| Summary: | In recent years, with the rapid development of mobile communication, D2D (Device-to-Device, D2D) cooperative communication network has become the main component of future communication network, which greatly improves the spectrum efficiency of the network and the quality of user communication. However, the existing D2D network resource allocation schemes have some problems, such as weak dynamic resource allocation capability and low user communication quality. In view of this challenge, this paper proposes a resource allocation algorithm for D2D cooperative communication networks based on improved Monte Carlo tree search. First, a double-chain deep deciduous Monte Carlo tree search (Dcdd-MCTS) resource allocation model is established, Then, the loss function composed of deciduous MCTS and parallel convolution network is used to update the parameters of the deep neural network model of Dcdd-MCTS. Then, the theory of optimal classification is used to solve the user’s transmit power. Finally, the optimal scheme of dynamic output resource allocation is output. The simulation results show that Dcdd-MCTS has good convergence. In the research on the distance between devices, compared with single-chain deep MCTS and joint optimization algorithm, the proposed algorithm in this paper increases the system throughput by 5%, 2%, respectively, and reduces the outage probability by 33%,18%.
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