移动通信2026，Vol.50Issue(2)：11-19,9.DOI:10.3969/j.issn.1006-1010.20251130-0004
基于LSTM-AE的动态信道图谱构建
Dynamic Channel Charting:an LSTM-AE-Based Approach
摘要
Abstract
With the development of the sixth-generation(6G)communication system,Channel State Information(CSI)plays a crucial role in improving network performance.Traditional Channel Charting(CC)methods map high-dimensional CSI data to low-dimensional spaces to help reveal the geometric structure of wireless channels.However,most existing CC methods focus on learning static geometric structures and ignore the dynamic nature of the channel over time,leading to instability and poor topological consistency of the channel charting in complex environments.To address this issue,this paper proposes a novel time-series channel charting approach based on the integration of Long Short-Term Memory(LSTM)networks and Auto encoders(AE)(LSTM-AE-CC).This method incorporates a temporal modeling mechanism into the traditional CC framework,capturing temporal dependencies in CSI using LSTM and learning continuous latent representations with AE.The proposed method ensures both geometric consistency of the channel and explicit modeling of the time-varying properties.Experimental results demonstrate that the proposed method outperforms traditional CC methods in various real-world communication scenarios,particularly in terms of channel charting stability,trajectory continuity,and long-term predictability.关键词
LSTM-AE/信道图谱/时序建模/信道状态信息/深度学习Key words
LSTM-AE/channel charting/temporal modeling/channel state information/deep learning分类
信息技术与安全科学引用本文复制引用
高塬,谢文静,刘一鸣,郭馨雨,胡斌涛,杜剑波,徐树公..基于LSTM-AE的动态信道图谱构建[J].移动通信,2026,50(2):11-19,9.基金项目
上海市自然科学基金项目"多点协作通信感知一体化网络关键技术研究"(25ZR1402148) （25ZR1402148）
江苏省高等学校基础科学(自然科学)研究面上项目"面向低空边缘智能计算卸载与缓存的优化策略研究"(25KJB510033) （自然科学）
