Federated learning (FL) has become a transformative paradigm for distributed machine learning over wireless networks. However, the performance of FL is hindered by the unreliable communication links between resource-constrained Internet of Things (IoT) devices and the central server. To overcome this challenge, we propose a novel framework that employs an unmanned aerial vehicle (UAV) as a mobile server to enhance the FL training process. By capitalizing on the UAV’s mobility, we establish strong line-of-sight connections with IoT devices, thereby enhancing communication reliability and capacity. To maximize training efficiency, we formulate a latency minimization problem that jointly optimizes bandwidth allocation, computing resources, transmit power for both the UAV and IoT devices, and the flight trajectory of the UAV. Subsequently, we analyze the required rounds of the IoT devices training and the UAV aggregation for FL convergence. Based on the convergence constraint, we transform the problem into three subproblems and develop an efficient alternating optimization algorithm to solve this problem. Additionally, we provide a thorough analysis of the algorithm’s convergence and computational complexity. Extensive numerical results demonstrate that the proposed algorithm-based scheme not only surpasses existing benchmark schemes in reducing latency up to 15.29%, but also achieves training efficiency that nearly matches the ideal scenario.

Intelligent reflecting surface (IRS) and rate-splitting multiple access (RSMA) technologies are at the forefront of enhancing spectrum and energy efficiency in the next generation multi-antenna communication systems. This paper explores a RSMA system with multiple IRSs, and proposes two purpose-driven scheduling schemes, i.e., the exhaustive IRS-aided (EIA) and opportunistic IRS-aided (OIA) schemes. The aim is to optimize the system weighted energy efficiency (EE) under the above two schemes, respectively. Specifically, the Dinkelbach, branch and bound, successive convex approximation, and the semidefinite relaxation methods are exploited within the alternating optimization framework to obtain effective solutions to the considered problems. The numerical findings indicate that the EIA scheme exhibits better performance compared to the OIA scheme in diverse scenarios when considering the weighted EE, and the proposed algorithm demonstrates superior performance in comparison to the baseline algorithms.

This paper studies the unmanned-aerial-vehicle (UAV)-enabled data collection for Internet-of-things (IoT) networks, in which multiple UAVs are dispatched to collect data over their correspondingly designated areas. In particular, we consider that the UAVs need to collect data in a timely manner. We further consider a practical segmented channel model, in which the air-to-ground wireless channel is assumed to follow Rayleigh or Rician fading when the corresponding line-of-sight (LoS) link is blocked or unblocked, respectively. Under this setup, we minimize the average Age-of-Information (AoI) for the IoT devices, by jointly optimizing the UAV trajectory, the collection scheduling, and the completion time. Since the problem is non-convex, and the dimension of optimization variables varies w.r.t. the completion time that needs to be optimized, the conventional methods are not applicable for efficiently solving the problem. To address this issue, we first propose a deep-reinforcement-learning (DRL) based algorithm to solve this problem in the special case with single UAV, and then exploit federated learning over multiple UAVs to efficiently train the model in a collaborative manner while preserving the data privacy. Numerical results verify that the proposed methods achieve significantly better performance than benchmarks in terms of the AoI, energy consumption, and completion time.

Recently, intelligent reflecting surface (IRS) has emerged as a promising technology, which can be applied in mobile edge computing (MEC) systems to achieve higher data transmission efficiency and reliability, by providing a reflective channel. Concurrently, rate-splitting multiple access (RSMA), as an innovative technology, is increasingly utilized in MEC systems to enhance data offloading efficiency and facilitate a better integration of computation and communication. In this paper, an IRS enabled MEC system with RSMA under user mobility is considered. Based on this system model, we propose an optimization problem that is aimed at maximizing the system's data transmission rate by jointly optimizing the RSMA power allocation and the IRS phase shift parameters. Although traditional optimization methods can be utilized to solve the considered problem, it is quite time consuming since the optimization methods are often iterative algorithms. To design low complexity algorithm, we propose a deep reinforcement learning (DRL) approach that can efficiently make good decisions quickly after training. Numerical results indicate that, compared to the baseline algorithms, the proposed DRL-based IRS-aided offloading algorithm under RSMA protocol achieves superior system performance.

This article considers a heterogeneous mobile-edge computing (HMEC) system with multiple energy-limited Internet of Things (IoT) devices and an unmanned aerial vehicle (UAV). The UAV can supply energy to all the IoT devices through wireless power transfer. To maximize the utilization of the communication and computation resources, all the IoT devices are divided into two groups, i.e., the active devices and the idle devices. The UAV and the idle devices assist the active devices in executing computing tasks. We formulate an optimization problem that maximizes the minimum task computation data volume among all the active devices by jointly optimizing the UAV trajectory and the communication and computation resource allocation. Since the problem is nonconvex, we decompose the problem into two subproblems: 1) the UAV trajectory design and the computation resource allocation and 2) the time allocation. We utilize a block coordinate descent approach to solve these two subproblems alternately. Simulation results demonstrate that the proposed algorithm can provide an optimized trajectory robust to different initializations. Additionally, compared to the benchmark algorithms, our proposed algorithm shows superior performance in terms of system efficiency and computation data volume.

This paper studies the over-the-air computation (AirComp) in an orthogonal frequency division multiplexing (OFDM) system with imperfect channel state information (CSI), in which multiple single-antenna wireless devices (WDs) simultaneously send uncoded signals to a multi-antenna access point (AP) for distributed functional computation over multiple subcarriers. In particular, we consider two scenarios with best-effort and error-constrained computation tasks, with the objectives of minimizing the average computation mean squared error (MSE) and the computation outage probability over the multiple subcarriers, respectively. Towards this end, we jointly optimize the transmit coefficients at the WDs and the receive beamforming vectors at the AP over subcarriers, subject to the maximum transmit power constraints at individual WDs. First, for the special case with a single receive antenna at the AP, we propose the semi-closed-form globally optimal solutions to the two problems using the Lagrange-duality method. It is shown that at each subcarrier, the WDs’ optimized power control policy for average MSE minimization follows a regularized channel inversion structure, while that for computation outage probability minimization follows an on-off regularized channel inversion, with the regularization dependent on the transmit power budget and channel estimation error. Next, for the general case with multiple receive antennas at the AP, we present efficient algorithms based on alternating optimization and convex optimization to find converged solutions to both problems. It is shown that with finite receive antennas at the AP, a non-zero computation MSE for AirComp is inevitable due to the channel estimation errors even when the transmit powers at WDs tend to infinity, while with massive receive antennas, the average MSE and outage probability vanish when the channel vectors are independent and identically distributed. Finally, numerical results are provided to demonstrate the effectiveness of the proposed designs.

Signal detection and modulation classification are two crucial tasks in various wireless communication systems. Different from prior works that investigate them independently, this paper studies the joint signal detection and automatic modulation classification (AMC) by considering a realistic and complex scenario, in which multiple signals with different modulation schemes coexist at different carrier frequencies. We first generate a coexisting RADIOML dataset (CRML23) to facilitate the joint design. Different from the publicly available AMC dataset, ignoring the signal detection step and containing only one signal, our synthetic dataset covers the more realistic multiple-signal coexisting scenario. Then, we present a joint framework for detection and classification (JDM) for such a multiple-signal coexisting environment, which consists of two modules for signal detection and AMC, respectively. In particular, these two modules are interconnected using a designated data structure called “proposal”. Finally, we conduct extensive simulations over the newly developed dataset, which demonstrate the effectiveness of our designs. Our code and dataset are now available as open-source resources athttps://github.com/Singingkettle/ChangShuoRadioData.

Recently, the cybertwin-driven intelligent internet of vehicles has received widespread consideration in modern smart cities which makes it possible to run high dimensional, low-latency tolerating, and computational-intensive tasks on the vehicles. Thanks to the development in mobile edge computing, the so-called vehicular edge computing allows mobile vehicles to offload their tasks to the road-side unit or hybrid access point due to the limited computation capability. In this paper, we consider a cybertwin-driven internet of vehicle system that provides computing services for mobile vehicles in local area network or wide area network aided with multi-intelligent reflecting surfaces. Based on this system model, we investigate an optimization problem to jointly maximize the sum of data rate in wide area network, and the sum of energy utilities of vehicles. However, in the proposed system model, it is complicated to design the optimal phase, scheduling and offloading decision policy. To solve this issue, we propose a block coordinate descent and deep reinforcement learning based intelligent IoV computing policy. Numerical results have verified that the proposed algorithm can achieve better IoV computing performance compared with four relative benchmark algorithms.