scholarly journals A Self-Organized Overlay Network Management Mechanism for Heterogeneous Environments

2011 ◽  
Vol 19 ◽  
pp. 25-38
Author(s):  
Tsutomu Inaba ◽  
Hiroyuki Takizawa ◽  
Hiroaki Kobayashi
2016 ◽  
Vol 54 ◽  
pp. 29-47 ◽  
Author(s):  
Luiz Fernando Carvalho ◽  
Sylvio Barbon ◽  
Leonardo de Souza Mendes ◽  
Mario Lemes Proença

2021 ◽  
Author(s):  
Wei Jiang ◽  
Mathias Strufe ◽  
Michael Gundall ◽  
Hans Dieter Schotten

Complexity and heterogeneity of the fifth generation (5G) and beyond mobile systems impose a great challenge on current network managing approaches, which are vulnerable, time-consuming and costly. The state-of-the-art research direction in this field is to apply machine learning (ML) techniques to realize intelligent and highly self-organized networking. Unlike the physical layer, theoretical analyses and numerical simulations on the management layer are generally infeasible or not scientifically rigorous enough. Therefore, in this paper, we present a software-defined and virtualized wireless test-bed that is established to evaluate ML-based network management. Based on open-source software and off-the-shelf hardware, this test-bed is easily reproducible, which in turn is hopeful to foster innovative works in this field.


2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Peng Yu ◽  
Lei Feng ◽  
Wenjing Li ◽  
Xuesong Qiu

Aiming at the lack of integrated energy-saving (ES) methods based on hybrid energy supplies in LTE heterogeneous networks, a novel ES management mechanism considering hybrid energy supplies and self-organized network (SON) is proposed. The mechanism firstly constructs ES optimization model with hybrid energy supplies. And then a SON framework is proposed to resolve the model under practical networks. According to the framework, we divide the ES problem into four stages, which are traffic variation prediction, regional Base Station (BS) mode determination, BS-user association, and power supply. And four corresponding low-complexity algorithms are proposed to resolve them. Simulations are taken on under LTE underlay heterogeneous networks. Compared with other algorithms, results show that our mechanism can save 47.4% energy consumption of the network, while keeping coverage, interference, and service quality above acceptable levels, which takes on great green-economy significance.


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