Context-Aware Self-Optimization: Evolution Based on the Use Case of Load Balancing in Small-Cell Networks

Aguilar-Garcı́a, Alejandro; Fortes, Sergio; Durán, Alfonso Fernández; Barco, Raquel

doi:10.1109/mvt.2015.2431753

Cited by 18 publications

(24 citation statements)

References 9 publications

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“…For example, the work [15] forecast the user distribution of potential high-loaded areas and suggested to activate the nearest idle cell to expand margins by considering the interference and the QoS. Another similar work is done in [10].…”

Section: Related Workmentioning

confidence: 99%

“…The optimisation block is divided into four minor functions: social data collection, social data filtering, 3-stage data analytics, and proactive optimisation. The first two functions (collection and filtering) are common in the related works, such as in the event-based network managements [24] and the context-aware load sharing [10]. The core contributions of this work are the functions of the 3-stage data analytics and proactive optimisation.…”

Section: The Framework Of Context-aware Proactive 5g Load Balancmentioning

confidence: 99%

“…the time and location of the irregularities (anomaly conditions) to provide a new ranking of the hotspots in the RoI with an event happening. The popular public events can influence the spatial traffic pattern with new temporal-spatial hotspots emerging [10] resulting in a dramatic rise of dropped calls [24]. Such forecasting of new hotspots can be an alert that the cells associated with the hotspots require optimisation.…”

Section: ) Stage 3 -Anomaly Detection: This Stage Is To Detectmentioning

confidence: 99%

“…• The controller-based algorithms are reactive to the problems, which is time-consuming in adjusting the offset, and limited in adapting the fast-changing load [10].…”

Section: Introductionmentioning

confidence: 99%

“…• The controller-based algorithms trigger all the neighbouring cells to balance the load, which is inefficient for intra-cell skewed UE-distribution [10]. The causes of the above challenges come from two main categories.…”

Section: Introductionmentioning

confidence: 99%

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Context-Aware Proactive 5G Load Balancing and Optimization for Urban Areas

Yang

Zhu

et al. 2020

IEEE Access

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In the fifth-generation (5G) mobile networks, the traffic is estimated to have a fast-changing and imbalance spatial-temporal distribution. It is challenging for a system-level optimisation to deal with while empirically maintaining quality of service. The 5G load balancing aims to address this problem by transferring the extra traffic from a high-load cell to its neighbouring idle cells. In recent literature, controller and machine learning algorithms are applied to assist the self-optimising and proactive schemes in drawing load balancing decisions. However, these algorithms lack the ability of forecasting upcoming high traffic demands, especially during popular events. This shortage leads to cold-start problems because of reacting to the changes in the heterogeneous dense deployment. Notably, the hotspots corresponding with skew load distribution will result in low convergence speed. To address these problems, this paper contributes to three aspects. Firstly, urban event detection is proposed to forecast the changes in cellular hotspots based on Twitter data for enabling context-awareness. Secondly, a proactive 5G load balancing strategy is simulated considering the prediction of the skewed-distributed hotspots in urban areas. Finally, we optimise this context-aware proactive load balancing strategy by forecasting the best activation time. This paper represents one of the first works to couple the real-world urban event detection with proactive load balancing. INDEX TERMS Context-aware, data analytics, proactive load balancing, 5G, machine learning.

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Section: Related Workmentioning

confidence: 99%

Section: The Framework Of Context-aware Proactive 5g Load Balancmentioning

confidence: 99%

Section: ) Stage 3 -Anomaly Detection: This Stage Is To Detectmentioning

confidence: 99%

“…• The controller-based algorithms are reactive to the problems, which is time-consuming in adjusting the offset, and limited in adapting the fast-changing load [10].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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