Solar Powered Cellular
Networks: Issues, Challenges
and Solutions
Biplab Sikdar
1
Overview
• Green cellular networks
• Motivating factors
• System description
• Case study and ongoing deployment
• Challenges and proposed solutions
2
Need for Greener Base Stations
• More than 6 billion mobile
subscribers and increasing
• 4 million base stations
worldwide (~ 25 MWh/year)
• Contribution to global energy
requirement: 3 %
• Contribution to global carbon
emissions: 2 %
Power consumption of a typical cellular network [2]
[2] Hasan, Ziaul, Hamidreza Boostanimehr, and Vijay K. Bhargava. "Green cellular networks: A survey, some research issues and
challenges." Communications Surveys & Tutorials, IEEE 13.4 (2011): 524-540.
Factors Motivating Solar
Powered BSs
• Telecom markets shifting to developing countries (specifically in
Africa and South Asia)
• Challenges:
• Poor grid connectivity
• Of 400,000 BSs in India, more than 70% of the BSs face power
cuts for more than 8 hours a day and many rural areas more than 20
hours a day
• Currently around 250,000 off-grid and 550,000 poor-grid BSs in
African and South Asian developing nations
• High operating expenditure (OPEX) for off-grid sites with diesel
generators
• The cost of running a site with diesel is around 10 times as compared
to using grid energy.
Factors Motivating Solar
Powered BSs
Solar map of world [6]
5
Factors Motivating Solar
Powered BSs
• Low OPEX: only battery replacement (every 3-5 years).
Typical lifetime of PV panels ~ 25-30 years
• Less maintenance/ Reduced site visits
• Government initiatives
• Government regulations: TRAI regulation: 50% rural
and 20% urban telecom towers to be powered by
renewable energy by 2015
• Government subsidy: TRAI subsidy on solar
deployments: North India ( 70%), other parts (30%)
Factors Motivating Solar
Powered BSs
• Greater disaster resistance:
• 2011 earthquake in Japan followed by a tsunami, more
than 6,700 cellular BSs experienced outages
• Solar powered BSs are immune to grid outages and can
restore their services faster.
• New base stations with low power consumption:
• Macro BSs typically have high consumption, requiring
large solar panel dimensions, thereby making solar
powered solutions impractical.
• Recent developments: macro BSs consuming 500-800 W
and smaller BSs consuming 50-120 W
Setup of Solar Powered BSs
http://www.topsunenergy.net/solar-telecom-system.htm
•
Modeling parameters:
• Base station power consumption
• Solar energy harvested by PV panels
• Battery (lifetime)
PV Panels
• Arrays of solar PV cells to convert solar energy to electricity
• DC rating: power generated when the solar power available
on panels is 1 kW/m2.
• 1 kW PV panel is typically 5 m2 in area and panel lifetime is
more than 25 years
• Factors affecting the power
produced by a PV panel:
• DC rating
• Geographical location
• Tilt of the PV panel
• DC-AC loss factor
Cross Section of a PV Panel
Batteries
• Stores charge for night/ bad weather hours.
• Lead Acid batteries a common storage option: cheap and time
tested
• Depth of discharge crucial in determining battery life.
Number of cycles vs DOD for a deep discharge lead acid battery [9]
Discharge-charge process and tolerable depth
of discharge [10]
Integrated Power Unit
• Power requirements of a BS: transceiver equipment, cooling,
miscellaneous loads (e.g. lights).
• IPU: manages power supply to loads, and conversion and storage of the
harvested solar energy
• Power management unit: controls the charging of the batteries and the
supply of power to the loads.
• Battery charge regulator: monitors the battery state and disconnects
them when level goes below a specified DOD (generally 50-80%).
• The DC-DC converters: supply power to the transceiver and store
power from the solar panels in the batteries
• DC-AC converters: supply power to AC loads
Current Deployment Scenario
Solar Powered BS Deployment as of December 2014
Current Deployment Scenario
• Bhutan Telecom Limited
(BTL):
• Solar powered BSs for
use in rural areas, with
ability to handle hundreds
of users (in a range of few
kms).
• BSs require only between
50-150 W of power and
have batteries designed for
3-7 day backup, aimed at
providing autonomy
during cloudy days.
Current Deployment Scenario
• Telkomsel Indonesia:
• 234 solar powered BSs in
2012
• Example: BS in Sangatta
• Average daily power
consumption: 26 KW
• Powered by 60 solar
panels each with a DC
rating of 205 W (giving a
total rating of 12.3 kW).
• 24 batteries each with
rating 2000 Ah for
autonomy of 4 days
Case Study: Tigo Ghana
• In 2012, 60% of the land area and 20% of the population (5
million people) of Ghana had no mobile coverage.
• Primary reasons: (i) the lack of necessary infrastructure such as
reliable grid power and (ii) too low average revenue per user
(ARPU) to justify the deployment costs.
• 2012: Tigo Ghana partnered with network solutions provider KNET and equipment manufacturer Altobridge to deploy 10 solar
powered BSs
• Compression techniques so that voice calls require rates of 4
kbps (compared to 14 kbps in conventional systems)
• Cell site average power consumption of 90 W (compared to
130 W or more)
Case Study: Tigo Ghana
Case Study: Tigo Ghana
• The BSs use satellites for backhaul, have a coverage range of 10
km, and capacity for up to 1500 subscribers.
• Lowering of costs brought about by the design optimizations:
return on investment for the operator in less than 24 months,
assuming 600 subscribers with ARPU of $4 per month.
• Currently there are has plans to expand to 300 additional sites,
some of which have already been implemented.
Challenges and Solutions
•
•
•
•
Economic challenges
Geographical limitations
Resource provisioning and dimensioning
Network management and resource allocation
Economic Challenges
• High CAPEX
• CAPEX/TCO (total cost of ownership) ratio decreased by around
40% between 2009 and 2013
• Government initiatives such as subsidies
• Market forces
• Awareness of environmental issues
• Government regulations
• Large BSs
• Powering a macro BS with power consumption of 3 kW would
require an area of around 180 m2 for the PV panels.
• However, larger BSs can still be cost effective, e.g. in the presence
of government subsidies, though the payback period is still high
(7-10 years).
Geographical Limitations
• Regions with Poor Solar Insolation
• Solar power may be used in conjunction with the grid to power the
BSs.
• Urban Deployments:
• PV panels should ideally be installed in open areas without
shadows from obstructions due to buildings or trees
• Difficult and expensive to procure such sites in urban areas.
• Long Stretches of Bad Weather
• Required size of the battery banks is very large
• Increases the CAPEX and the possibility of outages during these
periods.
Resource Provisioning
• The successful deployment of a solar powered BS requires
meticulous dimensioning of the PV panels and backup
batteries
• Trade-off between CAPEX and outage
• Dimensioning depends on
• Solar irradiation profile
• BS load profile
• Desired outage probability
System Resources
• PV Panels
• Number of PV panels: nPV
• DC rating of each panel: Epanel
• Overall DC rating: 𝑃𝑉𝑤 = 𝑛𝑃𝑉 𝐸𝑝𝑎𝑛𝑒𝑙
• Cost of PV panels: CPV ($/kW)
• Batteries
• Number of batteries: nb
• Capacity of each battery: Ebat
• Overall battery bank capacity: 𝐵𝑐𝑎𝑝 = 𝑛𝑏 𝐸𝑏𝑎𝑡
• Battery lifetime: Lb
• Cost of batteries: CBat ($/battery)
BS Power Consumption
•
•
•
•
•
NTRX : Number of transceivers
P0 : power consumption at no traffic.
Δp : constant for a BS
Pmax: power consumption at maximum traffic
K: Normalized traffic
1400
BS power consumption (W)
• LTE Base station ( ~ 0.6-1.4 kW)
• Traffic dependent power consumption [7]
1200
1000
800
600
400
0
0.1
0.2
0.3 0.4 0.5 0.6 0.7
Normalized traffic (K)
0.8
BS power consumption [7]
[7] Auer, Gunther, et al. "Cellular energy efficiency evaluation framework."Vehicular Technology Conference (VTC
Spring), 2011 IEEE 73rd. IEEE, 2011.
0.9
1
BS Traffic
Calls are modelled as a Poisson process with rate dependent on time of
the day and call duration exponentially distributed with mean of 2
minutes [8].
Normalized Traffic: 𝐾 𝑡 = 𝑁𝑡
𝑁𝑚𝑎𝑥
1300
0.9
Weekday
0.8
Base station power consumption (W)
0.7
Normalized traffic
Saturday
Sunday
1200
0.6
0.5
0.4
0.3
0.2
1100
1000
900
800
0.1
0
0
24
48
72
96
120
144
Hour
Typical Normalized traffic for a sample week
168
700
0
2
4
6
8
10
12
14
16
18
20
22
Hour of the day
Average BS power consumption on weekdays/weekend
[8] P. De Melo, et al., “Surprising patterns for the call duration distribution of mobile phone users,” Machine Learning and
Knowledge Discovery in Databases, pp. 354-369, Springer, 2010.
Solar Data for 10 years
Worst month for each year is selected
Daily solar power generation is computed and days are sorted
1-α %
Good weather days
Find minimum and maximum values of
power generated during a given hour
Uniformly divide the region between
minimum and maximum into 4 regions
Calculate the average value of the solar
power generated during each of hour
for each of the regions from empirical
data for good weather days
For each hour calculate the probability
of transition from each of the regions
to each of the region in the next hour
α%
Bad weather days
Modeling Solar Energy Resources
• State of system
• Daily transitions
• Hourly transitions
Battery Charge/discharge
Dynamics
• Battery charge level
• Outage:
Optimal System Dimensioning
• Cost:
• Number of batteries required over Trun years:
• Optimization problem:
Simulation Setup
• LTE base station system
• 10MHz Bandwidth
• 2 × 2 Multi Input Multi Output (MIMO)
• 3 sectors each with 2 transceivers (NT RX = 6)
• Lead acid batteries. (12 V, 200 Ah)
• Cost: $ 280/battery
• PV panels
• Cost: $ 1000/kW
• Locations considered
• Miami (USA)
• Mumbai (India)
• New Delhi (India)
Results: Battery Lifetime vs
Number of Batteries
Number of batteries vs battery battery lifetime for the three locations for PV wattage of 14 kW
Results: Battery Sizing
Number of batteries required for a given outage for the three locations for PV wattage= 14 kW
Results: PV Panel Sizing
PV wattage vs number of batteries required for various outage probabilities for Mumbai.
Deploying Small Cells
• Small cells:
• Reduced transmitter to mobile terminal distance
• Higher data rates
• Reduced transmit power requirement
• Increasing network capacity and spectral efficiency
Image: mwrf.com
Deploying Small Cells
• The main challenge: determine the number of BSs to deploy
and their locations
• Trade-off between outage probability and the number of BSs:
• Preferable to have more small cell BSs with less energy
harvesting (EH) resources rather than few BSs with larger EH
resources [13].
• Approach: obtain the required number of small cell BSs
keeping in mind only the desired outage probability, with other
parameters(like the macro BSs and their location) kept as fixed.
• The small cell locations are determined by factors such as the
spatial distribution of traffic hotspots and solar insolation.
Network Management and Resource
Allocation
• Resources available:
• Energy harvested by the BSs
• Transmission power level at which the BSs choose to operate
• Spectrum available for transmission
• Challenge: stochastic nature of the traffic intensity and solar
insolation
• Most widely explored problem: minimize the overall energy
consumption of the network through a variety of
mechanisms.
• Existing methodologies for resource allocation and
management consider both centralized and distributed
mechanisms
Load Balancing
• Objective: preventing the BSs from running out of energy or
being over-loaded
• Constraints and factors: available energy, the expected
harvested energy in the near future, and the traffic load at the
BSs
• Strategy: BSs cooperate by dynamically changing the area
covered and traffic handled by each BS, in accordance to the
energy available at each BS.
• Two main techniques for load balancing among BSs:
• Dynamic user association
• BS beacon power control
Energy and Delay Aware Downlink
Power Control and User Association
• Two key challenges while operating solar powered BSs:
• avoiding energy outages
• ensuing reliable quality of service (network latency).
• Problem formulation: minimize network latency given the
constrained energy availability at the BSs
• System model:
• Set of BSs B offering coverage to a geographical region R
• User locations are denoted by 𝑥 ∈ 𝑅
• Downlink transmit power vector of the BSs: P
• Power level of BS j: 𝑃 𝑗 ∈ 0, 𝜔, 2𝜔, ⋯ , 𝑃𝑚𝑎𝑥
• Traffic arrival at location x: Poisson point process with rate
𝜆 𝑥 and an average file size of 1 𝜇 𝑥 .
Energy and Delay Aware Downlink
Power Control and User Association
• Assuming BS j is serving the users at location x, the rate
offered by the BS to the users is
𝑐𝑗 𝑥 = 𝐵𝑊𝑗 log 2 1 + 𝑆𝐼𝑁𝑅𝑗 (𝑥)
• BS load 𝜌𝑗 (fraction of time BS j is busy)
𝜆 𝑥
𝜌𝑗 =
𝑢𝑗 𝑥 𝑑𝑥
𝑅 𝜇 𝑥 𝑐𝑗 (𝑥)
• Network latency indicator
𝐷=
𝑗∈𝐵
𝜌𝑗
1 − 𝜌𝑗
Energy and Delay Aware Downlink
Power Control and User Association
• Problem formulation
24
min
𝐸,𝑃,𝜌
𝐷𝑡
𝑡=1
subject to: 𝜌 ∈ 𝐹,
∀𝑖
24
𝑡=1 𝐿𝑡 𝑗 ≤ 𝐺 𝑗 , ∀𝑗 ∈ 𝐵
• Approach:
• Temporal energy allocation
• BS downlink power control
• User association reconfiguration
Energy and Delay Aware Downlink
Power Control and User Association
• Temporal Energy Provisioning
• Green energy is allocated to a given hour in proportion to
the BS power consumption during that hour.
• To avoid battery degradation, batteries are disconnected
from the BS if the battery level goes below a certain
threshold state of charge
Energy and Delay Aware Downlink
Power Control and User Association
• Transmission power control
• Non-convex optimization problem with respect to the BS
power levels
• Two factors affecting power levels for the BSs:
• Avoid energy deficiency (i.e. 𝜃 𝑗 > 1)
• Avoid traffic overload at a BS (i.e. 𝜌𝑗 > 1)
• Strain index
Ψ 𝑗 = max 0, 𝜃 𝑗 − 1 + max 0, 𝜌𝑗 − 1
Energy and Delay Aware Downlink
Power Control and User Association
Energy and Delay Aware Downlink
Power Control and User Association
Energy and Delay Aware Downlink
Power Control and User Association
• User association
• For any given set of BS power levels and green energy
allocation find the optimal user association policy
• BSs periodically measure their traffic loads and use it to
determine their coalition factors which are advertised to MTs.
• These coalition factors are used by the MTs to associate with
the BSs so as to minimize the objective function.
• The BSs and MTs update their association until convergence
• Achieved by a transformation of the original problem
min
𝜌
𝑗𝜖𝐵
subject to: 𝜌 ∈ 𝐹,
𝐷 𝜌𝑗 + 𝑒 𝜃
∀𝑖
𝜌𝑗
Energy and Delay Aware Downlink
Power Control and User Association
Energy and Delay Aware Downlink
Power Control and User Association
BS On/Off Strategies
• Cellular networks are provisioned for peak-hour traffic: may
be possible to turn off some BSs during off-peak hours
• Strategies:
• Determine the minimum number of BSs required to serve the
area, with the desired quality of coverage as a constraint.
• The switching decision may also take into account the energy
availability of the BSs.
• The problem of minimizing the overall energy consumption of
a set of BSs, subject to a limit on the load on any BS, is known
to be NP-complete.
• Heuristics: greedily assigning MTs to BSs with higher loads so
that the number of the BSs that have no associated MTs (and
thus can be turned off) is maximized.
Coordinated Multipoint (CoMP)
• BSs cooperate to jointly serve MTs
• Combat inter-cell interference in dense deployment scenarios
• Enhancing network efficiency and overall QoS for users.
• Implementation:
• Form clusters of transmit points for CoMP transmissions and
the allocation of resources to the transmit points.
• Extent of cooperation and which BSs should cooperate to serve
the MTs is decided based on the resources available at the BSs
• Objective: maximize the system performance or to minimize
the energy costs.
• Cluster formation and resource allocation problems are tightly
coupled and optimization problems to solve them jointly
generally lead to non-convex formulations.
Conclusions
• Solar powered BSs are a viable solution for providing
network coverage in areas without reliable grid power.
• Solar powered BSs are expected to play a greater role in the
future
• Growing awareness of environmental issues and the
push towards green engineering solutions
• Technological advances in battery and PV panels
• Open problems:
• Network management
• Resource allocation
References
[1]: Bogucka, Hanna, and Oliver Holland. ”Multi-Layer Approach to Future Green Mobile
Communications.” Intelligent Transportation Systems Magazine, IEEE 5.4 (2013): 28-37.
[2] Hasan, Ziaul, Hamidreza Boostanimehr, and Vijay K. Bhargava. "Green cellular
networks: A survey, some research issues and challenges." Communications Surveys &
Tutorials, IEEE 13.4 (2011): 524-540.
[3]: http://www.trai.gov.in/
[4]: http://www.gsma.com
[5]: http://solargis.info/doc/free-solar-radiation-maps
[6] http://www.topsunenergy.net/solar-telecom-system.htm
[7] Auer, Gunther, et al. "Cellular energy efficiency evaluation framework."Vehicular
Technology Conference (VTC Spring), 2011 IEEE 73rd. IEEE, 2011.
[8] P. De Melo, et al., “Surprising patterns for the call duration distribution of mobile phone
users,” Machine learning and knowledge discovery in databases, pp. 354-369, Springer,
2010.
[9] http://www.usbattery.com/
[10] http://quizlet.com/19225890/ree-554-ch-06-batteries-flash-cards/
[11] Vinay Chamola and Biplab Sikdar “Resource provisioning and Dimensioning for
Solar powered Cellular Base Stations,” Proc. IEEE GLOBECOM, Austin 2014
Back up slides
51
National University of Singapore
(NUS)
Key Components
Solar Panels
-Mono/ Polycrystalline silicon panels
-Efficiency: 16-18 %
-Cost: $ 1000/ kW
-Dimension: 1 kW: 5 m^2
-Life Duration: 25-30 years
Fig 8: A PV 250 W PV panel (~.25 m^2) [12]
[8] http://www.thesolarbiz.com/Yingli-245-Watt-Poly-Solar-Panel_2#gsc.tab=0
52
National University of Singapore
(NUS)
Factors effecting energy generated by
DC
Ratingpower
Solar
Geographical Location
Fig 9: Solar map of world [6]
Tilt
DC-AC loss factor
Fig 10: Tilted solar panel [9]
[9] http://www.volker-quaschning.de/articles/fundamentals1/index_e.php
53
National University of Singapore
(NUS)