A Comprehensive Review on Various Estimation Techniques for Multi Input Multi Output Channel

لقد تطورت مشكلة تقدير القناة اللاسلكية بسبب بعض التأثيرات غير المرغوب فيها للخواص الفيزيائية للقناة على الإشارات المرسلة. في نهاية المستقبل، التشوه، والتأخير، والتوهين، والتداخلات، ونوبات الطور هي أكثر المشكلات التي تواجهها مع الإشارات المستقبلة. من أجل التغلب على تأثيرات القناة وتوفير جودة كاملة تقريبًا لنقل البيانات، يلزم تقدير معلومات القناة. في أنظمة المخرجات متعددة المدخلات والمخرجات (MIMO)، يعتبر تقدير القناة خطوة أكثر تعقيدًا مقارنة بأنظمة المخرجات ذات المدخلات المفردة، SISO، نظرًا لأن عدد القنوات الفرعية التي تحتاج إلى تقدير أكبر بكثير من انظمة SISO. الهدف الأساسي من هذه الورقة البحثية هو مراجعة شاملة لاغلب الخوارزميات الشهيرة والفعالة التي تم ابتكارها لحل مشكلة تقدير قناة MIMO في أنظمة الاتصالات اللاسلكية. في هذه الورقة، تم تصنيف هذه التقنيات إلى ثلاث مجموعات: غير المكفوفين، شبه الأعمى وتقدير أعمى. لكل مجموعة، يتم تقديم توضيح مختصر لخوارزميات التقدير المألوفة. وأخيرًا، نقارن بين هذه التقنيات استنادًا إلى التعقيد الحسابي والكمون ودقة التقدير.The problem of wireless channel estimation has been evolving due to some undesirable effects of channel physical properties on transmitted signals. At the receiver end, distortions, delays, attenuations, interferences, and phase shifts are the most issues encounter together with the received signals. In order to overcome channel effects and provide almost a perfect quality of data transmission, channel parameter estimation is needed. In Multiple Input-Multiple Output systems (MIMO), channel estimation is a more complicated step as compared with the Single Input-Single Output systems, SISO, because of the fact that the number of sub-channels that needs estimate is much greater than SISO systems. The fundamental objective of this research paper is to go over the famous and efficient algorithms that have been innovated to solve the problem of MIMO channel estimation in wireless communication systems. In this paper, these techniques have been classified into three groups: non-blind, semi-blind and blind estimation. For each group, a brief illustration is presented for familiar estimation algorithms. Finally, we compare between these techniques based on computational complexity, latency and estimation accuracy

Artificial neural networks for location estimation and co-cannel interference suppression in cellular networks

This thesis reports on the application of artificial neural networks to two important problems encountered in cellular communications, namely, location estimation and co-channel interference suppression. The prediction of a mobile location using propagation path loss (signal strength) is a very difficult and complex task. Several techniques have been proposed recently mostly based on linearized, geometrical and maximum likelihood methods. An alternative approach based on artificial neural networks is proposed in this thesis which offers the advantages of increased flexibility to adapt to different environments and high speed parallel processing. Location estimation provides users of cellular telephones with information about their location. Some of the existing location estimation techniques such as those used in GPS satellite navigation systems require non-standard features, either from the cellular phone or the cellular network. However, it is possible to use the existing GSM technology for location estimation by taking advantage of the signals transmitted between the phone and the network. This thesis proposes the application of neural networks to predict the location coordinates from signal strength data. New multi-layered perceptron and radial basis function based neural networks are employed for the prediction of mobile locations using signal strength measurements in a simulated COST-231 metropolitan environment. In addition, initial preliminary results using limited available real signal-strength measurements in a metropolitan environment are also reported comparing the performance of the neural predictors with a conventional linear technique. The results indicate that the neural predictors can be trained to provide a near perfect mapping using signal strength measurements from two or more base stations. The second application of neural networks addressed in this thesis, is concerned with adaptive equalization, which is known to be an important technique for combating distortion and Inter-Symbol Interference (ISI) in digital communication channels. However, many communication systems are also impaired by what is known as co-channel interference (CCI). Many digital communications systems such as digital cellular radio (DCR) and dual polarized micro-wave radio, for example, employ frequency re-usage and often exhibit performance limitation due to co-channel interference. The degradation in performance due to CCI is more severe than due to ISI. Therefore, simple and effective interference suppression techniques are required to mitigate the interference for a high-quality signal reception. The current work briefly reviews the application of neural network based non-linear adaptive equalizers to the problem of combating co-channel interference, without a priori knowledge of the channel or co-channel orders. A realistic co-channel system is used as a case study to demonstrate the superior equalization capability of the functional-link neural network based Decision Feedback Equalizer (DFE) compared to other conventional linear and neural network based non-linear adaptive equalizers.This project was funded by Solectron (Scotland) Ltd

Autoregressive models for text independent speaker identification in noisy environments

The closed-set speaker identification problem is defined as the search within a set of persons for the speaker of a certain utterance. It is reported that the Gaussian mixture model (GMM) classifier achieves very high classification accuracies (in the range 95% - 100%) when both the training and testing utterances are recorded in sound proof studio, i.e., there is neither additive noise nor spectral distortion to the speech signals. However, in real life applications, speech is usually corrupted by noise and band-limitation. Moreover, there is a mismatch between the recording conditions of the training and testing environments. As a result, the classification accuracy of GMM-based systems deteriorates significantly. In this thesis, we propose a two-step procedure for improving the speaker identification performance under noisy environment. In the first step, we introduce a new classifier: vector autoregressive Gaussian mixture (VARGM) model. Unlike the GMM, the new classifier models correlations between successive feature vectors. We also integrate the proposed method into the framework of the universal background model (UBM). In addition, we develop the learning procedure according to the maximum likelihood (ML) criterion. Based on a thorough experimental evaluation, the proposed method achieves an improvement of 3 to 5% in the identification accuracy. In the second step, we propose a new compensation technique based on the generalized maximum likelihood (GML) decision rule. In particular, we assume a general form for the distribution of the noise-corrupted utterances, which contains two types of parameters: clean speech-related parameters and noise-related parameters. While the clean speech related parameters are estimated during the training phase, the noise related parameters are estimated from the corrupted speech in the testing phase. We applied the proposed method to utterances of 50 speakers selected from the TIMIT database, artificially corrupted by convolutive and additive noise. The signal to noise ratio (SNR) varies from 0 to 20 dB. Simulation results reveal that the proposed method achieves good robustness against variation in the SNR. For utterances corrupted by covolutive noise, the improvement in the classification accuracy ranges from 70% for SNR = 0 dB to around 4% for SNR = 10dB, compared to the standard ML decision rule. For utterances corrupted by additive noise, the improvement in the classification accuracy ranges from 1% to 10% for SNRs ranging from 0 to 20 dB. The proposed VARGM classifier is also applied to the speech emotion classification problem. In particular, we use the Berlin emotional speech database to validate the classification performance of the proposed VARGM classifier. The proposed technique provides a classification accuracy of 76% versus 71% for the hidden Markov model, 67% for the k-nearest neighbors, 55% for feed-forward neural networks. The model gives also better discrimination between high-arousal emotions (joy, anger, fear), low arousal emotions (sadness, boredom), and neutral emotions than the HMM. Another interesting application of the VARGM model is the blind equalization of multi input multiple output (MIMO) communication channels. Based on VARGM modeling of MIMO channels, we propose a four-step equalization procedure. First, the received data vectors are fitted into a VARGM model using the expectation maximization (EM) algorithm. The constructed VARGM model is then used to filter the received data. A Baysian decision rule is then applied to identify the transmitted symbols up to a permutation and phase ambiguities, which are finally resolved using a small training sequence. Moreover, we propose a fast and easily implementable model order selection technique. The new equalization algorithm is compared to the whitening method and found to provide less symbol error probability. The proposed technique is also applied to frequency-flat slow fading channels and found to provide a more accurate estimate of the channel response than that provided by the blind de-convolution exploiting channel encoding (BDCC) method and at a higher information rate

Wireless Channel Equalization in Digital Communication Systems

Our modern society has transformed to an information-demanding system, seeking voice, video, and data in quantities that could not be imagined even a decade ago. The mobility of communicators has added more challenges. One of the new challenges is to conceive highly reliable and fast communication system unaffected by the problems caused in the multipath fading wireless channels. Our quest is to remove one of the obstacles in the way of achieving ultimately fast and reliable wireless digital communication, namely Inter-Symbol Interference (ISI), the intensity of which makes the channel noise inconsequential. The theoretical background for wireless channels modeling and adaptive signal processing are covered in first two chapters of dissertation. The approach of this thesis is not based on one methodology but several algorithms and configurations that are proposed and examined to fight the ISI problem. There are two main categories of channel equalization techniques, supervised (training) and blind unsupervised (blind) modes. We have studied the application of a new and specially modified neural network requiring very short training period for the proper channel equalization in supervised mode. The promising performance in the graphs for this network is presented in chapter 4. For blind modes two distinctive methodologies are presented and studied. Chapter 3 covers the concept of multiple cooperative algorithms for the cases of two and three cooperative algorithms. The select absolutely larger equalized signal and majority vote methods have been used in 2-and 3-algoirithm systems respectively. Many of the demonstrated results are encouraging for further research. Chapter 5 involves the application of general concept of simulated annealing in blind mode equalization. A limited strategy of constant annealing noise is experimented for testing the simple algorithms used in multiple systems. Convergence to local stationary points of the cost function in parameter space is clearly demonstrated and that justifies the use of additional noise. The capability of the adding the random noise to release the algorithm from the local traps is established in several cases

Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images

This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, Specificity (SPE) = 95.91%, Sensitivity (SEN) = 92.44%, and Dice-score = 0.95. These results indicate that computer aided stroke severity diagnosis support is possible. Such systems might lead to progress in stroke diagnosis by enabling healthcare professionals to improve diagnosis and management of stroke patients with the same resources

Kaotik haberleşme sistemlerinde gözü kapalı kanal denkleştirme

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu çalışmada, SISO ve MIMO kaotik haberleşme sistemlerinde gözü kapalı kanal denkleştirme problemi ele alınmıştır. SISO FIR filtreler olarak modellenen kanalların denkleştirilmesi için uyarlamalı özyinelemeli ve yinelemeli olmayan uyarlamalı iki algoritma geliştirilmiştir. SISO algoritmalarının ilkinde denkleştirici IIR ikincisinde ise FIR uyarlamalı bir filtre olarak tasarlanmıştır. Algoritmalar, kaotik işaretlerin kısa süreli öngörülebilirliklerinden yaralanılarak çıkartılmıştır. Algoritmaların performansları detaylı simülasyonlar yapılarak tespit edilmiştir. Geliştirilen algoritmaların literatürde sıklıkla kullanılan MNPE yönteminden daha yüksek performans sağladıkları gösterilmiştir. Uyarlamalı özyinelemeli algoritma en iyi denkleştirme sonuçlarını vermesine rağmen önemli iki sınırlamaya sahiptir. Yinelemeli olmayan uyarlamalı algoritma performansın bir miktar düşmesi karşılığında bu iki sınırlamayı kaldırmaktadır. Ayrıca, yinelemeli olmayan algoritma optimum sabit filtre ile kıyaslanmış ve algoritmanın optimum sabit filtreye oldukça yakın sonuçlar verdiği gösterilmiştir. Daha sonra, MIMO klasik haberleşme sistemleri için geliştirilen kavramlara benzer bir şekilde, SISO kaotik haberleşme durumunda kullanılan maliyet fonksiyonu MIMO durumuna uyarlanarak elde edilen gözü kapalı bir kanal denkleştirme algoritması önerilmiştir. Algoritma geliştirilmeden önce, MIMO bir kanal için mükemmel denkleştirmeyi gerçekleştirebilecek bir denkleştiricinin varlığı ve tekliği için gerek ve yeter koşullar tespit edilmiştir. Kanalın bilindiği varsayılarak optimum sabit filtre tasarlanmıştır. Literatürde MIMO kaotik haberleşme sistemleri için geliştirilen bir algoritma olmadığından algoritmanın performansı optimum sabit filtrenin performansı ile kıyaslanmıştır. Simülasyonlar vasıtasıyla, MIMO uyarlamalı denkleştiricinin giriş işaretlerini doğru bir şekilde kestirdiği ve önerilen algoritmanın optimum sabit filtreye oldukça yakın sonuçlar verdiği gösterilmiştir.In this study, blind channel equalization problem for SISO and MIMO chaotic communication systems is investigated. An adaptive autoregressive filter and a non-recursive adaptive filter are developed for equalizing SISO channels that are modelled as FIR filters. Equalizer is designed as an adaptive IIR filter in the first SISO algorithm while it is modelled as an adaptive FIR filter in the second algorithm. Algorithms are derived by exploiting short time predictability of chaotic signals. Simulation results are provided to demonstrate effectiveness of the proposed adaptive algorithms. Proposed algorithms are shown to give better equalization results compared to the frequently used MNPE method. Even though the adaptive autoregressive algorithm gives the best equalization results, it has two serious limitations. Non-recursive adaptive algorithm avoid these limitations at the cost of slightly decreased performance. In addition, the non-recursive algorithm was compared to the optimum filter and it was shown to exhibit performance similar to that of optimum filter. Finally, similar to blind equalization methods for MIMO classical communication systems, a novel blind channel equalization algorithm is developed for MIMO chaotic communication systems by modifying the cost function used in SISO chaotic equalization algorithms. Existence and uniqueness conditions fort he MIMO reconstruction filters are investigated before deriving the adaptive MIMO algorithm. An optimum fixed filter is developed for MIMO chaotic communication systems. Since there do not exist a method for comparison, the proposed algorithm is compared to the optimum fixed filter. That the adaptive MIMO equalizer estimates the input signals correctly and it gives results very close to that of the optimum fixed filter are shown via simulations

Fuzzy filters based communication channels equalization

Orientador: João Marcos Travassos RomanoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Esta tese objetiva o estudo da utilização de fuzzy na equalização supervisionada e não-supervisionada de canais de comunicação digital. O trabalho se divide em basicamente duas partes. Na primeira, é feita uma revisão das técnicas de equalização supervisionada empregando filtros nebulosos mostradas na literatura. Na segunda parte concentramse as principais contribuições originais, voltadas ao estabelecimento de um paradigma sólido para equalização não-linear cega. Primeiramente, demonstramos que o teorema de Benveniste-Goursat-Ruget não garante a equalização quando do uso de não lineares como os fuzzy. Como alternativa, optamos por um critério baseado no erro de predição aliado a estruturas nebulosas, opção esta que se mostra plenamente justificada pela demonstração da equivalência entre os preditores fuzzy e o estimador de mínimo erro quadrático médio. Para efetuar o treinamento desta estrutura foi proposto um algoritmo baseado em clusterização não-supervisionada que combina estratégias evolutivas com técnicas de busca local. Porem, resultados de simulações computacionais são apresentados afim de avaliar e comparar com as soluções clássicas o desempenho dos equalizadores e das técnicas de treinamento descritos no trabalhoAbstract: The objective of this thesis is to study the application of fuzzy to supervised and unsupervised digital channel equalization. Our work is basically divided in two main parts. In the one, we make an extensive review of supervised fuzzy equalization techniques. In the second part, we present original contributions towards the establishment of a solid paradigm for blind nonlinear equalization. In this part, we demonstrate that the Benveniste-Goursat-Ruget theorem is not valid for nonlinear equalizers such as fuzzy liters. As a viable alternative, we propose an approach based on the predictionerror criterion and a fuzzy logic system. The eectiveness of which is cornered by the demonstration of the equivalence between the fuzzy predictor and the minimum eansquare error estimator. Secondly, we propose a training scheme founded on an unsupervised clustering algorithm that combines evolutionary strategies and local search techniques. Lastly, we present results of computational simulations to assess the performance of the equalizers and training techniques introduced in our workMestradoTelecomunicações e TelemáticaMestre em Engenharia Elétric

Igualación ciega de canal para receptores de comunicaciones digitales

En el presente Proyecto Fin de Carrera se presenta una solución al problema de la ISI en los receptores de comunicaciones digitales. Dicho problema surge a raíz de la digitalización de los sistemas de comunicaciones, que comenzó a mediados del siglo XX con la llamada revolución industrial, y supuso uno de los factores que mayor impacto ha tenido en el enorme desarrollo económico y social de la época.In the present thesis it is presents a solution to the problem of the ISI in digital communications receivers. The problem arises as a result of the digitization of communications systems, which began in the mid-20th century with the so-called industrial revolution, and was one of the factors that greater impact has had on the enormous economic and social development of the era.Ingeniería en Tecnologías de Telecomunicació

Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization

As a complicated ubiquitous phenomenon encountered in ultrasound imaging, speckle can be treated as either annoying noise that needs to be reduced or the source from which diagnostic information can be extracted to reveal the underlying properties of tissue. In this study, the application of Independent Component Analysis (ICA), a relatively new statistical signal processing tool appeared in recent years, to both the speckle texture analysis and despeckling problems of B-mode ultrasound images was investigated. It is believed that higher order statistics may provide extra information about the speckle texture beyond the information provided by first and second order statistics only. However, the higher order statistics of speckle texture is still not clearly understood and very difficult to model analytically. Any direct dealing with high order statistics is computationally forbidding. On the one hand, many conventional ultrasound speckle texture analysis algorithms use only first or second order statistics. On the other hand, many multichannel filtering approaches use pre-defined analytical filters which are not adaptive to the data. In this study, an ICA-based multichannel filtering texture analysis algorithm, which considers both higher order statistics and data adaptation, was proposed and tested on the numerically simulated homogeneous speckle textures. The ICA filters were learned directly from the training images. Histogram regularization was conducted to make the speckle images quasi-stationary in the wide sense so as to be adaptive to an ICA algorithm. Both Principal Component Analysis (PCA) and a greedy algorithm were used to reduce the dimension of feature space. Finally, Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel were chosen as the classifier for achieving best classification accuracy. Several representative conventional methods, including both low and high order statistics based methods, and both filtering and non-filtering methods, have been chosen for comparison study. The numerical experiments have shown that the proposed ICA-based algorithm in many cases outperforms other algorithms for comparison. Two-component texture segmentation experiments were conducted and the proposed algorithm showed strong capability of segmenting two visually very similar yet different texture regions with rather fuzzy boundaries and almost the same mean and variance. Through simulating speckle with first order statistics approaching gradually to the Rayleigh model from different non-Rayleigh models, the experiments to some extent reveal how the behavior of higher order statistics changes with the underlying property of tissues. It has been demonstrated that when the speckle approaches the Rayleigh model, both the second and higher order statistics lose the texture differentiation capability. However, when the speckles tend to some non-Rayleigh models, methods based on higher order statistics show strong advantage over those solely based on first or second order statistics. The proposed algorithm may potentially find clinical application in the early detection of soft tissue disease, and also be helpful for better understanding ultrasound speckle phenomenon in the perspective of higher order statistics. For the despeckling problem, an algorithm was proposed which adapted the ICA Sparse Code Shrinkage (ICA-SCS) method for the ultrasound B-mode image despeckling problem by applying an appropriate preprocessing step proposed by other researchers. The preprocessing step makes the speckle noise much closer to the real white Gaussian noise (WGN) hence more amenable to a denoising algorithm such as ICS-SCS that has been strictly designed for additive WGN. A discussion is given on how to obtain the noise-free training image samples in various ways. The experimental results have shown that the proposed method outperforms several classical methods chosen for comparison, including first or second order statistics based methods (such as Wiener filter) and multichannel filtering methods (such as wavelet shrinkage), in the capability of both speckle reduction and edge preservation