Machine Learning-aided, Robust Wideband Spectrum Sensing for Cognitive Radios

Machine Learning Aided Efficient and Robust Algorithms for Spectrum Knowledge Acquisition in Wideband Autonomous Cognitive Radios

Wideband Spectrum Sensing and Signal Classification for Autonomous Self-Learning Cognitive Radios

In this dissertation, we develop a novel cognitive radio (CR) architecture, referred to as the Radiobot [1], whose goals go beyond dynamic spectrum access (DSA) to achieve the main features of cognition, notably, self-learning and self-reconfiguration. The proposed CR architecture is based on a sequence of signal processing and machine learning techniques that enable the Radiobot to sense a wid...

Total Variation Minimization Based Compressive Wideband Spectrum Sensing for Cognitive Radios

Wideband spectrum sensing is a critical component of a functioning cognitive radio system. Its major challenge is the too high sampling rate requirement. Compressive sensing (CS) promises to be able to deal with it. Nearly all the current CS based compressive wideband spectrum sensing methods exploit only the frequency sparsity to perform. Motivated by the achievement of a fast and robust detec...

Security of Spectrum Learning in Cognitive Radios

Due to delay and energy constraints, a cognitive radio may not be able to perform spectrum sensing in all available channels. Therefore, a sensing policy is needed to decide which channels to sense. The channel selection problem is the problem of designing such a sensing policy to maximize throughput while avoiding interference to primary users. The channel selection problem can be formulated a...

Algorithm for wideband spectrum sensing based on sparse Fourier transform1

In this paper we present a novel sub-Nyquist algorithm to perform Wideband Spectrum Sensing (WSS) for Cognitive Radios (CRs) by using the recently developed Sparse Fast Fourier Transform (sFFT) algorithms. In this case, we developed a noise-robust sub-Nyquist WSS algorithm with reduced sampling cost, by modifying the Nearly Optimal sFFT algorithm; this was accomplished by using Gaussian windows...