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International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume: 3 | Issue: 2 | Jan-Feb 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 - 6470
@ IJTSRD | Unique Reference Paper ID – IJTSRD21381 | Volume – 3 | Issue – 2 | Jan-Feb 2019 Page: 373
Modeling and Analysis of Energy Efficient Cellular Networks
Iram Masood Hamdani1, Raj Kumar Jain2
1M.Tech Scholar, 2Assistant Professor
Department of Electronics & Communication Engineering,
Arya Institue of Engineering & Technology, Kukas, Rajasthan, India
ABSTRACT
Energy consumption has become a primary concern in design and operation of wireless communication networks due to two
main reasons- Environmental concerns and Cost. The next generation network systems will have to consider energy efficient
designs in any aspect. The 5G network which is most awaited today though proposes better data rates but also speaks about
energy efficiency in its agenda.
KEYWORDS: 5G, beam forming, MIMO
I. INTRODUCTION
5G networks is the most awaited mobile technology
development in the next 5 years. A lot of talks, proposalsand
ideas have been put forward in many conferences,
congresses and events across the globe regarding the
requirements and deployment of this technology. Next-
generation cellular communication systems, or 5G, will be
assisted by technologies that produce significant
improvements in cell throughput. In recent years, various
studies have focused on massive multiple input multiple
output (MIMO) systems, which are considered to play a
significant role in coming 5G technology. The technology till
date has mainly concentrated on the data rates. But, energy
also becoming an important parameter for the human
survival hence plays a vital role in the enhancement of
technology. Realizing this, industriesareworkingonmoving
to renewable energy sources like solar or windfor powering
the base stations. The results are promising and seem to
reduce the stress on non-renewable resources. Massive
MIMO is another area where a lot of research is being
concentrated on, wherein the number of antennas is very
large when compared to the number of users that the base
station is serving. As the antennas increase, the amount of
power consumed will greatly reduce. It is definitelytruethat
the initial cost of installation of Massive MIMO will be very
expensive as the hardware requirement is very high when
compared to the current number of antennas that the
telecom industry is using. But the energy savings by using
Massive MIMO yield great results in the long run. A broad
study of different techniques for energy efficiency reveals
that beamforming also plays a very crucial role.
Beamforming though has been into existence for more than
over a decade, continuousimprovementsin themethodology
keeps it ahead of many other technologies used for the
common goal.
II. Problem Definition
Currently operating wireless networks have been mainly
designed and deployed to maximize user’s performanceand
focus on throughput, data rates and reliability, while usually
paying less attention to energy efficiency.Thefuturedesigns
of wireless networks need to consider energy efficiency,
since it is now a priority of the ICT industry to attain energy
efficiency gains. Seeing this, a new research discipline called
green cellular networks, concentrating on environmental
influences of cellular networks, has been formed and
attracted many researchers. The term green is originally a
nickname for the dedicated efforts to reduce unnecessary
greenhouse gases like (CO2) emissions from industries. The
problem statement is described in the following points.
As for analysis the architectureof4GLTE Network isnot
based on energy efficient concept even if there was no
traffic load the network still consumes energy.
To minimize the cost energy consumption should be
minimum hence the need of perfect energy efficient
network.
As for the environmental considerations are concerned
we need energy efficient networks.
III. Methodology
This work will adopt a research methodology that combines
the theory model with empirical evaluation and refinement
of the proposed scheme on MATLAB simulation tool.
MATLAB is a useful high-level developmentenvironmentfor
systems which require mathematical modeling, numerical
computations, data analysis, and optimizationmethods. This
is because MATLAB consists of various toolboxes, specific
components, and graphical design environment that help to
model different applicationsandbuild custommodelseasier.
Cellular network takes Energy consumption (in watt or
joule/s) (Cost) as input and gives us Data throughput (in
bit/s) (Benefit) as output. In order to measure how cost and
benefit are balancing each other,wedefinebenefit costratio:
Energy efficiency [bit/joule] =
For, Energy efficiency to be high, Energy consumption
should be low. Also, due to environmental conditions and
energy being non-renewable, energyconsumptionshould be
low. Also, energy costs money. In 4G network, as traffic load
increases, we go from a rather high initial Energy
consumption to slightly higherone, and Energyefficiency for
that reason has grown steadily as the traffic load increases.
The reason for initial cost when we don’t have any traffic is
because of the Architecture of 4G. We have designed 4G
network in the way that when no traffic is there, still energy
is consumed by the network. We want energy consumption
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Reference Paper ID – IJTSRD21381 | Volume – 3 | Issue – 2 | Jan-Feb 2019 Page: 374
to be linear with traffic load and Energy efficiencyberoughly
high all the time. Now to find Energy Efficient Network
Design, we have used Energy Efficiency Optimization
method. The optimization methods are described below:
1. Select network design variables; M, K, ρ, λ, τ
2. Model throughput and energyconsumptionasfunctions
of these variables.
3. Solve :
Maximize (M, K, ρ, λ, τ)
Optimization Variables:
M = Number of Antennas
K = Number of Active Users
Ρ = Transmitted power
λ = Base station density
τ = Pilot re-use factor (Frame: U channel users)
Simulation Parameters
PARAMETER SYMBOL VALUE
Frame Length U 400
Bandwidth B 20 MHz
Pathloss exponent Α 3.76
Noise over pathloss at 1 km (B No )/ω 33 dBm
Amplifier efficiency Η 0.39
Static power C0 0 10W
Circuit power per active user C1 0 0.1W
Circuit power per BS antenna C0 1 0.2W
Signal processing coefficient C1 1 3.12 mW
Coding/decoding/backhaul A 1.5*10-9 J/bit
MODELING DATA THROUGHPUT
Data throughput = K.(1 - ) Blog2(1 + SINR)
K= Multiplexed users
(1 - ) = Data fraction per frame
Blog2(1 + SINR) = Data rate per user
Pathloss exponent: α
MODELING ENERGY CONSUMPTION
It depends strongly on hardware.
Characterized by parameters: µ, C0,0, C0,1, C1,0, C1,1, A
IV. Results
The results are very important for research and developmentwork toprovetheproblemdefinition practically.Inmy research I
am using MATLAB tool to simulate the results.
Average user rate and energy efficiency
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Reference Paper ID – IJTSRD21381 | Volume – 3 | Issue – 2 | Jan-Feb 2019 Page: 375
Average area rate and energy efficiency
Average user rate, average area rate and energy efficiency
V. Conclusion
The aim of this paper is to present some of the most
promising or potential modern wireless technologies
capable in achieving positive energy savings over the
existing methods, and to understand their potential and
relations in energy savings. The work is an initial research
towards achieving energy efficient 5G networks. A detailed
initial study was done on the potential places where there is
maximum power consumption in whole mobile network.
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Modeling and Analysis of Energy Efficient Cellular Networks

  • 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume: 3 | Issue: 2 | Jan-Feb 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 - 6470 @ IJTSRD | Unique Reference Paper ID – IJTSRD21381 | Volume – 3 | Issue – 2 | Jan-Feb 2019 Page: 373 Modeling and Analysis of Energy Efficient Cellular Networks Iram Masood Hamdani1, Raj Kumar Jain2 1M.Tech Scholar, 2Assistant Professor Department of Electronics & Communication Engineering, Arya Institue of Engineering & Technology, Kukas, Rajasthan, India ABSTRACT Energy consumption has become a primary concern in design and operation of wireless communication networks due to two main reasons- Environmental concerns and Cost. The next generation network systems will have to consider energy efficient designs in any aspect. The 5G network which is most awaited today though proposes better data rates but also speaks about energy efficiency in its agenda. KEYWORDS: 5G, beam forming, MIMO I. INTRODUCTION 5G networks is the most awaited mobile technology development in the next 5 years. A lot of talks, proposalsand ideas have been put forward in many conferences, congresses and events across the globe regarding the requirements and deployment of this technology. Next- generation cellular communication systems, or 5G, will be assisted by technologies that produce significant improvements in cell throughput. In recent years, various studies have focused on massive multiple input multiple output (MIMO) systems, which are considered to play a significant role in coming 5G technology. The technology till date has mainly concentrated on the data rates. But, energy also becoming an important parameter for the human survival hence plays a vital role in the enhancement of technology. Realizing this, industriesareworkingonmoving to renewable energy sources like solar or windfor powering the base stations. The results are promising and seem to reduce the stress on non-renewable resources. Massive MIMO is another area where a lot of research is being concentrated on, wherein the number of antennas is very large when compared to the number of users that the base station is serving. As the antennas increase, the amount of power consumed will greatly reduce. It is definitelytruethat the initial cost of installation of Massive MIMO will be very expensive as the hardware requirement is very high when compared to the current number of antennas that the telecom industry is using. But the energy savings by using Massive MIMO yield great results in the long run. A broad study of different techniques for energy efficiency reveals that beamforming also plays a very crucial role. Beamforming though has been into existence for more than over a decade, continuousimprovementsin themethodology keeps it ahead of many other technologies used for the common goal. II. Problem Definition Currently operating wireless networks have been mainly designed and deployed to maximize user’s performanceand focus on throughput, data rates and reliability, while usually paying less attention to energy efficiency.Thefuturedesigns of wireless networks need to consider energy efficiency, since it is now a priority of the ICT industry to attain energy efficiency gains. Seeing this, a new research discipline called green cellular networks, concentrating on environmental influences of cellular networks, has been formed and attracted many researchers. The term green is originally a nickname for the dedicated efforts to reduce unnecessary greenhouse gases like (CO2) emissions from industries. The problem statement is described in the following points. As for analysis the architectureof4GLTE Network isnot based on energy efficient concept even if there was no traffic load the network still consumes energy. To minimize the cost energy consumption should be minimum hence the need of perfect energy efficient network. As for the environmental considerations are concerned we need energy efficient networks. III. Methodology This work will adopt a research methodology that combines the theory model with empirical evaluation and refinement of the proposed scheme on MATLAB simulation tool. MATLAB is a useful high-level developmentenvironmentfor systems which require mathematical modeling, numerical computations, data analysis, and optimizationmethods. This is because MATLAB consists of various toolboxes, specific components, and graphical design environment that help to model different applicationsandbuild custommodelseasier. Cellular network takes Energy consumption (in watt or joule/s) (Cost) as input and gives us Data throughput (in bit/s) (Benefit) as output. In order to measure how cost and benefit are balancing each other,wedefinebenefit costratio: Energy efficiency [bit/joule] = For, Energy efficiency to be high, Energy consumption should be low. Also, due to environmental conditions and energy being non-renewable, energyconsumptionshould be low. Also, energy costs money. In 4G network, as traffic load increases, we go from a rather high initial Energy consumption to slightly higherone, and Energyefficiency for that reason has grown steadily as the traffic load increases. The reason for initial cost when we don’t have any traffic is because of the Architecture of 4G. We have designed 4G network in the way that when no traffic is there, still energy is consumed by the network. We want energy consumption
  • 2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Reference Paper ID – IJTSRD21381 | Volume – 3 | Issue – 2 | Jan-Feb 2019 Page: 374 to be linear with traffic load and Energy efficiencyberoughly high all the time. Now to find Energy Efficient Network Design, we have used Energy Efficiency Optimization method. The optimization methods are described below: 1. Select network design variables; M, K, ρ, λ, τ 2. Model throughput and energyconsumptionasfunctions of these variables. 3. Solve : Maximize (M, K, ρ, λ, τ) Optimization Variables: M = Number of Antennas K = Number of Active Users Ρ = Transmitted power λ = Base station density τ = Pilot re-use factor (Frame: U channel users) Simulation Parameters PARAMETER SYMBOL VALUE Frame Length U 400 Bandwidth B 20 MHz Pathloss exponent Α 3.76 Noise over pathloss at 1 km (B No )/ω 33 dBm Amplifier efficiency Η 0.39 Static power C0 0 10W Circuit power per active user C1 0 0.1W Circuit power per BS antenna C0 1 0.2W Signal processing coefficient C1 1 3.12 mW Coding/decoding/backhaul A 1.5*10-9 J/bit MODELING DATA THROUGHPUT Data throughput = K.(1 - ) Blog2(1 + SINR) K= Multiplexed users (1 - ) = Data fraction per frame Blog2(1 + SINR) = Data rate per user Pathloss exponent: α MODELING ENERGY CONSUMPTION It depends strongly on hardware. Characterized by parameters: µ, C0,0, C0,1, C1,0, C1,1, A IV. Results The results are very important for research and developmentwork toprovetheproblemdefinition practically.Inmy research I am using MATLAB tool to simulate the results. Average user rate and energy efficiency
  • 3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Reference Paper ID – IJTSRD21381 | Volume – 3 | Issue – 2 | Jan-Feb 2019 Page: 375 Average area rate and energy efficiency Average user rate, average area rate and energy efficiency V. Conclusion The aim of this paper is to present some of the most promising or potential modern wireless technologies capable in achieving positive energy savings over the existing methods, and to understand their potential and relations in energy savings. The work is an initial research towards achieving energy efficient 5G networks. A detailed initial study was done on the potential places where there is maximum power consumption in whole mobile network. References: [1] A. S. Andrae and T. Edler, “On global electricityusageof communicationtechnology: trends to 2030,” Challenges, vol. 6, no. 1, pp. 117–157, 2015. [2] MobiThinking, “Global mobile statistics.” [Online]. Available: http://mobithinking.com/mobile- marketing-tools/latest-mobile-stats [3] S. Vandris and K.-U. Killiches, “Power optimization in wireless heterogeneous networks,” LSI White paper. [4] G. Auer, V. Giannini, C. Desset, I. Godor, P. Skillermark, M. Olsson, M. A.Imran, D. Sabella, M. J. Gonzalez, O. Blume et al., “How much energy isneeded to run a wireless network?” IEEE Wireless Communications, vol. 18,no. 5, 2011 [5] O. Jumira and S. Zeadally, “Energy efficiency in cellular networks,” EnergyEfficiency in Wireless Networks,pp. 1–16. [6] J. M. Elmirghani, T. Klein, K. Hinton, T. El-Gorashi, A. Q. Lawey, andX. Dong, “Greentouch greenmeter core network power consumption modelsand results,” in Green Communications (OnlineGreencomm), 2014 IEEE On-line Conference on. IEEE, 2014, pp. 1–8. [7] O. Arnold, F. Richter, G. Fettweis, and O. Blume, “Power consumption modeling of different base station types in heterogeneouscellularnetworks,” in FutureNetwork and Mobile Summit, 2010. IEEE, 2010, pp. 1–8. [8] S. Cui, A. J. Goldsmith, and A. Bahai, “Energy- constrained modulation optimization,” IEEE transactions on wireless communications, vol. 4, no. 5, pp.2349–2360, 2005. [9] H. Holtkamp, G. Auer, S. Bazzi, and H. Haas, “Minimizing base station powerconsumption,” IEEE Journal on Selected Areas in Communications, vol. 32,no. 2, pp. 297–306, 2014. [10] R. Combes, S. E. Elayoubi, A. Ali, L. Saker, and T.Chahed, “Optimal onlinecontrol for sleep mode in green base
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