Unmanned aerial vehicles (UAVs) are regarded as a promising solution for mobile-edge computing (MEC) systems due to their flexibility and capability to provide computing services to ground terminals (GTs). By leveraging UAVs, the latency in computation tasks can be reduced significantly, particularly in disaster scenarios. Additionally, reconfigurable intelligent surfaces (RISs) have emerged as a novel technology for enhancing the wireless propagation environment in wireless networks. This article proposes a multi-UAV-assisted MEC system where computation tasks of GTs can be computed locally or partially offloaded to UAVs. Furthermore, practical RIS phase shift designs are considered to enhance the communication performance between GTs and UAVs. To minimize the system delay and achieve fairness among GTs, the computation offloading strategy, trajectory of the UAVs are optimized using a Markov decision process. Simultaneously, the RIS phase shift is optimized through an alternating optimization algorithm. Additionally, a cooperative multiagent deep reinforcement learning framework is developed to obtain a optimal solution by employing the multiagent twin delayed deep deterministic policy gradient (MATD3) algorithm. Numerical results indicate that MATD3 can effectively improve the system delay and fairness performance of the RIS-assisted multi-UAV MEC system, as compared to benchmark solutions.

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum–energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

In urban environments, direct communication links between a base station (BS) and user equipment (UEs) are often obstructed by buildings. To mitigate these blockages, we integrate unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance system flexibility and improve transmission efficiency. This paper investigates an RIS-assisted multi-user multiple-input single-output (MU-MISO) downlink system, where the RIS is mounted on a UAV. To maximize the system rate while minimizing the UAV's energy consumption and flight duration, we formulate a multi-objective optimization problem. To address this problem, we propose a hybrid algorithm that integrates the soft deep deterministic policy gradient (SD3) algorithm with a graph neural network (GNN) architecture, named SD3-GNN-RIS. The original problem is decomposed into two subproblems: joint active beamforming at the BS and passive beamforming at the RIS, optimized via a GNN-based approach, and three-dimensional (3D) UAV trajectory optimization, formulated as a Markov decision process and solved using the SD3 algorithm. Simulation results demonstrate the superior performance of the proposed algorithm compared to baseline methods in terms of system rate, energy efficiency, and UAV trajectory optimization.

The proliferation of computation-intensive and delay-sensitive services in intelligent transportation systems, such as autonomous driving and vehicle-mounted infotainment services, presents a significant challenge for vehicular users (VUs) with limited resources. To address this issue, multi-access edge computing (MEC) has been considered a favorable solution to mitigate computation delay. This paper considers computation offloading for an air-ground integrated computing platform in vehicular networks. Specifically, we first propose a multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm to optimize the trajectory of UAVs. Then, an algorithm named federated upgraded dueling double deep Q network (FUD3QN) is proposed to meet quality of service (QoS) requirements. The algorithm allocates cross-domain resources after offloading decision-making, aiming to minimize delay and energy consumption while meeting reliability requirements, maximum tolerable delay, communication requirements, and computing limitations. Addressing the NP-hard problem, we employ a multi-agent federated learning and upgraded dueling double deep Q network algorithm (UD3QN) with centralized training and distributed execution. Simulation results illustrate that the MATD3-FUD3QN algorithm proposed significantly surpasses the baselines, highlighting the advantages of introducing UAVs to enhance transmission quality.