A New Deep Spiking Architecture for Reconstruction of Compressed data in Cognitive Radio Networks

Cognitive Radio (CR) offers a spectrum sharing solution to handle the massive amount of devices operating in the same spectrum. In this work a sub-Nyquist compressive sensing technique is proposed that allows secondary users to sense and utilize idle spectrum. Reconstruction of compressed sparse dat...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Amr, Reem, Zaher, Nawal A., Gasser, Safa M., Eldiasty, Sherif K.
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Cognitive radio
Complexity theory
Compressed sensing
Convergence
Energy Efficiency
Energy requirements
Energy storage
Image reconstruction
Neural networks
Reconstruction
Sensors
Signal to noise ratio
Sparse matrices
Spectrum sensing
Spiking
Spiking Neural Network
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Summary:Cognitive Radio (CR) offers a spectrum sharing solution to handle the massive amount of devices operating in the same spectrum. In this work a sub-Nyquist compressive sensing technique is proposed that allows secondary users to sense and utilize idle spectrum. Reconstruction of compressed sparse data is achieved through a dual stage sophisticated reconstruction algorithm. The reconstruction uses a classical fast Orthogonal Matching Persuit (OMP), followed by a new spiking deep Residual neural Network (ResNet) architecture. The proposed architecture is obtained through a novel distributed conversion technique that is proposed to convert deep architectures to a spiking neural networks. The reconstructed data is compared in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity (SSIM) to the compressed data and the ground truth. Super Resolution Convolutional Neural Network (SRCNN) and a Deep ResNet are also used for reconstruction. The proposed algorithm outperforms SRCNN and the unconverted ResNet, specially at low Channel SNR (CSNR). In addition, the proposed algorithm results in a 68% reduction in both storage and energy requirements, which makes it suitable for implementation on User Equipment (UE).
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3213816