DRX‐based energy‐efficient supervised machine learning algorithm for mobile communication networks

The continuous traffic increase of mobile communication systems has the collateral effect higher energy consumption, affecting battery lifetime in user equipment (UE). An effective solution for saving is to implement a discontinuous reception (DRX) mode. However, guaranteeing desired quality experience (QoE) while simultaneously challenge; but undoubtedly both efficiency and QoE have been essential aspects provision real-time services, such as voice over Internet protocol (VoIP), LTE, broadband 4G networks beyond. This paper focuses on human communications proposes Gaussian process regression algorithm that capable recognizing patterns silence predicts its duration conversations, with prediction error low 1.87%. proposed machine learning mechanism saves by switching OFF/ON radio frequency interface, order extend UE autonomy without harming QoE. Simulation results validate effectiveness compared related literature, showing improvements savings more than 30% ensuring level computational cost.