Large Models Enabled Ubiquitous Wireless Sensing

TL;DR

This paper demonstrates how large language models can be used to predict spatial channel state information in wireless MIMO-OFDM systems, improving network management and performance.

Contribution

It introduces a novel framework leveraging environment data for spatial CSI prediction using language models, advancing data-driven wireless sensing methods.

Findings

Experimental results show improved CSI prediction accuracy.

The proposed approach enhances wireless network management.

It bridges language models with wireless channel estimation.

Abstract

In the era of 5G communication, the knowledge of channel state information (CSI) is crucial for enhancing network performance. This paper explores the utilization of language models for spatial CSI prediction within MIMO-OFDM systems. We begin by outlining the significance of accurate CSI in enabling advanced functionalities such as adaptive modulation. We review existing methodologies for CSI estimation, emphasizing the shift from traditional to data-driven approaches. Then a novel framework for spatial CSI prediction using realistic environment information is proposed, and experimental results demonstrate the effectiveness. This research paves way for innovative strategies in managing wireless networks.