| 基于CNN的MIMO-OFDM可见光通信接收机研究 |
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| 引用本文： | 聂康宁,林邦姜,骆加彬,万翔宇,潘亚东. 基于CNN的MIMO-OFDM可见光通信接收机研究[J]. 福州大学学报(自然科学版), 2024, 52(2) |
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| 作者姓名： | 聂康宁 林邦姜 骆加彬 万翔宇 潘亚东 |
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| 作者单位： | 福州大学电气工程与自动化学院,中国科学院海西研究院泉州装备制造研究中心,中国科学院海西研究院泉州装备制造研究中心,中国科学院海西研究院泉州装备制造研究中心,中国科学院海西研究院泉州装备制造研究中心 |
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| 基金项目： | 福建省自然科学基金资助项目（2022J01499） |
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| 摘 要： | 限制可见光通信（VLC）系统传输性能的主要因素是发光二极管（LED）的有限带宽以及它的非线性效应导致的信号失真。多输入多输出（MIMO）-正交频分复用（OFDM）技术虽然可以提升系统容量、频谱效率,但由于OFDM技术较高的峰均功率比(PAPR),使得VLC系统受到更多的非线性损伤。针对以上问题,本文提出一种基于卷积神经网络（CNN）的VLC接收机。该接收机通过对接收端失真信号和发射端原始信号的学习,能够实现MIMO-OFDM可见光系统的信号解调,有效提升系统对非线性失真的抑制能力并具有较低的复杂度。实验结果表明,与最小二乘法（LS）接收机相比,CNN接收机能有效补偿信号受到的线性和非线性失真以及不同用户间的信号串扰,平均误码率（BER）提升超过一个数量级,同时有效克服LED带宽受限问题,比特传输速率提升53%。
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| 关 键 词： | 多输入多输出 正交频分复用 卷积神经网络 信号补偿 可见光通信 |
| 收稿时间： | 2023-04-06 |
| 修稿时间： | 2023-05-08 |
Research on CNN-based MIMO-OFDM visible light communication receiver |
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| 聂康宁,linbangjiang,luoJiabin,wanxiangyu and panyadong. Research on CNN-based MIMO-OFDM visible light communication receiver[J]. Journal of Fuzhou University(Natural Science Edition), 2024, 52(2) |
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| Authors: | 聂康宁 linbangjiang luoJiabin wanxiangyu panyadong |
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| Affiliation: | School of Electrical Engineering and Automation, Fuzhou University,Quanzhou Equipment Manufacturing Research Center, Haixi Institute, Chinese Academy of Sciences,Quanzhou Equipment Manufacturing Research Center, Haixi Institute, Chinese Academy of Sciences,Quanzhou Equipment Manufacturing Research Center, Haixi Institute, Chinese Academy of Sciences,Quanzhou Equipment Manufacturing Research Center, Haixi Institute, Chinese Academy of Sciences |
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| Abstract: | The main factors limiting the transmission performance of visible light communication (VLC) systems are the limited bandwidth of light-emitting diodes (LEDs) and the signal distortion caused by their nonlinearity. Although multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) technology can improve system capacity and spectral efficiency, the high peak-to-average power ratio (PAPR) of OFDM technology causes VLC systems to suffer from more nonlinear damage. To address these issues, this paper proposes a VLC receiver based on convolutional neural network (CNN). The receiver can learn from the distorted signal at the receiver and the original signal at the transmitter to achieve signal demodulation in MIMO-OFDM visible light systems, effectively improving the system''s ability to suppress nonlinear distortion and having lower complexity. The experimental results show that compared with the least square (LS) receiver, the CNN receiver can effectively compensate for the linear and nonlinear distortions of the signal as well as the inter-user signal interference, and the average bit error rate (BER) is improved by more than an order of magnitude. At the same time, it can effectively overcome the problem of LED bandwidth limitation, and the bit transmission rate is increased by 53% compared with the LS receiver. |
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| Keywords: | Multiple-input-multiple-output Orthogonal frequency division multiplexing Convolutional neural network Signal compensation Visible light communication |
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