Research on CNN-based MIMO-OFDM visible light communication receiver
To overcome challenges such as signal interference,high peak-to-average power ratio,limited bandwidth of light-emitting diodes(LED),and nonlinear effects in multiple-input multiple-output(MIMO)-orthogonal frequency-division multiplexing(OFDM)visible light communication(VLC)systems,a VLC receiver based on convolutional neural network(CNN)is proposed.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 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 is improved by more than an order of magnitude.At the same time,it can effectively over-come the problem of LED bandwidth limitation,and the bit transmission rate is increased by 53％compared with the least square receiver.
multiple-input-multiple-outputorthogonal frequency division multiplexingconvolutional neural networksignal compensationvisible light communication
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维普
