  An attractive research direction for future communication systems is the
design of new waveforms that can both support high throughputs and present
advantageous signal characteristics. Although most modern systems use
orthogonal frequency-division multiplexing (OFDM) for its efficient
equalization, this waveform suffers from multiple limitations such as a high
adjacent channel leakage ratio (ACLR) and high peak-to-average power ratio
(PAPR). In this paper, we propose a learning-based method to design OFDM-based
waveforms that satisfy selected constraints while maximizing an achievable
information rate. To that aim, we model the transmitter and the receiver as
convolutional neural networks (CNNs) that respectively implement a
high-dimensional modulation scheme and perform the detection of the transmitted
bits. This leads to an optimization problem that is solved using the augmented
Lagrangian method. Evaluation results show that the end-to-end system is able
to satisfy target PAPR and ACLR constraints and allows significant throughput
gains compared to a tone reservation (TR) baseline. An additional advantage is
that no dedicated pilots are needed.
