Network planning tool based on network classification and load prediction

Hammami, Seif Eddine; Afifi, Hossam; Marot, Michel; Gauthier, Vincent

doi:10.1109/wcnc.2016.7565166

Cited by 23 publications

(12 citation statements)

References 29 publications

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“…Previous studies [7]- [9] investigated machine learningbased traffic prediction for realizing proactive control. The authors in [7] utilized artificial neural networks, while those in [8] utilized deep learning, which is a type of neural network.…”

Section: Resource Adjustmentsmentioning

confidence: 99%

Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction

Hirayama

Miyazawa

Jibiki

et al. 2021

IEICE Trans. Inf. & Syst.

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Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.

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Section: Resource Adjustmentsmentioning

confidence: 99%

Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction

Hirayama

Miyazawa

Jibiki

et al. 2021

IEICE Trans. Inf. & Syst.

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“…The multi-agent reinforcement learning approach [8] , [4], is a generalization of the single model and is modeled by a stochastic game. The multi-agent RL employs a joint action, which is the combination of actions to be executed by each agent at state k.…”

Section: A Multi-agent Reinforcement Learning Approachmentioning

confidence: 99%

“…On the other hand, the promising development of artificial intelligence techniques and machine learning tools may speed up the development of cellular networks and leed to develop such dynamic and self-adaptive tools that help the network operators to better manage their infrastructure and integrate new techniques. Machine learning techniques have the advantage of exploiting the plethora of data generated by cellular networks to improve these dynamic deployment techniques [4].…”

Section: Introductionmentioning

confidence: 99%

Drone-Assisted Cellular Networks: A Multi-Agent Reinforcement Learning Approach

Hammami

Afifi

Moungla

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Drone-cell technology is emerging as a solution to support and backup the cellular network architecture. cell-drones are flexible and provide a more dynamic solution for resource allocation in both scales: spatial and geographic. They allow to increase the bandwidth availability anytime and everywhere according the continuous rate demands. Their fast deployment provide network operators with a reliable solution to face sudden network overload or peak data demands during mass events, without interrupting services and guaranteeing better QoS for users. With these advantages, drone-cell network management is still a complex task. We propose in this paper, a multiagent reinforcement learning approach for dynamic drones-cells management. Our approach is based on an enhanced joint action selection. Results show that our model speed up network learning and provide better network performance.

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“…In [18] a prediction method based on cluster analysis is proposed, whose main goal is to obtain general information about the multidimensional situation of LTE traffic to assign resources more efficiently. The network can be analyzed using only time-dependent data, applying kmeans, and Support Vector Machine (SVM) methods [19]. In addition, useful information about network future behavior can be given to plan optimal access.…”

Section: Introductionmentioning

confidence: 99%

A Quality of Experience Management Framework for Mobile Users

Algar

Diego

Fernández-Isabel

et al. 2019

Wireless Communications and Mobile Computing

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Voice transmission is no longer the main usage of mobile phones. Data transmissions, in particular Internet access, are very common actions that we might perform with these devices. However, the spectacular growth of the mobile data demand in 5G mobile communication systems leads to a reduction of the resources assigned to each device. Therefore, to avoid situations in which the Quality of Experience (QoE) would be negatively affected, an automated system for degradation detection of video streaming is proposed. This approach is named QoE Management for Mobile Users (QoEMU). QoEMU is composed of several modules to perform a real-time analysis of the network traffic, select a mitigation action according to the information of the traffic and to some predefined policies, and apply these actions. In order to perform such tasks, the best Key Performance Indicators (KPIs) for a given set of video traces are selected. A QoE Model is trained to define a global QoE for the set of traces. When an alert regarding degradation in the quality appears, a proper mitigation plan is activated to mitigate this situation. The performance of QoEMU has been evaluated over a degradation situation experiments with different video users.

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Network planning tool based on network classification and load prediction

Cited by 23 publications

References 29 publications

Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction

Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction

Drone-Assisted Cellular Networks: A Multi-Agent Reinforcement Learning Approach

A Quality of Experience Management Framework for Mobile Users

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