Learning OFDM Waveforms with PAPR and ACLR Constraints
This addresses signal quality and efficiency issues in future communication systems, representing an incremental improvement over existing OFDM methods.
The paper tackled the problem of high adjacent channel leakage ratio (ACLR) and peak-to-average power ratio (PAPR) in OFDM waveforms by proposing a learning-based method using convolutional neural networks to design waveforms that satisfy constraints while maximizing information rate, achieving significant throughput gains compared to a tone reservation baseline.
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.