ETSI Workshop

Transcription

1 Security Level:Limited distribution ETSI Workshop Methods to estimate future Radio Access Network (RAN) deployment and energy consumption. Case study European network reducing energy consumption while supporting traffic evolution. Rev B Tomas Edler Senior Expert Energy Efficiency Huawei Technologies Sweden tomas.edler@huawei.com HUAWEI TECHNOLOGIES CO., LTD.

2 Content Introduction RAN (Radio Access Network) evolution energy assessment RAN Evolution studies examples European theoretical country RAN European country RAN Huawei global trends study Proposal for ETSI EE EEPS HUAWEI Page 2

3 Introduction Can operators mitigate traffic growth with RAN energy efficiency? Depends on traffic evolution, network evolution strategy, assumptions and used method Results from 3 different studies: Different methods (theoretical country, physical country, global trends) Different areas (countries, global) Different network evolution strategies Different period , , ETSI EE has standardized methods to assess energy efficiency of RAN equipment and legacy RAN. We propose a new work item to find feasible methods to estimate energy performance for evolution of a current RAN. HUAWEI Page 3

4 RAN Evolution Assessment impact factors Spectrum Strategy Technology Roadmaps (Standards, Radio concepts, features) Traffic Evolution (services, devices) Area (Topology, Population distribution..) RAN/Base Station (Base Station types: Macro/HetNET, Cloud-RAN, Site: Site efficiency...) RAN capacity utilization Known and unknown (disruptive) factors Historic: SMS, Smartphones, Future?: IoT, 8K3D, Self driven cars, Augmented reality, Holographic Video, new 5G services... HUAWEI Page 4

5 Traffic growth: Exponential Amsterdam Persistent ~ Moore s Law Driven tech evolution (smartphones, tablets, laptops, 2G>3G>4G..) Int l internet Exch. Source: bsigroup.com Mobile Data Growth Sweden: 90%/Y estimates CAGR 61-67% 67% CISCO: +61%/Y, 117X Huawei study: +67%/Y Beyond 2020 estimates: METIS: 1000X ( ? = CAGR 61%) RAN HUAWEI Page 5

6 Efficiency Evolution kbps/w HetNet Massive MIMO? Efficiency evolution. Transferred bits/w Total cell average (high load) Traffic 1000 growth 10x per 5Y (60%/Y) WCDMA/DCH WCDMA /HSPA LTE LTE-A LTE-NG 5G End of Moore,s Law? 0.1 GSM GPRS EDGE Substantial improvement of spectral & BS eff Less to win on BS eff. Close to Shannon EC of processing must be reduced. New Paradigm! Hybrid Analog/Digital? HUAWEI Page 6

7 Efficiency factors: Dynamic Power Management Energy consumption must scale with load In field large static part Scaling characteristics Sleep modes Shut down of redundant resources Dynamic design Power Consumption - not scaling with traffic Potential for power saving - scaling with traffic Source: Bath Green Radio Conference 2009, Orange presentation HUAWEI Page 7

8 Europe RAN Evolution study Theoretical study equal cell load per area PS traffic growth, 67%/Y, 170X in period Traffic growth from a PS (Packet Switch) resource view Multi-RAT evolution 2-4G Spectrum refarming Higher utilization of RAN Typical mature central EU countries Analysis area sqkm , ,0 1000,0 100,0 10,0 Subs/ Sqkm Subs/ SQkm DU U SU R 5 ISD, km DU 0,2% % of sites % of subs DU U 11% 2% DU U SU R ISD, km 90% of sites R 27% SU 62% R 20% SU 36% U 42% HUAWEI Page 8

9 Traffic Traffic Evolution, Busy Hour traffic Voice coder data more demanding kbps voice = 30kbps (GSM)-75kbps(HSPA) kbps Equal PS resource for 2G/3G mix considered Year Growth Total kbps 0,56 0,84 1,48 3,14 7,63 20,11 35,7 PS kbps 0,113 0,316 0,881 2,46 6,88 19,21 170,0 CS kbps 0,45 0,525 0,6 0,675 0,75 0,9 2 E BH 0,015 0,015 0,015 0,015 0,015 0,015 1 CS kbps/e of PS PS data growth 170X Total traffic growth only 36X as CS voice is dominating until 2013 considering the QoS of voice coder bits 10,00 Total 36X Growth Subscriber Traffic evolution PS 170X Growth +67%/Y Total kbps 1,00 CS kbps Load % Daily Traffic Profile CS kbps Hr/Day 0, Voice X-over Voice 80% % HUAWEI Page 9

10 Spectrum & Equipment refarming 2010 RAT/BW 2020 RAT/BW Ban Ba Macro BS 2012 Macro/DBS d nd HW refresh New RAT/BAND/HW DU U SU R DU U SU R Note L Cap & New Coverage L10 - L10 L10 8 Cap & New Coverage 9 G4C G2C G2C G2C G3C U5 18 G4C G2C G2C G2C L20 L Cap New 21 U5 U5 U5 U5 U20 U Cap 26 L Cap New G2C U5 G1C U5 G1C U5 9 GU Multi-RAT Radio Units Micro/Pico BS New Micro/Pico BS U Cap 100% Macro Reuse of sites 50% Macro Base Stations +u-base stations 50% DBS HUAWEI Page 10

11 Moore s law vs trends (2X eff in 18 months) Cell capacity utilization in SU, R Low hanging fruits 100% Busy Hour Load % 90% of sites G U DU U SU R 100% Busy Hour Load % Low utilization G U L Source:IEEEcomputersociety. org/ mahc DU U SU R HUAWEI Page 11

12 RAN Results MWh/Y Total energy saving 50% Improvement mechanisms - Eff gains 2G 4G: RAT gain, Single carrier => multi carrier, Flexibility of multi RAT BS - Refarming of spectrum and Hardware - Utilization eff gain - Energy saving features - Dynamic power management ie shut down of redundant d resources. Total energy saving 50% Traffic growth PS only 170X TB/MWh 80,00 70,00 60,00 Traffic growth total : 36X 30,0000 Total Efficiency gain: 70X 50,00 40,00 20,00 10, Eff gain 70X (avg) 39-77X DU-SU Energy Consumption, Sqkm Network EC, 65% CAGR EC, 65% ESF* 165kb/J U SU R DU 000 0, HUAWEI Page 12

13 Europe RAN Evolution study Central European country 2014: Actual field RAN energy consumption and traffic 2020 traffic evolution: 2G decline 3G growth 4G massive growth. Solution A: No swap of RAN equipment Energy consumption will increase by 2020 Solution B Refresh of RAN equipment Band refarming HetNet t solutions prioritized iti for capacity growth Energy consumption may reduce by 2020 HUAWEI Page 13

14 Evolution of electricity usage of Wireless access networks (E w ) E E Global ICT electricity usage based on anticipated user traffic limits, extrapolation of global traffic and energy efficiency evolution trends. 3 scenarios: Best, expected and worst case. Voice,2010 n 2G,2010 n VT S2G 2010 n 2010 n MDT 12 EI 2010 n V, EI (100% EE%) 100 2G,2010 Energy Consumption 3-5G (=N) (100% EE%) 100 n n Best case electricity usage (TWh) of Wireless Access Networks (E w ) Year Expected case electricity usage (TWh) of Wireless Access Networks (E w ) Tot: G/3G voice 2G data 3G data 4G data 5G data E n (100% EE%) 100 3G,2010 n SNG2010 n MDT2010 n 12 EI3G, G/3G voice 70 2G data 60 3G data Source: Andrae, A.S.G.; Edler, T. On Global Electricity Usage of Communication Technology: Trends to Challenges 2015,, 6,, G data 40 5G data Year HUAWEI Page 14

15 Proposal for ETSI EE EEPS New WI Methods how to deploy and assess an energy efficient future RAN, including expected impact of evolution of traffic, services and networks First task: Review current methods, studies and results to find a best practice for prediction. Result presented as a Technical Report Second task: Define a guideline standard. HUAWEI Page 15

16 Thank You HUAWEI TECHNOLOGIES CO., LTD.