Agentic AI enhances mobile networks by improving device cloud teamwork
ASTRA: Toward Agentic AI for Intelligent Device-Network-Cloud Synergy in Next-Generation Mobile Communication
Emerging Technologies
Summary
Mobile networks today struggle to adjust quickly and smartly to what users want because they rely on fixed protocols and delayed reactions. The authors propose ASTRA, a new AI system made of agents on devices, networks, and clouds that talk using meaningful messages to coordinate better. This system can predict user needs and optimize how network resources are shared before problems happen. Simulations show ASTRA can increase data speeds by shifting users from crowded cells and reduce disruptions when moving fast between network areas.
What this means in practice
- •For telecom network operators: Optimize cell usage and handovers to improve network capacity and user experience using proactive AI agents across device, network, and cloud layers.
- •For data center network managers: Coordinate network resource allocation between data centers and edge devices through semantic intent messages to improve service delivery efficiency.
Authors
Yalong Guo, Jinbo Tan, Ying Wang, Fan Zhang, Jintao Wang, Changyong Pan
Abstract
The evolution toward next-generation mobile communication systems demands intelligence-native networks capable of autonomously adapting to user intent, yet the prevailing 3GPP protocol-driven device-network-cloud (DNC) architecture imposes three structural bottlenecks: protocol-constrained decision spaces confining optimization to predefined parameter subsets, cascaded information asymmetry from lossy interface compression that strips semantic context and causes intent miscalibration, and inherently reactive coordination mechanisms that trigger actions only after performance degradation. This paper proposes an autonomous agentic AI paradigm named Agentic Synergy for Telecommunication Resource Autonomy (ASTRA), which introduces a three-tier agent layer, including device agent, network agent, and cloud agent, decoupling network intelligence from the underlying hardware infrastructure. These agents collaborate through bidirectional semantic channels, including semantic intent messages, capability abstraction messages, global directives, and peer coordination, executing a six-phase cycle of perceive, reason and predict, communicate, decide, act, and learn that transforms reactive protocol-driven operations into proactive, intent-calibrated optimization over the full decision space. Validated through system-level simulations in two representative scenarios, ASTRA achieves a 13.1\% average throughput gain in dense-crowd cell selection by redistributing UEs from congested cells via semantic load exchange, and an 18.2\% passive handover reduction in high-speed mobility through predictive trajectory-aware coordination, providing initial evidence that the proposed agentic framework accesses solution regions structurally inaccessible under protocol-constrained architectures.