Sparse Residual Component Selection for Low-Complexity CSI Feedback in Massive MIMO

Abstract

Efficient and reliable transmission of channel state information (CSI) is crucial for maintaining optimal performance in massive multiple-input multiple-output (MIMO) wireless communication systems. However, the feedback overhead remains a significant challenge, particularly in frequency division duplex (FDD) systems with large antenna arrays. In this paper, we propose a novel CSI feedback compression scheme based on sparse residual component selection. The proposed method builds a training-set statistical mean baseline from the training dataset and identifies the K positions whose expected residual magnitude is largest, yielding a fixed importance mask shared between the user equipment (UE) and the base station (BS). Only the residual values at selected positions are encoded and transmitted using a CsiNet-style convolutional autoencoder, while the remaining positions are approximated by the statistical baseline at the BS without any additional feedback. As compression becomes more aggressive, the encoder and decoder operate on a smaller spatial grid at high compression, directly reducing computational cost. Experiments on the COST 2100 dataset show that the proposed method achieves competitive reconstruction quality compared with CsiNet, CsiNet+, and Sobel selective processing, while significantly reducing computational overhead at intermediate and high compression levels.