A Machine Learning Model to Resource Allocation Service for Access Point on Wireless Network

Militani, Davi Ribeiro; Vieira, Samuel Terra; Valadao, Everthon; Neles, Katia; Rosa, Renata Lopes; Rodríguez, Demóstenes Zegarra

doi:10.23919/softcom.2019.8903853

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…The simulation results presented in their work show the improved performance in terms of convergence rate, utilization of resources, and satisfactory fulfillment of VN requirements. The RF-based automated Access Point (AP) selection scheme was proposed in [171] for heterogeneous wireless networks. The experimental results show gains in the conditions of the wireless channels concerning the average throughput, and it performed better than received signal strength-based AP selection schemes.…”

Section: Intelligent Resource Adaptationmentioning

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: Intelligent Resource Adaptationmentioning

confidence: 99%

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

2022

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“…Other researchers try to use machine learning in the optimization of resource allocation in wireless networks. [15] explores the use of machine learning algorithms for AP selection strategy and found that the Random Forest algorithm demonstrated superior performance in terms of accuracy and complexity in both the training and testing phases. [16] discusses the capacity maximization problem in wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Communication Resource Allocation of Raft in Wireless Network

Yu¹,

Sun²,

Li³

et al. 2023

IEEE Sensors J.

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The distributed consensus intends to improve the reliability of critical decision making in wireless connected autonomous systems. The performance of distributed consensus heavily depends on the reliability of wireless links, which should be stochastic with limited communication resources. Therefore, advanced communication resource allocation schemes are needed to achieve high reliability and low latency for the distributed consensus. This article first derives optimized resource allocation schemes for the distributed consensus. The optimal number of nodes for the best reliability performance of the distributed consensus is also investigated to solve the inadequate overall communication resources issue. The revealed derivation and simulation results can provide guidelines to deploy the appropriate paradigm of communication resource allocation in autonomous wireless systems.

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“…Sentiment prediction of sentences posted on OSN [7] can be performed by machine learning (ML) algorithms. The ML algorithms can be used in several research areas [8][9][10][11][12], obtaining high accuracy in review datasets using a recursive neural tensor network (RNTN), according to [13]. High accuracy is reached when the sentiment and emotions are determined from images and speech signals [14].…”

Section: Introductionmentioning

confidence: 99%

Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning

Vieira

Rosa

Rodríguez

et al. 2021

Sensors

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A quality monitoring system for telecommunication services is relevant for network operators because it can help to improve users’ quality-of-experience (QoE). In this context, this article proposes a quality monitoring system, named Q-Meter, whose main objective is to improve subscriber complaint detection about telecommunication services using online-social-networks (OSNs). The complaint is detected by sentiment analysis performed by a deep learning algorithm, and the subscriber’s geographical location is extracted to evaluate the signal strength. The regions in which users posted a complaint in OSN are analyzed using a freeware application, which uses the radio base station (RBS) information provided by an open database. Experimental results demonstrated that sentiment analysis based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-recurrent neural network (RNN) with the soft-root-sign (SRS) activation function presented a precision of 97% for weak signal topic classification. Additionally, the results showed that 78.3% of the total number of complaints are related to weak coverage, and 92% of these regions were proved that have coverage problems considering a specific cellular operator. Moreover, a Q-Meter is low cost and easy to integrate into current and next-generation cellular networks, and it will be useful in sensing and monitoring tasks.

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A Machine Learning Model to Resource Allocation Service for Access Point on Wireless Network

Cited by 8 publications

References 32 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

Communication Resource Allocation of Raft in Wireless Network

Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning

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