While cellular networks are the foundation of smart cities, they consume a lot of energy, enhancing global warming. Putting base stations (BSs) with low traffic to sleep saves energy but also reduces traffic prediction accuracy. In a new study, researchers address this trade-off using machine learning technique to switch off BSs based on their contribution to prediction accuracy. The new scheme reduces power consumption and demonstrates a prediction accuracy superior to benchmark schemes.
