Variable-Length Wideband CSI Feedback via Loewner Interpolation and Deep Learning

TL;DR

This paper introduces a novel variable-length CSI feedback scheme for U6G band massive MIMO systems, utilizing Loewner Interpolation and deep learning to improve accuracy and efficiency over traditional methods.

Contribution

It proposes a dynamic basis generation method with Loewner Interpolation and a flexible, neural network-based auto-encoder for variable-length feedback in wideband channels.

Findings

Achieves higher CSI feedback accuracy with less or equal feedback overhead.

Improves spectral efficiency compared to baseline schemes.

Supports flexible feedback length with a single trained model.

Abstract

In this paper, we propose a variable-length wideband channel state information (CSI) feedback scheme for Frequency Division Duplex (FDD) massive multiple-input multipleoutput (MIMO) systems in U6G band (6425MHz-7125MHz). Existing compressive sensing (CS)-based and deep learning (DL)- based schemes preprocess the channel by truncating it in the angular-delay domain. However, the energy leakage effect caused by the Discrete Fourier Transform (DFT) basis will be more serious and leads to a bottleneck in recovery accuracy when applied to wideband channels such as those in U6G. To solve this problem, we introduce the Loewner Interpolation (LI) framework which generates a set of dynamic bases based on the current CSI matrix, enabling highly efficient compression in the frequency domain. Then, the LI basis is further compressed in the spatial domain through a neural network. To achieve a flexible trade-off between feedback overhead and recovery accuracy, we design a rateless auto-encoder trained with tail dropout and a multi-objective learning schedule, supporting variable-length feedback with a singular model. Meanwhile, the codewords are ranked by importance, ensuring that the base station (BS) can still maintain acceptable reconstruction performance under limited feedback with tail erasures. Furthermore, an adaptive quantization strategy is developed for the feedback framework to enhance robustness. Simulation results demonstrate that the proposed scheme could achieve higher CSI feedback accuracy with less or equal feedback overhead, and improve spectral efficiency compared with baseline schemes.