Beamforming Design for Active RIS-Aided Over-the-Air Computation

Over-the-air computation (AirComp) is emerging as a promising technology for wireless data aggregation. However, its performance is hampered by users with poor channel conditions. To mitigate such a performance bottleneck, this paper introduces an active reconfigurable intelligence surface (RIS) into the AirComp system. Specifically, we begin by exploring the ideal RIS model and propose a joint optimization of the transceiver design and RIS configuration to minimize the mean squared error (MSE) between the target and estimated function values. To manage the resultant tri-convex optimization problem, we employ the alternating optimization (AO) technique to decompose it into three convex subproblems, each solvable optimally. Subsequently, we investigate two specific cases and analyze their respective asymptotic performance to reveal the superiority of the active RIS in mitigating the MSE relative to its passive counterpart. Lastly, we adapt our transceiver and RIS configuration design to account for the self-interference of the active RIS. To handle the resultant highly non-convex problem, we further devise a two-layer AO framework. Simulation results demonstrate the superiority of the active RIS in enhancing AirComp performance compared to its passive counterpart

Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation

Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model aggregation in the central server. In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently been proposed and attracted immediate attention. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To combat this effect, the concept of dynamic learning rate (DLR) is proposed in this work. We begin our discussion by considering multiple-input-single-output (MISO) scenario, since the underlying optimization problem is convex and has closed-form solution. We then extend our studies to more general multiple-input-multiple-output (MIMO) case and an iterative method is derived. Extensive simulation results demonstrate the effectiveness of the proposed scheme in reducing the aggregate distortion and guaranteeing the testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present the asymptotic analysis and give a near-optimal receive beamforming design solution in closed form, which is verified by numerical simulations

Optimizing Over-The-Air Computation in IRS-Aided C-RAN Systems

Over-the-air computation (AirComp) is an efficient solution to enable federated learning on wireless channels. AirComp assumes that the wireless channels from different devices can be controlled, e.g., via transmitter-side phase compensation, in order to ensure coherent on-air combining. Intelligent reflecting surfaces (IRSs) can provide an alternative, or additional, means of controlling channel propagation conditions. This work studies the advantages of deploying IRSs for AirComp systems in a large-scale cloud radio access network (C-RAN). In this system, worker devices upload locally updated models to a parameter server (PS) through distributed access points (APs) that communicate with the PS on finite-capacity fronthaul links. The problem of jointly optimizing the IRSs' reflecting phases and a linear detector at the PS is tackled with the goal of minimizing the mean squared error (MSE) of a parameter estimated at the PS. Numerical results validate the advantages of deploying IRSs with optimized phases for AirComp in C-RAN systems.Comment: to appear in Proc. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 202

Federated learning empowered ultra-dense next-generation wireless networks

The evolution of wireless networks, from first-generation (1G) to fifth generation (5G), has facilitated real-time services and intelligent applications powered by artificial intelligence (AI) and machine learning (ML). Nevertheless, prospective applications like autonomous driving and haptic communications necessitate the exploration of beyond fifth-generation (B5G) and sixth-generation (6G) networks, leveraging millimeter-wave (mmWave) and terahertz (THz) technologies. However, these high-frequency bands experience significant atmospheric attenuation, resulting in high signal propagation loss, which necessitates a fundamental reconfiguration of network architectures and paves the way for the emergence of ultra-dense networks (UDNs). Equipped with massive multiple-input multiple-output (mMIMO) and beamforming technologies, UDNs mitigate propagation losses by utilising narrow line-of-sight (LoS) beams to direct radio waves toward specific receiving points, thereby enhancing signal quality. Despite these advancements, UDNs face critical challenges, which include worsened mobility issues in dynamic UDNs due to the susceptibility of LoS links to blockages, data privacy concerns at the network edge when implementing centralised ML training, and power consumption challenges stemming from the deployment of dense small base stations (SBSs) and the integration of cutting edge techniques like edge learning. In this context, this thesis begins by investigating the prevailing issue of beam blockage in UDNs and introduces novel frameworks to address this emerging challenge. The main theme of the first three contributions is to tackle beam blockages and frequent handovers (HOs) through innovative sensing-aided wireless communications. This approach seeks to enhance the situational awareness of UDNs regarding their surroundings by using a variety of sensors commonly found in urban areas, such as vision and radar sensors. While all these contributions share the common goal of proposing sensing-aided proactive HO (PHO) frameworks that intelligently predict blockage events in advance and performs PHO, each of them presents distinctive framework features, contributing significantly to the improvement of UDN operations. To provide further details, the first contribution adhered to conventional centralised model training, while the other contributions employed federated learning (FL), a decentralised collaborative training approach primarily designed to safeguard data privacy. The utilisation of FL technology offers several advantages, including enhanced data privacy, scalability, and adaptability. Simulation results from all these frameworks have demonstrated the remarkable performance of the proposed latency-aware frameworks in improving UDNs’ reliability, maintaining user connectivity, and delivering high levels of quality of experience (QoE) and throughput when compared to existing reactive HO procedures lacking proactive blockage prediction. The fourth contribution is centred on optimising energy management in UDNs and introduces FedraTrees, a lightweight algorithm that integrates decision tree (DT)-based models into the FL setup. FedraTrees challenges the conventional belief that FL is exclusively suited for Neural Network (NN) models by enabling the incorporation of DT models within the FL context. While FedraTrees offers versatility across various applications, this thesis specifically applies it to energy forecasting tasks with the aim of achieving the energy efficiency requirement of UDNs. Simulation results demonstrate that FedraTrees performs remarkably in predicting short-term energy patterns and surpasses the state-of-the-art long short-term memory (LSTM)-based federated averaging (FedAvg) algorithm in terms of reducing computational and communication resources demands