Spectrum Sensing in Cognitive Radio Using CNN-RNN and Transfer Learning

Cognitive radio has been proposed to improve spectrum utilization in wireless communication. Spectrum sensing is an essential component of cognitive radio. The traditional methods of spectrum sensing are based on feature extraction of a received signal at a given point. The development in artificial intelligence and deep learning have given an opportunity to improve the accuracy of spectrum sensing by using cooperative spectrum sensing and analyzing the radio scene. This research proposed a hybrid model of convolution and recurrent neural network for spectrum sensing. The research further enhances the accuracy of sensing for low SNR signals through transfer learning. The results of modelling show improvement in spectrum sensing using CNN-RNN compared to other models studied in this field. The complexity of an algorithm is analyzed to show an improvement in the performance of the algorithm.publishedVersio

Wireless Interference Identification with Convolutional Neural Networks

The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless interference identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII approaches and has a classification accuracy greater than 95% for signal-to-noise ratio of at least -5 dB

Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum

This paper presents a spectral attention-driven reinforcement learning based intelligent method for effective and efficient detection of important signals in a wideband spectrum. In the work presented in this paper, it is assumed that the modulation technique used is available as a priori knowledge of the targeted important signal. The proposed spectral attention-driven intelligent method is consists of two main components, a spectral correlation function (SCF) based spectral visualization scheme and a spectral attention-driven reinforcement learning mechanism that adaptively selects the spectrum range and implements the intelligent signal detection. Simulations illustrate that the proposed method can achieve high accuracy of signal detection while observation of spectrum is limited to few ranges via effectively selecting the spectrum ranges to be observed. Furthermore, the proposed spectral attention-driven machine learning method can lead to an efficient adaptive intelligent spectrum sensor designs in cognitive radio (CR) receivers.Comment: 6 pages, 11 figure