A Framework for Developing and Evaluating Algorithms for Estimating Multipath Propagation Parameters from Channel Sounder Measurements

A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by channel sounders at millimeter-wave frequencies. Sounders equipped with an omnidirectional transmitter and a receiver with a uniform planar array (UPA) are considered. An accurate mathematical model is developed for the spatial frequency response of the sounder that incorporates the non-ideal cross-polar beampatterns for the UPA elements. Due to the limited Field-of-View (FoV) of each element, the model is extended to accommodate multi-FoV measurements in distinct azimuth directions. A beamspace representation of the spatial frequency response is leveraged to develop three progressively complex algorithms aimed at solving the singlesnapshot maximum likelihood estimation problem: greedy matching pursuit (CLEAN), space-alternative generalized expectationmaximization (SAGE), and RiMAX. The first two are based on purely specular MPCs whereas RiMAX also accommodates diffuse MPCs. Two approaches for performance evaluation are proposed, one with knowledge of ground truth parameters, and one based on reconstruction mean-squared error. The three algorithms are compared through a demanding channel model with hundreds of MPCs and through real measurements. The results demonstrate that CLEAN gives quite reasonable estimates which are improved by SAGE and RiMAX. Lessons learned and directions for future research are discussed.Comment: 17 page

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

Near-field Ultra-wideband mmWave Channel Characterization Using Successive Cancellation Beamspace UCA Algorithm

Of the wide palette of 5G features, ultra-wide bandwidth and large-scale antenna configuration are regarded as the essential enabling technology components at millimeter wave (mmWave) communication. Accurate knowledge of delay and angle information of multipath components is essential for many applications in mmWave systems. There is a strong need for a low computation-cost channel estimation algorithm for such systems, where typically adopted far-field and narrowband assumptions might be violated. In this work, a generic yet novel beamspace uniform circular array (UCA) beamforming algorithm with successive cancellation scheme is proposed to jointly detect the impinging angle and delay of the multipath components. The proposed algorithm is computationally cheap and it works for ultra-wideband (UWB) systems in the near-field conditions. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed algorithm, compared to the state-of-art works

Low-Rank Channel Estimation for Millimeter Wave and Terahertz Hybrid MIMO Systems

Massive multiple-input multiple-output (MIMO) is one of the fundamental technologies for 5G and beyond. The increased number of antenna elements at both the transmitter and the receiver translates into a large-dimension channel matrix. In addition, the power requirements for the massive MIMO systems are high, especially when fully digital transceivers are deployed. To address this challenge, hybrid analog-digital transceivers are considered a viable alternative. However, for hybrid systems, the number of observations during each channel use is reduced. The high dimensions of the channel matrix and the reduced number of observations make the channel estimation task challenging. Thus, channel estimation may require increased training overhead and higher computational complexity. The need for high data rates is increasing rapidly, forcing a shift of wireless communication towards higher frequency bands such as millimeter Wave (mmWave) and terahertz (THz). The wireless channel at these bands is comprised of only a few dominant paths. This makes the channel sparse in the angular domain and the resulting channel matrix has a low rank. This thesis aims to provide channel estimation solutions benefiting from the low rankness and sparse nature of the channel. The motivation behind this thesis is to offer a desirable trade-off between training overhead and computational complexity while providing a desirable estimate of the channel

Analysis of the sum rate for massive MIMO using 10 GHz measurements

Orientador: Gustavo FraidenraichTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta um conjunto de contribuições para caracterização e modelagem de canais reais de rádio abordando aspectos relacionados com as condições favoráveis de propagação para sistemas massive MIMO. Discutiremos como caracterizar canais de rádio em um ambiente real, processamento de dados e análise das condições favoráveis de propagação. Em uma segunda parte, focamos na determinação teórica de alguns aspectos da tecnologia de massive MIMO utilizando propriedades de distribuições matriciais Wishart. Inicialmente, apresentamos uma contribuição sobre a aplicação do algoritmo ESPRIT, para estimar parâmetros de um conjunto de dados multidimensional. Obtivemos dados por varredura em frequência de um Analisador Vetorial de Rede e os adaptamos para o algoritmo ESPRIT. Mostramos como remover a influência do ganho de padrão de antenas e como utilizar um gerador de modelo de canal baseado nas medidas reais de canal de rádio. As medidas foram feitas na frequência de 10.1 GHz com largura de faixa de 500 MHz. Utilizando um gerador de modelo de canal, fomos além do universo das simulações por distribuições Gaussianas. Introduzimos o conceito de propagação favorável e analisamos condições de linha-de-visada usando arranjos lineares uniformes e arranjos retangulares uniformes de antena. Como novidade da pesquisa, mostramos os benefícios de explorar um número extra de graus de liberdade devido à escolha dos formatos de arranjo de antenas e ao aumento do número de elementos. Esta propriedade é observada ao analisarmos a distribuição dos autovalores de matrizes Gramianas. Em seguida, estendemos o mesmo raciocínio para as matrizes de canal geradas a partir de informações reais e verificamos se as propriedades ainda permaneceriam válidas. Na segunda parte deste trabalho, incluímos mais de uma antena no terminal móvel e calculamos a probabilidade de indisponibilidade para várias configurações de antenas e número arbitrário de usuários. Esboçamos inicialmente a formulação para a informação mútua e, em seguida, calculamos os resultados exatos em uma situação com dois usuários e duas antenas, tanto na estação base (EB) como nos terminais de usuário(TU). Visto que as formulações para a derivação exata dos casos com mais antenas e mais usuários mostrou-se muito intrincada, propusemos uma aproximação Gaussiana para simplificar o problema. Esta aproximação foi validada por simulações Monte Carlo para diferentes relações sinal/ruídoAbstract: This thesis presents a set of contributions for channel modeling and characterization of real radio channels delineating aspects related with the favorable propagation for massive MIMO systems. We will discuss about how to proceed for characterizing radio channels in an real environment , data processing, and analysis of favorable conditions. In a second part, we focused on determination of some theoretical aspects of the Massive MIMO technology using properties of Wishart distribution matrices. We initially present a contribution on the application of ESPRIT algorithm for estimating a multidimensional set of measured data. We have obtained data by frequency sweep carried out by a vector network analyzer(VNA) and adapted it to fit in the ESPRIT algorithm. We show how to remove antenna pattern gain using virtual antenna arrays and how to use a channel model generator based on radio channel measurements of real environments. The measurements were conducted at the frequency of 10.1 GHz and 500 MHz bandwidth. By using a channel model generator, we have explored beyond the simulation of Gaussian Distributions. We will introduce the concept of favorable propagation and analyze the line-of-sight conditions using ULA and URA array shapes. As a research novelty, we will show the benefits of exploiting an extra degree of freedom due to the choice of the antenna shapes and amount of antenna elements. We observe these properties through the distribution of the Gramian Matrices. Next, we extend the same rationale to channel matrices generated from real channels and we verify that the properties are still valid. In a second part of the research work, we included more than one antenna in the mobile terminals and calculated the outage probability for several antenna configurations and arbitrary number users. We introduce a formulation for mutual information and then we calculate exact results in a case with two users with two antennas in both Base Station (BS) and User Terminals (UT). Since the formulations to the exact derivation for cases with more antennas and users seems to be intricate, we propose a Gaussian approximation solution to simplify the problem. We validated this approximation with Monte Carlo simulations for different signal-to-noise ratiosDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétrica248416/2013-8CNPQCAPE

A Novel SAGE Algorithm for Estimating Parameters of Wideband Spatial Nonstationary Wireless Channels with Antenna Polarization

In this article, a novel space-alternating generalized expectation-maximization (SAGE) algorithm is proposed for parameter estimations of wideband spatial nonstationary wireless channels with antenna polarization (SAGE-WSNSAP). Compared with the traditional SAGE algorithm, the proposed SAGE-WSNSAP algorithm adds spatial nonstationarity by introducing birth-death coefficients at both transmitter (Tx) and receiver (Rx) sides into the parametric model. To reduce the complexity of the SAGE-WSNSAP algorithm, a coarse-to-fine search method is adopted in the initialization step. In addition, multiple-input multiple-output (MIMO) channel measurements are conducted to validate the proposed algorithm. The measurement results of the angle-delay power spectral density (PSD) and average delay PSD are compared with those estimated by the far-field SAGE algorithm, the near-field SAGE algorithm, and the proposed algorithm. It is found that the estimation results using the proposed SAGE-WSNSAP algorithm show higher similarity to measurement results than using the other two SAGE algorithms. In comparison to the far-field and near-field SAGE algorithms, the SAGE-WSNSAP algorithm can extract more effective multipath components (MPCs) and improve the power extraction ratios.</p