Threshold Setting Algorithms for Spectrum Sensing in Cognitive Radio Networks
Abstract
As the demand for wireless communication services grows quickly, spectrum scarcity has
been on the rise sharply. In this context, cognitive radio (CR) is being viewed as a
new intelligent technology to solve the deficiency of fixed spectrum assignment policy in
wireless communications. Spectrum sensing is one of the most fundamental technologies
to realise dynamic spectrum access in cognitive radio networks. It requires high accuracy
as well as low complexity. In this thesis, a novel adaptive threshold setting algorithm
is proposed to optimise the trade-off between detection and false alarm probability in
spectrum sensing while satisfying sensing targets set by the IEEE 802.22 standard. The
adaptive threshold setting algorithm is further applied to minimise the error decision
probability with varying primary users' spectrum utilisations. A closed-form expression
for the error decision probability, satisfied SNR value, number of samples and primary
users' spectrum utilisation ratio are derived in both fixed and the proposed adaptive
threshold setting algorithms. By implementing both Welch and wavelet based energy
detectors, the adaptive threshold setting algorithm demonstrates a more reliable and
robust sensing result for both primary users (PUs) and secondary users (SUs) in comparison with the conventional fixed one. Furthermore, the wavelet de-noising method is
applied to improve the sensing performance when there is insu cient number of samples. Finally, a novel database assisted spectrum sensing algorithm is proposed for a
secondary access of the TV White Space (TVWS) spectrum. The proposed database
assisted sensing algorithm is based on the developed database assisted approach for
detecting incumbents like Digital Terrestrial Television (DTT) and Programme Making
and Special Events (PMSE), but assisted by spectrum sensing to further improve the
protection to primary users. Monte-Carlo simulations show a higher SUs' spectrum efficiency can be obtained for the proposed database assisted sensing algorithm than the
existing stand-alone database assisted or sensing models.
Authors
Wang, NanCollections
- Theses [3831]