Green 5G enabling technologies: an overview

Zhang, Shunliang; Cai, Xiao; Zhou, Weihua; Wang, Yongming

doi:10.1049/iet-com.2018.5448

Cited by 32 publications

(18 citation statements)

References 58 publications

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“…Various surveys has been conducted on 5G technologies [12], [16], energy efficiency [14], [17]- [19], and on enabling technologies such CRAN [20]- [23], SDN [21], NFV [6], UDN [24], massive MIMO [25], [26], HetNets [27] and MEC [28]- [30]. The surveys on energy efficiency is either limited to the energy harvesting techniques, system design [18], virtualization [21] or architecture [19].…”

Section: B Comparison With Existing Literaturementioning

confidence: 99%

“…The proposed work presented resource allocation to increase energy efficiency using the benefits of the Rayleigh fading channel model for massive MIMO. Authors in [16] worked not only on energy efficiency as well as on end to end delay. Besides spectrum efficiency, increase in bandwidth, deploying small cells, D2D/M2M communication, and ultra-dense networks, energy efficiency is another interlinked challenge that needs to be addressed.…”

Section: Energy Efficiency Overviewmentioning

confidence: 99%

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

et al. 2020

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As the world pushes toward the use of greener technology and minimizes energy waste, energy efficiency in the wireless network has become more critical than ever. The next-generation networks, such as 5G, are being designed to improve energy efficiency and thus constitute a critical aspect of research and network design. The 5G network is expected to deliver a wide range of services that includes enhanced mobile broadband, massive machine-type communication and ultra-reliability, and low latency. To realize such a diverse set of requirement, 5G network has evolved as a multi-layer network that uses various technological advances to offer an extensive range of wireless services. Several technologies, such as software-defined networking, network function virtualization, edge computing, cloud computing, and small cells, are being integrated into the 5G networks to fulfill the need for diverse requirements. Such a complex network design is going to result in increased power consumption; therefore, energy efficiency becomes of utmost importance. To assist in the task of achieving energy efficiency in the network machine learning technique could play a significant role and hence gained significant interest from the research community. In this paper, we review the state-of-art application of machine learning techniques in the 5G network to enable energy efficiency at the access, edge, and core network. Based on the review, we present a taxonomy of machine learning applications in 5G networks for improving energy efficiency. We discuss several issues that can be solved using machine learning regarding energy efficiency in 5G networks. Finally, we discuss various challenges that need to be addressed to realize the full potential of machine learning to improve energy efficiency in the 5G networks. The survey presents a broad range of ideas related to machine learning in 5G that addresses the issue of energy efficiency in virtualization, resource optimization, power allocation, and incorporating enabling technologies of 5G can enhance energy efficiency.

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Section: B Comparison With Existing Literaturementioning

confidence: 99%

Section: Energy Efficiency Overviewmentioning

confidence: 99%

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

et al. 2020

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“…In several other studies, data offloading has been considered as an energy-efficient approach. Furthermore, authors in [11] have put together several techniques for not only reducing the energy consumption from the traditional energy sources but also for surveying newer Energy Efficiency (EE) schemes in the End-to-End (E2E) system. One of the remarkable mentions by the authors includes the implementation of 3rd Generation Partnership Project (3GPP) compliant EE manager that would be responsible for monitoring energy demands in an E2E session and for implementation of the policies needed for catering to the ongoing energy demand.…”

Section: Background On Energy Efficiencymentioning

confidence: 99%

A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G

Usama

Erol‐Kantarci

2019

Sensors

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The rapidly increasing interest from various verticals for the upcoming 5th generation (5G) networks expect the network to support higher data rates and have an improved quality of service. This demand has been met so far by employing sophisticated transmission techniques including massive Multiple Input Multiple Output (MIMO), millimeter wave (mmWave) bands as well as bringing the computational power closer to the users via advanced baseband processing units at the base stations. Future evolution of the networks has also been assumed to open many new business horizons for the operators and the need of not only a resource efficient but also an energy efficient ecosystem has greatly been felt. The deployment of small cells has been envisioned as a promising answer for handling the massive heterogeneous traffic, but the adverse economic and environmental impacts cannot be neglected. Given that 10% of the world’s energy consumption is due to the Information and Communications Technology (ICT) industry, energy-efficiency has thus become one of the key performance indicators (KPI). Various avenues of optimization, game theory and machine learning have been investigated for enhancing power allocation for downlink and uplink channels, as well as other energy consumption/saving approaches. This paper surveys the recent works that address energy efficiency of the radio access as well as the core of wireless networks, and outlines related challenges and open issues.

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“…These applications require high data rates, improved quality of experience (QoE), low latency, longer battery lifespan, robust security protections, and optimal bandwidth utilization [2], [11]. To fulfill these demands, operators are constantly deploying networking resources, where capital expenditure is increasing dramatically [8], [12].…”

Section: Introductionmentioning

confidence: 99%

A Review on Green Caching Strategies for Next Generation Communication Networks

et al. 2020

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In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching.

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Green 5G enabling technologies: an overview

Cited by 32 publications

References 58 publications

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

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

A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G

A Review on Green Caching Strategies for Next Generation Communication Networks

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