Estimation of the Radio Channel Parameters using the SAGE Algorithm

This paper presents the problem of estimating the parameters of a given number of superimposed signals, as is the case of the received signal in wireless communications. Based on the description of the received signal in the frequency domain, one version of the SAGE (Space-Alternating Generalized Expectation-Maximization) algorithm is presented, allowing the estimation, for each impinging ray, the delay, azimuth, elevation and complex amplitude. Ray retrieval results are presented in synthetic channels, using data generated with the extended Saleh Valenzuela (ESV) model, and also in real channels

Intelligent joint channel parameter estimation techniques for mobile wireless positioning applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile wireless positioning has recently received great attention. For mobile wireless communication networks, an inherently suitable approach is to obtain the parameters that are used for positioning estimates from the radio signal measurements between a mobile device and one or more xed base stations. However, obtaining accurate estimates of these location-dependent channel parameters is a challenging task. The focus of this thesis is on the estimation of these channel parameters for mobile wireless positioning applications. In particular, we investigate novel estimators that jointly estimate more than one type of channel parameters. We rst perform a comprehensive critical review on the most recent and popular joint channel parameter estimation techniques. Secondly, we improve a state-of-the-art technique, namely the Space Alternating Generalised Expectation maximisation (SAGE) algorithm by employing adaptive interference cancellation to improve the estimation accuracy of weaker paths. Thirdly, a novel intelligent channel parameter estimation technique using Evolution Strategy (ES) is proposed to overcome the drawbacks of the existing iterative maximum likelihood methods. Furthermore, given that in reality it is di cult to obtain the number of multipath in advance, we propose a two tier Hierarchically Organised ES to jointly estimate the number of multipath as well as the channel parameters. Finally, we extend the proposed ES method to further estimate the Doppler shift in mobile environments. Our proposed intelligent joint channel estimation techniques are shown to exhibit excellent performance even with low Signal to Noise Ratio (SNR) channel conditions as well as robust against uncertainties in initialisations.EPSRC and Cambridge Silicon Radi

Modelos de canal para sistemas MIMO

Doutoramento em Engenharia ElectrotécnicaSystems equipped with multiple antennas at the transmitter and at the receiver, known as MIMO (Multiple Input Multiple Output) systems, offer higher capacities, allowing an efficient exploitation of the available spectrum and/or the employment of more demanding applications. It is well known that the radio channel is characterized by multipath propagation, a phenomenon deemed problematic and whose mitigation has been achieved through techniques such as diversity, beamforming or adaptive antennas. By exploring conveniently the spatial domain MIMO systems turn the characteristics of the multipath channel into an advantage and allow creating multiple parallel and independent virtual channels. However, the achievable benefits are constrained by the propagation channel’s characteristics, which may not always be ideal. This work focuses on the characterization of the MIMO radio channel. It begins with the presentation of the fundamental results from information theory that triggered the interest on these systems, including the discussion of some of their potential benefits and a review of the existing channel models for MIMO systems. The characterization of the MIMO channel developed in this work is based on experimental measurements of the double-directional channel. The measurement system is based on a vector network analyzer and a two-dimensional positioning platform, both controlled by a computer, allowing the measurement of the channel’s frequency response at the locations of a synthetic array. Data is then processed using the SAGE (Space-Alternating Expectation-Maximization) algorithm to obtain the parameters (delay, direction of arrival and complex amplitude) of the channel’s most relevant multipath components. Afterwards, using a clustering algorithm these data are grouped into clusters. Finally, statistical information is extracted allowing the characterization of the channel’s multipath components. The information about the multipath characteristics of the channel, induced by existing scatterers in the propagation scenario, enables the characterization of MIMO channel and thus to evaluate its performance. The method was finally validated using MIMO measurements.Os sistemas equipados com múltiplas antenas no emissor e no recetor, conhecidos como sistemas MIMO (Multiple Input Multiple Output), oferecem capacidades mais elevadas, permitindo melhor rentabilização do espectro e/ou utilização de aplicações mais exigentes. É sobejamente sabido que o canal rádio é caracterizado por propagação multipercurso, fenómeno considerado problemático e cuja mitigação tem sido conseguida através de técnicas como diversidade, formatação de feixe ou antenas adaptativas. Explorando convenientemente o domínio espacial os sistemas MIMO transformam as características multipercurso do canal numa mais-valia e permitem criar vários canais virtuais, paralelos e independentes. Contudo, os benefícios atingíveis são condicionados pelas características do canal de propagação, que poderão não ser sempre as ideais. Este trabalho centra-se na caracterização do canal rádio para sistemas MIMO. Inicia-se com a apresentação dos resultados fundamentais da teoria da informação que despoletaram todo o entusiamo em torno deste tipo de sistemas, sendo discutidas algumas das suas potencialidades e uma revisão dos modelos existentes para sistemas MIMO. A caracterização do canal MIMO desenvolvida neste trabalho assenta em medidas experimentais do canal direcional adquiridas em dupla via. O sistema de medida é baseado num analisador de redes vetorial e numa plataforma de posicionamento bidimensional, ambos controlados por um computador, permitindo obter a resposta em frequência do canal rádio nos vários pontos correspondentes à localização dos elementos de um agregado virtual. As medidas são posteriormente processadas com o algoritmo SAGE (Space-Alternating Expectation-Maximization), de forma a obter os parâmetros (atraso, direção de chegada e amplitude complexa) das componentes multipercurso mais significativas. Seguidamente, estes dados são tratados com um algoritmo de classificação (clustering) e organizados em grupos. Finalmente é extraída informação estatística que permite caracterizar o comportamento das componentes multipercurso do canal. A informação acerca das características multipercurso do canal, induzidas pelos espalhadores (scatterers) existentes no cenário de propagação, possibilita a caracterização do canal MIMO e assim avaliar o seu desempenho. O método foi por fim validado com medidas MIMO