统一的空间-时间-消息干扰对齐:一个端到端的学习方法.
Elaheh Sadeghabadi1, Steven Blostein1
1Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了Deep-STMIA,这是一个针对多用户MISO广播频道的深度学习框架. 它有效地管理不完美的通道信息下的干扰,优于现有的方法.
科学领域:
- 无线通信系统无线通信系统
- 信息理论是信息理论.
- 机器学习用于信号处理.
背景情况:
- 多用户 MISO 广播频道面临性能降低,原因是发射器 (CSIT) 的频道状态信息不完善.
- 传统的干扰对齐 (IA) 技术在CSI不确定性下与连续干扰取消 (SIC) 错误传播作斗争.
- 现有的方法,如分费多重访问 (RSMA),在实际,非理想的CSIT场景中存在局限性.
研究的目的:
- 开发一个新的深度学习框架,Deep-STMIA,用于在MU-MISO广播频道中进行强大的干扰管理.
- 在不完美,延迟和定量化CSIT下解决传统IA和SIC方法的局限性.
- 为了适应性地减轻错误传播,并在各种CSIT条件下优化性能.
主要方法:
- 建议Deep-STMIA,一个端到端的深度学习框架,使用基于神经网络的自动编码器.
- 实施结构性消息域规范化,以共同优化空间,时间和消息域.
- 在各种CSIT缺陷下对基准和最先进的方法进行绩效评估.
主要成果:
- 深度STMIA在极端的CSIT制度中展示了性能匹配自由度 (DoF) 的最佳基准.
- 该框架在实际不完美的CSIT场景中显著优于分费多重访问 (RSMA).
- 深度STMIA有效地减轻了CSI不确定性和量子化引起的错误传播.
结论:
- 深度STMIA提供了一个强大的,适应性解决方案,用于干扰管理在MU-MISO系统与实际的CSIT约束.
- 与传统方法相比,深度学习方法提供了卓越的性能和稳定性.
- 这一框架促进了高效的无线通信系统的实际部署.
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