abstract: Dynamic channel selection in cognitive radio consists of two main phases. The first phase is spectrum sensing, during which the channels that are occupied by the primary users are detected. The second phase is channel selection, during which the state of the channel to be used by the secondary user is estimated. The existing cognitive radio channel selection literature assumes perfect spectrum sensing. However, this assumption becomes problematic as the noise in the channels increases, resulting in high probability of false alarm and high probability of missed detection. This thesis proposes a solution to this problem by incorporating the estimated state of channel occupancy into a selection cost function. The problem of optimal single-channel selection in cognitive radio is considered. A unique approach to the channel selection problem is proposed which consists of first using a particle filter to estimate the state of channel occupancy and then using the estimated state with a cost function to select a single channel for transmission. The selection cost function provides a means of assessing the various combinations of unoccupied channels in terms of desirability. By minimizing the expected selection cost function over all possible channel occupancy combinations, the optimal hypothesis which identifies the optimal single channel is obtained. Several variations of the proposed cost-based channel selection approach are discussed and simulated in a variety of environments, ranging from low to high number of primary user channels, low to high levels of signal-to-noise ratios, and low to high levels of primary user traffic. / Dissertation/Thesis / M.S. Electrical Engineering 2014

As wireless technologies evolve, novel innovations and concepts are required to dynamically and automatically alter various radio parameters in accordance with the radio environment. These innovations open the door for cognitive radio (CR), a new concept in telecommunications. CR makes its decisions using an inference engine, which can learn and adapt to changes in radio conditions. Fuzzy logic (FL) is the proposed decision-making algorithm for controlling the CR's inference engine. Fuzzy logic is well-suited for vague environments in which incomplete and heterogeneous information is present. In our proposed approach, FL is used to alter various radio parameters according to experience gained from different environmental conditions. FL requires a set of decision-making rules, which can vary according to radio conditions, but anomalies rise among these rules, causing degradation in the CR's performance. In such cases, the CR requires a method for eliminating such anomalies. In our model, we used a method based on the Dempster-Shafer (DS) theory of belief to accomplish this task. Through extensive simulation results and vast case studies, the use of the DS theory indeed improved the CR's decision-making capability. Using FL and the DS theory of belief is considered a vital module in the automation of various radio parameters for coping with the dynamic wireless environment. To demonstrate the FL inference engine, we propose a CR version of WiMAX, which we call CogMAX, to control different radio resources. Some of the physical parameters that can be altered for better results and performance are the physical layer parameters such as channel estimation technique, the number of subcarriers used for channel estimation, the modulation technique, and the code rate. / Ph. D.

Being one of the fastest growing fields of engineering, wireless has gained the attention of researchers and commercial businesses all over the world. Extensive research is underway to improve the performance of existing systems and to introduce cutting edge wireless technologies that can make high speed wireless communications possible. The first part of this dissertation deals with discrete channel models that are used for simulating error traces produced by wireless channels. Most of the time, wireless channels have memory and we rely on discrete time Markov models to simulate them. The primary advantage of using these models is rapid experimentation and prototyping. Efficient estimation of the parameters of a Markov model (including its number of states) is important to reproducing and/or forecasting channel statistics accurately. Although the parameter estimation of Markov processes has been studied extensively, its order estimation problem has been addressed only recently. In this report, we investigate the existing order estimation techniques for Markov chains and hidden Markov models. Performance comparison with semi-hidden Markov models is also discussed. Error source modeling in slow and fast fading conditions is also considered in great detail. Cognitive Radio is an emerging technology in wireless communications that can improve the utilization of radio spectrum by incorporating some intelligence in its design. It can adapt with the environment and can change its particular transmission or reception parameters to execute its tasks without interfering with the licensed users. One problem that CR network usually faces is the difficulty in detecting and classifying its low power signal that is present in the environment. Most of the time traditional energy detection techniques fail to detect these signals because of their low SNRs. In the second part of this thesis, we address this problem by using higher order statistics of incoming signals and classifying them by using the pattern recognition capabilities of HMMs combined with cased-based learning approach. This dissertation also deals with dynamic spectrum allocation in cognitive radio using HMMs. CR networks that are capable of using frequency bands assigned to licensed users, apart from utilizing unlicensed bands such as UNII radio band or ISM band, are also called Licensed Band Cognitive Radios. In our novel work, the dynamic spectrum management or dynamic frequency allocation is performed by the help of HMM predictions. This work is based on the idea that if Markov models can accurately model spectrum usage patterns of different licensed users, then it should also correctly predict the spectrum holes and use these frequencies for its data transmission. Simulations have shown that HMMs prediction results are quite accurate and can help in avoiding CR interference with the primary licensed users and vice versa. At the same time, this helps in sending its data over these channels more reliably. / Ph. D.

Předkládaná disertační práce je zaměřena na "heterogenní propojení mobilních zařízení v bezdrátových systémech 5. generace". Navzdory nepochybnému pokroku v rámci navržených komunikačních řešení postrádají mobilní sítě nastupující generace dostatečnou šířku pásma a to hlavně kvůli neefektivnímu využívání rádiového spektra. Tato situace tedy v současné době představuje řadu otázek v oblasti výzkumu. Hlavním cílem této disertační práce je proto návrh nových komunikačních mechanismů pro komunikaci mezi zařízeními v bezprostřední blízkosti s asistencí mobilní sítě a dále pak návrh a implementace algoritmů pro dynamické přidělování frekvenčního spektra v nastupujících mobilních sítích 5G. Navrhnuté komunikační mechanismy a algoritmy jsou následně komplexně vyhodnoceny pomocí nově vyvinutých simulačních nástrojů (kalibrovaných s využitím 3GPP trénovacích dat) a zejména pak v experimentální mobilní síti LTE-A, která se nachází v prostorách Vysokého učení technického v Brně, Česká Republika. Získané praktické výsledky, které jsou podpořeny zcela novou matematickou analýzou ve speciálně navržených charakteristických scénářích, představují řešení pro vlastníka spektra v případě požadavků na jeho dynamické sdílení. Tato metoda tedy představuje možnost pro efektivnější využití spektra v rámci mobilních sítí 5G bez degradace kvality služeb (QoS) a kvality zážitků (QoE) pro koncové uživatele. Vědecký přínos dosažených výsledků dokazuje fakt, že některé z principů představených v této disertační práci byly zahrnuty do celosvětově uznávaného standardu (specifikace) 3GPP Release 12.