DRL based binary computation offloading in wireless powered mobile edge computing

Abstract

Abstract This paper considers the wireless powered mobile edge computing combining wireless power transmission (WPT) and mobile edge computing, where the hybrid access point (HAP) uses multiple radio beams to charge multiple wireless devices (WDs) and WD adopts the binary offloading mode to offload computation workload to HAP via FDMA. It is aimed to maximize the sum computation rate (SCR) of WDs by jointly optimizing the binary offloading decision, transmit power of each radio beam, and WPT duration. Due to the strong coupling between the offloading decision and other optimization variables, the SCR maximization is formulated as a mixed integer nonlinear programming problem. To address this challenging problem, an online DRL‐based decoupling optimization algorithm is proposed. Specifically, the original problem is first split into a top‐problem of optimizing binary offloading decision and a sub‐problem of optimizing transmit powers and WPT duration under given offloading decision. Then a self‐learning DRL framework is designed to output the near‐optimal offloading decisions. Finally, for the sub‐problem, based on the Lagrangian dual theory, an efficient approach to fast obtain the closed‐form expression of the optimal solution is proposed. The simulation results show that the proposed DRL‐based algorithm can achieve the near‐maximal SCR with low computational complexity.

Keywords

• data communication
• mobile computing
• wireless sensor networks