With advances in communications technologies, there is a constant need for higher data rates. One possible solution to overcome this need is to allocate additional bandwidth. However, due to spectrum scarcity this is no longer feasible. In addition, the results of spectrum measurement campaigns discovered the fact that the available spectrum is under-utilized. One of the most significant solutions to solve the under- utilization of radio-frequency (RF) spectrum is the cognitive radio (CR) concept. A valid mathematical model that can be applied for most practical scenarios and also captures the random fluctuations of the spectrum is necessary. This model provides a significant insight and also a better quantitative understanding of such systems and this is the topic of this dissertation. Compact mathematical formulations that describe the realistic spectrum usage would improve the recent theoretical work to a large extent. The data generated for such models, provide a mean for a more realistic evaluation of the performance of CR systems. However, measurement based models require a large amount of data and are subject to measurement errors. They are also likely to be subject to the measurement time, location, and methodology. In the first part of this dissertation, we introduce cognitive radio networks and their role on solving the problem of under-utilized spectrum. In the second part of this dissertation, we target the random variable which accounts for the fraction of available subcarriers for the secondary users in an OFDMA based CR system. The time and location dependency of the traffic is taken into account by a non-homogenous Poisson Point Process (PPP). In the third part, we propose a comprehensive statistical model for user activity, spectrum occupancy, and system throughput in the presence of mutual interference in an OFDMA-based CR network which accounts for the sensing procedure of spectrum sensor, spectrum demand-model and spatial density of primary users, system objective for user satisfaction which is to support as many users as possible, and environment-dependent conditions such as propagation path loss, shadowing, and channel fading. In the last part of this dissertation, unlike the second and the third parts that the modeling is theoretical and based on limiting assumptions, the spectrum usage modeling is based on real data collected from an extensive measurement.

Thesis (M.S.)--George Mason University, 2008. / Vita: p. 89. Thesis director: David D. Hwang. Submitted in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering. Title from PDF t.p. (viewed Mar. 9, 2009). Includes bibliographical references (p. 85-88). Also issued in print.

Thesis (M.S.)--Rutgers University, 2009. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 72-73).

Thesis (M. Eng.)--Victoria University (Melbourne, Vic.), 2009.

Cognitive Radio (CR) technology creates the opportunity for unlicensed users to make use of the spectral band provided it does not interfere with any licensed user. It is a prominent tool with spectrum sensing functionality to identify idle channels and let the unlicensed users avail them. Thus, the CR technology provides the consumers access to a very large spectrum, quality spectral utilization, and energy efficiency due to spectral load balancing. However, the full potential of the CR technology can be realized only with CRs equipped with accurate mechanisms to predict/sense the spectral holes and vacant spectral bands without any prior knowledge about the characteristics of traffic in a real-time environment. Multi-layered perception (MLP), the popular neural network trained with the back-propagation (BP) learning algorithm, is a keen tool for classification of the spectral bands into "busy" or "idle" states without any a priori knowledge about the user system features. In this dissertation, we proposed the use of an evolutionary algorithm, Bacterial Foraging Optimization Algorithm (BFOA), for the training of the MLP NN. We have compared the performance of the proposed system with the traditional algorithm and with the Hybrid GA-PSO method. With the results of a simulation experiment that this new learning algorithm for prediction of channel states outperforms the traditional BP algorithm and Hybrid GA-PSO method with respect to classification accuracy, probability of misdetection, and Probability of false alarm.

Ever since the dawn of radio communication systems, the antenna has been the key component in the construction and performance of every wireless system. With the proliferation of new radio systems, a cognitive radio is a radio that has the capability to sense, learn, and autonomously adapt to its environment. The hardware components are essential to optimizing performance. Antenna hardware for cognitive radio applications presents distinctive problems, since in theoretical terms, a cognitive radio can operate anywhere in the spectrum. The purpose of this thesis is to investigate a particular type of cognitive radio system and examine the potential affects the antenna will have on the system. The thesis will provide an overview of fundamental antenna properties, the performance characteristics of the particular antenna used in this research, and the system characteristics when the antenna is integrated. This thesis will also illustrate how the antenna and its properties affect the overall public safety cognitive radio performance. This information can be used to establish antenna selection criteria for optimum system performance. / Master of Science

This thesis presents a performance evaluation system for cognitive radio. It considers performance as a complex, multi-dimensional function. Typically such a function would take some record of actions as an argument; however, a key contribution of this work is the addition of background information to the domain of the performance function. Including this information generalizes the performance function across many radios and applications, with the additional cost of complicating the domain. Thus the presented evaluation system organizes the domain information into sets. These sets are divided into two categories, one capturing necessary information that is external to the radio and on capturing necessary information that internal to the radio. These categories highlight the fact that neither the true actions nor the true performance is directly observable at the onset of evaluation. This arises because a cognitive radio can only express its actions in terms of the available knobs and meters, which together form the radio's language. Some understanding of this language and its limitations is required to fully understand the radio's expression of its actions. This parallelism of actions and performance suggests implementing the evaluation method as a composite form of the performance function. The composite performance function is made up of two sub-functions, one of which producing action information and one of which producing performance information. Specifically, the first sub-function is used to determine general measures of the actions' influence on performance; these are labeled Measures of Effectiveness. The second sub-function uses these Measures of Effectiveness to determine application specific performance values, called Measures of Performance. This work covers both these measures in detail. Each measure is determined as the result of a neural network based interpolation. This thesis also provides an examination of artificial neural networks in the scope of performance evaluation. Once these concepts are explored, a walk-through evaluation is presented. The four phases are the Setup Phase, the Logging Phase, the Training Phase, and the Evaluation Phase. Each phase is structured to provide the information necessary to determine the final performance. These phases detail the process of evaluation and discuss the realization of concepts explored earlier. This work concludes with a comparative evaluation example that proves the worth of the presented approach. A full evaluation system is outlined by this thesis and the foundational details for the system are explored in detail. / Master of Science

No description available.

No / he automatic classification of digital modulated signals has been subject to extensive studies over the last decade, with numerous scholarly articles and research studies published. This paper provides an insightful guidance and discussion on the most practical approaches of automatic modulation classification (AMC) in cognitive radio (CR) using likelihood based (LB) statistical tests. It also suggests a novel idea of storing the known constellation sets on the receiver side using a look-up table (LUT) to detect the transmitted replica. Relevant performance measures with simulated comparisons in flat fading additive white Gaussian noise (AWGN) channels are examined. Namely, the average likelihood ratio test (ALRT), generalized LRT (GLRT) and hybrid LRT (HLRT) are particularly illustrated using linearly phase-modulated signals such as M-ary phase shift keying (MPSK) and quadrature amplitude modulation (MQAM). When the unknown signal constellation is estimated using the maximum likelihood (ML) method, results indicate that the HLRT performs well and near optimal in most situations without extra computational burden.

The rapid rise in computational performance offered by computer systems has greatly increased the number of practical software defined radio applications. The addition of FPGAs to these flexible systems has resulted in platforms that can address a multitude of applications with performance levels that were once only known to ASICs. This work presents an embedded heterogeneous scalable cluster platform with software defined radio applications. The Xilinx Zynq chip provides a hybrid platform consisting of an embedded ARM general-purpose processing core and a low-power FPGA. The ARM core provides all of the benefits and ease of use common to modern high-level software languages while the FPGA segment offers high performance for computationally intensive components of the application. Four of these chips were combined in a scalable cluster and a task assigner was written to automatically place data flows across the FPGAs and ARM cores. The rapid reconfiguration software tFlow was used to dynamically build arbitrary FPGA images out of a library of pre-built modules. / Master of Science