Power Modeling of Base Stations - Wireless@KTH · PDF filePower Modeling of Base Stations. Björn Debaillie, Claude Desset. ... Total Energy = 14 TWh/yr. ... • No loss • Internal/external

Power Modeling of Base Stations

Björn Debaillie, Claude DessetImec, Belgium

5GrEEn Summerschool, August 2014, Stockholm, Sweden

© imec 2014 | Confidential – Personal use only | Power Modeling of Base Stations| 5GrEEn Summerschool Aug. 2014 | page 2

• Massive amount of communications devices• Massive growth in traffic volume (both in data & signaling)• Challenging requirements (latency, coverage, throughput availability,...)

This evolution should be affordable and sustainable© imec 2014 | Confidential – Personal use only | Power Modeling of Base Stations| 5GrEEn Summerschool Aug. 2014 | page 3

Total Energy =

14 TWh/yr

10kW per eachother elements

Power consumption of mobile communications

Based on: ETSI RRS05_024, NSN – version 2011

With 80%, the base stations are by far the main consumers

Total Energy =

4 TWh/yr0.1W per user for 5 billion

Subscriptions

Total Energy =

75 TWh/yr1.7kW per each of the 5 million

Base Stations

Total Energy =

<1 TWh/yr1kW per each of the 17,000Controllers

Ener

gy U

se

Users Base Station Network Control Core & Servers


Base station functional components

Cooling

Digital control

Power Supply Unit

Digital Signal processing

A/D conversion

Analog TRx

Power Amplifier

Radio Heads

antenna

A/D conversion

Analog TRx

Power Amplifier

antenna

A/D conversion

Analog TRx

Power Amplifier

antenna



Base Band Unit

From/to backhaul and neighbor cells I/Q samples

Digital connection• No loss• Internal/external (CPRI)

Analog connectionFeeder loss ifo cable length

Radio Heads• at least one per sectorCan be:• integrated with BBU (classic BS)• independent (RRH)• integrated with antenna (AAA)Can be multi-technology (e.g. LTE + GSM)

# antennas depend on:• number of sectors• MIMO, beamforming• supported antennas


Base station power breakdowna quantified example

Cooling

Digital control

Power Supply Unit


A/D conversion

Analog TRx

Power Amplifier

Radio Heads

antenna

A/D conversion

Analog TRx

Power Amplifier

antenna

A/D conversion

Analog TRx

Power Amplifier

antenna



Base Band Unit

85W

115W

90W

980W

25W

205W

25W

205W

50% feeder loss

~300W in cabinet 70% to RRH

3x230W in RRH

3x20W EIRP

20% efficiency

Only 6% of the power is transmitted into the air

Indicative values based on EARTH power model 2012(macro-cell baseline scenario full load)


Power efficiency evolution of base stations


The EARTH project

EARTH project (energy aware radio and network technologies):• 50% reduction of the energy consumption in LTE based access networks• effective and collaborative energy saving mechanisms in the wireless

networks, their components, and its radio interfaces, while maintaining the users perceived quality of service and system capacity

• January 2010 – June 2012

Components

Power Amplifier & Transceiver, Load-adaptive Hardware

Deployment Network Management

Dynamic operation; Sleep modes, Bandwidth Adaptation,…

off

Zzz

Smallcell

Small Cells with Overlay Macro Cell

cellssmall PA

RF in

DC supplyDC supply


EARTH power model

A matlab tool that provides realistic power consumption values of the base station and its components over different scenarios:

• Focuses on opportunities in LTE networks• Covers different base station types (macro, micro, pico, femto-cells) • Considering BAU (Earth OFF) and novel hardware (EARTH ON)• Provides network mgnt and deployment layers realistic hardware values• Enables analysis of the power consumption at component level

The main scaling parameter is the traffic load


Load adaptive base stations

• Data traffic varies during the day• Wireless access networks are dimensioned for estimated peak demand• Power amplifier efficiency decreases at low load• Signaling traffic should be preserved

Day 1

Dat

a Tr

affic

Network capacity

Pow

er C

onsu

mpt

ion

Data Load

Sleep mode

Minimal load

Day 2 Day 3

Maximal load

Daily data traffic profile for cellular systems in a dense urban environment


Load adaptive base stationsa quantified example (continued)

ETSI load definitions- Low load =10% PRF (no data; only signaling)- Medium load = 30% PRF (data + signaling)- Busy hour = 50% PRF (data + signaling)- Average = 6/24 low + 10/24 medium + 8/24 busy = 31.7% PRF

Data load [%] 100%0%

Pmax

Total BS power consumption (EARTH 2012 macro-cell baseline system, BAU)

~60% Pmax

Sleep mode

~60% of Pmax does not scale with the data traffic• DSP, cooling, and power supply is poorly dependent on the traffic• Signaling is continuously emitted (10% of PRF)


Power modeling can be easy

Simply measure the base station power consumption• 3 point measurements: mute, no load, full load• Linear interpolation

But...• Load is the only scaling parameter; covers only limited scenarios • Are power values representative for other base stations (types/size)?• Power breakdown over the different hardware components?• Impact of technology evolution?• Etc.

... has limited usage capabilitiesPo

wer

Con

sum

ptio

n

Traffic Load© imec 2014 | Confidential – Personal use only | Power Modeling of Base Stations| 5GrEEn Summerschool Aug. 2014 | page 12

Power breakdown depends on base station type

Basic power model is insufficient with future base stations

0%

20%

40%

60%

80%

100%

Macro Micro Pico Femto

64%

47%36% 32%

7%

7%

9% 10%

5%

5%

7% 6%

7%

12%16%

13%

9% 29% 33% 39%

8%

Pow

er c

onsu

mpt

ion

brea

kdow

nPA Main Supply DC-DC RF BB Cooling


EARTH power model

First extensive base station power model• Addressing flexible load-adaptive adaptations• Multiple BS types, scenarios, parameters...• Includes power optimization strategies (e.g. depending on traffic load)• Embeds duty-cycle scenarios over the traffic load• Contains ‘hidden’ parameters and assumptions

PA

RF

BB

DC

PS

CO

10 20 30 40 50 60 70 80 90 1000

100

200

300

400

500

600

700

800

900

1000

Relative RF Output Power [%]

BS

Pow

er C

onsu

mpt

ion

[W]

Macro-Cell Baseline System (EARTH OFF)


Beyond the EARTH power model

Shortcomings when going beyond EARTH• No consistent definition of the model parameters• User interface sometimes mixes scenarios and design• Limited support for new systems and technologies• No (de)activation information

Need to go beyond EARTH: from 2x to 1000x EE improvements• Much broader range of scenarios needed• 2020 extrapolation needed

Basic power model structure can be reused• Split into main components, reference power and scaling rules• Power optimization enhancements


How to improve the BS power and scalability

Indicative list of techniques only

Data load [%] 100%0%

Total BS power consumption (power scalable architectures)

Process technology (static HW improvements)

Deactivation• Multiple levels + fast reconfiguration (mirco-sleep)• Components and subcomponents

Scalability• Performance scaling (MCS, SiNAD)• Scalable MIMO Efficiency

• Smaller cells• BS architecture• Beamforming• Massive MIMO


The GreenTouch project

Deliver architectures, specifications and roadmap of demonstrated key technologies to increase the network EE by a factor 1000

from 2010 to 2015

• Bell Labs initiated Global Research Consortium representing industry, government and academic organizations

• New innovation Model for sustainability• May 2010 – 2015• Focus on energy efficiency, sustainability and growth• Holistic and ambitious: Goal of 1000x• 60 member organizations with 350+ leading scientist

• IMEC’s power modeling received 9-months funding from GreenTouch in 2013


Why IMEC ?

“Imec is a research institute in nano-electronics and -technology, delivering industry-relevant technology solutions for ICT, healthcare and energy.”

• We are not a network vendor or operator• We have no specific activities on base station design or access networks

• In-house expertise in future processing technologies• Green radio program focusing on radio solutions for handsets• High and practical expertise in energy efficient radio system design• 5+ years ahead of the component market

• Gained substantial knowledge and credibility over different projects• High interaction and openness with industrial partners


GreenTouch power model outline

• Key model capabilities and features

• Hierarchical model architecture and parameters

• Base station and network architectures

• Technology evolution

• Base station (de)activation and sleep levels

• User interface and practical examples


GreenTouch power modelMatlab tool which quantifies the power consumption and

(de)activation delays of base stations and its (sub-)components

Flexible model- Multiple base-station architectures and components- Embeds hardware energy-scaling (traffic load, MIMO, deployment...)- Hardware technology evolution (towards 2020 and beyond)

Include transition effects- Active, idle, sleep... incl. reactivation delay

Clear and user friendly interface- Separating the user/scenario and hardware parameters- Enables co- and re-simulation










Power model architecture

• Goal: user friendly interface and convenient configuration & usage

• Hierarchical architecture with layers corresponding to abstraction levels

• Specific layers for normal users and model designers

• Each layer comes with specific parameters

Base station hardware (static due to installation)

Dynamic system configuration (dynamic to network variability)

Translation from user-defined parameters to physical components

Physical parameters to (sub)components configuration

Base station definition

System scalability

Translation layer

Physical scalability

Power consumption tables and algorithms

Power model core

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Model designers

Model users


Layer 1

Base station definition specifies the installed hardware▸ Static base station characteristics▸ Maximal capabilities (antenna chains, nominal output power...)



Parameter Values Default

Base station type large, small, data (BCG2), signal (BCG2), LSAS large

Year of deployment 2010 – 2020 2020

Number of sectors any integer >= 1 3 for large or signal, 1 for small, data or LSAS

Number of antennas (per sector) 1 - 8 for all types except LSAS (any integer >= 1)

4 for large, 2 for small and data, 1 for signal, 200 for LSAS

Maximum output power any (dBm,limited to the implemented PA model)

Maximum bandwidth 1.4, 5, 10, 20 [MHz] 10 for all except 1.4 for signal

Feeder loss >= 0 (dB) 3 dB for large, 0 dB for other types

TDD/FDD operation TDD, FDD FDD


Layer 2

Parameter Values Default Notes

System load fractional between 0 and 1 100% load (value 1) total system load

Data load fractional between 0 and 1 100% load (value 1)data + data-related signaling

load, after removing fixed signaling

Traffic throughput value in Mbps / will determine load based on average MCS

Data-related signaling between 0 (full signaling) and 1 (full data) 0

Fixed signaling overhead between 0 and system_load 0.1

Idle time profile any value > 0 71.4e-6 will be rounded to integer number of OFDM symbols (default = 1)

Idle reactivation constraint any value between 0 and idle_time = idle_time

Reduced bandwidth between 0 and 1 1

RF power control power modification [dB] 0 negative value for reducing power

MIMO configuration integer in [1 ; antennas] = antennas not used for LSAS

MCS2-6 for modulation, 0 and 1 for coding_rate, integer between 1

and 15 for CQI

6 for modulation, 1/2 for coding rate

specified as either modulation and coding rate or CQI (not both)

Spatial multiplexing integer in [1 ; antennas] 30 LSAS only


System scalability

Specifies the current hardware configuration• Covers link or network variability• Covers high level hardware flexibility


Layer 2

Advanced data/signal load definitionMultiple parameters to accommodate various systems/users

100%

0%

Data + data-related

signalling

Fixed signalling20%

80%System load

Fixed signaling

Data load

0%

100%

75%

Total resources Remaining resources

fractional signalling

0%

100%

10%


System scalability


Layer 3

Layer 3 defines the energy-scaling strategy when translating the user-defined parameters to physical components- Deactivation strategies▸ Sleeping strategy (duty-cycling) or continuous operation▸ Optimizing sleeping strategy based on scenario and components

- Automatic transmit power control strategy▸ Output power scaling (Y/N) at reduced load, bandwidth, #antennas

- Power-performance trade-offs▸ Scalable components (switching modes at, e.g., reduced bandwidth)▸ Down-scaling signal accuracy in order to save power

- Less PA linearity, fewer digital quantization bits, reduced dynamic range or EVM of analog components...

- Special role for LSAS


Translation layer


Layer 4 & 5

For model designers!Model of each (sub-)component▸ Power consumption in reference scenario

▸ Scaling rules w.r.t. scenario parameters (layer 2)

▸ Extrapolation to different hardware designs (layer 1)

▸ Levels of deactivation and delay

▸ Local trade-offs (PA linearity, quantization, analog accuracy...)




Physical parameters to (sub)components configuration


System scalability

Translation layer

Physical scalability

Power consumption tables and algorithms

Power model core

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Model designers

Model users


Layer 4 & 5

Source of power consumption values and scaling factors• Imec expertise on design of scalable radios in advanced technology• Industrial partner interaction• Literature (publications, conferences, data sheets, ...)• Extrapolation, estimated guesses, ... • EARTH power model• Benchmark with other power models










Architectures and components of future base stations

Large-cell base stationDiscrete components, heterodyne architecture, feedback path for calibration, cooling elements, multiple supply structures , ...

Small-cell base stationIntegrated analog/digital circuitry, direct conversion, light calibration, ...

BCG2 and LSAS architectures are more uniqueBCG2 for signaling and data: large and small-cell with specific featuresLSAS a bit of both, with specific features (no PA, relaxed dyn. range resol.)

Large-cell base station Small-cell base stationBCG2-signalling BCG2-dataLSAS










Technology evolution towards 2020

Process and design technology evolutionProcess technology (silicon)▸ Technology scaling (Moore’s law)

per scaling step:- 50% increases the Gops/W- 20% reduction in analog circuit

power consumption

▸ Leakage problem!- Passive power vs active power- In todays baseband processors, 30% power loss due to leakage

▸ Emerging technologies required to sustain scaling- Material, processing, interconnection, ...- e.g. FinFET, 3D stacking, ...


Technology evolution towards 2020

Process and design technology evolutionDesign technology (architecture)▸ Main trend in large-cell base station

- Main focus on performance, less on efficiency and size- PA linearization: Doherty structure, Digital Predistortion

▸ Main trend in small-cell base station- Power efficiency (battery lifetime) : switching and digital PA’s - Reconfigurable radios (multi-mode): simple circuits with massive control- Design flexibility and accuracy: digital transceivers

→ Distribute and weight the process and design technology over the base station (sub-)components










Base station (de)activation and sleep levels

EARTH indicated that base station deactivation is most promising for EE enhancement during low traffic load, but could not be fully quantified because of limited capabilities of the power model

GreenTouch targets more advanced network architectures and mgmt

Power model should embed base station deactivation information!

Implemented at (sub-)component level of high accuracyNote: (sub-)component deactivated = UL/DL radio not operational


Component (de)activation time and power

Quantified at (sub-)component level• deactivation and reactivation delay• on/off or multiple sleep levels • power consumption over sleep levels and transitions


Base station (de)activation and sleep levels










GT_Power script (layer 1)

Characterization of installed base station

Advantages of using script▸ Configuring a scenario and saving the script for future use/reference▸ Scenarios very different from default without long command-line▸ Empty value [] possible to use default or dependent settings

Base station type

Antennas

Bandwidth

Sectors

Output power



Configuration of the base station in operation

Load (data + signaling)

Fractional use of:• Antennas• Bandwidth• Output power

Dynamism (idle time)

Spectral efficiency (MCS)

LSAS frame structure (if applicable)



Specific power-saving and architecture-related optionsOutput power adaptation• With load, bandwdith, antennas

Duty-cycling and sleeping

Specific architecture parameters• PA control optimization• RRH selection (reduced cooling)• Backhauling model• Derivation of asymmetric MIMO modes


Function call illustrating model outputs

Function call without parameters:default configuration (2020, 4x4, 10 MHz...)[Power in W, 3 sectors]

Details (for 1 sector)• Per component• For [downlink, uplink]

Throughput [Mbps] is indicative only, based on input parameters• Power model does not consider link budget, coverage, error rate...• Coupling needed between power model and system/network simulator


Load reduction impact

50% load (continuously)

50% load (duty-cycled)

Some more reduction from duty-cycling

Default sleep time = 1 OFDM symbol, 71 µs• Limits the power savings


Deeper sleep reduces power

1 frame sleep time = 10 msMore savings

System enters deeper sleep modes


References for further readings

Power model related projects

This power model references• Claude Desset, Björn Debaillie, Filip Louagie, “Towards a Flexible and Future-Proof Power Model for Cellular Base Stations”,

Tyrrhenian International Workshop on Digital Communications (TIWDC), Sept. 2013• Claude Desset, Björn Debaillie, Vito Giannini, Albrecht Fehske, Gunther Auer, Hauke Holtkamp, Wieslawa Wajda, Dario Sabella, Fred

Richter, Manuel J. Gonzalez, Henrik Klessig, Istvan Godor, Magnus Olsson, Muhammad Ali Imran, Anton Ambrosy, and Oliver Blume., "Flexible power modeling of LTE base stations“, in WCNC, Paris, France, April 2012.

• Gunther Auer, Vito Giannini, Istvan Godor, Per Skillermark, Magnus Olsson, Muhammad Ali Imran, Dario Sabella, Manuel J. Gonzalez, Claude Desset, Oliver Blume, and Albrecht Fehske, “How much energy is needed to run a wireless network?”, IEEE Wireless Communications Magazine, special issue on Technologies for Green Radio Communication Networks, vol. 18, no. 4, Oct. 2011.

• Dietrich Zeller, Magnus Olsson, Oliver Blume, Albrecht Fehske, Dieter Ferling, William Tomaselli, István Gódor, "Sustainable wireless broadband access to the future Internet - The EARTH project“, chapter in book "The future Internet - Future Internet Assembly 2013: Validated Results and New Horizons," by Alex Galis and Anastasius Gavras, pp. 247-271.

This power model Other power models


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Power Modeling of Base Stations - Wireless@KTH · PDF filePower Modeling of Base Stations. Björn Debaillie, Claude Desset. ... Total Energy = 14 TWh/yr. ... • No loss • Internal/external