Thesis (M.A. in National Security Affairs)--Naval Postgraduate School, March 2003. Thesis (M.S. in Computer Science and M.S. in Modeling, Virtual Environments and Simulation)--Naval Postgraduate School, March 2003. / Thesis advisor(s): Dan Moran, John Hiles, Rudy Darken. Includes bibliographical references (p. 134-138). Also available online.

Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xvi, 135 p.: ill. Includes abstract and vita. Co-advisors: Wu-Chi Feng, Dong Xuan, Dept. of Computer ad information Science. Includes bibliographical references (p. 129-135).

Parameter estimation plays a key role in many signal processing applications. Traditional parameter estimation relies on centralized method which requires gathering of all information dispersed over the network in a central processing unit. As the scale of network increases, centralized estimation is not preferred since it requires not only the knowledge of network topology but also heavy communications from peripheral nodes to central processing unit. Besides, computation at the control center cannot scale indefinitely with the network size. Therefore, distributed estimation which involves only local computation at each node and limited information exchanges between immediate neighbouring nodes is needed. In this thesis, for local observations in the form of a pairwise linear model corrupted by Gaussian noise, belief propagation (BP) algorithm is investigated to perform distributed estimation. It involves only iterative updating of the estimates with local message exchange between immediate neighboring nodes. Since convergence has always been the biggest concern when using BP, we establish the convergence properties of asynchronous vector form Gaussian BP under the pairwise model. It is shown analytically that under mild condition, the asynchronous BP algorithm converges to the optimal estimates with estimation mean square error (MSE) at each node approaching the centralized Bayesian Cram´er-Rao bound (BCRB) regardless of the network topology. The proposed framework encompasses both classes of synchronous and asynchronous algorithms for distributed estimation and is robust to random link failures. Two challenging parameter estimation problems in large-scale networks, i.e., network-wide distributed carrier frequency offsets (CFOs) estimation, and global clock synchronization in sensor network, are studied based on BP. The proposed algorithms do not require any centralized information processing nor knowledge of the global network topology and are scalable with the network size. Simulation results further verify the established theoretical analyses: the proposed algorithms always converge to the optimal estimates regardless of network topology. Simulations also demonstrate the MSE at each node approaches the corresponding centralized CRB within a few iterations of message exchange. Furthermore, distributed estimation is studied for the linear model with unknown coefficients. Such problem itself is challenging even for centralized estimation as the nonlinear property of the observation model. One problem following this model is the power state estimation with unknown sampling phase error. In this thesis, distributed estimation scheme is proposed based on variational inference with parallel update schedule and limited message exchange between neighboring areas, and the convergence is guaranteed. Simulation results show that after convergence the proposed algorithm performs very close to that of the ideal case which assumes perfect synchronization, and centralized information processing. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

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Modern networks are a reliable and effective medium for moving large volumes of data, however when considering the exponential growth of content, bandwidth and server availability is a finite resource. In many cases, the same content is being sent to multiple users, each consuming resources. Multicast offers scaling, that allows groups of users to receive common content at little extra network cost. While multicast deployment has been slow in terrestrial networks, the multicast service is ideally suited to IP-based broadband satellite networks. Despite this, many current broadband satellite systems do not provide any or limited support for IP multicast services. This thesis seeks to change this and identifies the protocols required to introduce support for IP multicast in broadband satellite systems. It examines the protocols required, with a focus on IP multicast routing using the Protocol Independent Multicast – Sparse Mode (PIM-SM) routing protocol. The robustness and overhead of the protocol are analyzed. This thesis proposes a set of architectures for the efficient delivery of IP multicast over an interactive satellite network. This thesis considers the key functions required to realize end-to-end IP multicast using the emergent 2nd Generation Digital Video Broadcast Return Link via Satellite (DVB-RCS2) specification. It also considers the differences between DVB-RCS2 and a mobile broadband satellite system and proposes appropriate methods for both use-cases. This facilitates merging satellite access networks with a multicast-enabled Internet backbone, enabling delivery of native IP multicast services to networks connected to a satellite terminal. The return link in a two-way broadband satellite network is often considered an expensive resource, which motivates the efficient use of this resource. This thesis therefore examines the applicability of header compression for a satellite system and proposes the use of Robust Header Compression (ROHC) to improve link efficiency.

published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy

published_or_final_version / Electrical Engineering / Master / Master of Philosophy

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