Unsupervised Learning-Based ISAC Waveforms

IF 5.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Wireless Communications Letters Pub Date : 2025-04-15 DOI:10.1109/LWC.2025.3560907

Janith Kavindu Dassanayake;Ranga Kulathunga;Gayan Amarasuriya Aruma Baduge;Mojtaba Vaezi

引用次数: 0

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.

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基于无监督学习的ISAC波形

基于经典优化理论的集成传感和通信（ISAC）的波形设计可能会由于传感和通信指标之间的权衡而变得计算详尽。为了解决这个问题，提出了一种基于无监督学习的ISAC波形设计。ISAC权衡是通过受约束的自定义损失函数捕获的。用户速率、检测概率和cram r- rao边界之间的权衡用于评估和比较性能增益。我们的研究结果揭示了基于学习的技术在平衡ISAC波形设计的性能和复杂性方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Wireless Communications Letters Engineering-Electrical and Electronic Engineering

CiteScore

12.30

自引率

6.30%

发文量

481

期刊介绍： IEEE Wireless Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of wireless communications. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of wireless communication systems.