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

Transcription

1 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

2 Case Study #1: Management of a Smart Base Station Power System for Green LTE Cellular Network in Malaysia

3 Why Energy Efficiency in wireless Communication?? Number of subscribers increased - 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Mobile Data Traffic (Exabytes per Month) Mobile-cellular subscriptions in (million) Source: (ITU) statistics database 0.9 Mobile Data Traffic Mobile data traffic increased Base stations increased Source: Cisco Report (2013) This increase has subsequently increased the overall energy consumption, operational costs and carbon footprint of cellular networks.

4 Cont Energy consumption in cellular networks taking into increase and will be increased more in the future Electricity Consumption (TWh/yr) Total= 260 (TWh/2012) Fig. 3. Worldwide electricity consumption of mobile Telecommunication networks Source: S. Lambert et al. (2012) 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 MtCO2 49 MtCO2 70 MtCO2 15% 14% 20% Mobile Sector Telecom Devices Total= 349 MtCO2 51% Fixed Narrowband Fixed Broadband Fig. 4. Forecast Carbon Footprint Contribution by Telecom for Source: L. Suarez et al. (2012) 179 MtCO2

5 Where is Energy Spent? Fig. 5. Energy consumption composition of a mobile operator. Each BS consumes approximately 25 MWh per year Fig. 6. Redundancy in the cellular coverage. BSs are densely deployed and overlapping, further waste of energy 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. T. Chen, Y. Yang, H. Zhang, and H. Kim, "Network Energy Saving Technologies for Green Wireless Access Networks", IEEE Commun. Mag., Octaber E. Oh, B. Krishnamachari, X. Liu, and Z. Niu, Towards Dynamic Energy-Efficient Operation of Cellular Network Infrastructure, IEEE Commun. Mag., June 2011

6 Cont Ø The power consumption grows proportionally with the number of cells P tot = N cells η P tx ( 1 σ ) PA feed ( )( )( ) 1 σ 1 σ 1 σ DC + MS P RF + P BB Cool STEP 1: Towards Energy-Efficient in cellular networks by reducing the number and size of active macro-cells according to traffic load conditions. 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.

7 Cont * * * * * * * Fig. 7. A cellular network in an urban scenario. [Blue cells represent a normal case with R org =750 m; black and green cells represent low traffic with R= 2R org = 1.5 km; and red cells represent idle traffic with R= 4R org = 3 km] * Master Cells (7 cells): can t be switch-off - work 24 hours

8 1 0.9 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 Time Period Traffic Category Total 12:00 AM - 2:00 AM Low Traffic 0.1 λ < < λ < 0.1 2:00 AM - 6:00 AM Idle Traffic λ < 0.2 6:00 AM - 8:00 AM Low Traffic λ 0.4 8:00 AM - 10:00 AM Low Traffic < λ 1 10:00 AM - 9:00 PM High Traffic λ 0.4 9:00 PM - 12:00 AM Low Traffic 3 Fig. 8. Categories and period of time for daily traffic

9 Cells switch-off (spatial diversity) Evaluate the impact of transmission power on cell size and coverage. Evaluate the impact of SINR on cell size Evaluate the impact of SINR on RSRP &MCS Evaluate the impact of cell size on data rate Evaluate the impact of transmitted power, MCS, and BW on the EE of LTE macro BS Evaluate a total power saving Evaluate a total cost saving Evaluate a Co 2 reduction The results show that energy savings of up to 48% can be obtained at 90% cell coverage for low and idle traffic cases.

10 Country overview Ø Malaysia lies entirely within the equatorial region. Ø Daily average global solar irradiation of approximately ( ) kwh/m2/day, and the average temperature per day ranges from 33 C during the day to 23 C at night. Fig. 9. Malaysia geographical map. Daily Radiation (kwh\m2\day) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Daily Radiation Clearness Index Clearness Index STEP 2: Develop an integration between cells switch-off approach (spatial diversity), and renewable resource energy (solar).

11 Cont 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 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. 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).

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

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17 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. Demand (W) Hour 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 Low Traffic High Traffic Low Traffic Idle Traffic High Traffic

18 Ø Three categories of power demand load, Therefore we have a three optimal design of hybrid power system as shown in Table 1 Table 1. Optimisation criteria for economic, technical and environmental aspects Optimisation criteria Unit Case 1 Case 2 Case 3 Period time [Hours] 11 hrs 20 hrs 24 hrs Daily demand [kw/day] Energy Model PV [kw] Battery [Unit] Converter [kw] Grid [kw] Economical IC [$] 5,780 7,200 7,200 Operating [$/yr] NPC [$] COE [$/kwh] Environment al CO 2 [Kg/yr] 1,673 2,510 3,070 SO 2 [Kg/yr] NO [Kg/yr]

19 Fig. 12. Monthly energy contribution of a solar system

20 Fig. 13. Energy purchased monthly for different cases

21 Energy contribution (%) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 37% 1,528 kwh/yr 36% 2,279 kwh/yr 35% 3,972 kwh/yr 63% 2,646 kwh/yr 64% 65% Case 1 Case 2 Case 3 Electric Grid PV 2,564 kwh/yr 4,858 kwh/yr Fig. 14 Annual energy contributions from different sources

22 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). Monthly OPEX savings (%) 40% 35% 30% 25% 20% 15% 10% 5% 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Case 1 Case 2 Case 3 Fig. 15. Monthly OPEX savings

23 Cost percentage of NPC (%) 100% 80% 60% 40% 20% 0% Case 1 Case 2 Case 3 Grid PV Battery Converter Fig. 16. Cash flow summary for hybrid PV/electric grid system NPC = TAC CRF TAC = total annualised cost ($) CRF = capital recovery factor

24 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.

25 Case Study #2: Conceptual Framework on TVWS Telemedicine Network for Rural Areas in Malaysia

26 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.

27 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.

28 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

29 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.

30 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.

31 Methodology

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

33 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 nonimplementation of cooperative joint resource allocation using sub-space techniques