DOI: http://doi.org/10.5281/zenodo.4066774
U.S. ISSN 2693 -1389
Journal Technological Science & Engineering (JTSE)
Vol. 1, No. 2, 2020
Renewable energy-focused hybrid supply system
for optimal powering the cellular base station
Saurav Biswas
Department of EEE, Bangladesh University of Business and Technology, Dhaka-1216, Bangladesh
Corresponding Author’s Email: sauravbiswas.eee@gmail.com
Abstract— With the enhancement of wireless
communication and their higher data demand, telecom network
operators are continuously deploying the cellular base stations
(BSs). This enormous growth of cellular BSs receiving a huge
amount of energy and creating immense pressure on the fossil
fuel reservation by releasing the greenhouse gas (GHG)
emissions. The main objective of this work to build a costeffective and environment-friendly cellular network powered by
the locally available renewable energy sources such as solar
photovoltaic (PV), wind turbine (WT), and biomass generator
(BG). This article addresses the key challenges of developing a
green mobile communication to minimize the net present cost
and GHG by maximum utilization of renewable energy. For
ensuring the guaranteed continuity of power supply, an adequate
battery bank is connected with the hybrid supply system. The
technical criteria, optimal component size, and energy issues of
the hybrid solar PV/WT/BG powered cellular BSs are critically
evaluated using HOMER optimization software considering the
dynamic fluctuation of the users and renewable energy sources.
Simulation results illustrate that the hybrid solar PV/WT/BG
system can satisfy the BS energy demand with the lowest value of
net present cost. Moreover, the battery bank can support the
cellular network for 162 hours during the emergency hours,
which is sufficient time for fixing the renewable energy sources.
In summary, the hybrid solar PV/WT/BG system along with
sufficient energy storage devices is an effective solution for
developing green cellular communication considering the
geographical location.
Keywords— Hybrid power, renewable energy, solar energy,
wind energy, biomass energy, cellular network, sustainability
I. INTRODUCTION
The rapid evolution of mobile traffic and wireless
multimedia networks has contributed to a remarkable rise in
the energy usage of wireless mobile communication over the
last decade [1]–[5]. Telecom operators are gradually deploying
more BSs to meet this ever-energy demand, leading to a rise in
a large portion of their operating expenses (OPEX). It has been
projected that global energy demand is expected to rise by
about 37 percent from 2013 to 2035, or by a further 37 percent
from 2013 to 2035 [6]–[10]. Consequently, the global energy
bill for cellular networks is over $10 billion per year and CO2
emissions are also expected to increase by 6% annually
through 2020 [11]–[15]. Another drawback is that Radio
Access Network (RAN) BSs contribute approximately 60-80
% of total energy expenditure and put a heavy burden on
conventional grid supplies to solve the problem of a stable
supply of electricity [16]–[20]. In response to the call for a
reduction in energy usage and the financial dimensions,
cellular operators have focused extensively on green
communications, with an emphasis on the environmental
effects [21]–[25]. Recently, the access networks created by
renewable, efficient, cost-effective, and clean energy resources
in green radio communications systems have created an
attractive solution that has attracted intense attention in both
industry and academia [26]–[30]. For example, in Africa,
Huawei [31] developed a 3G-supported green cellular BS
based solar PV energy to reduce soul reliance on grid energy.
A diesel generator (DG) is usually used to power the
remote cellular BSs if, due to the geographical position, the
grid connection is not accessible or not suitable. From an
economic, technological, and environmental viewpoint, the
notion of putting the diesel generator as the primary source has
become much less viable [32]–[36]. It is estimated that the
greenhouse gas outflow of the ICT sector will grow to 170
metric-tons in 2014 to 235 metric-tons by 2020. Besides, fossil
fuel supplies are constantly declining, and the cost of fuel is
rising in nature [37]–[41]. In addition, the lower performance
and regular operation and maintenance make the system even
less efficient due to DG failure and subsequent outage.
Motivated by the above problems, the renewable energy source
(RES) and the mix of RES and non-RES / Grid are an excellent
solution for network operators to achieve a minimum of green
connectivity. Besides, numerous studies [42]–[48] have been
undertaken to curb energy costs through the adoption of
renewable energy sources to power large-scale cellular
networks, and research in this area is not yet saturated. But the
authors have not studied the successful solution for the realtime pattern of cellular networks. During the low traffic time,
this approach can result in a huge dissipation of electricity.
Meeting the rapidly rising enormous demand for electricity
from non-renewable energy sources harms both the economic
and environmental impacts of higher capital costs and
increasing emissions of greenhouse gases. Also, non-renewable
resources are natural resources that, on a scale comparable to
their use, cannot be remade. As a result, researchers have
drawn deep attention to energy processing from renewable
energy sources to generate sustainable and environmentally
friendly energy [49]–[54]. Moreover, renewable energy
sources such as solar, wind, biomass, etc. are reusable, cheap,
clean, and accessible in many regions, while the technical
development of renewable energy production is cost-effective,
efficient, and reliable [55]–[60]. In [61], the authors explored
the feasibility of deploying renewable energy-based cellular
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DOI: http://doi.org/10.5281/zenodo.4066774
U.S. ISSN 2693 -1389
Journal Technological Science & Engineering (JTSE)
Vol. 1, No. 2, 2020
networks in different regions around the world and estimated
that approximately 320,100 renewable energy-based off-grid
BSs were deployed by 2014 and approximately 3,89,800 offgrid BSs will be running green energy by 2020.
The key contribution of this work to develop a hybrid solar
PV/WT/BG-powered green cellular network in Bangladesh
considering the dynamic nature of renewable energy sources
and traffic rates. The technical criteria, economic feasibility,
energy issues, and greenhouse gas emissions of the proposed
system are critically evaluated using the HOMER optimization
software package.
II.
SYSTEM ARCHITECTURE
Figure 2: Solar radiation profile for the selected area.
Where f the capacity factor is the ratio of actual electrical
energy output to the maximum possible electrical energy
output over a given period. The characteristic of the biomass
generator is shown in Fig. 2. On the other hand, the monthly
statistic of biomass available in the selected location is
presented in Fig. 3.
Figure 1: System model in the HOMER platform.
The architecture of the proposed system in the HOMER
platform is shown in Fig. 1. The major component of the
proposed system is solar PV panel, wind turbine, biomass
generator, battery bank, and converter. The short description
and mathematical modeling of the major component of the
proposed system are presented below:
Figure 3: Characteristic curve of the biomass generator.
A. Solar PV panel
Solar PV panel collects the solar intensity and converts it
into DC electrical energy. The detail of the electrical energy
generated the solar PV system is shown below [34]
E SPV = SPV PSH SPV
(1)
Where SPV the rated solar PV, PSH is the solar intensity in
kWh/m2/day and SPV is the efficiency of the solar PV. The
daily average solar intensity and clearness index of the selected
Chattogram division is shown in Fig. 2.
B. Biomass Generator
Biomass generator process biomass for generating green
energy. In this work, the selected main source of biomass is
agriculture residue such as rick husk. The amount of energy
generated by the biomass generator is calculated as follows
[34]
EBM = PBM (365 24 f )
Figure 4: Biomass profile of the selected area.
C. Wind Generator
The annual power generated by the wind turbine is
calculated by the following equation [34]
(2)
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P=
1
pV 3C p
2
(3)
31 | P a g e
DOI: http://doi.org/10.5281/zenodo.4066774
U.S. ISSN 2693 -1389
Journal Technological Science & Engineering (JTSE)
Vol. 1, No. 2, 2020
Where p is the air density of the selected location, V is the
air velocity, and Cp is the coefficient of the Betz limit.
HOMER calculates the harvested energy based on the above
equation for the given time period. The characteristic of the
wind turbine is shown in Fig. 5. In this work, the Whisper200
wind turbine has been selected for its satisfied wind speed
versus output power characteristic. The monthly statistic of the
wind speed of the selected location is shown in Fig. 6.
capacity of a single battery in (Ah), BDOD is the battery depth
of discharge, and LBS is the average daily BS load in kWh.
F
igure 7: Characteristic of Trojan L16P battery
E. Load Profile
The amount of power consumed by the cellular network
as a function of traffic intensity ( ) can be approximated as
follows [34]
Pin = M sec ( P1 + p Pmax ( − 1)) if
Figure 5: Characteristic curve of the wind turbine.
0 1 (5)
Where Msec is the number of antennas in the base station,
P1, is the maximum power consumption, Pmax is the
transmitted power, is the traffic intensity, p is the load
dependency of the power consumption. The dynamic traffic
intensity of the selected location is illustrated in Fig. 8. The
power consumed by the different component of the base
station under 10 MHz system bandwidth is shown in Table 1.
Table 1: Power consumed by the different parameters of
the BS under BW=10 MHz [34].
Components
BS
Figure 6: Wind profile of the selected area.
PA
D. Battery Bank
For providing sufficient backup power in this work ‘Trojan
L16P’ battery has been selected due to the low cost, and
superior charging/discharging characteristic. The characteristic
of the selected battery is shown in Fig. 7. The capacity of the
battery bank that is required to support the backup power is
calculated as follows [34]
Baut =
N batt Vnom Qnom BDOD (24h / day)
LBS
(4)
Where N batt is the number of batteries, Vnom is the
nominal voltage of a single battery in V, Qnom is the nominal
Total PA [W]
RF
Parameters
Pmax [W]
Feeder loss σfeed [dB]
Back-off [dB]
Max PA out [dBm]
PA efficiency, ηPA [%]
PTX [W]
PRX [W]
Total RF, PRF [W]
Baseband (B)
Radio (inner Tx/Rx) [W]
Turbo code (outer Tx/Rx) [W]
Processors [W]
Total B, PB [W]
DC-DC
σDC [%]
Cooling
σCool [%]
Main Supply
σMS [%]
Sectors
Antennas
Total Power [W]
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Value
20
0
8
51
31.1
64.4
6.8
6.1
12.9
10.8
8.8
10
29.6
7.5
0
9
3
2
754.8
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DOI: http://doi.org/10.5281/zenodo.4066774
U.S. ISSN 2693 -1389
Journal Technological Science & Engineering (JTSE)
Vol. 1, No. 2, 2020
The DC and AC load profiles of the cellular network under
10 MHz bandwidth are shown in Fig. 9 and Fig. 10
respectively.
B. Cost Modeling
HOMER is the optimization tool used in this study to
evaluate the optimal solar power system that satisfies userspecified constraints with the lowest net present cost (NPC)
including the capital costs (CC), replacement costs (RC),
operation and maintenance costs (OMC), and salvage value (S)
within the project duration. The NPC is computed as follows
[23]
Figure 8: Dynamic traffic demand for the selected area.
III.
NPC =
TAC
= CC + RC + OMC − S
CRF
(6)
SYSTEM IMPLEMENTATION
A. Simulation Setup
In this simulation setup, assignment length and the annual
hobby rate have been regarded 20 years and 6.75 %
respectively, which shows the long-term sustainability of the
proposed system. For ensuring the higher-high-quality of
service annual ability shortage has been chosen 0%. Moreover,
dual-axis tracking mode PV panels are modeled and 10%
returned to power is reserved to serve the BS load, even if the
renewable strength generation all of the sudden decreases.
HOMER decides at every time step to satisfy the energy
demand of BSs and preserve provision for backup energy at the
decrease net current cost.
In this simulation setup, the lifetime of the mission is
regarded as 10 years with an annual activity fee of 6.75%. The
normal fees together with capital cost, replacement cost, OMC
cost, and salvage fees are often depending on this assignment's
lifecycle and annual interest rate.
Total annualized cost (TAC) value and capital recovery
factor (CRF) is described as follows [23]
TAC = TACCC + TAC RC + TACOMC
CRF =
i (1 + i )
(7)
N
(8)
(1 + i ) N − 1
Where N is the project duration and i is the annual real
interest rate.
IV. RESULTS AND DISCUSSIONS
The result analysis section has been performed to
emphasize the five key aspects: (i) Optimal Architecture, (ii)
Technical criteria, (iii) Energy yield analysis, (iv) Cost
analysis, and (v) GHG emissions. Additionally, relations of
network sustainability, cost-effectiveness, and greenhouse gas
emissions with that of various parameters like system
bandwidth, transmission power, and daily solar radiation
intensities are thoroughly investigated and critically analyzed.
A. Optimal Architecture
The optimal architecture of the proposed system under 10
MHz bandwidth is shown in Table 2. The optimal system
architecture has been found simulating the system considering
the average solar intensity, wind speed, and biomass available
in the selected location. From the optimal results, it is seen that
the proposed system is technically feasible due to its smaller
size.
Figure 9: DC load profile under 10 MHz bandwidth.
Table 2: The optimal size of the hybrid solar PV/WT/BG
system under 10 MHz bandwidth.
PV
(kW)
3.5
Figure 10: AC load profile under 10 MHz bandwidth.
WT
(kW)
2
Battery
(Units)
64
Converter
(kW)
1.4
B. Energy yield analysis
The annual energy generated by the different renewable
energy sources is shown in Fig. 11. It is clearly seen that solar
PV and wind turbines generated a higher amount of energy due
to the superior solar intensity and wind speed on the selected
location. It is also seen that the biomass generator contributes
to a lower amount of energy. This is happening because the
energy generation from BG involves a small amount of CO2.
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DOI: http://doi.org/10.5281/zenodo.4066774
U.S. ISSN 2693 -1389
Journal Technological Science & Engineering (JTSE)
Vol. 1, No. 2, 2020
demand. The amount of energy contributed by the different
renewable energy sources under different system bandwidth is
shown in Fig. 13. Fig. 13 tells that the biomass generator
contributes always minimum energy due to the involvement of
a small amount of greenhouse gas by the biomass generator.
However, the biomass generator supplies the backup power
during the shortage and outage of renewable energy shortage.
Figure 11: Energy breakdown for 10 MHz system bandwidth.
C. Cost analysis
Different types of costs that are associated with the supply
system are evaluated with the HOMER optimization software.
The net present cost includes all the costs such as capital cost,
replacement cost, operation and maintenance cost, fuel cost,
salvage value. The net present cost of the proposed system for
10 MHz system bandwidth is shown in Fig. 14. It clearly
mentioned that among the different types of component Trojan
L16P involves the highest amount of cost due to a smaller
amount of battery life and a huge amount of battery
requirement.
Figure 12: Monthly power contribution by the RES.
Figure 14: Net present cost of the proposed system.
Figure13: Energy generated under different system bandwidth.
The monthly statistic of power contributed by the different
renewable energy sources is shown in Fig. 12. The monthly
statistic shows that the solar PV panel, wind turbine, and
biomass generator complement each other. In the case of
lower solar intensity solar PV generated higher energy. On the
other hand, the wind turbine generates higher energy during
the higher wind speed. Moreover, the biomass generator
provides additional energy when the solar PV panel and the
wind turbine is not sufficient to fulfill the required energy
Figure 15: Cost breakdown of the proposed system.
The individual cost breakdown for the different types of
costs is shown in Fig. 15. In line with our expectations, the
initial capital cost the highest level of cost, and fuel cost is the
lowest due to the minimum amount of biomass involvement in
this project. The cost breakdown curves also represent that the
small amount of capital cost can be returned after completing
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DOI: http://doi.org/10.5281/zenodo.4066774
U.S. ISSN 2693 -1389
Journal Technological Science & Engineering (JTSE)
Vol. 1, No. 2, 2020
the project which is indicated in the figure as negative salvage
value. Moreover, the proposed system involves a small
number of operating costs due to the operation and
maintenance of the supply system. The detail of the individual
cost under 10 MHz system bandwidth is presented in Table 3.
CONCLUSION
Table 3: Individual cost breakdown.
Components
IC
($)
RC
($)
OMC
($)
Fuel
($)
PV
WT
1,500
0
162
600
225
540
emits a smaller amount of greenhouse gases. Table 5 also
shows that among the different types of pollutant carbon
dioxide is the highest amount. However, this higher volume of
carbon content can be maintained at a lower value by using
modern technology.
Salvage
($)
Total
($)
0
-81
1,581
0
-108
1,257
BG
660
0
0
99
-75
684
Battery
19,200
9,991
6,914
0
0
36,105
Converter
40
15
11
0
-7
59
Total
22,000
10,232
7,627
99
-271
39,686
The main objective of this work to develop a sustainable,
reliable, and cost-effective supply system by utilizing the
locally available renewable energy sources. Extensive
simulations are carried out to find the optimal system
architecture, and quantitative advantages of the proposed
scheme under different network conditions. Simulation results
tell that the proposed hybrid solar PV+BG+WT system along
with sufficient energy storage devices can fulfill the base
station energy demand. The battery bank can carry the BS
load demand without support from renewable energy sources
during the intermittency time. It is also found that the
renewable energy-focused supply system can save the
environment by minimizing greenhouse gas emissions. In the
future, the authors intend to expand the proposed dynamic
model demonstrating to the operations of geologically
dispersed several base stations building a trained algorithm for
load balancing steerage of approaching solicitations relying
upon the geological range of electrical energy fee and
renewable energy generations.
Conflicts of Interest: The authors declare no conflicts of
interest.
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Figure 16: Cost of electricity for different system bandwidth.
[1]
An extensive comparison of per unit electricity generation
cost for different system bandwidth considering the average
renewable energy sources is shown in Fig. 16. In line with our
expectation, the per-unit electricity generation cost is
decreasing with the increment of the system bandwidth. This
is happening because the higher system bandwidth has a
comparatively lower net present cost.
[2]
[3]
D. GHG emissions
Table 4: Carbon contents for the 10 MHz bandwidth.
Waste product
Value (kg/yr)
CO2
0.349
CO
0.00189
Unburned hydrocarbons
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Particulate matter
0.000332
SO2
0
NO
0.0166
[4]
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with energy cooperation,” IEEE Access, 2018, 6(1), 60900-60920.
[37] Jahid A., and M. S. Hossain, “Performance analysis of MC-DS-CDMA
wireless communications over Rayleigh fading using maximal ratio
combining diversity technique,” European Scientific Journal, 2015,
11(36), 262-674.
[38] Jahid A., M. S. Hossain, and R. Islam, “Performance analysis of
DWDM system with optical amplifiers in cascade considering the effect
of crosstalk,” Journal of Electrical and Electronic Engineering, 2015,
3(5), 110-116.
[39] Hossain M. S., and M. F. Rahman, “Hybrid solar PV/Biomass powered
energy efficient remote cellular base stations,” International Journal of
Renewable Energy Research (IJRER), 2020, 10(1), 329–342.
[40] Jahid A., A. B. Shams, and M. F. Hossain, “Green energy driven cellular
networks with JT CoMP technique,” Physical Communication, 2018,
28, 58-68.
[41] Haque M. E., and M. S. Hossain, "Reliability and accuracy analysis of
BPSK and QAM modulation scheme for assessing structural health
data," European Journal of Scientific Research, 2014, 127(1), 75-86.
[42] Haque M. E., M. Asikuzzaman, I. U. Khan, I. H. Ra, M. S. Hossain, S.
B. H. Shah, “Comparative study of IoT-based topology maintenance
protocol in a wireless sensor network for structural health monitoring,”
Remote Sensing, 2020, 12(15), 2358.
[43] Hossain M. S., M. N. S. K. Shabbir, and M. F. Rahman, "Control of
power factor and reduction of THD for a three phase grid connected
inverter, "International Conference on Electrical & Electronic
Engineering (ICEEE), Rajshahi, Bangladesh, 2017, pp. 1-4.
[44] Haque M. E., M. F. M. Zain, and M. S. Hossain, "Throughput and queue
computations based on sensor network topology for overseeing high-rise
building SH data," European Journal of Scientific Research, 2015,
128(1), 45-59.
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DOI: http://doi.org/10.5281/zenodo.4066774
U.S. ISSN 2693 -1389
Journal Technological Science & Engineering (JTSE)
Vol. 1, No. 2, 2020
[45] Shams A. B., A. Jahid, and M. F. Hossain, “A CoMP based LTE-A
simulator for green communications”, IEEE International Conference on
Wireless Communications, Signal Processing and Networking
(WiSPNET), Chennai, India, 2017, 1751-1756.
[46] Miah S, M. H. Miah, M. S. Hossain, and M. H. Ahsan, “Study of the
homogeneity of glass fiber reinforced polymer composite by using
neutron radiography technique,” American Journal of Construction and
Building Materials, 2018 (2), 22-28.
[47] Ilius P. M., P. M. Rakibul, M. Z. Heider, and M. S. Hossain,
"Optimization of response and stability of dc motor using tachometer
and amplifier," American Academic & Scholarly Research Journal,
2013(5), 35-53.
[48] Jahid A, P. Das, and S. P. Majumder, “Bit error rate (BER) performance
of a free space optical (FSO) link considering the effect of cloudinduced fading,” Global Journal of Researches in Engineering, 2015 (6),
43-48.
[49] Jahid A, and Z. Alam, “Performance analysis of DWDM system
considering the effects of cascaded optical amplifiers with optimum
receiver gain,” American Journal of Engineering Research (AJER),
2015 (8), 1-8.
[50] Alsharif M. H. et al., “Artificial intelligence technology for diagnosing
COVID-19 cases: a review of substantial issues,” European Review for
Medical and Pharmacological Sciences, 2020 (24), 9226-9233.
[51] Sharif, A. Jahid, M. S. Islam, M. E. Hossain, and A. S. M. T. Islam,
“Analytical evaluation of BER considering effect of cross-polarization
in a polarization diversity 4×4 MIMO satellite to ground link,”
International Conference on Robotics, Electrical and Signal Processing
Techniques (ICREST), Dhaka, Bangladesh, 2019, pp. 116-119.
[52] Sadath M. A., M. S. Islam, M. S. Hossain, and M. Faisal,"Ultra-high
birefringent low loss suspended elliptical core photonic crystal fiber for
terahertz applications," 2020 (59), 1-8.
[53] Iqbal M. A., M. Manzoor-E-Elahee, A. Jahid, and S. P. Majumder,
"Performance proposition of limited-wavelength-interchange crossconnects considering Coherent and Incoherent crosstalk," International
Conference on Electrical & Computer Engineering (ICECE 2010),
Dhaka, 2010, pp. 610-613.
[54] Hossain M. F., A. U. Mahin, T. Debnath, F. B. Mosharrof, and K. Z.
Islam, “Recent research in cloud radio access network (C-RAN) for 5G
cellular systems- a survey,” Journal of Network and Computer
Applications, 2019 (139), 31-48.
[55] Kawser M. T. et al., “Improvement in DRX power saving for nonrealtime traffic in LTE,” ETRI Journal, 2016, 38(4), 622-633.
[56] Al-Amin M. R., S. S. Chowdhury, and K. Z. Islam, Design, “Simulation
of an edge-coupled band pass filter at X band,” IEEE International
Conference on Materials, Electronics & Information Engineering
(ICMEIE), Rajshahi, Bangladesh, 2015, 1-5.
[57] Mousumi U. S., M. Asaduzzaman, M. A. Zardar, and K. Z. Islam,
“Techno-economic evaluation of hybrid supply system for sustainable
powering the Saint Martin Island in Bangladesh”, Journal of Energy &
Technology (JET), 2020 (1), 9-23.
[58] Hossain M. S., K. Z. Islam, M. E. Hossain, and S. Biswas, “Technoeconomic analysis of optimal solar power system for LTE cellular base
stations,” Journal of Technological Science & Engineering (JTSE), 2020
(1), 11-20.
[59] Rahaman M. M., E. Mahfuj, M. M. Haque, R. S. Shekdar, and K. Z.
Islam, “Educational robot for learning programming through Blockly
based mobile application,” Journal of Technological Science &
Engineering (JTSE) 2020 (1), 21-25.
[60] Jahid A., A. B. Shams, and M. F. Hossain, “Energy efficiency of JT
CoMP based green powered LTEA cellular networks,” IEEE
International Conference on Wireless Communications, Signal
Processing and Networking (WiSPNET), Chennai, India, 2017, 17391745.
[61] Ghazzai H., E. Yaacoub, A. Kadri, H. Yanikomeroglu, and M. S.
Alouini, “Next-generation environment-aware cellular networks:
Modern green techniques and implementation challenges,” IEEE
Access, 2016, 4(1), 5010–5029.
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