Particle filtering for joint symbol and code delay estimation in DS spread spectrum systems in multipath environment

We develop a new receiver for joint symbol, channel characteristics, and code delay estimation for DS spread spectrum systems under conditions of multipath fading. The approach is based on particle filtering techniques and combines sequential importance sampling, a selection scheme, and a variance reduction technique. Several algorithms involving both deterministic and randomized schemes are considered and an extensive simulation study is carried out in order to demonstrate the performance of the proposed methods.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers

Monte Carlo algorithms can be used to estimate the state of a system given relative observations. In this dissertation, these algorithms are applied to physical layer communications system models to estimate channel state information, to obtain soft information about transmitted symbols or multiple access interference, or to obtain estimates of all of these by joint estimation. Initially, we develop and analyze a multiple access technique utilizing mutually orthogonal complementary sets (MOCS) of sequences. These codes deliberately introduce inter-chip interference, which is naturally eliminated during processing at the receiver. However, channel impairments can destroy their orthogonality properties and additional processing becomes necessary. We utilize Monte Carlo algorithms to perform joint channel and symbol estimation for systems utilizing MOCS sequences as spreading codes. We apply Rao-Blackwellization to reduce the required number of particles. However, dense signaling constellations, multiuser environments, and the interchannel interference introduced by the spreading codes all increase the dimensionality of the symbol state space significantly. A full maximum likelihood solution is computationally expensive and generally not practical. However, obtaining the optimum solution is critical, and looking at only a part of the symbol space is generally not a good solution. We have sought algorithms that would guarantee that the correct transmitted symbol is considered, while only sampling a portion of the full symbol space. The performance of the proposed method is comparable to the Maximum Likelihood (ML) algorithm. While the computational complexity of ML increases exponentially with the dimensionality of the problem, the complexity of our approach increases only quadratically. Markovian structures such as the one imposed by MOCS spreading sequences can be seen in other physical layer structures as well. We have applied this partitioning approach with some modification to blind equalization of frequency selective fading channel and to multiple-input multiple output receivers that track channel changes. Additionally, we develop a method that obtains a metric for quantifying the convergence rate of Monte Carlo algorithms. Our approach yields an eigenvalue based method that is useful in identifying sources of slow convergence and estimation inaccuracy.Ph.D.Committee Chair: Douglas B. Williams; Committee Member: Brani Vidakovic; Committee Member: G. Tong zhou; Committee Member: Gordon Stuber; Committee Member: James H. McClella

Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions

Efficient blind symbol rate estimation and data symbol detection algorithms for linearly modulated signals

Blind estimation of unknown channel parameters and data symbol detection represent major open problems in non-cooperative communication systems such as automatic modulation classification (AMC). This thesis focuses on estimating the symbol rate and detecting the data symbols. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. The thesis consists of two parts: a symbol rate estimator and a symbol detector. First, the symbol rate is estimated using the EM algorithm. In the EM algorithm, it is difficult to obtain the closed form of the log-likelihood function and the density function. Therefore, both functions are approximated by using the Particle Filter (PF) technique. In addition, the symbol rate estimator based on cyclic correlation is proposed as an initialization estimator since the EM algorithm requires initial estimates. To take advantage of the cyclostationary property of the received signal, there is a requirement that the sampling period should be at least four times less than the symbol period on the receiver side. Second, the blind data symbol detector based on the PF algorithm is designed. Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage inter-symbol interference, which is caused by over- sampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coe±cients, and the Mixture Kalman Filter (MKF) algorithm is used to marginalize out the fading channel coe±cients. At the end, two resampling schemes are adopted

Stochastic Signal Processing and Power Control for Wireless Communication Systems

This dissertation is concerned with dynamical modeling, estimation and identification of wireless channels from received signal measurements. Optimal power control algorithms, mobile location and velocity estimation methods are developed based on the proposed models. The ultimate performance limits of any communication system are determined by the channel it operates in. In this dissertation, we propose new stochastic wireless channel models which capture both the space and time variations of wireless systems. The proposed channel models are based on stochastic differential equations (SDEs) driven by Brownian motions. These models are more realistic than the time invariant models encountered in the literature which do not capture and track the time varying characteristics of the propagation environment. The statistics of the proposed models are shown to be time varying, and converge in steady state to their static counterparts. Cellular and ad hoc wireless channel models are developed. In urban propagation environment, the parameters of the channel models can be determined from approximating the band-limited Doppler power spectral density (DPSD) by rational transfer functions. However, since the DPSD is not available on-line, a filterbased expectation maximization algorithm and Kalman filter to estimate the channel parameters and states, respectively, are proposed. The algorithm is recursive allowing the inphase and quadrature components and parameters to be estimated on-line from received signal measurements. The algorithms are tested using experimental data, and the results demonstrate the method’s viability for both cellular and ad hoc networks. Power control increases system capacity and quality of communications, and reduces battery power consumption. A stochastic power control algorithm is developed using the so-called predictable power control strategies. An iterative distributed algorithm is then deduced using stochastic approximations. The latter only requires each mobile to know its received signal to interference ratio at the receiver

Clock Synchronization in Wireless Sensor Networks: An Overview

The development of tiny, low-cost, low-power and multifunctional sensor nodes equipped with sensing, data processing, and communicating components, have been made possible by the recent advances in micro-electro-mechanical systems (MEMS) technology. Wireless sensor networks (WSNs) assume a collection of such tiny sensing devices connected wirelessly and which are used to observe and monitor a variety of phenomena in the real physical world. Many applications based on these WSNs assume local clocks at each sensor node that need to be synchronized to a common notion of time. This paper reviews the existing clock synchronization protocols for WSNs and the methods of estimating clock offset and clock skew in the most representative clock synchronization protocols for WSNs

A Comparison of Parametric and Sample-Based Message Representation in Cooperative Localization

Location awareness is a key enabling feature and fundamental challenge in present and future wireless networks. Most existing localization methods rely on existing infrastructure and thus lack the flexibility and robustness necessary for large ad hoc networks. In this paper, we build upon SPAWN (sum-product algorithm over a wireless network), which determines node locations through iterative message passing, but does so at a high computational cost. We compare different message representations for SPAWN in terms of performance and complexity and investigate several types of cooperation based on censoring. Our results, based on experimental data with ultra-wideband (UWB) nodes, indicate that parametric message representation combined with simple censoring can give excellent performance at relatively low complexity

Performance analysis of filtering based chaotic synchronization and development of chaotic digital communication schemes

Ph.DDOCTOR OF PHILOSOPH

Bayesian nonparametrics for time series modeling

Mención Internacional en el título de doctorIn many real-world signal processing problems, an observed temporal sequence can be explained by several unobservable independent causes, and we are interested in recovering the canonical signals that lead to these observations. For example, we may want to separate the overlapping voices on a single recording, distinguish the individual players on a financial market, or recover the underlying brain signals from electroencephalography data. This problem, known as source separation, is in general highly underdetermined or ill-posed. Methods for source separation generally seek to narrow the set of possible solutions in a way that is unlikely to exclude the desired solution. However, most classical approaches for source separation assume a fixed and known number of latent sources. This may represent a limitation in contexts in which the number of independent causes is unknown and is not limited to a small range. In this Thesis, we address the signal separation problem from a probabilistic modeling perspective. We encode our independence assumptions in a probabilistic model and develop inference algorithms to unveil the underlying sequences that explain the observed signal. We adopt a Bayesian nonparametric (BNP) approach in order to let the inference procedure estimate the number of independent sequences that best explain the data. BNP models place a prior distribution over an infinite-dimensional parameter space, which makes them particularly useful in probabilistic models in which the number of hidden parameters is unknown a priori. Under this prior distribution, the posterior distribution of the hidden parameters given the data assigns higher probability mass to those configurations that best explain the observations. Hence, inference over the hidden variables is performed using standard Bayesian inference techniques, which avoids expensive model selection steps. We develop two novel BNP models for source separation in time series. First, we propose a non-binary infinite factorial hidden Markov model (IFHMM), in which the number of parallel chains of a factorial hidden Markov model (FHMM) is treated in a nonparametric fashion. This model constitutes an extension of the binary IFHMM, but the hidden states are not restricted to take binary values. Moreover, by placing a Poisson prior distribution over the cardinality of the hidden states, we develop the infinite factorial unbounded-state hidden Markov model (IFUHMM), and an inference algorithm that can infer both the number of chains and the number of states in the factorial model. Second, we introduce the infinite factorial finite state machine (IFFSM) model, in which the number of independent Markov chains is also potentially infinite, but each of them evolves according to a stochastic finite-memory finite state machine model. For the IFFSM, we apply an efficient inference algorithm, based on particle Markov chain Monte Carlo (MCMC) methods, that avoids the exponential runtime complexity of more standard MCMC algorithms such as forward-filtering backward-sampling. Although our models are applicable in a broad range of fields, we focus on two specific problems: power disaggregation and multiuser channel estimation and symbol detection. The power disaggregation problem consists in estimating the power draw of individual devices, given the aggregate whole-home power consumption signal. Blind multiuser channel estimation and symbol detection involves inferring the channel coefficients and the transmitted symbol in a multiuser digital communication system, such as a wireless communication network, with no need of training data. We assume that the number of electrical devices or the number of transmitters is not known in advance. Our experimental results show that the proposed methodology can provide accurate results, outperforming state-of-the-art approaches.En multitud de problemas reales de procesado de señal, se tiene acceso a una secuencia temporal que puede explicarse mediante varias causas latentes independientes, y el objetivo es la recuperación de las señales canónicas que dan lugar a dichas observaciones. Por ejemplo, podemos estar interesados en separar varias señales de voz solapadas en una misma grabación, distinguir los agentes que operan en un mismo mercado financiero, o recuperar las señales cerebrales a partir de los datos de un electroencefalograma. Este problema, conocido como separación de fuente, es en general sobredeterminado. Los métodos de separación de fuente normalmente tratan de reducir el conjunto de posibles soluciones de tal manera que sea poco probable excluir la solución deseada. Sin embargo, en la mayoría de métodos clásicos de separación de fuente, se asume que el número de fuentes latentes es conocido. Esto puede representar una limitación en aplicaciones en las que no se conoce el número de causas independientes y dicho número no está acotado en un pequeño intervalo. En esta Tesis, consideramos un enfoque probabilístico para el problema de separación de fuente, en el que las asunciones de independencia se pueden incluir en el modelo probabilístico, y desarrollamos algoritmos de inferencia que permiten recuperar las señales latentes que explican la secuencia observada. Nos basamos en la utilización de métodos bayesianos no paramétricos (BNP) para permitir al algoritmo estimar adicionalmente el número de secuencias que mejor expliquen los datos. Los modelos BNP nos permiten definir una distribución de probabilidad sobre un espacio de dimensionalidad infinita, lo cual los hace particularmente útiles para su aplicación en modelos probabilísticos en los que el número de parámetros ocultos es desconocido a priori. Bajo esta distribución de probabilidad, la distribución a posteriori sobre los parámetros ocultos del modelo, dados los datos, asignará una mayor densidad de probabilidad a las configuraciones que mejor expliquen las observaciones, evitando por tanto los métodos de selección de modelo, que son computacionalmente costosos. En esta Tesis, desarrollamos dos nuevos modelos BNP para la separación de fuente en secuencias temporales. En primer lugar, proponemos un modelo oculto de Markov factorial infinito (IFHMM) no binario, en el que tratamos de manera no paramétrica el número de cadenas paralelas de un modelo oculto de Markov factorial (FHMM). Este modelo constituye una extensión del IFHMM binario, pero se elimina la restricción de que los estados ocultos sean variables binarias. Además, imponiendo una distribución de Poisson sobre la cardinalidad de los estados ocultos, desarrollamos el modelo oculto de Markov factorial infinito con estados no acotados (IFUHMM), y un algoritmo de inferencia con la capacidad de inferir tanto el número de cadenas como el número de estados del modelo factorial. En segundo lugar, proponemos un modelo de máquina de estados factorial infinita (IFFSM), en el que el número de cadenas de Markov paralelas e independientes también es potencialmente infinito, pero cada una de ellas evoluciona según un modelo de máquina de estados estocástica con memoria finita. Para el IFFSM, aplicamos un eficiente algoritmo de inferencia, basado en métodos Markov chain Monte Carlo (MCMC) de partículas, que evita la complejidad exponencial en tiempo de ejecución de otros algoritmos MCMC más comunes, como el de filtrado hacia adelante y muestreo hacia atrás. A pesar de que nuestros modelos son aplicables en una amplia variedad de campos, nos centramos en dos problemas específicos: separación de energía, y estimación de canal y detección de símbolos en un sistema multi-usuario. El problema de separación de energía consiste en, dada la señal de potencia total consumida en una casa, estimar de manera individual el consumo de potencia de cada dispositivo. La estimación de canal y detección de símbolos consiste en inferir los coeficientes de canal y los símbolos transmitidos en un sistema de comunicaciones digital multiusuario, como una red de comunicaciones inalámbrica, sin necesidad de transmitir símbolos piloto. Asumimos que tanto el número de dispositivos eléctricos como el número de transmisores es en principio desconocido y no acotado. Los resultados experimentales demuestran que la metodología propuesta ofrece buenos resultados y presenta mejoras sobre otros métodos propuestos en la literatura.Beca FPU (referencia AP-2010-5333)Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Antonio Artés Rodríguez.- Secretario: Juan José Murillo Fuentes.- Vocal: Konstantina Pall