Joint Management of Energy Consumption, Maintenance Costs, and User Revenues in Cellular Networks With Sleep Modes

2017 ◽  
Vol 1 (2) ◽  
pp. 167-181 ◽  
Author(s):  
Andrea Baiocchi ◽  
Luca Chiaraviglio ◽  
Francesca Cuomo ◽  
Valentina Salvatore
Keyword(s):  
Energy Consumption ◽  
Cellular Networks ◽  
Maintenance Costs ◽  
Joint Management

scholarly journals Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhuofan Liao ◽  
Jingsheng Peng ◽  
Bing Xiong ◽  
Jiawei Huang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Physical Distance ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Average Delay ◽  
Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

Urban Design Supported by "Green" Streets - Urban Design for Financial Technology Island of Beijing "TBD"

2013 ◽  
Vol 671-674 ◽  
pp. 2476-2479
Author(s):  
Jian Guang Yu ◽  
Jian Kun Yu ◽  
Hong Wei Ma ◽  
Lei Xu
Keyword(s):  
Energy Consumption ◽  
Urban Design ◽  
Transport System ◽  
Traffic Congestion ◽  
Implementation Strategy ◽  
Urban Transport ◽  
Maintenance Costs ◽  
Building Maintenance ◽  
Financial Technology

Green transport is a concept to build a comprehensive urban transport system, which aims to reduce traffic congestion and energy consumption, promoting a more friendly environment and saving building maintenance costs. From the perspective of urban design, this paper focuses on how to integrate green transport into urban design and discusses the implementation strategy of green transport.

Stackelberg Game Theoretic Framework in Cognitive Green Heterogeneous Networks

2017 ◽  
pp. 269-286
Author(s):  
Chungang Yang ◽  
Pengyu Huang ◽  
Jia Xiao ◽  
Lingxia Wang ◽  
Jiandong Li
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Cognitive Radio ◽  
Cellular Networks ◽  
Communication Networks ◽  
Cognitive Radio Networks ◽  
Stackelberg Game ◽  
Radio Networks ◽  
Heterogeneous Cellular Networks ◽  
Game Theoretic

Game theory has found an extensive application in wireless communication networks including cognitive radio networks, heterogeneous cellular networks, cooperative relay networks. Also, cognitive radio networks, green communications and heterogeneous cellular networks have attracted a wide attention on improve the spectrum efficiency and energy efficiency; therefore, the capacity, the coverage and the energy consumption. However, interference problem and energy consumption are critical for these networks. Introducing hierarchy among different decision-making players in cognitive, heterogeneous, green, cooperative cellular networks can both save energy and mitigate interference, thus enhance throughput. Stackelberg game suits to model, analyze and design the distributed algorithms in these hierarchical decision-making networking scenarios. In this chapter, we introduce basics of Stackelberg game and survey the extensive applications of Stackelberg game in cognitive, heterogeneous, cooperative cellular networks with the emphasis on resource management, green commutations design and interference management. This chapter highlights the potentials and applications with the promising vision of Stackelberg game theoretic framework for future cognitive green heterogeneous cellular networks.

Low-Carb: Reducing energy consumption in operational cellular networks

2013 ◽  
Author(s):  
M. S. Ilyas ◽  
G. Baig ◽  
M. A. Qureshi ◽  
Q. U. A. Nadeem ◽  
A. Raza ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Cellular Networks ◽  
Reducing Energy

scholarly journals Energy Cost Minimization in Heterogeneous Cellular Networks with Hybrid Energy Supplies

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Bang Wang ◽  
Qiao Kong ◽  
Qiang Yang
Keyword(s):  
Energy Consumption ◽  
Cellular Networks ◽  
Minimization Problem ◽  
Energy Cost ◽  
Cost Minimization ◽  
Energy Allocation ◽  
Green Energy ◽  
User Association ◽  
Heterogeneous Cellular Networks ◽  
Cost Minimization Problem

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.

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