Reconfigurable Intelligent Surfaces (RIS) enhance wireless communication by passively beamforming signals to improve Signal-to-Noise Ratio (SNR) and creating virtual paths between the transmitter and receiver when direct paths are blocked. A major challenge in RIS deployment is the complexity of channel acquisition and estimation required for effective beamforming and signal recovery. To address this, we propose using Independent Component Analysis (ICA), a statistical signal processing technique, to separate data symbols from different users at a base transceiver station without needing channel acquisition or phase optimization. To the best of our knowledge, we are the first to propose using ICA for this purpose. Our numerical results show that ICA can effectively separate and decode user data, simplifying RIS-enabled communication systems.
