通过Meta-Learning加速对多天线频率选择通道的线性预测器的训练
Sangwoo Park1, Osvaldo Simeone1
1Department of Engineering, King's College London, London WC2R 2LS, UK.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了使用传输和元学习进行无线通信的高效通道预测算法. 这些方法可以通过更少的试点符号进行准确的预测,这对于5G系统至关重要.
科学领域:
- 无线通信工程 无线通信工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 有效的频道预测对于多天线频率选择性频道至关重要,需要最小的试点符号.
- 现有的方法在动态无线环境中使用有限的数据而扎.
研究的目的:
- 为多天线系统开发新的数据驱动的通道预测算法.
- 为了更快的适应,将转移学习和元学习与降级道参数化集成在一起.
- 为了提高预测准确度,使用较少的试点符号.
主要方法:
- 提出了新的通道预测算法,将转移和元学习与降级参数化结合起来.
- 开发了线性预测器,优化过去数据以适应当前.
- 为线性预测模型引入了一种新的长短期分解 (LSTD).
- 应用平衡传播 (EP) 和交替最小平方 (ALS) 用于基于LSTD的预测.
主要成果:
- 证明了转移和元学习在减少用于通道预测的试点符号要求方面的有效性.
- 在5G标准通道模型中展示了拟议的LSTD参数化的好处.
- 通过利用具有明显传播特征的先前的数据实现了高效的通道预测.
结论:
- 转移和元学习显著减少无线系统的通道预测中的飞行员开销.
- 拟议的LSTD参数化为高效和准确的频道预测提供了一个有希望的方法.
- 开发的算法为下一代无线通信系统提供了强大的数据驱动策略.
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