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Nonlinear Channel Estimation Based on Multilevel PN Sequences in OFDM Systems
"... Nonlinear distortion of power amplifier is one of the major factor that degrades the performance of OFDM systems.Instead of estimating the nonlinearity at transmitter such as ordinary predistortion (PD), this paper estimates the nonlinearity of amplifier at the receiver. The problem is that nonline ..."
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Nonlinear distortion of power amplifier is one of the major factor that degrades the performance of OFDM systems.Instead of estimating the nonlinearity at transmitter such as ordinary predistortion (PD), this paper estimates the nonlinearity of amplifier at the receiver. The problem is that nonlinear and multipath dispersion mix together at receiver end. And their parameters are generally unknown, so conventional method doesn’t work well. In this paper, an algorithm that estimates both the nonlinear parameters of the power amplifier and the impulse response of the wireless channel is proposed. Multilevel PN sequences are transmitted to construct a Vandermonde matrix, which separates the nonlinearity and linearity into different subspaces. The separation makes it easy to estimate the nonlinearity and linearity independently. 1.
Machine Learning in Wireless Relay Channels
"... This document describes our project applying machine learning techniques to modern wireless communication. In particular, we study classifying modulation schemes in the decodeandforward relay channel based on instantaneous channel information. Machine learning is an unconventional tool in communic ..."
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This document describes our project applying machine learning techniques to modern wireless communication. In particular, we study classifying modulation schemes in the decodeandforward relay channel based on instantaneous channel information. Machine learning is an unconventional tool in communication because of the accuracy and simplicity of underlying models and the overwhelming need
Support Vector Machines for Robust Channel Estimation in OFDM
"... Abstract—A new support vector machine (SVM) algorithm for coherent robust demodulation in orthogonal frequencydivision multiplexing (OFDM) systems is proposed. We present a complex regression SVM formulation specifically adapted to a pilotsbased OFDM signal. This novel proposal provides a simpler ..."
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Abstract—A new support vector machine (SVM) algorithm for coherent robust demodulation in orthogonal frequencydivision multiplexing (OFDM) systems is proposed. We present a complex regression SVM formulation specifically adapted to a pilotsbased OFDM signal. This novel proposal provides a simpler scheme than an SVM classification method. The feasibility of our approach is substantiated by computer simulation results obtained for IEEE 802.16 broadband fixed wireless channel models. These experiments allow to scrutinize the performance of the OFDMSVM system and the suitability of theHuber cost function, in the presence of nonGaussian impulse noise interfering with OFDM pilot symbols. Index Terms—Channel estimation, complex, orthogonal frequencydivision multiplexing (OFDM), robust estimation, support vector machine (SVM). I.
Machine Learning manuscript No. (will be inserted by the editor) Incorporating Prior Knowledge in Support Vector Regression
"... Abstract This paper explores the addition of constraints to the linear programming formulation of the support vector regression problem for the incorporation of prior knowledge. Equality and inequality constraints are studied with the corresponding types of prior knowledge that can be considered for ..."
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Abstract This paper explores the addition of constraints to the linear programming formulation of the support vector regression problem for the incorporation of prior knowledge. Equality and inequality constraints are studied with the corresponding types of prior knowledge that can be considered for the method. These include particular points with known values, prior knowledge on any derivative of the function either provided by a prior model or available only at some specific points and bounds on the function or any derivative in a given domain. Moreover, a new method for the simultaneous approximation of multiple outputs linked by some prior knowledge is proposed. This method also allows consideration of different types of prior knowledge on single outputs while training on multiple outputs. Synthetic examples show that incorporating a wide variety of prior knowledge becomes easy, as it leads to linear programs, and helps to improve the approximation in difficult cases. The benefits of the method are finally shown on a reallife application, the estimation of incylinder residual gas fraction in spark ignition engines, which is representative of numerous situations met in engineering. Key words Support Vector Regression – kernel approximation – prior knowledge – multioutputs 1
Article FilterType Variable Selection Based on Information Measures for Regression Tasks
, 2012
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Survey on Noise Estimation and Removal Methods through SVM
"... The Support vector machine is statistical learning method but it is also recognized as another approach to solve and simplify data classification. SVM have been discovered as one of the successful classification techniques for many areas and application and it works on different datasets and gives a ..."
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The Support vector machine is statistical learning method but it is also recognized as another approach to solve and simplify data classification. SVM have been discovered as one of the successful classification techniques for many areas and application and it works on different datasets and gives appropriate result. There is a noise or irrelevant data present in datasets which leads to poor result so to remove those meaningless data some approaches are introduced for better result. In this paper an introduction of SVM (Support Vector Machine) and various noise estimation and noise removal methods based on support vector machine is presented.
doi:10.1155/2008/491503 Research Article Digital Communication Receivers Using Gaussian Processes for Machine Learning
"... We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the informat ..."
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We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform stateoftheart nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to stateoftheart nonlinear machine learning tools, such as support vector machines. Copyright © 2008 F. PérezCruz and J. J. MurilloFuentes. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
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"... Structure characterization is one of many interesting problems in material science, the latter recently garnering significant attention in the form of the Materials Genome Initiative [4, 5], which ultimately seeks to understand the atomistic blueprint for key materials. Researchers across various sc ..."
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Structure characterization is one of many interesting problems in material science, the latter recently garnering significant attention in the form of the Materials Genome Initiative [4, 5], which ultimately seeks to understand the atomistic blueprint for key materials. Researchers across various scientific disci
An Investigation of Machine Learning Methods Applied to Structure Prediction in Condensed Matter
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A Unified SVM Framework for Signal Estimation
"... This paper presents a review in the form of a unified framework for tackling estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The paper formalizes our developments in the area of DSP with SVM principles. The use of SVMs for DSP is already mature, and has g ..."
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This paper presents a review in the form of a unified framework for tackling estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The paper formalizes our developments in the area of DSP with SVM principles. The use of SVMs for DSP is already mature, and has gained popularity in recent years due to its advantages over other methods: SVMs are flexible nonlinear methods that are intrinsically regularized and work well in lowsamplesized and highdimensional problems. SVMs can be designed to take into account different noise sources in the formulation and to fuse heterogeneous information sources. Nevertheless, the use of SVMs in estimation problems has been traditionally limited to its mere use as a blackbox model. Noting such limitations in the literature, we take advantage of several properties of Mercer’s kernels and functional analysis to develop a family of SVM methods for estimation in DSP. Three types of signal model equations are analyzed. First, when a specific timesignal structure is assumed to model the underlying system that generated the data, the linear signal model (so called Primal Signal Model formulation) is first stated and analyzed. Then, nonlinear versions of the signal structure can be readily developed by following two different approaches. On the one hand, the signal model equation is written in reproducing kernel Hilbert spaces (RKHS) using the wellknown RKHS Signal Model formulation, and Mercer’s kernels are readily used in SVM nonlinear algorithms. On the other hand, in the alternative and not so common Dual Signal Model formulation, a signal expansion is made by using an auxiliary signal model equation given by a nonlinear regression of each time instant in the observed time series.