Deep Reinforcement Learning-based Power Control and Bandwidth Allocation Policy for Weighted Cost Minimization in Wireless Networks.

Mobile edge computing (MEC) can dispatch its powerful servers close by to assist with the computation workloads that intelligent wireless terminals have offloaded. The MEC server's physical location is closer to the intelligent wireless terminals, which can satisfy the low latency and high reliability demands. In this paper, we formulate an MEC framework with multiple vehicles and service devices that considers the priority and randomness of arriving workloads from roadside units (RSUs), cameras, laser radars (Lidar) and the time-varying channel state between the service device and MEC server (MEC-S). To minimize the long-term weighted average cost of the proposed MEC system, we transit this issue (cost minimization problem) into the Markov decision process (MDP). Furthermore, considering the difficulty realizing the state transition probability matrix, the dimensional complexity of the state space, and the continuity of the action space, we propose a deterministic policy gradient (MADDPG)-based bandwidth partition and power allocation optimization policy. The proposed MADDPG-based policy is a model-free deep reinforcement learning (DRL) method, which can effectively deal with continuous action space and further guide multi-agent to execute decision-making. The comprehensive results verify that the proposed MADDPG-based optimization scheme has fine convergence and performance that is better than that of the other four baseline algorithms. [ABSTRACT FROM AUTHOR]