Delay - Energy Efficient Computation Offloading and Resources Allocation in Heterogeneous Network

Recently, mobile applications such as augmented reality, virtual reality and face recognition have become more and more ubiquitous. These delay- sensitive applications demand intensive computation and high energy consumption. However, mobile devices have limited battery power and computation resources that influence the quality of experience (QoE) of mobile users. Mobile edge computing (MEC) has become a key technology to meet these demands. The crucial challenges regarding MEC paradigm are computation offloading decision, spectrum and computation resource allocation for offloading users. In order to deal with these challenges, this paper formulates a joint delay-energy optimization problem by jointly considering spectrum resource Allocation, computation resource Allocation and computation Offloading decision (AAO). Further, we transform the original problem into convex optimization and solve the optimization problem through an alternating direction method of multipliers (ADMM) algorithm in a distributed way. Finally, simulation results validate the efficiency of the proposed AAO in saving delay and energy consumption.