基于元深度强化学习的蜂窝网链路自适应方法OA

To address the stringent requirements for reliability and data rate in cellular networks,an efficient and highly generalizable link adaptation method was proposed.First,to ensure reliable wireless communication transmission,a con-strained modulation and coding scheme selection strategy was designed to meet the block error rate requirement.Second,to overcome the poor generalization capability of traditional algorithms in unknown transmission environments,a meta-learning mechanism was integrated with deep reinforcement learning.Through offline training followed by online fine-tuning,rapid policy convergence was achieved.Simulation results demonstrate that,while strictly satisfying the block er-ror rate requirement,the proposed method achieves higher data rate performance and stronger generalization capability compared with traditional link adaptation methods.