Wang, Ziming
(2018)
Compact Digital Predistortion for Multi-band
and Wide-band RF Transmitters.
PhD thesis, National University of Ireland Maynooth.
Abstract
This thesis is focusing on developing a compact digital predistortion (DPD) system
which costs less DPD added power consumptions. It explores a new theory
and techniques to relieve the requirement of the number of training samples and
the sampling-rate of feedback ADCs in DPD systems. A new theory about the
information carried by training samples is introduced. It connects the generalized
error of the DPD estimation algorithm with the statistical properties of
modulated signals. Secondly, based on the proposed theory, this work introduces
a compressed sample selection method to reduce the number of training samples
by only selecting the minimal samples which satisfy the foreknown probability
information. The number of training samples and complex multiplication operations
required for coefficients estimation can be reduced by more than ten
times without additional calculation resource. Thirdly, based on the proposed
theory, this thesis proves that theoretically a DPD system using memory polynomial
based behavioural modes and least-square (LS) based algorithms can be
performed with any sampling-rate of feedback samples. The principle, implementation
and practical concerns of the undersampling DPD which uses lower
sampling-rate ADC are then introduced. Finally, the observation bandwidth of
DPD systems can be extended by the proposed multi-rate track-and-hold circuits
with the associated algorithm. By addressing several parameters of ADC
and corresponding DPD algorithm, multi-GHz observation bandwidth using only
a 61.44MHz ADC is achieved, and demonstrated the satisfactory linearization
performance of multi-band and continued wideband RF transmitter applications
via extensive experimental tests.
| Item Type: |
Thesis
(PhD)
|
| Academic Unit: |
Faculty of Science and Engineering > Electronic Engineering |
| Item ID: |
12565 |
| Depositing User: |
IR eTheses
|
| Date Deposited: |
09 Mar 2020 17:16 |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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