Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO

TL;DR

This paper introduces a variable length CSI feedback strategy for FDD massive MIMO systems using PCA for compression, which reduces overhead and improves sum rate performance.

Contribution

It proposes a novel adaptive feedback design using PCA and optimized bit allocation, outperforming fixed feedback methods in efficiency and effectiveness.

Findings

Improves zero-forcing sum rate by 17% over CsiNetPro.

Reduces model parameters by 23.4 times.

Requires eight times fewer training samples.

Abstract

To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division duplex (FDD) systems, the CSI is acquired through a feedback signal from the user equipment (UE). However, this may lead to an important overhead in FDD massive MIMO systems. Focusing on these systems, in this study, we propose a novel strategy to design the CSI feedback. Our strategy allows to optimally design variable length feedback, that is promising compared to fixed feedback since users experience channel matrices differently sparse. Specifically, principal component analysis (PCA) is used to compress the channel into a latent space with adaptive dimensionality. To quantize this compressed channel, the feedback bits are smartly allocated to the latent space dimensions by minimizing the normalized mean squared error (NMSE) distortion. Finally, the quantization codebook is determined with k-means clustering. Numerical simulations show that our strategy improves the zero-forcing beamforming sum rate by 17%, compared to CsiNetPro. The number of model parameters is reduced by 23.4 times, thus causing a significantly smaller offloading overhead. At the same time, PCA is characterized by a lightweight unsupervised training, requiring eight times fewer training samples than CsiNetPro.