Cluster-based resource allocation scheme with QoS guarantee in ultra-dense networks

Ultra-dense networks (UDN) can provide extremely high throughput and data rate. However, there are severe interference due to dense and random deployment of femto base stations (FBSs). To mitigate interference and allocate network resource efficiently while ensuring quality of service (QoS) of user...

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Bibliographic Details
Published in:IET communications Vol. 12; no. 7; pp. 861 - 867
Main Authors: Li, Wenchao, Zhang, Jing
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 24.04.2018
Subjects:
cluster‐based resource allocation scheme
dynamic network topology
FBS
femtobase station
femtocellular radio
greedy algorithms
greedy search algorithm
greedy‐based compensatory resource allocation algorithm
interference mitigation
interference suppression
modified k‐means clustering algorithm
orthogonal resource block
pattern clustering
QoS
quality of service
Research Article
resource allocation
search problems
UDN
ultradense network
user equipment
femtobase station
interference suppression
greedy algorithms
user equipment
quality of service
greedy-based compensatory resource allocation algorithm
UE
RB
greedy search algorithm
resource allocation
pattern clustering
QoS
dynamic network topology
orthogonal resource block
femtocellular radio
FBS
ultradense network
modified k-means clustering algorithm
search problems
UDN
interference mitigation
cluster-based resource allocation scheme
ISSN:1751-8628, 1751-8636
Online Access:Get full text
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Description
Summary:Ultra-dense networks (UDN) can provide extremely high throughput and data rate. However, there are severe interference due to dense and random deployment of femto base stations (FBSs). To mitigate interference and allocate network resource efficiently while ensuring quality of service (QoS) of user equipments (UEs), a cluster-based resource allocation scheme for UDN is proposed in this paper. Two stages, clustering and resource allocation, are involved in the scheme. In clustering stage, a modified K-means clustering algorithm is advanced to divide FBSs into different disjoint clusters dynamically according to the density of FBSs. Thus the number of clusters can be adjusted flexibly to fit for the dynamic network topology. In resource allocation stage, a greedy-based compensatory resource allocation algorithm (GCRAA) is further proposed to maximize the throughput of UDN. Herein the orthogonal resource blocks (RBs) are initially assigned among the UEs with a greedy algorithm. In order to ensure the fairness and QoS of UEs, a compensatory resource allocation algorithm is further proposed to allocate the remaining RBs. The simulated results show that the proposed resource allocation scheme can mitigate the interference in UDN effectively, and improve the system throughput while ensuring the QoS for UEs.
ISSN:1751-8628
1751-8636
DOI:10.1049/iet-com.2017.1331