Set Transformer-Based Beamforming Design for Cell-Free Integrated Sensing and Communication

Existing cell-free integrated sensing and communication (CF-ISAC) beamforming algorithms predominantly rely on classical optimization techniques, which often entail high computational complexity and limited scalability. Meanwhile, recent learning-based approaches have difficulty capturing the global interactions and long-range dependencies among distributed access points (APs), communication users, and sensing targets. To address these limitations, we propose the first Set Transformer-based CF-ISAC beamforming framework (STCIB). By exploiting attention mechanisms, STCIB explicitly models global relationships among network entities, naturally handles unordered input sets, and preserves permutation invariance across APs, users, and targets. The proposed framework operates in an unsupervised manner, eliminating the need for labeled training data, and supports three design regimes: (i) sensing-centric, (ii) communication-centric, and (iii) joint ISAC optimization. We benchmark STCIB against a convolutional neural network (CNN) baseline and two state-of-the-art optimization algorithms: the convex-concave procedure algorithm (CCPA) and augmented Lagrangian manifold optimization (ALM-MO). Numerical results demonstrate that STCIB consistently outperforms the CNN, achieving substantially higher ISAC performance with only a negligible increase in runtime. For instance, in regime (iii), at $η$=0.4, STCIB improves the sensing and communication sum rates by 14.8 % and 31.6 %, respectively, relative to the CNN, while increasing runtime by only 0.26 %. Compared with CCPA and ALM-MO, STCIB offers significantly lower computational cost while maintaining modest performance gains. In regime (i), for a 3.0 bps/Hz communication threshold, the runtime of STCIB is only 0.1 % and 0.3 % of that required by CCPA and ALM-MO, respectively, while improving the sensing sum rate by 4.45 % and 5.9 %.

AI-Empowered Integrated Sensing and Communications

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.

Cell-Free Integrated Sensing and Communication: Principles, Advances, and Future Directions

Cell-free (CF) integrated sensing and communication (ISAC) combines CF architecture with ISAC. CF employs distributed access points, eliminates cell boundaries, and enhances coverage, spectral efficiency, and reliability. ISAC unifies radar sensing and communication, enabling simultaneous data transmission and environmental sensing within shared spectral and hardware resources. CF-ISAC leverages these strengths to improve spectral and energy efficiency while enhancing sensing in wireless networks. As a promising candidate for next-generation wireless systems, CF-ISAC supports robust multi-user communication, distributed multi-static sensing, and seamless resource optimization. However, a comprehensive survey on CF-ISAC has been lacking. This paper fills that gap by first revisiting CF and ISAC principles, covering cooperative transmission, radar cross-section, target parameter estimation, ISAC integration levels, sensing metrics, and applications. It then explores CF-ISAC systems, emphasizing their unique features and the benefits of multi-static sensing. State-of-the-art developments are categorized into performance analysis, resource allocation, security, and user/target-centric designs, offering a thorough literature review and case studies. Finally, the paper identifies key challenges such as synchronization, multi-target detection, interference management, and fronthaul capacity and latency. Emerging trends, including next-generation antenna technologies, network-assisted systems, near-field CF-ISAC, integration with other technologies, and machine learning approaches, are highlighted to outline the future trajectory of CF-ISAC research.