WIMAX traffic forecasting based on neural networks in wavelet domain

Railean, Ion; Stolojescu, Cristina; Moga, Sorin; Lenca, Philippe

doi:10.1109/rcis.2010.5507338

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…The GRU and RNN with ReLU and identity matrix provided similar performance results with LSTM; however, the GRU incurred lesser computation cost. Using ANN and genetic algorithms, Railean et al In [252], Eailean et al proposed a traffic forecasting scheme based on Stationary Wavelet Transfer (SWT) for WiMAX traffic. Several ANN configurations, such as forecasts for similar days and all days, have been evaluated in this work to achieve better results as compared to traditional ANN-based schemes.…”

Section: Long-term Traffic Forecastingmentioning

confidence: 99%

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

2022

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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.

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Section: Long-term Traffic Forecastingmentioning

confidence: 99%

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

2022

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“…In the context of cellular networks, these algorithms can be found applied to solve all different kinds of issues, from radio parameters configuration [230], coverage and capacity optimization [231]- [233], HO optimization [234], [235], load balancing [236], resource optimization [57], to cell outage management [237]- [239].…”

Section: F Heuristic Algorithmsmentioning

confidence: 99%

“…This time, however, the authors use a regression based NN and aim to predict the path loss of a radio link, in order to optimize the BSs transmission power. Another solution is shown by Railean et al in [57]. In this work, the authors develop an approach for traffic forecasting by combining stationary wavelet transforms, NN, and GA.…”

Section: G Resource Optimizationmentioning

confidence: 99%

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

417

200

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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.

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“…Here, Wavelet Transform offers a highly beneficial pre-processing approach for forecasting data that can enhance the performance of prediction strategies as a whole. The proposed forecasting model in this research deviates from the decomposition of time series in the wavelet domain [12].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the WIMAX Traffic via modified Artificial Neural Network Models

Daw¹,

Seman²,

Saudi³

2014

IJAIA

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This paper attempts to present a new approach of forecasting the WiMAX traffic by exploiting Artificial Neural Networks (ANN). To develop the model, actual data is gathered from the LibyaMax network that spans the duration of 180 days in total. Traffic data is separated into three cases based on the base stations involved (A, B and AB). The model implements traffic prediction by emphasizing on the maximum and minimum number of online user whereby two different learning algorithms

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WIMAX traffic forecasting based on neural networks in wavelet domain

Cited by 11 publications

References 12 publications

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

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

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

Forecasting the WIMAX Traffic via modified Artificial Neural Network Models

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