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

TL;DR

This paper introduces a semantic-aware wireless agent network framework that optimizes energy efficiency through integrated learning and communication, enabling intelligent collaboration among agents.

Contribution

It proposes a novel hierarchical algorithm for joint semantic compression, power optimization, and topology evolution in agent networks, addressing redundancy and collaboration challenges.

Findings

The framework achieves higher energy efficiency than benchmarks.

Semantic compression reduces redundancy in data transmission.

Topology evolution improves long-term network performance.

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.