AI-Empowered Integrated Sensing and Communications

2025-04-17

AI Summary

In ISAC systems under spectrum and hardware constraints, achieving an optimal trade-off between sensing and communication performance remains challenging; conventional model-driven approaches suffer from incomplete modeling and high computational complexity. Method: This paper proposes an AI-driven unified waveform and beamforming co-design framework. It innovatively integrates unsupervised learning with neural network optimization to jointly design waveforms, constellation diagrams, and beamformers in an end-to-end manner—eliminating reliance on accurate channel or target models. Contribution/Results: The framework enables dynamic balancing of high sensing accuracy and communication throughput while drastically reducing algorithmic complexity for real-time deployment. Experimental results demonstrate its superior energy efficiency, cost-effectiveness, and generalizability across diverse scenarios. This work establishes a novel data-driven paradigm for physical-layer ISAC design.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

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. Since model-based analytical approaches may be suboptimal or overly complex, deep learning emerges as a powerful tool for developing data-driven signal processing algorithms, particularly when optimal algorithms are unknown or when known algorithms are too complex for real-time implementation. 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 article explores the application of artificial intelligence (AI) in ISAC designs to enhance efficiency and reduce complexity. 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 two case studies on waveform and beamforming design, demonstrating how unsupervised learning and neural network-based optimization can effectively balance performance, complexity, and implementation constraints.

Problem

• Overcoming spectrum and hardware limitations in ISAC systems
• Balancing sensing and communication performance with AI-driven designs
• Developing unified waveforms and beamformers using deep learning

Innovation

• AI-driven unified waveform and beamformer design
• Deep learning for data-driven signal processing
• Unsupervised learning for performance-complexity balance