Denoising The Wireless Channel Corrupted Images Using Machine Learning

Sufian, Muhammad; Khan, Abdullah; Saeed, Takreem; Khan, Zaffar M.

doi:10.1109/snpd.2019.8935803

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Both the denoising and detection networks used the Adam optimizer with a learning rate of 0.001 in the model. For each set of SNR = [0, 5, 10, 15, 25, 30], we randomly generated 1,000,000 sets of data for performance testing. To obtain a better experimental comparison, the generalization coefficient η was uniformly set to 0.3 for all cases in this study.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In [1], before channel estimation, denoising the received signal improves the quality of estimation. A convolutional neural network (CNN) was proposed to denoise images corrupted by noisy channels in [10], which uses batch normalization and residual learning to train the model with unknown noise levels. The two-phase cascade network Chan-nelNet proposed in [11] takes the pilots as the vectorized input image with low resolution.…”

Section: Introductionmentioning

confidence: 99%

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A joint denoising and deep learning detector for OFDM‐IM

et al. 2023

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Only a subset of subcarriers are activated in orthogonal frequency division multiplexing‐index modulation (OFDM‐IM), which achieves higher energy efficiency and resists frequency offset. In the OFDM‐IM, the energy of the received signal is computed and then combined with pre‐processed signal to create the input of detection network. Inspired by image denoising technology, this study enhances the detection performance by denoising the pre‐processed data and improving the energy distribution in the OFDM‐IM system. First, considering that the noise reduction process of the pre‐processed signal can effectively mitigate the distortion by noise which affects the detection accuracy, this study proposes a two‐phase neural network termed as Deep‐Denoising‐IM through the combination of a noise reduction network and a deep learning detection method. Then, to better determine the position of the active carriers, a joint decision method of the denoised data and the received signal is designed as the IQ signal of the denoised data may change the quadrant of original signal distribution. In addition, the pre‐processed data sample has insufficient diversity. Considering that data enhancement can increase the noise of the signal samples, this study proposes a method to strengthen the silent carriers in the model training phase, which improves the generalization ability of the model and further enhances the denoising performance. Simulation results show that Deep‐Denoising‐IM outperforms the existing detectors in terms of mean square error (MSE) and bit error rate (BER) under the Rayleigh fading channel.

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Section: Simulation Resultsmentioning

confidence: 99%