A comparative study of learning architecture for digital predistortion

Yu, Zhijian; Zhu, Erni

doi:10.1109/apmc.2015.7411819

Cited by 7 publications

(2 citation statements)

References 7 publications

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“…In ref. [25], the DPD coefficients vector and their adaptive DPD coefficients vector are derived in closed matrix form. An adaptive DPD approach based on a cascade of an adaptive ILA and a static DLA using a linear interpolation look-up-table is proposed in ref.…”

Section: Related Workmentioning

confidence: 99%

Performance of digital predistorter in system with analogue to digital converter

Elaskary¹,

Mehana

Fahmy

et al. 2022

IET Communications

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The baseband digital pre‐distortion (DPD) signal processing technique is the most cost‐effective linearisation method among other techniques to address the nonlinearity effect of the power amplifier. For communication systems containing DPD and power amplifier, it is difficult to acquire performance metrics closed‐forms for any DPD architecture since there was no mathematical expression for each DPD coefficient. Usually, researchers look for more efficient DPD algorithms for DPD coefficients (compared to the existing ones) in terms of computational complexity, delay, power consumption etc. Consequently, performance is evaluated through intensive simulation. In this paper, it is shown how one can exploit the results of the recent work to mathematically model the indirect learning architecture DPD and efficiently derive important measures in communication systems. Expressions of the normalised mean square error (NMSE) and achievable rate for the OFDM communication system are derived. The DPD is modelled mathematically by exploiting the DPD coefficients closed‐form expressions. The derived expressions of the NMSE and achievable rate are verified through numerical simulations. Furthermore, closed‐form expressions for the NMSE and achievable rate bounds for the OFDM communication system are derived. The performance measures bounds are found assuming a perfect linearisation scenario. The analytical expressions are validated through numerical simulations.

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Section: Related Workmentioning

confidence: 99%

Performance of digital predistorter in system with analogue to digital converter

Elaskary¹,

Mehana

Fahmy

et al. 2022

IET Communications

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“…The functional block diagram of the direct learning architecture DPD system is depicted in Figure , where the bold vectors

boldx

boldz

boldy

, and

bolde

represent the baseband input signal, predistorted signal, output signal of PA, and the error signal between

boldx

and

boldy

, respectively, and they both have the length of

N

.…”

Section: Conventional Dla Dpdmentioning

confidence: 99%

One-bit in-phase observation for direct learning-based digital predistortion with modified frequency-domain delay estimation and alignment

Zhang

Wang

et al. 2017

Int J RF Microw Comput Aided Eng

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This article proposes a novel digital predistortion (DPD) implementation method for RF power amplifiers. The new approach adopts only one 1‐bit comparator in the feedback path to observe the in‐phase (I) or the quadrature (Q) component of the error signal between the input and the output signals. To this end, the theoretical derivation of the in‐phase observation based on direct learning architecture (DLA) DPD is first given in this article, by combining the existing 1‐bit method and the low‐cost in‐phase observation. To facilitate the delay estimation and alignment, a modified iterative frequency‐domain delay estimation is presented, which only acquires either I or Q components of the output signal to achieve satisfied delay estimation. Experimental results show that the proposed DPD method decreased the normalized mean square error (NMSE) and the adjacent channel power ratio (ACPR) to less than −42 and −51 dB, respectively, which indicates that the proposed DPD system can achieve comparable performance as the existing DPD identification techniques with lower implementation complexity.

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A generalized model based on canonical piecewise linear functions for digital predistortion

Wang

Shang

2016

2016 Asia-Pacific Microwave Conference (APMC)

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A comparative study of learning architecture for digital predistortion

Cited by 7 publications

References 7 publications

Performance of digital predistorter in system with analogue to digital converter

Performance of digital predistorter in system with analogue to digital converter

One-bit in-phase observation for direct learning-based digital predistortion with modified frequency-domain delay estimation and alignment

A generalized model based on canonical piecewise linear functions for digital predistortion

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