Journal
2020 IEEE 3RD INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND SIGNAL PROCESSING (ICICSP 2020)
Volume -, Issue -, Pages 348-352Publisher
IEEE
DOI: 10.1109/icicsp50920.2020.9232124
Keywords
Channel estimation; OFDM; Neural networks; Deep learning; Signal detection
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Funding
- National Students Innovation and Research Training Program (SRTP) [201910286060]
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In this paper, a simplified deep neural network is proposed, which can be used for channel estimation and signal detection in OFDM system and reduce complexity. To be specific, the method of deep learning is introduced to optimize the channel estimation module of OFDM system. By building deep neural networks and training parameters at the signal-to-noise ratio of 10dB and 25dB, respectively, the channel estimation results can be optimized at a wider range of signal-to-noise ratio. In addition, the influence of training model size for channel estimation and signal detection is also researched. Compared with some other artificial intelligence aided OFDM receivers, proposed deep neural networks has shorter training time and simpler architecture. The simulation results show that by using proposed deep neural networks and training method in OFDM channel estimation, smaller mean square error and lower bit error rate can be obtained, especially in the case of clipping distortion and wide range of signal-to-noise ratio.
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