Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-18T15:12:37.320Z Has data issue: false hasContentIssue false

Recursive learning-based joint digital predistorter for power amplifier and I/Q modulator impairments

Published online by Cambridge University Press:  01 July 2010

Lauri Anttila*
Affiliation:
Department of Communications Engineering, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland. Phone: +358-3-31155127; Fax: +358-3-31153808.
Peter Händel
Affiliation:
Royal Institute of Technology, ACCESS Linnaeus Center, Signal Processing Lab, Stockholm, Sweden.
Olli Mylläri
Affiliation:
Department of Communications Engineering, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland. Phone: +358-3-31155127; Fax: +358-3-31153808.
Mikko Valkama
Affiliation:
Department of Communications Engineering, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland. Phone: +358-3-31155127; Fax: +358-3-31153808.
*
Corresponding author: L. Anttila Email: [email protected]

Abstract

The main implementation impairments degrading the performance of direct-conversion radio transmitters are in-phase/quadrature (I/Q) mismatch, local oscillator (LO) leakage, and power amplifier (PA) nonlinear distortion. In this article, we propose a recursive least-squares-based learning algorithm for joint digital predistortion (PD) of frequency-dependent PA and I/Q modulator impairments. The predistorter is composed of a parallel connection of two parallel Hammerstein (PH) predistorters and an LO leakage compensator, yielding a predistorter which as a whole is fully linear in the parameters. In the parameter estimation stage, proper feedback signal from the transmitter radio frequency (RF) stage back to the digital parts is deployed, combined with the indirect learning architecture and recursive least-squares training. The proposed structure is one of the first techniques to explicitly consider the joint estimation and mitigation of frequency-dependent PA and I/Q modulator impairments. Extensive simulation and measurement analysis is carried out to verify the operation and efficiency of the proposed PD technique. In general, the obtained results demonstrate linearization and I/Q modulator calibration performance clearly exceeding the performance of current state-of-the-art reference techniques.

Type
Original Article
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2010

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]Mak, P.-I.; Seng-Pan, U.; Martins, R.P.: Transceiver architecture selection: review, state-of-the-art survey and case study. IEEE Circuits Syst. Mag., 7 (2007), 625.CrossRefGoogle Scholar
[2]Fettweis, G.; Löhning, M.; Petrovic, D.; Windisch, M.; Zillmann, P.; Rave, W.: Dirty RF: a new paradigm. Springer Int. J. Wirel. Inf. Netw., 14 (2007), 133148.CrossRefGoogle Scholar
[3]Zou, Y.; Valkama, M.; Renfors, M.: Performance analysis of spatial multiplexing MIMO-OFDM systems under frequency-selective I/Q imbalances, in Proc. Int. Wireless Communication and Mobile Computing Conf., Leipzig, Germany, June 2009.CrossRefGoogle Scholar
[4]Katz, A.: Linearization: reducing distortion in power amplifiers. IEEE Microw. Mag., December 2 (2001), 3749.CrossRefGoogle Scholar
[5]Kim, W.-J. et al. : Digital predistortion linearizes wireless power amplifiers. IEEE Microw. Mag., September 6 (2001), 5461.Google Scholar
[6]Cavers, J.K.: The effect of quadrature modulator and demodulator errors on adaptive digital predistorters for amplifier linearization. IEEE Trans. Veh. Technol., 46 (1997), 456466.CrossRefGoogle Scholar
[7]Cavers, J.K.: New methods for adaptation of quadrature modulators and demodulators in amplifier linearization circuits. IEEE Trans. Veh. Technol., 46 (1997), 707716.CrossRefGoogle Scholar
[8]Ding, L.; Ma, Z.; Morgan, D.R.; Zierdt, M.; Zhou, G.T.: Compensation of frequency-dependent gain/phase imbalance in predistortion linearization systems. IEEE Trans. Circuits Syst. – Part I: Regul. Pap., 55 (2008), 390397.CrossRefGoogle Scholar
[9]Anttila, L.; Valkama, M.; Renfors, M.: Frequency-selective I/Q mismatch calibration of wideband direct-conversion transmitters. IEEE Trans. Circuits Syst. – Part II: Express Briefs, 55 (2008), 359363.Google Scholar
[10]Huang, X.; Caron, M.: Efficient transmitter self-calibration and amplifier linearization techniques, in Proc. IEEE Int. Symp. on Circuits and Systems, New Orleans, LA, May 2007, 265268.CrossRefGoogle Scholar
[11]Kim, Y.-D.; Jeong, E.-R.; Lee, Y.H.: Adaptive compensation for power amplifier nonlinearity in the presence of quadrature modulation/demodulation errors. IEEE Trans. Signal Process., 55 (2007), 47174721.CrossRefGoogle Scholar
[12]Hilborn, D.S.; Stapleton, S.P.; Cavers, J.K.: An adaptive direct conversion transmitter. IEEE Trans. Veh. Technol., 43 (1994), 223233.CrossRefGoogle Scholar
[13]Cao, H.; Tehrani, A.S.; Fager, C.; Eriksson, T.; Zirath, H.: I/Q imbalance compensation using a nonlinear modeling approach. IEEE Trans. Microwave Theory Tech., 57 (2009), 513518.Google Scholar
[14]Anttila, L.; Händel, P.; Valkama, M.: Joint mitigation of power amplifier and I/Q modulator impairments in broadband direct-conversion transmitters. IEEE Trans. Microwave Theory Tech., 58 (2010).CrossRefGoogle Scholar
[15]Ding, L. et al. : A robust predistorter constructed using memory polynomials. IEEE Trans. Commun., 52 (2004), 159165.CrossRefGoogle Scholar
[16]Isaksson, M.; Wisell, D.; Rönnow, D.: A comparative analysis of behavioral models for RF power amplifiers. IEEE Trans. Microwave Theory Tech., 54 (2006), 348359.CrossRefGoogle Scholar
[17]Isaksson, M.; Rönnow, D.: A parameter-reduced Volterra model for dynamic RF power amplifier modeling based on orthonormal basis functions. Int. J. RF Microw. Comput.-Aid. Eng., 17 (2007), 542551.CrossRefGoogle Scholar
[18]Raich, R.; Zhou, G.T.: Orthogonal polynomials for complex Gaussian processes. IEEE Trans. Signal Process., 52 (2004), 27882797.CrossRefGoogle Scholar
[19]Schetzen, M.: Theory of pth-order inverses of nonlinear systems. IEEE Trans. Circuits Syst., CAS-23 (1976), 285291.CrossRefGoogle Scholar
[20]Eun, C.; Powers, E.J.: A new Volterra predistorter based on the indirect learning architecture. IEEE Trans. Signal Process., 45 (1997), 223227.Google Scholar
[21]Morgan, D.R. et al. : A generalized memory polynomial model for digital predistortion of RF power amplifiers. IEEE Trans. Signal Process., 54 (2006), 38523860.CrossRefGoogle Scholar
[22]Haykin, S.: Adaptive Filter Theory, 3rd ed., Prentice-Hall, Upper Saddle River, NJ, 1996.Google Scholar
[23]DeGroat, R.D.; Dowling, E.M.: The data least squares problem and channel equalization. IEEE Trans. Signal Process., 41 (1993), 407411.CrossRefGoogle Scholar
[24]Griliches, Z.; Ringstad, V.: Errors-in-the-variables bias in nonlinear contexts. Econometrica, 38 (1970), 368370.CrossRefGoogle Scholar
[25]Nentwig, M.: Delay Estimation by FFT. Blog Article, available at http://www.dsprelated.com/showarticle/26.php.Google Scholar
[26]Ding, L.: Digital Predistortion of Power Amplifiers for Wireless Applications, Ph.D. Dissertation, Georgia Institute of Technology, Atlanta, GA, 2004.Google Scholar
[27]Rapp, C.: Effects of HPA-nonlinearity on a 4-DPSK/OFDM-signal for a digital sound broadcasting system, in Proc. Second European Conf. on Satellite Communications, Liege, Belgium, October 22–24, 1991, 179184.Google Scholar
[28]3GPP Technical Specification Group Radio Access Network, Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access (E-UTRAN); Overall Description; Stage 2, Technical Report TS 36.300, V1.0.0, March 2007.Google Scholar
[29]GNU radio website: http://gnuradio.org/.Google Scholar