Journal Article10.1109/TCSI.2007.895522
A Recursive Blind Adaptive Identification Algorithm and Its Almost Sure Convergence
M.S. Radenkovic,Tamal Bose +1 more
10
TL;DR: It is proved that the parameter estimates converge almost surely (a.s.) toward a scalar multiple of the true parameters of the least-squares type arguments.
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Abstract: This paper presents a novel blind adaptive identification algorithm based on least-squares type arguments. Parameter estimates are recursively updated with each output measurement, without resorting to any matrix inversion operation. It is proved that the parameter estimates converge almost surely (a.s.) toward a scalar multiple of the true parameters. Possible application of this algorithm to the channel equalization problem is discussed.
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Citations
System identification in communication with chaotic systems
Henri Huijberts,Henk Nijmeijer,R.M.A. Willems +2 more
- 01 Jan 1999
TL;DR: In this paper, the authors considered communication using chaotic systems from a control point of view and showed that parameter identification methods may be effective in building reconstruction mechanisms, even when a synchronizing system is not available.
141
Blind Adaptive Equalization of MIMO Systems: New Recursive Algorithms and Convergence Analysis
TL;DR: It is proved that the developed algorithms are globally convergent with probability one and an algorithm for the case of time-varying parameters is presented.
15
All-Adaptive Blind Matched Filtering for the Equalization and Identification of Multipath Channels—A Practical Approach
Adem Coskun,Izzet Kale +1 more
TL;DR: A novel architecture is proposed to simplify a potential hardware implementation of the blind matched filter receiver into an all-adaptive format which replaces all the matrix operations and does not need for any extra step to estimate the noise variance.
Blind adaptive identification of 2-channel systems using bias-compensated RLS algorithm
TL;DR: A bias‐compensated recursive least‐squares algorithm is proposed, which can estimate the unbalanced noises in real time and obtain the consistent estimation of channel characteristics.
5
Blind adaptive identification and equalization using bias-compensated NLMS methods
Zhen Zhang,Lijuan Jia,Ran Tao,Yue Wang +3 more
TL;DR: This paper proposes two new blind adaptive identification and equalization algorithms based on second-order statistics that estimate the variances of the noise online, and therefore, the noise-induced bias can be removed.
1
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A new approach to blind identification and equalization of multipath channels
Lang Tong,G. Xu,Thomas Kailath +2 more
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