A new call admission control scheme based on pattern prediction for mobile wireless cellular networks

Santamaria, Amilcare Francesco; Lupia, Andrea

doi:10.1109/wts.2015.7117261

Cited by 3 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Applications include: mobility management [211]- [215], resource optimization [216], [217], fault detection [218] and cell outage management [219].…”

Section: E Markov Modelsmentioning

confidence: 99%

“…Several works have been published covering the optimization of CAC, such as: [149], [177]- [182], [206], [216]. In [149], for example, the authors propose a CAC function that relies not only on information about the system resources, such as available bandwidth, but also on predictions made regarding system utilization and call dropping probability.…”

Section: G Resource Optimizationmentioning

confidence: 99%

“…Most of these solutions rely on estimating a set of parameters, such as effective bandwidth and mobility information in [177], [181], cpu load in [178], or queue load in [179], to determine whether to accept or reject a call. On the other hand, the work in [216], propose a different approach to solve the CAC problem. In this work, the authors utilize a generic predictor scheme (in this case a Markov-based scheme) integrated with a threshold based statistical bandwidth multiplexing scheme in order to perform CAC for both active and passive requests.…”

Section: G Resource Optimizationmentioning

confidence: 99%

“…, [213], [215] [256] - [216], [217] - Another example of a self-healing framework can be seen in [6]. In this paper, the authors show a reactive backhaul solution for 5G networks, which involves aspects of self-configuration, self-optimization and self-healing.…”

Section: H Coordination Of Son Functionsmentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

View full text Add to dashboard Cite

Abstract-In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

show abstract

“…Applications include: mobility management [211]- [215], resource optimization [216], [217], fault detection [218] and cell outage management [219].…”

Section: E Markov Modelsmentioning

confidence: 99%

Section: G Resource Optimizationmentioning

confidence: 99%

Section: G Resource Optimizationmentioning

confidence: 99%

Section: H Coordination Of Son Functionsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

View full text Add to dashboard Cite

show abstract

“…Lebih dari itu, konsep ini sangat diperlukan di berbagai bidang, misalnya untuk memenuhi atau menjaga Service Level Agreement (SLA), manajemen jaringan, rekayasa transmisi data, menyediakan sumber daya untuk aplikasi real-time, mengendalikan kongesti, dan mendeteksi gangguan atau serangan terhadap jaringan internet [1] [2]. Prediksi ketersediaan bandwidth juga merupakan komponen penting sebagai mekanisme admission control untuk menyediakan jaminan Quality of Service (QoS) pada jaringan kabel maupun nirkabel [3]. Walaupun demikian, pada era industri 4.0, sebagian besar benda-benda yang saling terkoneksi dengan internet pada umumnya menggunakan jaringan nirkabel.…”

Section: Pendahuluanunclassified

Pengaruh Interferensi Hidden Node Terhadap Model Prediksi Ketersediaan Bandwidth Di Jaringan Nirkabel

Rizal

2020

JUTI

View full text Add to dashboard Cite

ABSTRAKKetersediaan bandwidth merupakan salah satu aspek penting untuk menjamin QoS dalam transmisi data, terutama pada jaringan nirkabel. Walaupun demikian, prediksi ketersediaan pada jaringan nirkabel masih sulit dilakukan karena medium transmisi dapat digunakan oleh beberapa node secara bersamaan. Selain itu jaringan nirkabel juga rentan terhadap pengaruh dari sinyal transmisi yang dihasilkan dari node lain, terutama hidden node. Beberapa penelitian telah dilakukan untuk mengembangkan model prediksi ketersediaan bandwidth. Walaupun demikian, belum terdapat mekanisme terstandarisasi yang digunakan untuk mengevaluasi ketersediaan bandwidth pada jaringan nirkabel. Selain itu tingkat akurasi dari setiap model juga masih belum diketahui ketika diimplementasikan pada jaringan nirkabel, terutama dengan keberadaan hidden node. Oleh karena itu penelitian ini berupaya untuk menginvestigasi kinerja dari setiap model untuk memprediksi ketersediaan bandwidth pada jaringan nirkabel dengan interferensi hidden node. Model prediksi yang dibandingkan adalah Distributed ABSTRACT QoS requirement guarantee can be determined by some factors. But, one of the most critical factor is the availability of bandwidth, particularly in wireless networks because in real environment the transmission medium can be used by several devices or noes simultaneously. It causes the availability of bandwidth prediction tend to more difficult. In addition, wireless networks are also vulnerable to transmission signals generated from other nodes. Many researches have been conducted to build the availability of bandwidth prediction model. However, there is no standardized mechanism used to evaluate them on wireless networks. Moreover, the level of accuracy of each model is still unknown when implemented on wireless networks, particularly with hidden nodes interference as neighboring nodes. Hence, this research aims to evaluate the performance of each model to predict the availability of bandwidth on wireless network with hidden node interference. These models are Distributed Lagrange Interpolation Based Available Bandwidth Estimation (DLI-ABE), Cognitive Passive Estimation of the Available Bandwidth (cPEAB), Improved Available Bandwidth (IAB), and Available Bandwidth Estimation (ABE). The experiments were carried out on a simulation scale that was developed using OMNet++ network simulator. The result showed that ABE model obtained the best accuracy rate of 85.25%.

show abstract

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

2022

View full text Add to dashboard Cite

The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with the ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of machine learning applications in any domain can only be ensured if intelligent capabilities cover all its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support distributed learning architecture, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. Firstly, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Secondly, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned above be addressed with improved ML architecture and agile operations and propose ML pipeline-based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain and multi-layer environment and provide decision-making on a global or holistic rather than individual function level.

show abstract

A new call admission control scheme based on pattern prediction for mobile wireless cellular networks

Cited by 3 publications

References 20 publications

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Pengaruh Interferensi Hidden Node Terhadap Model Prediksi Ketersediaan Bandwidth Di Jaringan Nirkabel

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

Contact Info

Product

Resources

About