How to Meet Increased Capacities by Future Green 5G Networks: A Survey

Bohli, Afef; Bouallègue, Ridha

doi:10.1109/access.2019.2907284

Cited by 37 publications

(10 citation statements)

References 97 publications

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“…According to [18], resource allocation, network planning and deployment, energy harvesting & transfer, and hardware solutions are the broad categories that can increase energy efficiency. According to the Shannon formula, with the increase in bandwidth, the energy consumption factor also rises [31]. Massive MIMO is a promising technology to deal with efficiency concepts in terms of both spectrum and energy.…”

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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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: 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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“…Green energy represents diverse modules of wireless communication. To cognize upcoming green networks founded on energyefficient discipline that meets the atomistic demands in terms of content, elements triggered by the upcoming mobile generation, 5G can be helpful to fulfill the required necessities [14]. Green Computing (GC) must take the effective circulation impression, to operate and recycle, this smart system can save up-to 15-20% energy.…”

Section: Introductionmentioning

confidence: 99%

Social and Economic Contribution of 5G and Blockchain With Green Computing: Taxonomy, Challenges, and Opportunities

et al. 2021

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In recent years Fifth Generation (5G) technology is the most recent advancement in a wireless communication network. There is the advent of using the 5G with diverse data structures. The Blockchain (BC) has become an approving adoption for decentralized, peer-to-peer, distributed transparent ledger systems with a diverse data structure. The use of 5G with BC is an emerging trend in communication technology. The elasticity of 5G with BC enables many applications to reciprocity information molds it a fast, transparent, consequential, and safe for transportation of data in this smart era. Green computing (GC) is presently the intense optimistic tactic for the integration of smart technology in a diverse and distributed world of power consumption. This Systematic Mapping Study (SMS) has been analyzed by cautiously elected publications between 2016 and 2020 in well-putative venus. This study analyzed the advanced research on power consumption solutions for BC-based 5G communication, Moreover, a taxonomy of 5G based on green BC and GC in various areas is presented. Furthermore, Green energy renewable communication (GERC) problems are being observed in this research by integrating three discrete technologies such as 5G with green BC and GC also along with smart systems. Lastly, the research gaps had been bestowed to render future directions for the researchers in 5G with green BC and GC as the solution for rechargeable data packets.

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“…Recently, the green communication in wireless networks, especially in the upcoming 5G networks, has become an urgent need [38][39][40][41][42]. Unfortunately, despite its complete spectrum efficiency studies, the energy-efficient architectures in NOMA systems have not yet been well developed.…”

Section: Introductionmentioning

confidence: 99%

User Clustering and Power Allocation for Energy Efficiency Maximization in Downlink Non-Orthogonal Multiple Access Systems

et al. 2021

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Non-orthogonal multiple access (NOMA) has been considered a promising technique for the fifth generation (5G) mobile communication networks because of its high spectrum efficiency. In NOMA, by using successive interference cancellation (SIC) techniques at the receivers, multiple users with different channel gain can be multiplexed together in the same subchannel for concurrent transmission in the same spectrum. The simultaneously multiple transmission achieves high system throughput in NOMA. However, it also leads to more energy consumption, limiting its application in many energy-constrained scenarios. As a result, the enhancement of energy efficiency becomes a critical issue in NOMA systems. This paper focuses on efficient user clustering strategy and power allocation design of downlink NOMA systems. The energy efficiency maximization of downlink NOMA systems is formulated as an NP-hard optimization problem under maximum transmission power, minimum data transmission rate requirement, and SIC requirement. For the approximate solution with much lower complexity, we first exploit a quick suboptimal clustering method to assign each user to a subchannel. Given the user clustering result, the optimal power allocation problem is solved in two steps. By employing the Lagrangian multiplier method with Karush–Kuhn–Tucker optimality conditions, the optimal power allocation is calculated for each subchannel. In addition, then, an inter-cluster dynamic programming model is further developed to achieve the overall maximum energy efficiency. The theoretical analysis and simulations show that the proposed schemes achieve a significant energy efficiency gain compared with existing methods.

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How to Meet Increased Capacities by Future Green 5G Networks: A Survey

Cited by 37 publications

References 97 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

Social and Economic Contribution of 5G and Blockchain With Green Computing: Taxonomy, Challenges, and Opportunities

User Clustering and Power Allocation for Energy Efficiency Maximization in Downlink Non-Orthogonal Multiple Access Systems

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