Dynamically Segmented IRS-Assisted UAV Computing Power Networks: Toward System Delay and Energy Consumption Optimization

In this paper, we propose a dynamically segmented Intelligent Reflecting Surface (IRS)-assisted Unmanned Aerial Vehicle (UAV) Computing Power Networks (CPNs) with tightly integrated communication and computing power resources. The IRS can be dynamically segmented and allocated to users, with computing resources allocated accordingly to satisfy their delay constraints. Considering the energy limitations of UAVs, we formulate a multi-objective optimization problem to minimize user delay and UAV energy consumption. To solve the problem, we propose a new scheme jointly considering UAVtrajectory,computing power allocation, reflecting element allocation,phase shift, and UAV-userassociation (TCPA) scheme. The phase alignment theory is utilized to determine the IRS phase shift control and decompose the problem into three subproblems based on the coupling of variables. Specifically, we use channel optimal matching to solve the first subproblem to obtain user association decisions. Then, we formulate a computing power communication matching subproblem, and propose a successive convex approximation scheme to solve it. The trajectory subproblem is optimized by a multi-agent deep reinforcement learning-based method. The evaluation results demonstrate that our proposed TCPA achieves high performance in terms of reward and system delay. Additionally, it demonstrates that integrating IRS and CPNs can effectively reduce the total system delay with only a marginal increase in energy consumption.