Deep Neural Network Assisted Monte Carlo Tree Search Algorithm to Solve Bandwidth Slicing Placement Problem

To solve the network slicing placement problem, the methods based on CNN/RNN were inadequate in handling the randomness of fluctuating channel quality and bandwidth needs for each network slice. While the Monte Carlo Tree Search (MCTS) methodology effectively deals with the unpredictability of each slice’s channel quality and bandwidth request to optimize throughput, it remains time-consuming in finding an optimal solution. The cause is that MCTS relies on a uniform distribution to randomly sample one possible solution, which leads to subpar sampling efficiency. Our objective is to integrate a deep neural network (DNN) to assist MCTS. Specifically, the DNN first analyses the current allocation situation to predict probability distributions for achieving optimizing throughput. MCTS then leverages this DNN-produced probability distribution to pinpoint the best allocation scenario. Experimental results indicate that the performance of DNN-based MCTS with only 50 search iterations surpasses that of the original MCTS with 4,000 search iterations.