Joint Offloading Decision and Resource Allocation for Multiuser NOMA-MEC Systems

Abstract

Mobile edge computing (MEC) is becoming a promising paradigm to provide computing services for smart mobile devices (SMDs) via offloading computation-intensive tasks to MEC servers deployed at the network edge. In this paper, in order to further improve the accessing capacity of MEC systems and minimize all users' computation overhead, taking advantage of the superior spectral efficiency of Non-Orthogonal Multiple Access (NOMA) technology, we introduce NOMA into MEC systems and investigate a multi-user computation offloading problem through jointly optimizing offloading decisions, communication and computation resources allocation. To tackle the formulated mixed integer nonlinear programming (MINLP) problem which is NP-hard, we iteratively update either the resource allocation or the offloading decision via fixing the other solution and efficiently solve it in polynomial time. Specifically, given a fixed offloading decision, the sub-channel assignment problem is solved via applying a many-To-one matching model with peer effects, the transmission power of SMDs is optimized by combing sequential convex programming and parametric convex programming, and the computation resources allocation is addressed by convex optimization. Furthermore, the results of resource allocation are applied to guide the offloading decision. Extensive simulations show that our proposed algorithm performs closely to the optimal solution, and compared with existing solutions, our algorithm can efficiently improve the accessing capacity of MEC systems and reduce the total computation overhead of all users.