Imagine your mobile network like a huge city of traffic lights. Today, most lights stay on even when no cars are passing. AI for greener 5G makes the ‘traffic lights’ of the network smart: they dim, sleep, or reroute traffic automatically so energy isn’t wasted when there’s little or no data traffic, while still keeping the roads (connections) flowing smoothly.
Mobile operators waste large amounts of electricity running 5G/6G radio access networks (RANs) at near-constant power, even when user traffic is low. This drives up OPEX, carbon footprint, and infrastructure strain. The surveyed AI techniques aim to dynamically adapt network configurations and power states to actual demand, minimizing energy use while maintaining required quality of service.
Deep, proprietary telemetry and traffic data across the RAN; integration with existing OSS/BSS and vendor-specific hardware controls; and optimization know-how for local constraints (spectrum, regulation, topology) make effective deployments hard to replicate quickly.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Real-time inference and control-loop latency at cell/site scale, plus the need to coordinate optimization decisions across thousands of base stations without degrading QoS or violating regulatory constraints.
Early Majority
The focus is on holistic, cross-layer energy efficiency in 5G-and-beyond RANs (including sleep modes, power control, traffic offloading, and advanced antenna management) using AI/ML, rather than only hardware efficiency or static planning; this enables fine-grained, dynamic, and context-aware ‘green’ operation that can respond to real traffic and environmental conditions.