Characterizing the Effects of Rapid LTE Deployment: A Data-Driven Analysis

Abdullah, Kareem; Korany, Noha O.; Khalafallah, Ayman; Ahmed, Saeed; Gaber, Ayman

doi:10.23919/tma.2019.8784522

Cited by 5 publications

(2 citation statements)

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“…Data throughput rates may fluctuate due to congestion from overloaded cells and inter-cell interference. The HO trigger time decisions of BSs depend on the user's location relative to the cell edge and sudden changes in the radio channel [79]. These throughput fluctuations affect the user's QoE.…”

Section: ) Throughputmentioning

confidence: 99%

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

et al. 2022

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The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.

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Section: ) Throughputmentioning

confidence: 99%

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

et al. 2022

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“…The study in [76] is a general measurement study of application performance in the rapid deployments of LTE networks. Data traces has been used from different major LTE providers.…”

Section: Measurement Of Different Abr Schemesmentioning

confidence: 99%

Adaptive Bitrate Video Streaming for Wireless nodes: A Survey

Nishat,

Gnawali,

Abdelhadi

2020

Preprint

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In today's Internet, video is the most dominant application and in addition to this, wireless networks such as WiFi, Cellular, and Bluetooth have become ubiquitous. Hence, most of the Internet traffic is video over wireless nodes. There is a plethora of research to improve video streaming to achieve high Quality of Experience (QoE) over the Internet. Many of them focus on wireless nodes. Recent measurement studies often show QoE of video suffers in many wireless clients over the Internet. Recently, many research papers have presented models and schemes to optimize the Adaptive BitRate (ABR) based video streaming for wireless and mobile users. In this survey, we present a comprehensive overview of recent work in the area of Internet video specially designed for wireless network. Recent research has suggested that there are some new challenges added by the connectivity of clients through wireless. Also these challenges become more difficult to handle when these nodes are mobile. This survey also discusses new potential areas of future research due to the increasing scarcity of wireless spectrum.

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