EARTH Summer School Oulu, 29 June 2011

Transcription

2 FP7 Project EARTH 15 Partners from 10 European countries Funding Scheme: IP Total Cost: 14.8 m EC Contribution: 9.5 m Duration: January June 2012 Project Coordinator: Dietrich Zeller, Alcatel- Lucent Technical Manager: Ylva Jading, Ericsson 2

3 Motivation of EARTH Today ICT CO 2 contribution equals global air traffic contribution. Wireless communication ¼15% of ICT. Mobile Broadband traffic is exploding and entering new markets Densification of mobile networks /additional roll-out High costs and long lead time for power infrastructure Higher ratio of off-grid and weak grid BS Increasing network operational costs due to increasing energy costs 3

4 EARTH Outline EARTH Energy Efficiency Analysis Life Cycle Analyis EARTH Carbon Footprint Model Energy Efficiency Evaluation Framework (E 3 F) Case Study: Performance Evaluation of a 3GPP LTE Rel-8 Network Fundamental Challenges Application of EARTH E3F to Elaborate Fundamental Trade-offs Green Radio Key Enablers Scaling Network Power Consumption with System Load Energy Efficiency Trade-offs Power Control vs Discontinuous Transmission (DTx) 4

6 Carbon Footprint Model Objective: Assess the impact of future wireless systems on the global carbon footprint Methodology: Historical trends are the basis for a forecast on the future growth of wireless communications until 2020 Assess power consumption per site o Consider various RAN technologies (GSM, WCDMA, LTE) Anticipate deployment trends Number of required sites (for mix of RAN technologies) Total power consumption of the network Carbon footprint * A. Fehske, et al., The Global Carbon Foot-print of Mobile Communications The Ecological and Economic Perspective, IEEE Communications Magazine, to appear

7 Key Drivers in Grows in Wireless Communications Moore s Law : Processing power and data storage double every 18 month Requires powerful information and communication systems that are able to transport the created information in acceptable time Carbon footprint of the mobile communications industry may increase substantially 7

8 MWh / RBS site Carbon footprint forecast Base Station Sites 16 RBS operation (average) RBS "sites" taken out of service RBS op. (average, no improv.) RBS operation (new) RBS op. (new, no improv.) kw 1.4 kw (no improvements after 2010) 1.1 kwwhole site (incl. cooling, power, transmission etc.) 1.0 kw (no improvements after 2010) RBS basic unit 0.9 kw Capture product mix and deployment factors and the current trends gets extrapolated Capacity also increases per site (/standard) 2 0 RBSs taken out of service or swaped will be important

9 Mtonnes CO 2 e Cellular Networks Global Carbon Footprint % 34% 30% 39% : 0.3% of global direct CO 2 0.2% of total global CO 2 e 1.6% of global economy [GSMA] Worst case: No improvements 19% 2020: Continous improvement of 8% per year Data centers & data transport RAN operation focus of EARTH Mobile devices operation and manufacturing 0.6% of global direct CO 2 0.4% of total global CO 2 e Operator activities RAN construction 9

10 Mtonnes CO 2 e Radio Access Network (RAN) Global Carbon Footprint Diesel Area 2 Non-diesel 100 xxx Worst case No improvements New base stations (-8%/year) 40 Installed base up to Installed base of all diesel sites will be modernized with hybrid operation until New diesel consumption Installed base diesel consumption 0.14% global direct CO % 0.09% total global CO 2 e 0.12% 10

11 Mtonnes CO 2 e Radio Access Network (RAN) Global Carbon Footprint Diesel Area 2 Area 3 Non-diesel xxx Area 6 Impact of EARTH Worst caseon newly installed base stations No improvements New base stations (incl. EARTH) 40 savings Installed base up to Installed base diesel consumption New diesel consumption Alternative energy modules (solar, etc.) Green Mobile Communications 1000x total traffic 3x base station sites 1.5x RAN energy consumption 11

12 Mtonnes CO 2 e Radio Access Network (RAN) Global Carbon Footprint Diesel Area 2 Area 3 Non-diesel xxx Area 6 Impact of EARTH Worst case on all (new & installed) base stations No improvements New base stations (incl. EARTH) 40 savings Installed base up to Installed base diesel consumption New diesel consumption Alternative energy modules (solar, etc.) Energy savings on already installed base stations (e.g. through software updates) may yield further CO 2 e reduction Potential for reduction in carbon footprint 12

13 Carbon Footprint Analysis Major findings: Served data rates have grown by about a factor of 1000 over the last 15 years The carbon footprint of mobile communications has only increased by a factor of 3 in this period Potential impact of EARTH on reducing the carbon footprint : New sites that consume only 50% energy (EARTH target): No further increase in the RAN carbon footprint EARTH innovation implemented in installed sites: Potential for significant carbon footprint reduction To further reduce RAN carbon footprint, diesel generators for off-grid sites may be replaced by alternative energy modules Indirect EARTH impact 13

14 Cell-edge rate EARTH Energy Efficiency Evaluation Framework (E 3 F) Reference System & Scenarios E 3 F Aggregation to Global Scale P in Base Station P out Power Model Metrics 1

15 Reference System & Scenarios Reference System & Scenarios E 3 F Define system parameters and scenarios so to ensure comparable results among project partners Based on LTE o 3GPP and ITU compliant evaluation methods Dynamic system simulations for different deployments o Dense urban, urban, suburban and rural Basis for traditional (small-scale, short-term) System Level Simulations 2

16 Reference Scenarios Baseline is hexagonal cell structure with 19 sites (57 sectors) Macro-cells are complemented by low power nodes Relays / repeaters Micro-, pico-, femto-cells Various types of base station cooperation Multiple radio access technologies (multi-rat) macro-cell relay micro/pico-cell backhaul femto-cell 3

18 The EARTH Power Model Realistic Power Model E 3 F P in Base Station P out Power Model 5

19 Base Station (BS) Power Model P in Base Station P out Power model Maps RF output power P out to the total BS supply power P in Benefits: Key enabler for holistic energy efficiency analysis Allows to assess improvements on node and network level with realistic assumptions on the BS power consumption Improvements on components (e.g. power amplifier) can be quantified on system level 6

20 Cooling Mains Supply DC-DC Base Station (BS) Power Model P in RF PA Base Station BB RF PA P out EARTH Power model Maps RF output power P out to the total BS supply power P in Power breakdown of different components for various BS types (macro, micro, pico, femto BS) Includes power amplifier (PA), RF, base band (BB) processing, DC-DC unit, cooling, mains supply Includes (basic) BS sleep (standby mode) 7

21 BS Power Consumption [W] BS Power Consumption [W] BS Power Consumption [W] BS Power Consumption [W] Power Model for Various BS Types MacroCell - 46dBm Max RF Output Power [%] 15 PicoCell - 21dBm Max RF Output Power [%] MS CO DC BB RF PA MS CO DC BB RF PA 150 MicroCell - 38dBm Max RF Output Power [%] FemtoCell - 17dBm Max RF Output Power [%] MS CO DC BB RF PA MS CO DC BB RF PA Power model PA dominant for macro BS, while BB dominates for small BS types Modest load dependency for macro BS, but to a much lesser extend for small BS types Poor efficiency at low loads Power Savings through sleep mode at zero load Linear approximation of power model is justified MS Main Supply CO Cooling BB Baseband RF Radio Frequency incl tranceiver PA Power Amplifier 8

22 BS Power Consumption [W] BS Power Consumption [W] BS Power Consumption [W] BS Power Consumption [W] Power Model for Various BS Types Technology Scaling MacroCell - 46dBm Max RF Output Power [%] PicoCell - 21dBm Max % RF Output Power [%] MS CO DC BB RF PA MS CO DC BB RF PA MicroCell - 38dBm Max RF Output Power [%] FemtoCell - 17dBm Max % RF Output Power [%] MS CO DC BB RF PA MS CO DC BB RF PA Power model EARTH reference 2010: o Base stations available on the market in 2010 EARTH reference 2012: o o BSs with components available 2010 It takes about 2 years until these components are integrated into BSs Significant improvements through enhanced PAs and silicon technology scaling MS Main Supply CO Cooling BB Baseband RF Radio Frequency incl tranceiver PA Power Amplifier 9

23 Linear Power Model The power cosumption breakdown suggests that linear approximation of power model is justified* Supply Power N TRX : No. of transceiver chains P in P 0 Benefits: Key enabler for holistic energy efficiency analysis o o P out Allows to assess improvements on node and network level with realistic assumptions on the BS power consumption RF Power P max Improvements on components (e.g. power amplifier) can be quantified on system level P s P max * G. Auer, et al., Cellular Energy Efficiency Evaluation Framework, GreeNet Workshop, Budpest, Hungary, May

25 Cell-edge rate EARTH Energy Efficiency Metrics Recommended use of Energy Consumption Metrics [J/bit] reflecting traditional capacity and data rate improvements o Increasing peak data rates quickly decreases [J/bit] [W/m 2 ] reflecting crucial coverage and service area aspects o High fixed cost for coverage makes [W/m 2 ] very important Metric communicated to ETSI TC EE Performance Metrics for Concept Evaluations Performance metrics follow 3GPP recommendations [TR36.814] Served cell throughput Mean, 5 th, 10 th, and 50 th percentile user throughput Cell-edge throughput E 3 F Metrics 12

26 Energy Effinciency Metric vs Energy Consumption (Cost) Metric EE Energy Efficiency (EE) Metric [bit/joule] EE EE Power At low powers, small improvements in EE result in a large increase of the metric At high output powers, large improvements in EE result in a comparably small increase of the metric May lead to false conclusions 13

27 Energy Effinciency Metric vs Energy Consumption (Cost) Metric Energy Consumption Metric [Joule/bit] ECI ECI Power Linear increase of energy consumption index with increasing power P Energy Consumption (Cost) Metric is selected for EARTH studies 14

29 Aggregation to Global Scale Country wide 24 hour Traffic & Deployment Model E 3 F Aggregation to Global Scale 16

30 From Small-Scale to Large-Scale Small-scale, shortterm system level simulations provide snapshots of the respective deployments Urban Dense Urban Aggregation to large-scale by weighted summing Based on Geographical data determine deployment mix and population densities Rural Suburban 17

31 Large-Scale Deployment Model Determine number of active subscribers for a given deployment High correlation of deployment densities within Europe Exception are nordic countries and Russia (excluded here) Population Areas in Europe Population Densities in Europe [citizens/km 2 ] Rural Sparsely populated Suburban Urban Dense urban 18

32 Large-Scale Deployment Model Determine number of active subscribers for a given deployment High correlation of deployment densities within Europe Exception are nordic countries and Russia (excluded here) Population Areas in Europe Population Densities in Europe [citizens/km 2 ] Rural Sparsely populated Rural Suburban Sparsely populated Urban Suburban Urban Dense urban Dense urban Sparsely populated areas are only covered by 2G networks not considered in evaluations 19

33 Aggregation to Global Scale Methodology 1. Define the average traffic demand per active subscriber for different terminal types 2. Define relevant scenarios of terminal/subscriber mixes 3. Daily traffic profile determines traffic volume per subscriber 4. Determine the number of active users per unit area for the considered terminal/subscriber mixes 5. Given the population densities for the respective deployments, the scenario specific network traffic per unit area in [Mbps/km 2 ] can be derived; 6. the total network traffic of an average European country is obtained by weighted summing of the scenario specific network traffic 20

34 Long-Term Large-scale Traffic Models Average number of subscribers s k [%] of the entire population for»2015 anticipated traffic demands are based on UMTS forum forecast* 3 terminal types k are considered: PC: s PC =20% Tablet: s tab =5% Smartphone: s fon =50% Reference PC of 2010: s PC =20% % *UMTS Forum, Mobile Traffic Forecasts , Report no. 44, May

35 Long-Term Large-scale Traffic Models Average traffic demand per active subscriber for»2015 Usage of mobile services strongly depends on operator policies anticipated traffic demands are based on UMTS forum forecast* 3 terminal types are considered: PC data demand: [0.25, 2] Mbit/s 2Mbit/s provides HDTV [8,64] GB/month/subscriber Tablet demands 1/2 of PC: [0.125, 1] Mbit/s Smartphone: demands 1/8 of PC: [31.25,250] kbit/s Reference PC of 2010: [31.25,125] kbit/s High-end 3G traffic in 2010 [1,4] GB/month/subscriber PC Tablet Smartphone PC (2010) day month Tarffic demand [MB] *UMTS Forum, Mobile Traffic Forecasts , Report no. 44, May

36 Long-Term Large-scale Traffic Models Assumptions for terminal & subscriber mixes in»2015 assumptions are based on UMTS forum forecast* Heavy users request an average data rate of: Heavy PC: r h PC=2Mbit/s Heavy Tablet: r h tab= 1Mbit/s Heavy Smartphone: r h fon= 0.25Mbit/s PC day month Ordinary users request 1/4 the rate of heavy users: Ordinary PC: r o PC=500kbit/s Ordinary Tablet: r o tab=250kbit/s Ordinary Smartphone: r o fon= 62.5kbit/s Tablet Smartphone PC (2010) day month Reference PC of 2010: r rpc =62.5 kbit/s Tarffic demand [MB] *UMTS Forum, Mobile Traffic Forecasts , Report no. 44, May

37 Traffic profile Average Daily Data Traffic Profile Actual traffic demand: Peak traffic demands are scaled by the daily traffic profile The EARTH daily traffic profile is extracted from operator data, and represents an exemplary European country Daily traffic profile determines the actual traffic demand for the considered terminal & subscriber mixes for a given deployment 24

38 Traffic profile Long-Term Large-scale Traffic Models Total Traffic demand per operator per unit area p d : population density for deployment d N op : number of operators Traffic demand per active subscriber r k PC day month Number of active subscribers (t) Tablet Smartphone PC (2010) MB

39 Peak Traffic [Mbit/s/km 2 ] Long-Term Large-scale Traffic Models Average Peak traffic demand R d per unit area is determined for: Area served by N op =3 operators Various deployments Various terminal & subscriber mixes low mid high Dense urban Urban Suburban Rural 20% of subscribers are heavy users* 50% of subscribers are heavy users all subscribers are heavy users Mbit/s/km 2 Deployment Scenario * most relevant according to UMTS Forum, Mobile Traffic Forecasts , Report no. 44, May

40 Global Energy Efficiency Evaluation Framework (E 3 F) E 3 F Block Diagram Global Metric (long term, large scale) Metric (short term, scenario specific) Large scale area & Long term traffic load Smallscale, shortterm system level evaluations BS P out P in power model system performance mobile Aggregation to global scale channel 27

41 Case Study: Energy Efficiency of LTE Short-term, small-scale evaluations: Macro-cellular network with regular hexagonal cell layout is implemented 19 sites, each with 3 sectors Inter-site distance (ISD) Dense urban and urban environments: 500 m Suburban and rural areas: 1732 m The users are uniformly distributed, with user densities corresponding to the respective deployment scenarios The simulation parameters are taken from 3GPP specifications [TR36.814], [TR25.814] 10 MHz bandwidth 2.1 GHz carrier frequency 2 2 MIMO transmission with adaptive rank adaption 28

42 Energy per bit [Joule/bit] Short-term, small-scale evaluations Energy efficiency [Joule/bit] severely degrades at low loads Suburban/rural scenarios are more energy efficient at low loads Larger cell size is more energy efficient However, high system throughputs may only be achieved in urban scenarios Small cell size boosts capacity 29

43 Power Consumption per Unit Area (P/A) Short-term, small-scale evaluations Only a modest dependency of the system throughput on the network power consumption Due to macro BS power model, which exhibits poor efficiency at low loads Urban scenario achieves more than 10 times higher system throughput than suburban/rural scenarios due to higher site density (urban ISD=500m, compared to suburban/rural ISD=1732m) However, suburban/rural scenarios consume more than 10 times less power 30

44 Power Consumption per Unit Area (P/A) Short-term, small-scale evaluations Only a modest dependency of the system throughput on the network power consumption Due to macro BS power model, which exhibits poor efficiency at low loads Long-term, large-scale evaluations: o o average power per area unit is 0.6 kw/km 2, independent of data usage scenario System operates a very low loads: on average less than 10 % of resources are utilized, even for the high data usage scenario 31

45 Compare E 3 F with Carbon Footprint Model kwh / year / subscription Assuming that all deployments are served by 3 operators for the E 3 F leads to grossly overestimated power consumption The network assumed for the E 3 F appears to be over provisioned The following measures allow to scale the E 3 F estimates with the carbon footprint model: Base station sharing: 3 times less base stations required Inter-site distance (ISD): the E 3 F uses 3GPP assumptions; increasing the LTE System, 100% traffic share Global statistics & trends ISD also results in a significant reduction in power consumption LTE System 30% traffic share Energy efficiency improvements in future base stations ,1 16,3 16,4 14,4 13,2 13,0 E 3 F: 3 networks E 3 F: 1 network Carbon footprint model Traffic Scenario 32

46 Fraction of time in % What about Real Networks? For comparison: measurements of system load for a real 3G network* Traffic in Mbps HSDPA traffic data more than 300 cells in a big European city measured for several weeks. Median traffic: 15 kbps 83% of samples below 1 Mbps Fraction of cells in % (sorted by throughput) * P. Frenger, P. Moberg, Y. Jading, and I. Godor, Reducing energy consumption in LTE with cell DTX, VTC-Spring

47 Energy Efficiency Analysis Main Achievements Carbon footprint model Quantify the impact of future wireless network on the carbon footprint until the year 2020 Assess the potential impact of energy savings provided by EARTH The EARTH Energy Efficiency Evaluation Framework (E 3 F) The EARTH E 3 F facilitates a holistic performance analysis at network level, comprising: o Realistic base station power model for various BS types o Long-term traffic model & large-scale deployment model that facilitate aggregation to global scale o Energy efficiency metrics that complement existing performance metrics Evaluate the energy consumption of a LTE Rel-8 cellular network Identify the fundamental challenges for energy savings Vast potential at low traffic loads 34

49 Fundamental Challenges Lessons Learned from EARTH Energy Efficiency Analysis High loads sustainable growth challenge Squeeze more bits through an already busy network without an increase in energy consumption Improve energy efficiency [Joule/bit] Already thoroughly investigated Improvement ¼300 times over the last 15 years Challenge to keep up with this trend in a subject well understood Low loads scale power consumption to network load On average more than 90% of radio resources are idle Yet only modest reduction in energy consumption of ¼20% with respect to maximum load Energy efficiency severely degrades at low loads Vast potential for energy savings in particular at low loads 2

50 User bitrate percentiles ( ) Application of EARTH E 3 F MIMO Energy Efficiency in LTE User bitrate percentiles ( ) Compare energy efficiency of LTE Rel-8 with different number of Tx antennas Number of Rx antennas is kept constant MIMO benefits from enhanced directivity & spatial multiplexing In particular cell-centre users benefit from MIMO X X X Urban Scenario, ISD=500m System throughput [Mbps/km 2 ] Suburban/Rural Scenario, ISD=1730m System throughput [Mbps/km 2 ] M TX = 2 M TX = 1 ISD: intersite distance 3

51 Supply Power Application of EARTH E 3 F MIMO Energy Efficiency in LTE X X X 6 MIMO leads to higher power consumption due to additional radio unit Trade-off between spectral efficiency and energy efficiency Results suggest to only activate additional Tx antennas on demand Note: MIMO may greatly benefit from improved hardware and sleep modes Urban Scenario, ISD=500m 776 W 410 W 0 Power Model Suburban/Rural Scenario, ISD=1730m 1340 W 694 W 120 W RF Power P/A [kw/km 2 ] M TX = 2 M TX = System throughput [Mbps/km 2 ] P/A [kw/km 2 ] M TX = 2 M TX = System throughput [Mbps/km 2 ] ISD: intersite distance 4

52 Supply Power [W] Application of EARTH E 3 F Finding the Optimal Cell Size Small cells: less power per site is needed to transport data Large cells: less sites consume less offset power Coverage and capacity conditions Results: Min bit-rate = 2 Mbps Min capacity 20 Mbps/km 2 Limits the feasible range of ISDs The offset power at zero load drives the power consumption A minimum appears only when the slope of the power model is large with respect to the zero load offset 3000 Power Model BS load [%] Power per Area [W] Inter-site distance (ISD) [m] 100%

53 Application of EARTH E 3 F Energy Efficiency of HetNets Total BS power in W Geometry Macro-cells: hexagonal cell layout with 3 sectors per site Micro-cells: N mic nodes per site randomly placed Users are uniformly distributed Urban scenario: inter-site distance ISD=500m BS operation: 2x2 MIMO, 10MHz bandwidth EARTH 2010 reference power model Snapshot of simulated SINR distributions Macro only Macro BS Micro BS HetNet, 10 micros per site 250 At idle times BS power consumption is reduced by ¼50% by exploiting sleep mode RF load in % 6

54 Served traffic Mbit/s/site Application of EARTH E 3 F Energy Efficiency of HetNets Results Complimentary deployment of small cells boosts capacity Additional small cells deployment can reduce network energy consumption for certain load conditions N mic = 0 N mic = 3 N mic = 10 N mic = 20 N mic = 30 Area power in kw/km Mbit/s/site 47 Mbit/s/site 92 Mbit/s/site 136 Mbit/s/site Offered traffic in Mbit/s/site Av. Number of micro BS deployed per site 7

56 Low High Key Enablers for Green Wireless Networks* Scaling Power Consumption to Network Load Components Improve power efficiency at low loads of power amplifier (macro-cell) and baseband engine (small BS types) Adaptive network reconfiguration Capacity gains of MIMO, carrier aggregation (CA) and heterogeneous networks (HetNets) do not come for free 00.00hrs Saved energy 12.00hrs Telecom traffic 24.00hrs Reconfigure system to long and/or short-term traffic variations Freq. Time. Traffic Switch off sites or RATs Switch off radio units HetNet to macro Freq. Bandwidth adaptation BS sleep modes Time. *L. Correia, et al., Challenges and Enabling Technologies for Energy Aware Mobile Radio Networks, IEEE Communications Magazine, vol. 48, no. 11, pp ,

57 Key Enablers for Green Wireless Networks Discontinuous transmission (DTX) in LTE Deactivate radio components when not transmitting Micro DTX (<1 ms) Possible in LTE Rel-8 72 center sub-carriers 1 slot (0,5ms) 1 subframe (1ms) Micro DTX CRS SSS PSS BCH DTX Short DTX (<10 ms) UEs perform mobility measurements on synchronization signals Standardization impact: Remove CRS; Only PSS/SSS and PBCH transmissions in empty cells 72 center sub-carriers 1 radio frame (10ms) Short DTX Long DTX (>10 ms) Low activity mode: Active period: Transmission of SSS/PSS and PBCH (short DTX) Idle period: No transmissions at all Standardization impact: cell search alternatives needed Normal mode (Only short DTX) Active Period (e.g. 100 ms) Long DTX (e.g. 900 ms) Low activity mode Long DTX Normal mode (Only short DTX) 3

58 Supply Power Key Enablers for Green Wireless Networks DTX in LTE: Technology Potential Explore technology potential for cell DTX in LTE Assume very low sleep power consumption Power Model 1310 W Without cell DTX the power usage scales badly due to high power consumption in idle mode Significant energy savings of DTX at low load, due to high probability of empty (sub)frames By not transmitting CRS (short DTX) further energy savings are achieved Very high saving potential as according to the EARTH E 3 F, more than 90% of resources carry no data P/A [W/km2 ] W 30W 0 Inter-site distance ISD=1200m 120 W RF Power No DTX Micro DTX Short DTX System load [Mbps/km 2 ] 4

59 Key Enablers for Green Wireless Networks Adaptive Transceiver For Macro BS o Stepwise adaptation of PA output power to required bandwidth Operating point adjustment Usage of variable bias and adapted supply voltages Deactivation of Components Switch off certain PA stages when no signal is transmitted 5

60 Key Enablers for Green Wireless Networks Transceiver for Small Base Station Types Common component tracks related to small-cell base-station Energy adaptive small-cell transceiver for dynamic signal load Pico/Femto-cell Base station Main supply DC-DC Expected power savings: Up to 40% of power reduction at low signal level 60% power savings in sleep mode BB-DL BB-UL RF-DL RF-UL PA Digital baseband engine RF transceiver Power amplifier Adaptive energy efficient PA - Operating point adjustment and PA deactivation - Tunable matching for PA load modulation BB-DL BB-UL RF-DL RF-UL PA LLL L Tx/Rx antenna port 6

62 Energy Efficient Resource Allocation Objective Investigate the theoretical limits of energy efficient resource allocation algorithms* Find lower limit of base station power consumption by combining Resource Sharing (TDMA) Power Control (PC) Sleep mode (DTX) Adhering to QoS requirements (minimum rate) Link 1 Link 2 *H. Holtkamp, G. Auer, H. Haas, On Minimizing Base Station Power Consumption, IEEE Vehicular Technology Conference (VTC), 2011 fall, San Francisco, USA. 2

63 Cooling Mains Supply DC-DC Energy Efficient Resource Allocation Objective Investigate the theoretical limits of energy efficient resource allocation algorithms Find lower limit of base station power consumption by combining Resource Sharing (TDMA) Power Control (PC) Sleep mode (DTX) Adhering to QoS requirements (minimum rate) Take into account BS power model Link 2 P in Base RF BB PA StationPA RF P out Link 1 3

64 Energy Efficient Resource Allocation Methodology From the Shannon capacity, find the sum transmit power as a function of rate, noise power and channel gain on both links. Transmit power: P Tx Resource share: µ Rate target: ³ min Noise power: N Channel gain on link i: G i Establish analytical solution for PC + TDMA on two links Add a power model to translate transmit power to supply power Add the option of sleep mode (DTX) Evaluation in simulation (1) 4

65 Energy Efficient Resource Allocation Methodology A base station s supply power is composed of the standby consumption P 0 and a load dependent part with load factor m and transmit power P Tx. In sleep mode, the base station consumes P s. Establish analytical solution for PC + TDMA on two links Add a power model to translate transmit power to supply power Add the option of sleep mode (DTX) Evaluation in simulation (2) 5

66 Energy Efficient Resource Allocation Methodology Determine numerically, which combination of transmit power per user and sleep duration minimizes the supply power consumption. 3GPP model 1 cell with 250m radius 8dB shadowing SD 10MHz BW 10 users Establish analytical solution for PC + TDMA on two links Add a power model to translate transmit power to supply power Add the option of sleep mode (DTX) Evaluation in simulation 6

67 P supply [W] P supply [W] Energy Efficient Resource Allocation Considered Power Models EARTH Reference 2010 Examine performance with different power models 1. LTE macro base station as deployed in 2010 Including sleep mode Only one sector considered P Tx [W] Power Model Base stations expected to be available on the market in 2014* Improved power efficiency Reduced sleep mode consumption P Tx [W] *L. Correia, et al., Challenges and Enabling Technologies for Energy Aware Mobile Radio Networks, IEEE Communications Magazine, vol. 48, no. 11, pp ,

68 P supply [W] P supply [W] P supply [W] P supply [W] Energy Efficient Resource Allocation Results 1. EARTH Reference 2010 Low traffic loads 200 Discontinuous transmission (DTx) is most 150efficient to save energy High 100 traffic loads Power control (PC) yields significant gains 50 Mid traffic loads 0Both DTx and PC attain comparable gains Cross-over point depends P Tx [W] on used power model 2. Power Model 2014 Combination of DTx and power control 200 (DTx + PC) provides additional gains 150Compared to bandwidth adaptation (P supply is scaled by amount of used 100resources), DTx + PC achieves significant 50 gains (30 50%) for all loads BUT, in LTE 0 Gains 0 of DTX 10 are compromised 20 30by control 40 signaling P Tx [W] No downlink power control EARTH Reference Required link rate [Mbps] P max reference % bandwidth adapt. PC only binary DTX PRAIS DTX PC 47% 18% Power model % 30% 30% Required link rate [Mbps] 8

69 Thank you 11