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Research Article
Meabh Loughman
Maynooth University: National University of Ireland Maynooth
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8901-2937
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As the physical makeup of cellular base-stations evolve into systems with multiple parallel transmission paths the effort involved in modelling these complex systems increases considerably. One task in particular which contributes to signal distortion on each signal path, is the power amplifier. In power amplifier modelling, Recursive Least Squares has been used in the past to train Volterra models with memory terms, however instability can occur when training the model weights. This manuscript provides a computationally efficient technique to detect the onset of instability and subsequently to inform the decision when to stop adaptive training of dynamic nonlinear behavioural models and avoid the onset of instability. This technique is experimentally validated using four different signal modulation schemes.
Keywords
Behavioural modeling , Power Amplifier , Recursive least squares (RLS) , Volterra Model
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Posted 01 Jun, 2021
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Received 31 May, 2021
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This preprint is under consideration at Wireless Personal Communications. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Meabh Loughman
Maynooth University: National University of Ireland Maynooth
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8901-2937
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 01 Jun, 2021
No community comments so far
Reviews received
Received 31 May, 2021
Reviewers invited
Invitations sent on 30 May, 2021
First submitted
On 21 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Systems and Networking
License:
This work is licensed under a CC BY 4.0 License. Read Full License
As the physical makeup of cellular base-stations evolve into systems with multiple parallel transmission paths the effort involved in modelling these complex systems increases considerably. One task in particular which contributes to signal distortion on each signal path, is the power amplifier. In power amplifier modelling, Recursive Least Squares has been used in the past to train Volterra models with memory terms, however instability can occur when training the model weights. This manuscript provides a computationally efficient technique to detect the onset of instability and subsequently to inform the decision when to stop adaptive training of dynamic nonlinear behavioural models and avoid the onset of instability. This technique is experimentally validated using four different signal modulation schemes.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
The full text of this article is available to read as a PDF.
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