Entel Peru Accelerates Towards Autonomous Network L4 with Huawei’s Agentic MBB Network
Why It Matters
The Entel‑Huawei partnership demonstrates a scalable path to autonomous 5G networks, delivering cost savings, higher reliability and a differentiated user experience across challenging geographies.
Key Takeaways
- •Entel partners with Huawei to deploy autonomous L4 network.
- •New Ambient Site digital twins enable proactive energy management.
- •Pilot of collaborative adaptive energy backup increased site uptime 60%.
- •Agentic MBB network delivers intelligent experience, energy savings, simplified O&M.
- •Roadmap targets full network autonomy and predictive AI by 2028.
Summary
Entel Peru announced a major step toward a Level‑4 autonomous mobile broadband (MBB) network, leveraging Huawei’s newly unveiled Agentic MBB solution. The collaboration aims to transform the carrier’s radio access network (RAN) from a passive data pipe into a self‑optimising, AI‑driven platform.
The operator has created a dedicated digital‑transformation office built around four pillars—business success, evolution customization, service delivery and technology innovation. By integrating 5G, artificial‑intelligence‑based planning, and Huawei’s Ambient Site digital‑twin technology, Entel expects gains in operational efficiency, energy consumption and fault‑resolution speed.
Fernando Angulo highlighted a pilot at the Parque Kordilleras site where Huawei’s collaborative adaptive energy‑backup system extended battery life from seven to eleven hours, a 60 % increase in availability. Yunru He explained that the Ambient Site digitises each tower, feeding real‑time sensor data to a “brain‑agent” that can remotely wake sleeping cells during traffic spikes and shut them down when demand falls.
If successful, the rollout could cut OPEX, improve customer experience and set a benchmark for other Latin‑American operators seeking fully autonomous networks. The three‑year roadmap envisions broader deployment of hybrid power solutions, expanded digital‑twin modelling and a transition to predictive, proactive network management.
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