Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach
Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Institutions: Technische Universität Berlin, Technische Universität Dresden
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: EXC 2050: Centre for Tactile Internet with HumanintheLoop (CeTI), Verbundprojekt: 6G Research and Innovation Cluster (6G-RIC) Offene und sichere 6G-Technologien: Weltmarktchance für Deutschland - 6G-RIC -, Teilvorhaben: Sub-THz Kommunikation, Software-Hardware Co-Design und Virtualisierung, Verbundprojekt: Digitale Transformation und Souveränität zukünftiger Kommunikationsnetze - 6G-life2 -, G5154814024, G6021886509
OpenAlex ID: W7214700933