用深度学习方法对集群感知通道进行估计,用于深水声通信.
Diya Wang1, Yonglin Zhang1, Yupeng Tai1
1State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China.
本研究引入了水下声学 (UWA) 通信的深度学习方法,通过利用集群稀疏结构来改进通道估计. 这种新的方法提高了准确性和稳定性,特别是在具有挑战性的低信号噪声比环境中.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 水下声学 (UWA) 通道通常呈现集群分散结构.
- 现有的算法利用时间域稀疏性进行UWA通道估计.
- 集群结构为增强道估计提供了潜力.
研究的目的:
- 为UWA直角频率分割复杂化 (OFDM) 系统提出基于深度学习的通道估计方法.
- 为了利用UWA频道的集群结构来改进估计.
- 为了提高UWA通道估计的准确性和稳定性.
主要方法:
- 开发了一个基于卷积神经网络 (CNN) 的集群检测模型.
- 提出了一个集群感知分布式压缩传感 (CS) 方法.
- 利用相邻的OFDM符号之间的联合稀疏性和有限的频道延迟扩展搜索空间.
主要成果:
- 美国有线电视新闻网的集群检测模型表现出比佩奇测试算法更高的准确性和稳定性,特别是在低信号噪声比率的情况下.
- 集群意识的分布式CS方法减少了噪音引起的错误.
- 与现有的稀疏UWA通道估计技术相比,模拟和海试证实了拟议方法的优越性能.
结论:
- 提出的基于深度学习的方法有效地利用了UWA道的集群结构.
- 这种方法显著提高了UWA-OFDM系统中的通道估计性能.
- 该方法为可靠的水下声学通信提供了有希望的进步.
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