Effective deep learning-based channel state estimation and signal detection for OFDM wireless systems

Hassan, Hassan A.; Mohamed, Mohamed A.; Essai, Mohamed H.; Esmaiel, Hamada; Mubarak, Ahmed S. A.; Omer, Osama A.

doi:10.2478/jee-2023-0022

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Common digital modulation types can be divided into Phase Shift Keying (PSK), Frequency Shift Keying (FSK), Quadrature Amplitude Modulation (QAM), and Pulse Amplitude Modulation (PAM). In multi-order digital modulation systems, the value of the modulation signal M takes different values, which is shown in Eq ( 2 ) [ 21 ].…”

Section: Design Of Signal Amc Model With Neural Network Fusionmentioning

confidence: 99%

Signal automatic modulation based on AMC neural network fusion

Yin,

Diao

2024

PLoS ONE

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With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further improve the communication quality and system processing efficiency, this study combines two different neural network algorithms to optimize the traditional signal automatic modulation classification method. In this paper, the basic technology involved in the communication process, including automatic signal modulation technology and signal classification technology, is discussed. Then, combining parallel convolution and simple cyclic unit network, three different connection paths of automatic signal modulation classification model are constructed. The performance test results show that the classification model can achieve a stable training and verification state when the two networks are connected. After 20 and 29 iterations, the loss values are 0.13 and 0.18, respectively. In addition, when the signal-to-noise ratio (SNR) is 25dB, the classification accuracy of parallel convolutional neural network and simple cyclic unit network model is as high as 0.99. Finally, the classification models of parallel convolutional neural networks and simple cyclic unit networks have stable correct classification probabilities when Doppler shift conditions are introduced as interference in practical application environment. In summary, the neural network fusion classification model designed can significantly improve the shortcomings of traditional automatic modulation classification methods, and further improve the classification accuracy of modulated signals.

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Section: Design Of Signal Amc Model With Neural Network Fusionmentioning

confidence: 99%

Signal automatic modulation based on AMC neural network fusion

Yin,

Diao

2024

PLoS ONE

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Enhancing MIMO-OFDM channel estimation in 5G and beyond with conditional self-attention generative adversarial networks

Alqahtani,

Pandiaraj,

Alshmrany

et al. 2024

Wireless Netw

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Artificial neural network-based sparse channel estimation for V2V communication systems

Rahim,

Essai,

Hamad

2024

Journal of Electrical Engineering

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Artificial neural networks (ANNs) have gained a lot of attention from researchers in the past few years and have been employed on a large scale. They have also been gaining momentum in wireless communication systems. For efficient vehicle-to-vehicle (V2V) channel communication, a sparse multipath channel issue must be studied. To minimize the multipath effect, a time reversal (TR) operation and time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) have been appealing because of their fast synchronization and active spectral efficiency. To improve the transceiver's execution in a frequency-selective fading channel environment, an OFDM system is used to reduce inter- symbol interference (ISI). Simultaneous Orthogonal Matching Pursuit (SOMP) channel state estimator algorithm suffer from high computational cost and high computational complexity. The ANN algorithm has better performance than SOMP algorithm. The proposed neural network technologies have lower complexity than the SOMP algorithm. The application of ANN is capable of solving complex problems, such as those encountered in image, signal processing and have been implemented for channel estimation in OFDM. The proposed ANN outperformed the SOMP algorithm with regard to signal compensation. Overall, the ANN algorithm achieved the best performance. This study proposes an ANN-based sparse channel state estimator. Regarding the bit error rate (BER) metric, the proposed estimator outperforms the channel estimation approach based on the SOMP. The simulation results confirm the efficacy of the proposed approach.

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Effective deep learning-based channel state estimation and signal detection for OFDM wireless systems

Cited by 3 publications

References 29 publications

Signal automatic modulation based on AMC neural network fusion

Signal automatic modulation based on AMC neural network fusion

Enhancing MIMO-OFDM channel estimation in 5G and beyond with conditional self-attention generative adversarial networks

Artificial neural network-based sparse channel estimation for V2V communication systems

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