Radio Resource Management for Ultra-dense Smallcell Networks: A Hybrid Spectrum Reuse Approach

被引:0
|
作者
Lin, ShangJing [1 ]
Yu, JianGuo [1 ]
Ni, Wei [2 ]
Liu, RenPing [3 ]
机构
[1] Beijing Univ Posts & Telecommun, Beijing, Peoples R China
[2] CSIRO Data 61, Sydney, NSW, Australia
[3] Univ Technol, Sydney, NSW, Australia
来源
2017 IEEE 85TH VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING) | 2017年
基金
中国国家自然科学基金;
关键词
Smallcell; resource management; frequency reuse; multi-agent Q-learning;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Smallcells have great potential to enhance cellular networks, complementing macrocells. Severe interference may occur, as smallcells are expected to be deployed and operated uncoordinatedly. However, existing resource management methods require significant overhead to suppress interference. We propose a new resource management approach which is able to mitigate the cross-tier and co-tier interference with substantially reduced overhead. The key idea is to categorize the smallcells into two regions based on a judiciously designed cross-tier interference criterion. Smallcells in the high-interference zone occupy orthogonal radio resources with the macrocell; smallcells in the other zone can reuse the resources that the macrocell is using. Another crucial aspect is that we formulate the resource sharing between the macrocell and smallcells in the low-interference zone to a multi-agent Q-learning process which assigns adequate transmit power levels in a decentralized manner to suppress the co/cross-tier interference. As a result, our approach is able to reduce the outage probabilities of macrocell users significantly to 0%, respectively, in a dense smallcell deployment (200 smallcells), as evidenced by simulation results.
引用
收藏
页数:7
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