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- W4225503331 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, an 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." @default.
- W4225503331 created "2022-05-05" @default.
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- W4225503331 date "2021-12-01" @default.
- W4225503331 modified "2023-10-07" @default.
- W4225503331 title "End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints" @default.
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- W4225503331 doi "https://doi.org/10.1109/gcwkshps52748.2021.9682132" @default.
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