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

@misc{kim2026shortlength,
  title = {{Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach}},
  author = {Muah Kim and Shuangyang Li and Tayyebeh Jahani-Nezhad and Rafael F. Schaefer and Giuseppe Caire},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.33605},
  eprint = {2609.33605},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.33605},
}

TY  - UNPB
TI  - Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach
AU  - Muah Kim
AU  - Shuangyang Li
AU  - Tayyebeh Jahani-Nezhad
AU  - Rafael F. Schaefer
AU  - Giuseppe Caire
PY  - 2026
DA  - 2026/09/27
PB  - arXiv
DO  - 10.48550/arxiv.2609.33605
UR  - https://arxiv.org/abs/2609.33605
AB  - 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 Cramér-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.
ER  -