基于期望傳播的差分空間調制信號檢測算法

邵華; 王淳; 曹荻非; 李衛(wèi); 張海君

doi:10.11999/JEIT240840

基于期望傳播的差分空間調制信號檢測算法

doi: 10.11999/JEIT240840 cstr: 32379.14.JEIT240840

1.
北京科技大學智能科學與技術學院北京 100083
2.
北京科技大學計算機與通信工程學院北京 100083

基金項目: 雄安科技創(chuàng)新專項(2022XAGG0114)，國家自然基金(62101030, 62102021)

詳細信息

作者簡介:
邵華：男，講師，研究方向為無線通信物理層算法，智能通信等

王淳：女，碩士生，研究方向為人工智能，智能系統(tǒng)，大模型語義通信等

曹荻非：男，博士生，研究方向為物聯(lián)網(wǎng)，高可靠通信

李衛(wèi)：女，教授，研究方向為物聯(lián)網(wǎng)通信

張海君：男，教授，研究方向為無線資源管控、高可靠通信網(wǎng)絡

通訊作者:
邵華　shaohua@ustb.edu.cn

中圖分類號: TN925
計量
- 文章訪問數(shù): 167
- HTML全文瀏覽量: 87
- PDF下載量: 27
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-10-08
- 修回日期: 2025-03-04
- 網(wǎng)絡出版日期: 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: 設計高效且復雜度低的檢測算法是差分空間調制(DSM)系統(tǒng)中的一大關鍵問題。該文提出了一種多相移鍵控差分空間調制系統(tǒng)的貝葉斯期望傳播(EP)信號檢測方法，將DSM的信號檢測問題轉化為待檢測信號的參數(shù)估計問題，通過迭代估計先驗和后驗分布的參數(shù)，獲得檢測信號的估計值。該算法將原始的信號檢測問題分解為天線域信息和星座域信息兩部分，其中天線域檢測通過期望傳播算法迭代求取，星座域比特通過迭代過程中最優(yōu)解調獲得，降低了算法復雜度。進一步地，該文針對傳統(tǒng)期望傳播方法中噪聲參數(shù)進行了擴展，在迭代過程中不斷調整噪聲項的矩估計，獲得了比傳統(tǒng)方案更好的性能。該文對所提近最優(yōu)解調方案進行了仿真驗證，結果表明所提方案性能優(yōu)于傳統(tǒng)線性檢測方案；所提的基于期望傳播的噪聲修正方案性能優(yōu)于傳統(tǒng)恒值方案；在不同天線配置和調制階數(shù)情況下，所提方案均能夠快速收斂。
- 差分空間調制 /
- MIMO檢測 /
- 期望傳播
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

圖 1 3×3 MIMO下不同算法性能對比

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圖 2 4×4 MIMO下不同算法性能對

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圖 3 5×5 MIMO下不同算法性能對比

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圖 4 不同調制階數(shù)下算法性能對比

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圖 5 不同迭代次數(shù)對于算法性能影響

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圖 6 噪聲項特殊處理的誤碼率性能對比

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1 基于期望傳播的DSM信號檢測流程

輸入：${{\boldsymbol{Y}}_t}$, ${{\boldsymbol{Y}}_{t - 1}}$, $P(a),a \in \{ 1,2, \cdots ,A\} $, $\sigma _n^2$
輸出：信息比特的對數(shù)似然比
(1) For $l = 1:T$
(2) 　根據(jù)式(23)更新噪聲分布參數(shù)
(3) 　根據(jù)式(15)、式(24)、式(25)計算${{\boldsymbol{s}}_t}$后驗概率分布$q({{\boldsymbol{s}}_t})$及　　　參數(shù)
(4) 　　For $a = 1:A$
(5) 　　　根據(jù)${\boldsymbol{P}}_t^l(a)$和式(18)計算最優(yōu)的信號域符號${\boldsymbol{S}}_a^l$
(6) 　　　將${\bar {\boldsymbol{\mu}} _s}$逆向量化為$\bar {\boldsymbol{S}}_t^l$，根據(jù)式(17)計算每個候選圖樣的　　　　　歐式距離${M_l}(a)$
(7) 　　End
(8) 　根據(jù)式(19)將${M_l}(a)$轉化為歸一化的天線圖樣概率分布　　　 $P_b^l(a)$
(9) 　根據(jù)式(21)更新先驗分布參數(shù)${\boldsymbol{\mu }}_{\boldsymbol{s}}^l$, ${\boldsymbol{\varSigma}} _{\boldsymbol{s}}^l$，作為下一輪迭代輸入。
(10) End
(11) 輸出步驟(6)中最大${P_b}(a)$對應的天線圖樣
(12) 輸出最大概率${P_b}(a)$的對應的信號域符號${{\boldsymbol{S}}_a}$
(13) 根據(jù)式(26)計算天線比特的${{\rm{LLR}}_{{\mathrm{ant}}}}$
(14) 根據(jù)式(27)計算星座比特的${{\rm{LLR}}_{{\mathrm{sig}}}}$
(15) 輸出DSM的所有比特的${\bf{LLR}}$(式(28))

下載: 導出CSV

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