Intelligent Resource Optimization for CPN-Enabled IoT by RIS-UAV-Aided NOMA-THz Communication

The integration of advanced technologies such as the sixth-generation (6 G) mobile communications, artificial intelligence (AI) and blockchain have given new impetus to the development of the Internet of Things (IoT). However, these applications require higher computational power and lower latency, which present challenges to traditional network architecture. To address these issues, a novel computing power network (CPN) architecture is proposed in this paper based on reconfigurable intelligent surface (RIS)-unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA)-Terahertz (THz) communication, and it aims to meet high computational demands. To increase the efficiency of the proposed architecture, it is crucial to rationally allocate computational and transmission resources. Therefore, a joint optimization problem is formulated to minimize system consumption, encompassing both time and energy usage. To achieve efficient resource allocation, an adaptive N-Step method based on soft actorcritic (SAC) algorithm is employed. Simulation results demonstrate the superiority of the proposed method over the existing baselines.