Low-Complexity Neural Networks for Baseband Signal Processing

This study investigates the use of neural networks for the physical layer in the context of Internet of Things. In such systems, devices face challenging energy, computational and cost constraints that advocate for a low-complexity baseband signal processing. In this work, low-complexity neural netw...

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Bibliographic Details
Published in2020 IEEE Globecom Workshops (GC Wkshps pp. 1 - 6
Main Authors Larue, Guillaume, Dhiflaoui, Mona, Dufrene, Louis-Adrien, Lampin, Quentin, Chollet, Paul, Ghauch, Hadi, Rekaya, Ghaya
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2020
Subjects
Baseband
Internet of Things
Neural networks
Performance evaluation
Physical layer
Signal processing
Signal processing algorithms
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Summary:This study investigates the use of neural networks for the physical layer in the context of Internet of Things. In such systems, devices face challenging energy, computational and cost constraints that advocate for a low-complexity baseband signal processing. In this work, low-complexity neural networks are proposed as promising candidates. They present adaptability to operating conditions, high performance to complexity ratio and also offer a good explainability, crucial in most communications systems that cannot rely on "black-box" solutions. Moreover, recent advances in dedicated hardware for neural networks processing bring new perspectives in terms of efficiency and flexibility that motivate their use at the physical layer. To illustrate how classical baseband signal processing algorithms can be translated to minimal neural networks, two models are proposed in this paper to realize single-path equalization and demodulation of M-QAM signals. These models are assessed using both simulation and experimentation and achieve near optimal performances.
DOI:10.1109/GCWkshps50303.2020.9367521