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.

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

This research addresses the performance degradation in receivers due to poor selectivity. Poor selectivity is expected to be a primary limitation on the performance of Dynamic-Spectrum-Access (DSA) and millimeter wave (mmWave) technologies. Both DSA and mmWave are highly desired technologies because they can address the spectrum-deficit problem that is currently challenging the wireless industry. Accordingly, addressing poor receiver selectivity is necessary to expedite the adoption of these technologies into the main street of wireless. This research develops two receiver design concepts to enhance the performance of poorly-selective receivers. The first concept is called cognitive RF front-end control (CogRF). CogRF operates by cognitively controlling the local-oscillator and sampling frequencies in receivers. This research shows that CogRF can fulfil the objective of pre-selectors by minimizing the effects of weak and moderately-powered neighboring-channel signals on the desired signal. This research shows that CogRF can be an alternative to high-performance pre-selectors, and hence, CogRF is a viable architecture to implement reliable DSA and mmWave receivers. The theoretical design and hardware implementation of a cognitive engine and a spectrum sensor of CogRF are reported in this dissertation. Measurement results show that CogRF significantly reduces the rate of communication outage due to interference from neighboring-channel signals in poorly-selective receivers. The results also indicate that CogRF can enable a poorly-selective receiver to behave like a highly-selective receiver. The second receiver design concept addresses very strong neighboring-channel signals. The performance of poorly selective receivers can easily suffer due to a strong, unfiltered neighboring-channel signal. A strong neighboring-channel signal is likely for a DSA radio that is operating in military radar bands. Traditionally, strong neighboring signals are addressed using an Automatic-Gain-Control (AGC) that attempt to accommodate the strong received signal into the dynamic range of the receiver. However, this technique potentially desensitizes the receiver because it sacrifices the Signal-to-Noise-Ratio (SNR) of the desired signal. This research proposes the use of auxiliary-receive path to address strong neighboring-channel signals with minimal penalty on the SNR of the desired signal. Through simulation based analysis, and hardware-based measurement, this research shows that the proposed technique can provide significant improvement in the neighboring-channel-interference handling capability of the receiver. / Ph. D.

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.