电子学报 ›› 2022, Vol. 50 ›› Issue (1): 36-44.DOI: 10.12263/DZXB.20210276

• 无线光通信及其组网技术 • 上一篇    下一篇

室内可见光通信系统中基于压缩感知的空移键控信号检测方法

左婷1, 王法松1, 张建康1, 李睿2   

  1. 1.郑州大学信息工程学院,河南 郑州 450001
    2.河南工业大学理学院,河南 郑州 450001
  • 收稿日期:2021-02-24 修回日期:2021-06-09 出版日期:2022-01-25 发布日期:2022-01-25
  • 作者简介:左 婷 女,1995年9月生,河南信阳人.现为郑州大学信息工程学院硕士研究生.研究方向为可见光通信信号检测技术. E-mail:15978370982@163.com
    王法松(通信作者) 男,1979年3月生,河南潢川人.工学博士,教授.2002年获中国地质大学(武汉)学士和硕士学位,2013年获西安电子科技大学博士学位.研究方向为可见光通信、稀疏信号处理. E-mail:iefswang@zzu.edu.cn
    张建康 男,1982年7月生,河南开封人.工学博士,副教授.2006年获北京邮电大学学士学位,2012年获郑州大学博士学位.研究方向为航空通信、演化计算. E-mail:iejkzhang@zzu.edu.cn
    李 睿 女,1979年7月生,河南郑州人.副教授.研究方向为优化理论与算法及其应用. E-mail:slxlirui@haut.edu.cn
  • 基金资助:
    国家自然科学基金(61571401);河南省科技攻关(192102210088);河南省高校科技创新人才(18HASTIT021);国家重点研发项目(019QY0302)

Space Shift Keying Signal Detection Approach Based on Compressed Sensing in Indoor VLC System

ZUO Ting1, WANG Fa-song1, ZHANG Jian-kang1, LI Rui2   

  1. 1.School of Information Engineering,Zhengzhou University,Zhengzhou,Henan 450001,China
    2.School of Sciences,Henan University of Technology,Zhengzhou,Henan 450001,China
  • Received:2021-02-24 Revised:2021-06-09 Online:2022-01-25 Published:2022-01-25

摘要:

针对基于空移键控(Space Shift Keying,SSK)的室内可见光通信(Visible Light Communications,VLC)系统中的信号检测,本文将其转换为稀疏信号重构问题,使得具有相对较低计算复杂度的压缩感知(Compressed Sensing,CS)稀疏重构算法成为基于SSK调制的室内VLC系统中一种很有竞争力的信号检测手段.为了满足稀疏重构的测量矩阵性质,提出了一种基于奇异值分解(Singular Value Decomposition,SVD)的测量矩阵预处理方法,在理论上保证了在室内VLC系统中使用基于CS的稀疏信号检测方法的可行性.然后通过采用CS中的正交匹配追踪(Orthogonal Matching Pursuit,OMP)和压缩采样匹配追踪(Compressive Sampling Matching Pursuit,CoSaMP)两种经典算法实现了对室内VLC系统SSK信号的检测,同时还提出了一种新的结合贪婪算法和极大似然算法的稀疏信号重构检测方法.最后,通过计算机仿真验证了该类算法在基于SSK调制的室内VLC系统中信号检测的有效性.仿真结果证实了在基于SSK调制技术的室内VLC系统中,所提的CS检测算法性能可以在实际应用场景的系统参数设置下达到比(Maximum Likelihood,ML)更好的误码率和计算复杂度.

长摘要
针对基于空移键控(Space shift keying,SSK)的室内可见光通信(Visible light communications,VLC)系统中的信号检测问题,本文根据实际的信号特征,将其转换为稀疏信号重构问题,使得具有相对较低计算复杂度的压缩感知(Compressed sensing,CS)稀疏重构算法成为基于SSK调制的室内VLC系统中一种很有竞争力的信号检测手段。为了满足稀疏重构算法对于测量矩阵的约束等距性的性质要求,提出了一种基于奇异值分解(Singular value decomposition,SVD)的测量矩阵预处理方法,在理论上保证了在室内VLC系统中使用基于CS的稀疏信号检测方法的可行性。然后通过采用CS中的正交匹配追踪(Orthogonal matching pursuit,OMP)和压缩采样匹配追踪(Compressive sampling matching pursuit,CoSaMP)两种经典算法实现了对室内VLC系统SSK信号的检测。同时为了进一步提升算法性能,本文还提出了一种新的结合贪婪算法和极大似然(Maximum likelihood,ML)的稀疏信号重构检测方法。另外,针对上述几种涉及的稀疏重构算法,本文在理论上系统的分析了各种方法的计算复杂度。最后,通过计算机仿真验证了所提出的预处理方法和信号检测算法在基于SSK调制的室内VLC系统中信号检测的有效性。仿真结果证实了在基于SSK调制技术的室内VLC系统中,所提的基于CS的稀疏信号检测算法性能可以在实际应用场景的系统参数设置下达到比ML更好的误码率和计算复杂度。

关键词: 可见光通信, 空移键控, 压缩感知, 极大似然算法, 信号检测

Abstract:

Aiming at the signal detection problem in indoor visible light communication(VLC) system based on space shift keying(SSK), in this paper, by converting signal detection into a sparse signal reconstruction problem, the sparsity signal reconstruction algorithm in compressed sensing(CS) becomes a competitive complement detection approach for its relatively low computational complexity in indoor VLC system. In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing approach of measurement matrix is proposed aided by singular value decomposition(SVD), which theoretically guarantees the feasibility of using sparse signal detection method based on CS in indoor VLC system. Then, by adopting classical orthogonal matching pursuit(OMP) algorithm and compressed sampling matching pursuit(CoSaMP) algorithm, the SSK signals are efficiently detected in the considered indoor VLC system. Meanwhile, a novel OMP combined with maximum likelihood(ML) detection algorithm is presented to detect sparse signal. Finally, the effectiveness of this algorithm in indoor VLC system based on SSK modulation is verified by computer simulations. The results show that for SSK modulation technology in VLC system with practical system parameters setting, the performance of the proposed CS detection algorithm can achieve better bit error rate and lower computation complexity than ML based detection method.

Extended Abstract
As a special MIMO modulation technology, space shift keying (SSK) transmit information signal via a single activated antenna index, this operation makes the transmitted signal vectors have sparsity property inherently, and SSK has been widely utilized in indoor visible light communication (VLC) system. Up to now, the detection methods in the above-mentioned indoor SSK-VLC systems are mainly based on the maximum likelihood (ML) method. Although the detection performance is optimal, the computational complexity brought by exhaustive search will increase dramatically when the number of LEDs at the transmitter is large. Therefore, finding a detection scheme with low complexity and a lower BER at the receiver is very important. Linear detection algorithms, such as zero forcing (ZF) and minimum mean square error (MMSE), have a significant complexity reduction compared with ML, but at the expense of error increase. Furthermore, these two approaches are only applicable to over-determined systems. In this paper, by converting signal detection into a sparse signal reconstruction problem, the sparsity signal reconstruction algorithm in compressed sensing (CS) becomes a competitive complement detection approach for its relatively low computational complexity in indoor VLC systems. In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing approach of measurement matrix is proposed aided by singular value decomposition (SVD). Then, by adopting the classical orthogonal matching pursuit (OMP) algorithm and compressed sampling matching pursuit (CoSaMP) algorithm, the SSK signals are efficiently detected in the considered indoor VLC system. Meanwhile, a novel OMP combined with an ML detection algorithm is presented to detect sparse signals. Finally, the effectiveness of this algorithm in indoor VLC systems based on SSK modulation is verified by computer simulations. The results show that for SSK modulation technology in a VLC system with practical system parameters setting, the performance of the proposed CS detection algorithm can achieve a better bit error rate and lower computation complexity than ML based detection method. Specifically, upon employing our proposed detection method, a signal-to-noise ratio (SNR) gain as high as 12 dB is observed when the bit error rate (BER) is 10-5.

Key words: visible light communication(VLC), space shift keying(SSK), compressed sensing(CS), maximum likelihood algorithm(ML), signal detection

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