现代电子技术2024，Vol.47Issue(17)：1-9,9.DOI:10.16652/j.issn.1004-373x.2024.17.001
基于深度强化学习的水声网络公平跨层MAC协议
Deep reinforcement learning based proportional-fair optimized cross-layer MAC protocol for underwater acoustic networks
摘要
Abstract
In view of the unfair channel allocation and energy-constrained nodes in underwater acoustic communication heterogeneous networks,a proportional-fair optimized cross-layer medium access control(POCL-MAC)protocol is proposed based on deep reinforcement learning(DRL).It strives to optimize fair channel access and power control by cross-layer joint.By feedback ACK packets,the channel conflict outcomes and receiver-side signal-to-noise ratio(SNR)under delay status are obtained.Autonomous learning is carried out based on the state,action and reward sequence of DRL to adjust the access slot and transmission power of cognitive users.Fairness function is employed to achieve proportional fairness between cognitive users and primary users' throughput performance in heterogeneous networks.A joint state sequence and independent reward function are designed to enhance the accuracy of sub-action decisions in cross-layer joint optimization without increasing neural network complexity.Simulation results demonstrate that the proposed algorithm can achieve near-optimal fairness throughput performance while exhibiting better energy utilization efficiency in comparison with the traditional DRL-based algorithms.关键词
深度强化学习/水声通信网络/MAC协议/冲突避免/功率优化/信道分配Key words
DRL/underwater acoustic communication network/MAC protocol/conflict avoidance/power optimization/channel allocation分类
电子信息工程引用本文复制引用
韩翔,张育芝,李梦凡,冯晓美..基于深度强化学习的水声网络公平跨层MAC协议[J].现代电子技术,2024,47(17):1-9,9.基金项目
陕西省教育厅科研项目(22JK0454) （22JK0454）
