Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model

• URL: http://arxiv.org/abs/2509.15258v1
• Date: Thu, 18 Sep 2025 07:51:25 GMT
• Title: Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model
• Authors: Zheng Yang, Guoxuan Chi, Chenshu Wu, Hanyu Liu, Yuchong Gao, Yunhao Liu, Jie Xu, Tony Xiao Han,
• Abstract summary: Generative Artificial Intelligence (GenAI) has made significant advancements in fields such as computer vision (CV) and natural language processing (NLP)<n>Recently, there has been growing interest in integrating GenAI into wireless sensing systems.<n>This survey investigates the convergence of GenAI and wireless sensing from two complementary perspectives.
• Score: 19.036684769661775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Artificial Intelligence (GenAI) has made significant advancements in fields such as computer vision (CV) and natural language processing (NLP), demonstrating its capability to synthesize high-fidelity data and improve generalization. Recently, there has been growing interest in integrating GenAI into wireless sensing systems. By leveraging generative techniques such as data augmentation, domain adaptation, and denoising, wireless sensing applications, including device localization, human activity recognition, and environmental monitoring, can be significantly improved. This survey investigates the convergence of GenAI and wireless sensing from two complementary perspectives. First, we explore how GenAI can be integrated into wireless sensing pipelines, focusing on two modes of integration: as a plugin to augment task-specific models and as a solver to directly address sensing tasks. Second, we analyze the characteristics of mainstream generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, and discuss their applicability and unique advantages across various wireless sensing tasks. We further identify key challenges in applying GenAI to wireless sensing and outline a future direction toward a wireless foundation model: a unified, pre-trained design capable of scalable, adaptable, and efficient signal understanding across diverse sensing tasks.

Related papers

• Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications [60.721304295812445]
  Federated learning (FL) has the potential to improve the overall loop of agentic AI.<n>We first summarize fundamentals of agentic AI and mainstream FL types. Then, we illustrate how each FL type can strengthen a specific component of agentic AI's loop.<n>We conduct a case study on using FRL to improve the performance of agentic AI's action decision in low-altitude wireless networks.
  arXiv  Detail & Related papers  (2026-03-02T11:26:56Z)
• Pinching Antennas Meet AI in Next-Generation Wireless Networks [95.7524555556776]
  Next-generation (NG) wireless networks must embrace innate intelligence in support of emerging applications.<n>This article explores the "win-win" cooperation between AI and Pinching antennas (PAs)
  arXiv  Detail & Related papers  (2025-11-03T21:32:00Z)
• Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks [3.511369967593153]
  Ambient intelligence (AmI) is a computing paradigm in which physical environments are embedded with sensing, computation, and communication.<n>We argue that Generative Artificial Intelligence (GenAI) is the creative core of such environments.<n>This chapter reviews foundational GenAI models, GANs, VAEs, diffusion models, and generative transformers, and connects them to practical AmI use cases.
  arXiv  Detail & Related papers  (2025-08-27T00:44:47Z)
• Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study [52.585121600169714]
  This article explores the potential and effectiveness of generative data augmentation in wireless networks.<n>We propose a general generative data augmentation framework for Wi-Fi gesture recognition.
  arXiv  Detail & Related papers  (2024-11-13T05:15:25Z)
• Generative AI for RF Sensing in IoT systems [10.326067512318163]
  Radio Frequency (RF) sensing stands out for its cost-effective and non-intrusive monitoring of human activities and environmental changes. Traditional RF sensing methods face significant challenges, including noise, interference, incomplete data, and high deployment costs. This paper investigates the potential of Generative AI (GenAI) to overcome these limitations within the Internet of Things ecosystem.
  arXiv  Detail & Related papers  (2024-07-10T09:51:44Z)
• Strategic Demand-Planning in Wireless Networks: Can Generative-AI Save Spectrum and Energy? [17.59973153669422]
  This article introduces the concept of strategic demand-planning through demand-labeling, demand-shaping, and demand-rescheduling.<n>GenAI is proposed as a powerful tool to facilitate demand-shaping in wireless networks.
  arXiv  Detail & Related papers  (2024-07-02T14:27:06Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning [27.97936220718295]
  We propose the GenAINet framework in which distributed GenAI agents communicate knowledge to accomplish arbitrary tasks.<n>Specifically, GenAI agents extract semantics from heterogeneous raw data, build and maintain a knowledge model representing the semantic relationships among pieces of knowledge.<n>Under this paradigm, different levels of collaboration can be achieved flexibly depending on the complexity of targeted tasks.
  arXiv  Detail & Related papers  (2024-02-26T15:03:46Z)
• At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence [11.847999494242387]
  Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. We outline the central role of GMs in pioneering areas of 6G network research, including semantic/THz/near-field communications, ISAC, extremely large antenna arrays, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy
  arXiv  Detail & Related papers  (2024-02-02T06:23:25Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks [63.246437631458356]
  Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native. This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
  arXiv  Detail & Related papers  (2023-09-23T00:05:39Z)

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.