一种面向6G网络通感一体化网络切片生成算法OA

The integration of wireless sensing and wireless communication has emerged as a new technological development trend for 6G networks and stimulated new application demands.This paper in-vestigates how 6G networks coordinate network resources to generate integrated sensing and communica-tion(ISAC)network slices tailored to scenario-specific requirements.For 6G internet of things(IoT)ap-plication scenarios,whether network slices can provide sufficiently low-latency communication capability is critical.To solve the nonlinear integer programming problem caused by slice capability requirements in 6G IoT scenarios,a resource allocation scheme based on deep learning and the deep Q-network(DQN)algorithm is proposed.Simulation results show that when the proportion of user equipment(UE)using both communication and sensing services simultaneously is less than 50%,the proposed algorithm re-duces the end-to-end delay by up to approximately 130 ms compared with baseline algorithms.With 100 physical resource blocks(PRBs),the proposed algorithm reduces the end-to-end delay by about 50%relative to the baseline algorithm.System-level simulations verify the effectiveness of the proposed algo-rithm,which can provide a reference for resource allocation in the field of 6G network slicing.