Energy Efficient Computation Offloading for Multi-Access MEC Enabled Small Cell Networks

Abstract

With the growing demands in both high data rate and high computation capability, mobile edge computing (MEC) enabled small cell networks (SCN) is regarded as a promising concept. Previous works on computation offloading address the following problems: offloading decision making, wireless and computation resource allocation. However, most existing works only focus on one or two of these problems and fail to consider the high interference, multi-access property, and limited resources. In this paper, we perform a comprehensive study on the computation offloading scheme. First, we present the computation offloading model in the multi- access and multi-channel interference environment and formulate the offloading decision, channel allocation, computation resource allocation as a mixed integer non-linear programming (MINLP) problem. Here the users need not only to decide whether to offload but also to determine where to offload. The objective is to minimize the energy consumption of the UEs. To solve this large-scale yet NP-hard problem, we then design a suboptimal algorithm named as genetic algorithm (GA) based computation algorithm (GACA). Finally, the convergence of this algorithm is studied by simulation, and the performance of the proposed algorithm is verified by comparing with the other baseline algorithms.