Loughman, Meabh and Byrne, Declan and Farrell, Ronan and Dooley, John
(2022)
Acceleration of Digital Pre- Distortion Training Using Selective Partitioning.
In: 2022 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR), 16 - 19 January 2022, Las Vegas, Nevada, USA.
Abstract
In recent years model and Digital Pre-Distortion
dimension reduction has been widely researched. The oper-
ations involved when running DPD are often far less than
those needed during the training of the DPD coefficients. The
proposed partitioned Least Squares (LS) adaptation allows a
selected subset of DPD coefficients to be updated while the
remaining coefficients are held constant. This technique allows
a more adaptive training procedure, improved interpretability
of the important DPD coefficient’s during training and the
ability to partition the DPD function into specific groups. The
Frisch-Waugh-Lovell (FWL) theorem is exploited to partition the
coefficients of a DPD basis function trained using LS regression.
The proposed methodology was experimentally validated with a
Generalized Memory Polynomial (GMP) DPD function, used to
linearize a 5W power amplifier (PA) driven by a 40MHz 5G-NR
signal.
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