Autonomous Telecommunication Networks: The Convergence of Agentic AI and AI-Optimized Hardware

Hara Krishna Reddy Koppolu, Goutham Kumar Sheelam, Venkata Bhardwaj Komaragiri

Abstract: The upcoming deployment of the sixth mobile telecommunication system (6G) offers the opportunity to make a radical change to the structure of the telecommunications industry, shifting from the current model of human-directed operations toward a model of learnable, adaptive, support-less autonomous operations. Operating the networks at scale makes it very complex to rely exclusively on humans, who are easily overwhelmed and can create significant operational delays. The resources needed to avoid excess dependence on humans are huge, driven not only by the size of the offered system but also by the fact that the systems are highly improbable, in such a way that the majority of failures happen infrequently and therefore the historical learning never covers the different possibilities. Additionally, waiting for human decisions can lead to unacceptable operational delays. Furthermore, the demands made on the networks are increasingly complex, requiring very precise responses. All these factors make it crucial to make autonomous networks a top priority for the future of our interconnections. The development of autonomous communication networks, operating both the physical and network layers, is not simple, nor is the journey to arrive at an operational stage. The purpose of this essay is to show the strategies leading to the absolute necessity of the convergence between the algorithms operating the communication networks and the optimizations performed to the telecommunication hardware. These concepts have already been developed in other industries. However, no articulation or in-depth examination of the convergence has been produced until now for telecommunications networks. In this essay, we put this convergence at the basis of artificial intelligence (AI)-enabled future autonomous networks, as well as on neutral network-based technologies. We conclude with a note on how the partnership between AI and hardware technologies will create the basis for autonomous telecom networks, and why we think that this is crucial.

Keywords: Autonomous networks, telecommunication, agentic AI, AI-optimized hardware, intelligent infrastructure, self-organizing networks, self-healing systems, network automation, edge computing, real-time inference, adaptive decision-making, hardware acceleration, policy-based control, AI-driven orchestration, zero-touch management, cognitive radio, machine learning integration, 6G architecture, multi-agent systems, reinforcement learning, neural processing units, programmable networks, data plane optimization, control plane intelligence, energy-efficient AI, ultra-low latency, end-to-end automation, dynamic resource allocation, network slicing