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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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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 23-23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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Robust multi-response surface optimisation based on Bayesian quantile model

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第10期61卷 3260-3278页

作者： Yang, Shijuan Wang, Jianjun Tu, Yiliu Han, Yunxia Ren, Xiaolei Ding, Chunfeng Chen, Xiaoying Nanjing Univ Sci & Technol Dept Management Sci & Engn 200 Xiaolingwei Nanjing 210094 Jiangsu Peoples R China Univ Calgary Dept Mech & Mfg Engn Nw Calgary AB Canada Yangzhou Univ Business Sch Yangzhou Jiangsu Peoples R China

In robust parameter design, model parameter uncertainty and quality of experimental data often affect the establishment of response surface models, which in turn affect the acquisition of the optimal operating conditions. This paper proposes a robust multi-response surface modelling and optimisation method based on Bayesian quantile regression, which is a robust regression technique insensitive to outliers, to address the above problems. We first incorporate quantile regression into the Bayesian framework and use Bayes's theorem to obtain posterior inference of model parameters. Then, the Monte Carlo-based expectation maximisation algorithm is used to estimate the model parameters, and the entropy-based overall desirability function is taken as an optimisation objective to obtain the optimal settings. The effectiveness of the proposed method is demonstrated by an additive manufacturing process and a simulation study. Compared with other existing methods, the proposed method can resist the disturbance of outliers, and thus obtain more accurate optimisation results.

关键词： Parameter design Bayesian inference quantile regression expectation maximisation algorithm entropy-based desirability function

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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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Joint channel estimation and ISI/ICI cancellation for MIMO-OFDM systems

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2011年第2期7卷 137-142页

作者： Chiu, Yi-Jen Chen, Chien-Sheng Chang, Ting-Wei Tainan Univ Technol Dept Informat Management Tainan Taiwan Taiwan Shoufu Univ Dept Digital Entertainment & Game Design Tainan Taiwan VIA Technol Inc Taipei Taiwan

In MIMO-OFDM systems, the multipath components whose delays exceed Cyclic Prefix cause Inter-Symbol Interference and Inter-Carrier Interference, which may degrade system performance severely. In this paper, we propose a joint channel estimation and ISI/ICI cancellation scheme in which a limited CP is used in a trade-off against high-rate performance in MIMO-OFDM systems. A channel estimation scheme based on the criterion of expectation-maximisation (EM) algorithm can be proposed through the use of a training symbol. Simulation results show that the proposed method can significantly enhance the overall MIMO-OFDM system performance after only a few iterations.

关键词： ISI inter-symbol interference ICI inter-carrier interference EM expectation maximisation algorithm

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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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Image patch prior learning based on random neighbourhood resampling for image denoising

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IET IMAGE PROCESSING 2020年第5期14卷 838-844页

作者： Ji, Jian Wei, Jiajie Fan, Guoliang Bai, Mengqi Huang, Jingjing Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Image patch priors become a popular tool for image denoising. The Gaussian mixture model (GMM) is remarkably effective in modelling natural image patches. However, GMM prior learning using the expectation maximisation (EM) algorithm is sensitive to the initialisation, often leading to low convergence rate of parameter estimation. In this study, a novel sampling method called random neighbourhood resampling (RNR) is proposed to improve the accuracy and efficiency of parameter estimation. An enhanced GMM (EGMM) learning algorithm is further developed by incorporating RNR into the EM algorithm to initialise and update the GMM prior. The learned EGMM prior is applied in the expected patch log-likelihood (EPLL) framework for image denoising. The effectiveness and performance of the proposed RNR and EGMM algorithm are demonstrated via extensive experimental results comparing with the state-of-the-art image denoising methods, the experimental results show the higher PSNR result of the denoised images using the proposed method. Meanwhile, the authors verified that the proposed method can efficiently reduce the time of image denoising compared with the basic EPLL method.

关键词： image denoising mixture models learning (artificial intelligence) Gaussian processes expectation-maximisation algorithm random neighbourhood resampling Gaussian mixture model natural image patches expectation maximisation algorithm parameter estimation RNR enhanced GMM learning algorithm expected patch log-likelihood framework EGMM algorithm image denoising methods image patch prior learning EM algorithm EPLL framework

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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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Hidden Markov model parameters estimation with independent multiple observations and inequality constraints

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IET SIGNAL PROCESSING 2014年第9期8卷 938-949页

作者： Xia, Lisha Fang, Huajing Liu, Xiaoyong Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China

In this study, the authors focus on hidden Markov model (HMM) parameters estimation with independent multiple observations and non-linear inequality constraints. The parameters estimation process is divided into four steps: initialisation, parameters pre-estimation, parameters re-estimation and termination. The pre-estimation results are used to approximate non-linear inequality constraints to linear inequality constraints. In parameters re-estimation step, the active-set optimisation is combined with the expectation maximisation (EM) algorithm in M-step and the active set-based EM algorithm is proposed to re-estimate HMM parameters when inequality constraints are not satisfied in pre-estimation. An auxiliary function is devised for reconstructing the optimisation objective function and the convergence of the proposed algorithm is also demonstrated. Simulation results indicate that the proposed algorithm provides better performance by modifying the random error of observation data appropriately and it is powerful for industry process fault diagnosis.

关键词： hidden Markov models parameter estimation nonlinear estimation approximation theory expectation-maximisation algorithm optimisation fault diagnosis hidden Markov model parameter estimation independent multiple observation HMM parameter estimation parameters pre-estimation step parameters reestimation step termination step initialisation step approximate nonlinear inequality constraint linear inequality constraint active-set optimisation expectation maximisation algorithm M-step algorithm active set-based EM algorithm optimisation objective function reconstruction random error modification industry process fault diagnosis

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