1. Dimensioning an FPGA for Real-Time Implementation of State of the Art Neural Network-Based HPA Predistorter
- Author
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Abdelhamid Louliej, Younes Jabrane, Víctor P. Gil Jiménez, Frédéric Guilloud, Faculté des Sciences Agadir (EMSHTC), Université Ibn Zohr [Agadir], Université Cadi Ayyad [Marrakech] (UCA), Carlos III University of Madrid, Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Equipe Communication System Design (Lab-STICC_COSYDE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Comunidad de Madrid, and Ministerio de Economía y Competitividad (España)
- Subjects
TK7800-8360 ,Computer Networks and Communications ,Orthogonal frequency-division multiplexing ,Computer science ,Ultra-wideband ,02 engineering and technology ,PAPR ,01 natural sciences ,Ingeniería Industrial ,pre-distortion ,ECMA-368 ,Pre-distortion ,0202 electrical engineering, electronic engineering, information engineering ,Electronic engineering ,Wireless ,Electrical and Electronic Engineering ,Field-programmable gate array ,Digital signal processing ,FPGA ,business.industry ,Amplifier ,010401 analytical chemistry ,020206 networking & telecommunications ,neural networks ,0104 chemical sciences ,[SPI.TRON]Engineering Sciences [physics]/Electronics ,Hardware and Architecture ,Control and Systems Engineering ,HPA ,Signal Processing ,Lookup table ,Electronics ,business ,Electrical efficiency ,Neural networks ,MB-OFDM - Abstract
Orthogonal Frequency Division Multiplexing (OFDM) is one of the key modulations for current and novel broadband communications standards. For example, Multi-band Orthogonal Frequency Division Multiplexing (MB-OFDM) is an excellent choice for the ECMA-368 Ultra Wide-band (UWB) wireless communication standard. Nevertheless, the high Peak to Average Power Ratio (PAPR) of MB-OFDM UWB signals reduces the power efficiency of the key element in mobile devices, the High Power Amplifier (HPA), due to non-linear distortion, known as the non-linear saturation of the HPA. In order to deal with this limiting problem, a new and efficient pre-distorter scheme using a Neural Networks (NN) is proposed and also implemented on Field Programmable Gate Array (FPGA). This solution based on the pre-distortion concept of HPA non-linearities offers a good trade-off between complexity and performance. Some tests and validation have been conducted on the two types of HPA: Travelling Wave Tube Amplifiers (TWTA) and Solid State Power Amplifiers (SSPA). The results show that the proposed pre-distorter design presents low complexity and low error rate. Indeed, the implemented architecture uses 10% of DSP (Digital Signal Processing) blocks and 1% of LUTs (Look up Table) in case of SSPA, whereas it only uses 1% of LUTs in case of TWTA. In addition, it allows us to conclude that advanced machine learning techniques can be efficiently implemented in hardware with the adequate design. This work was partly funded by projects TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/ AEI/FEDER, UE) and MFOC (Madrid Flight on Chip—Innovation Cooperative Projects Comunidad of Madrid—HUBS 2018/MadridFlightOnChip).
- Published
- 2021
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