A Scalable Machine Learning Approach Enabled RIS Optimization with Implicit Channel Estimation
Year: 2025
Type: conference-paper
Abstract: The reconfigurable intelligent surface (RIS) is considered as a key enabler of the next-generation mobile radio systems. While attracting extensive interest from academia and industry due to its passive nature and low cost, scalability of RIS elements and requirement for channel state information (CSI) are two major difficulties for the RIS to become a reality. In this work, we introduce an unsupervised machine learning (ML) enabled optimization approach to configure the RIS. The dedicated neural network (NN) architecture RISnet is combined with an implicit channel estimation method. The RISnet learns to map from received pilot signals to RIS configuration directly without explicit channel estimation. Simulation results show that the proposed algorithm outperforms baselines significantly.
Authors: Bile Peng, Vahid Jamali, Eduard Jorswieck
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415524260