Peng Liu

With the widespread use of machine learning, especially deep learning, in high-mobility vehicular networks, vehicle-to-everything (V2X) communication has received lots of attention from academia and industry. However, the fast dynamic network environment brings out big challenges to the resource allocation issue in V2X communications. Considering the difficulty of accurately obtaining channel state information (CSI) in dynamic vehicular network environments, we propose a joint double deep Q-network (DDQN) based centralized spectrum allocation and distributed power control algorithm for V2X communications to utilize the spectrum resource more effectively and reduce the multiple interference between V2X links. The proposed algorithm decouples the channel matching and transmission power selection to balance the resource utilization and quality of user experience satisfaction levels. In the first stage, we present a graph theory based centralized spectrum matching scheme to reduce the multiple interference between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, and improve the system capacity of vehicular networks. Secondly, through predicting the future dynamic CSI trends of terminal vehicles based on the locally obtained CSI information, we propose a distributed DDQN based power control scheme to enhance the system reliability and meet the quality of terminal user experience requirements. The simulation results show that our proposed algorithm performs better compared the traditional existing schemes.

With more and more distributed deployment of internet of things (IoTs) in 5/6G networks, the unmanned aerial vehicles (UAVs) have attracted much interest for mobile edge computing service outside the coverage of terrestrial cellular systems. However, due to the interference and coverage hole issues of UAV-assisted aerial 3D communications, how to design a suitable UAV trajectory is crucial for the performance of future 6G-UAV. In order to achieve a balance between the communication quality and mission accomplishment, we define an environmental model with random interference and formulate a trajectory optimization problem to improve the communication coverage performance in high mobility conditions. By setting an interference point in the highly dynamic networks, we propose a novel trajectory optimization algorithm based on deep reinforcement learning (DRL) to minimize the overall task execution delay and expected outage duration. The received data information of UAVs is used for double deep q-network (DDQN) training while the prioritized empirical replay architecture in the prioritized replay deep q network (DQN) is utilized to select the best UAV action, avoid the UAV flying into the random interference points, and reduce the random interference. Simulation results demonstrate the superiority of the proposed algorithm.