Green NOMA based MU-MIMO transmission for MEC in 6G Networks

Mobile edge computing (MEC) is a distributed computing paradigm that brings computing and data storage closer to the network's edge. This paper considers a multiple-input multiple-output (MIMO) uplink scenario where non-orthogonal multiple access (NOMA) users partially offload their data to a MEC server. The objective of this study is to minimize the total energy consumption during local computing, task offloading, and MEC computing. To this end, the base station optimizes power allocation vectors and task assignment coefficients under time and power constraints. The generalized singular value decomposition-based linear beamforming method integrates NOMA, MIMO, and MEC technologies. Due to the non-convex nature of the problem, a successive convex optimization (SCA) and alternation optimization (AO) based low-complex solution is proposed. The impact of the delay tolerance, the size of the offloaded task, and the distance of the users are investigated concerning energy consumption. The simulation results show that the proposed method achieves better energy performance than the orthogonal multiple access (OMA) schemes. More importantly, the performance gap increases when the data size is large, and the delay requirement is stringent, as 6G networks require.