Computation offloading in STAR-RIS aided U-MEC systems: A delay-minimization deep reinforcement learning approach

Abstract

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a new wireless communication technology with bidirectional transmission. Unlike conventional reflective RIS, STAR-RIS offers unique advantages in multipath propagation, signal enhancement, and coverage. Through beamforming and intelligent signal processing, STAR-RIS provides users with additional transmission paths. In this context, this paper proposes a novel STAR-RIS-assisted unmanned aerial vehicle (UAV) -enabled mobile edge computing (U-MEC) emergency communication network. Considering the practical needs of post-disaster emergency communication, we formulate a problem of minimizing the maximum processing delay for all user tasks, while satisfying the battery energy constraints of UAV and guaranteeing the minimum transmission rate for users. To solve the problem, we comprehensively consider STAR-RIS beamforming, user task offload ratio, UAV flight trajectory, and resource allocation. Furthermore, we design an augmented Lagrangian-based reward-constrained twin delayed deep deterministic policy gradient (ALRCTD3) algorithm. The algorithm combines double-Q learning, state normalization, augmented Lagrangian relaxation method, and dual gradient descent strategy. Experimental results show that the proposed algorithm significantly outperforms existing deep reinforcement learning (DRL) algorithms and benchmark schemes in terms of performance.