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Wednesday, July 22, 2020 | History

7 edition of Blind equalization and system identification found in the catalog.

Blind equalization and system identification

batch processing algorithms, performance and applications

  • 244 Want to read
  • 39 Currently reading

Published by Springer in London, UK .
Written in English


Edition Notes

StatementChong-Yung Chi ... [et al.].
Classifications
LC ClassificationsTK
The Physical Object
Paginationxiii, 469 p. :
Number of Pages469
ID Numbers
Open LibraryOL22723182M
ISBN 101846280222

Journal Articles and Book Chapters. J. M. Walsh, P. A Jr., and P. Duhamel, “On Blind Equalization of Bi-Orthogonal Signaling,” submitted to IEEE Trans. on ``Blind Equalization Using the Constant Modulus Criterion: A Review,'' Proceedings of the IEEE, Special Issue on Blind System Identification and Estimation, October   Identification and equalization of Room Transfer Functions (RTFs) is an important topic with several applications in acoustic signal processing. RTFs are often modelled as finite impulse response filters characterized by orders of thousands of taps and non-minimum phase. In practice, only approximate estimates of the actual RTFs are available due to measurement noise, limited estimation .

The problem of blind demodulation of multiuser information symbols in a high-rate code-division multiple-access (CDMA) network in the presence of both mult Blind equalization and multiuser detection in dispersive CDMA channels - IEEE Journals & Magazine. and/or equalization of the channel. However, there are impor-tant applications, such as digital TV broadcasting, in which the use of a training sequence is very costly. In these cases, blind equalization techniques have proved viable alternatives. It is well established that when the receiver’s matched filter.

As a key technology of digital broadcast and TV, blind equalization overcomes inter-symbol interference to improve the effect of receiving signals. A new QAM blind equalization algorithm based on fuzzy neural network classifier was proposed. Reference - Book Blind Equalization & System Identification, batch processing Algorithm, performance & applications, by Chong-Yung Chi, Springer. Chapter 4 defines blind linear equalization for Single input single output (SISO).


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Blind equalization and system identification Download PDF EPUB FB2

"Blind Equalization and System Identification" provides such a unified treatment presenting theory, performance analysis, simulation, implementation and applications.

This is a textbook for graduate courses in discrete-time random processes, statistical signal processing, and blind equalization and system by: This is a textbook for graduate-level courses in discrete-time random processes, statistical signal processing, and blind equalization and system identification.

It contains material which will also interest researchers and practicing engineers working in digital communications, source separation, speech processing, image processing, seismic. Blind Equalization and Identification (Signal Processing and Communications): Medicine & Health Science Books @ ed by: It highlights basic operating conditions and potential for malfunction.

The authors also address concepts and principles of blind algorithms for single input multiple output (SIMO) systems and multi-user extensions of SIMO equalization and identification. Blind inverse problems include blind source separation (BSS) and independent component analysis (ICA) [1][2][3], blind deconvolution (BDE) [4,5], blind channel/system identification (BID) and.

Introduction The blind system identification (BSI) and blind channel equalization (BCE) problems addressed in this paper can be formulated as follows: A sequence of input signal u[khi] is transmitted at sampling rate li == l/hi to a continuous time system via an impulse generator or.

Blind equalization and identification [2] is a bandwidth efficient solution by eliminating training data and maximizing channel capacity for true information transmission. Another way to handle. Blind Equalization and Identification book.

Blind Equalization and Identification. DOI link for Blind Equalization and Identification. Blind Equalization and Identification book. By Zhi Ding, Ye Li. Edition 1st Edition. First Published eBook Published 8 October.

In early ’s, we investigated blind system identification and equalization. In order to compensate for channel distortion, channel parameters have to be identified explicitly or implicitly. Blind signal processing estimates channel/system parameters only by means of statistics of the system outputs without using any training sequences.

Tong, G. Xu, and T. Kailath, “A new approach to blind identification and equalization of multipath channels,” in Record of the 25th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Nov.

4–6,pp. – Blind equalization is a digital signal processing technique in which the transmitted signal is inferred from the received signal, while making use only of the transmitted signal statistics.

Hence, the use of the word blind in the name. Blind equalization is essentially blind deconvolution applied to digital eless, the emphasis in blind equalization is on online estimation.

"Blind Equalization and System Identification" provides such a unified treatment presenting theory, performance analysis, simulation, implementation and is a. Blind identification is a very significant problem in many contexts where only the output of transmission channels can be observed. The solutions that can be found in the literature are limited to.

Title: Blind identification and equalization based on second-order statistics: a time domain approach - Information Theory, IEEE Transactions on. Get this from a library. Blind equalization and system identification: batch processing algorithms, performance and applications. [Chong-Yung Chi;] -- Discrete-time signal processing has had a momentous impact on advances in engineering and science over recent decades.

The rapid progress of digital and mixed-signal integrated circuits in processing. Then, we extend the identification algorithms for single-input/multiple- output (FIR-SIMO) channels, such as the algebraic algorithm and the subspace algorithms to the identification of the MIMO FIR channels.

The MIMO systems can also be directly equalized using blind techniques. Publications > Books & Book Chapters Books. and C.-Y. Chen, Blind Equalization and System Identification: Batch Processing Algorithms, Performance and Applications, Springer Chong-Yung Chi, and Y.

Wang, “Convex analysis for non-negative blind source separation with application in imaging,” Chapter 7 in Convex Optimization in. Home Browse by Title Theses New algorithms for blind equalization and blind source separation/phase recovery New algorithms for blind equalization and blind source separation/phase recovery January Tsatsanis, “Time-varying system identification: A deterministic blind approach using antenna arrays - Liu, Giannakis, et al.

- 3 Giannakis, “Self-recovering equalization of time-selective fading channels using redundant filterbank precoders - Scaglione, Barbarossa, et al. - This paper proposes a joint blind equalization and modulation identification algorithm for higher order QAM modulated signals in multi-path environments.

Compared with the existing algorithms, the complexity of the proposed algorithm is reduced and the proposed algorithm is robust to carrier phase offset in the multi-path channels. Simulation results demonstrate the feasibility at middle or.

Karim Abed Meraim, Wanzhi Qiu, “Blind System Identification”, Proceedings of the IEEE Vol, No.8, Mirai Oshiro, Hiroshi Ochi, “Noise Robust Blind System Identification Using LMS Method”, Roc. 43rd IEEE Wdwest Symp.

on Circuits and Systems, Lanslng MI, Aug 8‐1 1, The normalized projection misalignment (NPM), which is a measure of blind system identification (BSI), is studied in the context of multichannel speech der On the evaluation of multichannel blind system identification from the viewpoint of system equalization - IEEE Conference Publication.select article Time-Varying System Identification and Channel Equalization Using Wavelets and Higher-Order Statistics.