Near-field beam focusing for extremely large-scale IRS-aided communication systems

An extremely large-scale intelligent reflecting surface (XL-IRS) aided communication system is studied. Although XL-IRS can effectively combat the double path-loss attenuation, the large aperture size introduces significant near-field effects. The complex near-field propagation and large number of XL-IRS elements can lead to optimality and complexity challenges in beam focusing design. Focusing on a spectral efficiency (SE) maximization problem, two unsupervised learning based algorithms are conceived for the joint optimization of base station and XL-IRS beam focusing, which operate without pre-training and exhibit strong robustness. Specifically, a dense-connected dilated autoencoder meta learning (DDAML) algorithm is proposed to achieve high SE by utilizing dense connections, while reasonably designing an autoencoder to reduce computational complexity. Furthermore, considering a need for low execution time in practical applications, a convolutional dilated autoencoder meta learning (CDAML) algorithm is also proposed to further reduce computational complexity. Simulation results show that the proposed DDAML algorithm achieves the highest SE, while the proposed CDAML algorithm significantly reduces computational complexity at the cost of limited SE loss. Moreover, the two proposed algorithms also demonstrate remarkable robustness in XL-IRS-aided near-field communications.