APP-SON: Application characteristics-driven SON to optimize 4G/5G network performance and quality of experience

Ouyang, Ye; Li, Zhongyuan; Su, Limin; Lu, Wenyuan; Lin, Zhenyi

doi:10.1109/bigdata.2017.8258085

Cited by 14 publications

(12 citation statements)

References 11 publications

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“…Now themes of this research will be presented in this section and the deficiency in the literature on this subject will be eliminate. Clustering 32, ( 100% ) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32] Clustering algorithm 18, ( 56% ) [3,5,6,7,8,9,11,12,13,20,21,22,23,24,25,29,31,32] Networks 29, ( 91% ) [1,<...>…”

Section: Resultsmentioning

confidence: 99%

“…Clustering procedures are likewise one of the ways to deal with topology control, which can sort out a network into a cluster based network. Various algorithm methods can be executed in this structure so as to accomplish explicit targets [1,7,8,9]. This study offers a review walkthrough on clustering procedures and the most applicable research exertions during the earlier years.…”

Section: Introductionmentioning

confidence: 99%

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Clustering Research in Fifth Generation Mobile Communication Networks

Serdar

2020

Journal of Engineering Technology and Applied Sciences

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Clustering procedures are utilized to broaden the life of systems and increment vitality proficiency. In this study, some clustering algorithms and traffic expectations consider in 5G communication systems are inspected. There is a need to explain how to improve the nature of client experience through clustering. Understanding the requirements of clients is basic to give the capacity to help various situations in smart frameworks. Client mindfulness or client situated plan is a challenge in clustering. Inquires about have demonstrated that the usage of clustering plans in 5G systems presents difficulties and that clustering procedures created with insightful system determination arrangements can be of extraordinary advantage. The present investigations are not perfect in unique frameworks with a wide assortment of client situations since they are performed in both homogeneous and low-level heterogeneous systems and can't work. Also, when the 5G happens, the issue will turn out to be more mind-boggling than customarily rearranged. Different challenges identified with the execution of clustering methods in 5G networks are introduced and examined.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering Research in Fifth Generation Mobile Communication Networks

Serdar

2020

Journal of Engineering Technology and Applied Sciences

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“…The authors in [66,67] presented a framework for self-optimization in 5G networks called APP-SON. It was designed to optimize some target network key performance indicators (KPIs) based on the mobile applications characteristics, by identifying similar application features and creating clusters using the Hungarian Algorithm Assisted Clustering (HAAC).…”

Section: Application Characterizationmentioning

confidence: 99%

“…In this work both fully connected and LSTM models were proposed for comparison and the temporal aspect of dataset was maintained. In [66], the authors adopted a dataset composed of historical data records for urban and rural areas. Unfortunately, the paper did not provide more details about the data used, but a deep learning model composed of fully connected layers was used to process this data.…”

Section: Fully Connected Modelsmentioning

confidence: 99%

“…The urban environment tops the studies as the most common scenario presented in the works [6,34,41,[44][45][46]48,66,67,72,75]. This is justifiable as urban scenarios are very dynamic, very challenging and heterogeneous, with the presence of different obstacles (persons, vehicles, and buildings).…”

Section: What Are the Most Common Scenarios Used To Evaluate The Intementioning

confidence: 99%

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When 5G Meets Deep Learning: A Systematic Review

et al. 2020

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This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration.

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5G heterogeneous network (HetNets): a self-optimization technique for vertical handover management

Kiran

Rao

2021

IJPCC

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Purpose Vertical handover has been grown rapidly due to the mobility model improvements. These improvements are limited to certain circumstances and do not provide the support in the generic mobility, but offering vertical handover management in HetNets is very crucial and challenging. Therefore, this paper presents a vertical handoff management method using the effective network identification method. Design/methodology/approach This paper presents a vertical handoff management method using the effective network identification method. The handover triggering schemes are initially modeled to find the suitable position for starting handover using computed coverage area of the WLAN access point or cellular base station. Consequently, inappropriate networks are removed to determine the optimal network for performing the handover process. Accordingly, the network identification approach is introduced based on an adaptive particle-based Sailfish optimizer (APBSO). The APBSO is newly designed by incorporating self-adaptive particle swarm optimization (APSO) in Sailfish optimizer (SFO) and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is utilized for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like end to end (E2E) delay, jitter, signal-to-interference-plus-noise ratio (SINR), packet loss, handover probability (HOP) are considered to find the best network. Findings The developed APBSO-based deep stacked autoencoder outperformed than other methods with a minimal delay of 11.37 ms, minimal HOP of 0.312, maximal stay time of 7.793 s and maximal throughput of 12.726 Mbps, respectively. Originality/value The network identification approach is introduced based on an APBSO. The APBSO is newly designed by incorporating self-APSO in SFO and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is used for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like E2E delay, jitter, SINR, packet loss and HOP are considered to find the best network. The developed APBSO-based deep stacked autoencoder outperformed than other methods with minimal delay minimal HOP, maximal stay time and maximal throughput.

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APP-SON: Application characteristics-driven SON to optimize 4G/5G network performance and quality of experience

Cited by 14 publications

References 11 publications

Clustering Research in Fifth Generation Mobile Communication Networks

Clustering Research in Fifth Generation Mobile Communication Networks

When 5G Meets Deep Learning: A Systematic Review

5G heterogeneous network (HetNets): a self-optimization technique for vertical handover management

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