Jointly Optimizing Helpers Selection and Resource Allocation in D2D Mobile Edge Computing

Device-to-Device (D2D) communication has attracted extensive researches because of its ability to reduce latency and improve the spectrum resource utilization. This paper studies a D2D Mobile Edge Computing (MEC) system which contains multiple busy smart devices (SDs) and multiple idle smart devices. To minimize the total energy consumption of the MEC system and satisfy the latency constraints of SDs, the computation intensive task of each busy SD can be partially offloaded to one or more idle SDs as helpers. Therefore, a joint optimization problem of helpers selection and communication and computation resources allocation is proposed. The problem is formulated as an integer-mixed non-convex optimization problem which is a NP-hard problem. We thus propose a two-phase iterative approach by jointly optimizing helpers selection and communication and computation resources allocation. In the first phase, we obtain the suboptimal helpers selection policy with convex optimization techniques and block coordinate descent method. In the second phase, the resource allocation strategy is achieved by applying block coordinate descent after obtaining the suboptimal helpers selection policy. The simulation results demonstrate that not only the proposed algorithm achieves fast convergence in both phases, but also the overall energy consumption is less than other benchmarks.