Hostname: page-component-cc8bf7c57-qfg88 Total loading time: 0 Render date: 2024-12-11T23:01:14.537Z Has data issue: false hasContentIssue false
  • English
  • Français

New order selection technique using information criteria applied to SISO and MIMO systems predistortion

Published online by Cambridge University Press:  05 March 2013

M.V. Amiri ,
S.A. Bassam ,
M. Helaoui  and
F.M. Ghannouchi
M.V. Amiri*
Affiliation:
iRadio Laboratory Electrical and Computer Engineering Department, Schulich School of Engineering, University of Calgary, ICT Building, 2500 University Drive NW, Calgary, T2N 1N4, Canada. Phone: +1 (403) 210-5408
S.A. Bassam
Affiliation:
iRadio Laboratory Electrical and Computer Engineering Department, Schulich School of Engineering, University of Calgary, ICT Building, 2500 University Drive NW, Calgary, T2N 1N4, Canada. Phone: +1 (403) 210-5408
M. Helaoui
Affiliation:
iRadio Laboratory Electrical and Computer Engineering Department, Schulich School of Engineering, University of Calgary, ICT Building, 2500 University Drive NW, Calgary, T2N 1N4, Canada. Phone: +1 (403) 210-5408
F.M. Ghannouchi
Affiliation:
iRadio Laboratory Electrical and Computer Engineering Department, Schulich School of Engineering, University of Calgary, ICT Building, 2500 University Drive NW, Calgary, T2N 1N4, Canada. Phone: +1 (403) 210-5408
*
Corresponding author: M.V. Amiri Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a new order selection technique of matrix memory polynomial technique that models the nonlinearities of single-branch and multi-branch transmitters. The new criteria take into account the complexity of the model in addition to its mean-square error in the selection criteria. The quasi-convexity of the proposed criteria was proven in this work. By using this proposed Akaike information criterion (AIC) and Bayesian information criterion (BIC) criteria, the model order selection was cast as a cost minimization problem. To minimize the criteria, modified gradient descent and simulated annealing algorithms were utilized which resulted in a considerable reduction in the number of search iterations. The performances of the criteria were shown by comparing the normalized mean square error (NMSE) of a higher-order model and the optimum model. It has been shown that the NMSE difference is <0.5 dB, but the complexity is much smaller.

Keywords

AICBICMIMORF crosstalkRadio transmitterPolynomial modelGradient descentSimulated annealing
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 5 , Special Issue 2: Special Issue on Power Amplifier Linearization , April 2013 , pp. 123 - 131
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Tehrani, A.S.; Haiying, C.; Afsardoost, S.; Eriksson, T.; Isaksson, M.; Fager, C.: A comparative analysis of the complexity/accuracy tradeoff in power amplifier behavioral models. IEEE Trans. Microw. Theory Tech., 58(6) (2010), 1510–1520.Google Scholar
[2]Isaksson, M.; Wisell, D.; Ronnow, D.: A comparative analysis of behavioral models for RF power amplifiers. IEEE Trans. Microw. Theory Tech., 54(1) (2006), 348–359.CrossRefGoogle Scholar
[3]Younes, M.; Hammi, O.; Kwan, A.; Ghannouchi, F.M.: An accurate complexity-reduced “PLUME” model for behavioral modeling and digital predistortion of RF power amplifiers. IEEE Trans. Ind. Electron., 58(4) (2011), 1397–1405.Google Scholar
[4]Ghannouchi, F.M.; Hammi, O.: Behavioral modeling and predistortion. IEEE Microw. Mag. 10(7) (2009), 52–64.Google Scholar
[5]Hammi, O.; Younes, M.; Ghannouchi, F.M.: Metrics and methods for benchmarking of RF transmitter behavioral models with application to the development of a hybrid memory polynomial model. IEEE Trans. Broadcast., 56(3) (2010), 350–357.Google Scholar
[6]Wood, J.; LeFevre, M.; Runton, D.; Nanan, J.-C.; Noori, B.H.; Aaen, P.H.: Envelope-domain time series (ET) behavioral model of a Doherty RF power amplifier for system design. IEEE Trans. Microw. Theory Tech., 54(8) (2006), 3163–3172.Google Scholar
[7]Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control, 19(6) (1974), 716–723.CrossRefGoogle Scholar
[8]Schwarz, G.: Estimating the Dimension of a Model. Ann. Stat. 6(2) (1978), 461–464.Google Scholar
[9]Stoica, P.; Selen, Y.: Model-order selection: a review of information criterion rules. IEEE Signal Process. Mag., 21(4) (2004), 36–47.Google Scholar
[10]Bozdogan, H.: Model selection and akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika, 52(3) (1987), 345–370.CrossRefGoogle Scholar
[11]Liavas, A.P.; Regalia, P.A.: On the behavior of information theoretic criteria for model order selection. IEEE Trans. Signal Process., 49(8) (2001), 1689–1695.Google Scholar
[12]Bassam, S.A.; Helaoui, M.; Ghannouchi, F.M.: Crossover digital predistorter for the compensation of crosstalk and nonlinearity in MIMO transmitters. IEEE Trans. Microw. Theory Tech. 57(5) part: 1, (2009), 1119–1128.CrossRefGoogle Scholar
[13]Amiri, M.V.; Bassam, S.A.; Helaoui, M.; Ghannouchi, F.M.: Matrix-based orthogonal polynomials for MIMO transmitter linearization, 15th IEEE International Workshop on Computer Aided Modeling, Analysis and Design of Communication Links and Networks (CAMAD), 2010 vol., no., 3–4 December 2010 57–60.Google Scholar
[14]Arora, J.S.; Huang, M.W.; Hsieh, C.C.: Methods for optimization of nonlinear problems with discrete variables: a review. Struct. Multidiscip. Optim. 8(2–3) (1994), 69–85.Google Scholar
[15]Rutenbar, R.A.: Simulated annealing algorithms: an overview. IEEE Circuits and Devices Mag., 5(1) (1989), 19–26.Google Scholar