This dissertation presents research performed in the areas of radio wave propagation measurement and modeling, smart antenna arrays, and software-defined radio development. A four-channel, wideband, software-defined receiver was developed to serve as a test bed for wideband measurements and antenna array experiments. This receiver was used to perform vector channel measurements in terrestrial and air-to-ground environments using an antenna array. ent results served as input to radio channel simulations based on three geometric channel models. The simulation results were compared to measurement results to evaluate the performance of the radio channel models under test. Criteria for evaluation include RMS delay spread, excess delay spread, signal envelope fading, antenna diversity gain, and gain achieved through the use of a two-dimensional rake receiver. This research makes contributions to the wireless communications field through analysis, development, measurement, and simulation that builds upon past theoretical and experimental results. Contributions include a software-defined radio architecture, based on object oriented techniques, that has been developed and successfully demonstrated using the wideband receiver. This research has produced new wideband vector channel measurements to provide extensive characterization results facilitating simulation of emerging wireless technology for commercial and military communications systems. Original ways of interpreting multipath component strength and correlation for antenna arrays have been developed and investigated. A novel geometric air-to-ground ellipsoidal channel model has been developed, simulated, and evaluated. Other contributions include an evaluation of two popular radio channel models, a geometric channel simulator for producing channel impulse responses, and analytical derivation results related to channel modeling geometries and multipath channel measurement processing. In addition to new results, existing theory and earlier research results are discussed. Fundamental theory for antenna arrays, vector channels, multipath characterization, and channel modeling is presented. Contemporary issues in software radio and object orientation are described, and measurement results from other propagation research are summarized. / Ph. D.

Large-dimensional communication systems are likely to play an important role in modern wireless communications, where dimensions can be in space, time, frequency and their combinations. Large dimensions can bring several advantages with respect to the performance of communication systems. Harnessing such large-dimension benefits in practice, however, is challenging. In particular, optimum signal detection gets prohibitively complex for large dimensions. Consequently, low-complexity detection techniques that scale well for large dimensions while achieving near-optimal performance are of interest. Belief Propagation (BP) is a technique that solves inference problems using graphical models. BP has been successfully employed in a variety of applications including computational biology, statistical signal/image processing, machine learning and artificial intelligence. BP is well suited in several communication problems as well; e.g., decoding of turbo codes and low-density parity check codes (LDPC), and multiuser detection. We propose a BP based algorithm for detection in large-dimension linear vector channels employing binary phase shift keying (BPSK) modulation, by adopting a Markov random field (MRF)graphical model of the system. The proposed approach is shown to achieve i)detection at low complexities that scale well for large dimensions, and ii)improved bit error performance for increased number of dimensions (a behavior we refer to as the ’large-system behavior’). As one application of the BP based approach, we demonstrate the effectiveness of the proposed BP algorithm for decoding non-orthogonal space-time block codes (STBC) from cyclic division algebras (CDA)having large dimensions. We further improve the performance of the proposed algorithm through damped belief propagation, where messages that are passed from one iteration to the next are formed as a weighted combination of messages from the current iteration and the previous iteration. Next, we extend the proposed BP approach to higher order modulation. through a novel scheme of interference cancellation. This proposed scheme exhibits large system behavior in terms of bit error performance, while being scalable to large dimensions in terms of complexity. Finally, as another application of the BP based approach, we illustrate the adoption and performance of the proposed BP algorithm for low-complexity near-optimal equalization in severely delay-spread UWBMIMO-ISI channels that are characterized by large number (tens to hundreds)of multipath components.