A Tutorial on AI-Empowered Integrated Sensing and Communications
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915796672839680 |
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| author | Vaezi, Mojtaba Baduge, Gayan Aruma Ollila, Esa Vorobyov, Sergiy A. |
| author_facet | Vaezi, Mojtaba Baduge, Gayan Aruma Ollila, Esa Vorobyov, Sergiy A. |
| contents | Integrating sensing and communication (ISAC) can help overcome the challenges of limited spectrum and expensive hardware, leading to improved energy and cost efficiency. While full cooperation between sensing and communication can result in significant performance gains, achieving optimal performance requires efficient designs of unified waveforms and beamformers for joint sensing and communication. Sophisticated statistical signal processing and multi-objective optimization techniques are necessary to balance the competing design requirements of joint sensing and communication tasks. As model-based approaches can be suboptimal or too complex, deep learning offers a powerful data-driven alternative, especially when optimal algorithms are unknown or impractical for real-time use. Unified waveform and beamformer design problems for ISAC fall into this category, where fundamental design trade-offs exist between sensing and communication performance metrics, and the underlying models may be inadequate or incomplete. This tutorial paper explores the application of artificial intelligence (AI) to enhance efficiency or reduce complexity in ISAC designs. We emphasize the integration benefits through AI-driven ISAC designs, prioritizing the development of unified waveforms, constellations, and beamforming strategies for both sensing and communication. To illustrate the practical potential of AI-driven ISAC, we present three case studies on waveform, beamforming, and constellation design, demonstrating how unsupervised learning and neural network-based optimization can effectively balance performance, complexity, and implementation constraints. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_13363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Tutorial on AI-Empowered Integrated Sensing and Communications Vaezi, Mojtaba Baduge, Gayan Aruma Ollila, Esa Vorobyov, Sergiy A. Information Theory Signal Processing Integrating sensing and communication (ISAC) can help overcome the challenges of limited spectrum and expensive hardware, leading to improved energy and cost efficiency. While full cooperation between sensing and communication can result in significant performance gains, achieving optimal performance requires efficient designs of unified waveforms and beamformers for joint sensing and communication. Sophisticated statistical signal processing and multi-objective optimization techniques are necessary to balance the competing design requirements of joint sensing and communication tasks. As model-based approaches can be suboptimal or too complex, deep learning offers a powerful data-driven alternative, especially when optimal algorithms are unknown or impractical for real-time use. Unified waveform and beamformer design problems for ISAC fall into this category, where fundamental design trade-offs exist between sensing and communication performance metrics, and the underlying models may be inadequate or incomplete. This tutorial paper explores the application of artificial intelligence (AI) to enhance efficiency or reduce complexity in ISAC designs. We emphasize the integration benefits through AI-driven ISAC designs, prioritizing the development of unified waveforms, constellations, and beamforming strategies for both sensing and communication. To illustrate the practical potential of AI-driven ISAC, we present three case studies on waveform, beamforming, and constellation design, demonstrating how unsupervised learning and neural network-based optimization can effectively balance performance, complexity, and implementation constraints. |
| title | A Tutorial on AI-Empowered Integrated Sensing and Communications |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2504.13363 |