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

Abstract: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.

Machine Learning (cs.LG); Information Theory (cs.IT)

arXiv:2609.33605 [cs.LG]

(or arXiv:2609.33605v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.33605 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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