Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier Systems

Abstract

High peak-to-average power ratio (PAPR) has been a major drawback of Filter bank Multicarrier (FBMC) in the 5G system. This research aims to calculate the PAPR reduction associated with the FBMC system. This research uses four techniques to reduce PAPR. They are classical tone reservation (TR). It combines tone reservation with sliding window (SW-TR). It also combines them with active constellation extension (TRACE) and with deep learning (TR-Net). TR-net decreases the greatest PAPR reduction by around 8.6 dB compared to the original value. This work significantly advances PAPR reduction in FBMC systems by proposing three hybrid methods, emphasizing the deep learning-based TRNet technique as a groundbreaking solution for efficient, distortion-free signal processing.

Keywords

• filter bank multi-carrier (fbmc)
• peak-to-average power ratio (papr)
• tone reservation (tr)
• sliding window tone reservation (sw-tr)
• offset quadrature amplitude modulation (oqam)
• trace detection (trace)
• tone reservation neural network (trnet)
• deep lea