mlsanityData to 14 Oct 2026

Waveform Learning for Next-Generation Wireless Communication Systems

arXiv 2109.00998 ↗First posted 2 Sep 2021cs.IT cs.LG eess.SPcited by 10

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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.

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

15 of 34 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2017An Introduction to Deep Learning for the Physical Layercited by 233
  4. 2020DeepRx: Fully Convolutional Deep Learning Receivercited by 52
  5. 20206G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunitiescited by 35
  6. 2020End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communicationcited by 33
  7. 2018Model-free Training of End-to-end Communication Systemscited by 30
  8. 2019ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detectioncited by 25
  9. 2019Trainable Communication Systems: Concepts and Prototypecited by 22
  10. 2021Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learningcited by 6
  11. 2021End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraintscited by 5
  12. 2021End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairmentcited by 4
3 more
  1. 2021End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shapingcited by 4
  2. 2019A Survey of NOMA: Current Status and Open Research Challengescited by 2
  3. 20205G New Radio Evolution Towards Sub-THz Communicationscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 3; 2026: 1.

10 papers.

  1. 20256G WavesFM: A Foundation Model for Sensing, Communication, and Localizationcited by 13
  2. 2024Building 6G Radio Foundation Models with Transformer Architecturescited by 9
  3. 2024Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learningcited by 5
  4. 2023Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approachcited by 4
  5. 2025Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systemscited by 3
  6. 2023Low PAPR MIMO-OFDM Design Based on Convolutional Autoencodercited by 2
  7. 2023Generative AI-Based Probabilistic Constellation Shaping With Diffusion Modelscited by 2
  8. 2026DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reductioncited by 1
  9. 2024Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Recevierscited by 0
  10. 2025A Study of Neural Polar Decoders for Communicationcited by 0