Thesis Cognitive Radio

Thesis Cognitive Radio-70
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.

This evaluation is performed for more realistic signals that suffer from the non-linear distortions, caused by the power amplifiers.ED is most preferred in CR because of simple implementation and semi-blind nature. So other combined techniques are preferred over the ED to enhance sensitivity of CR.So, thesis proposes Two Stage Spectrum Sensing as preferred in IEEE 802.22 standard.For further information, including about cookie settings, please read our Cookie Policy .By continuing to use this site, you consent to the use of cookies.In particular, energy can be harvested from the radio waves in the radio frequency spectrum.For ensuring reliable performance, energy prediction has been proposed as a key component for optimizing the energy harvesting because it equips the harvesting nodes with adaptation to the energy availability.It has also been shown that optimal time and frequency attained using energy predictive model can be used for defining the scheduling policies of the harvesting nodes.Last, it has been shown that wirelessly powered PUs having energy harvesting capabilities can attain energy gain from the transmission of SU and SU can attain the throughput gain from the extra transmission time allocated for energy harvesting PUs.A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy has been presented.In addition to this, the thesis investigates the energy efficient cognitive radio systems because energy harvesting enables the perpetual operation of the wireless networks without the need of battery change.

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