Wake-Up Scheduling for Energy-Efficient Mobile Devices

Abstract

Recently, discontinuous reception mechanisms (DRX) and wake-up schemes (WuS) have been proposed to enhance the energy efficiency of 5G mobile devices and prolong the battery lifetime. The existing DRX and WuS use commonly pre-configured parameters that cannot be adjusted dynamically. In this paper, a novel wake-up scheduling (WuSched) concept is introduced to further improve the energy efficiency of WuS-enabled mobile devices while controlling the buffering delay in a dynamic manner. The main idea of WuSched is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically based on actual traffic arrivals. For this purpose, two different optimization approaches of the wake-up scheduling concept are proposed, analyzed, and compared, namely offline and online wake-up schedulers (WuSched-Offline and WuSched-Online). First, the WuSched-Offline is analyzed analytically for Poisson traffic arrivals and optimized (offline) to balance the average delay and power consumption. Second, the WuSched-Online is proposed to take online decisions based on traffic prediction, which is able to deal with general and more complex traffic models. Towards this end, we develop a framework for the prediction of packet arrivals based on recurrent neural networks. Numerical results show that both wake-up schedulers outperform the ordinary WuS-based system where wake-up scheduler is not deployed. In particular, for predefined delay requirements of video streaming, audio streaming, and mixed traffic flow, the WuSched-Online reduces the power consumption of the baseline WuS by up to 36%, 28% and 9%, respectively. Results also show that the WuSched-Offline has slightly better energy efficiency than the WuSched-Online in the case of Poisson packet arrivals, as it is optimized for that, while its power consumption is slightly higher than that of the WuSched-Online scheduler for realistic traffic scenarios.

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

“…Similarly, the authors in [232] shed some light on BLEcompatible sensor devices enriched with a WUR, and their results demonstrate EE gains in low-latency applications (under 2s). In [233], the authors optimize operational parameters, determined by BS at the beginning of the session, to save energy. Meanwhile, an accurate traffic forecasting model is proposed in [218] to optimize the wake-up parameters, achieving up to 35% EC reduction.…”

Stochastic modeling for bandwidth part switching based DRX mechanism in 5G NR networks

“…In the WuS, the UE monitors narrowband channel for wake-up signalling to get the message for the arrival of packet(s). Recently, wake-up scheduling was proposed to improve the power saving of WuS enabled UE under predefined delay [15].…”

“…Numerous mathematical analysis are proposed to model the DRX mechanism by several researchers. Most of these studies considered that the data arrival follows Poisson process [7,[14][15][16]. To match with the realistic scenario, where data packets arrive in bulk, compound Poisson process is the best suited to model the arrival process [9].…”

Data-Driven Energy Conservation in Cellular Networks: A Systems Approach

“…If we could accurately forecast utilization at such a small timescale (every millisecond), we could make real-time decisions to switch off cells and achieve larger energy savings. In fact, prediction of packet arrivals in each TTI has been found to be feasible in [27,28]; however, an analysis of the PRB utilization at this granularity has not been studied so far. Our analysis has found that the utilization trace at the TTI level for each pair of coverage and capacity cells is actually a stationary time series, without any trend or seasonality -thus, it does not have a predictable pattern in the long-term [29].…”

“…Trinh et al [27] propose the use of LSTM networks to predict traffic (in Mbps) at the TTI-level (1 ms) and find that LSTM outperforms ARIMA models. More recently, Rostami et al [28] present an LSTM network to predict traffic (packet arrival times) in the next 1 to 30 TTIs. They find that the prediction accuracy is high when predicting arrivals up to 15 TTIs ahead.…”