Xiaoyi Fan
ABSTRACT
Base stations have been massively deployed nowadays to afford the explosive demand to infrastructure-based mobile networking services, including both cellular networks and commercial WiFi access points. To maintain high service availability, backup battery groups are usually installed on base stations and serve as the only power source during power outages, which can be prevalent in rural areas or during severe weather conditions such as hurricanes or snow storms. Therefore, being able to understand and predict the battery group working condition is of immense technical and commercial importance as the first step towards a cost-effective battery maintenance on minimizing service interruptions.
In this paper, we conduct a systematical analysis on a real world dataset collected from the battery groups installed on the base stations of China Mobile, with totally 1,550,032,984 records from July 28th, 2014 to February 17th, 2016. We find that the working condition degradation of a battery group may be accelerated under various situations and can cause premature failures on batteries in the group, which can hardly be captured by nowadays maintenance procedure and easily lead to a power-outage-triggered service interruption to a base station. To this end, we propose BatPro, a battery profiling framework, to precisely extract the features that cause the working condition degradation of the battery group. We formulate the prediction models for both battery voltage and lifetime and develop a series of solutions to yield accurate outputs. By real world trace-driven evaluations, we demonstrate that our BatPro approach can precisely predict the battery voltage and lifetime with the RMS error less than 0.01 v.
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Index Terms
- On Backup Battery Data in Base Stations of Mobile Networks: Measurement, Analysis, and Optimization
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