BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Wen Chen

Wen Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SPInformation Theorymath.IThep-phArtificial Intelligenceastro-ph.GAcond-mat.mtrl-scihep-exastro-ph.HEastro-ph.IMComputer Visioneess.IVeess.SYEmerging TechnologiesInformation RetrievalMachine Learningmath.OCMultiagent SystemsNetworking and Internet Architecturenucl-thphysics.app-phphysics.chem-phphysics.opticsq-fin.GNSystems and Control

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
91works
0followers
25topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

91 published item(s)

preprint2026arXiv

Engineering Favorable Propagation: Near-Field IRS Deployment for Spatial Multiplexing

In intelligent reflecting surface IRS assisted multiple input multiple output MIMO systems, a strong line of sight LoS link is required to compensate for the severe cascaded path loss. However, such a link renders the effective channel highly rank deficient and fundamentally limits spatial multiplexing. To overcome this limitation, this paper leverages the large aperture of sparse arrays to harness near field spherical wavefronts, and establishes a deterministic deployment criterion that strategically positions the IRS in the near field of a base station BS. This placement exploits the spherical wavefronts of the BS IRS link to engineer decorrelated channels, thereby fundamentally overcoming the rank deficiency issue in far field cascaded channels. Based on a physical channel model for the sparse BS array and the IRS, we characterize the rank properties and inter user correlation of the cascaded BS IRS user channel. We further derive a closed form favorable propagation metric that reveals how the sparse array geometry and the IRS position can be tuned to reduce inter user channel correlation. The resulting geometry driven deployment rule provides a simple guideline for creating a favorable propagation environment with enhanced effective degrees of freedom. The favorable channel statistics induced by our deployment criterion enable a low complexity maximum ratio transmission MRT precoding scheme. This serves as the foundation for an efficient algorithm that jointly optimizes the IRS phase shifts and power allocation based solely on long term statistical channel state information CSI. Simulation results validate the effectiveness of our deployment criterion and demonstrate that our optimization framework achieves significant performance gains over benchmark schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Integrating Movable Antennas and Intelligent Reflecting Surfaces (MA-IRS): Fundamentals, Practical Solutions, and ISAC

Movable antennas (MAs) and intelligent reflecting surfaces (IRSs) enable active antenna repositioning and passive phase-shift tuning for channel reconfiguration, respectively. Integrating MAs and IRSs boosts spatial degrees of freedom, significantly enhancing wireless network capacity, coverage, and reliability. In this article, we first present the fundamentals of MA-IRS integration, involving clarifying the key design issues, revealing performance gain, and identifying the conditions where MA-IRS synergy persists. Then, we examine practical challenges and propose pragmatic design solutions, including optimization schemes, hardware architectures, deployment strategies, and robust designs for hardware impairments and mobility management. In addition, we highlight how MA-IRS architectures uniquely support advanced integrated sensing and communication, enhancing sensing performance and dual-functional flexibility. Overall, MA-IRS integration emerges as a compelling approach toward next-generation reconfigurable wireless systems.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Joint Antenna Rotation and IRS Beamforming for Multi-User Uplink Communications

Rotatable antenna (RA) enhances wireless coverage through directional gain steering, yet suffers from performance degradation under physical blockages. Intelligent reflecting surface (IRS) establishes reflective paths to bypass obstacles, but suffers from angular mismatch when deployed in the side-lobe region of base station (BS) antennas. To address this issue, we propose a new RA-enabled IRS-assisted multi-user uplink system, in which the BS antennas are capable of flexibly adjusting their 3D orientations to align their boresights with the IRS. We formulate a sum rate maximization problem by jointly optimizing the antenna 3D rotations, receive beamforming and IRS phase shifts. To tackle this non-convex problem, we propose an efficient alternating optimization (AO) algorithm. Specifically, we iteratively update the antenna rotations via projected gradient ascent (PGA), compute the receive beamforming via a closed-form solution, and optimize the IRS phase shifts via fractional programming (FP). Numerical results demonstrate that the proposed system yields significant performance gains over conventional fixed-antenna systems, especially under large angular misalignments.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Joint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoV

This paper investigates a multi-Unmanned Aerial Vehicle (UAV) joint base station-assisted Internet of Vehicles (IoV) task offloading system in dense urban environments. To minimize system delay and energy consumption under strict coupling constraints, the complex non-convex optimization problem is decoupled into a hierarchical execution framework. First, a sequential distributed optimization algorithm based on Second-Order Cone Programming (SOCP) is proposed to optimize the 3D flight trajectory of each UAV, ensuring adaptive network coverage. Second, a novel hybrid resource scheduling paradigm synergizing Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) is developed. Within this framework, the DRL agent dictates the initial resource allocation, while the LLM acts as a semantic macro-scheduler to rectify long-tail allocation imbalances for failed and surplus tasks. Crucially, a reward decoupling mechanism is introduced to isolate DRL training from external LLM interventions, thereby ensuring policy convergence. Finally, the task offloading ratios are precisely determined via Linear Programming (LP) within an alternating optimization loop. Simulation results demonstrate that the proposed method significantly outperforms traditional multi-agent reinforcement learning baselines in terms of task success rate and system efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Parallel Latent Reasoning for Sequential Recommendation

Capturing complex user preferences from sparse behavioral sequences remains a fundamental challenge in sequential recommendation. Recent latent reasoning methods have shown promise by extending test-time computation through multi-step reasoning, yet they exclusively rely on depth-level scaling along a single trajectory, suffering from diminishing returns as reasoning depth increases. To address this limitation, we propose \textbf{Parallel Latent Reasoning (PLR)}, a novel framework that pioneers width-level computational scaling by exploring multiple diverse reasoning trajectories simultaneously. PLR constructs parallel reasoning streams through learnable trigger tokens in continuous latent space, preserves diversity across streams via global reasoning regularization, and adaptively synthesizes multi-stream outputs through mixture-of-reasoning-streams aggregation. Extensive experiments on three real-world datasets demonstrate that PLR substantially outperforms state-of-the-art baselines while maintaining real-time inference efficiency. Theoretical analysis further validates the effectiveness of parallel reasoning in improving generalization capability. Our work opens new avenues for enhancing reasoning capacity in sequential recommendation beyond existing depth scaling.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Rethinking Soft Interference Cancellation (IC) for MIMO: A Hard-Decision IC Inspired Recursive Scheme

Multiple-input multiple-output (MIMO) technology has been regarded as one of the most important technologies to enable emerging applications in current and next generation wireless communication systems, for which signal detection methods have been endowed with higher requirements, such as finer bit-error ratio (BER) performance, lower complexity, and smaller memory. Existing detectors mainly include hard-decision-based ordered successive interference cancellation (HD-OSIC) schemes with relatively simple implementation, and linear-minimum-mean-squareerror-based iterative soft interference cancellation (LMMSE-ISIC) schemes exhibiting near-optimal BER performance, whose advantages are combined by the detector developed in this paper. Specifically, we first elaborate that the LMMSE-ISIC scheme is the extension of the HD-OSIC counterpart, via comparing our proposed reordered description based on the equivalent channel matrix for the LMMSE-ISIC detection process with the other. Then, we propose a recursive scheme with speed advantage and memory saving for LMMSE-ISIC by extending that for HDOSIC, where the LMMSE-ISIC estimate and the filtering bias are updated highly efficiently. Compared to the existing best low-complexity LMMSE-ISIC scheme, theoretically, the required computations and memory units in each iteration of our proposed scheme decrease by at least 87.50% and 80.00%, respectively, and simulation results demonstrate that our proposed scheme always yields identical BER performance.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Two-Scale Spatial Deployment for Cost-Effective Wireless Networks via Cooperative IRSs and Movable Antennas

This paper proposes a two-scale spatial deployment strategy to ensure reliable coverage for multiple target areas, integrating macroscopic intelligent reflecting surfaces (IRSs) and fine-grained movable antennas (MAs). Specifically, IRSs are selectively deployed from candidate sites to shape the propagation geometry, while MAs are locally repositioned among discretized locations to exploit small-scale channel variations. The objective is to minimize the total deployment cost of MAs and IRSs by jointly optimizing the IRS site selection, MA positions, transmit precoding, and IRS phase shifts, subject to the signal-to-noise ratio (SNR) requirements for all target areas. This leads to a challenging mixed-integer non-convex optimization problem that is intractable to solve directly. To address this, we first formulate an auxiliary problem to verify the feasibility. A penalty-based double-loop algorithm integrating alternating optimization and successive convex approximation (SCA) is developed to solve this feasibility issue, which is subsequently adapted to obtain a suboptimal solution for the original cost minimization problem. Finally, based on the obtained solution, we formulate an element refinement problem to further reduce the deployment cost, which is solved by a penalty-based SCA algorithm. Simulation results demonstrate that the proposed designs consistently outperform benchmarks relying on independent area planning or full IRS deployment in terms of cost-efficiency. Moreover, for cost minimization, MA architectures are preferable in large placement apertures, whereas fully populated FPA architectures excel in compact ones; for worst-case SNR maximization, MA architectures exhibit a lower cost threshold for feasibility, while FPA architectures can attain peak SNR at a lower total cost.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Two-Timescale Design for Movable Antenna-Enabled Multiuser MIMO Systems

Movable antennas (MAs), which can be swiftly repositioned within a defined region, offer a promising solution to the limitations of fixed-position antennas (FPAs) in adapting to spatial variations in wireless channels, thereby improving channel conditions and communication between transceivers. However, frequent MA position adjustments based on instantaneous channel state information (CSI) incur high operational complexity, making real-time CSI acquisition impractical, especially in fast-fading channels. To address these challenges, we propose a two-timescale transmission framework for MA-enabled multiuser multiple-input-multiple-output (MU-MIMO) systems. In the large timescale, statistical CSI is exploited to optimize MA positions for long-term ergodic performance, whereas, in the small timescale, beamforming vectors are designed using instantaneous CSI to handle short-term channel fluctuations. Within this new framework, we analyze the ergodic sum rate and develop efficient MA position optimization algorithms for both maximum-ratio-transmission (MRT) and zero-forcing (ZF) beamforming schemes. These algorithms employ alternating optimization (AO), successive convex approximation (SCA), and majorization-minimization (MM) techniques, iteratively optimizing antenna positions and refining surrogate functions that approximate the ergodic sum rate. Numerical results show significant ergodic sum rate gains with the proposed two-timescale MA design over conventional FPA systems, particularly under moderate to strong line-of-sight (LoS) conditions. Notably, MA with ZF beamforming consistently outperforms MA with MRT, highlighting the synergy between beamforming and MAs for superior interference management in environments with moderate Rician factors and high user density, while MA with MRT can offer a simplified alternative to complex beamforming designs in strong LoS conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Wireless Communication with Cross-Linked Rotatable Antenna Array: Architecture Design and Rotation Optimization

Rotatable antenna (RA) technology can harness additional spatial degrees of freedom by enabling the dynamic three-dimensional orientation control of each antenna. Unfortunately, the hardware cost and control complexity of traditional RA systems is proportional to the number of RAs. To address the issue, we consider a cross-linked (CL) RA structure, which enables the coordinated rotation of multiple antennas, thereby offering a cost-effective solution. To evaluate the performance of the CL-RA array, we investigate a CL-RA-aided uplink system. Specifically, we first establish system models for both antenna element-level and antenna panel-level rotation. Then, we formulate a sum rate maximization problem by jointly optimizing the receive beamforming at the base station and the rotation angles. For the antenna element-level rotation, we derive the optimal solution of the CL-RA array under the single-user case. Subsequently, for two rotation schemes, we propose an alternating optimization algorithm to solve the formulated problem in the multi-user case, where the receive beamforming and the antenna rotation angles are obtained by applying the minimum mean square error method and feasible direction method, respectively. In addition, considering the hardware limitations, we apply the genetic algorithm to address the discrete rotation angles selection problem. Simulation results show that by carefully designing the row-column partition scheme, the performance of the CL-RA architecture is quite close to that of the flexible antenna orientation scheme. Moreover, the CL antenna element-level scheme surpasses the CL antenna panel-level scheme by 25% and delivers a 128% performance improvement over conventional fixed-direction antennas.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Dual-IRS Aided Near-/Hybrid-Field SWIPT: Passive Beamforming and Independent Antenna Power Splitting Design

This paper proposes a novel dual-intelligent reflecting surface (IRS) aided interference-limited simultaneous wireless information and power transfer (SWIPT) system with independent power splitting (PS), where each receiving antenna applies different PS factors to offer an advantageous trade-off between the useful information and harvested energy. We separately establish the near- and hybrid-field channel models for IRS-reflected links to evaluate the performance gain more precisely and practically. Specifically, we formulate an optimization problem of maximizing the harvested power by jointly optimizing dual-IRS phase shifts, independent PS ratio, and receive beamforming vector in both near- and hybrid-field cases. In the near-field case, the alternating optimization algorithm is proposed to solve the non-convex problem by applying the Lagrange duality method and the difference-of-convex (DC) programming. In the hybrid-field case, we first present an interesting result that the AP-IRS-user channel gains are invariant to the phase shifts of dual-IRS, which allows the optimization problem to be transformed into a convex one. Then, we derive the asymptotic performance of the combined channel gains in closed-form and analyze the characteristics of the dual-IRS. Numerical results validate our analysis and indicate the performance gains of the proposed scheme that dual-IRS-aided SWIPT with independent PS over other benchmark schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

High-efficiency broadband active metasurfaces via reversible metal electrodeposition

Realizing active metasurfaces with substantial tunability is important for many applications but remains challenging due to difficulties in dynamically tuning light-matter interactions at subwavelength scales. Here, we introduce reversible metal electrodeposition as a versatile approach for enabling active metasurfaces with exceptional tunability across a broad bandwidth. As a proof of concept, we demonstrate a dynamic beam-steering device by performing reversible copper (Cu) electrodeposition on a reflective gradient metasurface composed of metal-insulator-metal resonators. By applying different voltages, the Cu atoms can be uniformly and reversibly electrodeposited and stripped around the resonators, effectively controlling the gap-surface plasmon resonances and steering the reflected light. This process experimentally achieved >90% diffraction efficiencies and >60% reflection efficiencies in both specular and anomalous modes, even after thousands of cycles. Moreover, these high efficiencies can be extended from the visible to the near- and mid-infrared regimes, demonstrating the broad versatility of this approach in enabling various active optical and thermal devices with different working wavelengths and bandwidths.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Integrating Movable Antennas and Intelligent Reflecting Surfaces for Coverage Enhancement

This paper investigates an intelligent reflecting surface (IRS)-aided movable antenna (MA) system, where multiple IRSs cooperate with a multi-MA base station to extend wireless coverage to multiple target areas. The objective is to maximize the worst-case signal-to-noise ratio (SNR) across all locations within these areas through joint optimization of MA positions, IRS phase shifts, and transmit beamforming. To achieve this while balancing the performance-cost trade-off, we propose three coverage-enhancement schemes: the area-adaptive MA-IRS scheme, where both MA positions and IRS phase shifts are adaptively adjusted for each target area; the area-adaptive MA-staIRS scheme, where only MA positions are adjusted, while IRS phase shifts remain unchanged after initial configuration (with staIRS denoting static IRSs); and the shared MA-staIRS scheme, where a common MA placement and static IRS configuration are applied across all areas. These schemes lead to challenging non-convex optimization problems with implicit objectives, which are difficult to solve optimally. To address these problems, we propose a general algorithmic framework that can solve each problem efficiently albeit suboptimally. Simulation results demonstrate that: 1) the proposed MA-based schemes consistently outperform their fixed-position antenna (FPA)-based counterparts under both area-adaptive and static IRS configurations, with the area-adaptive MA-IRS scheme achieving the best worst-case SNR; 2) as transmit antennas are typically far fewer than IRS elements, the area-adaptive MA-staIRS scheme may underperform the baseline FPA scheme with area-adaptive IRSs in worst-case SNR, but a modest increase in antenna number can reverse this; 3) under a fixed total cost, the optimal MA-to-IRS-element ratio for worst-case SNR maximization is empirically found to be proportional to the reciprocal of their unit cost ratio.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Movable Antennas Enabled Wireless-Powered NOMA: Continuous and Discrete Positioning Designs

This paper investigates a movable antenna (MA)-enabled wireless-powered communication network (WPCN), where multiple wireless devices (WDs) first harvest energy from the downlink (DL) signal broadcast by a hybrid access point (HAP) and then transmit information in the uplink (UL) using non-orthogonal multiple access. Unlike conventional WPCNs with fixed-position antennas (FPAs), this MA-enabled WPCN allows the MAs at the HAP and the WDs to adjust their positions twice: once before DL wireless power transfer and once before DL wireless information transmission. Our goal is to maximize the system sum throughput by jointly optimizing the MA positions, the time allocation, and the UL power allocation. Considering the characteristics of antenna movement, we explore both continuous and discrete positioning designs, which, after formulation, are found to be non-convex optimization problems. Before tackling these problems, we rigorously prove that using identical MA positions for both DL and UL is the optimal strategy in both scenarios, thereby greatly simplifying the problems and enabling easier practical implementation of the system. We then propose alternating optimization-based algorithms for the resulting simplified problems. Simulation results show that: 1) the proposed continuous MA scheme can enhance the sum throughput by up to 395.71% compared to the benchmark with FPAs, even when additional compensation transmission time is provided to the latter; 2) a step size of one-quarter wavelength for the MA motion driver is generally sufficient for the proposed discrete MA scheme to achieve over 80% of the sum throughput performance of the continuous MA scheme; 3) when each moving region is large enough to include multiple optimal positions for the continuous MA scheme, the discrete MA scheme can achieve comparable sum throughput without requiring an excessively small step size.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Phase transitions of eutectic high entropy alloy AlCoCrFeNi2.1 under shock compression

High entropy alloys (HEAs) are a new class of metals that exhibit unique mechanical performance. Among HEAs, additively manufactured eutectic high entropy alloys (AM-EHEAs) have recently emerged as candidate materials for use in extreme conditions due to their simultaneous high strength and ductility. However, the deformation and structural evolution of AM-EHEAs under conditions of high pressure have not been well characterized, limiting their use in extreme applications. We present dynamic compression experiments and molecular dynamics simulations studying the structural evolution of AM-EHEA AlCoCrFeNi2.1 when compressed to pressures up to 400 GPa. Our in-situ X-ray diffraction measurements capture the appearance of fcc and bcc phases at different pressure conditions, with pure- and mixed-phase regions. Understanding the phase stability and structural evolution of the AM EHEA offers new insights to guide the development of high-performance complex materials for extreme conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Synchronous Differential Hot-charge Emission Spectroscopy: The Principle

Energy-level alignment (ELA) at buried interfaces between electrode and molecular materials sets charge injection barriers, carrier selectivity, and ultimately device efficiency, yet it is challenging to quantify under operating conditions. Hot-charge emission spectroscopy (HotES) probes ELA by injecting ballistic carriers across a tunneling oxide. Yet, the technique inherently convolutes the molecular response with a strong, energy-dependent tunneling background, complicating the isolation of the true ELA. We introduce synchronous differential HotES (sd-HotES), defined as the ratio of the differential conductance of the hot-charge and tunneling channels of the HotES. Physical modeling and numerical simulations validate that this ratio directly reconstructs the intrinsic molecular charge transmission, enabling the threshold-free and probe-bias-insensitive extraction of ELA. By effectively eliminating the masking tunneling background, sd-HotES substantially boosts detection sensitivity; weak spectral features previously hidden in conventional HotES become clearly resolvable, as demonstrated in lock-in simulations including realistic noise. This study establishes the fundamental operating principles of sd-HotES and highlights it as a powerful, broadly applicable strategy for accessing buried interface properties for the study of molecular and hybrid devices.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Cooperative Cellular Localization with Intelligent Reflecting Surface: Design, Analysis and Optimization

Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cross sections (RCS) of moving targets and the high complexity for measurement association give rise to weak echo signals and discrepancies in the measurements. To tackle this issue, we propose a novel approach for multi-target localization by leveraging the controllable signal reflection capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are strategically mounted on the targets (e.g., vehicles and robots), enabling effective association of multiple measurements and facilitating the localization process. We aim to minimize the maximum Cramér-Rao lower bound (CRLB) of targets by jointly optimizing the target association, the IRS phase shifts, and the dwell time. However, solving this CRLB optimization problem is non-trivial due to the non-convex objective function and closely coupled variables. For single-target localization, a simplified closed-form expression is presented for the case where base stations (BSs) can be deployed flexibly, and the optimal BS location is derived to provide a lower performance bound of the original problem ...

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Active IRS Aided Multiple Access for Energy-Constrained IoT Systems

We investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with user grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Beamforming Optimization for Active Intelligent Reflecting Surface-Aided SWIPT

In this paper, we study an active IRS-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, an active IRS is deployed to assist a multi-antenna access point (AP) to convey information and energy simultaneously to multiple single-antenna information users (IUs) and energy users (EUs). Two joint transmit and reflect beamforming optimization problems are investigated with different practical objectives. The first problem maximizes the weighted sum-power harvested by the EUs subject to individual signal-to-interference-plus-noise ratio (SINR) constraints at the IUs, while the second problem maximizes the weighted sum-rate of the IUs subject to individual energy harvesting (EH) constraints at the EUs. The optimization problems are non-convex and difficult to solve optimally. To tackle these two problems, we first rigorously prove that dedicated energy beams are not required for their corresponding semidefinite relaxation (SDR) reformulations and the SDR is tight for the first problem, thus greatly simplifying the AP precoding design. Then, by capitalizing on the techniques of alternating optimization (AO), SDR, and successive convex approximation (SCA), computationally efficient algorithms are developed to obtain suboptimal solutions of the resulting optimization problems. Simulation results demonstrate that, given the same total system power budget, significant performance gains in terms of operating range of wireless power transfer (WPT), total harvested energy, as well as achievable rate can be obtained by our proposed designs over benchmark schemes (especially the one adopting a passive IRS). Moreover, it is advisable to deploy an active IRS in the proximity of the users for the effective operation of WPT/SWIPT.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CATCH: Chasing All Transients Constellation Hunters Space Mission

In time-domain astronomy, a substantial number of transients will be discovered by multi-wavelength and multi-messenger observatories, posing a great challenge for follow-up capabilities. We have thus proposed an intelligent X-ray constellation, the Chasing All Transients Constellation Hunters (CATCH) space mission. Consisting of 126 micro-satellites in three types, CATCH will have the capability to perform follow-up observations for a large number of different types of transients simultaneously. Each satellite in the constellation will carry lightweight X-ray optics and use a deployable mast to increase the focal length. The combination of different optics and detector systems enables different types of satellites to have multiform observation capabilities, including timing, spectroscopy, imaging, and polarization. Controlled by the intelligent system, different satellites can cooperate to perform uninterrupted monitoring, all-sky follow-up observations, and scanning observations with a flexible field of view (FOV) and multi-dimensional observations. Therefore, CATCH will be a powerful mission to study the dynamic universe. Here, we present the current design of the spacecraft, optics, detector system, constellation configuration and observing modes, as well as the development plan.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning

Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting. Aiming to maximize the long-term average achievable system rate, an optimization problem is formulated by jointly designing the transmit beamforming at the base station (BS) and discrete phase shift beamforming at the IRSs, with the constraints on transmit power, user data rate requirement and IRS energy buffer size. Considering time-varying channels and stochastic arrivals of energy harvested by the IRSs, we first formulate the problem as a Markov decision process (MDP) and then develop a novel multi-agent Q-mix (MAQ) framework with two layers to decouple the optimization parameters. The higher layer is for optimizing phase shift resolutions, and the lower one is for phase shift beamforming and power allocation. Since the phase shift optimization is an integer programming problem with a large-scale action space, we improve MAQ by incorporating the Wolpertinger method, namely, MAQ-WP algorithm to achieve a sub-optimality with reduced dimensions of action space. In addition, as MAQ-WP is still of high complexity to achieve good performance, we propose a policy gradient-based MAQ algorithm, namely, MAQ-PG, by mapping the discrete phase shift actions into a continuous space at the cost of a slight performance loss. Simulation results demonstrate that the proposed MAQ-WP and MAQ-PG algorithms can converge faster and achieve data rate improvements of 10.7% and 8.8% over the conventional multi-agent DDPG, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Energy-Efficient Backscatter Aided Uplink NOMA Roadside Sensor Communications under Channel Estimation Errors

This work presents non-orthogonal multiple access (NOMA) enabled energy-efficient alternating optimization framework for backscatter aided wireless powered uplink sensors communications for beyond 5G intelligent transportation system (ITS). Specifically, the transmit power of carrier emitter (CE) and reflection coefficients of backscatter aided roadside sensors are optimized with channel uncertainties for the maximization of the energy efficiency (EE) of the network. The formulated problem is tackled by the proposed two-stage alternating optimization algorithm named AOBWS (alternating optimization for backscatter aided wireless powered sensors). In the first stage, AOBWS employs an iterative algorithm to obtain optimal CE transmit power through simplified closed-form computed through Cardano's formulae. In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal reflection coefficient for roadside sensors under their quality of service (QoS) and a circuit power constraint. The global optimal exhaustive search (ES) algorithm is used as a benchmark. Simulation results demonstrate that the AOBWS algorithm can achieve near-optimal performance with very low complexity, which makes it suitable for practical implementations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Energy-Efficient IRS-Aided NOMA Beamforming for 6G Wireless Communications

This manuscript presents an energy-efficient alternating optimization framework based on intelligent reflective surfaces (IRS) aided non-orthogonal multiple access beamforming (NOMA-BF) system for 6G wireless communications. Specifically, this work proposes a centralized IRS-enabled design for the NOMA-BF system to optimize the active beamforming and power allocation coefficient (PAC) of users at the transmitter in the first stage and passive beamforming at IRS in the 2nd stage to maximize the energy efficiency (EE) of the network. However, an increment in the number of supportable users with the NOMA-BF system will lead to NOMA user interference and inter-cluster interference (ICI). To mitigate the effect of ICI, first zero-forcing beamforming along with efficient user clustering algorithm is exploited and then NOMA user interference is tackled efficiently through a proposed iterative algorithm that computes PAC of NOMA user through simplified closed-form expression under the required system constraints. In the 2nd stage, the problem of passive beamforming is solved through a technique based on difference-of-convex (DC) programming and successive convex approximation (SCA). Simulation results demonstrate that the proposed alternating framework for energy-efficient IRS-assisted NOMA-BF system can achieve convergence within a few iterations and provide efficient performance in terms of EE of the system with low complexity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Is Fermi 1544-0649 a misaligned blazar? discovering the jet structure with VLBI

Fermi J1544-0649 is a transient GeV source first detected during its GeV flares in 2017. Multi-wavelength observations during the flaring time demonstrate variability and spectral energy distribution(SED) that are typical of a blazar. Other than the flare time, Fermi J1544-0649 is quiet in the GeV band and looks rather like a quiet galaxy (2MASX J15441967-0649156) for a decade. Together with the broad absorption lines feature we further explore the "misaligned blazar scenario". We analyzed the Very Long Baseline Array (VLBA) and East Asian VLBI Network (EAVN) data from 2018 to 2020 and discovered the four jet components from Fermi J1544-0649. We found a viewing angle around 3.7° to 7.4°. The lower limit of the viewing angle indicates a blazar with an extremely low duty cycle of the gamma-ray emission, the upper limit of it supports the "misaligned blazar scenario". Follow-up multi-wavelength observations after 2018 show Fermi J1544-0649 remains quiet in GeV, X-ray, and optical bands. Multi-messenger search of neutrinos is also performed, and an excess of 3.1 σ significance is found for this source.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Joint Active and Passive Beamforming Design for IRS-Aided Radar-Communication

In this paper, we study an intelligent reflecting surface (IRS)-aided radar-communication (Radcom) system, where the IRS is leveraged to help Radcom base station (BS) transmit the joint of communication signals and radar signals for serving communication users and tracking targets simultaneously. The objective of this paper is to minimize the total transmit power at the Radcom BS by jointly optimizing the active beamformers, including communication beamformers and radar beamformers, at the Radcom BS and the phase shifts at the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) required by communication users, the minimum SINR required by the radar, and the cross-correlation pattern design. In particular, we consider two cases, namely, case I and case II, based on the presence or absence of the radar cross-correlation design and the interference introduced by the IRS on the Radcom BS. For case I where the cross correlation design and the interference are not considered, we prove that the dedicated radar signals are not needed, which significantly reduces implementation complexity and simplifies algorithm design. Then, a penalty-based algorithm is proposed to solve the resulting non-convex optimization problem. Whereas for case II considering the cross-correlation design and the interference, we unveil that the dedicated radar signals are needed in general to enhance the system performance. Since the resulting optimization problem is more challenging to solve as compared with the case I, the semidefinite relaxation (SDR) based alternating optimization (AO) algorithm is proposed. Simulation results demonstrate the effectiveness of proposed algorithms and also show the superiority of the proposed scheme over various benchmark schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Secure Intelligent Reflecting Surface Aided Integrated Sensing and Communication

In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. Based on the availability of perfect channel state information (CSI) of all involved user links and the accurate target location at the BS, two scenarios are considered and two different optimization algorithms are proposed. For the ideal scenario where the CSI of the user links and the target location are perfectly known at the BS, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. On the other hand, for the more practical scenario where the CSI is imperfect and the target location is uncertain, a robust algorithm based on the $\cal S$-procedure and sign-definiteness approaches is proposed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving a trade-off between the communication quality and the sensing quality.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Path forward to N$^3$LO

The LHC experiments will achieve percent level precision measurements of processes key to some of the most pressing questions of contemporary particle physics: What is the nature of the Higgs boson? Can we successfully describe the interaction of fundamental particles at high energies? Is there physics beyond the Standard Model at the LHC? The capability to predict and describe such observables at next-to-next-to-next-to-leading order (N$^3$LO) in QCD perturbation theory is paramount to fully exploit these experimental measurements. We describe the current status of N$^3$LO predictions and highlight their importance in the upcoming precision phase of the LHC. Furthermore, we identify key conceptual and mathematical developments necessary to see wide-spread N$^3$LO phenomenology come to fruition.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A CRC-aided Hybrid Decoding for Turbo Codes

Turbo codes and CRC codes are usually decoded separately according to the serially concatenated inner codes and outer codes respectively. In this letter, we propose a hybrid decoding algorithm of turbo-CRC codes, where the outer codes, CRC codes, are not used for error detection but as an assistance to improve the error correction performance. Two independent iterative decoding and reliability based decoding are carried out in a hybrid schedule, which can efficiently decode the two different codes as an entire codeword. By introducing an efficient error detecting method based on normalized Euclidean distance without CRC check, significant gain can be obtained by using the hybrid decoding method without loss of the error detection ability.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Novel Alternative Optimization Method for Joint Power and Trajectory Design in UAV-Enabled Wireless Network

This letter aims to maximize the average throughput via the joint design of the transmit power and trajectory for unmanned aerial vehicle (UAV)-enabled network. The conventional way to tackle this problem is based on the alternating optimization (AO) method by iteratively updating power and trajectory until convergence, resulting in a non-convex trajectory subproblem which is difficult to deal with. To develop more efficient methods, we propose a novel AO method by incorporating both power and trajectory into an intermediate variable, and then iteratively updating power and the newly introduced variable. This novel variable transformation makes it easier to decompose the original problem into two convex subproblems, namely a throughput maximization subproblem and a feasibility subproblem. Consequently, both of these subproblems can be solved in a globally optimal fashion. We further propose a low-complexity algorithm for the feasibility subproblem by exploiting the alternating directional method of multipliers (ADMM), whose updating step is performed in closed-form solutions. Simulation results demonstrate that our proposed method reduces the computation time by orders of magnitude, while achieving higher performance than the conventional methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A PAPR Reduction Method Based on Artificial Bee Colony Algorithm for OFDM Signals

One of the major drawbacks of orthogonal frequency division multiplexing (OFDM) signals is the high peak to average power ratio (PAPR) of the transmitted signal. Many PAPR reduction techniques have been proposed in the literature, among which, partial transmit sequence (PTS) technique has been taken considerable investigation.However, PTS technique requires an exhaustive search over all combinations of allowed phase factors, whose complexity increases exponentially with the number of sub-blocks. In this paper, a newly suboptimal method based on modified artificial bee colony (ABC-PTS) algorithm is proposed to search the better combination of phase factors. The ABC-PTS algorithm can significantly reduce the computational complexity for larger PTS subblocks and offers lower PAPR at the same time. Simulation results show that the ABC-PTS algorithm is an efficient method to achieve significant PAPR reduction.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Achieving Global Optimality for Joint Source and Relay Beamforming Design in Two-Hop Relay Channels

This paper deals with joint source and relay beamforming (BF) design for an amplify-and-forward (AF) multi-antenna multirelay network. Considering that the channel state information (CSI) from relays to destination is imperfect, we aim to maximize the worst case received signal-to-noise ratio (SNR). The associated optimization problem is then solved in two steps. In the first step, by fixing the source BF vector, a semi-closed form solution of the relay BF matrices is obtained, up to a power allocation factor. In the second step, the global optimal source BF vector is obtained based on the Polyblock outer Approximation (PA) algorithm. We also propose two low-complexity methods for obtaining the source BF vector, which are different in their complexities and performances. The optimal joint source-relay BF solution obtained by the proposed algorithms serves as the benchmark for evaluating the existing schemes and the proposed low-complexity methods. Simulation results show that the proposed robust design can significantly reduce the sensitivity of the channel uncertainty to the system performance.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Achieving Optimality in Robust Joint Optimization of Linear Transceiver Design

This paper presents new results on linear transceiver designs in a multiple-input-multiple-output (MIMO) link. By considering the minimal total mean-square error (MSE) criterion, we prove that the robust optimal linear transceiver design has a channel-diagonalizing structure, which verifies the conjecture in the previous work \cite{JW_2011}. Based on this property, the original design problem can be transformed into a scalar problem, whose global optimal solution is first obtained in this work. Simulation results show the performance advantages of our solution over the existing schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Adaptive Resource Allocation for Improved DF Aided Downlink Multi-user OFDM Systems

In this letter, we propose a joint resource allocation algorithm for an OFDM-based multi-user system assisted by an improved Decode-and-Forward (DF) relay. We aim at maximizing the sum rate of the system by jointly optimizing subcarrier pairing, subcarrier pair-user assignment, and power allocation in such a single DF relay system. When the relay does not perform any transmission on some subcarriers in the second phase, we further allow the source to transmit new symbols on these inactive subcarriers. We effectively solve the formulated mixed integer programming problem by using continuous relaxation and dual minimization methods. Numerical results verify the theoretical analysis, and illustrate the remarkable gains resulted from the extra direct-link transmissions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Ameso Optimization: a Relaxation of Discrete Midpoint Convexity

In this paper we introduce the Ameso optimization problem, a special class of discrete optimization problems. We establish its basic properties and investigate the relation between Ameso optimization and the convex optimization. Further, we design an algorithm to solve a multi-dimensional Ameso problem by solving a sequence of one-dimensional Ameso problems. Finally, we demonstrate how the knapsack problem can be solved using the Ameso optimization framework.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An Improved Square-root Algorithm for V-BLAST Based on Efficient Inverse Cholesky Factorization

A fast algorithm for inverse Cholesky factorization is proposed, to compute a triangular square-root of the estimation error covariance matrix for Vertical Bell Laboratories Layered Space-Time architecture (V-BLAST). It is then applied to propose an improved square-root algorithm for V-BLAST, which speedups several steps in the previous one, and can offer further computational savings in MIMO Orthogonal Frequency Division Multiplexing (OFDM) systems. Compared to the conventional inverse Cholesky factorization, the proposed one avoids the back substitution (of the Cholesky factor), and then requires only half divisions. The proposed V-BLAST algorithm is faster than the existing efficient V-BLAST algorithms. The expected speedups of the proposed square-root V-BLAST algorithm over the previous one and the fastest known recursive V-BLAST algorithm are 3.9~5.2 and 1.05~1.4, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Binary Representaion for Non-binary LDPC Code with Decoder Design

The equivalent binary parity check matrices for the binary images of the cycle-free non-binary LDPC codes have numerous bit-level cycles. In this paper, we show how to transform these binary parity check matrices into their cycle-free forms. It is shown that the proposed methodology can be adopted not only for the binary images of non-binary LDPC codes but also for a large class of binary LDPC codes. Specifically, we present an extended $p$-reducible (EPR) LDPC code structure to eliminate the bit-level cycles. For the non-binary LDPC codes with short length symbol-level cycles, the EPR-LDPC codes can largely avoid the corresponding short length bit-level cycles. As to the decoding of the EPR-LDPC codes, we propose a hybrid hard-decision decoder and a hybrid parallel decoder for binary symmetric channel and binary input Gaussian channel, respectively. A simple code optimization algorithm for these binary decoders is also provided. Simulations show the comparative results and justify the advantages, i.e., better performance and lower decoding complexity, of the proposed binary constructions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Block Distributed Compressive Sensing Based Doubly Selective Channel Estimation and Pilot Design for Large-Scale MIMO Systems

The doubly selective (DS) channel estimation in the large-scale multiple-input multiple-output (MIMO) systems is a challenging problem due to the large number of the channel coefficients to be estimated, which requires unaffordable and prohibitive pilot overhead. In this paper, firstly we conduct the analysis about the common sparsity of the basis expansion model (BEM) coefficients among all the BEM orders and all the transmit-receive antenna pairs. Then a novel pilot pattern is proposed, which inserts the guard pilots to deal with the inter carrier interference (ICI) under the superimposed pilot pattern. Moreover, by exploiting the common sparsity of the BEM coefficients among different BEM orders and different antennas, we propose a block distributed compressive sensing (BDCS) based DS channel estimator for the large-scale MIMO systems. Its structured sparsity leads to the reduction of the pilot overhead under the premise of guaranteeing the accuracy of the estimation. Furthermore, taking consideration of the block structure, a pilot design algorithm referred to as block discrete stochastic optimization (BDSO) is proposed. It optimizes the pilot positions by reducing the coherence among different blocks of the measurement matrix. Besides, a linear smoothing method is extended to large-scale MIMO systems to improve the accuracy of the estimation. Simulation results verify the performance gains of our proposed estimator and the pilot design algorithm compared with the existing schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Capacity Performance of Relay Beamformings for MIMO Multi-Relay Networks with Imperfect R-D CSI at Relays

In this paper, we consider a dual-hop Multiple Input Multiple Output (MIMO) wireless relay network in the presence of imperfect channel state information (CSI), in which a source-destination pair both equipped with multiple antennas communicates through a large number of half-duplex amplify-and-forward (AF) relay terminals. We investigate the performance of three linear beamforming schemes when the CSI of relay-to-destination (R-D) link is not perfect at the relay nodes. The three efficient linear beamforming schemes are based on the matched-filter (MF), zero-forcing (ZF) precoding and regularized zero-forcing (RZF) precoding techniques, which utilize the CSI of both S-D channel and R-D channel at the relay nodes. By modeling the R-D CSI error at the relay nodes as independent complex Gaussian random variables, we derive the ergodic capacities of the three beamformers in terms of instantaneous SNR. Using Law of Large Number, we obtain the asymptotic capacities, upon which the optimized MF-RZF is derived. Simulation results show that the asymptotic capacities match with the respective ergodic capacities very well. Analysis and simulation results demonstrate that the optimized MF-RZF outperforms MF and MF-ZF for any power of R-D CSI error.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Comments on A New Parity Check Stopping Criterion for Turbo Decoding

A parity-check stopping (PCS) criterion for turbo decoding is proposed in [1], which shows its priority compared with the stopping criteria of Sign Change Ratio (SCR), Sign Difference Ratio (SDR), Cross Entropy (CE) and improved CEbased (Yu) method. But another well-known simple stopping criterion named Hard-Decision-Aided (HDA) criterion has not been compared in [1]. In this letter, through analysis we show that using max-log-MAP algorithm, PCS is equivalent to HDA; while simulations demonstrate that using log-MAP algorithm, PCS has nearly the same performance as HDA.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Compressed Channel Estimation with Position-Based ICI Elimination for High-Mobility SIMO-OFDM Systems

Orthogonal frequency-division multiplexing (OFDM) is widely adopted for providing reliable and high data rate communication in high-speed train systems. However, with the increasing train mobility, the resulting large Doppler shift introduces intercarrier interference (ICI) in OFDM systems and greatly degrades the channel estimation accuracy. Therefore, it is necessary and important to investigate reliable channel estimation and ICI mitigation methods in high-mobility environments. In this paper, we consider a typical HST communication system and show that the ICI caused by the large Doppler shift can be mitigated by exploiting the train position information as well as the sparsity of the conventional basis expansion model (BEM) based channel model. Then, we show that for the complex-exponential BEM (CE-BEM) based channel model, the ICI can be completely eliminated to get the ICI-free pilots at each receive antenna. After that, we propose a new pilot pattern design algorithm to reduce the system coherence and hence can improve the compressed sensing (CS) based channel estimation accuracy. The proposed optimal pilot pattern is independent of the number of receive antennas, the Doppler shifts, the train position, or the train speed. Simulation results confirms the performance merits of the proposed scheme in high-mobility environments. In addition, it is also shown that the proposed scheme is robust to the respect of high mobility.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Compute-and-Forward Network Coding Design over Multi-Source Multi-Relay Channels

Network coding is a new and promising paradigm for modern communication networks by allowing intermediate nodes to mix messages received from multiple sources. Compute-and-forward strategy is one category of network coding in which a relay will decode and forward a linear combination of source messages according to the observed channel coefficients, based on the algebraic structure of lattice codes. The destination will recover all transmitted messages if enough linear equations are received. In this work, we design in a system level, the compute-and-forward network coding coefficients by Fincke-Pohst based candidate set searching algorithm and network coding system matrix constructing algorithm, such that by those proposed algorithms, the transmission rate of the multi-source multi-relay system is maximized. Numerical results demonstrate the effectiveness of our proposed algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep neural network for optimal retirement consumption in defined contribution pension system

In this paper, we develop a deep neural network approach to solve a lifetime expected mortality-weighted utility-based model for optimal consumption in the decumulation phase of a defined contribution pension system. We formulate this problem as a multi-period finite-horizon stochastic control problem and train a deep neural network policy representing consumption decisions. The optimal consumption policy is determined by personal information about the retiree such as age, wealth, risk aversion and bequest motive, as well as a series of economic and financial variables including inflation rates and asset returns jointly simulated from a proposed seven-factor economic scenario generator calibrated from market data. We use the Australian pension system as an example, with consideration of the government-funded means-tested Age Pension and other practical aspects such as fund management fees. The key findings from our numerical tests are as follows. First, our deep neural network optimal consumption policy, which adapts to changes in market conditions, outperforms deterministic drawdown rules proposed in the literature. Moreover, the out-of-sample outperformance ratios increase as the number of training iterations increases, eventually reaching outperformance on all testing scenarios after less than 10 minutes of training. Second, a sensitivity analysis is performed to reveal how risk aversion and bequest motives change the consumption over a retiree's lifetime under this utility framework. Third, we provide the optimal consumption rate with different starting wealth balances. We observe that optimal consumption rates are not proportional to initial wealth due to the Age Pension payment. Forth, with the same initial wealth balance and utility parameter settings, the optimal consumption level is different between males and females due to gender differences in mortality.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Design of Convergence-Optimized Non-binary LDPC Codes over Binary Erasure Channel

In this letter, we present a hybrid iterative decoder for non-binary low density parity check (LDPC) codes over binary erasure channel (BEC), based on which the recursion of the erasure probability is derived to design non-binary LDPC codes with convergence-optimized degree distributions. The resulting one-step decoding tree is cycle-free and achieves lower decoding complexity. Experimental studies show that the proposed convergence-optimization algorithm accelerates the convergence process by 33%.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Design of Low Complexity Non-binary LDPC Codes with an Approximated Performance-Complexity Tradeoff

By presenting an approximated performance-complexity tradeoff (PCT) algorithm,a low-complexity non-binary low density parity check (LDPC) code over q-ary-input symmetric-output channel is designed in this manuscript which converges faster than the threshold-optimized non-binary LDPC codes in the low error rate regime. We examine our algorithm by both hard and soft decision decoders.Moreover, simulation shows that the approximated PCT algorithm has accelerated the convergence process by 30% regarding the number of the decoding iterations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Distributed Caching for Data Dissemination in the Downlink of Heterogeneous Networks

Heterogeneous cellular networks (HCN) with embedded small cells are considered, where multiple mobile users wish to download network content of different popularity. By caching data into the small-cell base stations (SBS), we will design distributed caching optimization algorithms via belief propagation (BP) for minimizing the downloading latency. First, we derive the delay-minimization objective function (OF) and formulate an optimization problem. Then we develop a framework for modeling the underlying HCN topology with the aid of a factor graph. Furthermore, distributed BP algorithm is proposed based on the network's factor graph. Next, we prove that a fixed point of convergence exists for our distributed BP algorithm. In order to reduce the complexity of the BP, we propose a heuristic BP algorithm. Furthermore, we evaluate the average downloading performance of our HCN for different numbers and locations of the base stations (BS) and mobile users (MU), with the aid of stochastic geometry theory. By modeling the nodes distributions using a Poisson point process, we develop the expressions of the average factor graph degree distribution, as well as an upper bound of the outage probability for random caching schemes. We also improve the performance of random caching. Our simulations show that (1) the proposed distributed BP algorithm has a near-optimal delay performance, approaching that of the high-complexity exhaustive search method, (2) the modified BP offers a good delay performance at a low communication complexity, (3) both the average degree distribution and the outage upper bound analysis relying on stochastic geometry match well with our Monte-Carlo simulations, and (4) the optimization based on the upper bound provides both a better outage and a better delay performance than the benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Efficient Beamforming for MIMO Relaying Broadcast Channel with Imperfect Channel Estimation

We consider a multiple-input multiple-output (MIMO) relaying boardcast channel in downlink cellular networks, where the base station and the relay stations are both equipped with multiple antennas, and each user terminal has only a single antenna. In practical scenarios, channel estimation is imperfect at the receivers. Aiming at maximizing the SINR at each user, we develop two robust linear beamforming schemes respectively for the single relay case and the multi-relay case. The two proposed schemes are based on sigular value decomposition (SVD), minimum mean square error (MMSE) and regularized zero-forcing (RZF). Simulation results show that the proposed scheme outperforms the conventional schemes with imperfect channel estimation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Efficient Linear Transmission Strategy for MIMO Relaying Broadcast Channels with Direct Links

In this letter, a novel linear transmission strategy to design the linear precoding matrix~(PM) at base station~(BS) and the beamforming matrix~(BM) at relay station~(RS) for multiple-input multiple-output~(MIMO) relaying broadcast channels with direct channel (DC) is proposed, in which a linear PM is designed at BS based on DC, and the RS utilizes the PM, the backward channel and the forward channel to design the linear BM. We then give a quite tight lower bound of the achievable sum-rate of the network with the proposed strategy to measure the performance. The sum-rates achieved by the proposed strategy is compared with other schemes without considering the DC in design in simulations, which shows that the proposed strategy outperforms the existing methods when RS is close to BS.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Energy-Efficient Buffer-Aided Relaying Systems with Opportunistic Spectrum Access

In this paper, an energy-efficient cross-layer design framework is proposed for cooperative relaying networks, which takes into account the influence of spectrum utilization probability. Specifically, random arrival traffic is considered and an adaptive modulation and coding (AMC) scheme is adopted in the cooperative transmission system to improve the system performance. The average packet dropping rate of the relay-buffer is studied at first. With the packet dropping rate and stationary distribution of the system state, the closed-form expression of the delay is derived. Then the energy efficiency for relay-assisted transmission is investigated, which takes into account the queueing process of the relay and the source. In this context, an energy efficiency optimization problem is formulated to determine the optimum strategy of power and time allocation for the relay-assisted cooperative system. Finally, the energy efficient switching strategy between the relay assisted transmission and the direct transmission is obtained, where packet transmissions have different delay requirements. In addition, energy efficient transmission policy with AMC is obtained. Numerical results demonstrate the effectiveness of the proposed design improving the energy efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Energy-Efficient NOMA Multicasting System for 5G Cellular V2X Communications with Imperfect CSI

Vehicle-to-everything (V2X) is a modern vehicular technology that improves conventional vehicle systems in traffic and communications. V2X communications demand energyefficient and high-reliability networking because of massive vehicular connections and high mobility wireless channels. Nonorthogonal multiple access (NOMA) is a promising solution for 5G V2X services that intend to guarantee high reliability, quality-of-service (QoS) provisioning, and massive connectivity requirements. In V2X, it is vital to inspect imperfect CSI because the high mobility of vehicles leads to more channel estimation uncertainties. Unlike existing literatures, we propose energy-efficient roadside units (RSUs) assisted NOMA multicasting system for 5G cellular V2X communications, and investigate the energy-efficient power allocation problem. The proposed system multicast the information through low complexity optimal power allocation algorithms used under channel outage probability constraint of vehicles with imperfect CSI, QoS constraints of vehicles, and transmit power limits constraint of RSUs. The formulated problem with the channel outage probability constraint is a nonconvex probabilistic optimization problem. This problem is solved efficiently by converting the probabilistic problem through relaxation into a non-probabilistic problem.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Enhancements of the 3GPP LTE-Advanced and the Prized Asset: Dynamic TDD Transmissions

In this paper, we perform a survey on new Third Generation Partnership Project (3GPP) Long Term Evolution-Advanced (LTE-Advanced) enhancements,covering the technologies recently adopted by the 3GPP in LTE Release 11 and those being discussed in LTE Release 12. In more details, we introduce the latest enhancements on carrier aggregation (CA), multiple-input multiple-output (MIMO) and coordinated multi-point (CoMP) as well as three-dimensional (3D) MIMO. Moreover, considering that network nodes will become very diverse in the future, and thus with heterogeneous network (HetNet) being a key feature of LTE-Advanced networks, we also discuss technologies of interest in HetNet scenarios, e.g., enhanced physical data control channel (ePDCCH), further enhanced inter-cell interference coordination (FeICIC) and small cells, together with energy efficiency concerns. In particular, we pay special attention to one of the most important enhancements in LTE Release 12, i.e., dynamic time division duplex (TDD) transmissions, and present performance results that shed new light on this topic.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Generalized Quadratic Matrix Programming: A Unified Framework for Linear Precoding With Arbitrary Input Distributions

This paper investigates a new class of non-convex optimization, which provides a unified framework for linear precoding in single/multi-user multiple-input multiple-output (MIMO) channels with arbitrary input distributions. The new optimization is called generalized quadratic matrix programming (GQMP). Due to the nondeterministic polynomial time (NP)-hardness of GQMP problems, instead of seeking globally optimal solutions, we propose an efficient algorithm which is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point. The idea behind this algorithm is to construct explicit concave lower bounds for non-convex objective and constraint functions, and then solve a sequence of concave maximization problems until convergence. In terms of application, we consider a downlink underlay secure cognitive radio (CR) network, where each node has multiple antennas. We design linear precoders to maximize the average secrecy (sum) rate with finite-alphabet inputs and statistical channel state information (CSI) at the transmitter. The precoding problems under secure multicast/broadcast scenarios are GQMP problems, and thus they can be solved efficiently by our proposed algorithm. Several numerical examples are provided to show the efficacy of our algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Generalized Wireless-Powered Communications: When to Activate Wireless Power Transfer?

Wireless-powered communication network (WPCN) is a key technology to power energy-limited massive devices, such as on-board wireless sensors in autonomous vehicles, for Internet-of-Things (IoT) applications. Conventional WPCNs rely only on dedicated downlink wireless power transfer (WPT), which is practically inefficient due to the significant energy loss in wireless signal propagation. Meanwhile, ambient energy harvesting is highly appealing as devices can scavenge energy from various existing energy sources (e.g., solar energy and cellular signals). Unfortunately, the randomness of the availability of these energy sources cannot guarantee stable communication services. Motivated by the above, we consider a generalized WPCN where the devices can not only harvest energy from a dedicated multiple-antenna power station (PS), but can also exploit stored energy stemming from ambient energy harvesting. Since the dedicated WPT consumes system resources, if the stored energy is sufficient, WPT may not be needed to maximize the weighted sum rate (WSR). To analytically characterize this phenomenon, we derive the condition for WPT activation and reveal how it is affected by the different system parameters. Subsequently, we further derive the optimal resource allocation policy for the cases that WPT is activated and deactivated, respectively. In particular, it is found that when WPT is activated, the optimal energy beamforming at the PS does not depend on the devices' stored energy, which is shown to lead to a new unfairness issue. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed optimal resource allocation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Genetic Algorithm Based Nearly Optimal Peak Reduction Tone Set Selection for Adaptive Amplitude Clipping PAPR Reduction

In tone reservation (TR) based OFDM systems, the peak to average power ratio (PAPR) reduction performance mainly depends on the selection of the peak reduction tone (PRT) set and the optimal target clipping level. Finding the optimal PRT set requires an exhaustive search of all combinations of possible PRT sets, which is a nondeterministic polynomial-time (NP-hard) problem, and this search is infeasible for the number of tones used in practical systems. The existing selection methods, such as the consecutive PRT set, equally spaced PRT set and random PRT set, perform poorly compared to the optimal PRT set or incur high computational complexity. In this paper, an efficient scheme based on genetic algorithm (GA) with lower computational complexity is proposed for searching a nearly optimal PRT set. While TR-based clipping is simple and attractive for practical implementation, determining the optimal target clipping level is difficult. To overcome this problem, we propose an adaptive clipping control algorithm.Simulation results show that our proposed algorithms efficiently obtain a nearly optimal PRT set and good PAPR reductions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Green MU-MIMO/SIMO Switching for Heterogeneous Delay-aware Services with Constellation Optimization

In this paper, we propose adaptive techniques for multi-user multiple input and multiple output~(MU-MIMO) cellular communication systems, to solve the problem of energy efficient communications with heterogeneous delay-aware traffic. In order to minimize the total transmission power of the MU-MIMO, we investigate the relationship between the transmission power and the M-ary quadrature amplitude modulation~(MQAM) constellation size and get the energy efficient modulation for each transmission stream based on the minimum mean square error~(MMSE) receiver.Since the total power consumption is different for MU-MIMO and multi-user single input and multiple output~(MU-SIMO), by exploiting the intrinsic relationship among the total power consumption model, and heterogeneous delay-aware services, we propose an adaptive transmission strategy, which is a switching between MU-MIMO and MU-SIMO. Simulations show that in order to maximize the energy efficiency and consider different Quality of Service (QoS) of delay for the users simultaneously, the users should adaptively choose the constellation size for each stream as well as the transmission mode.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Hybrid Precoding For Millimeter Wave MIMO Systems: A Matrix Factorization Approach

This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. The precoding problem is a joint optimization of analog and digital precoders, and we treat it as a matrix factorization problem with power and constant modulus constraints. Our work presents three main contributions: First, we present a sufficient condition and a necessary condition for hybrid precoding schemes to realize unconstrained optimal precoders exactly when the number of data streams Ns satisfies Ns = minfrank(H);Nrfg, where H represents the channel matrix and Nrf is the number of radio frequency (RF) chains. Second, we show that the coupled power constraint in our matrix factorization problem can be removed without loss of optimality. Third, we propose a Broyden-Fletcher-Goldfarb-Shanno (BFGS)-based algorithm to solve our matrix factorization problem using gradient and Hessian information. Several numerical results are provided to show that our proposed algorithm outperforms existing hybrid precoding algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Increasing Security Degree of Freedom in Multi-user and Multi-eve Systems

Secure communication in the Multi-user and Multi-eavesdropper (MUME) scenario is considered in this paper. It has be shown that secrecy can be improved when the transmitter simultaneously transmits information-bearing signal to the intended receivers and artificial noise to confuse the eavesdroppers. Several processing schemes have been proposed to limit the co-channel interference (CCI). In this paper, we propose the increasing security degree of freedom (ISDF) method, which takes idea from the dirty-paper coding (DPC) and ZF beam-forming. By means of known interference pre-cancelation at the transmitter, we design each precoder according to the previously designed precoding matrices, rather than other users' channels, which in return provides extra freedom for the design of precoders. Simulations demonstrate that the proposed method achieves the better performance and relatively low complexity.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Integer-Forcing Linear Receiver Design with Slowest Descent Method

Compute-and-forward (CPF) strategy is one category of network coding in which a relay will compute and forward a linear combination of source messages according to the observed channel coefficients, based on the algebraic structure of lattice codes. Recently, based on the idea of CPF, integer forcing (IF) linear receiver architecture for MIMO system has been proposed to recover different integer combinations of lattice codewords for further original message detection. In this paper, we consider the problem of IF linear receiver design with respect to the channel conditions. Instead of exhaustive search, we present practical and efficient suboptimal algorithms to design the IF coefficient matrix with full rank such that the total achievable rate is maximized, based on the slowest descent method. Numerical results demonstrate the effectiveness of our proposed algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Joint Optimization of User Association, Subchannel Allocation, and Power Allocation in Multi-cell Multi-association OFDMA Heterogeneous Networks

Heterogeneous network is a novel network architecture proposed in Long-Term-Evolution~(LTE), which highly increases the capacity and coverage compared with the conventional networks. However, in order to provide the best services, appropriate resource management must be applied. In this paper, we consider the joint optimization problem of user association, subchannel allocation, and power allocation for downlink transmission in Multi-cell Multi-association Orthogonal Frequency Division Multiple Access (OFDMA) heterogeneous networks. To solve the optimization problem, we first divide it into two subproblems: 1) user association and subchannel allocation for fixed power allocation; 2) power allocation for fixed user association and subchannel allocation. Subsequently, we obtain a locally optimal solution for the joint optimization problem by solving these two subproblems alternately. For the first subproblem, we derive the globally optimal solution based on graph theory. For the second subproblem, we obtain a Karush-Kuhn-Tucker (KKT) optimal solution by a low complexity algorithm based on the difference of two convex functions approximation (DCA) method. In addition, the multi-antenna receiver case and the proportional fairness case are also discussed. Simulation results demonstrate that the proposed algorithms can significantly enhance the overall network throughput.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Joint Power Allocation and Precoding for Network Coding based Cooperative Multicast Systems

In this letter, we propose two power allocation schemes based on the statistical channel state information (CSI) and instantaneous s->r CSI at transmitters respectively for a 2-N-2 cooperative multicast system with non-regenerative network coding.Then the isolated precoder and the distributed precoder are respectively applied to the schemes to further improve the system performance by achieving the full diversity gain. Finally, we demonstrate that joint instantaneous s->r CSI based power allocation and distributed precoder design achieve the best performance.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Joint Shortening and Puncturing Optimization for Structured LDPC Codes

The demand for flexible broadband wireless services makes the pruning technique, including both shortening and puncturing, an indispensable component of error correcting codes. The analysis of the pruning process for structured lowdensity parity-check (LDPC) codes can be considerably simplified with their equivalent representations through base-matrices or protographs. In this letter, we evaluate the thresholds of the pruned base-matrices by using protograph based on extrinsic information transfer (PEXIT). We also provide an efficient method to optimize the pruning patterns, which can significantly improve the thresholds of both the full-length patterns and the sub-patterns. Numerical results show that the structured LDPC codes pruned by the improved patterns outperform those with the existing patterns.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Joint Source and Relay Design for MIMO Relaying Broadcast Channels

In this letter, we address the optimal source and relay matrices design for the multiple-input multiple-output~(MIMO) relaying broadcast channels~(BC) with direct links~(DLs) based on weighted sum-rate criterion.This problem is nonlinear nonconvex and its optimal solution remains open. To develop an efficient way to solve this problem,we first set up an equivalent problem, and solve it by decoupling it into two tractable subproblems. Finally, we propose a general linear iterative design algorithm based on alternative optimization. This iterative design algorithm is convergent since the solution of each subproblem is optimal and unique. The advantage of the proposed iterative design scheme is demonstrated by numerical experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Joint Source and Relay Design for Multi-user MIMO Non-regenerative Relay Networks with Direct Links

In this paper, we investigate joint source precoding matrices and relay processing matrix design for multi-user multiple-input multiple-output~(MU-MIMO) non-regenerative relay networks in the presence of the direct source-destination~(S-D) links. We consider both capacity and mean-squared error~(MSE) criterions subject to the distributed power constraints, which are nonconvex and apparently have no simple solutions. Therefore, we propose an optimal source precoding matrix structure based on the point-to-point MIMO channel technique, and a new relay processing matrix structure under the modified power constraint at relay node, based on which, a nested iterative algorithm of jointly optimizing sources precoding and relay processing is established. We show that the capacity based optimal source precoding matrices share the same structure with the MSE based ones. So does the optimal relay processing matrix. Simulation results demonstrate that the proposed algorithm outperforms the existing results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network

Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death. Biopsy of gastric mucosa is a standard procedure in gastric cancer screening test. However, manual pathological inspection is labor-intensive and time-consuming. Besides, it is challenging for an automated algorithm to locate the small lesion regions in the gigapixel whole-slide image and make the decision correctly.To tackle these issues, we collected large-scale whole-slide image dataset with detailed lesion region annotation and designed a whole-slide image analyzing framework consisting of 3 networks which could not only determine the screening result but also present the suspicious areas to the pathologist for reference. Experiments demonstrated that our proposed framework achieves sensitivity of 97.05% and specificity of 92.72% in screening task and Dice coefficient of 0.8331 in segmentation task. Furthermore, we tested our best model in real-world scenario on 10,315 whole-slide images collected from 4 medical centers.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Leakage-Based Robust Beamforming for Multi-Antenna Broadcast System with Per-Antenna Power Constraints and Quantized CDI

In this paper, we investigate the robust beamforming schemes for a multi-user multiple-input-single-output (MU-MISO) system with per-antenna power constraints and quantized channel direction information (CDI) feedback. Our design objective is to maximize the expectation of the weighted sum-rate performance by means of controlling the interference leakage and properly allocating the power among user equipments (UEs).First, we prove the optimality of the non-robust zero-forcing (ZF) beamforming scheme in the sense of generating the minimum amount of average inter-UE interference under quantized CDI. Then we derive closed-form expressions of the cumulative density function (CDF) of the interference leakage power for the non-robust ZF beamforming scheme, based on which we adjust the leakage thresholds and propose two robust beamforming schemes under per-antenna power constraints with an iterative process to update the per-UE power allocations using the geometric programming (GP). Simulation results show the superiority of the proposed robust beamforming schemes compared with the existing schemes in terms of the average weighted sum-rate performance.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Limited Feedback based Adaptive Power Allocation and Subcarrier Pairing for OFDM DF Relay Networks with Diversity

A limited feedback based dynamic resource allocation algorithm is proposed for a relay cooperative network with Orthogonal Frequency Division Multiplexing (OFDM) modulation. A communication model where one source node communicates with one destination node assisted by one half-duplex Decode-and-Foward (DF) relay is considered in this paper. We first consider the \emph{selective} DF scheme, in which some relay subcarriers will keep idle if they are not advantageous to forward the received symbols. Furthermore, we consider the \emph{enhanced} DF scheme where the idle subcarriers are used to transmit new messages at the source. We aim to maximize the system instantaneous rate by jointly optimizing power allocation and subcarrier pairing on each subcarrier based on the Lloyd algorithm. Both sum and individual power constraints are considered. The joint optimization turns out to be a mixed integer programming problem. We then transform it into a convex optimization by continuous relaxation, and achieve the solution in the dual domain.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Linear Precoding for Fading Cognitive Multiple Access Wiretap Channel with Finite-Alphabet Inputs

We investigate the fading cognitive multiple access wiretap channel (CMAC-WT), in which two secondary-user transmitters (STs) send secure messages to a secondary-user receiver (SR) in the presence of an eavesdropper (ED) and subject to interference threshold constraints at multiple primary-user receivers (PRs). We design linear precoders to maximize the average secrecy sum rate for multiple-input multiple-output (MIMO) fading CMAC-WT under finite-alphabet inputs and statistical channel state information (CSI) at STs. For this non-deterministic polynomial time (NP)-hard problem, we utilize an accurate approximation of the average secrecy sum rate to reduce the computational complexity, and then present a two-layer algorithm by embedding the convex-concave procedure into an outer approximation framework. The idea behind this algorithm is to reformulate the approximated average secrecy sum rate as a difference of convex functions, and then generate a sequence of simpler relaxed sets to approach the non-convex feasible set. Subsequently, we maximize the approximated average secrecy sum rate over the sequence of relaxed sets by using the convex-concave procedure. Numerical results indicate that our proposed precoding algorithm is superior to the conventional Gaussian precoding method in the medium and high signal-to-noise ratio (SNR) regimes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Low Complexity Iterative Receiver Design for Sparse Code Multiple Access

Sparse code multiple access (SCMA) is one of the most promising methods among all the non-orthogonal multiple access techniques in the future 5G communication. Compared with some other non-orthogonal multiple access techniques such as low density signature (LDS), SCMA can achieve better performance due to the shaping gain of the SCMA codewords. However, despite of the sparsity of the codewords, the decoding complexity of the current message passing algorithm (MPA) utilized by SCMA is still prohibitively high. In this paper, by exploring the lattice structure of SCMA codewords, we propose a low complexity decoding algorithm based on list sphere decoding (LSD). The LSD avoids the exhaustive search for all possible hypotheses and only considers signal within a hypersphere. As LSD can be viewed a depth-first tree search algorithm, we further propose several methods to prune the redundancy visited nodes in order to reduce the size of the search tree. Simulation results show that the proposed algorithm can reduce the decoding complexity substantially while the performance loss compared with the existing algorithm is negligible.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

MMSE Based Greedy Antenna Selection Scheme for AF MIMO Relay Systems

We propose a greedy minimum mean squared error (MMSE)-based antenna selection algorithm for amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems. Assuming equal-power allocation across the multi-stream data, we derive a closed form expression for the mean squared error (MSE) resulted from adding each additional antenna pair. Based on this result, we iteratively select the antenna-pairs at the relay nodes to minimize the MSE. Simulation results show that our algorithm greatly outperforms the existing schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On Dynamic Time Division Duplex Transmissions for Small Cell Networks

Motivated by the promising benefits of dynamic Time Division Duplex (TDD), in this paper, we use a unified framework to investigate both the technical issues of applying dynamic TDD in homogeneous small cell networks (HomSCNs), and the feasibility of introducing dynamic TDD into heterogeneous networks (HetNets). First, HomSCNs are analyzed, and a small cell BS scheduler that dynamically and independently schedules DL and UL subframes is presented, such that load balancing between the DL and the UL traffic can be achieved. Moreover, the effectiveness of various inter-link interference mitigation (ILIM) schemes as well as their combinations, is systematically investigated and compared. Besides, the interesting possibility of partial interference cancellation (IC) is also explored. Second,based on the proposed schemes, the joint operation of dynamic TDD together with cell range expansion (CRE) and almost blank subframe (ABS) in HetNets is studied. In this regard, scheduling polices in small cells and an algorithm to derive the appropriate macrocell traffic off-load and ABS duty cycle under dynamic TDD operation are proposed. Moreover, the full IC and the partial IC schemes are investigated for dynamic TDD in HetNets. The user equipment (UE) packet throughput performance of the proposed/discussed schemes is benchmarked using system-level simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Optimal Binary/Quaternary Adaptive Signature Design for Code-Division Multiplexing

We consider signature waveform design for synchronous code division multiplexing in the presence of interference and wireless multipath fading channels. The adaptive real/complex signature that maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR filter is the minimum-eigenvalue eigenvector of the disturbance autocovariance matrix. In digital communication systems, the signature alphabet is finite and digital signature optimization is NP-hard. In this paper, first we convert the maximum-SINR objective of adaptive binary signature design into an equivalent minimization problem. Then we present an adaptive binary signature design algorithm based on modified Fincke-Pohst (FP) method that achieves the optimal exhaustive search performance with low complexity. In addition, with the derivation of quaternary-binary equivalence, we extend and propose the optimal adaptive signature design algorithm for quaternary alphabet. Numerical results demonstrate the optimality and complexity reduction of our proposed algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

PAPR Reduction Method Based on Parametric Minimum Cross Entropy for OFDM Signals

The partial transmit sequence (PTS) technique has received much attention in reducing the high peak to average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. However, the PTS technique requires an exhaustive search of all combinations of the allowed phase factors, and the search complexity increases exponentially with the number of sub-blocks. In this paper, a novel method based on parametric minimum cross entropy (PMCE) is proposed to search the optimal combination of phase factors. The PMCE algorithm not only reduces the PAPR significantly, but also decreases the computational complexity. The simulation results show that it achieves more or less the same PAPR reduction as that of exhaustive search.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Position Based Compressed Channel Estimation and Pilot Design for High Mobility OFDM Systems

With the development of high speed trains (HST) in many countries, providing broadband wireless services in HSTs is becoming crucial. Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communications due to its high spectral efficiency. However, OFDM is sensitive to the time selectivity caused by high-mobility channels, which costs large spectrum or time resources to obtain the accurate channel state information (CSI). Therefore, the channel estimation in high-mobility OFDM systems has been a long-standing challenge. In this paper, we first propose a new position-based high-mobility channel model,in which the HST's position information and Doppler shift are utilized to determine the positions of the dominant channel coefficients. %In this way, we can reduce the estimation complexity and to design the transmitted pilot.Then, we propose a joint pilot placement and pilot symbol design algorithm for compressed channel estimation. It aims to reduce the coherence between the pilot signal and the proposed channel model, and hence can improve the channel estimation accuracy. Simulation results demonstrate that the proposed method achieves better performances than existing channel estimation methods over high-mobility channels. Furthermore, we give an example of the designed pilot codebook to show the practical applicability of the proposed scheme.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Position-Based Interference Elimination for High Mobility OFDM Channel Estimation in Multi-cell Systems

Orthogonal frequency-division multiplexing (OFD-M) and multi-cell architecture are widely adopted in current high speed train (HST) systems for providing high data rate wireless communications. In this paper, a typical multi-antenna OFDM HST communication system with multi-cell architecture is considered, where the inter-carrier interference (ICI) caused by high mobility and multi-cell interference (MCI) are both taken into consideration. By exploiting the train position information, a new position-based interference elimination method is proposed to eliminate both the MCI and ICI for a general basis expansion model (BEM). We show that the MCI and ICI can be completely eliminated by the proposed method to get the ICI-free pilots at each receive antenna. In addition, for the considered multi-cell HST system, we develop a low-complexity compressed channel estimation method and consider the optimal pilot pattern design. Both the proposed interference elimination method and the optimal pilot pattern are robust to the train speed and position,as well as the multi-cell multi-antenna system. Simulation results demonstrate the benefits and robustness of the proposed method in the multi-cell HST system.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Power Adaptive Network Coding for a Non-Orthogonal Multiple-Access Relay Channel

In this paper we propose a novel power adapted network coding (PANC) for a non-orthogonal multiple-access relay channel (MARC), where two sources transmit their information simultaneously to the destination with the help of a relay. Different from the conventional XOR-based network coding (CXNC), the relay in our PANC generates network coded bits by considering the coefficients of the source-to-relay channels, and forwards each bit with a pre-optimized power level. Specifically, by defining a symbol pair as two symbols from the two sources, we first derive the exact symbol pair error rate (SPER) of the system. Noting that the generations of the exact SPER are complicated due to the irregularity of the decision regions caused by random channel coefficients, we propose a coordinate transform (CT) method to simplify the derivations of the SPER. Next, we prove that with a power scaling factor at relay, our PANC scheme can achieve full diversity gain, i.e., two-order diversity gain, of the system, while the CXNC can only achieve one-order diversity gain due to multi-user interference. In addition, we optimize the power levels at the relay to equivalently minimize the SPER at the destination concerning the relationship between SPER and minimum Euclidean distance of the received constellation. Simulation results show that (1) the SPER derived based on our CT method can well approximate the exact SPER with a much lower complexity; (2) the PANC scheme with power level optimizations and power scaling factor design can achieve full diversity, and obtain a much higher coding gain than the PANC scheme with randomly chosen power levels.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Power Allocation in the High SNR Regime for A Multicast Cell with Regenerative Network Coding

This letter focuses on power allocation schemes for a basic multicast cell with wireless regenerative network coding (RNC). In RNC, mixed signals received from the two sources are jointly decoded by the relay where decoded symbols are superposed in either the complex field (RCNC) or Galois field (RGNC) before being retransmitted. We deduce the optimal statistical channels state information (CSI) based power allocation and give a comparison between the two RNCs. When instantaneous CSI is available at each transmitter, we propose a suboptimal power allocation for RCNC, which achieves better performance.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Probabilistic Caching for Small-Cell Networks with Terrestrial and Aerial Users

The support for aerial users has become the focus of recent 3GPP standardizations of 5G, due to their high maneuverability and flexibility for on-demand deployment. In this paper, probabilistic caching is studied for ultra-dense small-cell networks with terrestrial and aerial users, where a dynamic on-off architecture is adopted under a sophisticated path loss model incorporating both line-of-sight and non-line-of-sight transmissions. Generally, this paper focuses on the successful download probability (SDP) of user equipments (UEs) from small-cell base stations (SBSs) that cache the requested files under various caching strategies. To be more specific, the SDP is first analyzed using stochastic geometry theory, by considering the distribution of such two-tier UEs and SBSs as Homogeneous Poisson Point Processes. Second, an optimized caching strategy (OCS) is proposed to maximize the average SDP. Third, the performance limits of the average SDP are developed for the popular caching strategy (PCS) and the uniform caching strategy (UCS). Finally, the impacts of the key parameters, such as the SBS density, the cache size, the exponent of Zipf distribution and the height of aerial user, are investigated on the average SDP. The analytical results indicate that the UCS outperforms the PCS if the SBSs are sufficiently dense, while the PCS is better than the UCS if the exponent of Zipf distribution is large enough. Furthermore, the proposed OCS is superior to both the UCS and PCS.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

QoS-Based Source and Relay Secure Optimization Design with Presence of Channel Uncertainty

In this letter, we study relay-aided networks with presence of single eavesdropper. We provide joint beamforming design of the source and relay that can minimize the overall power consumption while satisfying our predefined quality-of-service (QoS) requirements. Additionally, we investigate the case that the channel between relay and eavesdropper suffers from channel uncertainty. Finally, simulation results are provided to verify the effectiveness of our algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Reduction of Feynman Integrals in the Parametric Representation

In this paper, the reduction of Feynman integrals in the parametric representation is considered. This method proves to be more efficient than the integration-by-part (IBP) method in the momentum space. Tensor integrals can directly be parametrized without performing tensor reductions. The integrands of parametric integrals are functions of Lorentz scalars, instead of four momenta. The complexity of a calculation is determined by the number of propagators that are present rather than the number of all the linearly independent propagators. Furthermore, the symmetries of Feynman integrals under permutations of indices are transparent in the parametric representation. Since all the indices of the propagators are nonnegative, an algorithm to solve those identities can easily be developed, which can be used for automatic calculations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Regularized Zero-Forcing for Multiantenna Broadcast Channels with User Selection

A multiantenna multiuser broadcast channel with transmitter beamforming and user selection is considered. Different from the conventional works, we consider imperfect channel state information (CSI) which is a practical scenario for multiuser broadcast channels. We propose a robust regularized zero-forcing (RRZF) beamforming at the base station. Then we show that the RRZF outperforms zero-forcing (ZF) and regularized ZF (RZF) beamforming even as the number of users grows to infinity. Simulation results validate the advantage of the proposed robust RZF beamforming.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Relativistic corrections to exclusive $χ_{cJ} + γ$ production from $e^+ e^-$ annihilation

We calculate in the non-relativistic QCD (NRQCD) factorization framework all leading relativistic corrections to the exclusive production of $χ_{cJ}+γ$ in $e^+ e^-$ annihilation. In particular, we compute for the first time contributions induced by octet operators with a chromoelectric field. The matching coefficients multiplying production long distance matrix elements (LDMEs) are determined through perturbative matching between QCD and NRQCD at the amplitude level. Technical challenges encountered in the non-relativistic expansion of the QCD amplitudes are discussed in detail. The main source of uncertainty comes from the not so well known LDMEs. Accounting for it, we provide the following estimates for the production cross sections at $\sqrt{s} = 10.6\textrm{ GeV}$: $σ(e^+ e^- \to χ_{ c0} + γ) = (1.3 \pm 0.4) \textrm{ fb}$, $σ(e^+ e^- \to χ_{ c1} + γ) = (15.4 \pm 6.7) \textrm{ fb}$, and $σ(e^+ e^- \to χ_{ c2} + γ) = (4.7 \pm 2.6) \textrm{ fb}$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Relay Beamforming Design with SIC Detection for MIMO Multi-Relay Networks with Imperfect CSI

In this paper, we consider a dual-hop Multiple Input Multiple Output (MIMO) wireless multi-relay network, in which a source-destination pair both equipped with multiple antennas communicates through multiple half-duplex amplify-and-forward (AF) relay terminals which are also with multiple antennas. Since perfect channel state information (CSI) is difficult to obtain in practical multi-relay network, we consider imperfect CSI for all channels. We focus on maximizing the signal-to-interference-plus-noise ratio (SINR) at the destination. We propose a novel robust linear beamforming at the relays, based on the QR decomposition filter at the destination node which performs successive interference cancellation (SIC). Using Law of Large Number, we obtain the asymptotic rate in the presence of imperfect CSI, upon which, the proposed relay beamforming is optimized. Simulation results show that the asymptotic rate matches with the ergodic rate well. Analysis and simulation results demonstrate that the proposed beamforming outperforms the conventional beamforming schemes for any power of CSI errors and SNR regions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Robust Beamforming Design for Sum Secrecy Rate Optimization in MU-MISO Networks

This paper studies the beamforming design problem of a multi-user downlink network, assuming imperfect channel state information known to the base station. In this scenario, the base station is equipped with multiple antennas, and each user is wiretapped by a specific eavesdropper where each user or eavesdropper is equipped with one antenna. It is supposed that the base station employs transmit beamforming with a given requirement on sum transmitting power. The objective is to maximize the sum secrecy rate of the network. Due to the uncertainty of the channel, it is difficult to calculate the exact sum secrecy rate of the system. Thus, the maximum of lower bound of sum secrecy rate is considered. The optimization of the lower bound of sum secrecy rate still makes the considered beamforming design problem difficult to handle. To solve this problem, a beamforming design scheme is proposed to transform the original problem into a convex approximation problem, by employing semidefinite relaxation and first-order approximation technique based on Taylor expansion. Besides, with the advantage of low complexity, a zero-forcing based beamforming method is presented in the case that base station is able to nullify the eavesdroppers' rate. When the base station doesn't have the ability, user selection algorithm would be in use. Numerical results show that the former strategy achieves better performance than the latter one, which is mainly due to the ability of optimizing beamforming direction, and both outperform the signal-to-leakage-and-noise ratio based algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Robust Joint Source-Relay-Destination Design Under Per-antenna Power Constraints

This paper deals with joint source-relay-destination beamforming (BF) design for an amplify-and-forward (AF) relay network. Considering the channel state information (CSI) from the relay to the destination is imperfect, we first aim to maximize the worst case received SNR under per-antenna power constraints. The associated optimization problem is then solved in two steps. In the first step, by revealing the rank-one property of the optimal relay BF matrix, we establish the semi-closed form solution of the joint optimal BF design that only depends on a vector variable. Based on this result, in the second step, we propose a low-complexity iterative algorithm to obtain the remaining unknown variable. We also study the problem for minimizing the maximum per-antenna power at the relay while ensuring a received signal-to-noise ratio (SNR) target, and show that it reduces to the SNR maximization problem. Thus the same methods can be applied to solve it. The differences between our result and the existing related work are also discussed in details. In particular, we show that in the perfect CSI case, our algorithm has the same performance but with much lower cost of computational complexity than the existing method. Finally, in the simulation part, we investigate the impact of imperfect CSI on the system performance to verify our analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

SCMA Spectral and Energy Efficiency with QoS

Sparse Code Multiple Access (SCMA) is one of the promising candidates for new radio access interface. The new generation communication system is expected to support massive user access with high capacity. However, there are numerous problems and barriers to achieve optimal performance, e.g., the multiuser interference and high power consumption. In this paper, we present optimization methods to enhance the spectral and energy efficiency for SCMA with individual rate requirements. The proposed method has shown a better network mapping matrix based on power allocation and codebook assignment. Moreover, the proposed method is compared with orthogonal frequency devision multiple access (OFDMA) and code devision multiple access (CDMA) in terms of spectral efficiency (SE) and energy efficiency (EE) respectively. Simulation results show that SCMA performs better than OFDMA and CDMA both in SE and EE.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Sequential and Incremental Precoder Design for Joint Transmission Network MIMO Systems with Imperfect Backhaul

In this paper, we propose a sequential and incremental precoder design for downlink joint transmission (JT) network MIMO systems with imperfect backhaul links. The objective of our design is to minimize the maximum of the sub-stream mean square errors (MSE), which dominates the average bit error rate (BER) performance of the system. In the proposed scheme,we first optimize the precoder at the serving base station (BS), and then sequentially optimize the precoders of non-serving BSs in the JT set according to the descending order of their probabilities of participating in JT. The BS-wise sequential optimization process can improve the system performance when some BSs have to temporarily quit the JT operations because of poor instant backhaul conditions. Besides, the precoder of an additional BS is derived in an incremental way, i.e., the sequentially optimized precoders of previous BSs are fixed, thus the additional precoder plays an incremental part in the multi-BS JT operations. An iterative algorithm is designed to jointly optimize the sub-stream precoder and sub-stream power allocation for each additional BS in the proposed sequential and incremental optimization scheme. Simulations show that, under the practical backhaul link conditions, our scheme significantly outperforms the autonomous global precoding (AGP) scheme in terms of BER performance.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Structured Distributed Compressive Channel Estimation over Doubly Selective Channels

For an orthogonal frequency-division multiplexing (OFDM) system over a doubly selective (DS) channel, a large number of pilot subcarriers are needed to estimate the numerous channel parameters, resulting in low spectral efficiency. In this paper, by exploiting temporal correlation of practical wireless channels, we propose a highly efficient structured distributed compressive sensing (SDCS) based joint multi-symbol channel estimation scheme. Specifically, by using the complex exponential basis expansion model (CE-BEM) and exploiting the sparsity in the delay domain within multiple OFDM symbols, we turn to estimate jointly sparse CE-BEM coefficient vectors rather than numerous channel taps. Then a sparse pilot pattern within multiple OFDM symbols is designed to obtain an ICI-free structure and transform the channel estimation problem into a joint-block-sparse model. Next, a novel block-based simultaneous orthogonal matching pursuit (BSOMP) algorithm is proposed to jointly recover coefficient vectors accurately. Finally, to reduce the CE-BEM modeling error, we carry out smoothing treatments of already estimated channel taps via piecewise linear approximation.Simulation results demonstrate that the proposed channel estimation scheme can achieve higher estimation accuracy than conventional schemes, although with a smaller number of pilot subcarriers.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Subcarrier Assignment and Power Allocation for SCMA Energy Efficiency

In this paper we propose resource allocation algorithm for uplink sparse code multiple access (SCMA) networks to maximize the energy efficiency (EE). Due to the joint optimization of factor graph matrix and power allocation matrix, the EE maximization is a non-convex mixed-integer non-linear program (MINLP) problem. After transforming the non-convex form of the uplink sum rate to a convex one and separating subcarrier assignment and power allocation, we propose an energy efficient subcarrier assignment algorithm. By applying the fractional programming theory based on Dinkelbach method, we then propose power allocation algorithm to maximize EE. Finally, the simulation results show that the proposed resource allocation algorithm can significantly increase the EE of the uplink SCMA network.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The hyperluminous, dust-obscured quasar W2246-0526 at z=4.6: detection of parsec-scale radio activity

WISE J224607.56$-$052634.9 (W2246-0526) is a hyperluminous ($L_{\rm bol}\approx 1.7\times 10^{14}~L_\odot$), dust-obscured and radio-quiet quasar at redshift $z=4.6$. It plays a key role in probing the transition stage between dusty starbursts and unobscured quasars in the co-evolution of galaxies and supermassive black holes (SMBHs). To search for the evidence of the jet activity launched by the SMBH in W2246-0526, we performed very long baseline interferometry (VLBI) observations of its radio counterpart with the European VLBI Network (EVN) plus the enhanced Multi Element Remotely Linked Interferometer Network (e-MERLIN) at 1.66 GHz and the Very Long Baseline Array (VLBA) at 1.44 and 1.66 GHz. The deep EVN plus e-MERLIN observations detect a compact (size $\leq32$ pc) sub-mJy component contributing about ten percent of its total flux density, which spatially coincides with the peak of dust continuum and [C II] emissions. Together with its relatively high brightness temperature ($\geq8\times10^{6}$ K), we interpret the component as a consequence of non-thermal radio activity powered by the central SMBH, which likely originates from a stationary jet base. The resolved-out radio emission possibly come from a diffuse jet, quasar-driven winds, or both, while the contribution by star formation activity is negligible. Moreover, we propose an updated geometry structure of its multi-wavelength active nucleus and shed light on the radio quasar selection bias towards the blazars at $z>4$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Towrad 5G Air Interface Technology: Sparse Code Muliple Access

The fifth generation wireless networks focus on the design of low latency, high data rate, high reliability, and massive connectivity communications. Non-orthogonal multiple access (NOMA) is an essential enabling technology to accommodate the wide range of communication requirements. By coordinating the massive devices within the same resource block on power domain, frequency domain or code domain, NOMA is superior to conventional orthogonal multiple access in terms of the network connectivity, the throughputs of system and etc. Sparse code multiple access (SCMA) is a kind of multi-carrier code domain NOMA and has been studied extensively. The challenges for designing a high quality SCMA system is to seek the feasible encoding and decoding schemes to meet the desired requirements. In this article, we present some recent progresses towards the design of multi-dimensional codebooks, the practical low complexity decoder, as well as the Grant-Free multiple access for SCMA system. In particular, we show how the SCMA codebooks construction are motived by the combined design of multi-dimensional constellation and factor graphs. In addition, various low complexity SCMA decoders are also reviewed with a special focus on sphere decoding. Moreover, based on the framework of belief propagation, the SCMA Grant-Free transmission is introduced and the problem of collision resolution is also discussed.

0 citations0 reviewsNo code linkedOpen access