land mobile satellite (LMS); machine learning (ML); peak-to-average power ratio (PAPR); Satellite communication system (SatCom); swarm intelligence
Abstract :
[en] High peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) signals presents a persistent challenge in satellite communications (SatCom), impacting signal quality and causing adjacent channel interference. This paper introduces a novel framework that combines the elastic net-based machine learning (ML) model with the partial transmit sequence (PTS) technique to effectively reduce PAPR. Additionally, the potential of artificial intelligence (AI) approaches are investigated, specifically swarm intelligence and ML methods, for high-performance, low-complexity solutions. In this regard, ML models are applied to mitigate PAPR in SatCom networks under the presence of a traveling wave tube amplifier (TWTA) model and a land mobile satellite (LMS) channel, employing 16-quadrature amplitude modulation (16-QAM). Compared with the baseline schemes, simulation results demonstrate that the proposed ML framework, integrating principal component analysis (PCA) with the elastic net learning model, achieves comparable PAPR performance and minimal computational complexity.
Disciplines :
Electrical & electronics engineering
Author, co-author :
GARCIA MORETA, Carla Estefania ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Vega, Francisco J. Martin; University of Malaga, Malaga, Spain
CAMANA ACOSTA, Mario Rodrigo ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
QUEROL, Jorge ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Althunibat, Saud; Al-Hussein Bin Talal University, Department of Communication Engineering, Ma'an, Jordan
Qaraqe, Khalid; College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
External co-authors :
yes
Language :
English
Title :
Machine Learning-Driven Framework for Reducing PAPR in Satellite Communication Systems
This work was supported by the European Space Agency (ESA) under Contract No. 4000130962/20/NL/NL/FE for the project \"Satellite Communications with AI-OFDM Waveform for PAPR and ACI Reduction (SAFARI).\" This research also was supported in part by the project sElf-evolving terrestrial/nonTerrestrial Hybrid nEtwoRks (ETHER). The ETHER Project was supported by the Smart Networks and Services Joint Undertaking (SNS JU) through the European Union's Horizon Europe Research and Innovation Programme under Grant 101096526. Additional support was provided by the Qatar Research, Development and Innovation (QRDI) Fund-part of Qatar Foundation-through grant NPRP14C-0909-210008.
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