Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

Optimum energy allocation for detection in wireless sensor networks

The problem of binary hypothesis testing in a wireless sensor network is studied in the presence of noisy channels and for non-identical sensors. We have designed a mathematically tractable fusion rule for which optimal energy allocation for individual sensors can be achieved. In this thesis we considered two methods for transmitting the sensor observations; binary modulation and M-ary modulation. In binary modulation we are able to allocate the energy among the sensors and protect the individual quantized bits where as the M-ary modulation provides optimum energy allocation only among the sensors. The goal is to design a fusion rule and an energy allocation for the nodes subject to a limit on the total energy of all the nodes so as to optimize a cost function. Two cost functions were considered; the probability of error and the J-divergence distance measure. Probability of error is the most natural criteria used for binary hypothesis testing problem. Distance measure is applied when it is difficult to obtain a closed form for the error probability. Results of optimal energy allocation and the resulting probability of error are presented for the two cost functions. Comparisons are drawn between the two cost functions regarding the fusion rule, energy allocations and the error probability

Data compression using adaptive transform coding. Appendix 1: Item 1

Adaptive low-rate source coders are described in this dissertation. These coders adapt by adjusting the complexity of the coder to match the local coding difficulty of the image. This is accomplished by using a threshold driven maximum distortion criterion to select the specific coder used. The different coders are built using variable blocksized transform techniques, and the threshold criterion selects small transform blocks to code the more difficult regions and larger blocks to code the less complex regions. A theoretical framework is constructed from which the study of these coders can be explored. An algorithm for selecting the optimal bit allocation for the quantization of transform coefficients is developed. The bit allocation algorithm is more fully developed, and can be used to achieve more accurate bit assignments than the algorithms currently used in the literature. Some upper and lower bounds for the bit-allocation distortion-rate function are developed. An obtainable distortion-rate function is developed for a particular scalar quantizer mixing method that can be used to code transform coefficients at any rate

Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems

© 2023 IEEE. This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/TVT.2023.3249353.[Abstract]: The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference.This work has been supported by grants ED431C 2020/15 and ED431G 2019/01 (to support the Centro de Investigación de Galicia “CITIC”) funded by Xunta de Galicia and ERDF Galicia 2014-2020; and by grants PID2019-104958RB-C42 (ADELE) and BES-2017-081955 funded by MCIN/AEI/10.13039/501100011033.Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G 2019/0

Vertical Optimizations of Convolutional Neural Networks for Embedded Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Sparsity in Linear Predictive Coding of Speech

nrpages: 197status: publishe