Resource allocation strategy based on deep reinforcement learning in 6G dense network - Fan YANG; Cheng YANG; Jie HUANG; Shilong ZHANG; Tao YU; Xun ZUO; Chuan YANG (Tongxin xuebao)
In order to realize no overlapping interference between cells, 6G dense network (DN) adopting resource allocation is the important technology of enhancing network performance.However, limited resources and dense distribution of nodes make it difficult to solve the problem of resource allocation through traditional optimization methods.To tackle the problem, a point-line graph coloring based overlapping interference model was formulated and a Dueling deep Q-network (DQN) based resource allocation method was proposed, which combined deep reinforcement learning (DRL) and the overlapping interference model.Specifically, the proposed method adopted the overlapping interference model and resource reuse rate to design the immediate reward.Then, generating 6G DN resource allocation strategies were independently learned by using Dueling DQN to achieve the goal of realizing resource allocation without overlapping interference between cells.The performance evaluation results show that the proposed method can effectively increase both network throughput and resource reuse rate, as well as enhance network performance.