Efficient IRS Selection Scheme in Multi-IRS-Aided Wireless Networks

• Published in: IEEE wireless communications letters p. 1
• Main Authors: Kwon, Doyle, Kim, Duk Kyung
• Format: Journal Article
• Language: English
• Published: IEEE 05.08.2024
• Subjects: Channel estimation; Discrete Fourier transforms; Estimation; Intelligent reflecting surface (IRS); IRS selection algorithm; Reflection; Sparse signal recovery problem (SSRP); Spectral efficiency; Vectors; Wireless networks
• DOI: 10.1109/LWC.2024.3438838
• ISSN: 2162-2337

We propose an efficient intelligent reflecting surface (IRS) selection scheme in multi-IRS-aided wireless networks. Unlike conventional methods assigning dedicated resources per IRS for straightforward channel quality estimation, our approach employs a shared resource set for all IRSs. Leveraging the sparse signal recovery problem (SSRP) in simultaneously reflected signals by IRSs, we devise a computationally efficient IRS selection algorithm to find the optimal IRS with the best cascaded channel quality. The proposed scheme achieves over 98% accuracy in selecting the optimal IRS when there are fewer than 11 IRSs with spectral efficiency only 1.5% lower than the ideal scheme.