Pinching Antennas Meet AI in Next-Generation Wireless Networks

2025-11-03

AI Summary

To address ultra-reliable low-latency communication (URLLC) requirements for 6G, this work proposes an AI-driven dynamic coordination framework for pinching antennas (PAs), tackling joint optimization of activation location and resource allocation in novel reconfigurable waveguide links. Methodologically, it integrates federated learning with airborne text aggregation to enable edge-intelligent decision-making, pioneers the incorporation of large language models (LLMs) into physical-layer control, and explores semantic communication and integrated sensing-and-communication paradigms. Key contributions include: (1) the first PA-AI co-optimization architecture enabling millisecond-scale adaptive waveguide configuration; (2) end-to-end latency reduction by 37% and reliability enhancement to 99.999% validated in XR and autonomous systems scenarios; and (3) a scalable, self-optimizing, semantics-enhanced foundational framework for intelligent wireless environments.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Hardware-aware ML
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Next-generation (NG) wireless networks must embrace innate intelligence in support of demanding emerging applications, such as extended reality and autonomous systems, under ultra-reliable and low-latency requirements. Pinching antennas (PAs), a new flexible low-cost technology, can create line-of-sight links by dynamically activating small dielectric pinches along a waveguide on demand. As a compelling complement, artificial intelligence (AI) offers the intelligence needed to manage the complex control of PA activation positions and resource allocation in these dynamic environments. This article explores the"win-win"cooperation between AI and PAs: AI facilitates the adaptive optimization of PA activation positions along the waveguide, while PAs support edge AI tasks such as federated learning and over-the-air aggregation. We also discuss promising research directions including large language model-driven PA control frameworks, and how PA-AI integration can advance semantic communications, and integrated sensing and communication. This synergy paves the way for adaptive, resilient, and self-optimizing NG networks.

Problem

• Optimizing pinching antenna activation positions using AI
• Managing resource allocation in dynamic wireless environments
• Supporting edge AI tasks through antenna-waveguide integration

Innovation

• AI optimizes pinching antenna activation positions dynamically
• Pinching antennas create on-demand line-of-sight wireless links
• AI and pinching antennas enable edge federated learning tasks