Abstract
The base station (BS) switching technique has recently attracted considerable attention for reducing power consumption in wireless networks. In this paper, we propose a novel BS switching and sleep mode optimization method to minimize the power consumption, while ensuring that the arriving user traffic is sufficiently covered. First, the user traffic in multiple time slots was predicted using the long-short term memory (LSTM) prediction model. Subsequently, we solved the Lyapunov optimization problem to obtain the optimal BS switching solution from the trade-off relationship between the reduced power consumption by BS switching and the user traffic handled in time series. Finally, we selected the sleep mode for the switched result by calculating the wake-up time and the power consumption ratio of each sleep mode. Simulation results confirm that the proposed algorithm successfully reduces the total power consumption by approximately 15% while preventing the user data queue from diverging in multiple time slots.
Highlights
User traffic in the wireless networks has exploded because of increase in the amount of various large-scale contents along with the commercialization of the fifth generation (5G) wireless systems
Jang et al.: base station (BS) Switching and Sleep Mode Optimization With long-short term memory (LSTM)-Based User Prediction in a short future time slot and solve the Lyapunov optimization problem to obtain the optimal BS switching result by analyzing the trade-off relationship between the power consumption reduced by BS switching and the user traffic to be accommodated
The power consumption and sleep models were defined by analyzing the BS hardware elements, whereas the user data traffic was considered as a queue
Summary
User traffic in the wireless networks has exploded because of increase in the amount of various large-scale contents along with the commercialization of the fifth generation (5G) wireless systems. We optimized the switching strategy of the BS (i.e., controlling the On/Off states) to reduce power consumption by considering the actual data traffic of users. G. Jang et al.: BS Switching and Sleep Mode Optimization With LSTM-Based User Prediction in a short future time slot and solve the Lyapunov optimization problem to obtain the optimal BS switching result by analyzing the trade-off relationship between the power consumption reduced by BS switching and the user traffic to be accommodated. The power consumption and sleep models were defined by analyzing the BS hardware elements, whereas the user data traffic was considered as a queue. We proposed a sleep mode section algorithm by analyzing the switched result set of BSs to determine the optimal result when applying the sleep depth over multiple time slots.
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