Softwarization and Optimization for Sustainable Future Mobile Networks: A Survey

Temesgene, Dagnachew Azene; Nunez-Martinez, Jose; Dini, Paolo

doi:10.1109/access.2017.2771938

Cited by 10 publications

(10 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper proposed that future cellular networks will have different system requirements, which include utilising energy-harvesting technologies and optimising tool sets [73].…”

Section: Review and Survey Articlesmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

show abstract

“…This paper proposed that future cellular networks will have different system requirements, which include utilising energy-harvesting technologies and optimising tool sets [73].…”

Section: Review and Survey Articlesmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Due to the tremendous traffic generated by mobile users, the future mobile network deployment is going to be extremely complex. The spectrum utilization in the densely populated network is a challenging task and, if machine learning is used as an optimization tool to optimize the C‐RAN, the resource allocation scheme will be enabled to downlink H‐CRAN . H‐CRAN aims the reduction of an interference problem to maximize the energy efficiency and to guarantee the QoS between RRH and all User Equipment (UE) present in the network.…”

Section: Related Workmentioning

confidence: 99%

An optimal multitier resource allocation of cloud RAN in 5G using machine learning

Bashir

Arul

Basheer

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The networks are evolving drastically since last few years in order to meet user requirements. For example, the 5G is offering most of the available spectrum under one umbrella. In this work, we will address the resource allocation problem in fifth-generation (5G) networks, to be exact in the Cloud Radio Access Networks (C-RANs). The radio access network mechanisms involve multiple network topologies that are isolated based on the spectrum bands and it should be enhanced with numerous access technology in the deployment of 5G network. The C-RAN is one of the optimal technique to combine all the available spectral bands. However, existing C-RAN mechanisms lacks the intelligence perspective on choosing the spectral bands. Thus, C-RAN mechanism requires an advanced tool to identify network topology to allocate the network resources for substantial traffic volumes. Therefore, there is a need to propose a framework that handles spectral resources based on user requirements and network behavior. In this work, we introduced a new C-RAN architecture modified as multitier Heterogeneous Cloud Radio Access Networks in a 5G environment. This architecture handles spectral resources efficiently. Based on the simulation analysis, the proposed multitier H-CRAN architecture with improved control unit in network management perspective enables augmented granularity, end-to-end optimization, and guaranteed quality of service by 15 percentages over the existing system.Trans Emerging Tel Tech. 2019;30:e3627.wileyonlinelibrary.com/journal/ett spectrum like WiFi. 2 The advanced antennas with multiple input and multiple output technologies can be adopted in the evolving 5G to provide higher data rate. 3,4 The colossal requirements of future 5G networks cannot be accomplished by the existing radio access networks (RANs), wherein the base band units (BBUs) and radio units are consolidated. 2 Meanwhile, the cloud-enabled services in 5G ecosystem cater heterogeneous communication framework utilizing advanced virtualization techniques. The network virtualization stimulates multiservice and multitenancy for efficient network operations and service provisions, which, in turn, offers new-fangled service-oriented and edge-cloud 5G architectures with enhanced Quality of Experience (QoE). 5 Thus, the current RAN architectures need to be further extended with various virtualization techniques to develop Cloud RAN (C-RAN). Cloud RANs abutments the correlation of Access Points (APs) to the pool of BBU via a troop of transport links in the Control Unit (CU), 6 which conquers the limitations in the traditional RAN. 7 However, C-RAN requires an intelligent technique to recognize the topology of the network in the mobility stage and to locate the resources, as the vast traffic volume created by the sampled radio signals are directly transported to the CU. 8 The next-generation networks are expected to learn consistently based on the varied user behavior and spectral stability that depends on both the user's environment and network behavior. 9 Thereby, sea...

show abstract

“…Aiming at the integration of pointing, navigating, timing, remote sensing, and communication resources, a novel architecture for a space resource integrated information platform was proposed in 2015 [14] for the enhancement of basic mission protection. For the era of 5G and beyond, multiple network standards, such as long-term evolution advanced (LTE-A) [15], the green network [16] and network-2030 [17], have been proposed and will coexist with each other for a period of time.…”

Section: Related Work a Heterogeneous Networkmentioning

confidence: 99%

Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process

et al. 2019

View full text Add to dashboard Cite

In the era of 5G and beyond, heterogeneous network orchestration has become a tremendous issue. The dilemma facing future systems is how to allocate integrated resources to satisfy multifarious services, which is an imperative but arduous task in forming a systematic mathematical model and quantifying the model with its multi-layer uncertainty characteristics. Aiming at the statistical representation and optimization in multimodal heterogenous networks for 5G and beyond, we propose a novel hybrid probability process (HPP) as a generalized surrogate model and a weighted degenerated upper confidence bound (WDUCB) criterion for Bayesian optimization (BO). We apply the proposed HPP-WDUCB combination to our developed simulation platform and configure several applications of the integration of space information network in next generation communication systems. And we compared the proposed method with other surrogate models and acquisition strategies from a range of perspectives. The experiment results yield significant applicability and excellent performance in multimodal system representation and optimization which provides an effective statistical modeling and orchestration references for network tuning.INDEX TERMS Bayesian optimization, heterogeneous network orchestration, 5G and beyond, hybrid probability process, weight degenerate upper confidence bound.

show abstract

Softwarization and Optimization for Sustainable Future Mobile Networks: A Survey

Cited by 10 publications

References 84 publications

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

An optimal multitier resource allocation of cloud RAN in 5G using machine learning

Bayesian Optimization for Multimodal Heterogeneous Network Orchestration via Hybrid Probability Process

Contact Info

Product

Resources

About