An improved signal detection algorithm for a mining-purposed MIMO-OFDM IoT-based system

Jikun Guo; Qing Zhao; Lixuan Guo; Shize Guo; Gen Liang; Jikun Guo; Qing Zhao; Lixuan Guo; Shize Guo; Gen Liang

doi:10.3934/era.2023200

Electronic Research Archive

2023, Volume 31, Issue 7: 3943-3962. doi: 10.3934/era.2023200

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An improved signal detection algorithm for a mining-purposed MIMO-OFDM IoT-based system

1.
School of Electronic and Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
2.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
3.
School of Electronics and Communication Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China
4.
The International Division of Senior High School, Affiliated with the Harbin Institute of Technology, Class of 2020, Harbin 150000, China

Received: 12 January 2023 Revised: 28 April 2023 Accepted: 05 May 2023 Published: 16 May 2023

The coal mine internet of things (IoT) communication system is used for real-time monitoring of mining production to ensure the safety and reliability of personnel and equipment in the mine. To eliminate multipath fading in the process of wireless communication in mines, multiple-output multiplexing (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies are introduced. In this paper, a wireless communication system architecture of IoT in mining based on MIMO-OFDM is constructed. Aiming to solve the problems of intersymbol interference and frequency selective fading at the receiver, an improved minimum mean square error ordered successive interferences cancellation (MMSE-OSIC) signal detection algorithm is proposed. First, the signal-to-interference plus noise ratio of the received signal is calculated and the calculation results are sorted. The lowest signal-to-noise ratio is selected as the weakest signal layer. Then, the MMSE-OSIC algorithm is used to extract all of the signals, except the weakest layer. Finally, a maximum likelihood (ML) algorithm is used to traverse the whole signal domain; the signal symbol with the smallest distance from the weakest signal layer is found as the original signal of the weakest signal layer, and it is combined with the signal detected by MMSE-OSIC; then, the final signal detection result is obtained. The simulation results show that, compared with three benchmark algorithms, the proposed MMSE-OSIC algorithm has better signal detection performance under the conditions of different modulation methods and different channel numbers.
- coal mine,
- IoT,
- MIMO-OFDM,
- signal detection,
- MMSE,
- ML
Citation: Jikun Guo, Qing Zhao, Lixuan Guo, Shize Guo, Gen Liang. An improved signal detection algorithm for a mining-purposed MIMO-OFDM IoT-based system[J]. Electronic Research Archive, 2023, 31(7): 3943-3962. doi: 10.3934/era.2023200

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Abstract

The coal mine internet of things (IoT) communication system is used for real-time monitoring of mining production to ensure the safety and reliability of personnel and equipment in the mine. To eliminate multipath fading in the process of wireless communication in mines, multiple-output multiplexing (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies are introduced. In this paper, a wireless communication system architecture of IoT in mining based on MIMO-OFDM is constructed. Aiming to solve the problems of intersymbol interference and frequency selective fading at the receiver, an improved minimum mean square error ordered successive interferences cancellation (MMSE-OSIC) signal detection algorithm is proposed. First, the signal-to-interference plus noise ratio of the received signal is calculated and the calculation results are sorted. The lowest signal-to-noise ratio is selected as the weakest signal layer. Then, the MMSE-OSIC algorithm is used to extract all of the signals, except the weakest layer. Finally, a maximum likelihood (ML) algorithm is used to traverse the whole signal domain; the signal symbol with the smallest distance from the weakest signal layer is found as the original signal of the weakest signal layer, and it is combined with the signal detected by MMSE-OSIC; then, the final signal detection result is obtained. The simulation results show that, compared with three benchmark algorithms, the proposed MMSE-OSIC algorithm has better signal detection performance under the conditions of different modulation methods and different channel numbers.

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