Compressed sensing detection of optical spatial modulation signal in turbulent channel

Hui-qin WANG; Li-hua SONG; Ming-hua CAO; Dao-bin WANG

doi:10.3788/OPE.20182611.2669

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Compressed sensing detection of optical spatial modulation signal in turbulent channel

Modern Applied Optics | 更新时间：2020-07-05

- Compressed sensing detection of optical spatial modulation signal in turbulent channel
- Optics and Precision Engineering Vol. 26, Issue 11, Pages: 2669-2674(2018)
- 作者机构：
  
  兰州理工大学计算机与通信学院, 甘肃兰州 730050
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182611.2669
  CLC： TN929.12
- Received：15 May 2018，
  
  Accepted：02 July 2018，
  
  Published：25 November 2018
- 稿件说明：
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Hui-qin WANG, Li-hua SONG, Ming-hua CAO, et al. Compressed sensing detection of optical spatial modulation signal in turbulent channel[J]. Optics and precision engineering, 2018, 26(11): 2669-2674.
DOI：

Hui-qin WANG, Li-hua SONG, Ming-hua CAO, et al. Compressed sensing detection of optical spatial modulation signal in turbulent channel[J]. Optics and precision engineering, 2018, 26(11): 2669-2674. DOI： 10.3788/OPE.20182611.2669.

摘要

在光空间调制系统中采用最大似然检测算法可以获得最优的误码性能，但算法的译码复杂度限制了其实际应用。本文依据光空间调制激光器映射向量，并结合脉冲位置调制方式下的脉冲向量构建了具有稀疏特性的发送信号。基于该稀疏特性，采用正交匹配追踪算法提出了一种基于压缩感知理论的光空间调制信号检测方法。仿真结果表明：该方法以少量误码性能损失为代价极大地降低了信号检测的复杂度。当激光器数目为64时，相比于最大似然检测算法，本文所提方法的复杂度降低了99.54%。同时，由于稀疏性的引入，该方法更适合于具有大规模激光器的无线光通信系统。

Abstract

The maximum likelihood detection algorithm produces the best bit error performance in optical spatial modulation systems

but the complexity of the decoding process limits its application in practical settings. To address this deficiency

a transmission signal with sparse characteristics was generated by mapping an optical spatial modulation laser vector and a pulse position modulation vector together. A compressed sensing-based optical spatial modulation signal detection method was proposed based on the resulting transmission signal with sparse characteristics and the utilization of an orthogonal matching pursuit algorithm. Simulation results show that this method can greatly reduce computational complexity at the expense of a small increase in the performance error. In comparison with the maximum likelihood detection algorithm

the complexity of the proposed method is reduced by 99.54% when 64 lasers are used. Moreover

this approach is more suitable for wireless optical communication systems with large-scale lasers

due to the introduction of sparsity.

关键词

Keywords

references

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