Channel Estimation and Tracking in STAR-RIS Aided Systems: A Recurrent Quantum Learning Framework

A modular quantum machine learning (QML) based framework for channel estimation and tracking in STAR-RIS aided wireless systems, is proposed. While STAR-RISs enable extensive 360-degree manipulation of the propagation channels compared to reflection-only RISs, it is not a trivial task to acquire and track channel information in STAR-RIS aided systems, as this needs to be done for the devices located in both the reflection zone and the transmission zone of the surface. Powered by quantum-based operations, which are characterized by quantum parallelism, QML holds the potential to handle high-dimensional channel estimation and tracking in STAR-RIS systems. Towards this end, a novel modular QML framework that employs different quantum-based learning modules is proposed. Its purpose, threefold in design, strives to (i) eliminate the noise inherited in the coarse channel information, (ii) estimate the channels of devices in the reflection and transmission regions, and (iii) update the estimated channels to accommodate the time-varying movements of these devices. The numerical results demonstrate that the proposed QML-based approach outperforms the one based on classical recurrent neural networks.