Adaptive sparse pre-distortion structure based on compressed sensing algorithm

A compressive sensing and self-adaptive technology, applied in the direction of code conversion, electrical components, synchronization/start-stop system, etc., can solve problems such as sampling rate limitation, achieve the goal of reducing sampling rate, increasing coefficient estimation weight, and improving the accuracy of reconstructed signals Effect

Pending Publication Date: 2020-11-03
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

However, when the output signal is collected in the feedback loop of the dual-frequency predistortion system, it is severely limited by the sampling rate of the ADC (Analog-to-digital Converter)

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  • Adaptive sparse pre-distortion structure based on compressed sensing algorithm
  • Adaptive sparse pre-distortion structure based on compressed sensing algorithm
  • Adaptive sparse pre-distortion structure based on compressed sensing algorithm

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[0015] The specific implementation of the present invention will be described in detail below in conjunction with the accompanying drawings. As a part of this specification, the principles of the present invention will be described through examples. Other aspects, features and advantages of the present invention will become clear through the detailed description. In the referenced drawings, the same reference numerals are used for the same or similar components in different drawings.

[0016] refer to Figure 1 to Figure 2 , the present invention aims at the problem that the sampling rate of the output signal is severely limited in the feedback loop of the dual-frequency predistortion system, and uses Compressed Sensing (CS) technology to compress the signal, which can effectively reduce the sampling pressure of the radio frequency front end. The CS theory is different from the traditional Nyquist sampling theorem. When sampling the signal, it no longer needs to process all th...

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Abstract

The invention discloses an adaptive sparse pre-distortion structure based on a compressed sensing algorithm, and the structure comprises the steps: building a memory effect compensator based on a piecewise linear function double-frequency power amplifier pre-distortion model, and enabling the compressed sensing adaptive sparse reconstruction algorithm to be applied to a pre-distortion system feedback loop; performing compressed sensing sampling in a pre-distortion feedback loop, reconstructing five-order and high-order intermodulation signals by using an adaptive sparsity algorithm APSP, dynamically adjusting an initial value and a step length of sparsity to be close to real sparsity according to the characteristics of feedback loop signals, and restoring the signals at high precision by using a subspace tracking algorithm, so as to improve the coefficient estimation weight, and improve the pre-distortion effect.

Description

technical field [0001] The invention belongs to the technical field of pre-distortion structures, in particular to an adaptive sparse pre-distortion structure based on a compressed sensing algorithm. Background technique [0002] The intelligence, multi-frequency and low energy consumption of modern communication systems make the efficient use of radio spectrum resources more important. The linearization method of RF power amplifier (Ratio Frequency power Amplifier, RF PA) in the multi-carrier communication system—Digital Predistortion (Digital Predistortion, DPD) has attracted extensive attention of experts at home and abroad because of its high precision and good stability. In the field of dual frequency digital predistortion system research. In 2008, Roblin et al. used a large signal network analyzer (Large Signal Network Analyzer, LSNA) to update the predistorter coefficients after obtaining the power amplifier parameters, thus producing a combination of predistortion s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/49H03M7/30
CPCH04L25/49H03M7/3059
Inventor 高明明王金玲南敬昌边廷玥梁琦张雪曼
Owner LIAONING TECHNICAL UNIVERSITY
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