Massive MIMO under multi-keyhole channels: Does the use-and-then-forget bounding technique work?

Aiming at the problems of poor signal detection effect caused by many interference factors in large-scale MIMO technology scene, this paper proposes a 5G massive MIMO signal detection algorithm based on deep learning. Firstly, the MIMO system model based on neural network is constructed, and Deep Neural Network (DNN) detection is introduced into the receiver of the traditional MIMO system to obtain the information bits or codewords and channel state information transmitted by transmitters. Then, the end-to-end training method is adopted to make neural network learn the mapping relationship of information bits or codewords transmitted by system transceivers. Furthermore, DNN detector is improved based on Simplified Message Passing Detection (sMPD) algorithm, and the correction factor is updated continuously to optimize network parameters to realize the accurate detection and decoding of the MIMO system. Finally, the proposed algorithm is experimentally analyzed based on the TensorFlow deep learning framework. Experimental results show that when signal-to-noise ratio is 10 dB, the bit error rate and mean square error are lower than 0.005 and 0.1, respectively.

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Massive MIMO under multi-keyhole channels: Does the use-and-then-forget bounding technique work?

Cited by 2 publications

References 14 publications

Two-Timescale Design for Simultaneous Transmitting and Reflecting RIS-Assisted Massive MIMO Systems With Imperfect CSI

Two-Timescale Design for Simultaneous Transmitting and Reflecting RIS-Assisted Massive MIMO Systems With Imperfect CSI

5G Massive MIMO Signal Detection Algorithm Based on Deep Learning

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