A coloring-based cluster resource allocation for ultra dense network

2016 ◽  
Author(s):  
Caidan Zhao ◽  
Xiaofei Xu ◽  
Zhibin Gao ◽  
Lianfen Huang
Keyword(s):  
Resource Allocation ◽  
Dense Network ◽  
Ultra Dense Network

scholarly journals LBS Based Resource Allocation in Ultra-Dense Network with Joint COMP Scenarios

2020 ◽  
pp. 433-438
Author(s):  
Ho Kyung Yu ◽  
◽  
Sung Hyun Oh ◽  
Jeong Gon Kim
Keyword(s):  
Resource Allocation ◽  
Dense Network ◽  
Ultra Dense Network

scholarly journals Performance Analysis of Dynamic Re-Clustering and Resource Allocation in Ultra Dense Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 60891-60899 ◽  
Author(s):  
Wenle Bai ◽  
Yaomin Li ◽  
Tong Yao ◽  
Haijun Zhang
Keyword(s):  
Resource Allocation ◽  
Performance Analysis ◽  
Dense Network ◽  
Ultra Dense Network

scholarly journals Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network

2020 ◽  
Author(s):  
Zhipeng Cheng ◽  
Minghui LiWang ◽  
Ning Chen ◽  
Hongyue Lin ◽  
Zhibin Gao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Dense Network ◽  
Ultra Dense Network

scholarly journals Location-Based Resource Allocation in Ultra-Dense Network with Clustering

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4022
Author(s):  
Seong Jung Kim ◽  
Jeong Gon Kim
Keyword(s):  
Resource Allocation ◽  
Communication Systems ◽  
Base Station ◽  
User Equipment ◽  
Base Stations ◽  
System Capacity ◽  
Subcarrier Allocation ◽  
Dense Network ◽  
Small Base ◽  
Ultra Dense Network

With the rapid deployment of present-day mobile communication systems, user traffic requirements have increased tremendously. An ultra-dense network is a configuration in which the density of small base stations is greater than or equal to that of the user equipment. Ultra-dense networks are considered as the key technology for 5th generation networks as they can improve the link quality and increase the system capacity. However, in an ultra-dense network, small base stations are densely positioned, so one user equipment may receive signals from two or more small base stations. This may cause a severe inter-cell interference problem. In this study, we considered a coordinated multi-point scenario, a cooperative technology between base stations to alleviate the interference. In addition, to suppress the occurrence of severe interference at the cell edges, link formation was carried out by considering the degree of cell load for each cluster. After the formation of links between all the base stations and user equipment, a subcarrier allocation procedure was performed. The subcarrier allocation method used in this study was based on the location of base stations with clustering to improve the data rate and reduce the interference between the clusters. Power allocation was based on the channel gain between the base station and user equipment. Simulation results showed that the proposed scheme delivered a higher sum rate than the other resource allocation methods reported previously for various types of user equipment.

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