An efficient mine MIMO signal detection method based on deep learning
A deep learning-based efficient MIMO signal detection model for mining environments was proposed,which was comprised of nonlinear mapping network and error correction network.The nonlinear mapping network is responsible for the initial recovery of received signals into binary bit signals,while the error correction network corrects the errors introduced by the nonlinear mapping network,thereby improving the signal detection accuracy.The performance of the proposed model was validated through simulations conducted in a mining MIMO communication system.The simulation results show that,in the simulated mine MIMO communication environment,its performance is superior to traditional receivers when the modulation method at the transmitter,channel coding method,and channel environment change.Additionally,compared to deep receiver models,the proposed model achieves higher detection efficiency.The method is a novel solution to the low decoding efficiency of intelligent receivers,and its performance advantage has been proved in the complex environment of underground mine.
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