深度学习基于无线通信系统的无线训练,没有反
Christopher P Davey1, Ismail Shakeel2, Ravinesh C Deo1
1School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究引入了一种新的无反方法,用于无线通信系统的空中培训. 该方法可以在不需要专门的反通道的情况下进行发射器和接收器模型训练,从而提高效率和安全性.
科学领域:
- 无线通信无线通信
- 深度学习 (Deep Learning) 是一种深度学习.
- 信号处理 信号处理
背景情况:
- 在无线系统中进行无线培训的深度学习面临由于频道不连续性的挑战.
- 现有的方法依赖于反道,增加了资源需求和对攻击的脆弱性.
- 仅仅在前链路上进行无反培训,就很难可靠地结束培训过程.
研究的目的:
- 提出一种新的无线通信系统空中训练方法,不需要反通道.
- 为了使发射器和接收器模型在没有连续通道探测的情况下同时进行训练.
- 提供一个更节约资源,更安全的培训模式.
主要方法:
- 通过通道传输随机样本来训练混合密度网络 (MDN) 以进行通道分布近似.
- 使用训练的MDN来训练发射器和接收器模型.
- 在培训期间使用区块错误率 (BLER) 测量作为停止标准.
主要成果:
- 拟议的方法成功地在没有反通道的情况下训练发射器和接收器模型.
- 区块错误率测量被证明是有效的监测培训完成.
- 实现了与小消息序列端到端自动编码器训练相当的性能.
结论:
- 新的无反方法使无线通信系统的高效和安全的无线训练成为可能.
- 这种方法通过消除对反通道的需求,简化了培训过程.
- 该技术表现出与现有方法可比的性能,提供了可行的替代方案.
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