A Denoising Autoencoder based wireless channel transfer function estimator for OFDM communication system

Wada, Tomohisa; Toma, Takao; Dawodi, Mursal; Baktash, Jawid Ahmad

doi:10.1109/icaiic.2019.8669044

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…Furthermore, the DNN has a powerful denoising capability, which can be applied to process low SNR signals [32], [33]. Comparing the second layer outputs of DTN [Figs.…”

Section: Doa Detection Network (Dtn)mentioning

confidence: 99%

Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR

Huang

Ren

et al. 2022

IEEE Access

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Signal source number detection is an essential issue for the direction of arrival (DOA) estimation in satellite communication systems. The performances of conventional and deep-learning-based signal source number detection methods will deteriorate when the signal-to-noise ratio is low or coherent signals exist. This paper proposes a DOA detection network (DTN) combined with the root weighted subspace fitting (root-WSF) method to tackle this challenge. The DTN uses the constructed deep neural networks (DNN) to denoise the received signals and captures the nonlinear mapping relationship between the received signals and the number of signal sources. The received signals in the complex-valued domain are directly treated as DTN's input, and the label of DTN is the one-hot encoding of the source number. It solves the issue that the classifier cannot well-handle the coherent signals and extends the values of discrete features to Euclidean space. Accordingly, the trained DTN can detect the signal source numbers with an average detection accuracy of 96.6%, and the root-WSF algorithm is applied as the rear stage of DOA estimation. Compared with the traditional DOA methods, the proposed DTN incorporated with the root-WSF algorithm features superior robustness, high DOA estimation accuracy, and enhanced resolution. INDEX TERMS Deep neural network (DNN), direction of arrival (DOA), DOA detection network (DTN), root weighted subspace fitting (root-WSF), satellite communication system.

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“…Furthermore, the DNN has a powerful denoising capability, which can be applied to process low SNR signals [32], [33]. Comparing the second layer outputs of DTN [Figs.…”

Section: Doa Detection Network (Dtn)mentioning

confidence: 99%

Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR

Huang

Ren

et al. 2022

IEEE Access

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“…Several pieces of literature have proposed a machine learning algorithm for the mitigation of the PAPR [4,[11][12][13]. Before this study, we will discuss the nonlinearity reduction technique, which resulted in PAs.…”

Section: Related Workmentioning

confidence: 99%

A Nonlinear Distortion Removal Based on Deep Neural Network for Underwater Acoustic OFDM Communication with the Mitigation of Peak to Average Power Ratio

Raza

Bilal

et al. 2020

Applied Sciences

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Machine learning and deep learning algorithms have proved to be a powerful tool for developing data-driven signal processing algorithms for challenging engineering problems. This paper studies the modern machine learning algorithm for modeling nonlinear devices like power amplifiers (PAs) for underwater acoustic (UWA) orthogonal frequency divisional multiplexing (OFDM) communication. The OFDM system has a high peak to average power ratio (PAPR) in the time domain because the subcarriers are added coherently via inverse fast Fourier transform (IFFT). This causes a higher bit error rate (BER) and degrades the performance of the PAs; hence, it reduces the power efficiency. For long-range underwater acoustic applications such as the long-term monitoring of the sea, the PA works in full consumption mode. Thus, it becomes a challenging task to minimize power consumption and unnecessary distortion. To mitigate this problem, a receiver-based nonlinearity distortion mitigation method is proposed, assuming that the transmitting side has enough computation power. We propose a novel approach to identify the nonlinear power model using a modern deep learning algorithm named frequentative decision feedback (FFB); PAPR performance is verified by the clipping method. The simulation results prove the better performance of the PA model with a BER with the shortest learning time.

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“…Chang et al [ 12 ] propose a convolutional neural network (CNN)-based hybrid cascade structure to replace the traditional equalizer in the communication system. Wada et al [ 13 ] use two fully connected layers (FC) as denoising autoencoders to reduce noise of modulation signals. Zhao et al [ 14 ] propose a deep neural network co-evolving simultaneously at two different scales to enhance the denoising ability of the model.…”

Section: Introductionmentioning

confidence: 99%

Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals

Duan

Chen

Zhang

et al. 2023

Sensors

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Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of −12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59∼74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect.

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A Denoising Autoencoder based wireless channel transfer function estimator for OFDM communication system

Cited by 8 publications

References 6 publications

Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR

Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR

A Nonlinear Distortion Removal Based on Deep Neural Network for Underwater Acoustic OFDM Communication with the Mitigation of Peak to Average Power Ratio

Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals

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