Load Balancing With Traffic Splitting for QoS Enhancement in 5G HetNets

In heterogeneous networks (HetNets), high user density and random small cell deployment often result in uneven User Equipment (UE) distributions among cells. This can lead to excessive resource usage in some cells and a degradation of Quality of Service (QoS) for users, even while resources in other...

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Bibliographic Details
Published in:IEEE transactions on network science and engineering 2024-11, Vol.11 (6), p.6272-6284
Main Authors: Manan, Abdul, Shahid, Syed Maaz, Kim, SungKyung, Kwon, Sungoh
Format: Article
Language:English
Subjects:
5G mobile communication
Algorithms
Computer architecture
dual connectivity
HetNets
Load balancing
Load distribution (forces)
Load management
Microprocessors
Network architecture
Optimization
Overloading
Partitioning algorithms
QoS
Quality of service
Quality of service architectures
Resource management
Splitting
Throughput
traffic splitting
User satisfaction
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Summary:In heterogeneous networks (HetNets), high user density and random small cell deployment often result in uneven User Equipment (UE) distributions among cells. This can lead to excessive resource usage in some cells and a degradation of Quality of Service (QoS) for users, even while resources in other cells remain underutilized. To address this challenge, we propose a load-balancing algorithm for 5G HetNets that employs traffic splitting for dual connectivity (DC) users. By enabling traffic splitting, DC allows UEs to receive data from both macro and small cells, thereby enhancing network performance in terms of load balancing and QoS improvement. To prevent cell overloading, we formulate the problem of minimizing load variance across 5G HetNet cells using traffic splitting. We derive a theoretical expression to determine the optimal split ratio by considering the cell load conditions. The proposed algorithm dynamically adjusts the data traffic split for DC users based on the optimal split ratio and, if necessary, offloads edge users from overloaded macro cells to underloaded macro cells to achieve uniform network load distribution. Simulation results demonstrate that the proposed algorithm achieves more even load distribution than other load balancing algorithms and increases network throughput and the number of QoS-satisfied users.
ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2024.3482365