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Precoding in downlink multi-carrier code division multiple access systems using expectation maximisation algorithm

IET COMMUNICATIONS 2008年第10期2卷 1279-1288页

作者： Haghbin, A. Azmi, P. Tarbiat Modares Univ Dept Elect Engn Tehran Iran

The presence of multiple access interference and hardware complexity of the mobile terminal are two major burdens for multi-carrier code division multiple access schemes. Both burdens can be overcome in downlink by precoding the transmitted signal using the knowledge of the channel state information. In multi-user precoding techniques, the transmitter is optimised to combat channel impairments through the use of new spreading sequences that are obtained by solving an optimisation problem based on some criterion. Among these optimisation problems, the problem based on maximum likelihood is hard and complex to solve. In the proposed precoding scheme, the well-suited expectation maximisation algorithm is used to solve this problem. The proposed precoding technique is simulated and its performance is analysed and compared with some other precoding and detecting techniques. The results show that the proposed scheme considerably outperforms the previous precoding schemes.

关键词： multiuser precoding techniques maximum likelihood decoding multiple access interference mobile terminal code division multiple access hardware complexity radiofrequency interference expectation maximisation algorithm optimisation problem precoding mobile radio expectation-maximisation algorithm channel state information downlink multicarrier code division multiple access systems multiuser channels

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Code-aided SNR estimation based on expectation maximisation algorithm

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ELECTRONICS LETTERS 2008年第15期44卷 924-925页

作者： Wu, N. Wang, H. Kuang, J. -M. Beijing Inst Technol Dept Elect Engn Beijing 100081 Peoples R China

A code-aided signal-to-noise ratio (SNR) estimator based on the expectation maximisation algorithm is proposed. The method iteratively uses the soft information from the channel decoder and significantly improves estimation precision in the low SNR regime. It can be extended to higher-order modulation such as MPSK and MQAM directly.

关键词： MQAM Communication channel equalisation and identification channel coding iterative method higher-order modulation channel estimation iterative decoding AWGN channel MPSK code-aided SNR estimation channel decoder Codes Other topics in statistics expectation maximisation algorithm expectation-maximisation algorithm AWGN channels

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Iterative detection for V-BLAST MIMO communication systems based on expectation maximisation algorithm

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ELECTRONICS LETTERS 2004年第11期40卷 684-685页

作者： Rad, KR Nasiri-Kenari, M Sharif Univ Technol Dept Elect Engn Wireless Res Lab Tehran Iran

By applying the expectation maximisation algorithm to the maximum likelihood detection of layered space-time codes, the conditional log-likelihood of a single layer is iteratively maximised, rather than maximising the intractable likelihood function of all layers. Computer simulations demonstrate the efficiency of the proposed detection scheme.

关键词： optimisation MIMO communication systems iterative detection multiple input multiple output systems Signal detection intractable likelihood function MIMO systems computer simulations Optimisation techniques Codes iterative decoding space-time codes expectation maximisation algorithm maximum likelihood detection conditional log-likelihood maximisation Other topics in statistics

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Constrained expectation maximisation algorithm for estimating ARMA models in state space representation

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2020年第1期2020卷 1-37页

作者： Galka, Andreas Moontaha, Sidratul Siniatchkin, Michael Bundeswehr Tech Ctr Ships & Naval Weap Naval Technol & Res WTD 71 Berliner Str 115 Eckernforde Germany Univ Kiel Inst Med Psychol & Med Sociol Preusserstr 1-9 Kiel Germany Potsdam Univ Digital Hlth Ctr Hasso Plattner Inst Rudolf Breitscheid Str 187 Potsdam Germany Clin Pediat & Adolescent Psychiat & Psychotherapy Remterweg 13a Bielefeld Germany

This paper discusses the fitting of linear state space models to given multivariate time series in the presence of constraints imposed on the four main parameter matrices of these models. Constraints arise partly from the assumption that the models have a block-diagonal structure, with each block corresponding to an ARMA process, that allows the reconstruction of independent source components from linear mixtures, and partly from the need to keep models identifiable. The first stage of parameter fitting is performed by the expectation maximisation (EM) algorithm. Due to the identifiability constraint, a subset of the diagonal elements of the dynamical noise covariance matrix needs to be constrained to fixed values (usually unity). For this kind of constraints, so far, no closed-form update rules were available. We present new update rules for this situation, both for updating the dynamical noise covariance matrix directly and for updating a matrix square-root of this matrix. The practical applicability of the proposed algorithm is demonstrated by a low-dimensional simulation example. The behaviour of the EM algorithm, as observed in this example, illustrates the well-known fact that in practical applications, the EM algorithm should be combined with a different algorithm for numerical optimisation, such as a quasi-Newton algorithm.

关键词： Kalman filtering State space modelling expectation maximisation algorithm

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Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter

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JOURNAL OF PROCESS CONTROL 2011年第4期21卷 585-601页

作者： Bavdekar, Vinay A. Deshpande, Anjali P. Patwardhan, Sachin C. Indian Inst Technol Dept Chem Engn Bombay 400076 Maharashtra India

The performance of Bayesian state estimators, such as the extended Kalman filter (EKE), is dependent on the accurate characterisation of the uncertainties in the state dynamics and in the measurements. The parameters of the noise densities associated with these uncertainties are, however, often treated as 'tuning parameters' and adjusted in an ad hoc manner while carrying out state and parameter estimation. In this work, two approaches are developed for constructing the maximum likelihood estimates (MLE) of the state and measurement noise covariance matrices from operating input-output data when the states and/or parameters are estimated using the EKF. The unmeasured disturbances affecting the process are either modelled as unstructured noise affecting all the states or as structured noise entering the process predominantly through known, but unmeasured inputs. The first approach is based on direct optimisation of the ML objective function constructed by using the innovation sequence generated from the EKF. The second approach - the extended EM algorithm - is a derivative-free method, that uses the joint likelihood function of the complete data, i.e. states and measurements, to compute the next iterate of the decision variables for the optimisation problem. The efficacy of the proposed approaches is demonstrated on a benchmark continuous fermenter system. The simulation results reveal that both the proposed approaches generate fairly accurate estimates of the noise covariances. Experimental studies on a benchmark laboratory scale heater-mixer setup demonstrate a marked improvement in the predictions of the EKE that uses the covariance estimates obtained from the proposed approaches. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Nonlinear state estimation Extended Kalman filter Covariance estimation Maximum likelihood estimates expectation maximisation algorithm

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System identification of nonlinear state-space models

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AUTOMATICA 2011年第1期47卷 39-49页

作者： Schon, Thomas B. Wills, Adrian Ninness, Brett Linkoping Univ Div Automat Control SE-58183 Linkoping Sweden Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia

This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More specifically, a Maximum Likelihood (ML) framework is employed and an expectation maximisation (EM) algorithm is derived to compute these ML estimates. The expectation (E) step involves solving a nonlinear state estimation problem, where the smoothed estimates of the states are required. This problem lends itself perfectly to the particle smoother, which provides arbitrarily good estimates. The maximisation (M) step is solved using standard techniques from numerical optimisation theory. Simulation examples demonstrate the efficacy of our proposed solution. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： System identification Nonlinear models Dynamic systems Monte Carlo method Smoothing filters expectation maximisation algorithm Particle methods

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Switching LDS detection for GNSS-based train integrity monitoring system

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IET INTELLIGENT TRANSPORT SYSTEMS 2017年第5期11卷 299-307页

作者： Li, Sihui Cai, Baigen Wei Shangguan Schnieder, Eckehard Toro, Federico Grasso Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Tech Univ Carolo Wilhelmina Braunschweig Inst Traff Safety & Automat Engn Braunschweig Germany

Train integrity whilst in service establishes the foundation for railway safety. This study investigates train integrity detection which reliably deduces whether the train consists remain intact. A switching linear dynamic system (SLDS) based train integrity detection method is proposed for Global Navigation Satellite System (GNSS) based train integrity Monitoring System (TIMS) using the relative distance, velocity and acceleration of the locomotive and the last van. There, expectation maximisation (EM) algorithm estimates the parameters of SLDS model while the Gaussian Sum Filter infers train integrity state. After that, to cope with false detection and misdetection, a verification procedure and train parting time estimation are designed. The approach is evaluated with both field trials and simulated data. Results show that the false alarm rate and misdetection rate of SLDS-based integrity detection approach are 0 and 0.09% respectively, which proves better than the estimated train length based detection model and Hidden Markov Model (HMM).

关键词： satellite navigation railway safety expectation-maximisation algorithm hidden Markov models GNSS-based train integrity monitoring system railway safety switching linear dynamic system train integrity detection method global navigation satellite system TIMS relative distance expectation maximisation algorithm EM algorithm SLDS model Gaussian sum filter verification procedure train parting time estimation false alarm rate misdetection rate train length based detection model hidden Markov model HMM

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Three-dimensional channel modelling using spherical statistics for multiple-input multiple-output systems

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IET COMMUNICATIONS 2009年第1期3卷 48-56页

作者： Mammasis, K. Stewart, R. W. Pfann, E. Freeland, G. Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland Steepest Ascent Ltd Glasgow G4 0UW Lanark Scotland

Recently, the third generation partnership standards bodies (3GPP/3GPP2) have defined a two-dimensional channel model for multiple-input multiple-output (MIMO) systems, where the propagating plane waves are assumed to arrive only from the azimuthal direction and therefore not include the elevation domain. As a result of this assumption, the derived angle-of-arrival (AoA) distribution is characterised only by the azimuth direction of these waves. The AoA distribution of multipaths is implemented with a novel three-dimensional approach. The von Mises-Fisher (VMF) probability density function is used to describe their distribution within the propagation environment in both azimuth and co-latitude. More specifically, the proposed model uses a mixture of VMF distributions. A mixture can be composed of any number of clusters and this is clutter specific. The parameters of the individual cluster of scatterers within the mixture are derived and an estimation of those parameters is achieved using the spherical K-means algorithm and also the expectation maximisation algorithm. Statistical tests are provided to measure the goodness of fit of the proposed model. The results indicate that the proposed model fits well with MIMO experimental data obtained from a measurement campaign in Germany.

关键词： Germany multiple-input multiple-output systems propagation environment spherical K-means algorithm spherical statistics direction-of-arrival estimation von Mises-Fisher probability density function 3G mobile communication expectation maximisation algorithm MIMO systems 3D channel modelling third generation partnership standards bodies expectation-maximisation algorithm angle-of-arrival distribution 3GPP-3GPP2 MIMO communication azimuth direction

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Data-Driven Scene Understanding with Adaptively Retrieved Exemplars

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IEEE MULTIMEDIA 2015年第3期22卷 82-92页

作者： Liu, Xionghao Yang, Wei Lin, Liang Wang, Qing Cai, Zhaoquan Lai, Jianhuang Sun Yat Sen Univ Pattern Recognit & Comp Vis Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Sch Adv Comp Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Comp sci Guangzhou 510275 Guangdong Peoples R China Huizhou Univ Comp Sci Huizhou Peoples R China Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510275 Guangdong Peoples R China

This novel, data-driven framework for semantic scene understanding works without pixelwise annotation or classifier training using a probabilistic expectation-Maximization formulation. It performs better than state-of... 详细信息

关键词： expectation maximisation algorithm Graph Theory Image Matching Image Retrieval Probability Statistical Analysis Visual Databases Adaptively Retrieved Exemplars Data Driven Approach Semantic Scene Understanding Target Image Parsing Image Database Unsegmented Annotated Images Pixel Labels Mutually Conditional Framework Bootstrapping Probabilistic expectation Maximization Formulation EM Formulation Appearance Matching Semantic Matching Assigned Labels Combinatorial Graphical Representation Super Pixel Extracted Vertices Graph Edges Spatial Neighbors Public Databases Semantic Segmentation Task Image Annotation Task Computer Graphics Semantics Optimization Image Edge Detection Image Segmentation Graphical Models Image Reconstruction Data Analysis Scene Understanding Semantic Segmentation Image Retrieval Graphical Model Image Annotation Multimedia Graphics Data Analysis

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Skew Gaussian mixture models for speaker recognition

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IET SIGNAL PROCESSING 2014年第8期8卷 860-867页

作者： Matza, Avi Bistritz, Yuval Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel

Gaussian mixture models (GMMs) are widely used in speech and speaker recognition. This study explores the idea that a mixture of skew Gaussians might capture better feature vectors that tend to have skew empirical distributions. It begins with deriving an expectation maximisation (EM) algorithm to train a mixture of two-piece skew Gaussians that turns out to be not much more complicated than the usual EM algorithm used to train symmetric GMMs. Next, the algorithm is used to compare skew and symmetric GMMs in some simple speaker recognition experiments that use Mel frequency cepstral coefficients (MFCC) and line spectral frequencies (LSF) as the feature vectors. MFCC are one of the most popular feature vectors in speech and speaker recognition applications. LSF were chosen because they exhibit significantly more skewed distribution than MFCC and because they are widely used [together with the related immittance spectral frequencies (ISF)] in speech transmission standards. In the reported experiments, models with skew Gaussians performed better than models with symmetric Gaussians and skew GMMs with LSF compared favourably with both skew symmetric and symmetric GMMs that used MFCC.

关键词： Gaussian processes mixture models speaker recognition vectors expectation-maximisation algorithm speaker recognition GMM speech recognition skew empirical distribution expectation maximisation algorithm EM algorithm two-piece skew Gaussian mixture model Mel frequency cepstral coefflcient MFCC line spectral frequency LSF immittance spectral frequency ISF speech transmission standard feature vectors

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