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Chapter 21: Overview of Energy Saving

Techniques for Mobile and Wireless Access Networks

1 Diogo Quintas, 1Oliver Holland, 1Hamid Aghvami, and 2Hanna Bogucka

1Centre for Telecommunications Research, King’s College London

2Poznan University of Technology, Poland

HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS

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Carbon Footprint of Mobile Networks

Embodied Energy Energy spent on manufacturing,

installation and decommission of equipment

Operational Energy Energy spent on the day to day

operation of the equipment

Operational Energy is the dominant part of the energy consumption… but,

As systems become more efficient the Embodied Energy will be dominant

4.3 kg CO 2

8.1 kg CO 2

9 kg CO 2

2.6 kg CO 2

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Base Stations Handsets

CO2

Emis

sion

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Operational

Embodied

Figure 1: Contribution to the carbon footprint of mobile wireless networks [1]

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Operational Energy

There are three main components of energy consumption by access equipment: Power Amplifiers Cooling Baseband Processing Other circuitry

The total power consumption has a load and RF power variant part AND a fixed consumption

Figure 2: Operational energy consumption breakdown [2]

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Operational Energy Modeling

Linear models scaling with the average RF power consumption and load have been proposed in the literature, modeling a Omni-directional BS* [3]:

lPP rft )1(

Parameter Typical Values found in Literature [3-6]

PA Efficiency () 3.1-7.8 (Macro BS), 4-5.5 (Micro BS)

Fixed power () 83-300 (Macro BS), 32-68 (Micro BS)

Proportional to load () Not scalable (Macro BS), ~0.5 (Micro BS)

* - The total power typically scales linearly with the number of PA’s and sectors

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Embodied Energy

Estimatives for the embodied energy of a base station set the total cost as 75GJ [7]

Semiconductor manufacure is the dominant factor in the embodied energy of mobile equipment

Figure 3: Embodied energy consumption breackdown [7]

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Towards a Life Cycle Perspective

To evaluate the enviormental impact of a new system design it is critical to have in mind the full life cycle‘s energy consumption

Facilitating the comparison between two systems/techniques with different life expectancie‘s (or time frames...) the full life cycle‘s energy consumption must be scaled into an energy cost per unit of time (usually measured in years)

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Extending the Life Cycle

Modular equipment with multiple reusable parts Parts of High end equipment could be reused in

lower end equipement after reaching their end of life.

Reconfigurable chips New technology could then be deployed by a

simple reconfiguration of the chip

Recycling valuable raw material In practice this is already done...

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Improving Hardware

Improvements on hardware design are the most effective way of reducing the operational energy consumption

Base station equipment consuming just 500W has been released by manufacturers

Little understanding of the impact of the new design paradigms in the manufacture phase

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Power Amplifiers

Three promising designs/techniques:

Doherty Amplifiers Envelope Tracking Digital Pre-distortion

Together these are expected to yield efficiency rates of up to 50% [8]

Comercial PA‘s have been anounced with an efficiency rate of 45%

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Processors

Power consumption of a processor varies quadraticaly with the voltage and linearly with the clock frequency

Dynamicaly adaptin the frequency and undervolting the processor leads to significant power savings

Multi core arquitechtures allow a fine-grained control off the power consumption

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Micro Sleep Modes

Switch off signaling during some timeslots

Power down the processor (under voltage)

Switch off the PA (DTX)

Ensure that the power consumption scales with the effective load (i.e. the instantaneous load).

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Whole System Design

Component level efficiency improvements can only reduce the operational power costs There are fundamental limits to achievable

efficiency gains by better designs They do little to improve the Energy

Consumption/Capacity trade off

The whole system has to be taken into consideration Network dimensioning Alternative networking paradigms Spectrum management

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“Green” Radio Interface

Theoretical capacity of current modulation schemes are pushing us closer to the Shannon bound. As the bound is approached the number of base

stations to provide capacity is reduced...

However, these new techniques require complex DSP, increasing both the embodied and operational energy of processors.

Simpler techniques are needed that still achieve capacity...

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System Dimensioning

Smaller cells tend to consume less RF power - however more base stations are needed to cover the same area

The fixed energy costs increase linearly with the number of access routers

For smaller micro base stations the embodied starts to dominate the Life Cycle consumption

Figure 8: Energy consumption per year to cover a 20 sq km area, with and without embodied energy

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Multi Hop Networks

Multi hop networking can effect a reduction by:

Increasing the capacity density of the network Decreasing the RF power levels in the network

However, relays can be inthemselves power hungry

The embodied energy of relays could be a problem.

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Relay Aided Networks

The operational power consumption can be reduced up to a factor of 10 depending on the required capacity density

Extrapolating from an economic analysis, if relays have an life cycle cost of less than 6% of a base station then energy is saved

Relay switch off paterns can further reduce the life cycle costs of these networks.

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Mobile Ad-Hoc Networks

Delay tolerant applications can use Mobile Ad-Hoc networks

Traffic can be shifted from the access network to the Ad-Hoc network, reducing the capacity density required

Effects on the energy efficiency are highly dependent on the spatial and temporal characteristics of delay tolerant traffic...

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Dynamic Spectrum Management

Utilizing the available spectrum bands in a more intellegent way can reduce energy consumption by: Moving users or traffic from one band to another

switching off all radio equipment in one of the bands

Adjusting sectorisation patterns allowing the switching off of some sectors

Moving users or traffic between bands to allow subsets of cells to be switched off

Enabling the switching off radio equipment in single band scenarios

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Alternative Source of Energy

Grid access is an increasingly important issue as mobile networks grow in emerging markets

On-site generation has to be used, bypassing the need for a grid (and associated losses...)

Coupled with an effective reduction of the power consumption of access networks, renewable energy is an option to reduce the carbon footprint

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Suitability Of Wind and Solar Power

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Wind Power (kWh)

Solar Power (kWh)

Total (kWh)

Macro sites have more space to deploy power generating equipment

BUT... consume much more power.

Figure : Energy availability throughout the year in London, United Kingdom. Data from

Energy yields from renewable source can be volatile, with clear seasonal patterns

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Conclusion

There are several techniques that can improve the access network‘s energy efficiency spanning several design dimensions:

Component level energy efficient design Network planning Spectrum management Renewable energy

Little research has been done from a life cycle prespective – recent energy efficient research has been focusing on the operational energy reduction

There is a risk of shifting the energy consumption of the operational phase to the manufacture phase.

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Selected References[1] T. Edler, “Green base stations how to minimize co2 emission in operator networks.” in

Next Generation Networks and Base stations Conference, Bath, UK, 2008.[2] H. Karl, “An overview of energy-efficiency techniques for mobile communication System,”

TU Berlin, Tech. Rep., 2003.[3] O. Arnold, F. Richter, G. Fettweis, and O. Blume, “Power consumption modeling of different

base station types in heterogeneous cellular networks,” in Future Network and Mobile Summit 2010

[4] M. Deruyck, E. Tanghe, W. Joseph, and L. Martens, “Modelling and optimization of power consumption in wireless access networks,” Comp Comms, In Press, Corrected Proof, 2011.

[5] W. Guo and T. OFarrell, “Green cellular network: Deployment solutions, sensitivity and tradeoffs,” in WiAd 2011, Jun 2011.

[6] L. Saker and S. Elayoubi, “Sleep mode implementation issues in green base stations,” in PIMRC 2010, sept. 2010, pp. 1683 –1688.

[7] I. Humar, X. Ge, L. Xiang, M. Jo, M. Chen, and J. Zhang, “Rethinking energy efficiency models of cellular networks with embodied energy,” Network, IEEE, vol. 25, no. 2, pp. 40 –49, march-april 2011.

[8] L. Correia, D. Zeller, O. Blume, D. Ferling, Y. Jading, I. Go anddor, G. Auer, and L. Van Der Perre, “Challenges and enabling technologies for energy aware mobile radio networks,” Comm Mag, IEEE, vol. 48, no. 11, pp. 66 –72, november 2010.

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Thanks for your attention!