Abstract
This paper addresses the power saving problem in mobile networks. Base station (BS) power and network traffic volume (NTV) models are first established. The BS power is modeled based on in-house equipment measurement by sampling different BS load configurations. The NTV model is built based on traffic data in the literature. Then, a threshold-based adaptive power saving method is discussed, serving as the benchmark. Next, a BS power control framework is created using Q-learning. The action-state function of the Q-learning is approximated via a deep convolutional neural network (DCNN). The DCNN-Q agent is designed to control the loads of cells in order to adapt to NTV variations and reduce power consumption. The DCNN-Q power saving framework is trained and simulated in a heterogeneous network including macrocells and microcells. It can be concluded that with the proposed DCNN-Q method, the power saving outperforms the threshold-based method.
Highlights
The proposed deep convolutional neural network (DCNN)-Q method is able to save 19% power compared to the threshold-based method and 42% compared to the always-full-load method
To investigate power saving for mobile networks, it is important to establish practical power and network traffic models
The thresholdbased method, which relies on heuristically set thresholds, serves as the benchmark and is able to reduce power consumption by 30% compared to always-full load
Summary
BACKGROUND In the era of data, information is flowing in an unprecedented way anytime everywhere It is reported in [1], that the number of mobile broadband subscriptions will be approaching eight billion by 2025. The amount of mobile data traffic is anticipated to grow at an exponential pace, reaching 160 extrabyte (EB, 1018 bytes) per month within the same time period. New emerging applications such as augmented reality (AR), virtual reality (VR), vehicle to everything (V2X), and internet of things (IoTs) are projected to have increasing contribution to the massive growth of data traffic. The use of massive multiple-input multiple-output (MIMO), which equips base stations (BSs) and user equipments (UEs) with an increasing number of
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