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

arXiv: https://arxiv.org/abs/2609.33605 · HTML: https://mlsanity.com/paper/2609.33605/

## 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.

## Builds on (most cited first, 16 of 16)

- [Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels](https://mlsanity.com/paper/1911.03038/) (2019)
- [End-to-End Learning of Communications Systems Without a Channel Model](https://mlsanity.com/paper/1804.02276/) (2018)
- [Joint Radar and Communication Design: Applications, State-of-the-art, and the Road Ahead](https://mlsanity.com/paper/1906.00789/) (2019)
- [ProductAE: Towards Training Larger Channel Codes based on Neural Product Codes](https://mlsanity.com/paper/2110.04466/) (2021)
- [End-to-End Learning for Integrated Sensing and Communication](https://mlsanity.com/paper/2111.02106/) (2021)
- [SVD-Embedded Deep Autoencoder for MIMO Communications](https://mlsanity.com/paper/2111.02359/) (2021)
- [Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE](https://mlsanity.com/paper/2209.01701/) (2022)
- [Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication](https://mlsanity.com/paper/2006.15027/) (2020)
- [On Outage-based Beamforming Design for Dual-Functional Radar-Communication 6G Systems](https://mlsanity.com/paper/2207.04921/) (2022)
- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels](https://mlsanity.com/paper/2105.01260/) (2021)
- [End-to-End Learning for SLP-Based ISAC Systems](https://mlsanity.com/paper/2401.05663/) (2024)
- [MinGRU-Based Encoder for Turbo Autoencoder Frameworks](https://mlsanity.com/paper/2503.08451/) (2025)
- [Scalable Synchronization and Reciprocity Calibration for Distributed Multiuser MIMO](https://mlsanity.com/paper/1310.7001/) (2013)
- [A Novel ISAC Transmission Framework based on Spatially-Spread Orthogonal Time Frequency Space Modulation](https://mlsanity.com/paper/2109.00440/) (2021)
- [Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling](https://mlsanity.com/paper/2310.18090/) (2023)

## 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.