Transformer-Based Sparse CSI Estimation for Non-Stationary Channels

TL;DR

This paper introduces a transformer-based framework for sparse CSI estimation in non-stationary wireless channels, significantly improving accuracy and reducing pilot overhead compared to traditional methods.

Contribution

It proposes a novel Flash-Attention Transformer model that combines model-driven pilot acquisition with data-driven CSI reconstruction, tailored for dynamic channel conditions.

Findings

Outperforms LMMSE and LSTM by ~13 dB NMSE

Reduces pilot overhead by 16 times

Enhances spectral efficiency and link reliability

Abstract

Accurate and efficient estimation of Channel State Information (CSI) is critical for next-generation wireless systems operating under non-stationary conditions, where user mobility, Doppler spread, and multipath dynamics rapidly alter channel statistics. Conventional pilot aided estimators incur substantial overhead, while deep learning approaches degrade under dynamic pilot patterns and time varying fading. This paper presents a pilot-aided Flash-Attention Transformer framework that unifies model-driven pilot acquisition with data driven CSI reconstruction through patch-wise self-attention and a physics aware composite loss function enforcing phase alignment, correlation consistency, and time frequency smoothness. Under a standardized 3GPP NR configuration, the proposed framework outperforms LMMSE and LSTM baselines by approximately 13 dB in phase invariant normalized mean-square error (NMSE) with markedly lower bit-error rate (BER), while reducing pilot overhead by 16 times. These results demonstrate that attention based architectures enable reliable CSI recovery and enhanced spectral efficiency without compromising link quality, addressing a fundamental bottleneck in adaptive, low-overhead channel estimation for non-stationary 5G and beyond-5G networks.