dc.contributor.author |
Asaka, OT
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dc.contributor.author |
Adejo, A
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|
dc.contributor.author |
Onumanyi, Adeiza J
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dc.contributor.author |
Bello-Salau, H
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dc.contributor.author |
Oluwamotemi, FT
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dc.date.accessioned |
2022-05-04T19:38:08Z |
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dc.date.available |
2022-05-04T19:38:08Z |
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dc.date.issued |
2021-07 |
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dc.identifier.citation |
Asaka, O., Adejo, A., Onumanyi, A.J., Bello-Salau, H. & Oluwamotemi, F. 2021. Load-driven resource allocation for enhanced interference mitigation in cellular networks. http://hdl.handle.net/10204/12385 . |
en_ZA |
dc.identifier.isbn |
978-1-6654-3493-5 |
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dc.identifier.isbn |
978-1-6654-3494-2 |
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dc.identifier.uri |
DOI: 10.1109/ICMEAS52683.2021.9739816
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|
dc.identifier.uri |
http://hdl.handle.net/10204/12385
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|
dc.description.abstract |
Cellular users are often considered to be uniformly distributed within the communication network for the purposes of simplified analysis. Based on this assumption, the inter-cell interference experienced by users has been handled using soft frequency reuse (SFR) techniques. However, in real networks, the distribution of users in the network regions are not uniform. Therefore, analysis for random deployment of users under SFR is essential for improved accuracy of analysis and better handling of interference. This research presents an SFR algorithm (Load-Driven SFR) that intelligently adjusts resource allocation parameters (base station bandwidth assignment) according to the load distribution in the network. Interference mitigation is enhanced and Load-Driven SFR outperforms several implementations of the standard SFR algorithm using fixed bandwidth allocation, especially for edge user’s SINR (up to 3.2% improvement) and edge user’s Capacity (up to 202% improvement). |
en_US |
dc.format |
Abstract |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/9739816 |
en_US |
dc.source |
1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), Abuja, Nigeria, 15-16 July 2021 |
en_US |
dc.subject |
5G networks |
en_US |
dc.subject |
Algorithms |
en_US |
dc.subject |
Cellular networks |
en_US |
dc.subject |
Frequency reuse |
en_US |
dc.subject |
Network load |
en_US |
dc.subject |
Resource allocation |
en_US |
dc.subject |
Simulation |
en_US |
dc.subject |
Soft frequency reuse |
en_US |
dc.subject |
User distribution |
en_US |
dc.title |
Load-driven resource allocation for enhanced interference mitigation in cellular networks |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
6pp |
en_US |
dc.description.note |
Copyright: 2021 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://ieeexplore.ieee.org/document/9739816 |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
Advanced Internet of Things |
en_US |
dc.identifier.apacitation |
Asaka, O., Adejo, A., Onumanyi, A. J., Bello-Salau, H., & Oluwamotemi, F. (2021). Load-driven resource allocation for enhanced interference mitigation in cellular networks. http://hdl.handle.net/10204/12385 |
en_ZA |
dc.identifier.chicagocitation |
Asaka, OT, A Adejo, Adeiza J Onumanyi, H Bello-Salau, and FT Oluwamotemi. "Load-driven resource allocation for enhanced interference mitigation in cellular networks." <i>1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), Abuja, Nigeria, 15-16 July 2021</i> (2021): http://hdl.handle.net/10204/12385 |
en_ZA |
dc.identifier.vancouvercitation |
Asaka O, Adejo A, Onumanyi AJ, Bello-Salau H, Oluwamotemi F, Load-driven resource allocation for enhanced interference mitigation in cellular networks; 2021. http://hdl.handle.net/10204/12385 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Asaka, OT
AU - Adejo, A
AU - Onumanyi, Adeiza J
AU - Bello-Salau, H
AU - Oluwamotemi, FT
AB - Cellular users are often considered to be uniformly distributed within the communication network for the purposes of simplified analysis. Based on this assumption, the inter-cell interference experienced by users has been handled using soft frequency reuse (SFR) techniques. However, in real networks, the distribution of users in the network regions are not uniform. Therefore, analysis for random deployment of users under SFR is essential for improved accuracy of analysis and better handling of interference. This research presents an SFR algorithm (Load-Driven SFR) that intelligently adjusts resource allocation parameters (base station bandwidth assignment) according to the load distribution in the network. Interference mitigation is enhanced and Load-Driven SFR outperforms several implementations of the standard SFR algorithm using fixed bandwidth allocation, especially for edge user’s SINR (up to 3.2% improvement) and edge user’s Capacity (up to 202% improvement).
DA - 2021-07
DB - ResearchSpace
DP - CSIR
J1 - 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), Abuja, Nigeria, 15-16 July 2021
KW - 5G networks
KW - Algorithms
KW - Cellular networks
KW - Frequency reuse
KW - Network load
KW - Resource allocation
KW - Simulation
KW - Soft frequency reuse
KW - User distribution
LK - https://researchspace.csir.co.za
PY - 2021
SM - 978-1-6654-3493-5
SM - 978-1-6654-3494-2
T1 - Load-driven resource allocation for enhanced interference mitigation in cellular networks
TI - Load-driven resource allocation for enhanced interference mitigation in cellular networks
UR - http://hdl.handle.net/10204/12385
ER -
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en_ZA |
dc.identifier.worklist |
25656 |
en_US |