中国邮电高校学报(英文) ›› 2015, Vol. 22 ›› Issue (4): 74-80.doi: 10.1016/S1005-8885(15)60670-7

• Networks • 上一篇    下一篇

Energy efficiency enhancement in heterogeneous networks: a joint resource allocation approach

孙昱婧,王永斌,李屹   

  1. Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 收稿日期:2014-03-28 修回日期:2015-05-28 出版日期:2015-08-28 发布日期:2015-08-28
  • 通讯作者: Yi LI E-mail:liyi@bupt.edu.cn

Energy efficiency enhancement in heterogeneous networks: a joint resource allocation approach

Yu-Jing SUN1, Yi LI   

  1. Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2014-03-28 Revised:2015-05-28 Online:2015-08-28 Published:2015-08-28
  • Contact: Yi LI E-mail:liyi@bupt.edu.cn

摘要: To support the drastic growth of wireless multimedia services and the requirements of ubiquitous access, numerous wireless infrastructures which consume enormous energy, such as macrocell, small cell, distributed antenna systems and wireless sensor networks, have been deployed. Under the background of environmental protection, improving the energy efficiency (EE) in wireless networks is becoming more and more important. In this paper, an EE enhancement scheme in heterogeneous networks (HetNets) by using a joint resource allocation approach is proposed. The HetNets consists of a mix of macrocell and small cells. Firstly, we model this strategic coexistence as a multi-agent system in which decentralized resource management inspired from Reinforcement Learning are devised. Secondly, a Q-learning based joint resource allocation algorithm is designed. Meanwhile, with the consideration of the time-varying channel characteristics, we take the long-term learning reward into account. At last, simulation results show that the proposed decentralized algorithm can approximate to centralized algorithm with low-complexity and obtain high spectral efficiency (SE) in the meantime.

关键词: heterogeneous networks, energy efficiency, reinforcement learning, decentralized resource allocation, joint resource allocation approach

Abstract: To support the drastic growth of wireless multimedia services and the requirements of ubiquitous access, numerous wireless infrastructures which consume enormous energy, such as macrocell, small cell, distributed antenna systems and wireless sensor networks, have been deployed. Under the background of environmental protection, improving the energy efficiency (EE) in wireless networks is becoming more and more important. In this paper, an EE enhancement scheme in heterogeneous networks (HetNets) by using a joint resource allocation approach is proposed. The HetNets consists of a mix of macrocell and small cells. Firstly, we model this strategic coexistence as a multi-agent system in which decentralized resource management inspired from Reinforcement Learning are devised. Secondly, a Q-learning based joint resource allocation algorithm is designed. Meanwhile, with the consideration of the time-varying channel characteristics, we take the long-term learning reward into account. At last, simulation results show that the proposed decentralized algorithm can approximate to centralized algorithm with low-complexity and obtain high spectral efficiency (SE) in the meantime.

Key words: heterogeneous networks, energy efficiency, reinforcement learning, decentralized resource allocation, joint resource allocation approach

中图分类号: