Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC

2026-04-01

AI Summary

This work addresses the challenges of semantic redundancy and the decoupling of communication, computation, and control in multi-agent collaboration by proposing a wireless agent network framework that jointly optimizes semantic compression, transmission power, and physical trajectories through a progressive knowledge aggregation mechanism. The approach innovatively introduces a topology evolution method based on fused potential fields, overcoming the myopic limitations of conventional greedy matching and providing theoretical guarantees for long-term energy minimization. A hierarchical optimization algorithm is devised, with an inner loop coordinating resource allocation and semantic awareness, and an outer loop driving topology evolution and trajectory control. Experimental results demonstrate that the proposed method significantly enhances energy efficiency and scalability in dynamic environments, outperforming existing benchmark approaches.

Abstract

The rapid development of agentic artificial intelligence (AI) is driving future wireless networks to evolve from passive data pipes into intelligent collaborative ecosystems under the emerging paradigm of integrated learning and communication (ILAC). However, realizing efficient agentic collaboration faces challenges not only in handling semantic redundancy but also in the lack of an integrated mechanism for communication, computation, and control. To address this, we propose a wireless agent network (WAN) framework that orchestrates a progressive knowledge aggregation mechanism. Specifically, we formulate the aggregation process as a joint energy minimization problem where the agents perform semantic compression to eliminate redundancy, optimize transmission power to deliver semantic payloads, and adjust physical trajectories to proactively enhance channel qualities. To solve this problem, we develop a hierarchical algorithm that integrates inner-level resource optimization with outer-level topology evolution. Theoretically, we reveal that incorporating a potential field into the topology evolution effectively overcomes the short-sightedness of greedy matching, providing a mathematically rigorous heuristic for long-term energy minimization. Simulation results demonstrate that the proposed framework achieves superior energy efficiency and scalability compared to conventional benchmarks, validating the efficacy of semantic-aware collaboration in dynamic environments.

Problem

• Agentic AI
• Semantic-Aware Collaboration
• Integrated Learning and Communication
• Wireless Agent Networks
• Energy Minimization

Innovation

• Agentic AI
• Semantic-Aware Collaboration
• Integrated Learning and Communication (ILAC)
• Wireless Agent Networks
• Energy Minimization