%0 Conference Paper %F Oral %T Data-driven and Model-driven Deep Learning Detection for RIS-aided Spatial Modulation %+ Laboratoire des signaux et systèmes (L2S) %A Liu, Jiang %A Renzo, Marco Di %< avec comité de lecture %B 2021 IEEE 4th 5G World Forum (5GWF) %C Montreal, Canada %I IEEE %P 88-92 %8 2021-10-13 %D 2021 %R 10.1109/5GWF52925.2021.00023 %Z Engineering Sciences [physics]Conference papers %X Reconfigurable intelligent surface (RIS) is regarded as a key technology for the next generation of wireless communications. Recently, the combination of RIS and spatial modulation (SM) or space shift keying (SSK) has attracted a lot of interest in the wireless communication area by achieving a trade-off between spectral and energy efficiency. In this paper, by generalizing RIS-aided SM/SSK system to a special case of conventional SM system, we investigated deep learning based detection in RIS-aided SM/SSK systems. Based on the idea of deep unfolding, we studied the model-driven deep learning detection for RIS-aided SM systems and compare the performance against the data-driven deep learning detectors. %G English %L hal-03448167 %U https://centralesupelec.hal.science/hal-03448167 %~ CNRS %~ SUP_LSS %~ OPENAIRE %~ SUP_TELECOMS %~ CENTRALESUPELEC %~ UNIV-PARIS-SACLAY %~ TEST-HALCNRS %~ UNIVERSITE-PARIS-SACLAY %~ GS-ENGINEERING %~ GS-COMPUTER-SCIENCE