Phase Shift Information Compression in IRS-aided Wireless Systems: Challenges and Opportunities

TL;DR

This paper addresses the challenge of reducing phase shift information delivery overhead in IRS-aided wireless systems by proposing a prompt-guided compression framework that enhances efficiency and adaptability.

Contribution

It introduces a novel prompt-guided PSI compression method utilizing task-aware prompts and meta-learning for real-time, scalable IRS configuration.

Findings

Improved PSI reconstruction accuracy

Enhanced robustness in dynamic environments

Outperforms baseline compression methods

Abstract

Intelligent reflecting surfaces (IRS) have emerged as a promising technology for future 6G wireless networks, offering programmable control of the wireless environment by adjusting the phase shifts of reflecting elements. However, IRS performance relies on accurately configuring the phase shifts of reflecting elements, which introduces substantial phase shift information (PSI) delivery overhead, especially in large-scale or rapidly changing environments. This paper first introduces the architecture of IRS-assisted systems and highlights real-world use cases where PSI delivery becomes a critical bottleneck. It then reviews current PSI compression approaches, outlining their limitations in adaptability and scalability. To address these gaps, we propose a prompt-guided PSI compression framework that leverages task-aware prompts and meta-learning to achieve efficient and real-time PSI delivery under diverse conditions. Simulation results show improved reconstruction accuracy and robustness compared to the baseline method. Finally, we discuss open challenges and outline promising directions for future research.