Soft Computing

, Volume 23, Issue 15, pp 6051–6063 | Cite as

A channel-aware expected energy consumption minimization strategy in wireless networks

  • Gaocai Wang
  • Qifei ZhaoEmail author
  • Tianxiao Xie
  • Guojun WangEmail author


With the rapid development of wireless network technology, energy saving has become a very important topic to build a green network in wireless networks. Due to the time-varying characteristics of the channel, it is possible to obtain a higher utilization for energy by using the channel with good state in wireless communications. From the view of the data transmission energy consumption of the whole wireless network, this paper proposes an expected energy consumption minimization strategy (E2CMS) for data transmission based on the optimal stopping theory. Considering the maximum transmission delay and a given receiving power, E2CMS delays data to transmit until the best expected channel state is detected. In order to solve the problem, firstly, we construct an energy consumption minimization problem with quality of service constraints. Then, we prove that E2CMS is a pure threshold strategy by the optimal stopping theory and obtain the power threshold by solving a fixed-point equation using backward induction. Finally, simulations are performed in a typical small-scale fading channel model. E2CMS is compared with a variety of different transmission scheduling strategies. The results show that E2CMS has lower average energy consumption for per unit data and significantly improves the network performance.


Wireless networks Channel awareness Optimal stopping theory Data transmission Energy consumption optimization 



This study was funded by the National Natural Science Foundation of China under Grant Nos. 61562006 and 61632009, 61772233, in part by the Natural Science Foundation of Guangxi Province under Grant No. 2016GXNSFBA380181.

Compliance with ethical standards

Conflict of interest

We declare that we have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electrical EngineeringGuangxi UniversityNanningPeople’s Republic of China
  2. 2.School of Computer Science and TechnologyGuangzhou UniversityGuangzhouPeople’s Republic of China

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