论文部分内容阅读
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。
Due to the influence of intersymbol interference, the bit error rate of the visible light communication system is increased. To this end, a receiving system based on artificial neural network (ANN) is proposed, which uses angle diversity receiving technology to collect signals and combines the obtained multiple groups of data to form a total output signal through a neural network. The receiving system can effectively reduce the impact of intersymbol interference on the system, improve the signal-to-noise ratio (SNR) of the received signal, and reduce the bit error rate (BER) of the system. Using Matlab software simulation signal transmission experiment to verify the performance and superiority of the system. Simulation results show that the bit error rate of the diversity receiving system based on neural network equalization is lower than that of the traditional system using single-input and single-output (SISO) when the SNR of source and environment is the same, And can reduce the impact of inter-symbol interference. Optimize the channel performance of visible light communication (VLC) system, and have broad application prospects.