Case Study #1: Management of a Smart Base Station Power System for Green LTE Cellular Network in Malaysia Case Study #2: Conceptual Framework On TVWS Telemedicine Network for Rural Area in Malaysia Rosdiadee Nordin, Universiti Kebangsaan Malaysia MALAYSIA

Case Study #1: Management of a Smart Base Station Power System for Green LTE

Cellular Network in Malaysia

Why Energy Efficiency in wireless Communication??

Number of subscribers increased

 

Mobile data traffic increased

Base stations increased

This increase has subsequently increased the overall energy consumption, operational costs and carbon footprint of cellular networks.

- 1,000 2,000 3,000 4,000 5,000 6,000 7,000

2005 2006 2007 2008 2009 2010 2011 2012 2013

Mobile-cellular subscriptions in (million)

0.9

1.6

2.8

0

1

2

3

2012 2013 2014

Mob

ile D

ata

Traf

fic

(Exa

byte

s per

Mon

th)

Mobile Data Traffic

Source: Cisco Report (2013)

Source: (ITU) statistics database

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects.

51%

20%

15%

14%

Mobile Sector Fixed Narrowband Telecom Devices Fixed Broadband

Fig. 4. Forecast Carbon Footprint Contribution by Telecom for 2020.

Source: L. Suarez et al. (2012)

179 MtCO2 70 MtCO2

51 MtCO2

49 MtCO2

Total= 349 MtCO2

0 50 100 150 200 250

2007

2008

2009

2010

2011

2012

Electricity Consumption (TWh/yr)

Fig. 3. Worldwide electricity consumption of mobile Telecommunication networks

Source: S. Lambert et al. (2012)

Total= 260 (TWh/2012)

Energy consumption in cellular networks taking into increase and will be increased more in the future.

Cont.

Where is Energy Spent?

BSs are densely deployed and overlapping, further waste of energy

Each BS consumes approximately 25 MWh per year

Fig. 6. Redundancy in the cellular coverage.

E. Oh, B. Krishnamachari, X. Liu, and Z. Niu, “Towards Dynamic Energy-Efficient Operation of Cellular Network Infrastructure”, IEEE Commun. Mag., June 2011

T. Chen, Y. Yang, H. Zhang, and H. Kim, "Network Energy Saving Technologies for Green Wireless Access Networks", IEEE Commun. Mag., Octaber 2011.

Fig. 5. Energy consumption composition of a mobile operator.

For an cellular operators, to expand and deliver their services to potential new customers, they must solve the problem of electricity supply in a reliable and cost-effective way.

Cont…

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STEP 1: Towards Energy-Efficient in cellular networks by reducing the number and size of active macro-cells according to traffic load conditions.

Ø The power consumption grows proportionally with the number of cells  

Ø In this work, the decision to determine which cells remain active depends on two considerations:

1.  The ease with which radio coverage can be provided to neighbouring cells to guarantee service.

2.  The largest possible number of neighbouring cells should be switched off to significantly reduce the energy required.

The optimal cells that satisfy these conditions are located in the middle of a cluster (and are called master cells) and can easily provide coverage to 6 neighbouring cells that will be switched off later.

*  

*  *  

*  

*  *  

*  

* Master Cells (7 cells): can’t be switch-off - work 24 hours

Fig. 7. A cellular network in an urban scenario. [Blue cells represent a normal case with Rorg =750 m; black and green cells represent low traffic

with R= 2Rorg= 1.5 km; and red cells represent idle traffic with R= 4Rorg= 3 km]

Cont…

Time Period Traffic Category Total

12:00 AM - 2:00 AM Low Traffic 2

2:00 AM - 6:00 AM Idle Traffic 4

6:00 AM - 8:00 AM Low Traffic 2

8:00 AM - 10:00 AM Low Traffic 2

10:00 AM - 9:00 PM High Traffic 11

9:00 PM - 12:00 AM Low Traffic 3

1.00 << λ

2.01.0 <≤ λ

4.02.0 ≤≤ λ

14.0 ≤< λ

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Fig. 8. Categories and period of time for daily traffic

2.01.0 <≤ λ

4.02.0 ≤≤ λ

The power consumed at the BSs is different, that because some of cells works 11hrs during high mobile traffic only, other work 9 hrs at low traffic, and 4 hrs at idle traffic.

Evaluate a total power saving  

Evaluate a total cost saving  

Evaluate a Co2 reduction  

Evaluate the impact of transmitted power, MCS, and BW on the EE of LTE macro BS  

Evaluate the impact of cell size on data rate  

Evaluate the impact of SINR on RSRP &MCS  

Evaluate the impact of SINR on cell size  

Evaluate the impact of transmission power on cell size and coverage.  

Cells switch-off (spatial diversity)  

The results show that energy savings of up to 48% can be obtained at 90% cell coverage for low and idle traffic cases.

Fig. 9. Malaysia geographical map.

Ø  Malaysia lies entirely within the equatorial region.

Ø  Daily average global solar irradiation of approximately (4.21 - 5.56) kWh/m2/day, and the average temperature per day ranges from 33°C during the day to 23°C at night.

STEP 2: Develop an integration between cells switch-off approach (spatial diversity), and renewable resource energy (solar).

Country overview

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Cle

arne

ss In

dex

Dai

ly R

adia

tion

(kW

h\m

2\da

y)

Daily Radiation Clearness Index

Ø  Renewable energy systems have the following advantages:

ü  Protection of the environment as there is no emission of CO and green house gases,

ü  Cost-effectiveness, ü Diversity of security power sources, ü  Rapid deployment, modular and easy to install, ü  Resources are abundant, free and inexhaustible.

The specific needs in power supply for BS such as cost effectiveness, efficiency, sustainability, reliability and positive impact on the environment can be met with the technological advances in renewable energy.  

Ø  More BSs are located in metropolitan areas because of the high population. All of these BSs are powered by the electric grid.

Ø  Hybrid of renewable energy resources and electric grid[Urban scenario]. Ø  Optimum criteria: economic, technical and environmental feasibility

analysis was performed through optimization software, Hybrid Optimization Model for Electric Renewables (HOMER).

Cont…

Fig. 10. System model of an adaptive power management scheme for a LTE-based BS powered by a smart grid

Ø  HOMER (http://homerenergy.com) - an optimization software package simulates various renewable energy sources system configurations and scales these configurations on the basis of the net present cost (NPC)

Ø  The NPC represents the life cycle cost of the system.

0 200 400 600 800

1000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Dem

and

(W)

Hour

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

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Case 1: Power demand of BSs operate at high traffic load only

Case 2: Power demand of BSs operate at high and low traffic loads

Case 3: Power demand of BSs operate at high, low, and idle traffic loads

The power consumed at the BSs is different as well as the period time. The figure that has given provides a vision about the power demand and the period time for each case.

Low Traffic

High Traffic

High Traffic

Low Traffic

Idle Traffic

Optimisation criteria Unit Case 1 Case 2 Case 3 Period time [Hours] 11 hrs 20 hrs 24 hrs Daily demand [kW/day] 10.62 16.13 19.13 Energy Model

PV [kW] 0.8 1.2 1.2 Battery [Unit] 5 5 5 Converter [kW] 1.2 1.0 1.0 Grid [kW] 0.8 0.8 0.8

Economical IC [$] 5,780 7,200 7,200 Operating [$/yr] 565 755 892 NPC [$] 13.01 16.85 18.61 COE [$/kWh] 0.26 0.22 0.21

Environmental

CO2 [Kg/yr] 1,673 2,510 3,070 SO2 [Kg/yr] 7.25 10.9 13.3 NO [Kg/yr] 3.55 5.32 6.51

Table 1. Optimisation criteria for economic, technical and environmental aspects

Ø Three categories of power demand load, Therefore we have a three optimal design of hybrid power system as shown in Table 1

Fig. 12. Monthly energy contribution of a solar system

Fig. 13. Energy purchased monthly for different cases

63% 64% 65%

37% 36% 35%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

100%

Case 1 Case 2 Case 3

Ene

rgy

cont

ribu

tion

(%)

Electric Grid PV

1,528 kWh/yr

2,646 kWh/yr

2,279 kWh/yr

3,972 kWh/yr

2,564 kWh/yr  

4,858 kWh/yr  

Fig. 14 Annual energy contributions from different sources

0% 5%

10% 15% 20% 25% 30% 35% 40%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mon

thly

OPE

X sa

ving

s (%

)

Month

Case 1 Case 2 Case 3

The simulation results show that the hybrid power system of the PV/electric grid can save up to 32% of the annual operational expenditure (OPEX).

Fig. 15. Monthly OPEX savings

0%

20%

40%

60%

80%

100%

Case 1 Case 2 Case 3

Cos

t per

cent

age

of N

PC (%

)

Grid PV Battery Converter

Fig. 16. Cash flow summary for hybrid PV/electric grid system

NPC = TACCRF

TAC = total annualised cost ($) CRF = capital recovery factor

CONCLUDING REMARKS It is in favor of both the network operators and the society to swiftly

address these challenges to minimize the environmental and financial

impact of such a fast growing and widely adopted technology.  

Case Study #2: Conceptual

Framework on TVWS Telemedicine Network for Rural

Areas in Malaysia

Introduction

•  TVWS is a technology proposed to add value in the wireless ecosystem.

•  Hence, it has been suggested as an enabling technology to maximize wireless utility in rural broadband services, emergency services and lately in transportation industry.

•  We are proposing using TVWS as a backbone for Medical Wireless Body Area Sensor Networks (MWBASN) for rural and semi-urban areas.

Problem Statement

“a healthy nation is a wealthy nation” ~ massive investment in the health sector. However, some problems continue to exist: •  Health services are grossly inadequate in some parts of the

country, particularly in East Malaysia and in the East Coast States of West Peninsular Malaysia.

•  Delays in constructing, equipping of medical facilities due to budget constraints.

Problem Statement cont…

•  Shortfall in number of manpower in health sector, both professional staff and technicians.

•  Decline in death rate with the resultant high proportion of aged society.

•  High propensity of relocating from urban to rural/semi-urban environment, especially the retirees

Research Objectives

Our goals are to deploy TVWS to enhance: •  Availability - TVWS as a backbone wireless media for making

healthcare available in rural and sparsely inhabited areas.

•  Affordability - The focus group are the rural areas, cost minimization in-terms of wireless access technology infrastructural roll-out is ensured.

•  Accessibility - Studies have shown that there are ample amount of unused spectrum in the rural areas and hence, end-to-end service accessibility both in real and non real time is assured.

Target Audience

Case 1 This is designed for the elderly people suffering from unpredictable diseases like high blood pressure (BP), heart disease, organ failures which can occur intermittently. As well as those living far away from medical centers.

Case 2 Our target focus for rural areas with limited trained medical experts. These group of patients are mobile and can be physically present at health centers.

Methodology  

Inter-Base Station Coexistence and Downlink Resource Allocation in TVWS Assisted by Grey Prediction Algorithm

Research Objectives

•  Introduction of financial modeling (grey series) to predict PU occupancy statistics in Cognitive Radio OFDMA Networks (CRON)

•  Novel primal algorithm for resource allocation in CRON based on skipping spectrum sensing and utilizing that time slot to extract channel state information

•  Mathematical formulation on overhead cost in non-implementation of cooperative joint resource allocation using sub-space techniques