Convergence-Optimal Quantizer Design of Distributed Contraction-based Iterative Algorithms with Quantized Message Passing

In this paper, we study the convergence behavior of distributed iterative algorithms with quantized message passing. We first introduce general iterative function evaluation algorithms for solving fixed point problems distributively. We then analyze the convergence of the distributed algorithms, e.g. Jacobi scheme and Gauss-Seidel scheme, under the quantized message passing. Based on the closed-form convergence performance derived, we propose two quantizer designs, namely the time invariant convergence-optimal quantizer (TICOQ) and the time varying convergence-optimal quantizer (TVCOQ), to minimize the effect of the quantization error on the convergence. We also study the tradeoff between the convergence error and message passing overhead for both TICOQ and TVCOQ. As an example, we apply the TICOQ and TVCOQ designs to the iterative waterfilling algorithm of MIMO interference game.Comment: 17 pages, 9 figures, Transaction on Signal Processing, accepte

Distributed video through telecommunication networks using fractal image compression techniques

The research presented in this thesis investigates the use of fractal compression techniques for a real time video distribution system. The motivation for this work was that the method has some useful properties which satisfy many requirements for video compression. In addition, as a novel technique, the fractal compression method has a great potential. In this thesis, we initially develop an understanding of the state of the art in image and video compression and describe the mathematical concepts and basic terminology of the fractal compression algorithm. Several schemes which aim to the improve of the algorithm, for still images are then examined. Amongst these, two novel contributions are described. The first is the partitioning of the image into sections which resulted insignificant reduction of the compression time. In the second, the use of the median metric as alternative to the RMS was considered but was not finally adopted, since the RMS proved to be a more efficient measure. The extension of the fractal compression algorithm from still images to image sequences is then examined and three different schemes to reduce the temporal redundancy of the video compression algorithm are described. The reduction in the execution time of the compression algorithm that can be obtained by the techniques described is significant although real time execution has not yet been achieved. Finally, the basic concepts of distributed programming and networks, as basic elements of a video distribution system, are presented and the hardware and software components of a fractal video distribution system are described. The implementation of the fractal compression algorithm on a TMS320C40 is also considered for speed benefits and it is found that a relatively large number of processors are needed for real time execution

On multiple-antenna communications: signal detection, error exponent and and quality of service

Motivated by the demand of increasing data rate in wireless communication, multiple-antenna communication is becoming a key technology in the next generation wireless system. This dissertation considers three different aspects of multipleantenna communication. The first part is signal detection in the multiple-input multiple-output (MIMO) communication. Some low complexity near optimal detectors are designed based on an improved version of Bell Laboratories Layered Space-Time (BLAST) architecture detection and an iterative space alternating generalized expectation-maximization (SAGE) algorithm. The proposed algorithms can almost achieve the performance of optimal maximum likelihood detection. Signal detections without channel knowledge (noncoherent) and with co-channel interference are also investigated. Novel solutions are proposed with near optimal performance. Secondly, the error exponent of the distributed multiple-antenna communication (relay) in the windband regime is computed. Optimal power allocation between the source and relay node, and geometrical relay node placement are investigated based on the error exponent analysis. Lastly, the quality of service (QoS) of MIMO/single-input single- output(SISO) communication is studied. The tradeoff of the end-to-end distortion and transmission buffer delay is derived. Also, the SNR exponent of the distortion is computed for MIMO communication, which can provide some insights of the interplay among time diversity, space diversity and the spatial multiplex gain

Connecting the Speed-Accuracy Trade-Offs in Sensorimotor Control and Neurophysiology Reveals Diversity Sweet Spots in Layered Control Architectures

Nervous systems sense, communicate, compute, and actuate movement using distributed components with trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, the resulting control can achieve remarkably fast, accurate, and robust performance due to a highly effective layered control architecture. However, this architecture has received little attention from the existing research. This is in part because of the lack of theory that connects speed-accuracy trade-offs (SATs) in the components neurophysiology with system-level sensorimotor control and characterizes the overall system performance when different layers (planning vs. reflex layer) act work jointly. In thesis, we present a theoretical framework that provides a synthetic perspective of both levels and layers. We then use this framework to clarify the properties of effective layered architectures and explain why there exists extreme diversity across layers (planning vs. reflex layers) and within levels (sensorimotor versus neural/muscle hardware levels). The framework characterizes how the sensorimotor SATs are constrained by the component SATs of neurons communicating with spikes and their sensory and muscle endpoints, in both stochastic and deterministic models. The theoretical predictions are also verified using driving experiments. Our results lead to a novel concept, termed ``diversity sweet spots (DSSs)'': the appropriate diversity in the properties of neurons and muscles across layers and within levels help create systems that are both fast and accurate despite being built from components that are individually slow or inaccurate. At the component level, this concept explains why there are extreme heterogeneities in the neural or muscle composition. At the system level, DSSs explain the benefits of layering to allow extreme heterogeneities in speed and accuracy in different sensorimotor loops. Similar issues and properties also extend down to the cellular level in biology and outward to our most advanced network technologies from smart grid to the Internet of Things. We present our initial step in expanding our framework to that area and widely-open area of research for future direction

Fourth Workshop on Information Theoretic Methods in Science and Engineering : Proceedings

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