educing energy consumption is a pressing issueeducing energy consumption is a pressing issueR in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, stateof-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long-short term memory (LSTM)-based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being false alarm and miss-detection probabilities respectively below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism in up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability.

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