TOWARDS OPTIMAL MIMO-OFDM WAVEFORMS : A LOW-PAPR TRANSMISSION STRATEGY WITH ARTIFICIAL NEURAL NETWORKS

A high peak-to-average power ratio (PAPR) is one of the most critical challenges in Orthogonal Frequency Division Multiplexing (OFDM) systems. It limits the efficiency of high power amplifiers and increases signal distortion. This problem is aggravated in Multiple-Input Multiple-Output (MIMO) OFDM systems due to the simultaneous transmission of multiple data streams, resulting in degraded Bit Error Rate (BER) performance and reduced power efficiency. To address this, we propose an intelligent PAPR reduction scheme based on Artificial Neural Networks (ANNs) to dynamically optimise the clipping threshold. Unlike traditional clipping techniques, which use a fixed threshold, our adaptive ANN-Clipping method learns to determine the optimal threshold according to the instantaneous statistical properties of the transmitted signal. This enables an efficient trade-off to be made between PAPR reduction and signal distortion while maintaining low computational complexity. Simulation results demonstrate the effectiveness of the proposed method, achieving an average PAPR of 2.76 dB, compared to 4.01 dB for conventional fixed clipping and 8.74 dB for the original OFDM signal. Furthermore, at a CCDF probability of 10-4 , the ANN-Clipping scheme achieves a PAPR of 3.04dB, which is a significant improvement on conventional PAPR reduction methods. These results confirm that the proposed approach significantly improves the performance of 5G and 6G wireless communication systems in terms of efficiency and robustness.