Large Models Enabled Ubiquitous Wireless Sensing

TL;DR

This research paves way for innovative strategies in managing wireless networks by exploring the utilization of language models for spatial CSI prediction within MIMO-OFDM systems.

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.

Figures (11)

• Figure 1: Modern classification of wireless channels
• Figure 2: Numerical Methods of Winprop, with more approximation introduced when the prolem's scale becomes larger.
• Figure 3: Locations of UEs and BS in LLM4CP's experiment setup. For consideration of simplicity, only LOS paths are shown. But remember that NLOS paths also exist and they are caused by randomly generated scatter points in the space, under QuaDriGa built-in algorithms.
• Figure 4: The LLM4CP and other baselines' NMSE performance in relation to various user velocities for TDD systems
• Figure 5: The LLM4CP and other baselines' NMSE performance in relation to various user velocities for FDD systems