Performance analysis of energy detector over generalised wireless channels in cognitive radio

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.This thesis extensively analyses the performance of an energy detector which is widely employed to perform spectrum sensing in cognitive radio over different generalised channel models. In this analysis, both the average probability of detection and the average area under the receiver operating characteristic curve (AUC) are derived using the probability density function of the received instantaneous signal to noise ratio (SNR). The performance of energy detector over an ŋ --- µ fading, which is used to model the Non-line-of-sight (NLoS) communication scenarios is provided. Then, the behaviour of the energy detector over к --- µ shadowed fading channel, which is a composite of generalized multipath/shadowing fading channel to model the lineof- sight (LoS) communication medium is investigated. The analysis of the energy detector over both ŋ --- µ and к --- µ shadowed fading channels are then extended to include maximal ratio combining (MRC), square law combining (SLC) and square law selection (SLS) with independent and non-identically (i:n:d) diversity branches. To overcome the problem of mathematical intractability in analysing the energy detector over i:n:d composite fading channels with MRC and selection combining (SC), two different unified statistical properties models for the sum and the maximum of mixture gamma (MG) variates are derived. The first model is limited by the value of the shadowing severity index, which should be an integer number and has been employed to study the performance of energy detector over composite α --- µ /gamma fading channel. This channel is proposed to represent the non-linear prorogation environment. On the other side, the second model is general and has been utilised to analyse the behaviour of energy detector over composite ŋ --- 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, signal denoising based on hybrid slantlet transform (HST) is employed to reduce the noise impact on the performance of energy detector. The combined SPT-HST approach improves the detection capability of energy detector with 97% and reduces the total computational complexity by nearly 19% in comparison with previously implemented work using filter-bank transforms. The aforementioned percentages are measured at specific SNR, number of selected samples and levels of signal decompositionMartyrs Foundatio

Closed-form Distribution and Analysis of a Combined Nakagami-lognormal Shadowing and Unshadowing Fading Channel , Journal of Telecommunications and Information Technology, 2016, nr 4

The realistic wireless channels face combined (time shared) Nakagami-lognormal shadowing and unshadowing fading because of time varying nature of radio channel and mobile user. These channels can be modeled as time-shared sum of multipath-shadowing and unshadowing Rician distributions. These fading create severe problems in long distance wireless systems where multipath fading is superimposed on shadowing fading (called multipath-shadowing fading). The multipath effect can be modeled using Rayleigh, Rician, Nakagami-m or Weibull distribution and shadowing effect is modeled using lognormal distribution. In this paper, authors present a new closed-form probability distribution function of a Nakagami-lognormal fading channel. Using this result, the closed-form expression of combined Nakagamilognormal shadowing and unshadowing fading is presented. The obtained closed-form result facilitates to derive the important performance metrics of a communication system such as amount of fading, outage probability, and average channel capacity in closed-form expressions

Analysis of Gaussian Quadratic Forms with Application to Statistical Channel Modeling

Finalmente, en el contexto de modelado de canal, la metodología de análisis de variables propuesta permite obtener dos nuevas generalizaciones del conocido modelo de desvanecimiento kappa-mu shadowed. Estas dos nuevas distribuciones, nombradas Beckmann fluctuante y kappa-mu shadowed correlado, incluyen como casos particulares a la gran mayoría de distribuciones de desvanecimientos usadas en la literatura, abarcando desde los modelos clásicos de Rayleigh y Rice hasta otros más generales y complejos como el Beckmann y el kappa-mu. Para ambas distribuciones, se presenta su caracterización estadística de primer orden, i.e., función generadora de momentos (MGF), PDF y CDF; así como los estadísticos de segundo orden del modelo Beckmann fluctuante. Fecha de lectura de Tesis Doctoral: 24 Enero 2020En esta tesis se presenta una nueva aproximación a la distribución de de formas cuadráticas gaussianas (FCGs) no centrales tanto en variables reales como complejas. Para ello, se propone un nuevo método de análisis de variables aleatorias que, en lugar de centrarse en el estudio de la variable en cuestión, se basa en la caracterización estadística de una secuencia de variables aleatorias auxiliares convenientemente definida. Como consecuencia, las expresiones obtenidas, con independencia del grado de precisión adquirido, siempre representan una distribución válida, siendo ésta su principal ventaja. Aplicando este método, se obtienen simples expresiones recursivas para la función densidad de probabilidad (PDF) y la función de distribución (CDF) de las FCGs reales definidas positivas. En el caso de las formas complejas, esta nueva forma de análisis conduce a aproximaciones para los estadísticos de primer orden en términos de funciones elementales (exponenciales y potencias), siendo más convenientes para cálculos posteriores que otras soluciones disponibles en la literatura. La tratabilidad matemática se ejemplifica mediante el análisis de sistemas de combinación por razón máxima (MRC) sobre canales Rice correlados, proporcionando aproximaciones cerradas para la probabilidad de outage y la probabilidad de error de bit