Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting

Abstract

Due to the limited battery capacity and computing capability of mobile users, the resource allocation strategy in device-to-device (D2D)-assisted edge computing system with hybrid energy harvesting is investigated in this paper. By employing magnetic induction-based wireless reverse charging technology, mobile user can supplement extra energy from nearby users when the energy harvested from ambient radio frequency sources is about to be exhausted. Moreover, mobile user can not only perform local computation, but also offload computing tasks to nearby users for auxiliary computation through D2D communication links or mobile edge computing (MEC) server under base station (BS) for edge computation. Due to the limited computing resources of MEC server, when the computing capability of the MEC server reaches the maximum value, an adjacent user under another nearby BS can be considered as a relay node. The computing tasks of the remaining users under the previous BS can be transferred to the MEC server with sufficient resources under another nearby BS by establishing D2D relay links. The objective of the resource allocation strategy is to maximize the energy efficiency under the constraints of computation delay and energy harvesting. The resource allocation problem is formulated as a mixed-integer nonlinear programming problem, which is not easy to obtain the optimal solution at low computational complexity. A suboptimal solution is obtained by adopting the quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the performance of the proposed strategy is superior to other benchmark strategies, and QPSO algorithm can achieve higher energy efficiency than the standard particle swarm optimization algorithm.

Keywords

• Hybrid energy harvesting
• device-to-device communication
• mobile edge computing
• resource allocation