mlsanityData to 7 Oct 2026

Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning

arXiv 2101.08213 ↗First posted 20 Jan 2021cs.IT cs.LG eess.SPcited by 6

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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

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017An Introduction to Deep Learning for the Physical Layercited by 227
  3. 2020DeepRx: Fully Convolutional Deep Learning Receivercited by 49
  4. 2020End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communicationcited by 31
  5. 2019Trainable Communication Systems: Concepts and Prototypecited by 22
  6. 2018End-to-end Deep Learning of Optical Fiber Communicationscited by 16

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Cited by year

Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 1; 2025: 1.

6 papers.