Wideband Spectrum Sensing and Signal Classification for Autonomous Self-Learning Cognitive Radios
نویسندگان
چکیده
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 wide frequency band and act autonomously by learning from past experience. To achieve its goals, the proposed CR is equipped with the following functionalities: 1) Wideband spectrum sensing, 2) non-parametric signal classification, 3) unsupervised learning and reasoning and 4) decentralized decision-making. To this end, we implement a blind spectrum sensing method based on joint energy/cyclostationary detection. Optimal wideband energy detector is designed based on the Neyman-Pearson (NP) criterion which maximizes the detection probability of primary signals, subject to a certain false alarm rate. Cyclostationary detection is
منابع مشابه
Machine Learning Aided Efficient and Robust Algorithms for Spectrum Knowledge Acquisition in Wideband Autonomous Cognitive Radios
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