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When high PAPR reduction meets CNN: A PRD framework.

Yaoqi Yang¹, Xianglin Wei², Renhui Xu³

¹Graduate School, Army Engineering University of PLA, Nanjing 210000, China.

Mathematical Biosciences and Engineering : MBE

|September 14, 2021

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This study introduces a novel algorithm to identify high Peak to Average Power Ratio (PAPR) sequences in Orthogonal Frequency Division Multiplexing (OFDM) systems. This method accurately detects problematic signals, enabling efficient PAPR reduction and improving system performance.

Area of Science:

Electrical Engineering
Signal Processing
Telecommunications

Background:

High Peak to Average Power Ratio (PAPR) is a critical performance limitation in Orthogonal Frequency Division Multiplexing (OFDM) systems.
Existing PAPR suppression techniques often involve computationally intensive pre-processing of all signals, leading to excessive overhead, especially at high transmission speeds.

Purpose of the Study:

To develop an efficient algorithm for identifying high PAPR sequences in OFDM systems without requiring Inverse Fast Fourier Transform (IFFT) operations.
To reduce the computational complexity associated with PAPR reduction methods.

Main Methods:

A Convolutional Neural Network (CNN) is trained to accurately identify high PAPR sequences.
A novel algorithm, termed PRD (PAPR Recognition and Discrimination), is proposed to detect these sequences prior to PAPR reduction.

Keywords:

convolutional neural network orthogonal frequency division multiplexing peak to average power ratio

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The PRD algorithm avoids IFFT operations, significantly reducing computational load.

Main Results:

The proposed PRD algorithm achieves a 92.3% accuracy in identifying high PAPR sequences.
The method demonstrates extremely low computational requirements compared to traditional approaches.
Effective PAPR reduction is achieved by targeting only identified high PAPR sequences.

Conclusions:

The PRD algorithm offers an efficient and accurate solution for identifying high PAPR sequences in OFDM systems.
This approach significantly reduces computational overhead, making it suitable for high-speed communication systems.
The findings pave the way for more practical and performant PAPR reduction strategies in OFDM.