【摘要】 230022）摘要：针对用户设备到基站（Base station, BS）的视距通信受阻时智能反射面（Intelligent reflecting surface, IRS）辅助通信系统的信道估计任务，提出了一种基于潜在特征空间噪声抑制的神经网络，可以实现精确的信道估计。该神经网络将变分自编码器（Variational auto-encoder, VAE）模型和UNet模型相结合，能够在进行信道估计的同时对输入信号中的噪声进行处理。首先，VAE模型的输入是纯净的基站接收信号，以最小化估计的纯净的基站接收信号与其真实值之间的误差为目标，使VAE模型的编码器映射出一个特征向量，作为纯净接收信号的潜在表示。其次，固定VAE模型部分，使用纯净的基站接收信号作为UNet模型的输入对整个神经网络进行训练，在此过程中，VAE模型学习到的纯净潜在特征向量有助于UNet模型的编码器学习到纯净的特征表示。接着，该特征被UNet模型的解码器解码以实现信道估计任务。最后，在估计阶段仅需利用UNet模型部分即可。仿真实验结果表明，本文所提出的信道估计方法可以有效抑制特征空间中的噪声信息，能以更低的时间复杂度更准确地估计出信道信息。更多还原

【Abstract】 In channel estimation tasks for intelligent reflecting surface(IRS)-assisted communication systems when line-of-sight communication between user equipment and base station(BS) is blocked, this paper proposes a neural network based on noise suppression in the latent feature space, which can realize accurate channel estimation. The neural network combines the variational auto-encoder(VAE) and UNet to reduce the noise in the input signal while performing channel estimation. Firstly, the VAE model takes noise-free BS received signals as input, with the objective of minimizing the error between the estimated noise-free BS received signals and their true value, so that the encoder of the VAE model maps a feature vector as a potential representation of the pure received signal. Secondly, the VAE model part is fixed. The entire network is trained using noisy BS received signals as input to the UNet model, in which the noisefree latent feature vectors learned by the VAE assist the encoder of the UNet model in learning noise-free feature representations. Subsequently, the pure feature representations are fed into the decoder of the UNet model to achieve the channel estimation task. Finally, during the estimation phase, only the UNet model part is utilized, which effectively reduces computational complexity. The results of simulation experiments demonstrate that the proposed channel estimation method can effectively suppress noisy information in the feature space, and can estimate the channel information more accurately with lower time complexity.更多还原