Joint Task Offloading and Resource Allocation in STAR-RIS assisted NOMA System

In this paper, the joint task offloading and resource allocation are investigated for the semi-grant-free (SGF) non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. Moreover, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are deployed to improve the quality of wireless communications under the mode switching protocol. Each MU can partially or fully offload its task to the base station (BS) based on its differentiated channel conditions and computing capacity in the proposed MEC system. We formulate the joint task offloading, channel assignment, power allocation, and the RIS coefficients design problem to save energy consumption. The formulated problem is modeled from a long-term optimization perspective as a multi-agent Markov game (MG). Then, a multi-agent deep reinforcement learning (MADRL) based joint task offloading and resource allocation (JTORA) algorithm is proposed to solve the problem. The simulation results confirm that the applied SGF-NOMA scheme can significantly reduce energy consumption under a stringent latency constraint. Moreover, the effectiveness of the STAR-RIS and the proposed algorithm are confirmed.