What question did this study set out to answer?

The aim is to develop a method to reduce the peak-to-average power ratio in OTFS using a neural network algorithm.

February 2, 2026Open Access

A Neural Network-Enabled OTFS-PAPR Reduction with Low Computational Complexity

MAM. I. Al-RayifKing Saud University EEE. E. EldukhriKing Saud University

Key Points

The aim is to develop a method to reduce the peak-to-average power ratio in OTFS using a neural network algorithm.
Utilized an Artificial Neural Network (ANN) for power ratio adjustment.
Evaluated matrix elements in the ISFFT process.
Applied fractional shifts based on predefined thresholds for DD data matrix elements.
Conducted simulations to assess performance and complexity.
Achieved significant reduction in PAPR.
Maintained system performance regarding Bit Error Rate (BER).
Maintained similar computational complexity compared to the standard OTFS.

Abstract

This study proposes a new solution to overcome the high peak-to-average power ratio (PAPR) in Orthogonal Time Frequency Space (OTFS) by using an Artificial Neural Network (ANN) algorithm. The algorithm checks the magnitude (power) of each element in the matrix of the first stage of the inverse symplectic finite Fourier transform (ISFFT) process against a pre-specified threshold and, consequently adjusts the elements whose magnitudes exceed the threshold. This is achieved by using the ANN algorithm to apply fractional shifts to the elements of the original delay-Doppler (DD) data matrix without changing their orientation. The simulation results demonstrated a significant PAPR reduction while maintaining the system performance in terms of the Bit Error Rate (BER), with almost the same computational complexity of the conventional OTFS system.

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