Modular Quantum Machine Learning for Channel Estimation in STAR-RIS Assisted Communication Systems

This work employs modular quantum machine learning (QML) to estimate the wireless channels in simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) aided communication systems. Although RISs, composed of low-energy phase-shifting elements, can enable controlled signal reflections to cover communication devices obstructed by blockages, the devices located behind the reflectiononly surfaces cannot be covered as these structures now become blockages themselves. STAR-RISs solve this issue by allowing the transmission signals to be conveyed to the devices located behind the STAR-RIS structures. However, acquiring accurate channel information of devices in the reflection and transmission regions of a STAR-RIS is not a trivial task. To address this issue, this paper proposes a novel modular QML scheme that employs different quantum-based learning modules to (i) eliminate the noise from the coarse channel information, and (ii) estimate the channels of the devices in the reflection and transmission regions.