In frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems, the reciprocity mismatch caused by receiver distortion seriously degrades the amplitude prediction performance of channel state information (CSI). To tackle this issue, from the perspective of distortion suppression and reciprocity calibration, a lightweight neural network-based amplitude prediction method is proposed in this paper. Specifically, with the receiver distortion at the base station (BS), conventional methods are employed to extract the amplitude feature of uplink CSI. Then, learning along the direction of the uplink wireless propagation channel, a dedicated and lightweight distortion-learning network (Dist-LeaNet) is designed to restrain the receiver distortion and calibrate the amplitude reciprocity between the uplink and downlink CSI. Subsequently, by cascading, a single hidden layer-based amplitude-prediction network (Amp-PreNet) is developed to accomplish amplitude prediction of downlink CSI based on the strong amplitude reciprocity. Simulation results show that, considering the receiver distortion in FDD systems, the proposed scheme effectively improves the amplitude prediction accuracy of downlink CSI while reducing the transmission and processing delay.

翻译：在频分双工（FDD）大规模多输入多输出（mMIMO）系统中，由接收机失真引起的互易性失配会严重降低信道状态信息（CSI）的幅值预测性能。针对这一问题，本文从失真抑制与互易性校准的角度，提出了一种基于轻量级神经网络的幅值预测方法。具体而言，在基站（BS）存在接收机失真时，采用传统方法提取上行CSI的幅值特征；随后，沿着上行无线传播信道方向进行学习，设计了一种专用的轻量级失真学习网络（Dist-LeaNet），以抑制接收机失真并校准上下行CSI之间的幅值互易性。在此基础上，通过级联方式构建了基于单隐藏层的幅值预测网络（Amp-PreNet），利用强幅值互易性完成下行CSI的幅值预测。仿真结果表明，在FDD系统考虑接收机失真的情况下，所提方案在降低传输与处理时延的同时，有效提升了下行CSI的幅值预测精度。