Complex CNN-Based Equalization for Communication Signal

Chang, Zexuan; Wang, Yongshi; Li, Hao; Wang, Zhigang

doi:10.1109/siprocess.2019.8868708

Cited by 23 publications

(9 citation statements)

References 9 publications

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“…Utilizing deep learning in optimizing the receiver performance has already been considered in several works. MLbased channel estimation has been studied in [13], [14], whereas [15] utilizes convolutional neural networks (CNNs) [16] for equalization. ML-based demapping has been considered in [17], where it was shown to achieve nearly the same accuracy as the optimal demapping rule, albeit with greatly reduced computational cost.…”

Section: A State-of-the-artmentioning

confidence: 99%

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

Pihlajasalo

Korpi

Honkala

et al. 2023

IEEE Trans. Wireless Commun.

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In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifically, three novel deep learning based convolutional neural network receivers are devised, containing layers in time-and/or frequency-domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Applicable training procedures are also described, such that the learned layers in the receiver processing properly generalize over different nonlinear distortion and multipath channel characteristics. Extensive set of numerical results is provided, in the context of 5G NR uplink (UL) incorporating also measured terminal power amplifier (PA) characteristics. The obtained results show that the proposed receiver systems are able to clearly outperform the classical linear minimum meansquared error (LMMSE) receiver as well as the existing ML receiver approaches, especially when the EVM is high compared to modulation order. This is particularly so when the devised ML receiver is of hybrid nature with layers both in time and frequency. The proposed ML receivers can thus facilitate pushing the terminal PA systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage. Through combining the obtained radio link performance results with link budget calculations, all carried out at the 28 GHz mmWave band, it is shown that the proposed ML receivers can enhance the network coverage in terms of maximum UL link distances by close to 100%, when compared to classical LMMSE receiver based networks.

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Section: A State-of-the-artmentioning

confidence: 99%

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

Pihlajasalo

Korpi

Honkala

et al. 2023

IEEE Trans. Wireless Commun.

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“…ML-aided radio reception has already been considered in several works, which have investigated implementing certain parts of the receiver chain with learned layers. For instance, channel estimation with neural networks has been studied in [5], [6], while [7] utilizes convolutional neural networks (CNNs) [8] for equalization. ML-based demapping has been considered in [9], where it was shown to achieve nearly the same accuracy as the optimal demapping rule, albeit with greatly reduced computational cost.…”

Section: State Of the Artmentioning

confidence: 99%

HybridDeepRx: Deep Learning Receiver for High-EVM Signals

Pihlajasalo

Korpi

Honkala

et al. 2021

2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network receiver is devised, containing layers in both timeand frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.

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“…Recently, deep learning has not only achieved great success in the fields of computer vision and natural language processing, but has also provided a new solution for the noise reduction of modulation signals. 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.…”

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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Complex CNN-Based Equalization for Communication Signal

Cited by 23 publications

References 9 publications

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

HybridDeepRx: Deep Learning Receiver for High-EVM Signals

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

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