  The reconfigurable intelligent surface (RIS) is considered a crucial
technology for the future of wireless communication. Recently, there has been
significant interest in combining RIS with spatial modulation (SM) or space
shift keying (SSK) to achieve a balance between spectral and energy efficiency.
In this paper, we have investigated the use of deep learning techniques for
detection in RIS-aided received SM (RSM)/received-SSK (RSSK) systems over
Weibull fading channels, specifically by extending the RIS-aided SM/SSK system
to a specific case of the conventional SM system. By employing the concept of
neural networks, the study focuses on model-driven deep learning detection
namely block deep neural networks (B-DNN) for RIS-aided SM systems and compares
its performance against maximum likelihood (ML) and greedy detectors. Finally,
it has been demonstrated by Monte Carlo simulation that while B-DNN achieved a
bit error rate (BER) performance close to that of ML, it gave better results
than the Greedy detector.
