Unsupervised Learning-Based ISAC Waveforms

Waveform designs for integrated sensing and communication (ISAC) via classical optimization theory may become computationally exhaustive due to trade-offs between sensing and communication metrics. To circumvent this, an unsupervised learning-based ISAC waveform design is proposed. The ISAC trade-of...

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Published inIEEE wireless communications letters Vol. 14; no. 9; pp. 2663 - 2667
Main Authors Dassanayake, Janith Kavindu, Kulathunga, Ranga, Baduge, Gayan Amarasuriya Aruma, Vaezi, Mojtaba
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.09.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Computational modeling
Correlation
Cramer-Rao bounds
Integrated sensing and communication
ISAC
Linear antenna arrays
Optimization
Performance evaluation
Radar
Real-time systems
Symbols
Tradeoffs
Unsupervised learning
Vectors
waveform Design
Waveforms
Online AccessGet full text
ISSN2162-2337
2162-2345
DOI10.1109/LWC.2025.3560907

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Abstract Waveform designs for integrated sensing and communication (ISAC) via classical optimization theory may become computationally exhaustive due to trade-offs between sensing and communication metrics. To circumvent this, an unsupervised learning-based ISAC waveform design is proposed. The ISAC trade-offs are captured via a custom loss function subject to constraints. The trade-offs between the user rates, probability of detection, and Cramér-Rao bound are used to evaluate and compare performance gains. Our results reveal the potential of learning-based techniques in balancing the performance and complexity of ISAC waveform designs.
AbstractList Waveform designs for integrated sensing and communication (ISAC) via classical optimization theory may become computationally exhaustive due to trade-offs between sensing and communication metrics. To circumvent this, an unsupervised learning-based ISAC waveform design is proposed. The ISAC trade-offs are captured via a custom loss function subject to constraints. The trade-offs between the user rates, probability of detection, and Cramér-Rao bound are used to evaluate and compare performance gains. Our results reveal the potential of learning-based techniques in balancing the performance and complexity of ISAC waveform designs.
Author Dassanayake, Janith Kavindu
Kulathunga, Ranga
Baduge, Gayan Amarasuriya Aruma
Vaezi, Mojtaba
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SubjectTerms Artificial neural networks
Computational modeling
Correlation
Cramer-Rao bounds
Integrated sensing and communication
ISAC
Linear antenna arrays
Optimization
Performance evaluation
Radar
Real-time systems
Symbols
Tradeoffs
Unsupervised learning
Vectors
waveform Design
Waveforms
Title Unsupervised Learning-Based ISAC Waveforms
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