Reduction of Power Envelope Fluctuations in OFDM Signals by using Neural Networks

Jabrane, Younes; Jiménez, Víctor P. Gil; Armada, Ana García; Said, Brahim Ait Es; Ouahman, Abdellah Ait

doi:10.1109/lcomm.2010.07.100385

Cited by 27 publications

(17 citation statements)

References 10 publications

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“…The main problem with these algorithms is the complexity and convergence mainly due to high number of iterations, although arbitrarily low PAPR signals can be obtained. In [11], a neural network (NN) technique, referred to as Multilayer Perceptrons (MLPs), to obtain signals with low envelope fluctuations has been developed. Indeed, NN have been widely applied in solving optimization problems [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Practical Guidelines for Approaching the Implementation of Neural Networks on FPGA for PAPR Reduction in Vehicular Networks

Louliej

Jabrane

Jiménez

et al. 2018

Sensors

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Nowadays, the sensor community has become wireless, increasing their potential and applications. In particular, these emerging technologies are promising for vehicles’ communications (V2V) to dramatically reduce the number of fatal roadway accidents by providing early warnings. The ECMA-368 wireless communication standard has been developed and used in wireless sensor networks and it is also proposed to be used in vehicular networks. It adopts Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) technology to transmit data. However, the large power envelope fluctuation of OFDM signals limits the power efficiency of the High Power Amplifier (HPA) due to nonlinear distortion. This is especially important for mobile broadband wireless and sensors in vehicular networks. Many algorithms have been proposed for solving this drawback. However, complexity and implementations are usually an issue in real developments. In this paper, the implementation of a novel architecture based on multilayer perceptron artificial neural networks on a Field Programmable Gate Array (FPGA) chip is evaluated and some guidelines are drawn suitable for vehicular communications. The proposed implementation improves performance in terms of Peak to Average Power Ratio (PAPR) reduction, distortion and Bit Error Rate (BER) with much lower complexity. Two different chips have been used, namely, Xilinx and Altera and a comparison is also provided. As a conclusion, the proposed implementation allows a minimal consumption of the resources jointly with a higher maximum frequency, higher performance and lower complexity.

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Section: Introductionmentioning

confidence: 99%

Practical Guidelines for Approaching the Implementation of Neural Networks on FPGA for PAPR Reduction in Vehicular Networks

Louliej

Jabrane

Jiménez

et al. 2018

Sensors

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“…Increase in the number of subcarriers will substantially increase the FFT/IFFT computations. In recent years, artificial neural network (NN) technique has been successfully applied in various communications signal processing areas due to the good generalization properties, including PAPR reduction of OFDM signals [7], [8]. In this letter, we propose a new CF based method for PAPR reduction of OFDM signals based on SCF and NN signal mapper.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Simplified Clipping and Filtering Technique for PAPR Reduction of OFDM Signals

Sohn

Kim

2015

IEEE Commun. Lett.

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Many iterative clipping and filtering (ICF) based techniques have been proposed that achieve similar peak-toaverage power ratio (PAPR) reduction of orthogonal frequency division multiplexing (OFDM) signals as the original ICF, but with lower complexity, such as the simplified clipping and filtering (SCF) technique. However, these low complexity methods require numerous complex fast Fourier transform (FFT) operations and parameter calculations. In this letter, we introduce a novel ICF method that uses an optimized mapper based on artificial neural network and SCF techniques. Compared to the conventional ICF based methods, the proposed scheme offers desirable cubic metric (CM) and bit error rate (BER) simulation results with significantly reduced computational complexity.Index Terms-OFDM, PAPR, cubic metric, clipping and filtering, neural networks.

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“…However, it undergoes a large envelope fluctuations, causing a loss in energy efficiency due to the need of power backoff at the High Power Amplifiers (HPA). Several proposals in the literature try to reduce their envelope fluctuations to fight this problem which is the most important drawbacks in multi-carrier modulations [2]- [5], Active Constellation Expansion (ACE) [6] is an interesting technique since it is able to achieve large reductions, it does not need side information and it only needs a small increase in transmit power. However, ACE requires many iterations for convergence and this, unfortunately, constitutes its main weakness.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization for PAPR reduction in OFDM signals with modified pilots

Ouqour

Jabrane

Said

et al. 2011

2011 International Conference on Multimedia Computing and Systems

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In this paper we test an elaborated algorithm based on particle Swarm Optimization for reducing the Peak to Average Power Ratio (PAPR) with respect to ACE-AGP (Active Constellations Expansion) in OFDM signals, by removing and then rearranging pilots in the original signals. Simulation results show that the proposed method doesn' undergo any degradation interm of PAPR reduction with a very low complexity.

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Reduction of Power Envelope Fluctuations in OFDM Signals by using Neural Networks

Cited by 27 publications

References 10 publications

Practical Guidelines for Approaching the Implementation of Neural Networks on FPGA for PAPR Reduction in Vehicular Networks

Practical Guidelines for Approaching the Implementation of Neural Networks on FPGA for PAPR Reduction in Vehicular Networks

Neural Network Based Simplified Clipping and Filtering Technique for PAPR Reduction of OFDM Signals

Particle swarm optimization for PAPR reduction in OFDM signals with modified pilots

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