Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

Mughees, Amna; Tahir, Mohammad; Sheikh, Muhammad Aman; Ahad, Abdul

doi:10.1109/access.2020.3029903

Cited by 50 publications

(24 citation statements)

References 110 publications

(123 reference statements)

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“…It is important for us to quantify amount of CO 2 savings because one of the goals of the proposed cell switching algorithm is to ensure that the carbon foot print or quantity of CO 2 emission associated with the UDHN is greatly reduced by reducing the amount of energy consumption of the network as most of the energy used to power the BSs are obtained from fossil fuels. Thus, a reduction in the energy consumption in the network leads to a reduction in amount of energy demanded which translates in lesser CO 2 emission thereby resulting in environmental conservation and prevention of global warming [4], [47].…”

Section: Resultsmentioning

confidence: 99%

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks

Abubakar¹,

Mollel²,

Öztürk³

et al. 2021

Preprint

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One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network but most of the proposed methods are computationally demanding which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that the THESIS produces a significant reduction in the energy consumption of the UDHN and is able to reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.

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Section: Resultsmentioning

confidence: 99%

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks

Abubakar¹,

Mollel²,

Öztürk³

et al. 2021

Preprint

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“…on the level of automation that can be inducted. The authors of [24] also highlight the importance of ML and DL to enable an autonomous 5G network, especially concerning its optimization in terms of energy. Likewise, the work in [25] is focused on data-driven proactive 5G networks and highlights the change from reactive to proactive and automated management, the technical enablers to make it a reality, and the main networking areas affected, including network-level traffic prediction, temporal dimension, and metadata analysis, and proactive caching with social concentration prediction.…”

Section: B Surveys On Anm For 5g Networkmentioning

confidence: 99%

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

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Mobile networks are facing an unprecedented demand for high-speed connectivity originating from novel mobile applications and services and, in general, from the adoption curve of mobile devices. However, coping with the service requirements imposed by current and future applications and services is very difficult since mobile networks are becoming progressively more heterogeneous and more complex. In this context, a promising approach is the adoption of novel network automation solutions and, in particular, of zero-touch management techniques. In this work, we refer to zero-touch management as a fully autonomous network management solution with human oversight. This survey sits at the crossroad between zero-touch management and mobile and wireless network research, effectively bridging a gap in terms of literature review between the two domains. In this paper, we first provide a taxonomy of network management solutions. We then discuss the relevant state-of-the-art on autonomous mobile networks. The concept of zero-touch management and the associated standardization efforts are then introduced. The survey continues with a review of the most important technological enablers for zero-touch management. The network automation solutions from the RAN to the core network, including end-toend aspects such as security, are then surveyed. Finally, we close this article with the current challenges and research directions.

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“…A large number of references has been also provided around channel and carrier shutdown techniques for dense small cell networks, and the implications of centralised versus distributed network architectures have been discussed. With a more practical -but still vanilla-5G perspective, the authors in [44] provide a comprehensive survey on how machine learning (ML) can be used to address the energy efficiency challenges encountered in generic 5G networks. Finally, the recent survey in [45] has provided the most up-to-date overview on power saving techniques supported by the 3GPP NR standard, covering developments in Release 15 and 16, and the potential upcoming ones in Release 17.…”

Section: Comparison To Previous 5g Energy Efficiency Surveysmentioning

confidence: 99%

A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning

Liu¹,

Domenico²,

Piovesan³

et al. 2021

Preprint

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Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low latency communications.In addition, the third generation partnership project (3GPP) new radio (NR) specification also provides tools to significantly decrease the energy consumption and the green house emissions of next generations networks, thus contributing towards information and communication technology (ICT) sustainability targets. In this survey paper, we thoroughly review the state-of-the-art on current energy efficiency research. We first categorise and carefully analyse the different power consumption models and energy efficiency metrics, which have helped to make progress on the understanding of green networks. Then, as a main contribution, we survey in detail -from a theoretical and a practical viewpoint-the main energy efficiency enabling features that 3GPP NR provides, together with their main benefits and challenges. Special attention is paid to four key technology features, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence capabilities. We dive into their implementation and operational details, and thoroughly discuss their optimal operation points and theoretical-trade-offs from an energy consumption perspective. This will help the reader to grasp the fundamentals of -and the status on-green networking. Finally, the areas of research where more effort is needed to make future networks greener are also discussed.

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Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

Cited by 50 publications

References 110 publications

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning

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