Synesthesia of Machines (SoM)-Enhanced Wideband Multi-User CSI Learning With LiDAR Sensing

TL;DR

This paper introduces a LiDAR-enhanced CSI learning network for wideband multi-user MIMO systems, leveraging environmental sensing to improve channel estimation accuracy and spectrum efficiency, especially in latency-sensitive scenarios.

Contribution

It presents a novel LE-CLN that integrates LiDAR data with channel estimation, transforming raw LiDAR inputs into signal-relevant features and adapting to channel conditions via attention mechanisms.

Findings

LE-CLN achieves higher estimation accuracy than benchmarks.

Spectrum efficiency is improved with reduced pilot transmissions.

Performance is enhanced in latency-sensitive applications.

Abstract

Light detection and ranging (LiDAR) has been utilized for optimizing wireless communications due to its ability to detect the environment. This paper explores the use of LiDAR in channel estimation for wideband multi-user multiple-input-multiple-output orthogonal frequency division multiplexing systems and introduces a LiDAR-enhanced Channel State Information (CSI) learning network (LE-CLN). By utilizing user positioning information, LE-CLN first calculates user-localized over-complete angular measurements. It then investigates the correlation between LiDAR and CSI, transforming raw LiDAR data into a low-complexity format embedded with signal propagation characteristics. LE-CLN also adapts the use of LiDAR based on channel conditions through attention mechanisms. Thanks to the unique wireless features offered by LiDAR, LE-CLN achieves higher estimation accuracy and spectrum efficiency compared to benchmarks, particularly in latency-sensitive applications where pilot transmissions are expected to be reduced.