Ucell and ZTE complete large-scale deployment of AIPowered green network solution in Uzbekistan

ZTE and Ucell completed full commercial deployment of an AI-powered energy-saving solution across Ucell’s network. The solution uses artificial intelligence analytics to optimize energy consumption dynamically according to real-time traffic patterns. It applies per-site and per-cell strategies that activate energy-saving modes during low-traffic periods while preserving service continuity and user experience. Network energy efficiency improved by 10.6%, measured as data traffic delivered per kilowatt-hour (GB/kWh). The system uses a distributed architecture with network-level AI for traffic forecasting and orchestration and base station-level AI for real-time execution and monitoring. Multi-dimensional shutdown techniques operate at symbol, channel, carrier, and equipment levels, with continuous machine-learning optimization to balance energy savings and performance.

"ZTE and Ucell, one of Uzbekistan's largest mobile operators, announced the full commercial deployment of ZTE's RAN AI‑powered energy‑saving solution across Ucell's network. This milestone significantly advances Ucell's commitment to a more sustainable and energy‑efficient telecommunications infrastructure."

"The AI-powered energy-saving solution leverages advanced artificial intelligence analytics to dynamically optimize network energy consumption based on real‑time traffic patterns. By enabling per‑site, per‑cell intelligent energy‑saving strategies, the system automatically activates energy‑saving modes during low‑traffic periods while ensuring seamless service continuity and a high‑quality user experience."

"A key achievement of the deployment is a measurable improvement in network energy efficiency. The solution has increased the energy efficiency ratio - defined as the volume of data traffic delivered per kilowatt‑hour (GB/kWh) - by 10.6% across the network. This means that for every unit of electricity consumed, the network now delivers 10.6% more data, directly contributing to lower carbon emissions and reduced operational costs."

"The AI-powered energy-saving solution employs a distributed computing architecture with dual‑layer intelligence consisting of network‑level AI for traffic forecasting and strategy orchestration, and base station‑level AI for real‑time execution and performance monitoring. It implements multi‑dimensional energy‑saving techniques including symbol‑level, channel‑level, carrier‑level, and equipment‑level shutdowns. These strategies are autonomously configured and continuously optimized through machine learning, ensuring an optimal balance between energy savings and network performance."

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