© University of Cancun, Mexico1 Chapter 21: Overview of Energy Saving Techniques for Mobile and Wireless Access Networks 1 Diogo Quintas, 1 Oliver Holland, 1 Hamid Aghvami, and 2 Hanna Bogucka 1Centre for Telecommunications Research, Kings College London 2Poznan University of Technology, Poland HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS

© University of Cancun, Mexico2 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 Figure 1: Contribution to the carbon footprint of mobile wireless networks [1]

© University of Cancun, Mexico3 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]

© University of Cancun, Mexico4 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]: ParameterTypical Values found in Literature [3-6] PA Efficiency ( ) (Macro BS), (Micro BS) Fixed power ( ) (Macro BS), (Micro BS) Proportional to load ( )Not scalable (Macro BS), ~0.5 (Micro BS) * - The total power typically scales linearly with the number of PAs and sectors

© University of Cancun, Mexico5 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]

© University of Cancun, Mexico6 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 cycles energy consumption Facilitating the comparison between two systems/techniques with different life expectancies (or time frames...) the full life cycles energy consumption must be scaled into an energy cost per unit of time (usually measured in years)

© University of Cancun, Mexico7 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...

© University of Cancun, Mexico8 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

© University of Cancun, Mexico9 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 PAs have been anounced with an efficiency rate of 45%

© University of Cancun, Mexico10 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

© University of Cancun, Mexico11 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).

© University of Cancun, Mexico12 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

© University of Cancun, Mexico13 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...

© University of Cancun, Mexico14 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

© University of Cancun, Mexico15 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.

© University of Cancun, Mexico16 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.

© University of Cancun, Mexico17 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...

© University of Cancun, Mexico18 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

© University of Cancun, Mexico19 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

© University of Cancun, Mexico20 Suitability Of Wind and Solar Power 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

© University of Cancun, Mexico21 Conclusion There are several techniques that can improve the access networks 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.

© University of Cancun, Mexico22 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, [2] H. Karl, An overview of energy-efficiency techniques for mobile communication System, TU Berlin, Tech. Rep., [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, [5] W. Guo and T. OFarrell, Green cellular network: Deployment solutions, sensitivity and tradeoffs, in WiAd 2011, Jun [6] L. Saker and S. Elayoubi, Sleep mode implementation issues in green base stations, in PIMRC 2010, sept. 2010, pp –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 [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.

© University of Cancun, Mexico23 Thanks for your attention!