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

Zhouxiang Zhao, Jiaxiang Wang, Zhaohui Yang, Kun Yang, Zhaoyang Zhang, Mingzhe Chen, Kaibin Huang

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.

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

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.

Paper Structure

This paper contains 46 sections, 4 theorems, 39 equations, 8 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Mobile Collaborative Sensing and Computing
  4. Semantic Communication and Generative AI
  5. Agentic AI and IoA
  6. Contributions
  7. System Model
  8. General Setup
  9. Scenario Description
  10. Workflow
  11. Network Geometry and Information State
  12. Geometric Constraints and Patrol Regions
  13. Information Source State Evolution
  14. Accumulated Data Payload Dynamics
  15. Mobility Model
  16. ...and 31 more sections

Key Result

Theorem 1

Problem pf.t1 is a convex optimization problem. $\blacktriangleleft$$\blacktriangleleft$

Figures (8)

  • Figure 1: An illustration of the considered wireless AI agent-assisted surveillance scenario.
  • Figure 2: An example of the progressive knowledge aggregation mechanism: the information of six agents is aggregated via pair transmission.
  • Figure 3: Joint move-compute-communicate protocol over time between two paired agents.
  • Figure 4: Convergence behavior of the proposed inner-level joint motion and resource optimization algorithm, illustrating the alternating BCD iterations and the SCA sub-iterations within the second BCD block.
  • Figure 5: Total energy consumption versus: (a) Channel reference gain $\beta_0$, (b) Path loss exponent $\delta$, (c) Computational capacity $f$, (d) Sender data payload $L_i$, (e) Initial distance between two agents, (f) Maximum latency constraint $T_{\max}$.
  • ...and 3 more figures

Theorems & Definitions (4)

  • Theorem 1
  • Lemma 1
  • Lemma 2
  • Lemma 3