Spectrum sensing is a base for the performance of all functions performed by the Cognitive Radio (CR).Cognitive radio recognizes the unused spectrum and shares it to secondary users (SU’s) without creating harmful interference to primary users (E.g. Literature discusses various SS techniques like ED, CSD, CMME with their advantages and disadvantages.Based on this, a new SVM algorithm has been proposed by combining SVM with FFA.
Two machine learning techniques, linear regression (LR) and decision trees (DT) have been utilized to predict the harvested energy using real-time power measurements in the radio spectrum.
Furthermore, the conventional energy harvesting cognitive radios do not assume any energy harvesting capability at the primary users (PUs).
In modern wireless communications the spectrum is allocated to fixed licensed users and on the other side the number of wireless devices are increasing rapidly, that has lead to spectrum crunch.
As the spectrum is precious it has to be utilized efficiently.
Firstly, bio-inspired techniques, including re y algorithm (FFA), fish school search (FSS) and particle swarm optimization (PSO), have been utilized in this thesis to evaluate the optimal weighting vectors for cooperative spectrum sensing (CSS) and spectrum allocation in the cognitive radio systems.