# Waveform Learning for Next-Generation Wireless Communication Systems

arXiv: https://arxiv.org/abs/2109.00998 · HTML: https://mlsanity.com/paper/2109.00998/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 2 Sep 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities](https://mlsanity.com/paper/2008.03213/) (2020)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Model-free Training of End-to-end Communication Systems](https://mlsanity.com/paper/1812.05929/) (2018)
- [ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection](https://mlsanity.com/paper/1905.10750/) (2019)
- [Trainable Communication Systems: Concepts and Prototype](https://mlsanity.com/paper/1911.13055/) (2019)
- [Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning](https://mlsanity.com/paper/2101.08213/) (2021)
- [End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints](https://mlsanity.com/paper/2106.16039/) (2021)
- [End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment](https://mlsanity.com/paper/2103.15856/) (2021)
- [End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping](https://mlsanity.com/paper/2106.15158/) (2021)
- [A Survey of NOMA: Current Status and Open Research Challenges](https://mlsanity.com/paper/1912.10561/) (2019)
- [5G New Radio Evolution Towards Sub-THz Communications](https://mlsanity.com/paper/2002.02659/) (2020)

## Built on by (most cited first, 10 of 10)

- [6G WavesFM: A Foundation Model for Sensing, Communication, and Localization](https://mlsanity.com/paper/2504.14100/) (2025)
- [Building 6G Radio Foundation Models with Transformer Architectures](https://mlsanity.com/paper/2411.09996/) (2024)
- [Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning](https://mlsanity.com/paper/2411.09849/) (2024)
- [Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach](https://mlsanity.com/paper/2309.08688/) (2023)
- [Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systems](https://mlsanity.com/paper/2510.25416/) (2025)
- [Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder](https://mlsanity.com/paper/2301.05017/) (2023)
- [Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models](https://mlsanity.com/paper/2311.09349/) (2023)
- [DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction](https://mlsanity.com/paper/2603.23544/) (2026)
- [Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers](https://mlsanity.com/paper/2409.02430/) (2024)
- [A Study of Neural Polar Decoders for Communication](https://mlsanity.com/paper/2510.03069/) (2025)

## Abstract (arXiv)

We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-based detector. The method maximizes an achievable information rate, while simultaneously satisfying constraints on the adjacent channel leakage ratio (ACLR) and peak-to-average power ratio (PAPR). This allows control of the tradeoff between spectral containment, peak power, and communication rate. Evaluation on an additive white Gaussian noise (AWGN) channel shows significant reduction of ACLR and PAPR compared to a conventional baseline relying on quadrature amplitude modulation (QAM) and root-raised-cosine (RRC), without significant loss of information rate. When considering a 3rd Generation Partnership Project (3GPP) multipath channel, the learned waveform and neural receiver enable competitive or higher rates than an orthogonal frequency division multiplexing (OFDM) baseline, while reducing the ACLR by 10 dB and the PAPR by 2 dB. The proposed method incurs no additional complexity on the transmitter side and might be an attractive tool for waveform design of beyond-5G systems.