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Researcher profile

Meixia Tao

Meixia Tao contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Information Theorymath.ITeess.SPMachine LearningArtificial IntelligenceNetworking and Internet ArchitectureRobotics

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Published work

24 published item(s)

preprint2026arXiv

Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models

Reconfigurable intelligent surface (RIS) has been recognized as a promising technology for next-generation wireless communications. However, the performance of RIS-assisted systems critically depends on accurate channel state information (CSI). To address this challenge, this letter proposes a novel channel estimation method for RIS-aided millimeter-wave (mmWave) systems based on diffusion models (DMs). Specifically, the forward diffusion process of the original signal is formulated to model the received signal as a noisy observation within the framework of DMs. Subsequently, the channel estimation task is formulated as the reverse diffusion process, and a sampling algorithm based on denoising diffusion implicit models (DDIMs) is developed to enable effective inference. Furthermore, a lightweight neural network, termed BRCNet, is introduced to replace the conventional U-Net, significantly reducing the number of parameters and computational complexity. Extensive experiments conducted under various scenarios demonstrate that the proposed method consistently outperforms existing baselines.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

CR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference

As large language models (LLMs) move from centralized clouds to mobile edge environments, efficient serving must balance latency, energy consumption, and accuracy under constrained device-edge resources. Query-level routing between lightweight on-device models and stronger edge models provides a flexible mechanism to navigate this trade-off. However, existing routers are designed for centralized cloud settings and optimize token-level costs, failing to capture the dynamic latency and energy overheads in wireless edge deployments. In this paper, we formulate mobile edge LLM routing as a deployment-constrained, cost-aware decision problem, and propose CR^2, a two-stage device-edge routing framework. CR^2 decouples a lightweight on-device margin gate from an edge-side utility selector for deferred queries. The margin gate operates on frozen query embeddings and a user-specified cost weight to predict whether local execution is utility-optimal relative to the best edge alternative under the target operating point. We further introduce a conformal risk control (CRC) calibration procedure that maps each operating point to an acceptance threshold, enabling explicit control of the marginal false-acceptance risk under the full-information utility reference. Experiments on the routing task show that CR^2 closely matches a full-information reference router using only device-side signals before deferral. Compared with strong query-level baselines, CR^2 consistently improves the deployable accuracy-cost Pareto frontier and reduces normalized deployment cost by up to 16.9% at matched accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Domain-Adaptive Communication-Rate Optimization for Sim-to-Real Humanoid-Robot Wireless XR Teleoperation

Wireless extended reality (XR) teleoperation provides embodied interaction capability for collecting humanoid robot demonstrations, but the large-scale adoption is restricted by the overhead of high-frequency motion transmission. This paper develops a system framework that integrates sampling, transmission, interpolation, and reconstruction and formulates a communication-rate optimization that aims to minimize the communication energy while maintaining the reconstruction accuracy of robot motion trajectories through dimension-wise sampling-rate control. Since acquiring real-time feedback from physical robots is limited by hardware costs, it is necessary to solve the problem through simulator interaction with offline real-domain data correction. To guide sim-to-real adaptation, we provide a PAC-Bayes generalization characterization that reveals the effects of latent density-ratio estimation, finite-sample deviation, and encoder bias. Building on this analysis, we propose a proximal policy optimization (PPO) method with density-ratio weighting and trust-region regularization. Experiments on public humanoid teleoperation dataset show that the proposed method improves the tradeoff between reconstruction error and communication energy consumption under sim-to-real distribution shift. We further analyze the effectiveness of the proposed algorithm across various wireless channels and dynamic motion trajectories.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

A Fundamental Tradeoff Among Storage, Computation, and Communication for Distributed Computing over Star Network

Coded distributed computing can alleviate the communication load by leveraging the redundant storage and computation resources with coding techniques in distributed computing. In this paper, we study a MapReduce-type distributed computing framework over star topological network, where all the workers exchange information through a common access point. The optimal tradeoff among the normalized number of the stored files (storage load), computed intermediate values (computation load) and transmitted bits in the uplink and downlink (communication loads) are characterized. A coded computing scheme is proposed to achieve the Pareto-optimal tradeoff surface, in which the access point only needs to perform simple chain coding between the signals it receives, and information-theretical bound matching the surface is also provided.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Applicable Regions of Spherical and Plane Wave Models for Extremely Large-Scale Array Communications

Extremely large-scale array (XL-array) communications can significantly improve the spectral efficiency and spatial resolution, and has great potential in next-generation mobile communication networks. A crucial problem in XL-array communications is to determine the boundary of applicable regions of the plane wave model (PWM) and spherical wave model (SWM). In this paper, we propose new PWM/SWM demarcations for XL-arrays from the viewpoint of channel gain and rank. Four sets of results are derived for four different array setups. First, an equi-power line is derived for a point-to-uniform linear array (ULA) scenario, where an inflection point is found at $\pm \fracπ{6}$ central incident angles. Second, an equi-power surface is derived for a point-to-uniform planar array (UPA) scenario, and it is proved that $\cos^2(ϕ) \cos^2(φ)=\frac{1}{2}$ is a dividing curve, where $ϕ$ and $φ$ denote the elevation and azimuth angles, respectively. Third, an accurate and explicit expression of the equi-rank surface is obtained for a ULA-to-ULA scenario. Finally, an approximated expression of the equi-rank surface is obtained for a ULA-to-UPA scenario. With the obtained closed-form expressions, the equi-rank surface for any antenna structure and any angle can be well estimated. Furthermore, the effect of scatterers is also investigated, from which some insights are drawn.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Characteristics of Channel Eigenvalues and Mutual Coupling Effects for Holographic Reconfigurable Intelligent Surfaces

As a prospective key technology for the next-generation wireless communications, reconfigurable intelligent surfaces (RISs) have gained tremendous research interest in both the academia and industry in recent years. Only limited knowledge, however, has been obtained about the channel eigenvalue characteristics and spatial degrees of freedom (DoF) of systems containing RISs, especially when mutual coupling (MC) is present between the array elements. In this paper, we focus on the small-scale spatial correlation and eigenvalue properties excluding and including MC effects, for RISs with a quasi-continuous aperture (i.e., holographic RISs). Specifically, asymptotic behaviors of far-field and near-field eigenvalues of the spatial correlation matrix of holographic RISs without MC are first investigated, where the counter-intuitive observation of a lower DoF with more elements is explained by leveraging the power spectrum of the spatial correlation function. Second, a novel metric is proposed to quantify the inter-element correlation or coupling strength in RISs and ordinary antenna arrays. Furthermore, in-depth analysis is performed regarding the MC effects on array gain, effective spatial correlation, and eigenvalue architectures for a variety of element intervals when a holographic RIS works in the radiation and reception mode, respectively. The analysis and numerical results demonstrate that a considerable amount of the eigenvalues of the spatial correlation matrix correspond to evanescent waves that are promising for near-field communication and sensing. More importantly, holographic RISs can potentially reach an array gain conspicuously larger than conventional arrays by exploiting MC, and MC has discrepant impacts on the effective spatial correlation and eigenvalue structures at the transmitter and receiver.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Joint Hybrid Beamforming and User Scheduling for Multi-Satellite Cooperative Networks

In this paper, we consider a cooperative communication network where multiple satellites provide services for ground users (GUs) (at the same time and on the same frequency). The communication and computational resources on satellites are usually restricted and the satellite-GU link determination affects the communication performance significantly when multiple satellites provide services for multiple GUs in a collaborative manner. Therefore, considering the limitation of the on-board radio-frequency chains, we first propose a hybrid beamforming method consisting of analog beamforming for beam alignment and digital beamforming for interference mitigation. Then, to establish appropriate connections between satellites and GUs, we propose a heuristic user scheduling algorithm which determines the connections according to the total spectral efficiency (SE) increment of the multi-satellite cooperative network. Next, a joint hybrid beamforming and user scheduling scheme is proposed to dramatically improve the performance of the multi-satellite cooperative network. Moreover, simulations are conducted to compare the proposed schemes with representative baselines and analyze the key factors influencing the performance of the multi-satellite cooperative network. It is shown that the proposed joint beamforming and user scheduling approach can provide 47.2% SE improvement on average as compared with its non-joint counterpart.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems

Fast and precise beam alignment is crucial to support high-quality data transmission in millimeter wave (mmWave) communication systems. In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner. Specifically, the proposed method first performs coarse channel measurement using the tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The proposed deep neural network (DNN) architecture is trained in two steps. First, the tier-1 PC and the tier-2 PC selector are trained jointly. After that, all the tier-2 PCs together with the optimal beam predictors are trained jointly. The learned hierarchical PCs can capture the features of propagation environment. Numerical results based on realistic ray-tracing datasets demonstrate that the proposed method is superior to the state-of-art beam alignment methods in both alignment accuracy and sweeping overhead.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Double-Sided Information Aided Temporal-Correlated Massive Access

This letter considers temporal-correlated massive access, where each device, once activated, is likely to transmit continuously over several consecutive frames. Motivated by that the device activity at each frame is correlated to not only its previous frame but also its next frame, we propose a double-sided information (DSI) aided joint activity detection and channel estimation algorithm based on the approximate message passing (AMP) framework. The DSI is extracted from the estimation results in a sliding window that contains the target detection frame and its previous and next frames. The proposed algorithm demonstrates superior performance over the state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach

One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters. Several model compression methods, such as gradient quantization and sparsification, have been proposed to improve the communication efficiency of model aggregation. However, the information-theoretic minimum communication cost for a given distortion of gradient estimators is still unknown. In this paper, we study the fundamental limit of communication cost of model aggregation in distributed learning from a rate-distortion perspective. By formulating the model aggregation as a vector Gaussian CEO problem, we derive the rate region bound and sum-rate-distortion function for the model aggregation problem, which reveals the minimum communication rate at a particular gradient distortion upper bound. We also analyze the communication cost at each iteration and total communication cost based on the sum-rate-distortion function with the gradient statistics of real-world datasets. It is found that the communication gain by exploiting the correlation between worker nodes is significant for SignSGD, and a high distortion of gradient estimator can achieve low total communication cost in gradient compression.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hybrid Spherical- and Planar-Wave Channel Modeling and Estimation for Terahertz Integrated UM-MIMO and IRS Systems

Integrated ultra-massive multiple-input multiple-output (UM-MIMO) and intelligent reflecting surface (IRS) systems are promising for 6G and beyond Terahertz (0.1-10 THz) communications, to effectively bypass the barriers of limited coverage and line-of-sight blockage. However, excessive dimensions of UM-MIMO and IRS enlarge the near-field region, while strong THz channel sparsity in far-field is detrimental to spatial multiplexing. Moreover, channel estimation (CE) requires recovering the large-scale channel from severely compressed observations due to limited RF-chains. To tackle these challenges, a hybrid spherical- and planar-wave channel model (HSPM) is developed for the cascaded channel of the integrated system. The spatial multiplexing gains under near-field and far-field regions are analyzed, which are found to be limited by the segmented channel with a lower rank. Furthermore, a compressive sensing-based CE framework is developed, including a sparse channel representation method, a separate-side estimation (SSE) and a dictionary-shrinkage estimation (DSE) algorithms. Numerical results verify the effectiveness of the HSPM, the capacity of which is only $5\times10^{-4}$ bits/s/Hz deviated from that obtained by the ground-truth spherical-wave-model, with 256 elements. While the SSE achieves improved accuracy for CE than benchmark algorithms, the DSE is more attractive in noisy environments, with 0.8 dB lower normalized-mean-square-error than SSE.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Joint Design of Hybrid Beamforming and Reflection Coefficients in RIS-aided mmWave MIMO Systems

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under the signal-to-interference-plus-noise ratio (SINR) constraints at all users. The problem is highly challenging to solve due to the non-convex SINR constraints as well as the unit-modulus phase shift constraints for both the RIS reflection coefficients and the analog beamformer. A two-layer penalty-based algorithm is proposed to decouple variables in SINR constraints, and manifold optimization is adopted to handle the non-convex unit-modulus constraints. {We also propose a low-complexity sequential optimization method, which optimizes the RIS reflection coefficients, the analog beamformer, and the digital beamformer sequentially without iteration.} Furthermore, the relationship between the power minimization problem and the max-min fairness (MMF) problem is discussed. Simulation results show that the proposed penalty-based algorithm outperforms the state-of-the-art semidefinite relaxation (SDR)-based algorithm. Results also demonstrate that the RIS plays an important role in the power reduction.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Coded Computing and Cooperative Transmission for Wireless Distributed Matrix Multiplication

Consider a multi-cell mobile edge computing network, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix. This is done through task offloading by means of input uploading, distributed computing at edge nodes (ENs), and output downloading. Task offloading may suffer long delay since servers at some ENs may be straggling due to random computation time, and wireless channels may experience severe fading and interference. This paper aims to investigate the interplay among upload, computation, and download latencies during the offloading process in the high signal-to-noise ratio regime from an information-theoretic perspective. A policy based on cascaded coded computing and on coordinated and cooperative interference management in uplink and downlink is proposed and proved to be approximately optimal for a sufficiently large upload time. By investing more time in uplink transmission, the policy creates data redundancy at the ENs, which can reduce the computation time, by enabling the use of coded computing, as well as the download time via transmitter cooperation. Moreover, the policy allows computation time to be traded for download time. Numerical examples demonstrate that the proposed policy can improve over existing schemes by significantly reducing the end-to-end execution time.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Deep-Learned Approximate Message Passing for Asynchronous Massive Connectivity

This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios. While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices. This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data. Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity. Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning. It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Joint Design of Hybrid Beamforming and Phase Shifts in RIS-Aided mmWave Communication Systems

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under signal-to-interference-plus-noise ratio (SINR) constraints. The problem is highly challenging due to the non-convex SINR constraints as well as the non-convex unit-modulus constraints for both the phase shifts at the RIS and the analog beamforming at the BS. A penalty-based algorithm in conjunction with the manifold optimization technique is proposed to handle the problem, followed by an individual optimization method with much lower complexity. Simulation results show that the proposed algorithm outperforms the state-of-art algorithm. Results also show that the joint optimization of RIS response matrix and BS hybrid beamforming is much superior to individual optimization.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Two-Way Passive Beamforming Design for RIS-Aided FDD Communication Systems

Reconfigurable intelligent surfaces (RISs) are able to provide passive beamforming gain via low-cost reflecting elements and hence improve wireless link quality. This work considers two-way passive beamforming design in RIS-aided frequency division duplexing (FDD) systems where the RIS reflection coefficients are the same for downlink and uplink and should be optimized for both directions simultaneously. We formulate a joint optimization of the transmit/receive beamformers at the base station (BS) and the RIS reflection coefficients. The objective is to maximize the weighted sum of the downlink and uplink rates, where the weighting parameter is adjustable to obtain different achievable downlink-uplink rate pairs. We develop an efficient manifold optimization algorithm to obtain a stationary solution. For comparison, we also introduce two heuristic designs based on one-way optimization, namely, time-sharing and phase-averaging. Simulation results show that the proposed manifold-based two-way optimization design significantly enlarges the achievable downlink-uplink rate region compared with the two heuristic designs. It is also shown that phase-averaging is superior to time-sharing when the number of RIS elements is large.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching

This paper investigates learning-based caching in small-cell networks (SCNs) when user preference is unknown. The goal is to optimize the cache placement in each small base station (SBS) for minimizing the system long-term transmission delay. We model this sequential multi-agent decision making problem in a multi-agent multi-armed bandit (MAMAB) perspective. Rather than estimating user preference first and then optimizing the cache strategy, we propose several MAMAB-based algorithms to directly learn the cache strategy online in both stationary and non-stationary environment. In the stationary environment, we first propose two high-complexity agent-based collaborative MAMAB algorithms with performance guarantee. Then we propose a low-complexity distributed MAMAB which ignores the SBS coordination. To achieve a better balance between SBS coordination gain and computational complexity, we develop an edge-based collaborative MAMAB with the coordination graph edge-based reward assignment method. In the non-stationary environment, we modify the MAMAB-based algorithms proposed in the stationary environment by proposing a practical initialization method and designing new perturbed terms to adapt to the dynamic environment. Simulation results are provided to validate the effectiveness of our proposed algorithms. The effects of different parameters on caching performance are also discussed.

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

Degrees of Freedom of Cache-Aided Wireless Cellular Networks

This work investigates the degrees of freedom (DoF) of a downlink cache-aided cellular network where the locations of base stations (BSs) are modeled as a grid topology and users within a grid cell can only communicate with four nearby BSs. We adopt a cache placement method with uncoded prefetching tailored for the network with partial connectivity. According to the overlapped degree of cached contents among BSs, we propose transmission schemes with no BS cooperation, partial BS cooperation, and full BS cooperation, respectively, for different cache sizes. In specific, the common cached contents among BSs are utilized to cancel some undesired signals by interference neutralization while interference alignment is used to coordinate signals of distinct cached contents. Our achievable results indicate that the reciprocal of per-user DoF of the cellular network decreases piecewise linearly with the normalized cache size $μ$ at each BS, and the gain of BS caching is more significant for the small cache region. Under the given cache placement scheme, we also provide an upper bound of per-user DoF and show that our achievable DoF is optimal when $μ\in\left[\frac{1}{2},1\right]$, and within an additive gap of $\frac{4}{39}$ to the optimum when $μ\in\left[\frac{1}{4},\frac{1}{2}\right)$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Exploiting Computation Replication for Mobile Edge Computing: A Fundamental Computation-Communication Tradeoff Study

Existing works on task offloading in mobile edge computing (MEC) networks often assume a task is executed once at a single edge node (EN). Downloading the computed result from the EN back to the mobile user may suffer long delay if the downlink channel experiences strong interference or deep fading. This paper exploits the idea of computation replication in MEC networks to speed up the downloading phase. Computation replication allows each user to offload its task to multiple ENs for repetitive execution so as to create multiple copies of the computed result at different ENs which can then enable transmission cooperation and hence reduce the communication latency for result downloading. Yet, computation replication may also increase the communication latency for task uploading, despite the obvious increase in computation load. The main contribution of this work is to characterize asymptotically an order-optimal upload-download communication latency pair for a given computation load in a multi-user multi-server MEC network. Analysis shows when the computation load increases within a certain range, the downloading time decreases in an inversely proportional way if it is binary offloading or decreases linearly if it is partial offloading, both at the expense of linear increase in the uploading time.

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

Mobile Communications, Computing and Caching Resources Optimization for Coded Caching with Device Computing

Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a general coded caching with device computing strategy for content computation, e.g., virtual reality (VR) rendering, to minimize the average transmission bandwidth with the caching capacity and the energy constraints of each mobile device, and the maximum tolerable delay constraint of each task. The key enabler is that because both coded data and stored data can be the data before or after computing, the proposed scheme has numerous edge computing and caching paths corresponding to different bandwidth requirement. We thus formulate a joint coded caching and computing optimization problem to decide whether the mobile devices cache the input data or the output data, which tasks to be coded cached and which tasks to compute locally. The optimization problem is shown to be 0-1 nonconvex nonsmooth programming and can be decomposed into the computation programming and the coded caching programming. We prove the convergence of the computation programming problem by utilizing the alternating direction method of multipliers (ADMM), and a stationary point can be obtained. For the coded cache programming, we design a low complexity algorithm to obtain an acceptable solution. Numerical results demonstrate that the proposed scheme provides a significant bandwidth saving by taking full advantage of the caching and computing capability of mobile devices.

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

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.

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