物联网学报2025，Vol.9Issue(3)：122-131,10.DOI:10.11959/j.issn.2096-3750.2025.00460
基于D3QN的Wi-Fi网络智能调制方法
Intelligent modulation method for Wi-Fi networks based on D3QN
摘要
Abstract
Rate adaptation(RA)technology is a key feature in Wi-Fi networks,capable of selecting the optimal data trans-mission rate based on real-time observed channel conditions.However,most existing rate adaptation algorithms exhibit two issues.Firstly,methods relying on cross-layer information feedback are often challenging to implement in practical applications.Secondly,the strategies employed are over-conservative in rate selection,opting for lower rates when the signal-to-noise ratio(SNR)varies between two selectable modulation levels.A rate adaptation algorithm based on the du-eling double deep Q-network(D3QN)in deep reinforcement learning was proposed to address these issues.This algo-rithm eliminated the need for cross-layer feedback and dynamically adjusted the data rate through the observation of physical layer information.Additionally,it referenced existing table-based rate adjustment methods during the design of the reward function and model loading phase.Simulation results show that the proposed algorithm can rapidly adapt to environ-mental changes and achieve higher throughput performance compared with four baseline methods across various scenarios.关键词
Wi-Fi网络/深度强化学习/双决斗深度Q网络/自适应调制Key words
Wi-Fi networks/deep reinforcement learning/D3QN/adaptive modulation分类
信息技术与安全科学引用本文复制引用
吴婷婷,方旭明..基于D3QN的Wi-Fi网络智能调制方法[J].物联网学报,2025,9(3):122-131,10.基金项目
国家自然科学基金资助项目(No.62071393) （No.62071393）
四川省重点研发计划项目(No.2024YFHZ0093)The Natural Science Foundation of China(No.62071393),Sichuan Science and Technology Program(No.2024YFHZ0093) （No.2024YFHZ0093）
