A Novel Clustering-Based Detector for RIS-Assisted Spatial Modulation Systems

Abstract

In this letter, we propose a novel unsupervised detector for RIS-assisted received spatial modulation (RIS-RSM) systems utilizing a clustering-based approach. The combination of reconfigurable intelligent surfaces (RIS) and spatial modulation (SM) presents a promising direction for beyond 5G (B5G) networks, enhancing spectral and energy efficiency. However, existing signal detection methods for RIS-RSM assume perfect channel state information (CSI), which is impractical due to the passive nature of RIS. To overcome this, we first transform the unsupervised detection problem of RIS-RSM into a clustering problem and apply unsupervised clustering algorithms from machine learning to eliminate the need for CSI acquisition. Given that traditional clustering algorithms like K-means are insufficient for this application, we propose a novel clustering detector by leveraging the unique amplitude and phase characteristics of the channel in RIS-RSM systems. Simulation results demonstrate that our proposed detector can maintain excellent detection performance that is basically consistent with the optimal detector ML without the need for CSI, marking a significant advancement in RIS-RSM signal detection.