Research on Mine OFDM Intelligent Receiver Based on Deep Learning

Author:

Li Xuhong,Pan Yong,Wang Anyi,Feng Zhiyuan

Abstract

Abstract coal is an important energy. Coal mining needs a series of equipment, among which communication equipment can ensure the safety of underground workers in the mining process and improve production efficiency. Aiming at the problems of multipath fading and anti-interference of signal transmission in coal mine, researchers propose to optimize the quality of signal transmission in coal mine with OFDM technology. Compared with traditional OFDM technology, this paper proposes a method of mine OFDM intelligent receiver based on deep learning, which aims to use deep learning technology to replace the signal recovery process of the receiver of traditional OFDM technology. The simulation results show that in terms of bit error rate, the signal recovery of the receiver based on deep learning technology is obviously better than that of the traditional OFDM receiver. This will improve the economic benefits of coal mining in the future.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Reference7 articles.

1. Current Situation and Development Trend of Multi System Fusion Data Analysis for Coal Mine Safety Monitoring[J];Zhang;International Journal of Computational and Engineering,2020

2. An Efficient ICI Mitigation Technique for MIMO-OFDM System in Time-Varying Channels[J];Kumar;Mathematical Modelling and Engineering Problems,2020

3. DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer[J];Zheng;IEEE Transactions on Cognitive Communications and Networking,2020

4. Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems;Hao,2018

5. Spectral efficiency of adaptive OFDM systems over high mobility Nakagami-m fading channels;Zhang,2020

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