NDLI: Dimensioning an FPGA for Real-Time Implementation of State of the Art Neural Network-Based HPA Predistorter

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Dimensioning an FPGA for Real-Time Implementation of State of the Art Neural Network-Based HPA Predistorter

Content Provider	MDPI
Author	Jiménez, Víctor Louliej, Abdelhamid Jabrane, Younes Guilloud, Frédéric
Copyright Year	2021
Description	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 Wideband (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.
Starting Page	1538
e-ISSN	20799292
DOI	10.3390/electronics10131538
Journal	Electronics
Issue Number	13
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2021-06-25
Access Restriction	Open
Subject Keyword	Electronics Hardware and Architecturee Ecma-368 Mb-ofdm Hpa Papr Pre-distortion Neural Networks Fpga
Content Type	Text
Resource Type	Article

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