首页|注意力去噪与复数LSTM的时变信道预测算法

注意力去噪与复数LSTM的时变信道预测算法

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随着无线通信技术的发展,高速场景下通信技术的研究也越来越广泛,其中获取到准确的信道状态信息对提升无线通信系统的性能具有重要的意义.针对正交频分复用系统在高速场景下,现有信道预测算法未考虑噪声影响及预测精度低的问题,提出了 一种注意力去噪与复数卷积LSTM的时变信道预测算法.首先,设计了一种通道注意力信道去噪网络对信道状态信息进行去噪处理,降低了噪声对信道状态信息的影响.然后,提出了基于复数卷积层和长短期记忆网络的信道预测模型,对去噪后历史时刻的信道状态信息进行特征提取,并且对未来时刻的信道状态信息进行预测;改进后的LSTM预测模型增强了对信道时序特征的提取能力,提高了信道预测的精度.最后,结合Adam优化器对未来时刻信道状态信息进行预测输出.仿真结果表明:与对比算法相比,所提基于注意力去噪与复数卷积LSTM的时变信道预测算法对信道状态信息的预测精度更高,能够适用于高速移动场景下的时变信道预测.
Time-varying channel prediction algorithm based on the attention denoising and complex LSTM network
With the development of wireless communication technology,the research on communication technology in high-speed scenario is becoming more and more extensive,one aspect of which is that obtaining accurate channel state information is of great significance to improving the performance of a wireless communication system.In order to solve the problem that the existing channel prediction algorithms for orthogonal Frequency Division multiplexing(OFDM)systems do not consider the influence of noise and the low prediction accuracy in high-speed scenarios,a time-varying channel prediction algorithm based on attention denoising and complex convolution LSTM is proposed.First,a channel attention channel denoising network is proposed to denoise the channel state information,which reduces the influence of noise on the channel state information.Second,a channel prediction model based on the complex convolutional layer and long short term memory(LSTM)is constructed.The channel state information at the historical moment after denoising is extracted,and then it is input into the channel prediction model to predict the channel state information at the future moment.The improved LSTM prediction model enhances the ability to extract channel timing features and improves the accuracy of channel prediction.Finally,the Adam optimizer is used to predict the channel state information at the future time.Simulation results show that the proposed time-varying channel prediction algorithm based on the attention denoising and complex convolutional LSTM network method has a higher prediction accuracy for the channel state information than the comparison algorithm.At the same time,the proposed method can be applied to the time-varying channel prediction in high-speed moving scenarios.

time-varying channel predictionhigh-speed scenariochannel attention denoisingcomplex convolution long short term memory networkorthogonal frequency division multiplexing

陈永、蒋丰源

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兰州交通大学 电子与信息工程学院,甘肃 兰州 730070

时变信道预测 高速场景 通道注意力去噪 复数卷积长短期记忆网络 正交频分复用

国家自然科学基金国家自然科学基金兰州交通大学基础拔尖人才项目

61963023618413032022JC36

2024

10.19665/j.issn1001-2400.20230203

西安电子科技大学学报(自然科学版)
西安电子科技大学

西安电子科技大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.837
ISSN：1001-2400
年,卷(期)：2024.51(1)
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