# Learning OFDM Waveforms with PAPR and ACLR Constraints

arXiv: https://arxiv.org/abs/2110.10987 · HTML: https://mlsanity.com/paper/2110.10987/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [Machine Learning in the Air](https://mlsanity.com/paper/1904.12385/) (2019)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Trainable Communication Systems: Concepts and Prototype](https://mlsanity.com/paper/1911.13055/) (2019)
- [End-to-end Deep Learning of Optical Fiber Communications](https://mlsanity.com/paper/1804.04097/) (2018)
- [DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations](https://mlsanity.com/paper/2010.16283/) (2020)
- [Machine Learning for MU-MIMO Receive Processing in OFDM Systems](https://mlsanity.com/paper/2012.08177/) (2020)
- [End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints](https://mlsanity.com/paper/2106.16039/) (2021)
- [End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping](https://mlsanity.com/paper/2106.15158/) (2021)

## Built on by (most cited first, 3 of 3)

- [Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective](https://mlsanity.com/paper/2208.08342/) (2022)
- [DeepTx: Deep Learning Beamforming with Channel Prediction](https://mlsanity.com/paper/2202.07998/) (2022)
- [Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder](https://mlsanity.com/paper/2301.05017/) (2023)

## Abstract (arXiv)

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.