Performance investigation of channel estimation for intelligent reflecting surface assisted wireless communications using neural network

• Published in: Engineering applications of artificial intelligence 2024-11, Vol.137, p.109133, Article 109133
• Main Authors: Ahmadinejad, Asma, Talebi, Siamak
• Format: Article
• Language: English
• Subjects: Artificial neural network; Channel estimation; Intelligent reflecting surface; Normalized mean-square-error
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2024.109133

Intelligent reflecting surface (IRS), in which a large number of tunable reflective elements are employed, can enhance the wireless propagation environment in an acceptable manner. Although this issue is operated via intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users, the optimal tuning of the phase shifters at the IRS is arduous. In other words, the passive nature of reflective elements causes some challenges in direct measuring the channels between the IRS, the BS and the users. Even though the conventional methods first estimate the channels and then optimize the system parameters, this paper utilizes an artificial intelligence procedure in which channel estimation has been carried out considering the levenberg-marquardt training algorithm. This is attained via performing some initial processes and trained and estimated each part at different neural network architectures. The estimated output of the system can be expressed via parameters of artificial neural networks which is an important stage for channel estimation. The aforesaid technique is not only making full use of the learning property of neural network, but also by applying this method, there is no need of any matrix computation and the proposed technique is less complex than the traditional pilot-based techniques. Simulation results demonstrate that the offered technique exhibits premiere performance in comparison with conventional common methods.