Set-membership adaptive algorithms based on time-varying error bounds and their application to interference suppression

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

doi:10.1109/its.2006.4433396

Cited by 10 publications

(10 citation statements)

References 10 publications

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“…Another approach for performing the estimation of the SM threshold is to use a time-varying threshold. The proposed estimation is inspired by the time-varying threshold [11], by avoiding the term containing σ 2 n , which requires previous knowledge of the measurement noise variance. The estimated thresholdγ(k) at iteration k is given by:…”

Section: B Time-varying Thresholdmentioning

confidence: 99%

Set-Membership Affine-Projection with Estimated Parameters for non-White Inputs

Ferreira¹,

Lima²,

Diniz³

2021

Anais Do XXXIX Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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This article proposes versions of the set-membership affine-projection algorithm that estimate an adequate threshold and a proper constraint-vector for a system identification scenario. A novel constraint-vector is proposed for a non-white input, since the Simple-Choice Constraint Vector is asymptotically optimum for white inputs. Non-white inputs are commonplace in communication systems when there is channel encoding or precoding in the transmitter. A fixed and a time-varying threshold set-membership version are used. Simulations show that the proposed algorithm presents lower mean-square error and updates in fewer iterations than the traditional Simple-Choice and Exponential-Decay Constraint Vectors. For the timevarying threshold, the computational complexity of the algorithm is similar to the Simple-Choice Constraint Vector.

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Section: B Time-varying Thresholdmentioning

confidence: 99%

Set-Membership Affine-Projection with Estimated Parameters for non-White Inputs

Ferreira¹,

Lima²,

Diniz³

2021

Anais Do XXXIX Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…Based on the SMF technique, a normalized least mean square (NLMS) algorithm with SMF has been proposed to reduce the data updates and iterations for getting the same or lower steady-state error floor, which is denoted as set-membership NLMS (SM-NLMS) algorithm [1]. Since the prior bound is important for SM-NLMS algorithm, error-bound development methods have been proposed such as parameter-dependent technique [4][5][6]. In recent years, the SM-NLMS algorithm has been widely used for channel estimation, adaptive control, echo cancellation, and system identification [3].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Channel Estimation Based on an Improved Norm-Constrained Set-Membership Normalized Least Mean Square Algorithm

Jin

Wang

2017

Wireless Communications and Mobile Computing

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An improved norm-constrained set-membership normalized least mean square (INCSM-NLMS) algorithm is proposed for adaptive sparse channel estimation (ASCE). The proposed INCSM-NLMS algorithm is implemented by incorporating an lp-norm penalty into the cost function of the traditional set-membership normalized least mean square (SM-NLMS) algorithm, which is also denoted as lp-norm penalized SM-NLMS (LPSM-NLMS) algorithm. The derivation of the proposed LPSM-NLMS algorithm is given theoretically, resulting in a zero attractor in its iteration. By using this proposed zero attractor, the convergence speed is effectively accelerated and the channel estimation steady-state error is also observably reduced in comparison with the existing popular SM-NLMS algorithms for estimating exact sparse multipath channels. The estimation behaviors are investigated via a typical sparse wireless multipath channel, a typical network echo channel, and an acoustic channel. The computer simulation results show that the proposed LPSM-NLMS algorithm is better than those corresponding sparse SM-NLMS and traditional SM-NLMS algorithms when the channels are exactly sparse.

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“…In this work, we present the set-membership normalized kernel least-mean square (SM-NKLMS) and the set-membership kernel affine projection (SM-KAP) adaptive algorithms, which can provide a faster learning than existing kernel-based algorithms and limit the size of the dictionary without compromising performance. Similarly to existing set-membership algorithms [4,15,22,5,44,3,6,43,42,45], the proposed SM-NKLMS and SM-KAP algorithms are equipped with variable step sizes and perform sparse updates that are useful for several applications [8,9,10,7,39,38,41,12,23,13,11,18,51,17,48,2,52,50,37,31,46,34,36,47,29]. Unlike existing kernel-based adaptive algorithms the proposed SM-NKLMS and SM-KAP algorithms deal with in a natural way with the kernel expansion because of the data selectivity based on error bounds that they implement.…”

Section: Introductionmentioning

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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Set-membership adaptive algorithms based on time-varying error bounds and their application to interference suppression

Cited by 10 publications

References 10 publications

Set-Membership Affine-Projection with Estimated Parameters for non-White Inputs

Set-Membership Affine-Projection with Estimated Parameters for non-White Inputs

Adaptive Channel Estimation Based on an Improved Norm-Constrained Set-Membership Normalized Least Mean Square Algorithm

Set-membership kernel adaptive algorithms

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