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- Title
MIMO Signal Detection Based on IM-LSTMNet Model.
- Authors
Huang, Xiaoli; Yuan, Yumiao; Li, Jingyu
- Abstract
Signal detection is crucial in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, yet classical detection methods often struggle with nonlinear issues in wireless channels. To handle this challenge, we propose a novel signal detection method for MIMO-OFDM system based on the fractional Fourier transform (FrFT), leveraging the robust time series processing capabilities of long short-term memory (LSTM) networks. Our innovative approach, termed IM-LSTMNet, integrates LSTM with convolutional neural networks (CNNs) and incorporates a Squeeze and Excitation Network to emphasize critical information, enhancing neural network performance. The proposed IM-LSTMNet is applied to the FrFT-based MIMO-OFDM system to improve signal detection performance. We compare the detection results of IM-LSTMNet with zero forcing (ZF), minimum mean square error (MMSE), simple LSTM neural network, and CNN–LSTM network by evaluating the bit error rate. Experimental results demonstrate that IM-LSTMNet outperforms ZF, MMSE, LSTM, and other methods, significantly enhancing system signal detection performance. This work offers a promising advancement in MIMO-OFDM signal detection, presenting a deep learning-based solution that effectively improves the system signal detection performance.
- Subjects
ORTHOGONAL frequency division multiplexing; MEAN square algorithms; SIGNAL detection; BIT error rate; CONVOLUTIONAL neural networks; DEEP learning
- Publication
Electronics (2079-9292), 2024, Vol 13, Issue 16, p3153
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics13163153