Joint Resource Optimization for Relay-Assisted NOMA-MEC Networks Based on Cloud-Edge Collaboration in CPPS

Due to the widespread deployment of intelligent acquisition and monitoring equipment in cyber-physical power system (CPPS), more and more multi-source power data tasks need to be transmitted and analyzed. However, limited network resources and a single cloud computing mode hinder the real-time data analysis and control of CPPS. To address the challenging problem that existing task processing scheme can no longer satisfy the real-time processing requirement of power data, in this paper, cooperative relay, mobile edge computing (MEC), and non-orthogonal multiple access (NOMA) technologies are introduced into the CPPS communication architecture, and a joint resource optimization scheme for relay-assisted NOMA-MEC networks based on cloud-edge collaboration is proposed. Specifically, a novel relay-assisted NOMA-MEC system model based on cloud-edge collaboration is proposed. This model allows each relay to serve multiple edge gateways simultaneously to raise resources utilization and adopts NOMA technology to reduce interference impact. Then, based on the proposed model, the tasks processing issue is modeled as a joint multi-objective optimization problem, aiming to allocate network resources reasonably and achieve a trade-off between system delay and energy consumption. For the variable coupling and non-convex characteristics of the proposed optimization problem, a channel performance-based relay selection method and a block coordinate descent (BCD)- based joint optimization algorithm are designed. Furthermore, the optimality conditions of the subproblems are also derived. Finally, extensive simulation results illustrate that the proposed scheme is superior to other baseline schemes.