LSTM projected layer neural network-based signal estimation and channel state estimator for OFDM wireless communication systems

Sebin J Olickal; Renu Jose; Sebin J Olickal; Renu Jose

doi:10.3934/electreng.2023011

AIMS Electronics and Electrical Engineering

2023, Volume 7, Issue 2: 187-195. doi: 10.3934/electreng.2023011

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Research article

LSTM projected layer neural network-based signal estimation and channel state estimator for OFDM wireless communication systems

Sebin J Olickal ^{1
,
,},
Renu Jose ²

1.
Rajiv Gandhi Institute of Technology, Kottayam (Affiliated to APJ Abdul Kalam Technological University, Kerala)
2.
Govt. Engineering College, Idukki

Received: 03 April 2023 Revised: 22 May 2023 Accepted: 09 June 2023 Published: 14 June 2023

Advanced wireless communication technologies, such as 5G, are faced with significant challenges in accurately estimating the transmitted signal and characterizing the channel. One of the major obstacles is the interference caused by the delay spread, which results from receiving multiple signal copies through different paths. To mitigate this issue, the orthogonal frequency division modulation (OFDM) technique is often employed. Efficient signal detection and optimal channel estimation are crucial for enhancing the performance of multi-carrier wireless communication systems. To this end, this paper proposes a Long Short Term Memory-Projected Layer (LSTM-PL) deep neural network(DNN) based channel estimator to detect received OFDM signal. The results show that the LSTM-PL algorithm outperforms traditional methods such as Least Squares(LS), Minimum Mean Square Error (MMSE) and other LSTM deep learning channel estimation methods like Long Short Term Memory(LSTM)-DNN and Bidirectional-LSTM(Bi-LSTM)-DNN, as evidenced by Symbol-Error Rate (SER) outcomes.
- OFDM,
- deep learning,
- channel estimation,
- LSTM,
- LSTM-PL
Citation: Sebin J Olickal, Renu Jose. LSTM projected layer neural network-based signal estimation and channel state estimator for OFDM wireless communication systems[J]. AIMS Electronics and Electrical Engineering, 2023, 7(2): 187-195. doi: 10.3934/electreng.2023011

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Abstract

Advanced wireless communication technologies, such as 5G, are faced with significant challenges in accurately estimating the transmitted signal and characterizing the channel. One of the major obstacles is the interference caused by the delay spread, which results from receiving multiple signal copies through different paths. To mitigate this issue, the orthogonal frequency division modulation (OFDM) technique is often employed. Efficient signal detection and optimal channel estimation are crucial for enhancing the performance of multi-carrier wireless communication systems. To this end, this paper proposes a Long Short Term Memory-Projected Layer (LSTM-PL) deep neural network(DNN) based channel estimator to detect received OFDM signal. The results show that the LSTM-PL algorithm outperforms traditional methods such as Least Squares(LS), Minimum Mean Square Error (MMSE) and other LSTM deep learning channel estimation methods like Long Short Term Memory(LSTM)-DNN and Bidirectional-LSTM(Bi-LSTM)-DNN, as evidenced by Symbol-Error Rate (SER) outcomes.

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