Energy-Saving Management Mechanism Based on Hybrid Energy Supplies for LTE Heterogeneous Networks

Yu, Peng; Feng, Lei; Li, Wenjing; Qiu, Xuesong

doi:10.1155/2016/3121538

Cited by 5 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mobile devices connect to the cloud hub over a wireless network and send their own tasks to the cloud hub. ere is no need to perform data management calculations or other processing [12], just provide data input and output in the cloud center. is 2 Mobile Information Systems feature allows users to download and install applications, store mobile storage memory, view data on a variety of devices and platforms, improve application compatibility, and cover mobile devices [13,14].…”

Section: Edge Server Deployment Algorithm Design Experimentsmentioning

confidence: 99%

Construction of an Intelligent APP for Dance Training Mobile Information Management Platform Based on Edge Computing

Gao

2021

Mobile Information Systems

View full text Add to dashboard Cite

In recent years, with the rapid development of modern technology and the continuous promotion of information technology, information technology has been widely used in modern performing arts. Information management has become the most practical and effective method and means in performing arts training management, but as the amount of various data grows exponentially, the requirements for computing processing power and speed for massive amounts of data and information are also increasing day by day. This article aims to study the use of edge computing to solve the problems of high latency and high cost when traditional cloud computing centers provide services. In response to these problems, this paper proposes a data acquisition and processing system architecture based on edge computing, which uses edge computing to mine the computing power of edge terminals in the network, performs partial or all calculations at the edge terminals, processes private data, and reduces cloud computing. The center’s computing, transmission bandwidth load, and energy consumption, combined with cloud computing, provide data acquisition, processing, and analysis solutions with low latency and high processing capabilities. This article details how to optimize edge server development to minimize access latency and consider network reliability when requesting access to edge servers. This paper uses the proposed edge server deployment algorithm and system load optimization, which can effectively reduce the network delay and system load of the edge server, and the experimental results show that the system performance is improved by 23.5% after effective optimization.

show abstract

Section: Edge Server Deployment Algorithm Design Experimentsmentioning

confidence: 99%

Construction of an Intelligent APP for Dance Training Mobile Information Management Platform Based on Edge Computing

Gao

2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…where the denominator provides the maximum achievable data rate and b τ is the bandwidth of a RB in the network, which is 180 kHz in LTE [10]. By obtaining P τ j and τ * ij from (2) and (3) respectively, we can calculate the minimum amount of power in W that has to be allocated by BS j to UE i as…”

Section: Density- Energy-and Spectrum-aware Base Station Schedulmentioning

confidence: 99%

“…That is why P 0 is considered in the problem formulations. Generally, P 0 is around 500 W and 15 W for macro and small cells, respectively [10]. This model can be expanded by including switching power consumption which is out of scope of this work [12], [13].…”

Section: Density- Energy-and Spectrum-aware Base Station Schedulmentioning

confidence: 99%

Density-Aware, Energy- and Spectrum-Efficient Small Cell Scheduling

Mollahasani

Onur

2019

IEEE Access

View full text Add to dashboard Cite

Future mobile networks have to be densified by employing small cells to handle the upsurge in traffic load. Although the amount of energy each small cell consumes is low, the total energy consumption of a large-scale network may be enormous. To enhance energy efficiency, we have to adapt the number of active base stations to the offered traffic load. Deactivating base stations may cause coverage holes, degrade the quality of service and throughput while redundant base stations waste energy. That is why we have to adapt the network to an effective density. In this paper, we show that achieving an optimal solution for adapting the density of base stations to the demand is NP-hard. We propose a solution that consists of two heuristic algorithms: a base station density adaptation algorithm and a cell-zooming algorithm that determines which base stations must be kept active and adapts transmit power of base stations to enhance throughput, energy, and spectral efficiency. We employ a multi-access edge cloud for taking a snapshot of the network state in nearly real time with a wider perspective and for collecting network state over a large area. We show that the proposed algorithm conserves energy up to 12% while the spectral efficiency and network throughput can be enhanced up to 30% and 26% in comparison with recent works, respectively. INDEX TERMS 5G mobile networks, densification, density-aware networking, energy-efficiency, green networks, multi-access edge cloud (MEC), self-organizing networks.

show abstract

“…It has been reported that the telecommunication industry is responsible for about 2 percent of the total CO 2 emissions worldwide, and the percentage is expected to double in 2020 [8]. To reduce the carbon footprint, recently, integrating some green energy, such as wind and solar power, into the existing cellular networks has became another research priority [9][10][11][12]. German mobile operator E-Plus (available: https:// nsn.com/news-events/publications/unite-magazine-issue-10/ e-plus-launches-germany-s-first-zero-co2-off-grid-base-station) has launched the first generation of green BSs by adopting a combination of solar and wind power only.…”

Section: Introductionmentioning

confidence: 99%

Energy Cost Minimization in Heterogeneous Cellular Networks with Hybrid Energy Supplies

Wang

Kong

Yang

2016

Mobile Information Systems

View full text Add to dashboard Cite

The ever increasing data demand has led to the significant increase of energy consumption in cellular mobile networks. Recent advancements in heterogeneous cellular networks and green energy supplied base stations provide promising solutions for cellular communications industry. In this article, we first review the motivations and challenges as well as approaches to address the energy cost minimization problem for such green heterogeneous networks. Owing to the diversities of mobile traffic and renewable energy, the energy cost minimization problem involves both temporal and spatial optimization of resource allocation. We next present a new solution to illustrate how to combine the optimization of the temporal green energy allocation and spatial mobile traffic distribution. The whole optimization problem is decomposed into four subproblems, and correspondingly our proposed solution is divided into four parts: energy consumption estimation, green energy allocation, user association, and green energy reallocation. Simulation results demonstrate that our proposed algorithm can significantly reduce the total energy cost.

show abstract

Energy-Saving Management Mechanism Based on Hybrid Energy Supplies for LTE Heterogeneous Networks

Cited by 5 publications

References 22 publications

Construction of an Intelligent APP for Dance Training Mobile Information Management Platform Based on Edge Computing

Construction of an Intelligent APP for Dance Training Mobile Information Management Platform Based on Edge Computing

Density-Aware, Energy- and Spectrum-Efficient Small Cell Scheduling

Energy Cost Minimization in Heterogeneous Cellular Networks with Hybrid Energy Supplies

Contact Info

Product

Resources

About