Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning
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Abstract (arXiv)
Orthogonal frequency division multiplexing (OFDM) is one of the dominant waveforms in wireless communication systems due to its efficient implementation. However, it suffers from a loss of spectral efficiency as it requires a cyclic prefix (CP) to mitigate inter-symbol interference (ISI) and pilots to estimate the channel. We propose in this work to address these drawbacks by learning a neural network (NN)-based receiver jointly with a constellation geometry and bit labeling at the transmitter, that allows CP-less and pilotless communication on top of OFDM without a significant loss in bit error rate (BER). Our approach enables at least 18% throughput gains compared to a pilot and CP-based baseline, and at least 4% gains compared to a system that uses a neural receiver with pilots but no CP.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 20 Jan 2021.
Builds on
6 of 11 references.
References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2017An Introduction to Deep Learning for the Physical Layercited by 227
- 2020DeepRx: Fully Convolutional Deep Learning Receivercited by 49
- 2020End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communicationcited by 31
- 2019Trainable Communication Systems: Concepts and Prototypecited by 22
- 2018End-to-end Deep Learning of Optical Fiber Communicationscited by 16
Built on by
Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
6 papers.
- 2021Waveform Learning for Next-Generation Wireless Communication Systemscited by 10
- 2024Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applicationscited by 6
- 2022Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifiercited by 5
- 2021End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shapingcited by 4
- 2025Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systemscited by 3
- 2021End-to-End Autoencoder Communications with Optimized Interference Suppressioncited by 2
- 2025Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systems29 Oct 2025
- 2024Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications8 Jan 2024
- 2022Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier14 Jan 2022
- 2021End-to-End Autoencoder Communications with Optimized Interference Suppression29 Dec 2021
- 2021Waveform Learning for Next-Generation Wireless Communication Systems2 Sep 2021
- 2021End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping29 Jun 2021