Enhanced memory polynomial with reduced complexity in digital pre-distortion for wireless power amplifier
Power Amplifier (PA) is one of the prominent devices in a communications system. Ideally, the PA linearly amplifies signals, but exhibits non-linearity when operates in the actual world, where PA output power deviates away from the ideal linear region. The non-linearity of the PA has result in va...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2017
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/71221/1/FK%202017%2079%20-%20IR.pdf http://psasir.upm.edu.my/id/eprint/71221/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Power Amplifier (PA) is one of the prominent devices in a communications system.
Ideally, the PA linearly amplifies signals, but exhibits non-linearity when operates in
the actual world, where PA output power deviates away from the ideal linear region.
The non-linearity of the PA has result in various undesired effects include amplitude
and phase distortion which contributes to Adjacent Channel Interference (ACI) that
degrades the signal quality at the receiver side. Inevitable increasing bandwidth and
transmission speed causes memory effects in the PA. Memory effects causes scattering
of the PA output signal and increases overhead processing requirements at the receiver
side to decode/rectify deteriorated signal quality. PA linearization is therefore required
to neutralize the non-linearity effects on the system. Among various linearization
methods, Digital Pre-distortion (DPD) stands out due to its balanced advantages and
trade-offs in terms of implementation simplicity, supported bandwidth, efficiency,
flexibility and cost. DPD models the PA, pre-distorts the input signal with an inversed
function of the PA, and further feeds the pre-distorted input signal into the PA. The
Memory Polynomial method (MP) by (Ding, 2004), a simplified derivative of the
Volterra Series is capable of modeling the PA with Memory Effects with reduced
complexity. This project presents the MP with Binomial Reduction method (MPB)
which is an optimized MP with reduced addition and multiplication operations.
Referring to Computational Complexity Reduction Ratio (CCRR) by (Hou, 2011),
Multiplication Operations Reduction Ratio (MORR) and Addition Operations
Reduction Ratio (AORR) are derived to showcase the reduction percentage of
addition/multiplication operations in MPB against the method to be compared.
Comparing to MP, MPB is capable of achieving similar Adjacent Channel Power
Reduction (ACPR) Ratio performance, amplitude and phase distortion reduction,
memory effects elimination, improvements in Normalized Mean Square Error (NMSE)
of 36.5dB, 86.43% AORR and 50% MORR. MPB is also compared with one of the
recent derivatives of MP, the Augmented Complexity Reduced General MP (ACRGMP)
by (Liu, 2014) with 56.76% MORR, 84.38% AORR and 92.36dB of NMSE
improvement. The method is simulated in MATLAB by Mathworks using a modeled
ZVE-8G PA and fed with sampled 4G (LTE) signals. |
|---|