Intelligent CAC and routing for multi-point connections

This study introduces Reinforcement Learning (RL) to solve the problem of Cull Admission Control (CAC)&Routing for multi-point connections in networks serving multiple service classes. The network system is trained to find out the optimal control policy which brings up the highest amount of reward in long-run. For a manageable solution and realizable training time, decomposition of network and connections into link level is implemented. To demonstrate the prominence of RL-based routing against MOSPF (Multicast extension to Open Shortest Path First) protocol, a routing protocol with high performance among available ones, we consider different criteria, including reward rate, call drop rate, and link usage rate.