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Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach

arXiv 2609.33605 ↗First posted 27 Sep 2026cs.LG cs.IT math.ITcited by 0

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Abstract (arXiv)

Integrated sensing and communication (ISAC) enables joint communication and sensing using a shared waveform, but its signal design is challenging due to the inherent trade-off between the two objectives, particularly in the short blocklength regime. This paper proposes an autoencoder (AE)-based framework for ISAC waveform design in noncoherent settings. We derive a modified Cram'er-Rao bound for multi-target delay estimation and analyze the maximum-likelihood decoding rule for noncoherent communication under correlated fading. These results reveal structural connections and trade-offs between communication and sensing objectives in waveform design. Based on this analysis, the AE learns waveform representations that jointly optimize both functionalities, with a tunable parameter controlling the trade-off. Simulation results show that the proposed design outperforms conventional schemes in both communication reliability and sensing accuracy, especially under short blocklength and fading conditions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 47 references that are in the map. First posted on arXiv 27 Sep 2026.

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References · 16

  1. 2019Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channelscited by 44
  2. 2018End-to-End Learning of Communications Systems Without a Channel Modelcited by 21
  3. 2019Joint Radar and Communication Design: Applications, State-of-the-art, and the Road Aheadcited by 19
  4. 2021ProductAE: Towards Training Larger Channel Codes based on Neural Product Codescited by 14
  5. 2021End-to-End Learning for Integrated Sensing and Communicationcited by 6
  6. 2021SVD-Embedded Deep Autoencoder for MIMO Communicationscited by 5
  7. 2022Concatenated Classic and Neural (CCN) Codes: ConcatenatedAEcited by 5
  8. 2020Deep-learning Autoencoder for Coherent and Nonlinear Optical Communicationcited by 4
  9. 2022On Outage-based Beamforming Design for Dual-Functional Radar-Communication 6G Systemscited by 3
  10. 2025Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunitiescited by 3
  11. 2021End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channelscited by 2
  12. 2024End-to-End Learning for SLP-Based ISAC Systemscited by 2
4 more
  1. 2025MinGRU-Based Encoder for Turbo Autoencoder Frameworkscited by 2
  2. 2013Scalable Synchronization and Reciprocity Calibration for Distributed Multiuser MIMOcited by 1
  3. 2021A Novel ISAC Transmission Framework based on Spatially-Spread Orthogonal Time Frequency Space Modulationcited by 1
  4. 2023Probabilistic Constellation Shaping for OFDM-Based ISAC Signalingcited by 1

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