Deep reinforcement learning-based task scheduling and resource allocation for NOMA-MEC in Industrial Internet of Things

Mobile Edge Computing (MEC) and Non-Orthogonal Multiple Access (NOMA) have been treated as promising technologies to process the delay-sensitive tasks in the Industrial Internet of Things (IIoT) network. The cooperation among multiple MEC servers is essential to improve the processing capacity of MEC systems. However, the dynamic IIoT environment with unknown changing models, including time-varying wireless channels, diversified task requests, and dynamic load on wireless resources and multiple MEC servers, may continuously affect the task offloading decision and NOMA user pairing, which brings great challenges to the resource management in the NOMA-MEC-based IIoT network. In order to solve this problem, we design a distributed deep reinforcement learning (DRL) based solution to improve the task satisfaction ratio by jointly optimizing the task offloading decision and the sub-channel assignment to support the binary computing offloading policy. For each IIoT device agent, to deal with the problem of partial state observability, the Recurrent Neural Network (RNN) is employed to predict the load states of sub-channels and MEC servers, which is further used for the decision of the RL agent. Simulation results show that the proposed prediction-based-DRL (P-DRL) method can achieve higher task satisfaction ratio than exiting schemes.