Agentic AI-Empowered Conversational Embodied Intelligence Networks in 6G
Mingkai Chen, Zijie Feng, Lei Wang, Yaser Khamayseh

TL;DR
This paper introduces CC-EIN, a novel collaborative network for embodied intelligent devices in 6G, enabling effective multimodal fusion, adaptive communication, and transparent decision-making for complex tasks.
Contribution
The paper presents a comprehensive CC-EIN framework integrating multimodal fusion, adaptive semantic communication, and interpretability, advancing embodied intelligence in 6G environments.
Findings
Achieves 95.4% task completion rate in simulations
Attains 95% transmission efficiency
Maintains semantic consistency and energy efficiency
Abstract
In the 6G era, semantic collaboration among multiple embodied intelligent devices (MEIDs) becomes crucial for complex task execution. However, existing systems face challenges in multimodal information fusion, adaptive communication, and decision interpretability. To address these limitations, we propose a collaborative Conversational Embodied Intelligence Network (CC-EIN) integrating multimodal feature fusion, adaptive semantic communication, task coordination, and interpretability. PerceptiNet performs cross-modal fusion of image and radar data to generate unified semantic representations. An adaptive semantic communication strategy dynamically adjusts coding schemes and transmission power according to task urgency and channel quality. A semantic-driven collaboration mechanism further supports task decomposition and conflict-free coordination among heterogeneous devices. Finally, the…
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Taxonomy
TopicsWireless Signal Modulation Classification · Advanced Wireless Communication Technologies · Opportunistic and Delay-Tolerant Networks
