Iterative Dichotomy PAPR Reduction Method for Multicarrier Waveforms

Tahkoubit, Khaled; Ali-Pacha, Adda; Shaïek, Hmaied; Roviras, Daniel

doi:10.1109/lcomm.2019.2935439

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…For = 2, we have shown in [17] that the two final signals have quite similar PAPR equal, approximately to 0.6 times the linear PAPR of the original signal.…”

Section: B Theoretical Analysis Of the Papr Achieved By Idp Methodsmentioning

confidence: 82%

“…The Iterative Dichotomy PAPR method [17] is a downward compatibility method, i.e. there is no side information sent to the receiver.…”

Section: Idp Reduction Methods a Methods Descriptionmentioning

confidence: 99%

“…In [17], we have restricted our theoretical analysis to a dichotomy order = 2. In the following, we will establish theoretical analysis for PAPR reached by IDP method for any higher dichotomy order.…”

Section: B Theoretical Analysis Of the Papr Achieved By Idp Methodsmentioning

confidence: 99%

“…Downward compatibility techniques are highly preferred in order to have a receiver independent of the used PAPR reduction technique. Recently, we proposed new PAPR reduction technique with downward compatibility [17]. This method has been applied to OFDM signal but it could be used for any kind of multicarrier modulation.…”

Section: Introductionmentioning

confidence: 99%

“…We have compared in [20] the energy efficiency between these two methods and we have shown that the performances with IDP technique are better. In [17], we have proposed the IDP method and we have restricted our theoretical analysis to the order = 2. Regarding our previous work, the main contributions of this paper are:…”

Section: Introductionmentioning

confidence: 99%

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Generalized Iterative Dichotomy PAPR Reduction Method for Multicarrier Waveforms

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5th generation systems (5G) will be based on Orthogonal Frequency Division Multiplexing (OFDM) multicarrier modulation technique. This modulation has a very high Peak to Average Power Ratio (PAPR). This high PAPR is a drawback when nonlinear Power Amplifiers (PA) are operated near the saturation region, for energy efficiency purposes, due to in-band and out-of-band impairments. In this paper, a new PAPR reduction technique called Iterative Dichotomy PAPR (IDP) is proposed. This method is suited for any multicarrier modulation and is studied here using realistic PA model. For evaluating the signal distortions, two main metrics have been considered to evaluate the performance of IDP technique: bit error rate (BER) and power spectrum density (PSD). The analytical expressions of the IDP technique for any dichotomy order is firstly described and subsequently the energy efficiency for different values is compared. We have shown that the proposed IDP method reduces the PAPR by 2.2 for = 2 and 4 for = 4. Regarding the energy efficiency, for the same Error Vector Magnitude (EVM) and the same PA power consumption, the output signal power obtained with the IDP based technique outperforms classical OFDM by 20%, 50% and 80% for = 2, = 3 and = 4, respectively.

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“…For = 2, we have shown in [17] that the two final signals have quite similar PAPR equal, approximately to 0.6 times the linear PAPR of the original signal.…”

Section: B Theoretical Analysis Of the Papr Achieved By Idp Methodsmentioning

confidence: 82%

“…The Iterative Dichotomy PAPR method [17] is a downward compatibility method, i.e. there is no side information sent to the receiver.…”

Section: Idp Reduction Methods a Methods Descriptionmentioning

confidence: 99%

Section: B Theoretical Analysis Of the Papr Achieved By Idp Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generalized Iterative Dichotomy PAPR Reduction Method for Multicarrier Waveforms

et al. 2021

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Low-Complexity PAPR Reduction by Coded Data Insertion on DVB-T2 Reserved Carriers

et al. 2023

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A Comprehensive Review of Machine Learning in Multi-objective Optimization

Clausen

et al. 2021

2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)

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In the real world, it is challenging to calculate a trade-off alternative with traditional classical methods for complex non-linear systems, which always involve multiple conflicting objectives. Such complicated systems urgently desire advanced methods to conquer the multi-objective optimization problems (MOPs). As a promising AI method, the development and application of Machine Learning (ML) attract increasingly more attention from researchers. The natures of ML methods, such as parallel computation possibility, no need for any priori assumptions, etc., ensure the effectiveness and efficiency for solving MOPs. However, as we know, there is no literature related to the comprehensive review of ML in multi-objective optimization domain until now. This literature review aims to provide researchers a global view of mainstream ML methods for MOO in a general domain and a reference for applying ML methods to solve a specific type of MOPs. In this paper, the general ML mainstream methods are summarized, based on which the literature relating to ML on MOPs are retrieved in comprehensive domains. The relevant literature is categorized according to the emphasis of object types, purposes and methods, and the categorization results are finally analyzed and discussed.

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Iterative Dichotomy PAPR Reduction Method for Multicarrier Waveforms

Cited by 16 publications

References 9 publications

Generalized Iterative Dichotomy PAPR Reduction Method for Multicarrier Waveforms

Generalized Iterative Dichotomy PAPR Reduction Method for Multicarrier Waveforms

Low-Complexity PAPR Reduction by Coded Data Insertion on DVB-T2 Reserved Carriers

A Comprehensive Review of Machine Learning in Multi-objective Optimization

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