Detector Design and ABEP Analysis for Spatial Scattering Shift Keying in mmWave Systems

To address millimeter-wave (mmWave) path loss, this paper proposes spatial scattering shift keying (SSSK), a modulation scheme encoding data bits onto the indices of selected channel scatterers, suitable for systems with large arrays, a single transmitter RF chain, and multiple receiver RF chains. We introduce the SSSK system model and analyze three detectors: low-complexity greedy, serial maximum likelihood (ML), and joint-processing parallel ML. Complexity analysis confirms that the greedy detector is the simplest, while Parallel ML is the most demanding. Analytical and simulation results for average bit error probability (ABEP) show that parallel ML achieves the best performance by leveraging multi-chain information, significantly outperforming the others. SSSK demonstrates viability against conventional modulations like amplitude phase modulation, offering competitive performance, particularly at higher data rates and high signal-to-noise ratios, effectively utilizing spatial diversity.

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Published
2025-08-10
DOI
https://doi.org/10.1109/iccc65529.2025.11148658
Primary Topic
Millimeter-Wave Propagation and Modeling
Type
article
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article

Detector Design and ABEP Analysis for Spatial Scattering Shift Keying in mmWave Systems

Qingqing Wu, Xusheng Zhu, Wen Chen, Zhenwei Jiang
Millimeter-Wave Propagation and Modeling
article

Detector Design and ABEP Analysis for Spatial Scattering Shift Keying in mmWave Systems

Qingqing Wu, Xusheng Zhu, Wen Chen, Zhenwei Jiang
article en

Abstract

To address millimeter-wave (mmWave) path loss, this paper proposes spatial scattering shift keying (SSSK), a modulation scheme encoding data bits onto the indices of selected channel scatterers, suitable for systems with large arrays, a single transmitter RF chain, and multiple receiver RF chains. We introduce the SSSK system model and analyze three detectors: low-complexity greedy, serial maximum likelihood (ML), and joint-processing parallel ML. Complexity analysis confirms that the greedy detector is the simplest, while Parallel ML is the most demanding. Analytical and simulation results for average bit error probability (ABEP) show that parallel ML achieves the best performance by leveraging multi-chain information, significantly outperforming the others. SSSK demonstrates viability against conventional modulations like amplitude phase modulation, offering competitive performance, particularly at higher data rates and high signal-to-noise ratios, effectively utilizing spatial diversity.

Shanghai Jiao Tong University (CN), Phicomm (China) (CN), University College London (GB), China United Network Communications Group (China) (CN)
Openalex Percentile: Top 77%
Millimeter-Wave Propagation and Modeling
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Detector Design and ABEP Analysis for Spatial Scattering Shift Keying in mmWave Systems — Qingqing Wu, Xusheng Zhu, et al. · (2025) | TGRS Research Map | TGRS