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

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 s...

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Bibliographic Details
Published in:Electronics (Basel) 2021-07, Vol.10 (13), p.1538
Main Authors: Louliej, Abdelhamid, Jabrane, Younes, Jiménez, Víctor P. Gil, Guilloud, Frédéric
Format: Article
Language:English
Subjects:
Broadband
Communications systems
Complexity
Digital signal processing
Distortion
Efficiency
Electronic devices
Electronics
Engineering Sciences
Field programmable gate arrays
Lookup tables
Machine learning
Neural networks
Nonlinearity
Orthogonal Frequency Division Multiplexing
Power amplifiers
Power efficiency
Power management
Spectrum allocation
Traveling wave tubes
Traveling waves
Ultrawideband
Universal Serial Bus
Wireless communications
Wireless networks
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10131538