2019
DOI: 10.1109/tmc.2018.2842733
|View full text |Cite
|
Sign up to set email alerts
|

Backup Battery Analysis and Allocation against Power Outage for Cellular Base Stations

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
14
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
5
2

Relationship

2
5

Authors

Journals

citations
Cited by 32 publications
(14 citation statements)
references
References 21 publications
0
14
0
Order By: Relevance
“…The recent breakthroughs in deep learning and hardware design have enabled researchers to train much more powerful models, which highly empower many applications such as crowdsourced delivery [11], network caching [12], energy management [13] and edge computing [14]. In the following we highlight the advantages of deep learning as compared with traditional machine learning methods, which demonstrates the benefits to apply deep learning in IoT applications.…”
Section: Introductionmentioning
confidence: 93%
“…The recent breakthroughs in deep learning and hardware design have enabled researchers to train much more powerful models, which highly empower many applications such as crowdsourced delivery [11], network caching [12], energy management [13] and edge computing [14]. In the following we highlight the advantages of deep learning as compared with traditional machine learning methods, which demonstrates the benefits to apply deep learning in IoT applications.…”
Section: Introductionmentioning
confidence: 93%
“…To solve the above‐mentioned problem keeping in mind the benefit of using DL, Ref. 119 proposed a DL‐based battery profiling strategy. Appliance‐level power profiling can be a handy tool for making some decisions 120 .…”
Section: Applications Of DL Techniques For Power Load Datamentioning
confidence: 99%
“…It is mostly used for backup purposes. The base station also uses battery backup system 119 . For the backup system, it is needed to predict future situations.…”
Section: Applications Of DL Techniques For Power Load Datamentioning
confidence: 99%
“…The recent advances of edge computing migrate the massive computation from the remote cloud to the local edge, enabling more low-latency and secure manufacturing [129]. And deep learning further empowers more effective local analysis and prediction [130] at the edge node of industry instead of the cloud. We summarize them in the next two parts as illustrated in Tab.…”
Section: E Smart Industrymentioning
confidence: 99%
“…Wu et al [125] focused on the remaining useful life estimation of the engineered system and proposed to use vanilla LSTM neural networks to get good remaining lifetime prediction accuracy. Wang et al [126] focused on the remaining lifetime analysis of backup batteries in the wireless base stations. They proposed to use DNN-based architecture to accurately predict the remaining energy and remaining lifetime of batteries, which further enables an informed power configuration among base stations.…”
Section: ) Smart Industrial Analysismentioning
confidence: 99%