An Efficient Network with Novel Quantization Designed for Massive MIMO CSI Feedback

2024/05/30 by Xinran Sun, Sun, Xinran, Zhengming Zhang +3 · 1 citation
Engineering · #Advanced Adaptive Filtering Techniques #Antenna Design and Optimization #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2405.20068

openalex publication_date 2024/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The efficacy of massive multiple-input multiple-output (MIMO) techniques heavily relies on the accuracy of channel state information (CSI) in frequency division duplexing (FDD) systems. Many works focus on CSI compression and quantization methods to enhance CSI reconstruction accuracy with lower feedback overhead. In this letter, we propose CsiConformer, a novel CSI feedback network that combines convolutional operations and self-attention mechanisms to improve CSI feedback accuracy. Additionally, a new quantization module is developed to improve encoding efficiency. Experiment results show that CsiConformer outperforms previous state-of-the-art networks, achieving an average accuracy improvement of 17.67% with lower computational overhead.

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• Residual Diffusion Models for Variable-Rate Joint Source Channel Coding of MIMO CSI

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