Iterative Predistortion of the Nonlinear Satellite Channel

Deleu, Thibault; Dervin, Mathieu; Kasai, Kazuhiko; Horlin, François

doi:10.1109/tcomm.2014.2337319

Cited by 11 publications

(9 citation statements)

References 21 publications

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“…In [4], the authors have proposed a new iterative predistortion algorithm, called SVA. Their study has shown that the performance improvement brought by SVA algorithm represents several dB on the MSE and up to 1.5 on the link budget with 32-APSK modulation, compared to state-ofthe-art pre-distorters based on memory polynomials [10] and look-up tables [11].…”

Section: A Pre-distortion Methodsmentioning

confidence: 99%

“…Therefore, if we define a Computation Unit (CU) as the amount of mathematical operations needed to output one symbol from the channel model depicted in Figure 4, the number of CU needed to pre-distort one symbol is equal to × × 3 where denotes the number of iterations. Preliminary approximations have been proposed in [4] to decrease the algorithm complexity by removing the channel simulations intended to evaluate , : one option is to tabulate it in look-up tables (LUTs), the other is to approximate the channel variation with a reduced Volterra model. In this paper, further optimizations have been brought for the purpose of prototyping SVA algorithm on a FPGA device: 1) Channel model optimization: The memory length ′ of SVA embedded channel model depicted in Figure 4 may be decreased by truncating the IMUX, OMUX and shaping filter impulse response, keeping in mind that the achievable gain mainly relies on the accuracy of channel model used to assess the Euclidian distance , and to a lesser extent on the accuracy of the channel output variation function , which can be approximated as proposed in [4].…”

Section: A Pre-distortion Methodsmentioning

confidence: 99%

“…In [4], an efficient pre-distortion technique, based on the socalled Small Variation Algorithm (SVA), has been proposed to mitigate the effects of the distortions induced by the satellite payload High Power Amplifier (HPA) and channelizing filters (IMUX, OMUX) in DVB-S2 transmissions. Improvement of the system spectral efficiency has been assessed for the higher DVB-S2 modulation scheme (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Non-Linearized Amplifier and Advanced Mitigation Techniques: DVB-S2X Spectral Efficiency Improvement

Lucciardi¹,

Potier²,

Buscarlet³

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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The latest standardization DVB-S2X increases the achievable spectral efficiency of the satellite communications by around 15% in AWGN channel. In order to benefit from those improvements, the strong non-linear distortions introduced by the payload have to be overcome, mostly taking high back-off on the amplifier operation point. Nowadays, on-board amplifiers are linearized before being deployed, allowing low-complexity transmitters and receivers at the detriment of the payload's cost and reduced energy efficiency. In this paper, various techniques are investigated for the purpose of spectral efficiency improvement while releasing the amplifier linearization constraint. Iterative pre-distortion at the transmitter, turbo-equalization at the receiver and appropriate waveforms for transmission through non-linearized payload appear as strong candidates considering the results of this study.

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Section: A Pre-distortion Methodsmentioning

confidence: 99%

Section: A Pre-distortion Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-Linearized Amplifier and Advanced Mitigation Techniques: DVB-S2X Spectral Efficiency Improvement

Lucciardi¹,

Potier²,

Buscarlet³

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

View full text Add to dashboard Cite

show abstract

“…The mitigation of non-linear distortion can either be done at the transmitter with predistortion [2]- [8] or at the receiver with equalization [9]. In the literature, the predistortion techniques are separated into two categories, the data predistortion, which operates at symbol rate [8], [10], and the signal predistortion which operates at a higher sample rate after the pulse shaping filter [2]- [7].…”

Section: Introductionmentioning

confidence: 99%

Iterative adaptive signal predistortion for satellite communications

Alibert

Amis

Langlais

et al. 2018

2018 IEEE 10th International Symposium on Turbo Codes &Amp; Iterative Information Processing (ISTC)

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The expected increase of data rates in future satellite communications (DVB-S2X) will require higher-order modulations and sharper roll-off, which makes the transmission more sensitive to non-linear interference due to on-board amplifiers. One solution is the predistortion of the signal that is carried out at the transmitter. Iterative predistortion based on the contraction mapping theorem has shown to be a good solution to mitigate the effects of nonlinearities but suffers from convergence depending on the chosen norm. In this paper, we propose to adapt the method with a feedback from the channel to ensure the convergence with the best linearization performance for a given number of iterations. We compare both schemes according to different figures of merit (adjacent channel interference factor, total degradation and normalized mean square error).

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“…The resulting signals can be used to act as references for adaptive control approaches, and give an indication on the importance of the memory effects in the power amplifier. Furthermore, an iterative approach can be used to obtain compensated input signals on a symbol level [13]: different blocks of input signal symbols are predistorted using an iterative algorithm. Both approaches do not compute an actual predistorter based on the iteratively predistorted signal, neither do they introduce the mature ILC framework from the control community to obtain these predistorted signals.…”

Section: Introductionmentioning

confidence: 99%

Obtaining the Preinverse of a Power Amplifier Using Iterative Learning Control

Schoukens

Hammenecker

Cooman

2017

IEEE Trans. Microwave Theory Techn.

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Abstract-Telecommunication networks make extensive use of power amplifiers to broaden the coverage from transmitter to receiver. Achieving high power efficiency is challenging and comes at a price: the wanted linear performance is degraded due to nonlinear effects. To compensate for these nonlinear disturbances, existing techniques compute the pre-inverse of the power amplifier by estimation of a nonlinear model. However, the extraction of this nonlinear model is involved and requires advanced system identification techniques.We used the plant inversion iterative learning control algorithm to investigate whether the nonlinear modeling step can be simplified. This paper introduces the iterative learning control framework for the pre-inverse estimation and predistortion of power amplifiers. The iterative learning control algorithm is used to obtain a high quality predistorted input for the power amplifier under study without requiring a nonlinear model of the power amplifier. In a second step a nonlinear pre-inverse model of the amplifier is obtained. Both the nonlinear and memory effects of a power amplifier can be compensated by this approach. The convergence of the iterative approach, and the predistortion results are illustrated on a simulation of a Motorola LDMOS transistor based power amplifier and a measurement example using the Chalmers RF WebLab measurement setup. I. INTRODUCTIONTo maximize their power efficiency current-day power amplifiers are most often operating close to saturation. This introduces nonlinear disturbances in the amplified signals. Such nonlinear disturbances introduce unwanted effects such as: spectral spreading, intersymbol interference, and constellation warping [1], [2]. Digital predistortion (DPD) allows to linearize the overall system behavior by predistorting the input of the amplifier such that the desired, linearized, output is obtained.The inversion of the response of a power amplifier is of critical importance for power amplifier linearization using DPD techniques. The two most common frameworks to obtain an inverse model of an high frequency power amplifier are the direct and the indirect learning architectures [3], [4]. The direct learning architecture (DLA) estimates the pre-inverse of the system directly [5]-[8], often using adaptive parameter estimation routines. The indirect learning architecture (ILA) first estimates the post-inverse of the system, which is used as a pre-inverse in a second step [9]. This paper proposes a two-step approach instead. First, iterative learning control (ILC) [10] is used to obtain the predistorted input u(t) of the power amplifier. In a second step a predistorter is estimated from the reference input r(t) to the predistorted input u(t) of the amplifier. This approach

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Iterative Predistortion of the Nonlinear Satellite Channel

Cited by 11 publications

References 21 publications

Non-Linearized Amplifier and Advanced Mitigation Techniques: DVB-S2X Spectral Efficiency Improvement

Non-Linearized Amplifier and Advanced Mitigation Techniques: DVB-S2X Spectral Efficiency Improvement

Iterative adaptive signal predistortion for satellite communications

Obtaining the Preinverse of a Power Amplifier Using Iterative Learning Control

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