Expectation Propagation-based Signal Detection for Differential Spatial Modulation

SHAO Hua; WANG Chun; CAO Difei; LI Wei; ZHANG Haijun

doi:10.11999/JEIT240840

Volume 47 Issue 3

Mar. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(3): 590-599

SHAO Hua, WANG Chun, CAO Difei, LI Wei, ZHANG Haijun. Expectation Propagation-based Signal Detection for Differential Spatial Modulation[J]. Journal of Electronics & Information Technology, 2025, 47(3): 590-599. doi: 10.11999/JEIT240840

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SHAO Hua, WANG Chun, CAO Difei, LI Wei, ZHANG Haijun. Expectation Propagation-based Signal Detection for Differential Spatial Modulation[J]. Journal of Electronics & Information Technology, 2025, 47(3): 590-599. doi: 10.11999/JEIT240840

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Expectation Propagation-based Signal Detection for Differential Spatial Modulation

doi: 10.11999/JEIT240840 cstr: 32379.14.JEIT240840

1.
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
2.
School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Funds: Science, Technology &Innovation Project of Xiongan New Area (2022XAGG0114), The National Natural Science Foundation of China (62101030, 62102021)

Received Date: 2024-10-08
Rev Recd Date: 2025-03-04

Available Online: 2025-03-14

Publish Date: 2025-03-01

Abstract

Abstract

Objective This research develops an efficient Bayesian Expectation Propagation (EP) detection method for Differential Spatial Modulation (DSM) systems using Multi-Phase Shift Keying (MPSK). DSM systems are notable for their advantage of not requiring Channel State Information (CSI), yet signal detection complexity remains a significant challenge. The detection problem is reformulated as a parameter estimation task, where a prior and a posterior distribution parameters are iteratively estimated to improve detection accuracy. By decoupling antenna-domain detection from constellation-domain information, computational complexity is reduced while maintaining high performance. Additionally, the traditional EP method is extended to account for variable noise variance, dynamically adjusting the noise term’s second-order estimate to enhance robustness. This research is essential for improving the practical applicability and performance of DSM systems, enabling efficient, low-complexity signal detection in modern wireless communication networks. Methods This research applies an EP approach to enhance the detection of DSM signals. The detection process is reformulated as a parameter estimation problem, where the a priori and a posteriori distribution parameters of the antenna domain and constellation domain are iteratively optimized. The EP algorithm decouples these domains, allowing independent iterative detection of antenna indices and optimal demodulation of constellation bits. This method effectively reduces computational complexity compared to existing detection schemes. Additionally, the traditional EP algorithm is extended by incorporating a variable noise variance mechanism. The second-order moment estimation of noisy random vectors is refined iteratively, improving detection robustness under varying noise conditions. Simulation experiments are conducted to evaluate the proposed scheme, and the results demonstrate superior detection performance and faster convergence across different system configurations. Results and Discussions Three detection algorithms—Zero-Forcing (ZF) detection, Minimum Mean Square Error (MMSE) detection, and Soft-input Soft-output (SISO) detection—are selected for performance comparison . Bit Error Rate (BER) comparisons for 3×3 (Figure 1), 4×4 (Figure 2), and 5×5 (Figure 3) antenna configurations are presented. Simulation results show that the proposed EP algorithm maintains similar BER performance across different antenna configurations, offering an advantage over existing linear schemes. Using a 4×4 MIMO antenna configuration, the proposed EP method outperforms the MMSE linear detection scheme across various modulation orders, with a significant performance gain observed from QPSK to 16PSK (Figure 4). Regardless of the antenna configuration, BER performance remains nearly unchanged after 1～3 iterations, with rapid convergence. Compared to a single iteration, three iterations provide a performance gain of approximately 1.5 dB (Figure 5). A comparison of BER performance between the constant noise variance in traditional EP and the non-uniform variance proposed in this study (Figure 6) shows that the non-uniform noise correction method outperforms the traditional approach, validating the effectiveness of the noise vector correction. Conclusions A detection algorithm based on Bayesian EP is proposed for use in DSM systems. The antenna domain and signal domain are estimated through iterative updates of the a prior and a posterior distribution parameters. The proposed algorithm outperforms traditional linear detection methods in terms of performance while offering lower complexity compared to conventional high-complexity maximum likelihood detection. Additionally, it can be extended to joint detection and decoding systems for enhanced performance.
- Differential Spatial Modulation (DSM),
- Multiple-Input Multiple-Output (MIMO) detection,
- Expectation Propagation (EP)

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References(36)

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