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Review of adaptive cell selection techniques in LTE-advanced heterogeneous networks

Poor cell selection is the main challenge in Picocell (PeNB) deployment in Long Term Evolution- (LTE-) Advanced heterogeneous networks (HetNets) because it results in load imbalance and intercell interference. A selection technique based on cell range extension (CRE) has been proposed for LTE-Advanc...

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Main Authors: Gadam, M. A., Abdulazeez Ahmed, Maryam, Ng, Chee Kyun, Noordin, Nor Kamariah, Sali, Aduwati, Hashim, Fazirulhisyam
Format: Article
Language:English
Published: Hindawi Limited 2016
Online Access:http://psasir.upm.edu.my/id/eprint/52881/1/Review%20of%20adaptive%20cell%20selection%20techniques%20in%20LTE-advanced%20heterogeneous%20networks.pdf
http://psasir.upm.edu.my/id/eprint/52881/
https://www.hindawi.com/journals/jcnc/2016/7394136/
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spelling my.upm.eprints.528812022-03-16T07:36:55Z http://psasir.upm.edu.my/id/eprint/52881/ Review of adaptive cell selection techniques in LTE-advanced heterogeneous networks Gadam, M. A. Abdulazeez Ahmed, Maryam Ng, Chee Kyun Noordin, Nor Kamariah Sali, Aduwati Hashim, Fazirulhisyam Poor cell selection is the main challenge in Picocell (PeNB) deployment in Long Term Evolution- (LTE-) Advanced heterogeneous networks (HetNets) because it results in load imbalance and intercell interference. A selection technique based on cell range extension (CRE) has been proposed for LTE-Advanced HetNets to extend the coverage of PeNBs for load balancing. However, poor CRE bias setting in cell selection inhibits the attainment of desired cell splitting gains. By contrast, a cell selection technique based on adaptive bias is a more effective solution to traffic load balancing in terms of increasing data rate compared with static bias-based approaches. This paper reviews the use of adaptive cell selection in LTE-Advanced HetNets by highlighting the importance of cell load estimation. The general performances of different techniques for adaptive CRE-based cell selection are compared. Results reveal that the adaptive CRE bias of the resource block utilization ratio (RBUR) technique exhibits the highest cell-edge throughput. Moreover, more accurate cell load estimation is obtained in the extended RBUR adaptive CRE bias technique through constant bit rate (CBR) traffic, which further improved load balancing as against the estimation based on the number of user equipment (UE). Finally, this paper presents suggestions for future research directions. Hindawi Limited 2016 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/52881/1/Review%20of%20adaptive%20cell%20selection%20techniques%20in%20LTE-advanced%20heterogeneous%20networks.pdf Gadam, M. A. and Abdulazeez Ahmed, Maryam and Ng, Chee Kyun and Noordin, Nor Kamariah and Sali, Aduwati and Hashim, Fazirulhisyam (2016) Review of adaptive cell selection techniques in LTE-advanced heterogeneous networks. Journal of Computer Networks and Communications, 2016. art. no. 7394136. pp. 1-12. ISSN 2090-7141; ESSN: 2090-715X https://www.hindawi.com/journals/jcnc/2016/7394136/ 10.1155/2016/7394136
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Poor cell selection is the main challenge in Picocell (PeNB) deployment in Long Term Evolution- (LTE-) Advanced heterogeneous networks (HetNets) because it results in load imbalance and intercell interference. A selection technique based on cell range extension (CRE) has been proposed for LTE-Advanced HetNets to extend the coverage of PeNBs for load balancing. However, poor CRE bias setting in cell selection inhibits the attainment of desired cell splitting gains. By contrast, a cell selection technique based on adaptive bias is a more effective solution to traffic load balancing in terms of increasing data rate compared with static bias-based approaches. This paper reviews the use of adaptive cell selection in LTE-Advanced HetNets by highlighting the importance of cell load estimation. The general performances of different techniques for adaptive CRE-based cell selection are compared. Results reveal that the adaptive CRE bias of the resource block utilization ratio (RBUR) technique exhibits the highest cell-edge throughput. Moreover, more accurate cell load estimation is obtained in the extended RBUR adaptive CRE bias technique through constant bit rate (CBR) traffic, which further improved load balancing as against the estimation based on the number of user equipment (UE). Finally, this paper presents suggestions for future research directions.
format Article
author Gadam, M. A.
Abdulazeez Ahmed, Maryam
Ng, Chee Kyun
Noordin, Nor Kamariah
Sali, Aduwati
Hashim, Fazirulhisyam
spellingShingle Gadam, M. A.
Abdulazeez Ahmed, Maryam
Ng, Chee Kyun
Noordin, Nor Kamariah
Sali, Aduwati
Hashim, Fazirulhisyam
Review of adaptive cell selection techniques in LTE-advanced heterogeneous networks
author_facet Gadam, M. A.
Abdulazeez Ahmed, Maryam
Ng, Chee Kyun
Noordin, Nor Kamariah
Sali, Aduwati
Hashim, Fazirulhisyam
author_sort Gadam, M. A.
title Review of adaptive cell selection techniques in LTE-advanced heterogeneous networks
title_short Review of adaptive cell selection techniques in LTE-advanced heterogeneous networks
title_full Review of adaptive cell selection techniques in LTE-advanced heterogeneous networks
title_fullStr Review of adaptive cell selection techniques in LTE-advanced heterogeneous networks
title_full_unstemmed Review of adaptive cell selection techniques in LTE-advanced heterogeneous networks
title_sort review of adaptive cell selection techniques in lte-advanced heterogeneous networks
publisher Hindawi Limited
publishDate 2016
url http://psasir.upm.edu.my/id/eprint/52881/1/Review%20of%20adaptive%20cell%20selection%20techniques%20in%20LTE-advanced%20heterogeneous%20networks.pdf
http://psasir.upm.edu.my/id/eprint/52881/
https://www.hindawi.com/journals/jcnc/2016/7394136/
_version_ 1728052802060550144
score 13.211869

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