Deploying reinforcement learning agents to automatically tune hundreds of tower antenna parameters.
Reinforcement learning agents learn optimal antenna scan patterns from historical pedestrian and traffic densities, reducing unneeded radar sweeps in empty areas.
SON algorithms adjust electronic antenna downtilt angles as foliage grows or weather shifts, eliminating coverage shadows automatically.
Automated SON optimization reduced tower energy consumption by 28% while improving vehicle tracking continuity by 44% across a major metropolitan deployment.