Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles, renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model, and the vehicle may also be affected by Byzantine attacks, leading to the deterioration of the vehicle data. However, based on deep reinforcement learning (DRL), we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL. At the same time, when aggregating AFL, we can focus on those vehicles with better performance to improve the accuracy and safety of the system. In this paper, we proposed a vehicle selection scheme based on DRL in VEC. In this scheme, vehicle s mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Pseudo MIMO (pMIMO): An Energy and Spectral Efficient MIMO-OFDM System

This article introduces an energy and spectral efficient multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission scheme designed for the future sixth generation (6G) wireless communication networks. The approach involves connecting each receiving radio frequency (RF) chain with multiple antenna elements and conducting sample-level adjustments for receiving beamforming patterns. The proposed system architecture and the dedicated signal processing methods enable the scheme to transmit a bigger number of parallel data streams than the number of receiving RF chains, achieving a spectral efficiency performance close to that of a fully digital (FD) MIMO system with the same number of antenna elements, each equipped with an RF chain. We refer to this system as a ''pseudo MIMO'' system due to its ability to mimic the functionality of additional invisible RF chains. The article begins with introducing the underlying principles of pseudo MIMO and discussing potential hardware architectures for its implementation. We then highlight several advantages of integrating pseudo MIMO into next-generation wireless networks. To demonstrate the superiority of our proposed pseudo MIMO transmission scheme to conventional MIMO systems, simulation results are presented. Additionally, we validate the feasibility of this new scheme by building the first pseudo MIMO prototype. Furthermore, we present some key challenges and outline potential directions for future research.

Employing High-Dimensional RIS Information for RIS-aided Localization Systems

Reconfigurable intelligent surface (RIS)-aided localization systems have attracted extensive research attention due to their accuracy enhancement capabilities. However, most studies primarily utilized the base stations (BS) received signal, i.e., BS information, for localization algorithm design, neglecting the potential of RIS received signal, i.e., RIS information. Compared with BS information, RIS information offers higher dimension and richer feature set, thereby significantly improving the ability to extract positions of the mobile users (MUs). Addressing this oversight, this paper explores the algorithm design based on the high-dimensional RIS information. Specifically, we first propose a RIS information reconstruction (RIS-IR) algorithm to reconstruct the high-dimensional RIS information from the low-dimensional BS information. The proposed RIS-IR algorithm comprises a data processing module for preprocessing BS information, a convolution neural network (CNN) module for feature extraction, and an output module for outputting the reconstructed RIS information. Then, we propose a transfer learning based fingerprint (TFBF) algorithm that employs the reconstructed high-dimensional RIS information for MU localization. This involves adapting a pre-trained DenseNet-121 model to map the reconstructed RIS signal to the MU's three-dimensional (3D) position. Empirical results affirm that the localization performance is significantly influenced by the high-dimensional RIS information and maintains robustness against unoptimized phase shifts.

Large-Scale RIS Enabled Air-Ground Channels: Near-Field Modeling and Analysis

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, this will result in relatively low computing accuracy. To tackle this challenge, we initially develop an analytical framework for sub-array partitioning. This framework divides the large-scale RIS array into multiple sub-arrays, effectively reducing modeling complexity while maintaining acceptable accuracy. Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled UAV-to-vehicle communication systems, which can be used to efficiently capture the sparse features in RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges. Furthermore, some important propagation characteristics of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (CFs), and channel capacities with respect to the different physical features of the RIS and non-stationary properties of the channel model are derived and analyzed. Finally, simulation results are provided to demonstrate that the proposed framework is helpful to achieve a good tradeoff between model complexity and accuracy for investigating the channel propagation characteristics, and therefore providing highly-efficient communications in RIS-enabled UAV-to-vehicle wireless networks.

Deep Learning-Based CSI Feedback for RIS-Aided Massive MIMO Systems with Time Correlation

In this paper, we consider an reconfigurable intelligent surface (RIS)-aided frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) downlink system.In the FDD systems, the downlink channel state information (CSI) should be sent to the base station through the feedback link. However, the overhead of CSI feedback occupies substantial uplink bandwidth resources in RIS-aided communication systems. In this work, we propose a deep learning (DL)-based scheme to reduce the overhead of CSI feedback by compressing the cascaded CSI. In the practical RIS-aided communication systems, the cascaded channel at the adjacent slots inevitably has time correlation. We use long short-term memory to learn time correlation, which can help the neural network to improve the recovery quality of the compressed CSI. Moreover, the attention mechanism is introduced to further improve the CSI recovery quality. Simulation results demonstrate that our proposed DLbased scheme can significantly outperform other DL-based methods in terms of the CSI recovery quality

Blockchain-Enabled Variational Information Bottleneck for IoT Networks

In Internet of Things (IoT) networks, the amount of data sensed by user devices may be huge, resulting in the serious network congestion. To solve this problem, intelligent data compression is critical. The variational information bottleneck (VIB) approach, combined with machine learning, can be employed to train the encoder and decoder, so that the required transmission data size can be reduced significantly. However, VIB suffers from the computing burden and network insecurity. In this paper, we propose a blockchain-enabled VIB (BVIB) approach to relieve the computing burden while guaranteeing network security. Extensive simulations conducted by Python and C++ demonstrate that BVIB outperforms VIB by 36%, 22% and 57% in terms of time and CPU cycles cost, mutual information, and accuracy under attack, respectively.

Tensor Decomposition-based Time Varying Channel Estimation for mmWave MIMO-OFDM Systems

In this paper, we consider the time-varying channel estimation in millimeter wave (mmWave) multiple-input multiple-output MIMO systems with hybrid beamforming architectures. Different from the existing contributions that considered single-carrier mmWave systems with high mobility, the wideband orthogonal frequency division multiplexing (OFDM) system is considered in this work. To solve the channel estimation problem under channel double selectivity, we propose a pilot transmission scheme based on 5G OFDM, and the received signals are formed as a fourth-order tensor, which fits the low-rank CANDECOMP/PARAFAC (CP) model. By further exploring the Vandermonde structure of factor matrix, a tensor-subspace decomposition based channel estimation method is proposed to solve the CP decomposition, where the uniqueness condition is analyzed. Based on the decomposed factor matrices, the channel parameters, including angles of arrival/departure, delays, channel gains and Doppler shifts are estimated, and the Cram\'{e}r-Rao bound (CRB) results are derived as performance metrics. Simulation results demonstrate the superior performance of the proposed method over other benchmarks. Furthermore, the channel estimation methods are tested based on the channel parameters generated by Wireless InSites, and simulation results show the effectiveness of the proposed method in practical scenarios.

Target Localization and Performance Trade-Offs in Cooperative ISAC Systems: A Scheme Based on 5G NR OFDM Signals

The integration of sensing capabilities into communication systems, by sharing physical resources, has a significant potential for reducing spectrum, hardware, and energy costs while inspiring innovative applications. Cooperative networks, in particular, are expected to enhance sensing services by enlarging the coverage area and enriching sensing measurements, thus improving the service availability and accuracy. This paper proposes a cooperative integrated sensing and communication (ISAC) framework by leveraging information-carrying orthogonal frequency division multiplexing (OFDM) signals transmitted by access points (APs). Specifically, we propose a two-stage scheme for target localization, where communication signals are reused as sensing reference signals based on the system information shared at the central processing unit (CPU). In Stage I, we measure the ranges of scattered paths induced by targets, through the extraction of time-delay information from the received signals at APs. Then, the target locations are estimated in Stage II based on these range measurements. Considering that the scattered paths corresponding to some targets may not be detectable by all APs, we propose an effective algorithm to match the range measurements with the targets and achieve the target location estimation. Notably, by analyzing the OFDM numerologies defined in fifth generation (5G) standards, we elucidate the flexibility and consistency of performance trade-offs in both communication and sensing aspects. Finally, numerical results confirm the effectiveness of our sensing scheme and the cooperative gain of the ISAC framework.

OTFS vs OFDM: Which is Superior in Multiuser LEO Satellite Communications

Orthogonal time frequency space (OTFS) modulation, a delay-Doppler (DD) domain communication scheme exhibiting strong robustness against the Doppler shifts, has the potentials to be employed in LEO satellite communications. However, the performance comparison with the orthogonal frequency division multiplexing (OFDM) modulation and the resource allocation scheme for multiuser OTFS-based LEO satellite communication system have rarely been investigated. In this paper, we conduct a performance comparison under various channel conditions between the OTFS and OFDM modulations, encompassing evaluations of sum-rate and bit error ratio (BER). Additionally, we investigate the joint optimal allocation of power and delay-Doppler resource blocks aiming at maximizing sum-rate for multiuser downlink OTFS-based LEO satellite communication systems. Unlike the conventional modulations relaying on complex input-output relations within the Time-Frequency (TF) domain, the OTFS modulation exploits both time and frequency diversities, i.e., delay and Doppler shifts remain constant during a OTFS frame, which facilitates a DD domain input-output simple relation for our investigation. We transform the resulting non-convex and combinatorial optimization problem into an equivalent difference of convex problem by decoupling the conditional constraints, and solve the transformed problem via penalty convex-concave procedure algorithm. Simulation results demonstrate that the OTFS modulation is robust to carrier frequency offsets (CFO) caused by high-mobility of LEO satellites, and has superior performance to the OFDM modulation. Moreover, numerical results indicate that our proposed resource allocation scheme has higher sum-rate than existed schemes for the OTFS modulation, such as delay divided multiple access and Doppler divided multiple access, especially in the high signal-to-noise ratio (SNR) regime.

Energy-Efficient Clustered Cell-Free Networking with Access Point Selection

Ultra-densely deploying access points (APs) to support the increasing data traffic would significantly escalate the cell-edge problem resulting from traditional cellular networks. By removing the cell boundaries and coordinating all APs for joint transmission, the cell-edge problem can be alleviated, which in turn leads to unaffordable system complexity and channel measurement overhead. A new scalable clustered cell-free network architecture has been proposed recently, under which the large-scale network is flexibly partitioned into a set of independent subnetworks operating parallelly. In this paper, we study the energy-efficient clustered cell-free networking problem with AP selection. Specifically, we propose a user-centric ratio-fixed AP-selection based clustering (UCR-ApSel) algorithm to form subnetworks dynamically. Following this, we analyze the average energy efficiency achieved with the proposed UCR-ApSel scheme theoretically and derive an effective closed-form upper-bound. Based on the analytical upper-bound expression, the optimal AP-selection ratio that maximizes the average energy efficiency is further derived as a simple explicit function of the total number of APs and the number of subnetworks. Simulation results demonstrate the effectiveness of the derived optimal AP-selection ratio and show that the proposed UCR-ApSel algorithm with the optimal AP-selection ratio achieves around 40% higher energy efficiency than the baselines. The analysis provides important insights to the design and optimization of future ultra-dense wireless communication systems.