Computation Offloading and Beamforming Optimization for Energy Minimization in Wireless-Powered IRS-Assisted MEC

Abstract

Intelligent reflecting surface (IRS) has been recently exploited as a symbiotic radio (SR) technology to improve energy-and spectral-efficiencies in wireless systems. In this paper, we consider a symbiotic IRS-assisted mobile edge computing (MEC) system that allows edge users to first harvest RF power from a hybrid access point (HAP) and then offload its computational workload to the MEC server associated with the HAP. We aim to minimize the HAP’s energy consumption by jointly optimizing the users’ offloading schemes, the HAP’s active beamforming, and the IRS’s passive beamforming strategies. We propose an optimization-driven hierarchical deep deterministic policy gradient (OH-DDPG) framework to decompose the energy minimization problem into the optimization and the learning sub-problems, respectively. The outer-loop DDPG learning method adapts the IRS’s passive beamforming strategy, while the inner-loop optimization deals with the other control variables with reduced dimensionality. Moreover, to improve the learning efficiency, we extend OH-DDPG to the multi-agent scenario. In particular, the HAP first estimates the users’ offloading strategy by the inner-loop optimization and shares it with all user agents. Then, each user agent refines its offloading decision using the DDPG algorithm independently. This can avoid signaling overhead among users and improve the multi-user learning efficiency. Simulation results show that the proposed OH-DDPG and the multi-user extension can achieve significant performance gains compared to the conventional model-free learning algorithms.