Multipath is known to be one of the most dominant sources of accuracy degradation in satellite-based navigation systems. Multipath may cause biased position estimates that could jeopardize high-precision applications. This paper considers the problem of tracking the time-variant synchronization parameters of both the line-of-sight signal (LOSS) and its multipath replicas. In particular, the proposed algorithm tracks time-delays, amplitudes, phases and proposes a procedure to extract Doppler shifts from complex amplitudes. However, the interest is focused on LOSS time-delay estimates, since those provide the means to compute user's position. The undertaken Bayesian approach is implemented by a particle filter. The selection of the importance density function, from which particles are generated, is performed using a Gaussian approximation of the posterior function. This selection provides a particle generating function close to the optimal, which yields to an efficient usage of particles. The complex-linear part of the model, i.e., complex amplitudes, is tackled by a Rao-Blackwellization procedure that implements a complex Kalman filter for each generated particle, thus reducing the computational load. Computer simulation results are compared to other Bayesian filtering alternatives (namely, the extended Kalman filter, the unscented Kalman filter and the sequential importance resampling algorithms) and the posterior Cramer-Rao bound.

A Bayesian Approach to Multipath Mitigation in GNSS Receivers

This dissertation provides the theory, design, and implementation of a cognitive engine, the enabling technology of cognitive radio. A cognitive radio is a wireless communications device capable of sensing the environment and making decisions on how to use the available radio resources to enable communications with a certain quality of service. The cognitive engine, the intelligent system behind the cognitive radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The cognitive engine presented here provides a general framework to build and test cognitive engine algorithms and components such as sensing technology, optimization routines, and learning algorithms. The cognitive engine platform allows easy development of new components and algorithms to enhance the cognitive radio capabilities. It is shown in this dissertation that the platform can easily be used on a simulation system and then moved to a real radio system.
The dissertation includes discussions of both theory and implementation of the cognitive engine. The need for and implementation of all of the cognitive components is strongly featured as well as the specific issues related to the development of algorithms for cognitive radio behavior. The discussion of the theory focuses largely on developing the optimization space to intelligently and successfully design waveforms for particular quality of service needs under given environmental conditions. The analysis develops the problem into a multi-objective optimization process to optimize and trade-off of services between objectives that measure performance, such as bit error rate, data rate, and power consumption. The discussion of the multi-objective optimization provides the foundation for the analysis of radio systems in this respect, and through this, methods and considerations for future developments. The theoretical work also investigates the use of learning to enhance the cognitive engine’s capabilities through feed-back, learning, and knowledge representation. 
The results of this work include the analysis of cognitive radio design and implementation and the functional cognitive engine that is shown to work in both simulation and on-line experiments. Throughout, examples and explanations of building and interfacing cognitive components to the cognitive engine enable the use and extension of the cognitive engine for future work.

Application of artificial intelligence to wireless communications

In an effort to improve radio spectrum management and promote a more efficient use of it, regulatory bodies are currently trying to adopt a new spectrum access model. At the same time, cognitive radio technology has received a lot of interest as a possible enabling technology. In this paper, we provide a brief description of the broad impact of cognitive radios in different markets. At Virginia Tech's Center for Wireless Telecommunications (CWT), we have designed a biologically inspired cognitive engine with dynamic spectrum access (DSA) as one of its intended applications. An experimental software simulation shows a 20 dB SINR improvement using cognitive techniques in an interference environment over that provided by current IEEE 802.11a service PHY standard

Cognitive radio applications to dynamic spectrum allocation: a discussion and an illustrative example

In this paper, we present the evolution of software-defined radio (SDR) technology and show how it is currently at the forefront of numerous advances within the wireless sector, enabling new applications considered unrealizable only a decade ago. Specifically, this paper focuses on SDR from a discrete-time sampling perspective and discusses the efforts that are currently being pursued in order to further bridge the gap between these discrete-time samples, the hardware used to generate this information from continuous-time over-the-air signaling waveforms, and the software and digital logic used to process these samples into digital data via baseband processing. Given the extensive deployment of SDR technology across a growing number of applications, such as national defense, public safety, connected vehicles, education, and scientific research and development activities, it is vitally important that the wireless community understands the features, advantages, and limitations of this technology. With its versatility, cost, and functionality continuously improving, SDR has become a viable solution for prototyping wireless transceivers and networks that are much more tailored to specific applications and performance requirements relative to available off-the-shelf wireless solutions. To highlight the advantages and current issues with SDR technology, this paper presents several examples using a recently released, commercially available SDR platform.

Software-Defined Radio: Bridging the Analog–Digital Divide

Software-defined radio is a rapidly developing field that is driving the development of and innovation in communications technology, and promises to significantly impact all communications sectors. Entities developing these SDR systems require a trained workforce that has been prepared with the mindset, knowledge, skills, and tools required to address both the system (breadth) and technical (depth) aspects of SDR systems. Developing SDRs necessarily involves a collection of disciplines including, but not limited to, electromagnetics, radio-frequency engineering, communications, digital signal processing, embedded systems, computer programming, and systems engineering. Whereas electrical engineering and computer science and engineering curricula at the university level may include courses in all of these areas, a student?s typical curriculum does not; nor does it usually involve the integration of all these topics. However, SDR can be employed as an integrative construct that facilitates systems thinking and cross-domain learning via peers. In this article, we present several significant educational efforts across six U.S. universities that have developed integrated curricula in SDR, most including a significant laboratory component.

Software-defined radio: a new paradigm for integrated curriculum delivery

MPM is a Shewhart/Deming (PDCA) process itself. The authors provide an excellent taxonomy for this decomposition process. Finally, at the bottom of this measurement tree are the “measurement constructs”—examples of fill-in-the-blank tables representing various metrics categories (for example, customer feedback, process compliance, effort, and financial and schedule performance). These constructs thoroughly address almost any measurement a project manager could need. This book also offers good boilerplates (examples), so the ramp-up time is minimized.

Software radio: A modern approach to radio engineering [Book Review]

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Software radio: A modern approach to radio engineering [Book Review]