Amplitude Prediction from Uplink to Downlink CSI against Receiver Distortion in FDD Systems

TL;DR

This paper introduces a lightweight neural network approach to improve downlink CSI amplitude prediction in FDD massive MIMO systems by mitigating receiver distortion effects, enhancing accuracy and reducing delays.

Contribution

A novel neural network-based method that calibrates amplitude reciprocity and suppresses receiver distortion for better downlink CSI prediction in FDD systems.

Findings

Significant improvement in amplitude prediction accuracy.

Effective suppression of receiver distortion effects.

Reduced transmission and processing delays.

Abstract

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.