5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network

Priya, Bhanu; Malhotra, Jyoteesh

doi:10.1007/s12652-021-03606-x

Cited by 10 publications

(3 citation statements)

References 68 publications

(54 reference statements)

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“…A definable cognitive computing capability must be built into the architecture, and the design should be based on a data-driven model that can predict customer demand. To maintain a satisfying QoE, the architecture must efficiently manage communication resources based on the QoS requirements, and the expected user-centric service [164].…”

Section: Standardizing Qoe Mapping To Network Performance Indicatorsmentioning

confidence: 99%

“…Several critical issues in this area include: managing conflicting changes in a system caused by multiple applications or network functions based on the same context, monitoring and collecting context information without overwhelming the core network's operation, and maintaining the acquired context's recentness by aligning the temporal scales of monitored information [44]. The context-aware quantized QoE model improves user experience while ensuring resource efficiency, while the invalid action masking approach reduces invalid RATs to accelerate convergence [164].…”

Section: The Role Of Contextual and Device Information In Qoementioning

confidence: 99%

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Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Nasralla

Khattak

Rehman

et al. 2023

Sensors

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Mobile-health (m-health) is described as the application of medical sensors and mobile computing to the healthcare provision. While 5G networks can support a variety of m-health services, applications such as telesurgery, holographic communications, and augmented/virtual reality are already emphasizing their limitations. These limitations apply to both the Quality of Service (QoS) and the Quality of Experience (QoE). However, 6G mobile networks are predicted to proliferate over the next decade in order to solve these limitations, enabling high QoS and QoE. Currently, academia and industry are concentrating their efforts on the 6G network, which is expected to be the next major game-changer in the telecom industry and will significantly impact all other related verticals. The exponential growth of m-health multimedia traffic (e.g., audio, video, and images) creates additional challenges for service providers in delivering a suitable QoE to their customers. As QoS is insufficient to represent the expectations of m-health end-users, the QoE of the services is critical. In recent years, QoE has attracted considerable attention and has established itself as a critical component of network service and operation evaluation. This article aims to provide the first thorough survey on a promising research subject that exists at the intersection of two well-established domains, i.e., QoE and m-health, and is driven by the continuing efforts to define 6G. This survey, in particular, creates a link between these two seemingly distinct domains by identifying and discussing the role of 6G in m-health applications from a QoE viewpoint. We start by exploring the vital role of QoE in m-health multimedia transmission. Moreover, we examine how m-health and QoE have evolved over the cellular network’s generations and then shed light on several critical 6G technologies that are projected to enable future m-health services and improve QoE, including reconfigurable intelligent surfaces, extended radio communications, terahertz communications, enormous ultra-reliable and low-latency communications, and blockchain. In contrast to earlier survey papers on the subject, we present an in-depth assessment of the functions of 6G in a variety of anticipated m-health applications via QoE. Multiple 6G-enabled m-health multimedia applications are reviewed, and various use cases are illustrated to demonstrate how 6G-enabled m-health applications are transforming human life. Finally, we discuss some of the intriguing research challenges associated with burgeoning multimedia m-health applications.

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Section: Standardizing Qoe Mapping To Network Performance Indicatorsmentioning

confidence: 99%

Section: The Role Of Contextual and Device Information In Qoementioning

confidence: 99%

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Nasralla

Khattak

Rehman

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, it provides a variety of benefits including efficient resource usage, improved scalability, and seamless communication. The efficient utilization of 5G HetNets presents a difficulty for context-aware RAT selection due to the diversity of RATs' frequency bands, protocols, and physical and Media access control (MAC) layer multiple access techniques (Priya and Malhotra) [4].…”

Section: Introductionmentioning

confidence: 99%

Signal Processing with Machine Learning for Context Awareness in 5G Communication Technology

Mohandas¹,

Lira

Gonzales

et al. 2023

Wireless Communications and Mobile Computing

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To meet users’ expectations for speed and reliability, 5th Generation (5G) networks and other forms of mobile communication of the future will need to be highly efficient, flexible, and nimble. Because of the expected density and complexity of 5G networks, sophisticated network control across all layers is essential. In this context, self-organizing network (SON) is among the essential solutions for managing the next generation of mobile communication networks. Self-optimization, self-configuration, and self-healing (SH) are typical SON functions. This research creates a framework for analyzing SH by exploring the impact of recovery measures taken in precarious stages of health. For this reason, our suggested architecture takes into account both detection and compensating operations. The system is broken down into some faulty states and the “fuzzy c-means” (FCM) approach is used to conduct the classifying. In the compensation process, the network is characterized as the Markov decision model (MDM), and the linear programming (LP) technique is implemented to find the most effective strategy for reaching a goal. Numerical findings acquired from a variety of situations with varying objectives show that the suggested method with optimized operations in the compensation stage exceeds the approach with randomly chosen actions.

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Model-Free QoE-Aware Seamless Handoff in Heterogeneous Wireless Networks

Singh,

Chaudhry,

Rishiwal

et al. 2024

Mobile Netw Appl

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5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network

Cited by 10 publications

References 68 publications

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Signal Processing with Machine Learning for Context Awareness in 5G Communication Technology

Model-Free QoE-Aware Seamless Handoff in Heterogeneous Wireless Networks

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