Deep learning based demodulation method for multiple access of visible light communication in 6G
Because the modulation bandwidth of the light emitting diode is narrow,the capacity of the visible light communication system is limited,the spectral efficiency and number of endpoint user can be improved by the mul-tiple access technique. However,there is strong inter-user interference among multiple access users in the visible light communication system. In view of this problem,by utilizing the correlation among received signals of the visible communication system,a multiple user detection and signal recovery method of multiple users for multiple access based on deep neural network was proposed. The transmitter model and the receiver model of the visible light commu-nication system were presented based on sparse code multiple access,the temporal convolutional network was ad-opted to learn the inter-signal temporal correlation of the long sequence,the output sequence was delivered to dense layer to learn the spatial mapping relationship of the signal sequence,in the end,signals of all users were recovered in the receiver of the visible light communication system. Experimental results indicate that the proposed signal recov-ery method improves the communication performance of the visible light communication system multiple access ef-fectively,and the proposed method can play an active role under the condition of different communication distances,different signal noise ratios and transmit speeds.
visible light communication systemmultiple accessdemodulation technologyshort range wireless communicationconvolutional neural network
万方数据
维普
