Reconfigurable Intelligent Surface-Aided Orthogonal Time Frequency Space and Its Deep Learning-Based Signal Detection

Abstract

Orthogonal time frequency space (OTFS) has emerged as a promising modulation for next-generation wireless systems. This two-dimensional (2D) modulation has the inherent capability of providing uninterrupted connectivity and improved performance in a high-mobility doubly-selective channel by mapping the information symbols to the delay-Doppler (DD) domain. In order to improve the directivity of OTFS signals, this paper considers a reconfigurable intelligent surface (RIS)-aided OTFS transmission for a high-speed environment. While OTFS mitigates the dispersion due to the highly mobile wireless channels by mapping the transmit symbols to the DD domain, RIS improves the directivity by appropriately shifting the phase of the signal in non-line-of-sight (NLOS) communication channels. We provide a concrete matrix-based mathematical model of the RIS-aided OTFS communication system. Capitalizing on the simple matrix multiplication-based model, a number of detectors can be used. These include the usual zero-forcing (ZF) and the minimum mean-squared error (MMSE) linear detectors, and a low-complexity message passing algorithm (MPA)-assisted detector. We evaluate the performance of these detectors for RIS-based OTFS systems. A deep learning (DL)-based signal detector is also proposed for the RIS-aided OTFS system. A significant improvement in bit-error-rate (BER) performance is achieved using the system considered, while the RIS-induced computational burden is not high. Furthermore, in NLOS communication, our system can ensure seamless connectivity with a reduced number of base stations (BS).