Joint task offloading and resource allocation in mobile edge computing with energy harvesting

Mobile edge computing (MEC) is considered to be a promising technique to enhance the computation capability and reduce the energy consumption of smart mobile devices (SMDs) in the sixth-generation (6G) networks. With the huge increase of SMDs, many applications of SMDs can be interrupted due to the limited energy supply. Combining MEC and energy harvesting (EH) can help solve this issue, where computation-intensive tasks can be offloaded to edge servers and the SMDs can also be charged during the offloading. In this work, we aim to minimize the total energy consumption subject to the service latency requirement by jointly optimizing the task offloading ratio and resource allocation (including time switching (TS) factor, uplink transmission power of SMDs, downlink transmission power of eNodeB, computation resources of SMDs and MEC server). Compared with the previous studies, the task uplink transmission time, MEC computation time and the computation results downloading time are all considered in this problem. Since the problem is non-convex, we first reformulate it, and then decompose it into two subproblems, i.e., joint uplink and downlink transmission time optimization subproblem (JUDTT-OP) and joint task offloading ratio and TS factor optimization subproblem (JTORTSF-OP). By solving the two subproblems, a joint task offloading and resource allocation with EH (JTORAEH) algorithm is proposed to solve the considered problem. Simulation results show that compared with other benchmark methods, the proposed JTORAEH algorithm can achieve a better performance in terms of the total energy consumption.