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Author: Carlos Estêvão Rolim Fernandes Publisher: ISBN: Category : Languages : en Pages : 151
Book Description
Modern telecommunication systems offer services demanding very high transmission rates. Channel identification appears as a major concern in this context. Looking forward better trade-offs between the quality of information recovery and suitable bit-rates, the use of blind techniques is of great interest. Making use of the special properties of the 4th-order output cumulants, this thesis introduces new statistical signal processing tools with applications in radio-mobile communication systems. Exploiting the highly symmetrical structure of the output cumulants, we address the blind channel identification problem by introducing a multilinear model for the 4th-order output cumulant tensor, based on the Parallel Factor (Parafac) analysis. The components of the new tensor model have a Hankel structure, in the SISO case. For (memoryless) MIMO channels, redundant tensor factors are exploited in the estimation of the channel coefficients. In this context, we develop blind identification algorithms based on a single-step least squares (SSLS) minimization problem. The proposed methods fully exploit the multilinear structure of the cumulant tensor as well as its symmetries and redundancies, thus enabling us to avoid any kind of pre-processing. Indeed, the SS-LS approach induces a solution based on a sole optimisation procedure, without intermediate stages, contrary to the vast majority of methods found in the literature. Using only the 4th-order cumulants, and exploiting the Virtual Array concept, we treat the source localization problem in multiuser sensor array processing. Exploiting a particular arrangement of the cumulant tensor, an original contribution consists in providing additional virtual sensors by improving the array resolution by means of an enhanced array structure that commonly arises when using 6th-order statistics. We also consider the problem of estimating the physical parameters of a multipath MIMO communication channel. Using a fully blind approach, we first treat the multipath channel as a convolutive MIMO model and propose a new technique to estimate its coefficients. This non-parametric technique generalizes the methods formerly proposed for the SISO and (memoryless) MIMO cases. Using a tensor formalism to represent the multipath MIMO channel, we estimate the physical multipath parameters by means of a combined ALS-MUSIC technique based on subspace algorithms. Finally, we turn our attention to the problem of determining the order of FIR channels in the context of MISO systems. We introduce a complete combined procedure for the detection and estimation of frequency-selective MISO communication channels. The new algorithm successively detects the signal sources, determines the order of their individual transmission channels and estimates the associated channel coefficients using a deflationary approach.
Author: Carlos Estêvão Rolim Fernandes Publisher: ISBN: Category : Languages : en Pages : 151
Book Description
Modern telecommunication systems offer services demanding very high transmission rates. Channel identification appears as a major concern in this context. Looking forward better trade-offs between the quality of information recovery and suitable bit-rates, the use of blind techniques is of great interest. Making use of the special properties of the 4th-order output cumulants, this thesis introduces new statistical signal processing tools with applications in radio-mobile communication systems. Exploiting the highly symmetrical structure of the output cumulants, we address the blind channel identification problem by introducing a multilinear model for the 4th-order output cumulant tensor, based on the Parallel Factor (Parafac) analysis. The components of the new tensor model have a Hankel structure, in the SISO case. For (memoryless) MIMO channels, redundant tensor factors are exploited in the estimation of the channel coefficients. In this context, we develop blind identification algorithms based on a single-step least squares (SSLS) minimization problem. The proposed methods fully exploit the multilinear structure of the cumulant tensor as well as its symmetries and redundancies, thus enabling us to avoid any kind of pre-processing. Indeed, the SS-LS approach induces a solution based on a sole optimisation procedure, without intermediate stages, contrary to the vast majority of methods found in the literature. Using only the 4th-order cumulants, and exploiting the Virtual Array concept, we treat the source localization problem in multiuser sensor array processing. Exploiting a particular arrangement of the cumulant tensor, an original contribution consists in providing additional virtual sensors by improving the array resolution by means of an enhanced array structure that commonly arises when using 6th-order statistics. We also consider the problem of estimating the physical parameters of a multipath MIMO communication channel. Using a fully blind approach, we first treat the multipath channel as a convolutive MIMO model and propose a new technique to estimate its coefficients. This non-parametric technique generalizes the methods formerly proposed for the SISO and (memoryless) MIMO cases. Using a tensor formalism to represent the multipath MIMO channel, we estimate the physical multipath parameters by means of a combined ALS-MUSIC technique based on subspace algorithms. Finally, we turn our attention to the problem of determining the order of FIR channels in the context of MISO systems. We introduce a complete combined procedure for the detection and estimation of frequency-selective MISO communication channels. The new algorithm successively detects the signal sources, determines the order of their individual transmission channels and estimates the associated channel coefficients using a deflationary approach.
Author: Pierre Comon Publisher: Academic Press ISBN: 0080884946 Category : Technology & Engineering Languages : en Pages : 856
Book Description
Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. - Covers the principles and major techniques and methods in one book - Edited by the pioneers in the field with contributions from 34 of the world's experts - Describes the main existing numerical algorithms and gives practical advice on their design - Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications - Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications
Author: Asoke Kumar Nandi Publisher: Springer Science & Business Media ISBN: 1475729855 Category : Technology & Engineering Languages : en Pages : 290
Book Description
In the signal-processing research community, a great deal of progress in higher-order statistics (HOS) began in the mid-1980s. These last fifteen years have witnessed a large number of theoretical developments as well as real applications. Blind Estimation Using Higher-Order Statistics focuses on the blind estimation area and records some of the major developments in this field. Blind Estimation Using Higher-Order Statistics is a welcome addition to the few books on the subject of HOS and is the first major publication devoted to covering blind estimation using HOS. The book provides the reader with an introduction to HOS and goes on to illustrate its use in blind signal equalisation (which has many applications including (mobile) communications), blind system identification, and blind sources separation (a generic problem in signal processing with many applications including radar, sonar and communications). There is also a chapter devoted to robust cumulant estimation, an important problem where HOS results have been encouraging. Blind Estimation Using Higher-Order Statistics is an invaluable reference for researchers, professionals and graduate students working in signal processing and related areas.
Author: Kareem Y. Bonna Publisher: ISBN: Category : Radio frequency Languages : en Pages : 47
Book Description
Channel Identification is an important part of wireless communication systems. Radio-Frequency (RF) signals are subject to reflection, refraction, and diffraction, attenuation, and other effects, that result in a distorted signal at a receiver, particularly over what are known as frequency-selective channels. Traditionally, such distortion is estimated using a ``training sequence" which is a known reference signal used to estimate, and then correct for, the distortion. However, use of training sequences is not always possible, for example in military applications where the source signal is not known, or in broadcast environments where there is a high cost of transmitting a signal. One potential solution is to estimate the channel blindly, that is, without knowledge of the transmitted signal. Blind Channel Identification (BCI) and Equalization has been a extensive topic of research since at least 1975. One strategy in Blind Channel Identification is to use the structure of the received signals in a Single Input Multiple Output (SIMO) system to estimate the channel. Research has occurred on a number of methods that exploit this in the past several decades. The subspace methods form the channel estimate in terms of a one-dimensional subspace constructed using the estimated second-order statistics of the received signals. Additionally, the use of sparsity in signal estimation has been a topic of interest as well, and has recently been used in certain cases to improve the robustness of the subspace methods in a number of works. In this thesis, the Cross-Relations and Noise-Subspace methods, both of which are SIMO BCI methods, as well as their sparse variant, are examined for a deterministic channel. The expected Normalized Projection Misalignment (NPM) is analytically approximated for all considered methods. In addition, it is compared to simulation results for a random source signal and several measured RF channels from earlier literature. Finally, the sensitivity of the sparse variant of the subspace methods as a function of the regularization parameter is studied using simulation for a set of measured RF channels from earlier literature.
Author: Dung Huy Han Publisher: ISBN: 9781267399847 Category : Languages : en Pages :
Book Description
In this dissertation, we present novel batch algorithms to tackle the multi-path fading effect of the wireless channels using convex optimization tools. We consider two major problems: channel equalization and channel shortening. Blind channel equalization has been widely investigated in the past decade. Blind algorithms are preferred because of their ability to equalize the channel without spending extra bandwidth. Existing works have proposed various blind channel equalization costs and characterized their convergence. Most of the blind signal recovery algorithms are implemented as stochastic gradient descent based adaptive schemes making them attractive to applications where the channel is slow varying. However, existing solutions for blind channel equalization often suffer from slow convergence and require long data samples. On the other hand, packet based data transmission in many practical digital communication systems makes it attractive to develop steepest descent implementation in order to speed up convergence. We focus on developing steepest decent implementation of several well-known blind signal recovery algorithms for multi-channel equalization and source separation. Our steepest descent formulation is more amenable to additional parametric and signal subspace constraints for faster convergence and superior performance. Most of the well-known blind channel equalization algorithms are based on higher-order statistics making the corresponding cost non-linear non-convex functions of the equalizer parameters. Therefore, the steepest descent implementations often converge to local optima. We develop batch algorithms that use modern optimization tools so that the global optima can be found in polynomial time. We convert our blind costs of interest into fourth-order functions and apply a semi-definite formulation to convert them into convex optimization problems so that they can be solved globally. Our algorithms work well not only for removing multipath fading effect in channel equalization problem but also for mitigating inter-channel interference in source separation problem. Nevertheless, in practical communication systems, pilot symbols are inserted to the packet for various purposes including channel estimation and equalization. Hence, the use of the pilot in conjunction with blind algorithms is more preferred. We investigate simple and practical means for performance enhancement for equalizing wireless packet transmission bursts that rely on short sequence as equalization pilots. Utilizing both the pilot symbols and additional statistical and constellation information about user data symbols, we develop efficient means for improving the performance of linear channel equalizers. We present two convex optimization algorithms that are both effective in performance enhancement and can be solved efficiently. We also propose a fourth-order training based cost so that it can be combined with other fourth-order blind costs and be solved efficiently using semi-definite programming. The simulation results show that with the help of very few pilots, the equalization can be done under very short packet length. Many modern communication systems adopt multicarrier modulation for optimum utilization of multi-path fading channel. Under this scenario, a cyclic prefix which is not shorter than the channel length is added to enable equalization. We study the problem of channel shortening in multicarrier modulation systems when this assumption is not met. We reformulate two existing second-order statistic based methods into semidefinite programming to overcome their shortcoming of local convergence. Our batch processor is superior to the conventional stochastic gradient algorithms in terms of achievable bit rate and signal to interference and noise ratio (SINR). Addressing the shortcoming of second-order statistic based costs, we propose a new criterion for blind channel shortening based on high order statistical information. The optimization criterion can be achieved through either a gradient descent algorithm or a batch algorithm using the aforementioned convex optimization for global convergence.
Author: George V. Tsoulos Publisher: Wiley-IEEE Press ISBN: Category : Technology & Engineering Languages : en Pages : 790
Book Description
Electrical Engineering Adaptive Antennas for Wireless Communications In the past decade, the wireless communications community recognized adaptive antennas as a core technology that would help existing systems overcome problems related to spectrum efficiency and provide a vehicle to achieve the ambitious requirements of next-generation networks. The communications industry has already begun to develop adaptive antenna systems for commercial use and at the same time is working with standardization institutes around the world to produce adaptive antenna-friendly standards. Adaptive Antennas for Wireless Communications is a concise, detailed resource of information for all critical issues related to this technology and is compiled from the original published work of experts in the field. The extensive literature covers: * Historical and background aspects * Radio channel simulation techniques and characteristics * Adaptive algorithm performance under a variety of conditions * Adaptive antenna performance in different operational environments * Design and implementation issues * Experimental results * Other issues such as network planning and recent novel techniques Adaptive Antennas for Wireless Communications is a valuable reference for helping consultants, researchers, communications professionals, academics, and students gain an in-depth understanding of adaptive antenna technology.
Author: Saeed V. Vaseghi Publisher: John Wiley & Sons ISBN: 0470094958 Category : Science Languages : en Pages : 480
Book Description
Signal processing plays an increasingly central role in the development of modern telecommunication and information processing systems, with a wide range of applications in areas such as multimedia technology, audio-visual signal processing, cellular mobile communication, radar systems and financial data forecasting. The theory and application of signal processing deals with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and hence, noise reduction and the removal of channel distortion is an important part of a signal processing system. Advanced Digital Signal Processing and Noise Reduction, Third Edition, provides a fully updated and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. Noise is the eternal bane of communications engineers, who are always striving to find new ways to improve the signal-to-noise ratio in communications systems and this resource will help them with this task. * Features two new chapters on Noise, Distortion and Diversity in Mobile Environments and Noise Reduction Methods for Speech Enhancement over Noisy Mobile Devices. * Topics discussed include: probability theory, Bayesian estimation and classification, hidden Markov models, adaptive filters, multi-band linear prediction, spectral estimation, and impulsive and transient noise removal. * Explores practical solutions to interpolation of missing signals, echo cancellation, impulsive and transient noise removal, channel equalisation, HMM-based signal and noise decomposition. This is an invaluable text for senior undergraduates, postgraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also appeal to engineers in telecommunications and audio and signal processing industries.
Author: Carlos Estêvão Rolim Fernandes
Author: Carlos Estêvão Rolim Fernandes
Author: Jing Liang
Author: Pierre Comon
Author: Ye Li
Author: Asoke Kumar Nandi
Author: Kareem Y. Bonna
Author: Dung Huy Han
Author: Yoke Yong Lai
Author: George V. Tsoulos
Author: Saeed V. Vaseghi