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Behavioral modeling and digital pre-distortion techniques for RF PAs in a 3 × 3 MIMO system

Published online by Cambridge University Press:  20 June 2019

Mahmoud Alizadeh Open the ORCID record for Mahmoud Alizadeh [Opens in a new window] ,
Peter Händel  and
Daniel Rönnow
Mahmoud Alizadeh*
Affiliation:
Department of Electrical Engineering, Mathematics and Science, University of Gävle, Gävle, Sweden Division of Information Science and Engineering, Royal Institute of Technology (KTH), Stockholm, Sweden
Peter Händel
Affiliation:
Division of Information Science and Engineering, Royal Institute of Technology (KTH), Stockholm, Sweden
Daniel Rönnow
Affiliation:
Department of Electrical Engineering, Mathematics and Science, University of Gävle, Gävle, Sweden
*
Author for correspondence: Mahmoud Alizadeh, E-mail: [email protected]
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Abstract

Modern telecommunications are moving towards (massive) multi-input multi-output (MIMO) systems in 5th generation (5G) technology, increasing the dimensionality of the systems dramatically. In this paper, the impairments of radio frequency (RF) power amplifiers (PAs) in a 3 × 3 MIMO system are compensated in both the time and the frequency domains. A three-dimensional (3D) time-domain memory polynomial-type model is proposed as an extension of conventional 2D models. Furthermore, a 3D frequency-domain technique is formulated based on the proposed time-domain model to reduce the dimensionality of the model, while preserving the performance in terms of model errors. In the 3D frequency-domain technique, the bandwidth of the system is split into several narrow sub-bands, and the parameters of the model are estimated for each sub-band. This approach requires less computational complexity, and also the procedure of the parameters estimation for each sub-band can be implemented independently. The device-under-test consists of three RF PAs including input and output cross-talk channels. The proposed techniques are evaluated in both behavioral modeling and digital pre-distortion (DPD) perspectives. The experimental results show that the proposed DPD technique can compensate the errors of non-linearity and memory effects in the both time and frequency domains.

Keywords

Radio frequency power amplifiernon-linearitymemory effectsmultiple-input multiple-output (MIMO)behavioral modellingdigital pre-distortion
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 11 , Issue 10 , December 2019 , pp. 989 - 999
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2019 

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References

1.Ghannouchi, FM, Hammi, O and Helaoui, M (2015) Behavioral Modeling and Predistortion of Wideband Wireless Transmitters. West Sussex, UK: John Wiley & Sons.Google Scholar
2.Larsson, EG, Edfors, O, Tufvesson, F and Marzetta, TL (2014) Massive MIMO for next generation wireless systems. IEEE Communications Magazine 52, 186–195.Google Scholar
3.Abdelaziz, M, Anttila, L and Valkama, M (2017) Reduced-complexity digital predistortion for massive MIMO. IEEE International Conference Acoustics, Speech, Signal Process. (ICASSP), New Orleans, LA, USA.Google Scholar
4.Vuolevi, J and Rahkonen, T (2003) Distortion in RF Power Amplifiers. Boston, MA, USA: Artech House.Google Scholar
5.Pedro, JC and Mass, SA (2005) A comparative overview of microwave and wireless power amplifier behavioral modeling approaches. IEEE Transactions on Microwave Theory and Techniques 53, 1150–1163.Google Scholar
6.Bassam, S, Helaoui, M and Ghannouchi, FM (2009) Crossover digital predistorter for the compensation of crosstalk and nonlinearity in MIMO transmitters. IEEE Transactions on Microwave Theory and Techniques 57, 1119–1128.Google Scholar
7.Schetzen, M (1980) The Volterra and Wiener Theory of Nonlinear System. New York, USA: John Wiley & Sons.Google Scholar
8.Wood, J (2014) Behavioral Modeling and Linearization of RF Power Amplifiers. Norwood, MA, USA: Artech House.Google Scholar
9.Mathews, CJ and Sicuranza, GL (2000) Polynomial Signal Processing. New York, USA: Wiley.Google Scholar
10.Amin, S, Landin, PN, Händel, P and Rönnow, D (2014) Behavioral modelling and linearization of crosstalk and memory effects in RF MIMO transmitters. IEEE Transactions on Microwave Theory and Techniques 62, 1–14.Google Scholar
11.Alizadeh, M, Amin, S and Rönnow, D (2017) Measurement and analysis of frequency-domain Volterra kernels of nonlinear dynamic 3 × 33 MIMO systems. IEEE Transactions on Instrumentation and Measurement 66, 1893–1905.Google Scholar
12.Khan, ZA, Zenteno, E, Händel, P and Isaksson, M (2018) Extraction of the third-Order 3 × 3 MIMO Volterra kernel outputs using multitone signals. IEEE Transactions on Microwave Theory and Techniques 66, 4985–4999.Google Scholar
13.Alizadeh, M, Rönnow, D, Händel, P and Isaksson, M (2017) A new block-structure modeling technique for RF power amplifiers in a 2 × 2 MIMO system. IEEE International ConferenceAdvanced Technologies, Systems and Services in Telecommunications (TELSIKS), Niš, Serbia.Google Scholar
14.Hausmair, K, Landin, PN, Gustavsson, U, Fager, C and Eriksson, T (2018) Digital predistortion for multi-antenna transmitters affected by antenna crosstalk. IEEE Transactions on Microwave Theory and Techniques 66, 1524–1535.Google Scholar
15.Chiu, M, Zeng, C and Liu, M (2008) Predistorter based on frequency domain estimation for compensation of nonlinear distortion in OFDM systems. IEEE Transactions on Vehicular Technology 57, 882–892.Google Scholar
16.Zhang, W and Xie, S (2016) Piece-wise vector fitting method with binary frequency domain partitioning algorithm for automatic modeling. IEEE International Conference Microwave Millimeter Wave Technology (ICMMT), Beijing, China.Google Scholar
17.Zhao, Y, Hao, Y, Alomainy, A and Parini, C (2006) UWB on-body radio channel modeling using ray theory and subband FDTD method. IEEE Transactions on Microwave Theory and Techniques 54, 1827–1835.Google Scholar
18.Zhang, Q and Chen, W (2017) Digital predistortion for 5G wideband power amplifiers using multiple band-limited feedback signals. Microwave Measurement Conference (ARFTG), Honololu, HI.Google Scholar
19.Liu, Y, Pan, W and Shao, S (2013) A new digital predistortion for wideband power amplifiers with constrained feedback bandwidth. IEEE Microwave Components Letters 23, 683–685.Google Scholar
20.Zargar, H, Banai, A and Pedro, JC (2015) A new double input-double output complex envelope amplifier behavioral model taking into account source and load mismatch effects. IEEE Transactions on Microwave Theory and Techniques 63, 766–774.Google Scholar
21.Vejdani Amiri, M, Helaoui, M and Ghannouchi, FM (2014) Streamlined MIMO cross-over digital predistortion. IEEE Radio Wireless Symposium (RWS), Newport Beach, CA, USA.Google Scholar
22.Morgan, DR, Ma, Z, Kim, J, Zierdt, MG and Pastalan, J (2006) A generalized memory polynomial model for digital predistortion of RF power amplifiers. IEEE Transactions on Signal Processing 54, 3852–3860.Google Scholar
23.Kim, J and Konstantinou, K (2001) Digital predistortion of wideband signals based on power amplifier model with memory. Electron Letters 37, 1417–1418.Google Scholar
24.Haykin, S (1996) Digital Communication Systems. New York, USA: Wiley.Google Scholar
25.Oppenheim, AV and Verghese, GC (2015) Signal Systems and Inference, 1st Edn. Upper Saddle River, NJ, USA: Prentice Hall.Google Scholar
26.Arokiamary, VJ (2009) Mobile Communications, 1st Edn. Pune, India: Technical Publications Pune.Google Scholar
27.Kay, SM (1998) Fundamentals of Statistical Signal Processing: Detection Theory, vol. II. Upper Saddle River, NJ, USA: Prentice-Hall.Google Scholar
28.Molisch, AF (2011) Wireless Communications, 2nd Edn. West Sussex, UK: John Wiley & Sons Ltd.Google Scholar
29.Eun, C and Powers, JE (1997) A new Volterra predistorter based on the indirect learning architecture. IEEE Transactions on Signal Processing 45, 223–227.Google Scholar
30.Rönnow, D (2018) Pth-order inverse of the Volterra series for multiple-input multiple-output non-linear dynamic systems. IET Circuits and Devices Systems 12, 403–412.Google Scholar
31.Isaksson, M, Wisell, D and Händel, P (2005) Nonlinear behavioral modeling of power amplifiers using radial-basis function neural networks. IEEE MTT-S International Microwave Symposium Digest, Long Beach, CA, USA.Google Scholar
32.Isaksson, M, Wisell, D and Rönnow, D (2006) A comparative analysis of behavioral models for RF power amplifiers. IEEE Transactions on Microwave Theory and Techniques 54, 348–359.Google Scholar
33.Zhu, A, Dooley, J and Brazil, TJ (2006) Simplified Volterra series based behavioral modeling of RF power amplifiers using deviation-reduction. IEEE MTT-S International Microwave Symposium Digest, San Francisco, CA, USA.Google Scholar
34.Strang, G (2006) Linear Algebra and Its Applications, 4th ed. Belmont, CA, USA: Thomson Brooks/Cole.Google Scholar
35.Guan, L and Zhu, A (2012) Optimized low-complexity implementation of least squares based model extraction for digital predistortion of RF power amplifiers. IEEE Transactions on Microwave Theory and Techniques 60, 594–603.Google Scholar
36.Tehrani, AS, Haiying, C, Afsardoost, S, Eriksson, T, Isaksson, M and Fager, C (2010) A comparative analysis of the complexity/accuracy tradeoff in power amplifier behavioral models. IEEE Transactions on Microwave Theory and Techniques 58, 1510–1520.Google Scholar
37.Barradas, FM, Cunha, TR and Pedro, JC (2017) Digital predistortion of RF PAs for MIMO transmitters based on the equivalent load. Integer Nonlinear Microwave Millimetre-wave Circuits Workshop (INMMiC), Graz, Austria.Google Scholar
38.Amin, S, Händel, P and Rönnow, D (2017) Digital predistortion of single and concurrent dual-band radio frequency GaN amplifiers with strong nonlinear memory effects. IEEE Transactions on Microwave Theory and Techniques 65, 2453–2464.Google Scholar