Delay Minimization for NOMA-Enabled Mobile Edge Computing in Industrial Internet of Things

Abstract

Mobile edge computing and nonorthogonal multiple access (NOMA) have been considered as promising technologies that can satisfy rigorous requirements of industrial Internet of Things systems. However, system dynamics, including channel states and computation task requests, may continuously change NOMA decoding order and computation uploading time, making it difficult to reduce latency using conventional highly complex optimization methods. In this article, we investigate a novel scheme that effectively reduces the average task delay to improve the quality of service for all users by jointly optimizing subchannel assignment (SA), offloading decision (OD), and computation resource allocation (CRA). To deal with the high complexity, the original multiserver problem is first decomposed into multiple single-server problems. Subsequently, each single-server problem is decoupled into CRA and SA/OD subproblems. Using convex optimization, a closed-form solution is derived for the optimal CRA action. Concurrently, the optimal SA/OD action is obtained using a distributed multiagent deep reinforcement learning algorithm. Simulation results reveal that the proposed scheme significantly outperforms the state-of-the-art schemes. In particular, it reduces the action decision duration by 30 times while achieving a near-optimal performance of up to 97% of the optimum under the exhaustive search scheme.