Deep Learning-Based Network-Wide Energy Efficiency Optimization in Ultra-Dense Small Cell Networks

被引:10
|
作者
Lee, Woongsup [1 ]
Lee, Howon [2 ]
Choi, Hyun-Ho [3 ]
机构
[1] Yonsei Univ, Grad Sch Informat, Seoul 03722, South Korea
[2] Hankyong Natl Univ, Sch Elect & Elect Engn, Anseong 17579, South Korea
[3] Hankyong Natl Univ, Sch ICT Robot & Mech Engn, Anseong 17579, South Korea
基金
新加坡国家研究基金会;
关键词
Deep neural network; energy efficiency; ultra-dense small cell network; optimization; activation; RESOURCE-ALLOCATION;
D O I
10.1109/TVT.2023.3237551
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In ultra-dense small cell networks (UDSCNs), where a significant number of small cell base stations (SBSs) coexist, the amount of power consumed at the SBSs can be extremely high, rendering the efficient management of power consumption for the SBSs particularly important. Herein, we propose a deep-learning-based resource allocation strategy to maximize network-wide energy efficiency in the UDSCN by optimally controlling the transmit power and user association. In this regard, a novel deep neural network (DNN) structure comprising three separate DNN units, each of which determines the activation of the SBSs, user association, and transmit power, as well as an unsupervised-learning-based training methodology are designed. Simulation results verify that the proposed scheme achieves a near-optimal performance while requiring a short computation time.
引用
收藏
页码:8244 / 8249
页数:6
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