基于神经网络的可见光通信接收系统的研究  

Research on Visible Light Communication Receiving System Based on Artificial Neural Networks

作　　者：关伟鹏 文尚胜[1,2] 黄伟明[1,2] 陈颖聪[1,2] 张广慧[1] 

机构地区：[1]华南理工大学材料科学与工程学院,广东广州510640 [2]华南理工大学发光材料与器件国家重点实验室,广东广州510640

出　　处：《中国激光》2015年第11期113-120,共8页Chinese Journal of Lasers

基　　金：广东省战略性新兴产业专项(2011A081301017;2012A080304012;2012A080304001);广州市科技计划(2013J4300021);国家级大学生创新创业训练计划项目(201510561003)

摘　　要：由于码间干扰的影响,导致可见光通信系统的误码率提升。为此,提出了一种基于人工神经元网络(ANN)的接收系统,采用角度分集接收技术采集信号,并通过神经元网络对所获得的多组数据进行合并优化构成总的输出信号。该接收系统可以有效地降低码间干扰对系统的影响,提高接收信号的信噪比(SNR),降低系统的误码率(BER)。采用Matlab软件模拟仿真信号传输实验以验证该系统的性能及优越性。仿真结果表明,在信源与环境的信噪比相同情况下,基于神经元网络均衡处理的分集接收系统误码率比传统的使用单输入单输出(SISO)技术的系统误码率更低,并且可以减弱码间干扰所带来的影响。优化了可见光通信(VLC)系统的信道性能,具有广阔的应用前景。As the inter symbol interference increases the bit error rate（BER） of the visible light communication system, a new artificial neural network （ANN） equalization receiving system is proposed. Based on angle diversity receiving technology and artificial neural networks, the system can not only reduce the influence of inter symbol inference, but also improve the signal to noise ratio （SNR） and decrease the bit error rate. The signal transmission test is simulated by Matlab. The simulation results show that the proposed system has lower bit error rate compared with the traditional system which uses single input and single output technology（SISO）, what＇s more, the former can weaken the influence of inter symbol interference under the same signal to noise ratios of the environment and signal source. This advanced system can optimize the channels performance of visible light communication system, and it obviously has a vast application prospect.

关 键 词：光通信 可见光通信 人工神经元网络 角度分集接收 误码率 码间干扰 

分 类 号：TN929.1[电子电信—通信与信息系统]