cs.NIApr 1, 2026

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

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

Organizations: College of Information Science and Electronic Engineering, Zhejiang University, and also with Zhejiang Provincial Key Laboratory of Info. Proc., Commun. & Netw. (IPCAN), Hangzhou 310027, China · Department of Engineering, King’s College London, London, WC2R 2LS, UK · Department of Electrical and Computer Engineering and Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA · Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China

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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.

Figures & tables

Fig. 1: An illustration of the considered wireless AI agent-assisted surveillance scenario.
Fig. 2: An example of the progressive knowledge aggregation mechanism: the information of six agents is aggregated via pair transmission.
Fig. 3: Joint move-compute-communicate protocol over time between two paired agents.
ParameterValue
Number of agents NN1010
Maximum latency Tmax⁡T_{\max}6 s6\text{\,}\mathrm{s}
Channel Bandwidth BB1 MHz1\text{\,}\mathrm{M}\mathrm{H}\mathrm{z}
Channel reference gain β0\beta_{0}−40 dB-40\text{\,}\mathrm{d}\mathrm{B}
Path loss exponent δ\delta3
Maximum transmit power Pimax⁡P_{i}^{\max}30 dBm30\text{\,}\mathrm{d}\mathrm{B}\mathrm{m}
TABLE I: Main System Parameters
Fig. 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.
Fig. 5: Total energy consumption versus: (a) Channel reference gain β0\beta_{0} , (b) Path loss exponent δ\delta , (c) Computational capacity ff , (d) Sender data payload LiL_{i} , (e) Initial distance between two agents, (f) Maximum latency constraint Tmax⁡T_{\max} .
Fig. 6: Average total energy consumption versus the number of total agents NN , which is based on the average result of 10 random runs.
Fig. 7: Impact of hyper-parameter ζ\zeta on total energy consumption.
Fig. 8: Visualization of the progressive knowledge aggregation process when N=6N=6 . The arrow signifies the directed communication link. The colored translucent circle denotes the patrol region assigned to each colored agent. When one agent transmits its information to another, its region information is fused to the received agent.
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