Energy-Aware Download Method in LTE Based Smartphone

Jie REN; Ling GAO; Hai WANG; QuanLi GAO; ZheWen ZHANG

doi:10.1587/transinf.2016EDP7349

Energy-Aware Download Method in LTE Based Smartphone

Jie REN, Ling GAO, Hai WANG, QuanLi GAO, ZheWen ZHANG

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Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.2 pp.304-312

Publication Date: 2017/02/01

Publicized: 2016/11/18

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016EDP7349

Type of Manuscript: PAPER

Category: Information Network

Authors

Jie REN
  Northwest University
Ling GAO
  Northwest University
Hai WANG
  Northwest University
QuanLi GAO
  Northwest University
ZheWen ZHANG
  Northwest University

Keyword

MDP, energy-aware, LTE, mobile devices, download method

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Jie REN, Ling GAO, Hai WANG, QuanLi GAO, ZheWen ZHANG, "Energy-Aware Download Method in LTE Based Smartphone" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 2, pp. 304-312, February 2017, doi: 10.1587/transinf.2016EDP7349.
Abstract: Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7349/_p

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@ARTICLE{e100-d_2_304,
author={Jie REN, Ling GAO, Hai WANG, QuanLi GAO, ZheWen ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Energy-Aware Download Method in LTE Based Smartphone},
year={2017},
volume={E100-D},
number={2},
pages={304-312},
abstract={Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).},
keywords={},
doi={10.1587/transinf.2016EDP7349},
ISSN={1745-1361},
month={February},}

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TY - JOUR
TI - Energy-Aware Download Method in LTE Based Smartphone
T2 - IEICE TRANSACTIONS on Information
SP - 304
EP - 312
AU - Jie REN
AU - Ling GAO
AU - Hai WANG
AU - QuanLi GAO
AU - ZheWen ZHANG
PY - 2017
DO - 10.1587/transinf.2016EDP7349
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2017
AB - Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).
ER -