Optimal matching between energy consumption of Base Stations and traffic load in green cellular networks

2012 ◽  
Author(s):  
Xiaowei Qin ◽  
Feng Chen ◽  
Guo Wei
Keyword(s):  
Energy Consumption ◽  
Cellular Networks ◽  
Base Stations ◽  
Traffic Load ◽  
Optimal Matching

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.

Energy Efficient Schemes for Base Station Management in 4G Broadband Systems

2014 ◽  
pp. 100-120 ◽  
Author(s):  
Alexandra Bousia ◽  
Elli Kartsakli ◽  
Angelos Antonopoulos ◽  
Luis Alonso ◽  
Christos Verikoukis
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Energy Savings ◽  
Base Station ◽  
Base Stations ◽  
Traffic Load ◽  
Network Resources ◽  
Load Variations ◽  
Mobile Cellular ◽  
Main Component

Reducing the energy consumption in wireless networks has become a significant challenge, not only because of its great impact on the global energy crisis, but also because it represents a noteworthy cost for telecommunication operators. The Base Stations (BSs), constituting the main component of wireless infrastructure and the major contributor to the energy consumption of mobile cellular networks, are usually designed and planned to serve their customers during peak times. Therefore, they are more than sufficient when the traffic load is low. In this chapter, the authors propose a number of BSs switching off algorithms as an energy efficient solution to the problem of redundancy of network resources. They demonstrate via analysis and by means of simulations that one can achieve reduction in energy consumption when one switches off the unnecessary BSs. In particular, the authors evaluate the energy that can be saved by progressively turning off BSs during the periods when traffic decreases depending on the traffic load variations and the distance between the BS and their associated User Equipments (UEs). In addition, the authors show how to optimize the energy savings of the network by calculating the most energy-efficient combination of switched off and active BSs.

scholarly journals Modeling Mobile Cellular Networks Based on Social Characteristics

2016 ◽  
Vol 11 (4) ◽  
pp. 480
Author(s):  
Ji Ma ◽  
Wei Ni ◽  
Jie Yin ◽  
Ren Ping Liu ◽  
Yuyu Yuan ◽  
...  
Keyword(s):  
Cellular Networks ◽  
Experimental Studies ◽  
Cellular Systems ◽  
Base Stations ◽  
Traffic Load ◽  
Social Characteristics ◽  
Mobile Cellular ◽  
Modelling Method ◽  
The Social ◽  
Network Operations

Social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. Traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. In this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. In the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. Spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. Based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. Experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consistency between measurements and model. In practice, our modelling method can be used for network planning and parameter dimensioning to facilitate cellular network design, deployments and operations.

scholarly journals On Reducing Energy Cost Consumption in Heterogeneous Cellular Networks using Optimal Time Constraint Algorithm

2021 ◽  
Author(s):  
V Kalpana ◽  
Divyendu Kumar Mishra ◽  
K. Chanthirasekaran ◽  
Anandakumar Haldorai ◽  
Srigitha. S. Nath ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Energy Cost ◽  
Time Constraint ◽  
Base Stations ◽  
Optimal Time ◽  
Energy Usage ◽  
Heterogeneous Cellular Networks ◽  
A New Technique

Abstract The increasing data demand in recent years has resulted in a considerable rise in heterogeneous cellular network energy usage. Advances in heterogeneous cellular networks with renewable energy supplied from base stations offer the cellular communications sector interesting options. Rising energy consumption, fuelled by huge growth in user count as well as usage of data, has emerged as the most pressing challenge for operators in fulfilling cost-cutting and environmental-impact objectives. The use of minimum power relay stations or base stations in conventional microcells is intended to lower cellular network's total energy usage. We examine the reasons, difficulties, and techniques for addressing the energy cost reduction issue for such renewable heterogeneous networks in this paper. Because of the variety of renewable energy as well as mobile traffic, then the issue related to a reduction in energy cost necessitates both spatial and temporal resource allotment optimization. In this paper, we proposed a new technique for reducing the energy consumption cost using the optimal time constraint algorithmic approach. We demonstrate that the proposed method has time as well as space complexity. Experimental simulations on actual databases with synthetic costs are used to confirm the usefulness and efficacy of our method.

Game Theoretic Infrastructure Sharing in Wireless Cellular Networks

2015 ◽  
pp. 368-398
Author(s):  
Alexandra Bousia ◽  
Elli Kartsakli ◽  
Angelos Antonopoulos ◽  
Luis Alonso ◽  
Christos Verikoukis
Keyword(s):  
Energy Consumption ◽  
Cellular Networks ◽  
Cost Savings ◽  
Mobile Network ◽  
Cost Effective ◽  
Base Stations ◽  
Traffic Demand ◽  
Game Theoretic ◽  
Mobile Network Operators ◽  
Low Traffic

The emerging traffic demand has fueled the rapid densification of cellular networks. The increased number of Base Stations (BSs) leads to augmented energy consumption and expenditures for the Mobile Network Operators (MNOs), especially during low traffic, when many of the BSs remain underutilized. Hence, the MNOs are encouraged to provide “green” and cost effective solutions for their networks. In this chapter, an innovative algorithm for infrastructure sharing in two-operator environments is proposed, based on BSs switching off during low traffic periods. Motivated by the conflicting interests of the operators, the problem is formulated in a game theoretic framework that enables the MNOs to act individually to estimate the switching off probabilities that reduce their financial cost. The authors analytically and experimentally estimate the potential energy and cost savings that can be accomplished. The obtained results show a significant reduction in both energy consumption and expenditures, thus giving the operators the necessary incentives for infrastructure sharing.

scholarly journals Dual-Level Game-Based Energy Efficiency and Fairness for Green Cellular Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Sungwook Kim
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
Communication Networks ◽  
Cooperative Game ◽  
Global Climate ◽  
Base Stations ◽  
Traffic Load ◽  
System Level ◽  
Bargaining Solution ◽  
Efficient System

In the recent decades, cellular networks have revolutionized the way of next generation communication networks. However, due to the global climate change, reducing the energy consumption of cellular infrastructures is an important and urgent problem. In this study, we propose a novel two-level cooperative game framework for improving the energy efficiency and fairness in cellular networks. For the energy efficiency, base stations (BSs) constantly monitor the current traffic load and cooperate with each other to maximize the energy saving. For the energy fairness, renewable energy can be shared dynamically while ensuring the fairness among BSs. To achieve an excellent cellular network performance, the concepts of theRaiffa Bargaining SolutionandJain’s fairnessare extended and practically applied to our dual-level cooperative game model. Through system level simulations, the proposed scheme is evaluated and compared with other existing schemes. The simulation results show that our two-level game approach outperforms the existing schemes in providing a better fair-efficient system performance.

scholarly journals Performance Analysis of Cell Zooming Based Centralized Algorithm for Energy Efficient in Surabaya

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Hani’ah Mahmudah ◽  
Okkie Puspitorini ◽  
Ari Wijayanti ◽  
Nur Adi Siswandari ◽  
Rosabella Ika Yuanita
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Cellular Network ◽  
Mobile User ◽  
Base Station ◽  
Base Stations ◽  
Traffic Load ◽  
Traffic Condition ◽  
Centralized Algorithm ◽  
Cell Zooming

The cellular subscribers’s growth over the years increases the traffic volume at Base Stations (BSs) significantly. Typically, in central business district (CBD) area, the traffic load in cellular network in the daytime is relatively heavy, and light in the daynight. But, Base Station still consumes energy normally. It can cause the energy consumption is wasted. On the other hand, energy consumption being an important issue in the world. Because, higher energy consumption contributes on increasing of emission. Thus, it requires for efficiency energy methods by switching BS dynamically. The methods are Lower-to-Higher (LH) and Higher-to-Lower (HL) scheme on centralized algorithm. In this paper propose cell zooming technique  which can adjusts the cell size dynamic based on traffic condition. The simulation result by using Lower-to-Higher (LH) scheme can save the network energy consumption up to 70.7917% when the number of mobile user is 37 users and 0% when the number of mobile user is more than or equal to 291 users. While, Higher-to-Lower (HL) scheme can save the network energy consumption up to 32.3303% when the number of mobile user is 37 users and 0% when the number of mobile user is more than or equal to 292 users. From both of these schemes, we can analyze that by using Lower-to-Higher (LH) scheme reduces energy consumption greater than using Higher-to-Lower (HL) scheme. Nevertheless, both of them can be implemented for energy-efficient method in CBD area. Eventually, the cell zooming technique by using two schemes on centralized algorithm which leads to green cellular network in Surabaya is investigated.

scholarly journals MABC: Power-Based Location Planning with a Modified ABC Algorithm for 5G Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Ruchi Sachan ◽  
Zahid Muhammad ◽  
Jaehoon (Paul) Jeong ◽  
Chang Wook Ahn ◽  
Hee Yong Youn
Keyword(s):  
Energy Consumption ◽  
High Capacity ◽  
Base Station ◽  
Base Stations ◽  
Traffic Load ◽  
Smart Devices ◽  
5G Networks ◽  
Power Requirement ◽  
Abc Algorithm ◽  
Real Coded Genetic Algorithm

The modernization of smart devices has emerged in exponential growth in data traffic for a high-capacity wireless network. 5G networks must be capable of handling the excessive stress associated with resource allocation methods for its successful deployment. We also need to take care of the problem of causing energy consumption during the dense deployment process. The dense deployment results in severe power consumption because of fulfilling the demands of the increasing traffic load accommodated by base stations. This paper proposes an improved Artificial Bee Colony (ABC) algorithm which uses the set of variables such as the transmission power and location of each base station (BS) to improve the accuracy of localization of a user equipment (UE) for the efficient energy consumption at BSes. To estimate the optimal configuration of BSes and reduce the power requirement of connected UEs, we enhanced the ABC algorithm, which is named a Modified ABC (MABC) algorithm, and compared it with the latest work on Real-Coded Genetic Algorithm (RCGA) and Differential Evolution (DE) algorithm. The proposed algorithm not only determines the optimal coverage of underutilized BSes but also optimizes the power utilization considering the green networks. The performance comparisons of the modified algorithms were conducted to show that the proposed approach has better effectiveness than the legacy algorithms, ABC, RCGA, and DE.

scholarly journals A Load-Balancing Energy Consumption Minimization Scheme in 5G Heterogeneous Small Cell Wireless Networks Under Coverage Probability Analysis

2017 ◽  
Vol 31 (07) ◽  
pp. 1759013 ◽  
Author(s):  
Zhu Xiao ◽  
Shuangchun Li ◽  
Xiaochun Chen ◽  
Dong Wang ◽  
Wenjie Chen
Keyword(s):  
Wireless Networks ◽  
Energy Consumption ◽  
Energy Saving ◽  
Power Efficiency ◽  
Success Probability ◽  
Small Cell ◽  
Base Stations ◽  
Traffic Load ◽  
Small Cells ◽  
Sleep Mode

Heterogeneous small cell networks (HSCN), as a promising paradigm to increase end-user data rates and improve the overall capacity, is expected to be a key cellular architecture in 5G wireless networks. However, energy consumed in HSCN is considerable due to the massive use of small cells. In this paper, we investigate the energy consumption issue which stems from the enormous number of running small cell base stations (SBSs) deploying in the HSCN. We first propose power consumption models so as to characterize the active state and the idle state of SBSs, respectively. Then two sleep modes for SBSs tier, i.e. random sleep mode and load-awareness dynamic sleep mode, are proposed. The random sleep is designed based on a binomial distribution of the SBS operation probability. Through the analysis on activeness of SBSs, we define the operation probability for the SBS applying the proposed dynamic sleep mode is associated to its traffic load level. The closed-form expressions of success probability for coverage, which is used to decide whether an active user can connect to a SBS successfully, are derived for the proposed sleep modes. Energy consumption minimizations are presented for the two proposed sleep modes under the success probability constraint. Simulation results prove the effectiveness of the proposed two sleep modes. Different energy saving gains can be achieved via using of the energy saving strategy. The superior of the dynamic sleep mode by comparing the random sleep is also verified in terms of energy consumption, success probability and power efficiency.

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