# AI-Native Integrated Sensing and Communications for Self-Organizing Wireless Networks: Architectures, Learning Paradigms, and System-Level Design

arXiv: https://arxiv.org/abs/2601.02398 · HTML: https://mlsanity.com/paper/2601.02398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 29 Dec 2025.

## Builds on (most cited first, 14 of 14)

- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Learning Optimal Resource Allocations in Wireless Systems](https://mlsanity.com/paper/1807.08088/) (2018)
- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey](https://mlsanity.com/paper/2301.03857/) (2023)
- [Computer Vision Aided Beam Tracking in A Real-World Millimeter Wave Deployment](https://mlsanity.com/paper/2111.14803/) (2021)
- [DeepWiFi: Cognitive WiFi with Deep Learning](https://mlsanity.com/paper/1910.13315/) (2019)
- [Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes](https://mlsanity.com/paper/2303.16113/) (2023)
- [Sensing-Aided 6G Drone Communications: Real-World Datasets and Demonstration](https://mlsanity.com/paper/2412.04734/) (2024)
- [Joint Beam Management and SLAM for mmWave Communication Systems](https://mlsanity.com/paper/2307.07936/) (2023)
- [Deep Reinforcement Learning Based Placement for Integrated Access Backhauling in UAV-Assisted Wireless Networks](https://mlsanity.com/paper/2312.14247/) (2023)
- [Integrated Sensing and Communication: Towards Multifunctional Perceptive Network](https://mlsanity.com/paper/2510.14358/) (2025)
- [Distributed Intelligent Sensing and Communications for 6G: Architecture and Use Cases](https://mlsanity.com/paper/2504.12765/) (2025)

## Abstract (arXiv)

Integrated Sensing and Communications (ISAC) is emerging as a foundational paradigm for next-generation wireless networks, enabling communication infrastructures to simultaneously support data transmission and environment sensing. By tightly coupling radio sensing with communication functions, ISAC unlocks new capabilities for situational awareness, localization, tracking, and network adaptation. At the same time, the increasing scale, heterogeneity, and dynamics of future wireless systems demand self-organizing network intelligence capable of autonomously managing resources, topology, and services. Artificial intelligence (AI), particularly learning-driven and data-centric methods, has become a key enabler for realizing this vision. This survey provides a comprehensive and system-level review of AI-native ISAC-enabled self-organizing wireless networks. We develop a unified taxonomy that spans: (i) ISAC signal models and sensing modalities, (ii) network state abstraction and perception from sensing-aware radio data, (iii) learning-driven self-organization mechanisms for resource allocation, topology control, and mobility management, and (iv) cross-layer architectures integrating sensing, communication, and network intelligence. We further examine emerging learning paradigms, including deep reinforcement learning, graph-based learning, multi-agent coordination, and federated intelligence that enable autonomous adaptation under uncertainty, mobility, and partial observability. Practical considerations such as sensing-communication trade-offs, scalability, latency, reliability, and security are discussed alongside representative evaluation methodologies and performance metrics. Finally, we identify key open challenges and future research directions toward deployable, trustworthy, and scalable AI-native ISAC systems for 6G and beyond.