History assisted energy efficient spectrum sensing in cognitive radio networks
AuthorsSyed, Tazeen Shabana
Subject Categories::P302 Radio studies
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AbstractThe ever-increasing wireless applications and services has generated a huge demand for the RF spectrum. The strict and rigid policy of spectrum management by the Federal Communications Commission (FCC) has rendered spectrum a valuable resource. The disproportion in the usage of spectrum between the licensed primary users (PUs) and the enormous unlicensed secondary users (SUs) in the band has created spectrum scarcity. This imbalance can be alleviated by the Dynamic Spectrum Access (DSA) based on Cognitive Radio Network (CRN) paradigm by significantly improving the efficiency of spectrum utilisation of the wireless networking systems. DSA enables unlicensed secondary users (SUs) also known as cognitive radios (CRs) to sense the spectral environment and access the licensed spectrum opportunistically without causing any interference to the licensed primary users (PUs). Spectrum sensing is the most prominent capability of CRs to effectively detect the presence or absence of licensed primary users (PUs) in the band. Sensing provides protection to primary users (PUs) from interference and creates opportunities of spectrum access to secondary users (SUs). However, scanning the spectrum continuously is critical and power intensive. The high-power consumption in battery operated CR devices reduces device lifetime thereby affecting the network performance. Research is being carried out to improve energy efficiency and offer viable solutions for extending lifetime for wireless devices. In this thesis, the work focuses on the energy efficient spectrum sensing of CR networks. The main aim is to reduce the percentage of energy consumption in the CR system in possible ways. Primarily, the conventional energy detection (ED) and the cyclostationary feature detection (CFD) spectrum sensing mechanisms were employed to sense the spectrum. Aiming on energy efficiency, a novel history assisted spectrum sensing scheme has been proposed which utilises an analytical engine database (AED). It generates a rich data set of spectrum usage history that can be used by CRs to make efficient sensing decisions modelled using Markov chain model. The usage of sensing history in decision making, results in decreasing the frequency of spectrum scanning by the CRs thereby reducing the processing cost and the sensing related energy consumption. It shows 17% improvement in energy saving compared to the conventional sensing scheme. The key performance parameters such as probability of miss detection (PMD), probability of false alarm (PF) and probability of detection (PD) were investigated using ROC curves. Extensive performance analysis is carried out by implementing two traditional sensing schemes ED and CFD in terms of computational cost and energy consumption and shows 50% improvement in effective energy saved by using history assisted spectrum sensing mechanism. Further, to address the high energy consumption during communication between CRs / stations (STAs) and the base station (BS), a novel energy efficient Group Control Slot allocation (GCSA) mac protocol has been proposed. Publish/Subscribe (PUB-SUB) and point-to-point messaging models have been implemented for data communication between BS, STAs and AED. The proposed mac protocol increases the number of STAs to enter in to sleep mode thereby conserving the energy consumed during idle state. Furthermore, cluster based co-operative spectrum sensing (CSS) is considered for reducing the energy utilised for data communication between CRs and BS by electing a cluster head (CH) using fuzzy logic-based clustering algorithm. The cluster head (CH) collects, aggregates data from cluster members and it is only the CHs that communicate to the BS. Thus, there is no communication between individual non-CH CRs and BS, thereby significantly reducing the energy consumption and improving the network lifetime of the CR system. Extensive simulations were performed in MATLAB and results are presented for all the proposed schemes.
CitationSyed, T.S. (2018) 'History Assisted Energy Efficient Spectrum Sensing in Cognitive Radio Networks'. PhD thesis. University of Bedfordshire.
PublisherUniversity of Bedfordshire
TypeThesis or dissertation
Description"A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the Degree of Doctor of Philosophy".
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