Computation Offloading in Energy Harvesting Powered MEC Network

Mobile edge computing (MEC) is a promising technique which migrates computational intensive tasks from smart devices to edge servers, so as to increase the computational capacity of smart devices while saving the battery energy. In this paper, we consider a MEC network system where the smart device with energy harvesting module and electricity storage chooses an offloading rate to offload computational task to one of edge servers. We formulate the offloading problem as a joint minimization problem of energy consumption and delay in the long-term, while meeting the smart device's quality of experience constraints. The challenge is that the varying renewable energy generation and the energy consumed by the current action will affect the next battery level. To address this problem, we use a reinforcement learning model to account for future dynamics in the environment. To this end, we develop an algorithm based on a deep Q noisy neural network that adjusts automatically the noise level at smart devices for exploration, and hence, replaces the epsilon-greedy policy, traditionally used in the Q-learning algorithm. Simulation results show that the proposed algorithm achieves lower energy consumption and better quality of experience, as compared to the celebrated deep Q-learning algorithm and the random scheme.