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

Abstract

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).