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Conference Papers Year : 2021

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

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.

Dates and versions

hal-03393750 , version 1 (21-10-2021)

Identifiers

Cite

Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis, Jean-Marie S Gorce. End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints. GLOBECOM 2021 - IEEE Global Communication Conference, Dec 2021, Madrid, Spain. pp.1-5, ⟨10.1109/GCWkshps52748.2021.9682132⟩. ⟨hal-03393750⟩
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