On the Performance of RIS-Aided Spatial Modulation for Downlink Transmission

In this study, we explore the performance of a reconfigurable reflecting surface (RIS)-assisted transmit spatial modulation (SM) system for downlink transmission, wherein the deployment of RIS serves the purpose of blind area coverage within the channel. At the receiving end, we present three detectors, i.e., maximum likelihood (ML) detector, two-stage ML detection, and greedy detector to recover the transmitted signal. By utilizing the ML detector, we initially derive the conditional pair error probability expression for the proposed scheme. Subsequently, we leverage the central limit theorem (CLT) to obtain the probability density function of the combined channel. Following this, the Gaussian-Chebyshev quadrature method is applied to derive a closed-form expression for the unconditional pair error probability and establish the union tight upper bound for the average bit error probability (ABEP). Furthermore, we derive a closed-form expression for the ergodic capacity of the proposed RIS-SM scheme. Monte Carlo simulations are conducted not only to assess the complexity and reliability of the three detection algorithms but also to validate the results obtained through theoretical derivation results.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Wireless Communications
Published
2024-09-23
DOI
https://doi.org/10.1109/twc.2024.3438767
Citations
15
Primary Topic
Advanced Wireless Communication Technologies
Type
article
Field-Weighted Citation Impact
3.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On the Performance of RIS-Aided Spatial Modulation for Downlink Transmission

Qingqing Wu, Xusheng Zhu, Wen Chen
15 citations
IEEE Transactions on Wireless Communications
Advanced Wireless Communication Technologies
3.13
article

On the Performance of RIS-Aided Spatial Modulation for Downlink Transmission

Qingqing Wu, Xusheng Zhu, Wen Chen
article en
15 citations

Abstract

In this study, we explore the performance of a reconfigurable reflecting surface (RIS)-assisted transmit spatial modulation (SM) system for downlink transmission, wherein the deployment of RIS serves the purpose of blind area coverage within the channel. At the receiving end, we present three detectors, i.e., maximum likelihood (ML) detector, two-stage ML detection, and greedy detector to recover the transmitted signal. By utilizing the ML detector, we initially derive the conditional pair error probability expression for the proposed scheme. Subsequently, we leverage the central limit theorem (CLT) to obtain the probability density function of the combined channel. Following this, the Gaussian-Chebyshev quadrature method is applied to derive a closed-form expression for the unconditional pair error probability and establish the union tight upper bound for the average bit error probability (ABEP). Furthermore, we derive a closed-form expression for the ergodic capacity of the proposed RIS-SM scheme. Monte Carlo simulations are conducted not only to assess the complexity and reliability of the three detection algorithms but also to validate the results obtained through theoretical derivation results.

IEEE Transactions on Wireless CommunicationsVol. 23(11)
Shanghai Jiao Tong University (CN)
Openalex Percentile: Top 8%
Advanced Wireless Communication Technologies
3.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

On the Performance of RIS-Aided Spatial Modulation for Downlink Transmission — Qingqing Wu, Xusheng Zhu, et al. · IEEE Transactions on Wireless Communications (2024) | TGRS Research Map | TGRS