Where big data meets 5G?

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.

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“…[25] DRL based resource management schemes for 5G HetNets. A [26] Big data analytics solution for 5G network.…”

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

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“…The first instance of SONs can be described as adaptive and autonomous systems, based on control loops and threshold comparison. In order to handle more complex scenarios, current state of the art is investigating the application of advanced techniques, such as Machine Learning (ML), data mining to SON [51], [67]- [70]. ML algorithms can be categorized in multiple ways, with a stronger focus on supervised learning, unsupervised learning, and reinforcement learning.…”

Section: B Learning Based Managementmentioning

confidence: 99%

“…Additionally, authors proposed learning algorithm for resource allocation in 5G [80], [81]. A practical framework based on SON, SDN, NFV, and Big Data is proposed [70], aiming to cluster, forecast and manage traffic from a huge number of BSs with different statistical traffic and multiple RATs. Authors of [82] leverage the recent trend in ML, to ensure coordination and avoid conflicts among different SON functions, with different time scales and inconsistent objectives, which would be the case in the Ultra-Dense Small-Cell Networks.…”

Section: B Learning Based Managementmentioning

confidence: 99%

On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis

Marzouk

Barraca

Radwan

2020

IEEE Commun. Surv. Tutorials

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An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications. To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management. While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS). Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation. In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing. Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA). After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency. This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks. Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions.

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“…The resultant big data poses challenges to be processed as it is significantly different from ordinary data. As mentioned above, in next generation networks, number of devices will increase by a factor of one thousand [49,50]. It becomes discernible that the size of data in 5G networks will be massive as well as diverse in nature.…”

Section: Big Data In Next Generation Networkmentioning

confidence: 99%

Big Data Perspective and Challenges in Next Generation Networks

2018

Self Cite

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With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.

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Where big data meets 5G?

Cited by 8 publications

References 9 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

On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis

Big Data Perspective and Challenges in Next Generation Networks

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