Maximum margin equalizers trained with the Adatron algorithm

Santamarı́a, Ignacio; Gonzalez, Rafael; Pantaleón, C.; Prı́ncipe, José C.

doi:10.1016/s0165-1684(02)00490-5

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…This problem is equivalent to maximizing the following quadratic form: (14) subject to , and where the input patterns are now given by (11). In this way, we arrive again at a conventional QP problem with real variables, whose solution is given by…”

Section: B Complex Channel and Qpsk Inputmentioning

confidence: 99%

“…Blind equalization is formulated as a support vector regression (SVR) problem [7], and an iterative procedure, which is denoted as iterative reweighted quadratic programming (IRWQP), is proposed to find the optimal regressor. Support vector machines (SVMs) have been successfully applied to linear and nonlinear supervised equalization problems [8]- [11]. In these works, the equalization problem is viewed as a supervised classification problem (with a training sequence), and the corresponding SV classifier is derived.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blind Equalization of Constant Modulus Signals Using Support Vector Machines

Santamarı́a

Pantaleón

Vielva

et al. 2004

IEEE Trans. Signal Process.

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Abstract-In this paper, the problem of blind equalization of constant modulus (CM) signals is formulated within the support vector regression (SVR) framework. The quadratic inequalities derived from the CM property are transformed into linear ones, thus yielding a quadratic programming (QP) problem. Then, an iterative reweighted procedure is proposed to blindly restore the CM property. The technique is suitable for real and complex modulations, and it can also be generalized to nonlinear blind equalization using kernel functions. We present simulation examples showing that linear and nonlinear blind SV equalizers offer better performance than cumulant-based techniques, mainly in applications when only a small number of data samples is available, such as in packet-based transmission over fast fading channels.

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Section: B Complex Channel and Qpsk Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind Equalization of Constant Modulus Signals Using Support Vector Machines

Santamarı́a

Pantaleón

Vielva

et al. 2004

IEEE Trans. Signal Process.

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“…As a result, a bigger amount of training data is necessary, so it may be a drawback in applications where measuring data are not trivial, such as antenna array applications; thus, simulation techniques arises as an alternative to calculate any training data. One of the most powerful SRM‐based learning methods is the use of support vector machines, particularly its regression framework: support vector regression (SVR) ; SVR provides excellent results for problems as channel equalization , device modeling , or synthesis on antenna arrays . It is used in this paper to obtain the accurate characterization of the coupling matrix of a predefined antenna array.…”

Section: Introductionmentioning

confidence: 99%

Support vector regression for near‐field multifocused antenna arrays considering mutual coupling

Muñiz

Ayestaran

Laviada

et al. 2015

Int J Numerical Modelling

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Support vector regression is proposed to obtain the coupling matrix that accounts for the real behavior of an antenna array in the near-field multifocusing framework. The obtained model can then be easily included in this synthesis method in order to obtain the feeding weights which satisfy the radiation pattern requirements under real conditions. An example of the performance of support vector regression using information from method of moments is also provided to demonstrate the performance and accuracy of the technique. SUPPORT VECTOR REGRESSION, NEAR-FIELD MULTIFOCUSING, MUTUAL COUPLING NEAR-FIELD MULTIFOCUSING FRAMEWORK UNDER REAL CONDITIONSThe aim of the NF-MF framework is obtaining the excitations to apply to the array elements so that the entire antenna focuses on the desired points simultaneously. Method explained in Reference [1] is used for this purpose, where an MP optimization or a PO synthesis of the feeding weights can be obtained according to the preferences of the application. Thus, excitations ! in (7) can be obtained using the NF-MF technique, with the following modifications to fulfill mutual coupling conditions.The commented synthesis technique in Reference [1] is based on a proper cost function defined by the maximum and minimum limits allowed for the field at each position of space, and its minimization is solved using an equivalent least squares problem and the Levenberg-Marquardt method [2]. A Jacobian matrix is then required, where some partial derivatives of the radiated field according to the

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“…In this paper we extend the previous work [4,5] in the following directions: first, we derive a robust algorithm for the blind estimation of a non-sparse channel when the channel order has been highly overestimated; secondly, to avoid the high computational cost in solving a QP problem, we use a fast and simple algorithm called the Adatron [6]; and finally, simulation results are provided to verify that the proposed algorithm outperforms existing robust methods even when the channel order is highly overestimated.…”

Section: Introductionmentioning

confidence: 95%

“…(10) is the main drawback of applying the SVM technique to practical estimation problems. Several techniques have been proposed to solve this problem, including the use of iterative reweighted least squares (IRWLS) techniques [7,8] and the Adatron algorithm [6,9]. The IRWLS requires a matrix inversion at each iteration so the computational burden could be considerably high even for a moderate number of data.…”

Section: The Adatron Algorithmmentioning

confidence: 99%

Robust Blind Simo Channel Estimation Using Adatron

Han

Prı́ncipe

Yang

et al.

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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In this paper we apply the structural risk minimization (SRM) principle to derive a blind single-input multiple-output (SIMO) channel estimation algorithm, which is robust to channel order overestimation. Specifically, the blind estimation is formulated as a support vector regression (SVR) problem in which the channel coefficients are the Lagrange multipliers of the dual problem. In this paper, we show that the SRM principle pushes to zero the small leading and trailing terms of the channel impulse response even when its order is highly overestimated. The main drawback of this approach is the high computational cost of the resulting quadratic programming (QP) problem. To alleviate this, in this paper we propose to use a simple and fast algorithm called the Adatron to solve the QP problem. Simulation results are provided to demonstrate the performance of our channel estimator.

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Maximum margin equalizers trained with the Adatron algorithm

Cited by 10 publications

References 25 publications

Blind Equalization of Constant Modulus Signals Using Support Vector Machines

Blind Equalization of Constant Modulus Signals Using Support Vector Machines

Support vector regression for near‐field multifocused antenna arrays considering mutual coupling

Robust Blind Simo Channel Estimation Using Adatron

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