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Lei Lei

Lei Lei appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Information Theorymath.ITMachine LearningNetworking and Internet ArchitectureArtificial IntelligenceComputer Visioneess.SPeess.SYphysics.opticsSystems and Controlastro-ph.GAastro-ph.IMastro-ph.SRDistributed, Parallel, and Cluster ComputingPerformanceRobotics

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Published work

24 published item(s)

preprint2026arXiv

DeformMaster: An Interactive Physics-Neural World Model for Deformable Objects from Videos

World models for deformable objects should recover not only geometry and appearance, but also underlying physical dynamics, interaction grounding, and material behavior. Learning such a model from real videos is challenging because deformable linear, planar, and volumetric objects evolve under high-dimensional deformation, noisy interactions, and complex material response. The model must therefore infer a physical state from visual observations, roll it forward under new interactions, and render the resulting dynamics with high visual fidelity. We present DeformMaster, a video-derived interactive physics--neural world model that turns real interaction videos into an online interactive model of deformable objects within a unified dynamics-and-appearance framework. DeformMaster preserves structured physical rollout while using a neural residual to compensate for unmodeled effects, grounds sparse hand motion as distributed compliant actuator for hand--continuum interaction, represents material response with spatially varying constitutive experts, and drives high-fidelity 4D appearance from the predicted physical evolution. Experiments on real-world deformable-object sequences demonstrate DeformMaster's ability to roll out future dynamics and render dynamic appearance, outperforming state-of-the-art baselines while supporting novel action rollout, material-parameter variation, and dynamic novel-view synthesis.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Physics-embedded neural computational electron microscopy for quantitative 4D nanometrology

The fusion of rigorous physical laws with flexible data-driven learning represents a new frontier in scientific simulation, yet bridging the gap between physical interpretability and computational efficiency remains a grand challenge. In electron microscopy, this divide limits the ability to quantify three-dimensional topography from two-dimensional projections, fundamentally constraining our understanding of nanoscale structure-function relationships. Here, we present a physics-embedded neural computational microscopy framework that achieves metrological three-dimensional reconstruction by deeply coupling a differentiable electron-optical forward model with deep learning. By introducing a Vision Field Transformer as a high-speed, differentiable surrogate for physical process analysis simulations, we establish an end-to-end, self-supervised optimization loop that enforces strict physical consistency with hardware geometry. This synergy enables single-shot, quantitative three-dimensional nanometrology with precision comparable to atomic force microscopy but at orders of magnitude higher throughput. Furthermore, we demonstrate the capability for four-dimensional (3D real space plus time) in situ characterization by tracking the dynamic evolution of surface nanostructure during copper redox, revealing hidden crystallographic kinetics invisible to conventional imaging. Our work not only redefines the limits of scanning electron microscopy but also establishes a generalizable archetype for solving ill-posed inverse problems across physical sciences, unlocking the full potential of simulation as a third pillar of discovery.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting

Neural rendering techniques, including NeRF and Gaussian Splatting (GS), rely on photometric consistency to produce high-quality reconstructions. However, in real-world scenarios, it is challenging to guarantee perfect photometric consistency in acquired images. Appearance codes have been widely used to address this issue, but their modeling capability is limited, as a single code is applied to the entire image. Recently, the bilateral grid was introduced to perform pixel-wise color mapping, but it is difficult to optimize and constrain effectively. In this paper, we propose a novel multi-scale bilateral grid that unifies appearance codes and bilateral grids. We demonstrate that this approach significantly improves geometric accuracy in dynamic, decoupled autonomous driving scene reconstruction, outperforming both appearance codes and bilateral grids. This is crucial for autonomous driving, where accurate geometry is important for obstacle avoidance and control. Our method shows strong results across four datasets: Waymo, NuScenes, Argoverse, and PandaSet. We further demonstrate that the improvement in geometry is driven by the multi-scale bilateral grid, which effectively reduces floaters caused by photometric inconsistency.

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preprint2022arXiv

A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud

Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud. In recent works, Reinforcement Learning (RL) has been introduced as a promising approach to learn the resource management policies to guide the scaling actions under the dynamic and uncertain cloud environment. However, RL methods face the following challenges in steering predictive autoscaling, such as lack of accuracy in decision-making, inefficient sampling and significant variability in workload patterns that may cause policies to fail at test time. To this end, we propose an end-to-end predictive meta model-based RL algorithm, aiming to optimally allocate resource to maintain a stable CPU utilization level, which incorporates a specially-designed deep periodic workload prediction model as the input and embeds the Neural Process to guide the learning of the optimal scaling actions over numerous application services in the Cloud. Our algorithm not only ensures the predictability and accuracy of the scaling strategy, but also enables the scaling decisions to adapt to the changing workloads with high sample efficiency. Our method has achieved significant performance improvement compared to the existing algorithms and has been deployed online at Alipay, supporting the autoscaling of applications for the world-leading payment platform.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

AVDDPG: Federated reinforcement learning applied to autonomous platoon control

Since 2016 federated learning (FL) has been an evolving topic of discussion in the artificial intelligence (AI) research community. Applications of FL led to the development and study of federated reinforcement learning (FRL). Few works exist on the topic of FRL applied to autonomous vehicle (AV) platoons. In addition, most FRL works choose a single aggregation method (usually weight or gradient aggregation). We explore FRL's effectiveness as a means to improve AV platooning by designing and implementing an FRL framework atop a custom AV platoon environment. The application of FRL in AV platooning is studied under two scenarios: (1) Inter-platoon FRL (Inter-FRL) where FRL is applied to AVs across different platoons; (2) Intra-platoon FRL (Intra-FRL) where FRL is applied to AVs within a single platoon. Both Inter-FRL and Intra-FRL are applied to a custom AV platooning environment using both gradient and weight aggregation to observe the performance effects FRL can have on AV platoons relative to an AV platooning environment trained without FRL. It is concluded that Intra-FRL using weight aggregation (Intra-FRLWA) provides the best performance for controlling an AV platoon. In addition, we found that weight aggregation in FRL for AV platooning provides increases in performance relative to gradient aggregation. Finally, a performance analysis is conducted for Intra-FRLWA versus a platooning environment without FRL for platoons of length 3, 4 and 5 vehicles. It is concluded that Intra-FRLWA largely out-performs the platooning environment that is trained without FRL.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information

The impact of Vehicle-to-Everything (V2X) communications on platoon control performance is investigated. Platoon control is essentially a sequential stochastic decision problem (SSDP), which can be solved by Deep Reinforcement Learning (DRL) to deal with both the control constraints and uncertainty in the platoon leading vehicle's behavior. In this context, the value of V2X communications for DRL-based platoon controllers is studied with an emphasis on the tradeoff between the gain of including exogenous information in the system state for reducing uncertainty and the performance erosion due to the curse-of-dimensionality. Our objective is to find the specific set of information that should be shared among the vehicles for the construction of the most appropriate state space. SSDP models are conceived for platoon control under different information topologies (IFT) by taking into account `just sufficient' information. Furthermore, theorems are established for comparing the performance of their optimal policies. In order to determine whether a piece of information should or should not be transmitted for improving the DRL-based control policy, we quantify its value by deriving the conditional KL divergence of the transition models. More meritorious information is given higher priority in transmission, since including it in the state space has a higher probability in offsetting the negative effect of having higher state dimensions. Finally, simulation results are provided to illustrate the theoretical analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids

Smart grids extremely rely on Information and Communications Technology (ICT) and smart meters to control and manage numerous parameters of the network. However, using these infrastructures make smart grids more vulnerable to cyber threats especially electricity theft. Electricity Theft Detection (EDT) algorithms are typically used for such purpose since this Non-Technical Loss (NTL) may lead to significant challenges in the power system. In this paper, an Ensemble Deep Convolutional Neural Network (EDCNN) algorithm for ETD in smart grids has been proposed. As the first layer of the model, a random under bagging technique is applied to deal with the imbalance data, and then Deep Convolutional Neural Networks (DCNN) are utilized on each subset. Finally, a voting system is embedded, in the last part. The evaluation results based on the Area Under Curve (AUC), precision, recall, f1-score, and accuracy verify the efficiency of the proposed method compared to the existing method in the literature.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Identifications of RR Lyrae stars and Quasars from the simulated data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the $uvgriz$ multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first year observation of Mephisto-W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~ 150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with $purity$ = 95.4 per cent and $completeness$ = 96.9 per cent for the RR Lyrae RFC and $purity$ = 91.4 per cent and $completeness$ = 90.2 per cent for the quasar RFC. We have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept - autonomous IoT (AIoT). The sensors collect information on the system status, based on which the intelligent agents in the IoT devices as well as the Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide a tutorial of DRL, and then propose a general model for the applications of RL/DRL in AIoT. Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model. Finally, the challenges and open issues for future research are identified.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization

In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage have triggered the development of intelligent energy-conserving scheduling solutions. In this paper, we investigate energy minimization for UAV-aided communication networks by jointly optimizing data-transmission scheduling and UAV hovering time. The formulated problem is combinatorial and non-convex with bilinear constraints. To tackle the problem, firstly, we provide an optimal relax-and-approximate solution and develop a near-optimal algorithm. Both the proposed solutions are served as offline performance benchmarks but might not be suitable for online operation. To this end, we develop a solution from a deep reinforcement learning (DRL) aspect. The conventional RL/DRL, e.g., deep Q-learning, however, is limited in dealing with two main issues in constrained combinatorial optimization, i.e., exponentially increasing action space and infeasible actions. The novelty of solution development lies in handling these two issues. To address the former, we propose an actor-critic-based deep stochastic online scheduling (AC-DSOS) algorithm and develop a set of approaches to confine the action space. For the latter, we design a tailored reward function to guarantee the solution feasibility. Numerical results show that, by consuming equal magnitude of time, AC-DSOS is able to provide feasible solutions and saves 29.94% energy compared with a conventional deep actor-critic method. Compared to the developed near-optimal algorithm, AC-DSOS consumes around 10% higher energy but reduces the computational time from minute-level to millisecond-level.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

NOMA-Enabled Multi-Beam Satellite Systems: Joint Optimization to Overcome Offered-Requested Data Mismatches

Non-Orthogonal Multiple Access (NOMA) has potentials to improve the performance of multi-beam satellite systems. The performance optimization in satellite-NOMA systems can be different from that in terrestrial-NOMA systems, e.g., considering distinctive channel models, performance metrics, power constraints, and limited flexibility in resource management. In this paper, we adopt a metric, Offered Capacity to requested Traffic Ratio (OCTR), to measure the requested-offered data (or rate) mismatch in multi-beam satellite systems. In the considered system, NOMA is applied to mitigate intra-beam interference while precoding is implemented to reduce inter-beam interference. We jointly optimize power, decoding orders, and terminal-timeslot assignment to improve the max-min fairness of OCTR. The problem is inherently difficult due to the presence of combinatorial and non-convex aspects. We first fix the terminal-timeslot assignment, and develop an optimal fast-convergence algorithmic framework based on Perron-Frobenius theory (PF) for the remaining joint power-allocation and decoding-order optimization problem. Under this framework, we propose a heuristic algorithm for the original problem, which iteratively updates the terminal-timeslot assignment and improves the overall OCTR performance. Numerical results verify that max-min OCTR is a suitable metric to address the mismatch issue, and is able to improve the fairness among terminals. In average, the proposed algorithm improves the max-min OCTR by 40.2% over Orthogonal Multiple Access (OMA).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Performance Modeling and Analysis of a Hyperledger-based System Using GSPN

As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of industry use cases ranging from governance to finance. In this paper, we propose a model to analyze the performance of a Hyperledgerbased system by using Generalised Stochastic Petri Nets (GSPN). This model decomposes a transaction flow into multiple phases and provides a simulation-based approach to obtain the system latency and throughput with a specific arrival rate. Based on this model, we analyze the impact of different configurations of ordering service on system performance to find out the bottleneck. Moreover, a mathematical configuration selection approach is proposed to determine the best configuration which can maximize the system throughput. Finally, extensive experiments are performed on a running system to validate the proposed model and approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Towards Power-Efficient Aerial Communications via Dynamic Multi-UAV Cooperation

Aerial base stations (BSs) attached to unmanned aerial vehicles (UAVs) constitute a new paradigm for next-generation cellular communications. However, the flight range and communication capacity of aerial BSs are usually limited due to the UAVs' size, weight, and power (SWAP) constraints. To address this challenge, in this paper, we consider dynamic cooperative transmission among multiple aerial BSs for power-efficient aerial communications. Thereby, a central controller intelligently selects the aerial BSs navigating in the air for cooperation. Consequently, the large virtual array of moving antennas formed by the cooperating aerial BSs can be exploited for low-power information transmission and navigation, taking into account the channel conditions, energy availability, and user demands. Considering both the fronthauling and the data transmission links, we jointly optimize the trajectories, cooperation decisions, and transmit beamformers of the aerial BSs for minimization of the weighted sum of the power consumptions required by all BSs. Since obtaining the global optimal solution of the formulated problem is difficult, we propose a low-complexity iterative algorithm that can efficiently find a Karush-Kuhn-Tucker (KKT) solution to the problem. Simulation results show that, compared with several baseline schemes, dynamic multi-UAV cooperation can significantly reduce the communication and navigation powers of the UAVs to overcome the SWAP limitations, while requiring only a small increase of the transmit power over the fronthauling links.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Energy-Efficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks

Wireless sensor networks (WSNs) are effective for locating and tracking people and objects in various industrial environments. Since energy consumption is critical to prolonging the lifespan of WSNs, we propose an energy-efficient LOcalization and Tracking} (eLOT) system, using low-cost and portable hardware to enable highly accurate tracking of targets. Various fingerprint-based approaches for localization and tracking are implemented in eLOT. In order to achieve high energy efficiency, a network-level scheme coordinating collision and interference is proposed. On the other hand, based on the location information, mobile devices in eLOT can quickly associate with the specific channel in a given area, while saving energy through avoiding unnecessary transmission. Finally, a platform based on TI CC2530 and the Linux operating system is built to demonstrate the effectiveness of our proposed scheme in terms of localization accuracy and energy efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Optimal Reliability in Energy Harvesting Industrial Wireless Sensor Networks

For Industrial Wireless Sensor Networks, it is essential to reliably sense and deliver the environmental data on time to avoid system malfunction. While energy harvesting is a promising technique to extend the lifetime of sensor nodes, it also brings new challenges for system reliability due to the stochastic nature of the harvested energy. In this paper, we investigate the optimal energy management policy to minimize the weighted packet loss rate under delay constraint, where the packet loss rate considers the lost packets both during the sensing and delivering processes. We show that the above energy management problem can be modeled as an infinite horizon average reward constraint Markov decision problem. In order to address the well-known curse of dimensionality problem and facilitate distributed implementation, we utilize the linear value approximation technique. Moreover, we apply stochastic online learning with post-decision state to deal with the lack of knowledge of the underlying stochastic processes. A distributed energy allocation algorithm with water-filling structure and a scheduling algorithm by auction mechanism are obtained. Experimental results show that the proposed algorithm achieves nearly the same performance as the optimal offline value iteration algorithm while requiring much less computation complexity and signaling overhead, and outperforms various existing baseline algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Power and Channel Allocation for Non-orthogonal Multiple Access in 5G Systems: Tractability and Computation

Network capacity calls for significant increase for 5G cellular systems. A promising multi-user access scheme, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is currently under consideration. In NOMA, spectrum efficiency is improved by allowing more than one user to simultaneously access the same frequency-time resource and separating multi-user signals by SIC at the receiver. These render resource allocation and optimization in NOMA different from orthogonal multiple access in 4G. In this paper, we provide theoretical insights and algorithmic solutions to jointly optimize power and channel allocation in NOMA. For utility maximization, we mathematically formulate NOMA resource allocation problems. We characterize and analyze the problems' tractability under a range of constraints and utility functions. For tractable cases, we provide polynomial-time solutions for global optimality. For intractable cases, we prove the NP-hardness and propose an algorithmic framework combining Lagrangian duality and dynamic programming (LDDP) to deliver near-optimal solutions. To gauge the performance of the obtained solutions, we also provide optimality bounds on the global optimum. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance in both throughput and fairness over orthogonal multiple access as well as over a previous NOMA resource allocation scheme.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Large-Scale MIMO Relaying Techniques for Physical Layer Security: AF or DF?

In this paper, we consider a large scale multiple input multiple output (LS-MIMO) relaying system, where an information source sends the message to its intended destination aided by an LS-MIMO relay, while a passive eavesdropper tries to intercept the information forwarded by the relay. The advantage of a large scale antenna array is exploited to improve spectral efficiency and enhance wireless security. In particular, the challenging issue incurred by short-distance interception is well addressed. Under very practical assumptions, i.e., no eavesdropper channel state information (CSI) and imperfect legitimate CSI at the relay, this paper gives a thorough secrecy performance analysis and comparison of two classic relaying techniques, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, asymptotical analysis is carried out to provide clear insights on the secrecy performance for such an LS-MIMO relaying system. We show that under large transmit powers, AF is a better choice than DF from the perspectives of both secrecy performance and implementation complexity, and prove that there exits an optimal transmit power at medium regime that maximizes the secrecy outage capacity.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Optimal Cell Clustering and Activation for Energy Saving in Load-Coupled Wireless Networks

Optimizing activation and deactivation of base station transmissions provides an instrument for improving energy efficiency in cellular networks. In this paper, we study optimal cell clustering and scheduling of activation duration for each cluster, with the objective of minimizing the sum energy, subject to a time constraint of delivering the users' traffic demand. The cells within a cluster are simultaneously in transmission and napping modes, with cluster activation and deactivation, respectively. Our optimization framework accounts for the coupling relation among cells due to the mutual interference. Thus, the users' achievable rates in a cell depend on the cluster composition. On the theoretical side, we provide mathematical formulation and structural characterization for the energy-efficient cell clustering and scheduling optimization problem, and prove its NP hardness. On the algorithmic side, we first show how column generation facilitates problem solving, and then present our notion of local enumeration as a flexible and effective means for dealing with the trade-off between optimality and the combinatorial nature of cluster formation, as well as for the purpose of gauging the deviation from optimality. Numerical results demonstrate that our solutions achieve more than 60% energy saving over existing schemes, and that the solutions we obtain are within a few percent of deviation from global optimum.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Optimal Power Allocation for Secure Communications in Large-Scale MIMO Relaying Systems

In this paper, we address the problem of optimal power allocation at the relay in two-hop secure communications. In order to solve the challenging issue of short-distance interception in secure communications, the benefit of large-scale MIMO (LS-MIMO) relaying techniques is exploited to improve the secrecy performance significantly, even in the case without eavesdropper channel state information (CSI). The focus of this paper is on the analysis and design of optimal power allocation for the relay, so as to maximize the secrecy outage capacity. We reveal the condition that the secrecy outage capacity is positive, prove that there is one and only one optimal power, and present an optimal power allocation scheme. Moreover, the asymptotic characteristics of the secrecy outage capacity is carried out to provide some clear insights for secrecy performance optimization. Finally, simulation results validate the effectiveness of the proposed scheme.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges

Heterogeneous Vehicular NETworks (HetVNETs) can meet various quality-of-service (QoS) requirements for intelligent transport system (ITS) services by integrating different access networks coherently. However, the current network architecture for HetVNET cannot efficiently deal with the increasing demands of rapidly changing network landscape. Thanks to the centralization and flexibility of the cloud radio access network (Cloud-RAN), soft-defined networking (SDN) can conveniently be applied to support the dynamic nature of future HetVNET functions and various applications while reducing the operating costs. In this paper, we first propose the multi-layer Cloud RAN architecture for implementing the new network, where the multi-domain resources can be exploited as needed for vehicle users. Then, the high-level design of soft-defined HetVNET is presented in detail. Finally, we briefly discuss key challenges and solutions for this new network, corroborating its feasibility in the emerging fifth-generation (5G) era.

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preprint2014arXiv

On Power and Load Coupling in Cellular Networks for Energy Optimization

We consider the problem of minimization of sum transmission energy in cellular networks where coupling occurs between cells due to mutual interference. The coupling relation is characterized by the signal-to-interference-and-noise-ratio (SINR) coupling model. Both cell load and transmission power, where cell load measures the average level of resource usage in the cell, interact via the coupling model. The coupling is implicitly characterized with load and power as the variables of interest using two equivalent equations, namely, non-linear load coupling equation (NLCE) and non-linear power coupling equation (NPCE), respectively. By analyzing the NLCE and NPCE, we prove that operating at full load is optimal in minimizing sum energy, and provide an iterative power adjustment algorithm to obtain the corresponding optimal power solution with guaranteed convergence, where in each iteration a standard bisection search is employed. To obtain the algorithmic result, we use the properties of the so-called standard interference function; the proof is non-standard because the NPCE cannot even be expressed as a closed-form expression with power as the implicit variable of interest. We present numerical results illustrating the theoretical findings for a real-life and large-scale cellular network, showing the advantage of our solution compared to the conventional solution of deploying uniform power for base stations.

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preprint2014arXiv

On the Secrecy Outage Capacity of Physical Layer Security in Large-Scale MIMO Relaying Systems with Imperfect CSI

In this paper, we study the problem of physical layer security in a large-scale multiple-input multiple-output (LS-MIMO) relaying system. The advantage of LS-MIMO relaying systems is exploited to enhance both wireless security and spectral efficiency. In particular, the challenging issue incurred by short interception distance is well addressed. Under very practical assumptions, i.e., no eavesdropper's channel state information (CSI) and imperfect legitimate channel CSI, this paper gives a thorough investigation of the impact of imperfect CSI in two classic relaying systems, i.e., amplify-and-forward (AF) and decode-and-forward (DF) systems, and obtain explicit expressions of secrecy outage capacities for both cases. Finally, our theoretical claims are validated by the numerical results.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Diversity of photonic differentiators based on flexible demodulation of phase signals

We theoretically prove a multifunctional photonic differentiation (DIFF) scheme based on phase demodulation using two cascaded linear filters. The photonic DIFF has a diversity of output forms, such as 1st order intensity DIFF, 1st order field DIFF and its inversion, 2nd order field DIFF, dependent on the relative shift between the optical carrier and the filter's resonant notches. As a proof, we also experimentally demonstrate the DIFF diversity using a phase modulator and two delay interferometers (DIs). The calculated average deviation is less than 7% for all DIFF waveforms. Our schemes show the advantages of flexible DIFF functions and forms, which may have different optical applications. For example, high order field differentiators can be used to generate complex temporal waveforms. And intensity differentiators are useful for ultra-wideband pulse generation.

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preprint2013arXiv

Energy-Efficient Optimization for Physical Layer Security in Multi-Antenna Downlink Networks with QoS Guarantee

In this letter, we consider a multi-antenna downlink network where a secure user (SU) coexists with a passive eavesdropper. There are two design requirements for such a network. First, the information should be transferred in a secret and efficient manner. Second, the quality of service (QoS), i.e. delay sensitivity, should be take into consideration to satisfy the demands of real-time wireless services. In order to fulfill the two requirements, we combine the physical layer security technique based on switched beam beamforming with an energy-efficient power allocation. The problem is formulated as the maximization of the secrecy energy efficiency subject to delay and power constraints. By solving the optimization problem, we derive an energy-efficient power allocation scheme. Numerical results validate the effectiveness of the proposed scheme.

0 citations0 reviewsNo code linkedOpen access