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arXiv · 2106.16039

End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints

Mathieu Goutay·Fayçal Ait Aoudia·Jakob Hoydis·Jean-Marie Gorce
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Abstract

Orthogonal frequency-division multiplexing (OFDM) is widely used in modern wireless networks thanks to its efficient handling of multipath environment. However, it suffers from a poor peak-to-average power ratio (PAPR) which requires a large power backoff, degrading the power amplifier (PA) efficiency. In this work, we propose to use a neural network (NN) at the transmitter to learn a high-dimensional modulation scheme allowing to control the PAPR and adjacent channel leakage ratio (ACLR). On the receiver side, a NN-based receiver is implemented to carry out demapping of the transmitted bits. The two NNs operate on top of OFDM, and are jointly optimized in and end-to-end manner using a training algorithm that enforces constraints on the PAPR and ACLR. Simulation results show that the learned waveforms enable higher information rates than a tone reservation baseline, while satisfying predefined PAPR and ACLR targets.

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Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis, Jean-Marie Gorce. 2021-06-30. End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints. https://arxiv.org/abs/2106.16039

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