Service Satisfaction Based User Selection and Resource Allocation for NOMA-Based Multi-Cell MEC Networks

Abstract

Mobile Edge Computing (MEC) is promising to enable low delay services with which users can offload computing intensive and delay sensitive tasks to the edge. Considering a multi-cell MEC (MC-MEC) network without sufficient resources to serve all users, user selection and non-orthogonal multiple access (NOMA) should be introduced. Then, to maximize the delay-aware average user service satisfaction degree (DA-AveUSD), user selection and resource allocation are jointly optimized (DA-JUSRA), which is modeled as a mixed integer nonlinear programming (MINLP) problem and proven to be NP-hard. To solve this problem, it is decomposed into two independent subproblems, i.e., the power allocation (PA) problem and the user selection, subchannel scheduling and computing resource allocation (USC) problem. Next, a convex evolutionary alternating optimization (CEAO) algorithm is proposed, which alternately applies the convex optimization method and the Karush-Kuhn-Tucker (KKT)-embedding enhanced elite genetic algorithm (KKT-embedding E2GA) to solve the PA and the USC problem, respectively. Simulations show that compared to the optimal exhaustive search algorithm, the proposed CEAO algorithm converges rapidly within a few iterations, with a gap in DA-AveUSD of less than 1% to the optimum performance. Next, compared to existing user selection schemes, DA-JUSRA with CEAO can enhance DA-AveUSD by more than 50% and yield a higher optimal load.