This dissertation deals with the distributed processing techniques for parameter estimation and efficient data-gathering in wireless communication and sensor networks. The estimation problem consists in inferring a set of parameters from temporal and spatial noisy observations collected by different nodes that monitor an area or field. The objective is to derive an estimate that is as accurate as the one that would be obtained if each node had access to the information across the entire network. With the aim of enabling an energy aware and low-complexity distributed implementation of the estimation task, several useful optimization techniques that generally yield linear estimators were derived in the literature. Up to now, most of the works considered that the nodes are interested in estimating the same vector of global parameters. This scenario can be viewed as a special case of a more general ...

In the field of adaptive filtering the most commonly applied filter structure is the transversal filter, also referred to as the tapped-delay line (TDL). The TDL is composed of a cascade of unit delay elements that are tapped, weighted and then summed. Thus, the output of a TDL is formed by a linear combination of its input signal at various delays. The weights in this linear combination are called the tap weights. The number of delay elements, or equivalently the number of tap weights, determines the duration of the impulse response of the TDL. For this reason, one often speaks of a finite impulse response (FIR) filter. In a general adaptive filtering scheme the adaptive filter aims to minimize a certain measure of error between its output and a desired signal. Usually, a quadratic cost criterion is taken: the so-called ...

Power reduction in digital communication systems can be achieved in many ways. Re- duction of the wordlengths used to represent data and control variables in the digital circuits comprising a communication system is an efective strategy, as register power consumption increases with wordlength. Another strategy is the reduction of the required data trans- mission rate, and hence speed of the digital circuits, by efficient source encoding. In this dissertation, applications of both of these power reduction strategies are investigated. The LMS adaptive filter, for which a myriad of applications exists in digital communi- cation systems, is optimized for performance with a power consumption constraint. This optimization is achieved by an analysis of the effects of wordlength reduction on both perfor- mance -transient and steady-state- as well as power consumption. Analytical formulas for the residual steady-state mean square error (MSE) due ...

Adaptive filtering techniques are used in a wide range of applications, including echo cancellation, adaptive equalization, adaptive noise cancellation, and adaptive beamforming. The performance of an adaptive filtering algorithm is evaluated based on its convergence rate, misadjustment, computational requirements, and numerical robustness. We attempt to improve the performance by developing new adaptation algorithms and by using "unconventional" structures for adaptive filters. Part I of this dissertation presents a new adaptation algorithm, which we have termed the Normalized LMS algorithm with Orthogonal Correction Factors (NLMS-OCF). The NLMS-OCF algorithm updates the adaptive filter coefficients (weights) on the basis of multiple input signal vectors, while NLMS updates the weights on the basis of a single input vector. The well-known Affine Projection Algorithm (APA) is a special case of our NLMS-OCF algorithm. We derive convergence and tracking properties of NLMS-OCF using a simple model ...

In this thesis we focus on the development of energy-efficient adaptive algorithms for Wireless Sensor Networks. Its contributions can be arranged in two main lines. Firstly, we focus on the efficient management of energy resources in WSNs equipped with finite-size batteries and energy-harvesting devices. To that end, we propose a censoring scheme by which the nodes are able to decide if a message transmission is worthy or not given their energetic condition. In order to do so, we model the system using a Markov Decision Process and use this model to derive optimal policies. Later, these policies are analyzed in simplified scenarios in order to get insights of their features. Finally, using Stochastic Approximation, we develop low-complexity censoring algorithms that approximate the optimal policy, with less computational complexity and faster convergence speed than other approaches such as Q-learning. Secondly, we ...

Adaptive filtering algorithms are powerful signal processing tools with widespread use in numerous engineering applications. Computational complexity is a key factor in determining the optimal implementation as well as real-time performance of the adaptive signal processors. To minimize the required hardware and/or software resources for implementing an adaptive filtering algorithm, it is desirable to mitigate its computational complexity as much as possible without imposing any significant sacrifice of performance. This thesis comprises a collection of thirteen peer-reviewed published works as well as an integrating material. The works are along the lines of a common unifying theme that is to devise new low-complexity adaptive filtering algorithms for communications and, more generally, signal processing applications. The main contributions are the new adaptive filtering algorithms, channel equalization techniques, and theoretical analyses listed below under four categories: 1) adaptive system identification • affine projection ...

In multiuser ultra-wideband (UWB) systems, a large number of multipath components (MPCs) are introduced by the channel. One of the main challenges for the receiver is to effectively suppress the interference with affordable complexity. In this thesis, we focus on the linear adaptive interference suppression algorithms for the direct-sequence ultrawideband (DS-UWB) systems in both time-domain and frequency-domain. In the time-domain, symbol by symbol transmission multiuser DS-UWB systems are considered. We first investigate a generic reduced-rank scheme based on the concept of joint and iterative optimization (JIO) that jointly optimizes a projection vector and a reduced-rank filter by using the minimum mean-squared error (MMSE) criterion. A low-complexity scheme, named Switched Approximations of Adaptive Basis Functions (SAABF), is proposed as a modification of the generic scheme, in which the complexity reduction is achieved by using a multi-branch framework to simplify the structure ...

Compensating or reducing the nonlinear distortion - usually resulting from a nonlinear system - is becoming an essential requirement in many areas. In this thesis adaptive digital predistortion techniques for a wide class of nonlinear systems are presented. For estimating the coefficients of the predistorter, different learning architectures are considered: the Direct Learning Architecture (DLA) and Indirect Learning Architecture (ILA). In the DLA approach, we propose a new adaptation algorithm - the Nonlinear Filtered-x Prediction Error Method (NFxPEM) algorithm, which has much faster convergence and much better performance compared to the conventional Nonlinear Filtered-x Least Mean Squares (NFxLMS) algorithm. All of these time domain adaptive algorithms require accurate system identification of the nonlinear system. In order to relax or avoid this strict requirement, the NFxLMS with Initial Subsystem Estimates (NFxLMS-ISE) and NFxPEM-ISE algorithms are proposed. Furthermore, we propose a frequency ...

In order to improve communication over a dispersive channel in a CDMA system, we have to re-establish the orthogonally of codes which are used when combining input signals from many users onto a single communication path, as otherwise the performance of such system is limited significantly by inter-symbol interference (ISI) and multiuser access interference (MAI). In order to achieve this, adaptive filters are employed. A variety of adaptive schemes to remove ISI and MAI have been reported in the literature, some of which rely on training sequences, such as the Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms, or on blind adaptation, such as the Constant Modulus Algorithm (CMA) or the Decision Directed algorithm (DD), which has similar convergence properties as the LMS in the absence of decision errors, the CMA is relatively slow compared to the DD ...

This thesis proposes different methods to reduce the computational complexity as well as increasing the adaptation rate of adaptive broadband beamformers. This is performed exemplarily for the generalised sidelobe canceller (GSC) structure. The GSC is an alternative implementation of the linearly constrained minimum variance beamformer, which can utilise well-known adaptive filtering algorithms, such as the least mean square (LMS) or the recursive least squares (RLS) to perform unconstrained adaptive optimisation. A direct DFT implementation, by which broadband signals are decomposed into frequency bins and processed by independent narrowband beamforming algorithms, is thought to be computationally optimum. However, this setup fail to converge to the time domain minimum mean square error (MMSE) if signal components are not aligned to frequency bins, resulting in a large worst case error. To mitigate this problem of the so-called independent frequency bin (IFB) processor, overlap-save ...

Today, adaptive algorithms represent one of the most frequently used computational tools for the processing of digital speech signals. This work investigates and analyzes the properties of adaptive algorithms in speech communication applications where rigorous conditions apply, such as noise and echo cancelation. Like other theses in this field do, it tries to tackle the ever-lasting problem of computational complexity vs. rate of convergence. It introduces some new adaptive methods that stem from the existing algorithms as well as a novel concept which has been entitled Optimal Step-Size (OSS). In the first part of the thesis we investigate some well-known, widely used adaptive techniques such as the Normalized Least Mean Squares (NLMS) and the Recursive Least Mean Squares (RLS). In spite of the fact that the NLMS and the RLS belong to the "simplest" principles, as far as complexity is ...

Direct-sequence code-divisionmultiple-access (DS-CDMA) and multiple-input multiple-output (MIMO) wireless networks form the physical layer of the current generation of mobile networks and are anticipated to play a key role in the next generation of mobile networks. The improvements in capacity, data-rates and robustness that these networks provide come at the cost of increasingly complex interference suppression and resource allocation. Consequently, efficient approaches to these tasks are essential if the current rate of progression in mobile technology is to be sustained. In this thesis, linear minimum mean-square error (MMSE) techniques for interference suppression and resource allocation in DS-CDMA and cooperative MIMO networks are considered and a set of novel and efficient algorithms proposed. Firstly, set-membership (SM) reduced-rank techniques for interference suppression in DS-CDMA systems are investigated. The principals of SM filtering are applied to the adaptation of the projection matrix and reduced-rank ...

Wireless sensor networks (WSNs) paved the way for accessing data previously unavailable by deploying sensors in various locations in space, each collecting local measurements of a target source signal. By exploiting the information resulting from the multitude of signals measured at the different sensors of the network, various tasks can be achieved, such as denoising or dimensionality reduction which can in turn be used, e.g., for source localization or detecting seizures from electroencephalography measurements. Spatial filtering consists of linearly combining the signals measured at each sensor of the network such that the resulting filtered signal is optimal in some sense. This technique is widely used in biomedical signal processing, wireless communication, and acoustics, among other fields. In spatial filtering tasks, the aim is to exploit the correlation between the signals of all sensors in the network, therefore requiring access to ...

Array processing is an area of study devoted to processing the signals received from an antenna array and extracting information of interest. It has played an important role in widespread applications like radar, sonar, and wireless communications. Numerous adaptive array processing algorithms have been reported in the literature in the last several decades. These algorithms, in a general view, exhibit a trade-off between performance and required computational complexity. In this thesis, we focus on the development of array processing algorithms in the application of beamforming and direction of arrival (DOA) estimation. In the beamformer design, we employ the constrained minimum variance (CMV) and the constrained constant modulus (CCM) criteria to propose full-rank and reduced-rank adaptive algorithms. Specifically, for the full-rank algorithms, we present two low-complexity adaptive step size mechanisms with the CCM criterion for the step size adaptation of the ...

Distributed networks comprising a large number of nodes, e.g., Wireless Sensor Networks, Personal Computers (PC’s), laptops, smart phones, etc., which cooperate with each other in order to reach a common goal, constitute a promising technology for several applications. Typical examples include: distributed environmental monitoring, acoustic source localization, power spectrum estimation, etc. Sophisticated cooperation mechanisms can significantly benefit the learning process, through which the nodes achieve their common objective. In this dissertation, the problem of adaptive learning in distributed networks is studied, focusing on the task of distributed estimation. A set of nodes sense information related to certain parameters and the estimation of these parameters constitutes the goal. Towards this direction, nodes exploit locally sensed measurements as well as information springing from interactions with other nodes of the network. Throughout this dissertation, the cooperation among the nodes follows the diffusion optimization ...