AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges

• URL: http://arxiv.org/abs/2501.14970v2
• Date: Tue, 05 Aug 2025 06:06:54 GMT
• Title: AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges
• Authors: Guangjin Pan, Yuan Gao, Yilin Gao, Wenjun Yu, Zhiyong Zhong, Xiaoyu Yang, Xinyu Guo, Shugong Xu,
• Abstract summary: AI/machine learning (ML)-based cellular positioning is becoming a key technology to overcome the limitations of traditional methods.<n>We categorize and summarize state-of-the-art (SOTA) research into two major classes: AI/ML-assisted positioning and direct AI/ML-based positioning.<n>We conclude by summarizing the challenges and opportunities of AI-driven wireless positioning.
• Score: 15.375888684755582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wireless positioning technologies hold significant value for applications in autonomous driving, extended reality (XR), unmanned aerial vehicles (UAVs), and more. With the advancement of artificial intelligence (AI), leveraging AI to enhance positioning accuracy and robustness has emerged as a field full of potential. Driven by the requirements and functionalities defined in the 3rd Generation Partnership Project (3GPP) standards, AI/machine learning (ML)-based cellular positioning is becoming a key technology to overcome the limitations of traditional methods. This paper presents a comprehensive survey of AI-driven cellular positioning. We begin by reviewing the fundamentals of wireless positioning and AI models, analyzing their respective challenges and synergies. We provide a comprehensive review of the evolution of 3GPP positioning standards, with a focus on the integration of AI/ML in current and upcoming standard releases. Guided by the 3GPP-defined taxonomy, we categorize and summarize state-of-the-art (SOTA) research into two major classes: AI/ML-assisted positioning and direct AI/ML-based positioning. The former includes line-of-sight (LOS)/non-line-of-sight (NLOS) detection, time of arrival (TOA)/time difference of arrival (TDOA) estimation, and angle prediction; the latter encompasses fingerprinting, knowledge-assisted learning, and channel charting. Furthermore, we review representative public datasets and conduct performance evaluations of AI-based positioning algorithms using these datasets. Finally, we conclude by summarizing the challenges and opportunities of AI-driven wireless positioning.

Related papers

• Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks [3.1695254838580618]
  fully realizing the potential of AI/ML in wireless communications requires a deep interdisciplinary understanding of both fields.<n>We outline open research challenges and opportunities where academic and industrial communities can contribute to shaping the future of AI-enabled wireless systems.
  arXiv  Detail & Related papers  (2025-06-13T04:41:51Z)
• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
  We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
  arXiv  Detail & Related papers  (2025-06-09T18:37:14Z)
• Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
  This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
  arXiv  Detail & Related papers  (2025-05-13T17:59:20Z)
• AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation [13.059865395440603]
  Assuring safety of artificial intelligence applied to safety-critical systems is of paramount importance. Research on safeguarding AI is being conducted in parallel to AI standardization and regulation.
  arXiv  Detail & Related papers  (2025-04-25T13:14:06Z)
• General Scales Unlock AI Evaluation with Explanatory and Predictive Power [57.7995945974989]
  benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems. We introduce general scales for AI evaluation that can explain what common AI benchmarks really measure. Our fully-automated methodology builds on 18 newly-crafted rubrics that place instance demands on general scales that do not saturate.
  arXiv  Detail & Related papers  (2025-03-09T01:13:56Z)
• Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models [16.16798813072285]
  The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices. This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models.
  arXiv  Detail & Related papers  (2025-03-08T02:59:51Z)
• Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities [156.35699837919677]
  This paper presents a comprehensive overview of AI and communication for 6G networks.<n>We first review the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G.<n>The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks.
  arXiv  Detail & Related papers  (2024-12-19T05:36:34Z)
• AI Horizon Scanning -- White Paper p3395, IEEE-SA. Part III: Technology Watch: a selection of key developments, emerging technologies, and industry trends in Artificial Intelligence [0.3277163122167434]
  Generative Artificial Intelligence (AI) technologies are in a phase of unprecedented rapid development following the landmark release of Chat-GPT. As the deployment of AI products rises geometrically, considerable attention is being given to the threats and opportunities that AI technologies offer. This manuscript is the third of a series of White Papers informing the development of IEEE-SA's p3995 it Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence Models'
  arXiv  Detail & Related papers  (2024-11-05T19:04:42Z)
• On the Combination of AI and Wireless Technologies: 3GPP Standardization Progress [13.799195145459972]
  Combing Artificial Intelligence (AI) and wireless communication technologies has become one of the major technologies trends towards 2030. Use AI to improve the efficiency of the wireless transmission and supporting AI deployment with wireless networks.
  arXiv  Detail & Related papers  (2024-06-17T00:11:04Z)
• A Survey on Vision-Language-Action Models for Embodied AI [71.16123093739932]
  Embodied AI is widely recognized as a key element of artificial general intelligence.<n>A new category of multimodal models has emerged to address language-conditioned robotic tasks in embodied AI.<n>We present the first survey on vision-language-action models for embodied AI.
  arXiv  Detail & Related papers  (2024-05-23T01:43:54Z)
• Networking Systems for Video Anomaly Detection: A Tutorial and Survey [55.28514053969056]
  Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has made significant progress. This article offers an exhaustive tutorial for novices in NSVAD.
  arXiv  Detail & Related papers  (2024-05-16T02:00:44Z)
• Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices [0.0]
  Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their computational capabilities, emphasizing the practicality of edge AI. Our study explores methods that enable distributed data processing through AI-enabled edge devices, enhancing collaborative learning capabilities.
  arXiv  Detail & Related papers  (2024-03-14T07:40:32Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration [56.330705072736166]
  We propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system.
  arXiv  Detail & Related papers  (2023-10-26T15:19:40Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI.<n>It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.<n>We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Towards Fairness Certification in Artificial Intelligence [31.920661197618195]
  We propose a first joint effort to define the operational steps needed for AI fairness certification. We will overview the criteria that should be met by an AI system before coming into official service and the conformity assessment procedures useful to monitor its functioning for fair decisions.
  arXiv  Detail & Related papers  (2021-06-04T14:12:12Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)
• Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
  Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Artificial intelligence (AI) is growing rapidly nowadays and has been very successful. We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
  arXiv  Detail & Related papers  (2020-09-24T07:11:31Z)
• Communication-Efficient Edge AI: Algorithms and Systems [39.28788394839187]
  Wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data. Such enormous data cannot all be sent from end devices to the cloud for processing. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
  arXiv  Detail & Related papers  (2020-02-22T09:27:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.