LAUSR

Multi-access mobile edge computing (MEC) is envisioned as a key enabling technology to support compute-intensive and delay-sensitive applications in railway Internet of Things (RIoT) networks. However, the time-varying channel variations in RIoT scenarios make it challenging to achieve efficient resource allocation. The emerging deep reinforcement learning (DRL) is able to respond to the above-mentioned challenge. In this paper, with the aim of reducing the total computational cost (weighted sum of consumed energy and delay), we investigate the dynamic resource management issue of joint subcarrier assignment, offloading ratio, power allocation and computation resource allocation in multi-access MEC assisted RIoT networks. To address this intractable mixed integer nonlinear programming issue, we put forward a hybrid DRL (HDRL) scheme, which is an integration of deep double Q-learning (DDQN) and deep deterministic policy gradient (DDPG). The HDRL algorithm is capable of learning the advisable strategies for actions including discrete-continuous hybrid variables. In HDRL algorithm, DDQN plays the role of making subcarrier assignment decision, and DDPG plays the role of making offloading ratio, power allocation as well as computation resource allocation decisions. Numerical results demonstrate that HDRL scheme can yield much less computational cost than the existing baselines for multi-access MEC assisted RIoT networks. In addition, the HDRL scheme is close to the near-optimal performance with comparatively low execution time.