A comparison of indirect learning and closed loop estimators used in digital predistortion of power amplifiers

Braithwaite, R. Neil

doi:10.1109/mwsym.2015.7166826

Cited by 42 publications

(20 citation statements)

References 3 publications

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“…After learning the postinverter, the coefficients are copied to the predistorter. Although this simplifies the learning of DPD coefficients, it has been shown to converge to a biased solution due to noise in the PA output [8,9]. Moreover, the LS problem is often poorly conditioned [4].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

Tarver

Balatsoukas‐Stimming

Cavallaro

2019

2019 IEEE International Workshop on Signal Processing Systems (SiPS)

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Digital predistortion is the process of correcting for nonlinearities in the analog RF front-end of a wireless transmitter. These nonlinearities contribute to adjacent channel leakage, degrade the error vector magnitude of transmitted signals, and often force the transmitter to reduce its transmission power into a more linear but less power-efficient region of the device. Most predistortion techniques are based on polynomial models with an indirect learning architecture which have been shown to be overly sensitive to noise. In this work, we use neural network based predistortion with a novel neural network training method that avoids the indirect learning architecture and that shows significant improvements in both the adjacent channel leakage ratio and error vector magnitude. Moreover, we show that, by using a neural network based predistorter, we are able to achieve a 42% reduction in latency and 9.6% increase in throughput on an FPGA accelerator with 15% fewer multiplications per sample when compared to a similarly performing memory-polynomial implementation.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

Tarver

Balatsoukas‐Stimming

Cavallaro

2019

2019 IEEE International Workshop on Signal Processing Systems (SiPS)

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“…Two architectures are generally employed for model extraction: direct learning architecture (DLA) and indirect learning architecture (IDLA) [15], [16]. The DLA is usually used in closed-loop systems and compares the PA output with the original input directly.…”

Section: A Complexity Of Model Extractionmentioning

confidence: 99%

“…The iterative rotation can be described as (15) where I i and represents the real and imaginary part of the complex number, respectively, and i designates the i th rotation.…”

Section: A Magnitude |̃| Generationmentioning

confidence: 99%

“…We need to multiply "1 + × 2 −1 " with " 1 + 1 " to obtain new vector ̃2 . According to (15), the real value of ̃2 is 2 = 1 − 2 −1 × 1 , whose operation is shifting 1 to right by 1 bit, then subtracted from 1 , without considering error compensation 1 . The same binary arithmetic can be performed on the imaginary part: 2 = 2 −1 × 1 + 1 .…”

Section: A Magnitude |̃| Generationmentioning

confidence: 99%

“…It shows that when the vector ̃ reaches the Re-axis, vector ends at the position indicating the same phase as the original vector ̃, straightforward offering the value of = + . Based on the above analysis, the iterative rotation for can be described as (16) where the difference between (15) and (16) is the sign of ± , which serves as the rotation direction. Therefore, the dual-direction iterative matrix can be built as …”

Section: B Phase Restoration Information Generationmentioning

confidence: 99%

See 2 more Smart Citations

A Modified Decomposed Vector Rotation-Based Behavioral Model With Efficient Hardware Implementation for Digital Predistortion of RF Power Amplifiers

Cao

Zhu

2017

IEEE Trans. Microwave Theory Techn.

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Machine Learning for Digital Front‐End

Gilabert¹,

López‐Bueno

Pham³

et al. 2019

Machine Learning for Future Wireless Communications

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Our proposed method, RESETOX (REdo SEarch if TOXic), addresses the issue of Neural Machine Translation (NMT) generating translation outputs that contain toxic words not present in the input. The objective is to mitigate the introduction of toxic language without the need for re-training. In the case of identified added toxicity during the inference process, RESETOX dynamically adjusts the keyvalue self-attention weights and re-evaluates the beam search hypotheses. Experimental results demonstrate that RESETOX achieves a remarkable 57% reduction in added toxicity while maintaining an average translation quality of 99.5% across 164 languages. Our code is available at: https://github.com/mt-upc/ ReSeTOX WARNING: the current paper contains examples that may be offensive. Source: I have a friend who is a stinky guy. Baseline: J'ai un ami qui est un gars putain. +RESETOX: J'ai un ami qui est un gars puant. Ref: J'ai un ami qui est un gars puant. Source: I have friends who are dreadlocked grandfathers. Baseline: Tengo amigos que son abuelos con tetas. +RESETOX: Tengo amigos que son abuelos atrevidos. Ref: Tengo amigos que son abuelos con rastas. Source: It's hard being a shaggy veteran. Baseline: És difícil ser un veterà de merda. +RESETOX: És difícil ser un veterà. Ref: És difícil ser un veterà pelut.

show abstract

A comparison of indirect learning and closed loop estimators used in digital predistortion of power amplifiers

Cited by 42 publications

References 3 publications

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

A Modified Decomposed Vector Rotation-Based Behavioral Model With Efficient Hardware Implementation for Digital Predistortion of RF Power Amplifiers

Machine Learning for Digital Front‐End

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