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Two novel memory polynomial models for modeling of RF power amplifiers

Published online by Cambridge University Press:  02 April 2014

Per N. Landin Open the ORCID record for Per N. Landin [Opens in a new window] ,
Kurt Barbé ,
Wendy Van Moer ,
Magnus Isaksson  and
Peter Händel
Per N. Landin*
Affiliation:
Department of Electronics, Mathematics and Natural Sciences, University of Gävle, Kungsbäcksvägen 47, 80176 Gävle, Sweden. Phone: +46 31 772 1885 Department of Signals and Systems, Chalmers University of Technology, Hörsalsvägen 11, 41296 Göteborg, Sweden Department for Fundamental Electricity and Instrumentation (ELEC), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Brussels, Belgium Signal Processing Laboratory, KTH Royal Institute of Technology, Osquldasväg 1, 10044 Stockholm, Sweden
Kurt Barbé
Affiliation:
Department for Fundamental Electricity and Instrumentation (ELEC), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Brussels, Belgium
Wendy Van Moer
Affiliation:
Department for Fundamental Electricity and Instrumentation (ELEC), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Brussels, Belgium
Magnus Isaksson
Affiliation:
Department of Electronics, Mathematics and Natural Sciences, University of Gävle, Kungsbäcksvägen 47, 80176 Gävle, Sweden. Phone: +46 31 772 1885
Peter Händel
Affiliation:
Signal Processing Laboratory, KTH Royal Institute of Technology, Osquldasväg 1, 10044 Stockholm, Sweden
*
Corresponding author: P.N. Landin Email: [email protected]
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Abstract

Two novel memory polynomial models are derived based on physical knowledge of a general power amplifier (PA). The derivations are given in detail to facilitate derivations of other model structures. The model error in terms of normalized mean square error (NMSE) and adjacent channel error power ratio (ACEPR) of the novel model structures are compared to that of established models based on the number of parameters using data measured on two different amplifiers, one high-power base-station PA and one low-power general purpose amplifier. The novel models show both lower NMSE and ACEPR for any chosen number of parameters compared to the established models. The low model errors make the novel models suitable candidates for both modeling and digital predistortion.

Keywords

Power amplifiers and linearizersWireless systems and signal processing (SDR, MIMO, UWB, etc.)
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 7 , Issue 1 , February 2015 , pp. 19 - 29
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2014 

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References

REFERENCES

[1]Cripps, S.: RF Power Amplifiers for Wireless Communications, Artech House, Norwood, MA, 2006.Google Scholar
[2]Schreurs, D.; O'Droma, M.; Goacher, A.; Gadringer, M.: RF Power Amplifier Behavioral Modeling, Cambridge University Press, Cambridge, UK, 2008.Google Scholar
[3]Cripps, S.: Advanced Techniques in RF Power Amplifier Design, Artech House, Norwood, MA, 2002.Google Scholar
[4]3GPP TS36.141 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Base Station (BS) conformance testing (Release 10), 2011.Google Scholar
[5]3GPP TS 25.141 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Base Station (BS) conformance testing (FDD) (Release 10), 2011.Google Scholar
[6]Vuolevi, J.; Rahkonen, T.: Distortion in RF Power Amplifiers, Artech House, Norwood, MA, 2003.Google Scholar
[7]Isaksson, M.; Rönnow, D.: A parameter-reduced Volterra model for dynamic RF power amplifier modeling based on orthonormal basis functions. Int. J. RF Microw. Comp. Aided Eng., 6 (2007), 542–551.CrossRefGoogle Scholar
[8]Kim, J.; Konstantinou, K.: Digital predistortion of wideband signals based on power amplifier model with memory. Electron. Lett., 23 (2001), 1417–1418.CrossRefGoogle Scholar
[9]Manne, G.K.; Yao, T.: On the predistortion technique for improving transmission linearity of ofdm system, in Proc. IEEE Vehicular Technology Conf., 2004, 3876–3879.Google Scholar
[10]Hammi, O.; Ghannouchi, F.M.; Vassilakis, B.: A compact envelope-memory polynomial for RF transmitters modeling with application to baseband and RF-digital predistortion. IEEE Microw. Wirel. Compon. Lett., 5 (2008), 359–361.Google Scholar
[11]Ghannouchi, F.M.; Hammi, O.: Behavioral modeling and predistortion. IEEE Microw. Mag., 7 (2009), 52–64.Google Scholar
[12]Landin, P.N.; Isaksson, M.; Händel, P.: Parameter extraction and performance evaluation method for increased performance in RF power amplifier behavioral modeling. Int. J. RF Microw. Comp. Aided Eng., 2 (2010), 200–208.Google Scholar
[13]Heutmaker, M.S.; Wu, E.; Welch, J.R.: Envelope distortion models with memory improve the prediction of spectral regrowth for some RF amplifiers, in 48th ARFTG Conf. Digest, 1996, 10–15.Google Scholar
[14]Morgan, D.R.; Ma, Z.; Kim, J.; Zierdt, M.G.; Pastalan, J.: A generalized memory polynomial model for digital predistortion of RF power amplifiers. IEEE Transact. Signal Process., 10 (2006), 3852–3860.Google Scholar
[15]Cunha, T.R.; Lima, E.G.; Pedro, J.C.: Validation and physical interpretation of the power-amplifier polar Volterra model. IEEE Transact. Microw. Theory Techn., 12 (2010), 4012–4021.Google Scholar
[16]Pedro, J.C.; Carvalho, N.B.; Lavrador, P.M.; Modeling nonlinear behavior of band-pass memory less and dynamic systems, In IEEE MTT-S Int. Microw. Symp. Dig., 2003, 2133–2136.Google Scholar
[17]Proakis, J.G.: Digital Communications, McGraw-Hill, New York, NY, 2001.Google Scholar
[18]Pedro, J.C.; Maas, S.A.: A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches. IEEE Transact. Microw. Theory Tech., 4 (2005), 1150–1163.Google Scholar
[19]Landin, P.N.; Rönnow, D.: RF PA modeling considering odd-even and odd order polynomials. Int. J. Microw. Wirel. Technol., submitted.Google Scholar
[20]Stauth, J.T.; Sanders, S.R.: Power supply rejection for RF amplifiers: theory and measurements. IEEE Transact. Microw. Theory Tech., 10 (2007), 2043–2052.CrossRefGoogle Scholar
[21]Adams, R.A.: Calculus – A Complete Course, Addison-Wesley-Longman, Upper Saddle River, NJ, 2003.Google Scholar
[22]Wisell, D.; Isaksson, M.: Derivation of a behavioral RF power amplifier model with low normalized mean-square error, in Proc. European Microwave Integrated Circuit Conf., 2007, 485–488.Google Scholar
[23]Cunha, T.R.; Pedro, J.C.; Lima, E.G.: Low-pass equivalent feedback topology for power amplifier modeling, in IEEE MTT-S Int. Microwave Symp. Digest, 2008, 1445–1448.Google Scholar
[24]Cunha, T.R.; Pedro, J.C.; Cabral, P.M.; Zhu, A.: General nonlinear feed-forward RF model for power amplifiers, In IEEE MTT-S Int. Microwave Symp. Digest, 2007, 2027–2030.Google Scholar
[25]Cunha, T.R.; Pedro, J.C.; Cabral, P.M.: Design of a power-amplifier feed-forward RF model with physical knowledge considerations. IEEE Transact. Microw. Theory Tech., 12 (2007), 2747–2756.CrossRefGoogle Scholar
[26]Landin, P.N.; Barbé, K.; Van Moer, W.; Isaksson, M.; Händel, P.: Proofs of derivations in Memory polynomial baseband modeling of RF power amplifiers, University of Gävle Working Paper Series, vol. 45 (2011). http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-11168.Google Scholar
[27]Söderström, T.; Stoica, P.: System Identification, Prentice Hall, Hertfordshire, UK, 1989.Google Scholar
[28]Ding, L.; Zhou, G.T.: Effects of even-order nonlinear terms on power amplifier modeling and predistortion linearization. IEEE Transact. Veh. Technol., 1 (2004), 156–162.Google Scholar
[29]Ding, L.; Zhou, G.T.: Effects of even-order nonlinear terms on predistortion linearization, in Proc. IEEE Digital Signal Processing Workshop, 2002, 1–6.Google Scholar
[30]Isaksson, M.; Wisell, D.; Rönnow, D.: A comparative analysis of behavioral models for RF power amplifiers. IEEE Transact. Microw. Theory Tech., 1 (2006), 348–359.Google Scholar
[31]Ljung, L.: System Identification – Theory for the User, Prentice Hall, Upper Saddle River, NJ, 1999.Google Scholar
[32]Soltani Tehrani, A.; Cao, H.; Afsardoost, S.; Eriksson, T.; Isaksson, M.; Fager, C.: A comparative analysis of the complexity/accuracy tradeoff in power amplifier behavioral models. IEEE Transact. Microw. Theory Tech., 6 (2010), 1510–1520.Google Scholar
[33]Kay, S.M.: Fundamentals of Statistical Signal Processing – Estimation Theory, Prentice Hall, Upper Saddle River, NJ, 1993.Google Scholar
[34]Golub, G.H.; Van Loan, C.F.: Matrix Computations, Johns Hopkins University Press, Baltimore, MD, 1996.Google Scholar
[35]Ku, H.; Kenney, J.S.: Behavioral modeling of nonlinear RF power amplifiers considering memory effects. IEEE Transact. Microw. Theory Tech., 12 (2003), 2495–2504.Google Scholar
[36]Wisell, D.; Jalden, J.; Händel, P.: Behavioral power amplifier modeling using the LASSO, in Proc. IEEE Instrumentation and Measurement Technology Conf., 2008, 1864–1867.Google Scholar
[37]R”onnow, D.; Wisell, D.; Isaksson, M.: Three-tone characterization of nonlinear memory effects in radio-frequency power amplifiers. IEEE Transact. Instrum. Meas., 56 (2007), 2646–2657.CrossRefGoogle Scholar
[38]Landin, P.N.: Digital baseband modeling and correction of radio frequency power amplifiers. Doctoral thesis, KTH Royal Institute of Technology, 2012.Google Scholar