Leveraging AI Agents for Autonomous Networks: A Reference Architecture and Empirical Studies

• URL: http://arxiv.org/abs/2509.08312v1
• Date: Wed, 10 Sep 2025 06:24:57 GMT
• Title: Leveraging AI Agents for Autonomous Networks: A Reference Architecture and Empirical Studies
• Authors: Binghan Wu, Shoufeng Wang, Yunxin Liu, Ya-Qin Zhang, Joseph Sifakis, Ye Ouyang,
• Abstract summary: This work bridges the gap between architectural theory and operational reality by implementing Joseph Sifakis's AN Agent reference architecture in a functional cognitive system.<n>We demonstrate sub-10 ms real-time control in 5G NR sub-6 GHz while achieving 6% higher downlink throughput than Outer Loop Link Adaptation (OLLA) algorithms.<n>These improvements confirm the architecture's viability in overcoming traditional autonomy barriers and advancing critical L4-enabling capabilities toward next-generation objectives.
• Score: 18.534083337294188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evolution toward Level 4 (L4) Autonomous Networks (AN) represents a strategic inflection point in telecommunications, where networks must transcend reactive automation to achieve genuine cognitive capabilities--fulfilling TM Forum's vision of self-configuring, self-healing, and self-optimizing systems that deliver zero-wait, zero-touch, and zero-fault services. This work bridges the gap between architectural theory and operational reality by implementing Joseph Sifakis's AN Agent reference architecture in a functional cognitive system, deploying coordinated proactive-reactive runtimes driven by hybrid knowledge representation. Through an empirical case study of a Radio Access Network (RAN) Link Adaptation (LA) Agent, we validate this framework's transformative potential: demonstrating sub-10 ms real-time control in 5G NR sub-6 GHz while achieving 6% higher downlink throughput than Outer Loop Link Adaptation (OLLA) algorithms and 67% Block Error Rate (BLER) reduction for ultra-reliable services through dynamic Modulation and Coding Scheme (MCS) optimization. These improvements confirm the architecture's viability in overcoming traditional autonomy barriers and advancing critical L4-enabling capabilities toward next-generation objectives.

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