Agentic TinyML for Intent-aware Handover in 6G Wireless Networks
On the map: LLM agents and tool use → Agentic AI for wireless networks
Abstract (arXiv)
As 6G networks evolve into increasingly AI-driven, user-centric ecosystems, traditional reactive handover mechanisms demonstrate limitations, especially in mobile edge computing and autonomous agent-based service scenarios. This manuscript introduces WAAN, a cross-layer framework that enables intent-aware and proactive handovers by embedding lightweight TinyML agents as autonomous, negotiation-capable entities across heterogeneous edge nodes that contribute to intent propagation and network adaptation. To ensure continuity across mobility-induced disruptions, WAAN incorporates semi-stable rendezvous points that serve as coordination anchors for context transfer and state preservation. The framework's operational capabilities are demonstrated through a multimodal environmental control case study, highlighting its effectiveness in maintaining user experience under mobility. Finally, the article discusses key challenges and future opportunities associated with the deployment and evolution of WAAN.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. Builds on 12 of its 18 references that are in the map. First posted on arXiv 2 Aug 2025.
Builds on
12 of 18 references.
Closest ancestors · 2
Same direction, newest first
- 2024When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment15 Jan
- 2023Large Language Model Enhanced Multi-Agent Systems for 6G Communications13 Dec
Other references · 10
- 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 769
- 2023Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directionscited by 32
- 2024WDMoE: Wireless Distributed Large Language Models with Mixture of Expertscited by 18
- 2022Intelligence at the Extreme Edge: A Survey on Reformable TinyMLcited by 13
- 2024WDMoE: Wireless Distributed Mixture of Experts for Large Language Modelscited by 12
- 2024Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computingcited by 6
- 2024Key Focus Areas and Enabling Technologies for 6Gcited by 6
- 2023Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunitiescited by 5
- 2024Follow-Me AI: Energy-Efficient User Interaction with Smart Environmentscited by 4
- 2025Smaller, Smarter, Closer: The Edge of Collaborative Generative AIcited by 2
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence
- 2026LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization
- 2025Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm
- 2026An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading
- 20266G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence
- 2026From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective
- 2025AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks
- 2025Advanced Architectures Integrated with Agentic AI for Next-Generation Wireless Networks