Power Allocation for Device-to-Device Interference Channel Using Truncated Graph Transformers

Power control for the device-to-device interference channel with single-antenna transceivers has been widely analyzed with both model-based methods and learning-based approaches. Although the learning-based approaches, i.e., datadriven and model-driven, offer performance improvement, the widely adopted graph neural network suffers from learning the heterophilous power distribution of the interference channel. In this paper, we propose a deep learning architecture in the family of graph transformers to circumvent the issue. Experiment results show that the proposed methods achieve the state-of-theart performance across a wide range of untrained network configurations. Furthermore, we show there is a trade-off between model complexity and generality.Comment: 6 pages, 5 figures. Accepted in IEEE International Mediterranean Conference on Communications and Networkin

Second-Order Coding Rate of Quasi-Static Rayleigh-Product MIMO Channels

With the development of innovative applications that require high reliability and low latency, ultra-reliable and low latency communications become critical for wireless networks. In this paper, the second-order coding rate of the coherent quasi-static Rayleigh-product MIMO channel is investigated. We consider the coding rate within O(1/\sqrt(Mn)) of the capacity, where M and n denote the number of transmit antennas and the blocklength, respectively, and derive the closed-form upper and lower bounds for the optimal average error probability. This analysis is achieved by setting up a central limit theorem (CLT) for the mutual information density (MID) with the assumption that the block-length, the number of the scatterers, and the number of the antennas go to infinity with the same pace. To obtain more physical insights, the high and low SNR approximations for the upper and lower bounds are also given. One interesting observation is that rank-deficiency degrades the performance of MIMO systems with FBL and the fundamental limits of the Rayleigh-product channel approaches those of the single Rayleigh case when the number of scatterers approaches infinity. Finally, the fitness of the CLT and the gap between the derived bounds and the performance of practical LDPC coding are illustrated by simulations

Handling Group Fairness in Federated Learning Using Augmented Lagrangian Approach

Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness issues, where models might be biased towards sensitive factors such as race or gender, even if they are trained using a legally compliant process. To redress this concern, this paper proposes a novel FL algorithm designed explicitly to address group fairness issues. We show empirically on CelebA and ImSitu datasets that the proposed method can improve fairness both quantitatively and qualitatively with minimal loss in accuracy in the presence of statistical heterogeneity and with different numbers of clients. Besides improving fairness, the proposed FL algorithm is compatible with local differential privacy (LDP), has negligible communication costs, and results in minimal overhead when migrating existing FL systems from the common FL protocol such as FederatedAveraging (FedAvg). We also provide the theoretical convergence rate guarantee for the proposed algorithm and the required noise level of the Gaussian mechanism to achieve desired LDP. This innovative approach holds significant potential to enhance the fairness and effectiveness of FL systems, particularly in sensitive applications such as healthcare or criminal justice.Comment: the main paper has 8 pages and the supplementary material has 12 pages. At the time of uploading, it is currently under review in ECA

Model-Driven Sensing-Node Selection and Power Allocation for Tracking Maneuvering Targets in Perceptive Mobile Networks

Maneuvering target tracking will be an important service of future wireless networks to assist innovative applications such as intelligent transportation. However, tracking maneuvering targets by cellular networks faces many challenges. For example, the dense network and high-speed targets make the selection of the sensing nodes (SNs), e.g., base stations, and the associated power allocation very difficult, given the stringent latency requirement of sensing applications. Existing methods have demonstrated engaging tracking performance, but with very high computational complexity. In this paper, we propose a model-driven deep learning approach for SN selection to meet the latency requirement. To this end, we first propose an iterative SN selection method by jointly exploiting the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). Then, we unfold the iterative algorithm as a deep neural network (DNN) and prove its convergence. The proposed model-driven method has a low computational complexity, because the number of layers is less than the number of iterations required by the original algorithm, and each layer only involves simple matrix-vector additions/multiplications. Finally, we propose an efficient power allocation method based on fixed point (FP) water filling (WF) and solve the joint SN selection and power allocation problem under the alternative optimization framework. Simulation results show that the proposed method achieves better performance than the conventional optimization-based methods with much lower computational complexity

How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels

Because of its privacy-preserving capability, federated learning (FL) has attracted significant attention from both academia and industry. However, when being implemented over wireless networks, it is not clear how much communication error can be tolerated by FL. This paper investigates the robustness of FL to the uplink and downlink communication error. Our theoretical analysis reveals that the robustness depends on two critical parameters, namely the number of clients and the numerical range of model parameters. It is also shown that the uplink communication in FL can tolerate a higher bit error rate (BER) than downlink communication, and this difference is quantified by a proposed formula. The findings and theoretical analyses are further validated by extensive experiments.Comment: Submitted to IEEE for possible publicatio

Integrated Sensing and Communication in Coordinated Cellular Networks

Integrated sensing and communication (ISAC) has recently merged as a promising technique to provide sensing services in future wireless networks. In the literature, numerous works have adopted a monostatic radar architecture to realize ISAC, i.e., employing the same base station (BS) to transmit the ISAC signal and receive the echo. Yet, the concurrent information transmission causes severe self-interference (SI) to the radar echo at the BS which cannot be effectively suppressed. To overcome this difficulty, in this paper, we propose a coordinated cellular network-supported multistatic radar architecture to implement ISAC. In particular, among all the coordinated BSs, we select a BS as the multistatic receiver to receive the sensing echo signal, while the other BSs act as the multistatic transmitters to collaborate with each other to facilitate cooperative ISAC. This allows us to spatially separate the ISAC signal transmission and radar echo reception, intrinsically circumventing the problem of SI. To this end, we jointly optimize the transmit and receive beamforming policy to minimize the sensing beam pattern mismatch error subject to both the communication and sensing quality-of-service requirements. The resulting non-convex optimization problem is tackled by a low-complexity alternating optimization-based suboptimal algorithm. Simulation results showed that the proposed scheme outperforms the two baseline schemes adopting conventional designs. Moreover, our results confirm that the proposed architecture is promising in achieving high-quality ISAC.Comment: 6 pages, 3 figure

Sensing Mutual Information with Random Signals in Gaussian Channels

Sensing performance is typically evaluated by classical metrics, such as Cramer-Rao bound and signal-to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the metric for sensing and communication, where researchers have proposed to utilize mutual information (MI) to measure the sensing performance with deterministic signals. However, the need to communicate in ISAC systems necessitates the use of random signals for sensing applications and the closed-form evaluation for the sensing mutual information (SMI) with random signals is not yet available in the literature. This paper investigates the achievable performance and precoder design for sensing applications with random signals. For that purpose, we first derive the closed-form expression for the SMI with random signals by utilizing random matrix theory. The result reveals some interesting physical insights regarding the relation between the SMI with deterministic and random signals. The derived SMI is then utilized to optimize the precoder by leveraging a manifold-based optimization approach. The effectiveness of the proposed methods is validated by simulation results

Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems

As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to the superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering the low complexity of the AMP algorithm and adaptability of GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches.Comment: 30 Pages, 7 Figures, and 4 Tables. This paper has been submitted to the IEEE for possible publication. arXiv admin note: text overlap with arXiv:2205.1062

Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms

As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space-air-ground integrated network (SAGIN) with diverse resource constraints. In this paper, we dynamically consider three typical classes of radio access network (RAN) slices, namely high-throughput slices, low-delay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay and the coverage area of these three classes of RAN slices are jointly optimized in a non-scalar form by considering the distinct channel features and service advantages of the terrestrial, aerial and satellite components of SAGINs. A joint central and distributed multi-agent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the inter-slice sub-channel and power sharing by relying on a centralized unit. Then it optimizes the intra-slice sub-channel and power allocation, and the virtual base station (vBS)/vUAV/virtual low earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.Comment: 19 pages, 14 figures, journa

Lightweight and Adaptive FDD Massive MIMO CSI Feedback with Deep Equilibrium Learning

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) needs to be sent from users back to the base station (BS), which causes prohibitive feedback overhead. In this paper, we propose a lightweight and adaptive deep learning-based CSI feedback scheme by capitalizing on deep equilibrium models. Different from existing deep learning-based approaches that stack multiple explicit layers, we propose an implicit equilibrium block to mimic the process of an infinite-depth neural network. In particular, the implicit equilibrium block is defined by a fixed-point iteration and the trainable parameters in each iteration are shared, which results in a lightweight model. Furthermore, the number of forward iterations can be adjusted according to the users' computational capability, achieving an online accuracy-efficiency trade-off. Simulation results will show that the proposed method obtains a comparable performance as the existing benchmarks but with much-reduced complexity and permits an accuracy-efficiency trade-off at runtime.Comment: submitted to IEEE for possible publicatio