TY - GEN
T1 - Machine Learning-Driven Framework for Reducing PAPR in Satellite Communication Systems
AU - Garcia, Carla E.
AU - Vega, Francisco J.Martin
AU - Camana, Mario R.
AU - Querol, Jorge
AU - Althunibat, Saud
AU - Qaraqe, Khalid
AU - Chatzinotas, Symeon
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Satellite communication system (SatCom)
KW - land mobile satellite (LMS)
KW - machine learning (ML)
KW - partial transmit sequence (PTS)
KW - peak-to-average power ratio (PAPR)
KW - swarm intelligence
UR - https://www.scopus.com/pages/publications/105019050199
U2 - 10.1109/VTC2025-Spring65109.2025.11174301
DO - 10.1109/VTC2025-Spring65109.2025.11174301
M3 - Conference contribution
AN - SCOPUS:105019050199
T3 - IEEE Vehicular Technology Conference
BT - 2025 IEEE 101st Vehicular Technology Conference, VTC 2025-Spring 2025 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 101st IEEE Vehicular Technology Conference, VTC 2025-Spring 2025
Y2 - 17 June 2025 through 20 June 2025
ER -