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.

通过考虑多节点计算资源的协作分配、传输资源块（RB）的分配以及系统的时变动态性，本文旨在通过联合考虑提出一种适用于合作 MEC 辅助的 RAN 切片系统中异构服务需求的最大 SSR 的方法。对系统进行抽象并使用循环图强化学习算法来智能地学习最佳混合 RA 策略，利用图神经网络（GCN）和深度确定性策略梯度（DDPG）有效地提取等效拓扑图的空间特征。此外，还设计了一种新的时间循环强化学习框架，将先前时刻策略网络的动作输出整合到后续时刻策略网络的状态输入中，以适应时变和上下文网络环境。此外，我们探讨了两种使用案例场景，以讨论所提出的 RGRL 算法的普遍优越性。仿真结果表明，所提出的算法在平均 SSR、性能稳定性和网络复杂性方面具有优势。