基于期望传播的差分空间调制信号检测算法

邵华; 王淳; 曹荻非; 李卫; 张海君

doi:10.11999/JEIT240840

基于期望传播的差分空间调制信号检测算法

doi: 10.11999/JEIT240840

邵华^1, ,,
王淳¹,
曹荻非²,
李卫²,
张海君²

1.
北京科技大学智能科学与技术学院北京 100083
2.
北京科技大学计算机与通信工程学院北京 100083

基金项目: 雄安科技创新专项(2022XAGG0114)，国家自然基金(62101030, 62102021)

详细信息

作者简介:
邵华：男，讲师，研究方向为无线通信物理层算法，智能通信等

王淳：女，硕士生，研究方向为人工智能，智能系统，大模型语义通信等

曹荻非：男，博士生，研究方向为物联网，高可靠通信

李卫：女，教授，研究方向为物联网通信

张海君：男，教授，研究方向为无线资源管控、高可靠通信网络

通讯作者:
邵华　shaohua@ustb.edu.cn

中图分类号: TN925
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- 文章访问数: 78
- HTML全文浏览量: 37
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2024-10-08
- 修回日期: 2025-03-04
- 网络出版日期: 2025-03-14
- 刊出日期: 2025-03-01

Expectation Propagation-based Signal Detection for Differential Spatial Modulation

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)

摘要

摘要: 设计高效且复杂度低的检测算法是差分空间调制(DSM)系统中的一大关键问题。该文提出了一种多相移键控差分空间调制系统的贝叶斯期望传播(EP)信号检测方法，将DSM的信号检测问题转化为待检测信号的参数估计问题，通过迭代估计先验和后验分布的参数，获得检测信号的估计值。该算法将原始的信号检测问题分解为天线域信息和星座域信息两部分，其中天线域检测通过期望传播算法迭代求取，星座域比特通过迭代过程中最优解调获得，降低了算法复杂度。进一步地，该文针对传统期望传播方法中噪声参数进行了扩展，在迭代过程中不断调整噪声项的矩估计，获得了比传统方案更好的性能。该文对所提近最优解调方案进行了仿真验证，结果表明所提方案性能优于传统线性检测方案；所提的基于期望传播的噪声修正方案性能优于传统恒值方案；在不同天线配置和调制阶数情况下，所提方案均能够快速收敛。
- 差分空间调制 /
- MIMO检测 /
- 期望传播
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)

HTML全文

图 1 3×3 MIMO下不同算法性能对比

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图 2 4×4 MIMO下不同算法性能对

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图 3 5×5 MIMO下不同算法性能对比

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图 4 不同调制阶数下算法性能对比

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图 5 不同迭代次数对于算法性能影响

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图 6 噪声项特殊处理的误码率性能对比

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1 基于期望传播的DSM信号检测流程

输入： ${{\boldsymbol{Y}}_t}$ , ${{\boldsymbol{Y}}_{t - 1}}$ , $P(a),a \in \{ 1,2, \cdots ,A\}$ , $\sigma _n^2$
输出：信息比特的对数似然比
(1) For $l = 1:T$
(2) 　根据式(23)更新噪声分布参数
(3) 　根据式(15)、式(24)、式(25)计算 ${{\boldsymbol{s}}_t}$ 后验概率分布 $q({{\boldsymbol{s}}_t})$ 及　　　参数
(4) 　　For $a = 1:A$
(5) 　　　根据 ${\boldsymbol{P}}_t^l(a)$ 和式(18)计算最优的信号域符号 ${\boldsymbol{S}}_a^l$
(6) 　　　将 ${\bar {\boldsymbol{\mu}} _s}$ 逆向量化为 $\bar {\boldsymbol{S}}_t^l$ ，根据式(17)计算每个候选图样的　　　　　欧式距离 ${M_l}(a)$
(7) 　　End
(8) 　根据式(19)将 ${M_l}(a)$ 转化为归一化的天线图样概率分布　　　 $P_b^l(a)$
(9) 　根据式(21)更新先验分布参数 ${\boldsymbol{\mu }}_{\boldsymbol{s}}^l$ , ${\boldsymbol{\varSigma}} _{\boldsymbol{s}}^l$ ，作为下一轮迭代输入。
(10) End
(11) 输出步骤(6)中最大 ${P_b}(a)$ 对应的天线图样
(12) 输出最大概率 ${P_b}(a)$ 的对应的信号域符号 ${{\boldsymbol{S}}_a}$
(13) 根据式(26)计算天线比特的 ${{\rm{LLR}}_{{\mathrm{ant}}}}$
(14) 根据式(27)计算星座比特的 ${{\rm{LLR}}_{{\mathrm{sig}}}}$
(15) 输出DSM的所有比特的 ${\bf{LLR}}$ (式(28))

下载: 导出CSV

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