End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints
This work addresses power amplifier efficiency issues in wireless communications, but it is incremental as it builds on existing OFDM and neural network approaches.
The paper tackled the problem of poor peak-to-average power ratio (PAPR) and adjacent channel leakage ratio (ACLR) in OFDM wireless networks by proposing an end-to-end neural network-based system to learn waveforms with constraints, resulting in higher information rates than a baseline method while meeting predefined targets.
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.