基于多智能体强化学习的异构网络CRE偏置动态优化算法OACSCDCSTPCD

To cope with the high throughput demand caused by the proliferation of wireless network users,a multi-agent reinforcement learning based dynamic optimization algorithm of cell range expansion(CRE)offset was proposed for in-terference scenarios in macro-pico heterogeneous networks.Based on the value decomposition network framework of collaborative multi-agent reinforcement learning,a personalized online local decision of CRE offset for all pico-base sta-tions was achieved by reasonably utilizing and interacting the intra-system user distribution and their interference levels among pico-base stations.Simulation results show that the proposed algorithm has significant advantages in increasing system throughput,balancing the throughput of each base station and improving edge-user throughput,compared to CRE=5 dB and distributed Q-learning algorithms.