Power Consumption and GoS Tradeoff in Cellular Mobile Networks With Base Station Sleeping and Related Performance Studies

Wu, Jingjin; Wong, E.W.M.; Chan, Yin-Chi; Zukerman, Moshe

doi:10.1109/tgcn.2020.3000277

Cited by 41 publications

(19 citation statements)

References 30 publications

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“…Therefore, we need to optimize the allocation of resource including channels and bandwidth, power control, and transmission scheduling policy to improve energy efficiency. 3) Summary: According to our above analysis, the strategies toward the green cellular networks in the 6G era mainly consists of the deployment and management of BSs [119]- [121], the power control [21], [122], and resource allocation [118], [123]- [125]. Another important direction which has been mentioned in Sec.…”

Section: A Power Consumption and Energy Efficiency Of Cellular Networkmentioning

confidence: 96%

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In current 5G and future 6G era, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, 6G will inevitably have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and even intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be adopted widely in 6G, further complicates the power control and network management. To address these challenges and reduce human intervene, Artificial Intelligence (AI) has been widely recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present the main considerations for green communications and survey the related research on AI-based green communications. We focus on how AI techniques are adopted to manage the network and improve energy harvesting toward the green era. We analyze how state-of-the-art Machine Learning (ML) and Deep Learning (DL) techniques can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and optimize the accuracy rate to accelerate the applications in 6G. Finally, we discuss the existing problems and envision the challenges for these emerging techniques in 6G.

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Section: A Power Consumption and Energy Efficiency Of Cellular Networkmentioning

confidence: 96%

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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“…At the same time, the operational power is consumed when ARC units are engaged in processing tasks [11]. The relevant components of ARCs, deployed in FNs such as micro BSs or embedded servers, can be de-activated when not in use for power saving, and activated upon the arrival of a task occupying at least one ARC unit [14] 2 . In a dynamic system such as a wireless network, the amount of the operational power consumption is affected by the real-time loads of ARC groups.…”

Section: Network Modelmentioning

confidence: 99%

“…We let ε 0 k ∈ R + and ε k ∈ R + represent the amounts of the idle power and operational power consumption per computing unit of ARC group k ∈ [K]. Similar power consumption models have been empirically justified and widely applied in existing research, where idle power and operational power values depend on specific hardware [9,14]. Detailed discussion about the power consumption of the network edge will be provided in Section 3.…”

Section: Network Modelmentioning

confidence: 99%

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Energy Efficient Priority-Based Task Scheduling for Computation Offloading in Fog Computing

Yin¹,

Fu²,

Wu³

et al. 2022

Preprint

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Fog computing offers a flexible solution for computational offloading for Internet of Things (IoT) services at the edge of wireless networks. It serves as a complement to traditional cloud computing, which is not cost-efficient for most offloaded tasks in IoT applications involving small-to-medium levels of computing tasks. Given the heterogeneity of tasks and resources in fog computing, it is vital to offload each task to an appropriate destination to fully utilize the potential benefit of this promising technology. In this paper, we propose a scalable priority-based index policy, referred to as the Prioritized Incremental Energy Rate (PIER), to optimize the energy efficiency of the network. We demonstrate that PIER is asymptotically optimal in a special case applicable for local areas with high volumes of homogeneous offloaded tasks and exponentially distributed task durations. In more general cases with statistically different offloaded tasks, we further demonstrate the improvement of PIER over benchmark policies in terms of energy efficiency and the robustness of PIER to different task duration distributions by extensive simulations. Our results show that PIER can perform better than benchmark policies in more than 78.6% of all simulation runs.

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“…An attractive energy-saving strategy in cellular networks is to set suitable sleep mechanisms for the BSs or access points (APs) according to the traffic intensity [91], [92], [93]. In a vehicular network, the energy consumption of RSUs can also be reduced through enabling the sleep modes.…”

Section: V2i Communicationsmentioning

confidence: 99%

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Wang¹,

Zhu²,

Hossain³

2021

Preprint

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As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literatures relevant to each of the scenarios are compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing, etc.) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition, etc.). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.

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Power Consumption and GoS Tradeoff in Cellular Mobile Networks With Base Station Sleeping and Related Performance Studies

Cited by 41 publications

References 30 publications

AI based Service Management for 6G Green Communications

AI based Service Management for 6G Green Communications

Energy Efficient Priority-Based Task Scheduling for Computation Offloading in Fog Computing

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

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