Performance Analysis of Energy Detector over Different Generalised Wireless Channels Based Spectrum Sensing in Cognitive Radio

This thesis extensively analyses the performance of an energy detector which iswidely employed to perform spectrum sensing in cognitive radio over different gener-alised channel models. In this analysis, both the average probability of detection andthe average area under the receiver operating characteristic curve (AUC) are derivedusing the probability density function of the received instantaneous signal to noiseratio (SNR). The performance of energy detector over an η− μ fading, which is usedto model the Non-line-of-sight (NLoS) communication scenarios is provided. Then,the behaviour of the energy detector over κ − μ shadowed fading channel, which isa composite of generalized multipath/shadowing fading channel to model the line-of-sight (LoS) communication medium is investigated. The analysis of the energydetector over both η − μ and κ − μ shadowed fading channels are then extended toinclude maximal ratio combining (MRC), square law combining (SLC) and squarelaw selection (SLS) with independent and non-identically (i.n.d) diversity branches.To overcome the problem of mathematical intractability in analysing the energydetector over i.n.d composite fading channels with MRC and selection combining(SC), two different unified statistical properties models for the sum and the maximumof mixture gamma (MG) variates are derived. The first model is limited by the valueof the shadowing severity index, which should be an integer number and has beenemployed to study the performance of energy detector over composite α− μ/gammafading channel. This channel is proposed to represent the non-linear prorogationenvironment. On the other side, the second model is general and has been utilised toanalyse the behaviour of energy detector over composite η−μ/gamma fading channel.Finally, a special filter-bank transform which is called slantlet packet transform(SPT) is developed and used to estimate the uncertain noise power. Moreover, signaldenoising based on hybrid slantlet transform (HST) is employed to reduce the noiseimpact on the performance of energy detector. The combined SPT-HST approachimproves the detection capability of energy detector with 97% and reduces the totalcomputational complexity by nearly 19% in comparison with previously implementedwork using filter-bank transforms. The aforementioned percentages are measured atspecific SNR, number of selected samples and levels of signal decomposition.