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A Novel Clustering-Based Detector for RIS-Assisted Spatial Modulation Systems
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DOI：10.1109/LCOMM.2024.3518234.png)
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Abstract
En 中文
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.
Keywords:
Receiving antennas
Detectors
Clustering algorithms
Reconfigurable intelligent surfaces
Signal detection
Modulation
Transmitting antennas
Vectors
Symbols
Reflector antennas
Unsupervised detection
reconfigurable intelligent surface (RIS)
K-means clustering (KMC)
signal detection
spatial modulation (SM)
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
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Cited Papers
K-Means Based Constellation Optimization for Index Modulated Reconfigurable Intelligent Surfaces
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