Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network
This work addresses resource allocation challenges in 5G/6G networks for improved service delivery, representing an incremental advance by combining existing techniques like graph neural networks and reinforcement learning in a novel framework.
The paper tackles maximizing the sum service rate for heterogeneous demands in a mobile edge computing-assisted radio access network slicing system by jointly optimizing computing and transmission resources under time-varying conditions, proposing a recurrent graph reinforcement learning algorithm that shows superiority in average sum service rate, stability, and complexity in simulations.
In this paper, we aim to maximize the SSR for heterogeneous service demands in the cooperative MEC-assisted RAN slicing system by jointly considering the multi-node computing resources cooperation and allocation, the transmission resource blocks (RBs) allocation, and the time-varying dynamicity of the system. To this end, we abstract the system into a weighted undirected topology graph and, then propose a recurrent graph reinforcement learning (RGRL) algorithm to intelligently learn the optimal hybrid RA policy. Therein, the graph neural network (GCN) and the deep deterministic policy gradient (DDPG) is combined to effectively extract spatial features from the equivalent topology graph. Furthermore, a novel time recurrent reinforcement learning framework is designed in the proposed RGRL algorithm by incorporating the action output of the policy network at the previous moment into the state input of the policy network at the subsequent moment, so as to cope with the time-varying and contextual network environment. In addition, we explore two use case scenarios to discuss the universal superiority of the proposed RGRL algorithm. Simulation results demonstrate the superiority of the proposed algorithm in terms of the average SSR, the performance stability, and the network complexity.