OFDM ido tsushin shisutemu ni okeru doitsu chaneru kansho jokyo hoshiki ni kansuru kenkyu

制度:新 ; 報告番号:甲3396号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2011/9/15 ; 早大学位記番号:新571

Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters

Advanced DSP Techniques for High-Capacity and Energy-Efficient Optical Fiber Communications

The rapid proliferation of the Internet has been driving communication networks closer and closer to their limits, while available bandwidth is disappearing due to an ever-increasing network load. Over the past decade, optical fiber communication technology has increased per fiber data rate from 10 Tb/s to exceeding 10 Pb/s. The major explosion came after the maturity of coherent detection and advanced digital signal processing (DSP). DSP has played a critical role in accommodating channel impairments mitigation, enabling advanced modulation formats for spectral efficiency transmission and realizing flexible bandwidth. This book aims to explore novel, advanced DSP techniques to enable multi-Tb/s/channel optical transmission to address pressing bandwidth and power-efficiency demands. It provides state-of-the-art advances and future perspectives of DSP as well

Orthogonal Time Frequency Space (OTFS) Modulation for Wireless Communications

The orthogonal time frequency space (OTFS) modulation is a recently proposed multi-carrier transmission scheme, which innovatively multiplexes the information symbols in the delay-Doppler (DD) domain instead of the conventional time-frequency (TF) domain. The DD domain symbol multiplexing gives rise to a direct interaction between the DD domain information symbols and DD domain channel responses, which are usually quasi-static, compact, separable, and potentially sparse. Therefore, OTFS modulation enjoys appealing advantages over the conventional orthogonal frequency-division multiplexing (OFDM) modulation for wireless communications. In this thesis, we investigate the related subjects of OTFS modulation for wireless communications, specifically focusing on its signal detection, performance analysis, and applications. In specific, we first offer a literature review on the OTFS modulation in Chapter~1. Furthermore, a summary of wireless channels is given in Chapter 2. In particular, we discuss the characteristics of wireless channels in different domains and compare their properties. In Chapter 3, we present a detailed derivation of the OTFS concept based on the theory of Zak transform (ZT) and discrete Zak transform (DZT). We unveil the connections between OTFS modulation and DZT, where the DD domain interpretations of key components for modulation, such as pulse shaping, and matched-filtering, are highlighted. The main research contributions of this thesis appear in Chapter 4 to Chapter 7. In Chapter 4, we introduce the hybrid maximum a posteriori (MAP) and parallel interference cancellation (PIC) detection. This detection approach exploits the power discrepancy among different resolvable paths and can obtain near-optimal error performance with a reduced complexity. In Chapter 5, we propose the cross domain iterative detection for OTFS modulation by leveraging the unitary transformations among different domains. After presenting the key concepts of the cross domain iterative detection, we study its performance via state evolution. We show that the cross domain iterative detection can approach the optimal error performance theoretically. Our numerical results agree with our theoretical analysis and demonstrate a significant performance improvement compared to conventional OTFS detection methods. In Chapter 6, we investigate the error performance for coded OTFS systems based on the pairwise-error probability (PEP) analysis. We show that there exists a fundamental trade-off between the coding gain and the diversity gain for coded OTFS systems. According to this trade-off, we further provide some rule-of-thumb guidelines for code design in OTFS systems. In Chapter 7, we study the potential of OTFS modulation in integrated sensing and communication (ISAC) transmissions. We propose the concept of spatial-spreading to facilitate the ISAC design, which is able to discretize the angular domain, resulting in simple and insightful input-output relationships for both radar sensing and communication. Based on spatial-spreading, we verify the effectiveness of OTFS modulation in ISAC transmissions and demonstrate the performance improvements in comparison to the OFDM counterpart. A summary of this thesis is presented in Chapter 8, where we also discuss some potential research directions on OTFS modulation. The concept of OTFS modulation and the elegant theory of DD domain communication may have opened a new gate for the development of wireless communications, which is worthy to be further explored

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