arXiv:2606.24416v1 Announce Type: new Abstract: Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective. This paper presents Agentic long-term performance optimization (Agentic-LTPO), a nested bilevel optimization framework that can be applied to adaptive physical layer problem configuration. The key idea is to employ agentic AI to generate upper-level configurations in a bilevel optimization structure, where evolving operator policies, environment summaries, and historica
The increasing complexity and dynamic nature of network operations, coupled with the rapid advancements in AI, make agentic approaches critical for optimizing physical layer systems.
This development allows network operators to adapt quickly to evolving policies and service requirements, maintaining efficiency and performance in highly dynamic environments.
Existing static optimization methods become obsolete as AI agents can now dynamically configure and optimize physical layer systems in real-time, based on changing conditions.
- · Telecommunication operators
- · AI software providers
- · Network infrastructure providers
- · Providers of static optimization software
- · Organizations slow to adopt agentic AI
Increased efficiency and adaptability of communication networks.
Reduced operational costs and improved quality of service in dynamic network environments.
Accelerated development of more complex and self-managing autonomous systems beyond telecommunications.
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Read at arXiv cs.AI