基于残留学习的卷积神经网络,用于改进VehA通道的通道估计.
Sunita Khichar1, Yahui Meng2, Abhishek Sharma3
1Department of Electrical Engineering, Chulalongkorn University, Bangkok, Thailand.
Scientific reports
|July 2, 2025
概括
本研究引入了一种新型的卷积神经网络 (CNN) 用于直角频率分割复杂化 (OFDM) 通道估计. 在高流动性无线通信中,CNN方法显著提高了准确性,并减少了错误.
科学领域:
- 无线通信系统工程 无线通信系统工程
- 对于电信的信号处理.
- 机器学习在网络中的应用.
背景情况:
- 准确的通道估计对于可靠的直角频率分割多重复合 (OFDM) 系统至关重要,特别是在高流动性环境中.
- 像最小平方 (LS) 和线性最小平均平方误差 (LMMSE) 这样的现有方法在准确性和计算负载方面存在局限性.
- 下一代无线系统需要更高效,更精确的频道估计技术.
研究的目的:
- 为OFDM系统开发一种新且高效的道估计框架.
- 克服传统道估计方法的局限性.
- 通过先进的机器学习,在高流动性场景中提高通信可靠性.
主要方法:
- 提出了一个新的基于卷积神经网络 (CNN) 的通道估计框架.
- 该框架纳入了残留学习,以解决消失的梯度和加速趋同.
- 使用代改进技术,逐步提高道估计的准确性.
主要成果:
- 拟议的CNN方法实现了高达30%的平均平方误差 (MSE) 比LS估计低.
- 与ChannelNet相比,它在12dB的信号噪声比率 (SNR) 中显示了15%的低MSE.
- 与FSRCNN相比,在低试点密度下观察到超过20%的MSE减少,在各种SNR中表现强.
结论:
- 开发的基于CNN的频道估计框架提供了卓越的性能和降低了计算复杂性.
- 与传统和现有的深度学习方法相比,它显著提高了准确性.
- 该框架非常适合在5G和未来的无线通信系统中实时实现.
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