Blind Adaptive Interference Suppression Based on Set-Membership Constrained Constant-Modulus Algorithms With Dynamic Bounds

Lamare, Rodrigo C. de; Diniz, Paulo S. R.

doi:10.1109/tsp.2012.2229995

Cited by 24 publications

(28 citation statements)

References 42 publications

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“…where P up = Pr(|e(i)| > γ) = 2Q γ σe denotes the probability of update of the set-membership algorithm [29] and σ e is the standard deviation of a Gaussian random variable associated with the error.…”

Section: B Statistical Analysismentioning

confidence: 99%

Set-membership kernel adaptive algorithms

Flores

Lamare

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In the last decade, a considerable research effort has been devoted to developing adaptive algorithms based on kernel functions. One of the main features of these algorithms is that they form a family of universal approximation techniques, solving problems with nonlinearities elegantly. In this paper, we present data-selective adaptive kernel normalized least-mean square (KNLMS) algorithms that can increase their learning rate and reduce their computational complexity. In fact, these methods deal with kernel expansions, creating a growing structure also known as the dictionary, whose size depends on the number of observations and their innovation. The algorithms described herein use an adaptive step-size to accelerate the learning and can offer an excellent tradeoff between convergence speed and steady state, which allows them to solve nonlinear filtering and estimation problems with a large number of parameters without requiring a large computational cost. The data-selective update scheme also limits the number of operations performed and the size of the dictionary created by the kernel expansion, saving computational resources and dealing with one of the major problems of kernel adaptive algorithms. A statistical analysis is carried out along with a computational complexity analysis of the proposed algorithms. Simulations show that the proposed KNLMS algorithms outperform existing algorithms in examples of nonlinear system identification and prediction of a time series originating from a nonlinear difference equation.

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Section: B Statistical Analysismentioning

confidence: 99%

Set-membership kernel adaptive algorithms

Flores

Lamare

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Substituting the optimal weight vector of WL beamformers denoted by (16) into (17), we can get the optimal SINR of WL beamformers as…”

Section: Output Performancementioning

confidence: 99%

“…Constrained constant modulus (CCM) criterion minimizes the deviation of the output signal from certain constant modulus while satisfying the constraints on the desired signal's response. Excellent performances have been obtained with CCM criterion in applications such as multiuser detection, interference suppression in CDMA system, and adaptive beamforming [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Constrained Widely Linear Beamforming Antijammer Technique for Satellite-Borne Distributed Sensor Data Acquisition System

Wang

Zhao

et al. 2016

International Journal of Distributed Sensor Networks

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Widely linear (WL) adaptive beamforming algorithms are proposed based on constrained minimum variance (CMV) and constrained constant modulus (CCM) criteria, respectively, to fully exploit both the desired signal and interferences' noncircularity. Modified conjugate gradient (MCG) algorithm is employed to ensure convergence with one iteration per sample. To further facilitate the adaptive processing, CCM criterion based WL beamforming algorithm is modified by second-order local approximation. Global convergence of the proposed algorithms is analyzed and constraints of parameters are given. Results of numerical simulations demonstrate that the proposed WL CCM beamformer has superior performance over WL CMV beamformer and conventional beamformers in convergence rate, robustness against direction of arrival (DOA) mismatch, and the noncircularity coefficient estimation error, while the former two have comparable low complexities without numerical instability.

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“…Adaptive algorithms can be classified into two main categories: non-blind adaptive algorithms and blind adaptive algorithms [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Non-blind adaptive algorithms require the transmission of a pilot training sequence.…”

Section: Introductionmentioning

confidence: 99%

Blind adaptive receiver for uplink multiuser massive MIMO systems

Shin

Seo

2019

ETRI Journal

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Herein, we consider uplink multiuser massive multiple‐input multiple‐output systems when multiple users transmit information symbols to a base station (BS) by applying simple space‐time block coding (STBC). At the BS receiver, two detection filters for each user are used to detect the STBC information symbols. One of these filters is for odd‐indexed symbols and the other for even‐indexed symbols. Using constrained output variance metric minimization, we first derive a special relation between the closed‐form optimal solutions for the two detection filters. Then, using the derived special relation, we propose a new blind adaptive algorithm for implementing the minimum output variance‐based optimal filters. In the proposed adaptive algorithm, filter weight vectors are updated only in the region satisfying the special relation. Through a theoretical analysis of the convergence speed and a computer simulation, we demonstrate that the proposed scheme exhibits faster convergence speed and lower steady‐state bit error rate than the conventional scheme.

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Blind Adaptive Interference Suppression Based on Set-Membership Constrained Constant-Modulus Algorithms With Dynamic Bounds

Cited by 24 publications

References 42 publications

Set-membership kernel adaptive algorithms

Set-membership kernel adaptive algorithms

Constrained Widely Linear Beamforming Antijammer Technique for Satellite-Borne Distributed Sensor Data Acquisition System

Blind adaptive receiver for uplink multiuser massive MIMO systems

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