Routing and Time-slot Allocation Method in Multi-Domain MTN Network

Large bearer networks are often managed by different operators, with each network forming an autonomous domain. These domains together constitute a multi-domain network. Metro transport network (MTN) is a new bearer network technology developed for 5G metropolitan bearer requirements. For the routing and time-slot allocation (RSA) problem in MTN, traditional algorithms are often based on heuristic rules or limited to single-domain environment, making them difficult to apply to more complex multi-domain scenarios. In view of the above issues, this paper proposes a hierarchical deep reinforcement learning (DRL) framework based on the asynchronous advantage actor critic (A3C) algorithm to solve the multi-domain MTN routing problem. And a time-slot allocation algorithm is proposed to optimize the time-slot resource utilization of the links. Compared to other classic heuristic algorithms, the proposed algorithm achieves an overall improvement of at least $42.1 \%$ in terms of the combined metrics of blocking rate and network load balancing, while the average utilization of link time-slot resources increases by $\mathbf{1. 5 \%}$.