Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals

6G (Next-generation mobile telephony) communication systems require modulation schemes robust against channel fading, and Orthogonal Time Frequency Space (OTFS) has emerged as a key technology to achieve this goal. However, OTFS exhibits high amplitude variations (high PAPR), making it particularly susceptible to High Power Amplifier (HPA) nonlinearities, which degrade spectral purity (ACPR or Adjacent Channel Power Ratio) and increase the Bit Error Rate (BER). Digital predistortion (DPD) is the most effective method for HPA linearization, but classical polynomial models struggle to capture complex nonlinearities especially when applied to demanding signals like OTFS. In this paper, we propose and evaluate an innovative DPD approach based on a feedforward neural network. A multi-criteria analysis demonstrates that this method significantly outperforms polynomial predistortion: it achieves precise predistortion function approximation with a Mean Squared Error (MSE) of 7.38 × 10sup-6/sup, improves ACPR by 22 dB (from -15 dB to -36 dB), and attains a BER nearly identical to that of a linear amplifier even in a Rayleigh fading channel. Moreover, it enables the HPA to operate in saturation (low IBO ou Input Back-off, ~70% efficiency) while preserving optimal transmission quality, thereby breaking the traditional trade-off between energy efficiency and linearity. Our approach is simple, robust, and computationally lightweight, paving the way for highly efficient 6G transmission chains tailored for mobile environments.