Statistical Method for MeasuringMobile Network Energy Consumption
Dave BossminResearcher, Energy Efficiency StandardizationEricsson
04 June 2015
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 2
› Reasons for Measuring Mobile Network Energy
Consumption
› Challenges with Measuring Large Items Such as Mobile
Networks
› Approaches Taken By Other Industries When Measuring
Large Items
› Explanation of the Statistical Method
Presentation OutlineMeasuring Mobile Network EE
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 3
Why Measure Mobile Network Energy Consumption?
Network Measurements Bring Network Understanding
› Primarily for Technical Reasons
– Many Energy-Saving Features Activated At Network Level
– Lab Measurements of Components In Isolation From Network Misses
Impact of these Features (e.g., Macro Cell Sleep)
– Extrapolations of Network Values from Lab Measurements are
Unrepresentative
› Benefits to Both Operators and Vendors
– Operators Gain Understanding and Ability to Improve the Energy
Consumption of their Networks
– Vendors Use Live Network Conditions to Demonstrate and Improve
Energy-Saving Features
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 4
Mobile Network MeasurementsNo Small Feat
5500 km
3500 km
13,000 Sites
* Canadian Cell Tower Locations2
Not The Typical “Box-In-The-Lab” Measurement Problem!
* Canadian Cell Tower Site Count1
300 km
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 5
Mobile Network MeasurementsProblem Statement
› Need to Measure Something Big
› Want to Limit to Expense and
Effort of Making the Measurement
› Need to Understand the Accuracy
of the Measurement
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 6
Mobile Network Measurements:Measure Every Site
Expensive
Accurate
Measured Site* Canadian Cell Tower Locations2
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 7
”From a survey of 1400 residents, 34% of respondents
support the government’s initiative.
This population estimate is accurate
within +/- 3.1%, 19 times out of 20.”
Cues from Other Industries:Measuring “Big Things”
Polling Industry Statement:
A Sample Size of Thousands Estimates a Population of Millions;
Significant Reduction in Measurement Effort
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 8
Guidance From Other Industries:Polling Industry
› Don’t “Measure” Every Person
– Doing So is Cost-Prohibitive
› Measure Small, Random Sample
› Sample Used to Estimate Entire
Population
› Statistical Analysis Used to
Determine Accuracy of the
Population Estimate
Apply Same Approach to
Mobile Network Energy Consumption Measurements
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 9
Statistical Method:Mobile Network Energy Consumption
› Network is a Population of
Base Station Sites
› Choose Random Sample of
Sites from Population
› Measure Average Energy
Consumption of the Sample
› Use Average to Estimate
Network Energy
ConsumptionMeasured Site
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 10
1. How Do We Know That Our Sample is Representative of
What Is Happening in the Overall Network?
2. How Do We Choose the Size of our Sample?
3. What Happens to Future Measurements as the Network
Grows and Changes?
Questions Regarding the Random Sample Approach
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 11
Meaning of “Representative Sample”
› Site Energy Consumption Has Many Influences
– Population Density, Climate, Traffic Load, Equipment Configuration
› Each Influence Is Distributed Differently Across Network
– Population Example: 60% Urban, 40% Rural
– Climate Example: 80% Warm, 20% Cool
Population Density3 Climate4Mobile Network
Random Sample Considers All Influences Simultaneously
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 12
XXXX----3333ssss XXXX----2222ssss XXXX----1111ssss XXXX XXXX++++1111ssss XXXX++++2222ssss XXXX++++3333ssss
› Sampled Energy Consumptions Have a Statistical Distribution
How Representative is the Sample?
χχχχ
ssss
Sample Mean
StandardDeviation
��
�Margin of
Error
› Total Energy Consumption Estimate
› Accuracy of Estimate
True MeanSample Mean
Statistical Analysis Will Indicate Sample Representativeness
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 13
Significance of theMargin of Error
Accuracy of the Energy Consumption Estimate is Known
› Satisfies Key Requirement of the Measurement
– Accuracy of Population Estimate
– Difference Between Sample and Population Distributions
› Eliminates Need To Manipulate Random Sample
– Avoids Introducing Bias into Sample
– Removes Guesswork From Overlapping Distributions
› End-User Has Much Control Over This Value
– Influenced By Sample Size “n”
– Diminishing Returns With Increasing “n”
±��
�
ssss
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 14
› Decide on Desired Level of Accuracy in the Estimate
– Margin of Error and Confidence Level (+/- 3%, 19 times out of 20)
› Develop First-Order Estimate of Population Statistics
– Population Mean (µ) and Population Standard Deviation (σ)
– Identify Major Site Configurations in Network
– Understand Energy Consumption Influences (e.g., climate, traffic load)
Determining Sample Size “n”
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2�
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› Requires “Back-of-the-Envelope” Calculation Exercise
– Based on Margin of Error Definition for Entire Population
* Formula for 95% Confidence Level
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 15
› If Measurement Equipment is Permanently Deployed
– Option 1: Network Grows Around Measurement Equipment
› Future Upgrades Can’t Be Based on Sites Being Measured
› Risk of Systemic Error
– Option 2: Randomly Add Measurement Equipment Each Year
› Full Direct Measurements Eventually Achieved
› Margin of Error Used in the Interim
› If Measurement Equipment is Temporarily Deployed
– New Sites Randomly Selected With Each New Measurement
› Note that Polling Firms Don’t Call Same People Each Time
Dealing with Growth and Changes to the Network
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 16
Summary: Statistical Method
› Measuring Network Energy Consumption
– Mobile Network is a Population of Base Station Sites
– Random Selection Considers All Site Energy Consumption Influences
Simultaneously
› Statistical Method Limits Expense and Effort
– Measuring Less Sites Significantly Eases Measurement Burden
› Statistical Method Provides Necessary Accuracy Statement
– Defined by Margin of Error
– Based on Spread of Energy Consumption and Size of Sample
– Operator Has Full Control Over Accuracy/Cost Trade-offs
Statistical Methods for Determining Mobile Network Energy Efficiency | Public | © Ericsson AB 2015 | 2015-06-04 | Page 17
1. Government of Canada (Industry Canada), Facts About Towers
– http://www.ic.gc.ca/eic/site/ic-gc.nsf/eng/07422.html
2. Canadian Cellular Towers Map
– http://www.ertyu.org/steven_nikkel/cancellsites.html
3. Government of Canada (Statistics Canada): Population Density By Dissemination
Area in Canada (2006)
– http://www.statcan.gc.ca/pub/91-003-x/2007001/figures/4129885-eng.htm
4. Government of Canada (Environment Canada): Surface Air Temperature
Climatology: Observations of June-July-August from period 1963-1993
– http://members.shaw.ca/driebergens/sports/cycling/images/summtemp.png
References