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

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.