Cellular traffic forecasting based on inverted transformer for mobile perception dual-level base station sleep control

Abstract

Due to the extensive implementation of the fifth generation wireless communication networks (5 G), numerous base stations are being strategically deployed in densely inhabited areas to address the escalating need for network traffic. Within wireless cellular networks, over 80% of energy consumption is primarily concentrated on the side of the base station. In comparison to 4 G base stations, the 5 G base stations exhibit notable enhancements in terms of bandwidth, peak rate, and transmission power. Nonetheless, they suffer from limited signal coverage and extensive resource consumption. To address this challenge effectively, the present study introduces a Mobile Perception Dual-Level (MPDL) base station sleep control strategy based on cellular traffic forecasting. The strategy aims to satisfy the demands for reduced power consumption and enhanced service quality by monitoring both the current network traffic of the base station and the user's mobility, consequently placing the base station into two different sleep states. Specifically, we first designed an inverted Transformer (iTransformer) model to accurately forecast cellular traffic. The model is capable of effectively capturing both the spatial and temporal attributes of cellular traffic. It also takes into account the impact of geographical features, such as Points of Interest (POIs) and the quantity of Base Stations (BSs), on the forecasting outcomes. Consequently, it attains precise predictions of the cellular traffic. Based on the forecasting outcomes of cellular traffic, this study presents a mobility perception dual-level sleep strategy. This strategy proposal differentiates sleep into deep sleep and light sleep, while also taking into account the mobility attributes of users. It incorporates considerations for Quality of Service (QoS) and energy consumption. We assess the performance and application impacts of cellular traffic forecasting and base station sleep strategy in real-world datasets. Several experiments and validations have confirmed that the MPDL, as proposed in this paper, effectively decreases power consumption by a range of 22% to 41% when compared to baselines.