Noise impact on the identification of digital predistorter parameters in the indirect learning architecture

Amin, Shoaib; Zenteno, Efrain; Landin, Per N.; Rönnow, Daniel; Isaksson, Magnus; Händel, Peter

doi:10.1109/swe-ctw.2012.6376285

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…While DPD is estimated using the received data from a reference receiver, which is designed to be low noise, EQ is evaluated at higher noise levels, from standard receivers. Further, the proposed equalizer is obtained as the channel inverse which prevents the received noise power being used into its estimation procedure and causes a bias in the estimates [49]. Moreover, DPD operates on nearly noiseless data from the transmitter, while the EQ operates on the received noisy data.…”

Section: Discussionmentioning

confidence: 99%

Low complexity predistortion and equalization in nonlinear multicarrier satellite communications

Zenteno

Piazza

Shankar

et al. 2015

EURASIP J. Adv. Signal Process.

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Aiming to reduce the power/mass requirements in satellite transponders and to reduce mission costs, joint amplification of multiple carriers using a single high-power amplifier (HPA) is being considered. In this scenario, a careful investigation of the resulting power efficiency is essential as amplification is nonlinear, and multicarrier signals exhibit enlarged peak-to-average power ratio. Thus, operating the amplifier close to saturation vastly increases signal distortion resulting in a severe degradation of performance, especially for higher order modulations. This paper proposes a reduced-complexity digital predistortion (DPD) scheme at the transmitter and a corresponding equalizer (EQ) at the receiver to mitigate these nonlinear effects. Scenarios include both the forward as well as the return links. In particular, the paper exploits the MIMO Volterra representation and builds on a basis pursuit approach using a LASSO (least absolute shrinkage and selection operator) algorithm to achieve an efficient basis representation, avoiding large computational complexity, to describe the selection of predistorter/equalizer model. The work further compares and contrasts the two mitigation techniques taking various system aspects into consideration. The gains in performance and amplification efficiency demonstrated by the use of DPD/ EQ motivate their inclusion in next-generation satellite systems.

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Section: Discussionmentioning

confidence: 99%

Low complexity predistortion and equalization in nonlinear multicarrier satellite communications

Zenteno

Piazza

Shankar

et al. 2015

EURASIP J. Adv. Signal Process.

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“…An advantage with this method is that the model is estimated as an inverse, which is how it will be used. A drawback is that the measured output is used as input, which risks causing a biased estimate [Amin et al, 2012].…”

Section: Methods Overviewmentioning

confidence: 99%

Inverse system identification with applications in predistortion

Jung

2019

Linköping Studies in Science and Technology. Dissertations

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Life is not easy. Finishing a PhD is not easy. One of the good things about the PhD is that you get a chance at the end to say thank you to the people who have helped you, which you might not get in life. This is my attempt to thank the ones who have helped me go through with this project. I'll start and end these acknowledgments with people without whom I think there would be no thesis (or not this, my thesis at least, others might still be able to write theirs). To my supervisor Dr. Martin Enqvist: I think I would have given up a long time ago if it wasn't for your encouragements. I am really impressed with how you always have time (or rather, take the time) to answer questions or concerns, and without feeling stressed. I know you have lots of other things to do and I really appreciate it. I also like how you can turn things around for a positive spin. Thank you for pushing me through this! Prof. Lennart Ljung and Prof. Torkel Glad, thanks for being co-supervisors and providing input. I should have talked, asked and learned more! I have had the pleasure of having three different bosses, who have all helped me sort out the work situation when life gets in the way. Prof. Lennart Ljung, Prof. Svante Gunnarsson and Dr. Martin Enqvist, you all seem to be able to guide the group forward as well as see the people and how to help. Lennart, thanks for letting me join the group! Svante, without your flexibility and willingness to adapt I could not have finished this thesis. Thank you! Martin, thanks for making every day bring-your-baby-to-work ! I am also grateful for the administrative help from Ninna Stensgård and her predecessor Åsa Karmelind. The upside of being on the slow side finishing your PhD is that you get to meet a lot of colleagues. I have really appreciated the Automatic control group and the amazing people in it! It is a group full of brilliant, fun, and hard working people, thank you all! I think my current office mate Kerstin is my favorite, but Patrik Leissner, Maryam Sadeghi Reineh and Gustaf Hendeby, you tie for second place (and you were all far less demanding)! Many colleagues I also consider friends and hope I will still see a lot of you in the future. Special shout outs to Manon Kok (for staying in touch and being a good friend), Daniel Petersson (for pepp and believing in me), Patrik Leissner (for friendship and helpfulness

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“…The behavior of the PA is captured in the construction of the basis functions because the models proposed in Section II-C are linear in the parameters, linear least square estimation (LSE) [25] can be used to estimate the model parameters by minimizing the cost function (11) The LSE solution can be written in matrix form as (12) where is the regression matrix and contains the estimated parameters for channel 1 in a 2 2 MIMO transmitter. In a realistic scenario, the measurement process is also be affected by in-phase/quadrature (I/Q) imbalance [26], [27], measurement noise [28], and phase noise. Other estimators such as BLUE [25] can be used to reduce the variance of the estimated parameters if the color of the noise is known.…”

Section: System Identificationmentioning

confidence: 99%

“…Note that the phase noise also causes the coherent averaging to produce a biased estimate that does not improve with the number of averages compared to additive noise [28] in which the bias terms tend to zero by increasing the number of averages.…”

Section: Appendix Analysis Of Phase Noise In Partially Noncoherent Mi...mentioning

confidence: 99%

Behavioral Modeling and Linearization of Crosstalk and Memory Effects in RF MIMO Transmitters

Amin

Landin

Händel

et al. 2014

IEEE Trans. Microwave Theory Techn.

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This paper proposes three novel models for behavioral modeling and digital pre-distortion (DPD) of nonlinear 2 2 multiple-input multiple-output (MIMO) transmitters in the presence of crosstalk. The proposed models are extensions of the singleinput single-output generalized memory polynomial model. Three types of crosstalk effects were studied and characterized as linear, nonlinear, and nonlinear & linear crosstalk. A comparative study was performed with previously published models for the linearization of crosstalk in a nonlinear 2 2 MIMO transmitter. The experiments indicate that, depending on the type of crosstalk, the selection of the correct model in the transmitter is necessary for behavioral modeling and sufficient DPD performance. The effects of coherent and partially noncoherent signal generation on the performance of DPD were also studied. For crosstalk levels of 30 dB, the difference in the normalized mean square error and adjacent channel power ratio was found to be 3-4 dB between coherent and partially noncoherent signal generation.

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Noise impact on the identification of digital predistorter parameters in the indirect learning architecture

Cited by 10 publications

References 6 publications

Low complexity predistortion and equalization in nonlinear multicarrier satellite communications

Low complexity predistortion and equalization in nonlinear multicarrier satellite communications

Inverse system identification with applications in predistortion

Behavioral Modeling and Linearization of Crosstalk and Memory Effects in RF MIMO Transmitters

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