Energy Efficiency in Wireless Networks

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Blekinge Tekniska Högskola

SE–371 79 Karlskrona

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Energy Efficiency in Wireless

Networks – Impact of Adaptive

Radio Unit Activation

Mozhgan Hedayati

This thesis is presented as part of Degree of

Master of Science in Electrical Engineering

Blekinge Institute of Technology

Jun 2010

Blekinge Institute of Technology

School of Engineering

Department of Applied Signal Processing

Supervisor: Prof. Abbas Mohammed

Examiner: Prof. Abbas Mohammed

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Abstract

Energy consumption in mobile networks, e.g. 3G networks, may be a substantial part of an operator's expenses. A large part of the total energy of the network is consumed by the radio access network. Radio Base Stations (RBSs) are the highest contributors to energy consumption and CO2 emissions in GSM and WCDMA mobile networks. While mobile network equipment is becoming more efficient, the increasing traffic demand and number of RBSs still increase the overall energy consumption of the networks. Putting the underutilized components of RBSs in sleep mode during low load can make the mobile networks more energy efficient. This study involves modeling the energy consumption of the macro base station components such as Radio Unit (RU), base band unit, cooling equipment, etc over different load scenarios. Based on the traffic load of each cell in the RBS, the number of active radio units needed for handling the traffic was selected and the rest of the radio units of the cell were put in sleep mode. A traffic scenario from a European city during 22 days is used to estimate the energy saving when using the above mentioned approach. Numeric results show that, for this traffic scenario an energy saving around 50% is achievable suggesting that there is a large potential for enhancing energy efficiency in mobile networks. Keywords: energy efficiency, sleep mode, energy saving, energy consumption, mobile network, cellular network, LTE, E-UTRAN

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Acknowledgements

I would like to express my deepest gratitude to Ove Linnell for believing in me and giving me this opportunity to do the thesis work at Ericsson Research in Linköping. My special thanks go to my supervisor, Mehdi Amirijoo for his invaluable support and guidance. I am indebted to Pål Frenger and Johan Moe for their comments and follow up of the thesis steps. I am grateful to my examiner, Abbas Mohammed, for his comments and follow up of the thesis steps. Furtheremore I am grateful to all the people at Ericsson who have been so nice and helpful to me during my stay at Ericsson. Mozhgan Hedayati

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Contents

1 Introduction .......................................................................................................... 14 1.1 Background ............................................................................................................ 14 1.2 Related work .......................................................................................................... 15 1.3 Method ................................................................................................................... 15 1.4 Assumptions .......................................................................................................... 16 1.5 Outline of the thesis ............................................................................................... 16

2 Energy consumption modeling in radio base stations ...................................... 17 2.1 Existing Technology .............................................................................................. 17 2.2 Modeling of Energy Consumption .......................................................................... 18 2.2.1 Deployment Scenarios .......................................................................................... 18 2.2.2 Models .................................................................................................................. 18 3 Simulator .............................................................................................................. 22 3.1 Overview ................................................................................................................ 22 3.2 Overal Simulation Executsion ................................................................................. 22 3.3 Propagation Models ............................................................................................... 23 3.3.1 Urban ..................................................................................................................... 23 3.3.2 Open Areas ............................................................................................................ 24 3.3.3 Slow Fading ........................................................................................................... 24 3.4 Physical Layer ........................................................................................................ 25 3.4.1 Reference Signal .................................................................................................... 25 3.4.2 User Plane Channels ............................................................................................. 25 3.5 RRM ....................................................................................................................... 27 3.5.1 Downlink Power Control ......................................................................................... 27 3.5.2 Uplink Power Control.............................................................................................. 27 4 Energy Consumption of Different Sleep Modes in Radio Networks ................. 29 4.1 Energy Consumption of Sites With the SISO Configuration .................................... 29 4.2 Energy Consumption of Sites With the MIMO Configuration ................................... 32 4.2.1 Sites With 4x10W Configuration ............................................................................. 34 4.2.2 Sites With 2x20W Configuration ............................................................................. 35 4.3 Conclusion ............................................................................................................. 36

5 Traffic Scenario .................................................................................................... 37 6 Simulation Results ............................................................................................... 42 6.1 Introduction ............................................................................................................ 42 6.2 Estimation of Energy Saving with Measured Traffic Data ...................................... 42 6.2.1 Energy Saving of Sites with the MIMO 4x10W Configuration ................................. 43 6.2.2 Energy Saving of Sites with the MIMO 2x20W Configuration ................................. 48 6.3 Estimation of Energy Saving in Different Scales of Measured Traffic Data ............ 54 6.4 Conclusion ............................................................................................................. 58

7 Summary ............................................................................................................... 60 7.1 Discussion and Conclusion .................................................................................... 60 7.2 Further Work .......................................................................................................... 60

References ............................................................................................................ 63

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List of figures

1.1 Energy consumption modleing of RBS with different improvement tracks...14 2.1 Energy consumption of a typical 3x1 40W over several output power........17 2.2 Energy consumption modeling of RBS.........................................................18 2.3 Energy consumption of AC/DC, cooling and cables over output power.......19 2.4 Energy consumption of CBU, fans, ASC/TMA and Iub................................19 2.5 Energy consumption of a radio unit with maximum 40W..............................20 2.6 Energy consumption of a radio unit with maximum 20W..............................21 2.7 Energy consumption of a radio unit with maximum 10W..............................21 3.1 Overal simulation loop..................................................................................22 3.2 Throughput of a set of MCSs........................................................................26 3.3 Assummed throughput vs SINR....................................................................27 3.4 Overview of using the siulator in modeling of RBS energy consumption......28 4.1 Site (RBS) energy consumption modeling....................................................31 4.2 Average energy consumtion of RBS over load in SISO configuration..........32 4.3 Example of using different sleep modes in two different MIMO configuration ......................................................................................................................33 4.4 Algorithm of site RU energy consumption modleing with and without applying sleep mode......................................................................................34 4.5 Average energy consumption of RBS over load in MIMO 4x10W.................35 4.6 Average energy consumption of RBS over load in MIMO 2x20W.................36 5.1 Average throughput of the network over time in HSPA system.....................37 5.2 Average throughput of the network over time in HSPA system.....................38 5.3 Distribution of throughput for cells of the network..........................................38 5.4 Algorithm of mapping the throughput to the output power.............................39 5.5 Average load ratio variation over throughput.................................................40 5.6 Average output power of the network over time in LTE network...................40 5.7 Distribution of output power for cells in the network......................................41 6.1 Cumulative number of active RUs during the 22 days..................................42 6.2 Algorithm of calculating energy saving......................................................... 43 6.3 Total energy consumption of the network in 4x10W cnfiguration..................43 6.4 Distribution of 1-sector sites energy consumption in the network in 4x10W configuration .................................................................................................45 6.5 Distribution of 2-sector sites energy consumption in the network in 4x10W configuration .................................................................................................45 6.6 Distribution of 3-sector sites energy consumption in the network in 4x10W configuration..................................................................................................46 6.7 Distribution of 4-sector sites energy consumption in the network in 4x10W configuration .................................................................................................46 6.8 Distribution of 5-sector sites energy consumption in the network in 4x10W configuration .................................................................................................47 6.9 Distribution of RBS energy consumption in the network in 4x10W configuration..................................................................................................47 6.10 Distribution of RBS energy saving in the network in 4x10W

configuration..................................................................................................48 6.11 Total energy consumption of the network in 2x20W cnfiguration..................49 6.12 Distribution of 1-sector sites energy consumption in the network in 2x20W configuration ................................................................................................50

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6.13 Distribution of 2-sector sites energy consumption in the network in 2x20W configuration ............................................................................................51 6.14 Distribution of 3-sector sites energy consumption in the network in 2x20W configuration.............................................................................................51 6.15 Distribution of 4-sector sites energy consumption in the network in 2x20W configuration ...........................................................................................52 6.16 Distribution of 5-sector sites energy consumption in the network in 2x20W configuration ...........................................................................................52 6.17 Distribution of RBS energy consumption in the network in 2x20W configuration............................................................................................53 6.18 Distribution of RBS energy saving in the network in 2x20W

configuration............................................................................................53

6.19 Cumulative number of active RUs over different scaled traffic in 4x10W configuration.............................................................................................55 6.20 Cumulative number of active RUs over different scaled traffic in 2x20W configuration.............................................................................................56 6.21 Energy consumption of RBS per day over different scaled traffic in 4x10W configuration.............................................................................................57 6.22 Energy consumption of RBS per day over different scaled traffic in 2x20W configuration.............................................................................................58

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List of tables

3.1 Parameters describing baseline link level performance for LTE................26

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Abbreviations

3G Third Generation 3GPP Third Generation Partnership Project AC/DC Alternating Current to Direct Current ASC Antenna System Controller CBU Central Base Unit DL Downlink DTX Discontinuous Transmission eNodeB E-UTRAN NodeB FU Filter Unit GSM Global System for Mobile communication HSPA High Speed Packet Access HSTX High Speed Transmitter ICT Information and Communication Technologies Iub The interface used for communication between the NodeB and the RNC KPI Key Performance Indicator LTE Long Term Evolution MIMO Multiple-Input Multiple-Output NodeB a logical node handing transmission/reception in multiple cells. Commonly, but not necessarily, corresponding to a base station. PDSCH Physical Downlink Shared Channel PDU Power Distribution Unit PHY Physical layer PRB Physical Resource Block PSS Primary Synchronization Signal PUSCH Physical Uplink Shared Channel RAN Radio Access Network RAT Radio Access Technology RAX Random Access Receiver RBS Radio Base Station RNC Radio Network Controller RRM Radio Resource Management RU Radio Unit RUIF Radio Unit Interface SISO Single-Input Single-Output SINR Signal to Interference Noise Ratio SSS Secondary Synchronization Signal TMA Tower mounted Amplifier TX Transmission UE User Equipment, the 3GPP name for the mobile terminal UL Uplink WCDMA Wideband Code Division Multiple Access

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1. Introduction

1.1 Background

Mobile network expenses related to energy consumption, may be a substantial part of an operator's network cost. A major part of the total network energy is consumed by the radio access network. The Information and Communication Technologies (ICT) are currently responsible for about 2% of global CO2 emissions and mobile telecom is about 0.2% of this total CO2 emission [4], [1]. So optimizing energy efficiency will not only reduce the network costs it will also reduce the environmental impact, and help to make communication more affordable for every one. Ericsson’s life cycle assessment of it’s own products shows that the Radio Base Stations (RBSs) are responsible for two-third of the total energy consumption and are the highest contributors of CO2 emissions in GSM and WCDMA mobile networks. There are three main approaches for increasing the energy efficiency of radio networks:

• Standardization changes (e.g. improving protocols with more efficient transmission)

• Product improvements (e.g. decreasing energy consumption of RBS components) and

• Network optimization (e.g. to put radio amplifiers and whole base stations, in sleep mode during certain intervals of time when they are not used or their load is sufficiently low)

Some elaboration has been done in standardization changes of RBS (e.g. 3GPP Rel10 and beyond) which can decrease the energy consumption of RBS (Figure 1.1 a). With product improvement, lower RBS energy consumption for both high and low loads could be expected. In the network optimization approach we can reduce the energy consumption of the constant part at no load by turning off of the hardware of nodes that are not used.

a) b) Figure 1.1: Energy consumption modeling of RBS with different improvement tracks. a) improvement from release 8 to release 10+ in standardization. b) product improvement and network optimization.

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In Figure 1.1 the circles show the energy consumption of two neighbor RBSs. The energy consumption of the low loaded RBS can be reduced by turning it off and delegating its load to the relatively higher (but not fully) loaded RBS. However the RBS which is supposed to handle the traffic of the turned off RBS will have a slightly higher consumption. This thesis work is about network optimization with main focus on turning off radio units based on load to decrease the energy consumption of the RBSs.

1.2 Related work

There have been several reports within Ericsson on energy efficiency; however, these have been removed in this report due to confidentiality. In an Ericsson white paper [1] the authors have discussed how to achieve energy efficient, sustainable mobile communications through network optimization, site optimization and alternative energy sources and suggest that a number of energy management techniques can be deployed at RBS sites to improve their energy efficiency, e.g., energy efficient radio equipment, remote sites, remote power network, advanced core network equipment, high efficiency power modules. Richter et al. (2009) discussed the metrics and the parameters to be used when studying energy efficiency [2]. They also made a comparison between micro and macro deployment in terms of energy efficiency and investigated the impact of deployment strategies on the power consumption of mobile radio networks considering layouts with different numbers of micro base stations per cell in addition to conventional macro sites. As a system performance metric, they introduce the concept of area power consumption and employ simulations to evaluate potential improvements of this metric through the use of micro base stations. Their results suggest that for scenarios with full traffic load, the use of micro base stations has a rather moderate effect on the area power consumption of a cellular network and that the power savings strongly depend on the offset power consumption (i.e. the constant part of the site power consumption) of both macro and micro sites (All base stations there seem to be omni sites). In this thesis we will simulate sectorized macro sites. Marsan et al. [3] evaluated the energy saving that can be achieved with the energy-aware cooperative management of the cellular access networks of two operators offering service over the same area. Their report suggests that switching between two networks (roaming sharing between two operators) can save a great amount of energy.

1.3 Method This thesis is done in the following steps:

1. Traffic to energy consumption mapping: The energy consumption of the RBS components, such as power amplifiers, signal processing, cooling, etc is studied. This reveals the components where their energy consumption increases with the load and those components where their energy consumption is independent from load. As a result, we will have a mapping

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for each component (group); i.e. a map which gives us the component(s) energy consumption at each traffic load.

2. Defining the sleep mode: We define the conditions for turning off the RUs in the cells. Depending on the cell load, we decide how many RUs can be turned off in the cell.

3. Estimating the energy saving: The energy consumption of the RBS is obtained by using the load measured from a European city and the mapping obtained from step 1 with and without using sleep mode at different loads. Given this we can now, estimate how much energy saving can be achieved by using the proposed sleep modes.

1.4 Assumptions Spatial multiplexing and spatial diversity are not modelled in our simulator and the radio units in the cells are used as a capacity extension. Zero watts energy consumption for RU sleep mode is used because the start up time is in the order of seconds. When all the radio units are switched off at no load traffic in a cell, we assume that another Radio Access Technology (RAT) for example WCDMA is on and maintains the coverage. When some of the radio units in the cell are put in sleep mode the Physical Broad Cast Channel and synchronization signals are transmitted with lower power and the coverage of the cell may decrease a little. For compensation of this impact, the Physical Broad Cast Channel and synchronization signals can be transmitted with higher power to keep the coverage.

1.5 Outline of the thesis

Chapter 2 gives a thorough analysis of the existing technology and modeling the energy consumption of different components of radio base station (RBS). An overview on the simulator which is used in this thesis work is provided in chapter 3. In chapter 4 the methods of modeling the energy consumption of RBS in SISO and MIMO configuration sites will be illustrated. The preparation of a traffic scenario for studying the amount of saving by applying sleep mode is explained in chapter 5. In chapter 6 the obtained results of energy saving will be presented by comparisons between the estimated energy consumption with sleep mode and without sleep mode. Summary and possible further investigations will be presented in chapter 7.

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2. Energy Consumption Modelling in Radio Base Stations

The energy consumption in a radio base station depends on the hardware architecture and the equipment capabilities. Base stations typically contain several components. The energy consumption of some of them depends on the output power, while that of others does not. This chapter gives a thorough analysis of the existing technology aiming at modeling the energy consumption of different components of RBS, such as radio unit, cooling, baseband unit etc., which will be used later in calculation and modeling of the RBS energy consumption.

2.1 Existing Technology Figure 2.1 shows the energy consumption of different components of a typical WCDMA base station over several load scenarios. The vertical axis is normalized to the input power of the fully loaded site, PRBS,FL. The horizontal axis is normalized to the maximum output power of the site, POut,Max. This radio base station contains three sectors, one radio unit per sector. As shown in this figure the energy consumption of the radio units depends on the output power and increases with it, but also it has a substantial constant part about 8% per radio unit at no load. At full load (i.e. when the output power increases to 40 W in each cell) the radio unit consumes around 19%. The power supply (AC/DC) and the cooling system are other significant contributors to the energy consumption. Their consumption increases from 19% at no load to around 32% at full load. Clearly from the figure the energy consumption of the base band unit is independent from the load and it is about 7%.

1 10 20 40 50 75 1000

20

40

60

80

100

Output Power [% Normalized to POut,Max

]

[%

Norm

alized to P

RB

S,F

L]

Site Energy Consumption

Cables + AC/DC + Cooling

Fans + Iub + ASC/TMA

RU1

RU2

RU3

CBU

Figure 2.1: Energy consumption of a typical RBS 3x1 40W over output power.

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2.2 Modeling of Energy Consumption

Energy consumption of an RBS can be modeled as a linear function of load. Figure 2.2 shows a schematic model for energy consumption of different RBS types.

Figure 2.2: Energy consumption modeling of different types of RBS. The energy consumption consists of a constant part at zero load which depends on the number of RUs of the RBS plus an increasing part with a slope which depends on the RBS type. In this report it is assumed that all sites in the network are of same type.

2.2.1 Deployment Scenarios

In this thesis three different deployment scenarios are considered and simulated, where all sites in each scenario have 3 sectors

• SISO configuration sites with 40 W RU (in this configuration each cell has just one radio unit with a maximum output power of 40 W).

• MIMO configuration sites with 2x20W RUs (the cells have two radio units with a maximum transmission power of 20 W).

• MIMO configuration sites with 4x10W RUs (each cell has four radio units with a maximum output power of 10 W).

2.2.2 Models

The energy consumption of the RBS has been divided into three parts:

• Energy consumption of AC/DC, cooling and cables

• Energy consumption of Central Base Unit (CBU), fans, Antenna System Controller / Tower Mounted Amplifier (ASC/TMA) and the interface used for communication between the NodeB and the RNC (Iub)

• Energy consumption of radio units Energy consumption of the first two parts are obtained from typical RBS energy consumption (Figure 2.1), and the last part is based on the Ericsson RU energy consumption measurements. More details about each of these three parts is given bellow. The Energy consumption of AC/DC, cooling and cables increases with the load as shown in Figure 2.3.

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5 10 15 20 25 30 35 400

20

40

60

80

100AC/DC + cooling + cables

Output power [w]

[ %

Norm

aliz

ed t

o P

RB

S,F

L ]

Figure 2.3: Energy consumption of AC/DC, cooling and cables over output power.

The energy consumption of CBU, fans, ASC/TMA and Iub as shown in Figure 2.4 is independent from load.

5 10 15 20 25 30 35 400

20

40

60

80

100CBU + fans + ASC/TMA + Iub

Output power [w]

[% N

orm

aliz

ed t

o P

RB

S,F

L ]

Figure 2.4: Energy consumption of CBU, fans, ASC/TMA and Iub.

Energy consumption of radio units (PRU,in) increases with load (Pout). The following graphs have been derived from measurements of Ericsson radio units and the measurements can not be shown in this report. Note, the energy consumption numbers differ from year to year and therefore a rough estimation could be sufficient. The characteristics for the energy consumption of radio units are estimated and given below:

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• RU 40W : PRU,in = A40W x Pout + P40W,NL [W] (2.1)

• RU 20W: PRU,in = A20W x Pout + P20W,NL [W] (2.2)

• RU 10W: PRU,in = A10W x Pout + P10W,NL [W] (2.3)

5 10 15 20 25 30 35 40

Radio Unit 40W

Output power [w]

Energ

y c

onsum

ption [

w]

Figure 2.5: Energy consumption of a radio unit with maximum 40W. (Numbers in vertical axis are removed due to Ericsson confidentiality).

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2 4 6 8 10 12 14 16 18 20

Radio Unit 20W

Output power [w]

Energ

y c

onsum

ption [

w]


1 2 3 4 5 6 7 8 9 10

Radio Unit 10W

Output power [w]

Energ

y c

onsum

ption [

w]


All figures above (Figure 2.5, 2.6, 2.7) have more or less the same fixed cost at no load (PNL = P40W,NL = P20W,NL = P10W,NL ) and they have almost the same slope (A40W = A40W = A40W ). Using four RUs 10W or two RUs 20W instead of one RU 40 W in each cell, results in an overall higher consumption since we have 4 and 2 RUs respectively compared to a 40W RU. For example an RU 40W consumes PNL at no load, while four RUs 10W consume 4 PNL and two RUs 20W consume 2 PNL.

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3. Simulator 3.1 Overview The simulator has been developed in MATLAB. It simulates a network with 19 sites with three cells (hexagonal cells) per site. The propagation type in this simulator is Okumura-Hata [5] [6] (urban) and shadow fading component is applied to the overall path loss. A 3-dimensional antenna pattern is further used [10].

3.2 Overall Simulation Execution The outer loop of the simulator is shown in Figure 3.1.

Figure 3.1: Overall simulation loop.

The outer loop will be terminated based on the number of snapshots and if the result has reached the steady state. For each snapshot the following are executed:

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• UE generation: Uniformly distributes UEs across the network. Each UE is associated with a certain required bit rate in the UL and DL.

• Path loss (Gain-matrix) calculation: Calculates the path loss between each UE generated in the last step and the related base station, and after adding the lognormal fading, stores the results in a so called Gain-matrix (G-matrix). During the execution of a snapshot the distance and lognormal fading are kept constant.

• Cell selection: UEs select the best server cells to connect (using Reference Signal Received Power) [7].

• Simulate physical layer, radio resource management etc: The Physical Layer (PHY) step consists of modeling of the physical channels, and link to system models mapping Signal to Interference Noise Ratio (SINR) to throughput. The simulator models reference signal and channels for user plane namely Physical Uplink Shared Channel (PUSCH) and Physical Downlink Shared Channel (PDSCH) based on a truncated Shannon model [10] which will be explained in section 4.4.2. Radio Resource Management (RRM) part performs the power control in downlink and uplink [6].

• Collect results: In this step data will be sampled and stored.

• Final snapshot: Based on number of snapshots and whether steady-state has been reached, simulation can be finished.

3.3 Propagation Models One of the most widely used models for path loss prediction in macrocells is Okumura-Hata propagation [5], which is used in the simulator. Okumura developed a set of curves based on the measurements and Hata formulated an empirical description into three different categories, namely open areas, suburban and urban areas. The following notation is used throughout this chapter:

• r is the distance between UE and RBS [km].

• fc is the carrier frequency [MHz].

• hb is the hight of the RBS [m].

• hm is the height of the UE [m].

• PL is the path loss between RBS and UE [dB].

3.3.1 Urban

The model is given by the following equation for urban areas [5]

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PL = 69.55 + 26.16 log fc - 13.82 log hb + A log r – B (4.1)

where A = 44.9 – 6.55 log hb (4.2) B = 4.78 (log fc)

2 + 18.33 log fc + 40.94. (4.3) In the simulator hb is assumed to be 45m, hm is 1m and the network is deployed for 900 MHz (fc = 900 MHz).Then the model in the simulator is given by PL = 95.5 + 34.11 log r. (4.4) 3.3.2 Open Areas (rural)

From Okumura-Hata equation, in the simulator the model is given by the following equations for open areas PL = 69.55 + 26.16 log fc – 13.82 log hb + (44.9 – 6.55 log hb) log r - C (4.5) C = 4.78(log (fc))

2 – 18.33 log fc + 40.94 (4.6) which gives that PL = 120.9 + 37.6 log r. (4.7) 3.3.3 Slow Fading (shadow fading) Slow fading occurs due to propagation close to obstacles or in an atmosphere with gases. The basic propagation mechanisms are:

• Reflection occurs when a radio wave strikes a large dimension size object (e.g. buildings, moving cars), compared to the wavelength.

• Diffraction occurs when radio wave is bent around sharp edges and depends on the relationship between the wavelength and the size of the obstacle.

• Scattering occurs when the radio wave travels through a medium containing a lot of small objects, compared to the wavelength, which influences the propagation.

There are many major obstacles, such as hills, large building, moving cars etc which obstruct the Line Of Sight (LOS) path between the transmitter and receiver. A mobile moving behind such objects will be exposed to shadowing. As a mobile moves through a shadowed environment, it receives a fluctuated signal level. This variation depends on the relative position of the mobile to the shadowing objects. The shadowing varies with the size of the obstacles and modeled as a log-normal function. The probability density function is given by

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2

20

2

)(

2

1)( SF

SS

eSpσ

πσ

−

−

= (4.8)

where S is the local average signal power expressed (and has a Gaussian distribution), S0 is the average signal power and σ is total shadow fading standard deviation which has been chosen equal to 8 dB in the simulator [5].

3.4 Physical Layer

The physical modeling consists of the physical channels, and link to system models mapping SINR to throughput. In the simulator reference signal and two user plane channels has been modeled.

3.4.1 Reference Signal The reference signal is used for mobility and cell selection, and UE needs this signal to select the best serving cell. The two synchronization signals Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS) are used to select the cells. The reference signal and the Synchronization signals are represented by one signal/channel, in the simulator which is named reference signal. If the Received Reference Signal Power (RSRP) at the UE is greater or equal than threshold RSRPdetect = -124dBm and the SINR of the reference signal ≥ -6 then the cell will be detectable [7]. 3.4.2 User Plane Channel The channels PUSCH and PDSCH carry the user data in the uplink and downlink respectively. The model which is used in simulator for mapping SINR to downlink/uplink user plane channel throughput is the truncated Shannon model [6]. LTE supports QPSK (2 bits/symbol), 16 QAM (4 bits/symbol), and 64 QAM (6 bits/symbol). The maximum throughput of a given Modulation and Coding Scheme (MCS) is the product of the coding rate and the number of bits per modulation symbol. Figure 3.2 shows the codeset of the upper envelope of the MCS-specific throughput versus SNR curves and the Shannon bound. Shannon bound in this figure represents the maximum theoretical throughput that can be achieved over an AWGN channel with a given SNR.

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0

1

2

3

4

5

6

7

-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20

SNR, dB

Thro

ughput, b

its p

er

second p

er

Hz

MCS-1 [QPSK,R=1/8]

MCS-2 [QPSK,R=1/5]

MCS-3 [QPSK,R=1/4]

MCS-4 [QPSK,R=1/3]

MCS-5 [QPSK,R=1/2]

MCS-6 [QPSK,R=2/3]

MCS-7 [QPSK,R=4/5]

MCS-8 [16 QAM,R=1/2]

MCS-9 [16 QAM,R=2/3]

MCS-10 [16 QAM,R=4/5]

MCS-11 [64 QAM,R=2/3]

MCS-12 [64 QAM,R=3/4]

MCS-13 [64 QAM,R=4/5]

Shannon

Figure 3.2: Throughput of a set of MCSs.

The following equations approximated the throughput over a channel with a given SINR. In this figure we see that the Shannon curve is a good approximation of the upper envelope curve based on a modification of the Shannon bound.

[ ]

maxmax

maxmin

min

SINR SINRfor ThrThr

SINRSINR SINRfor αS(SINR)Thr

SINR SINRfor 0Thr

//

>=

<<=

<=

=HzsbitsThroughput (4.9)

where:

• S(SINR) is the Shannon bound: S(SINR) = log2(1+SINR) bps/Hz

• α is the attenuation factor, representing implementation losses

• SINRmin of the code set (see Figure 3.2)

• Thrmax is the maximum throughput of the code set

• SINRmax is the SINR at which max throughput is reached S-1(Thrmax),dB

Table 3.1 Parameters describing baseline link level performance for LTE Parameter DL UL Notes

α, attenuation 0.6 0.4 Represents implementation losses

SNIRmin, dB -6.5 -6.5 Based on QPSK, 1/8 rate (DL) & 1/5 rate (UL)

Thrmax, bps/Hz 4.4 2.0 Based on 64QAM 4/5 (DL) & 16QAM 3/4 (UL)

Figure 3.3 shows the truncated Shannon model with parameters proposed in table 3.1.

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0

1

2

3

4

5

-15 -10 -5 0 5 10 15 20 25SNIR, dB

Th

rou

gh

pu

t, b

ps

/Hz Shannon

DL

UL

Figure 3.3: Assumed throughput vs SINR.

3.5 RRM 3.5.1 Downlink Power Control Uniform power distribution is assumed for the DL, i.e., the total base station transmit power is distributed among the Physical Resource Blocks (PRBs). This means that the power allocated to a PRB is constant and the power assigned to unused PRBs is simply ‘lost’ (is not redistributed over used PRBs). Therefore Power Spectral channel bandwidth is given by

channel

max

BW

PPSD = [dBW/Hz] (4.10)

where Pmax is the cell maximum transmission power and BWchannel is the channel bandwidth. 3.5.2 Uplink Power Control LTE employs closed-loop and open loop Power Control (PC) with fractional pathloss (PL) compensation. Studies, however, show that open-loop power control together with fractional PL compensation gives good results [8]. In the simulator open-loop PC [9] is implemented and UL transmit PSD is given by

α

PLPPSD ×= 0 [W/Hz] (4.11)

P0 is the desired received power density, which has the average amount around -179

dB and α is fractional pathloss compensation is equal to 1. An overview on how the simulator is used in this report illustrated in Figure 3.4. The number of UEs per cell is used as the input to the simulator, and the load ratio (allocated bandwidth over maximum bandwidth) of cells will be calculated as the output to obtain the energy consumption of the cell.

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Figure 3.4: Overview of using the simulator in modeling of RBS energy consumption.

Number of

UE/cell Simulator

Load ratio/cell

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4 Energy Consumption of Different Sleep Modes in Radio Networks In this chapter a definition of different sleep mode levels comes first and then energy consumption of different configurations will be illustrated. Different levels of sleep modes are used:

• MIMO: When the data traffic of the site is high, it needs to use more than one radio unit per each cell to handle the traffic. In this case the site will need to use more than one active RU per cell and therefore, it will work in a MIMO configuration. By MIMO is meant that more than one RU is active.

• SISO: For sites with low-medium data traffic only one antenna branch in each cell will suffice to transmit the pilot, the common channels and to handle the user traffic. Therefore, other RUs in the cell can be turned off and the site can go to SISO configuration with three active radio units (one active RU in each cell).

• Omni: In the low load traffic case, just one radio unit will be enough for the entire site to handle the traffic. The site uses the omni configuration and uses one radio unit connected to the three sectors.

• Shut down: Sites with no traffic load can turn off the RUs in all sectors (traffic is handled by another radio access technology (e.g. WCDMA)).

Even though there are no UEs connected to a sector, the output power is not zero since the broadcast channel and physical signals needs to be transmitted. In all current cellular networks, (e.g. GSM, HSPA, LTE) the base stations transmit several additional signals in their cells continuously. Examples of such signals and channels are:

• Reference signals (pilot), which is used by mobile terminal to estimate the downlink channel in order to coherently receive the transmitted data and to perform handover/cell selection decisions according to quality.

• Synchronization signals, which is used to be able to keep in synchronization with the network.

• Broadcast channel, which carries part of the system information, required by the terminal in order to connect to the network.

4.1 Energy Consumption of Sites With the SISO Configuration Sites with SISO configuration (i.e., the sites have only one antenna and RU) have one radio unit with maximum output power of 40 W per each cell. The energy consumption of the RBS is simulated by using the load ratio obtained from the radio network simulator. Figure 4.1 shows the modeling of energy consumption in each RBS. The load ratio of each cell in the simulator is introduced as the input to this algorithm, where the output power of each cell is given by:

Pout = Pmax x Load Ratio (5.1)

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where the Pmax is the maximum output power of the cell, which is 40W in the simulator. From energy consumption modeling of RU (Figures 2.5, 2.6 and 2.7) and according to the output power in each cell, the energy consumption of that cell’s radio units is obtained. For calculating the energy consumption of AC/DC, cooling and cables, since Figure 2.1 is for an RBS with three sectors (when all sectors have the same load), the energy consumption of the AC/DC cooling and cables (Figure 2.3) is divided by three to obtain the consumption related to each sector. According to the obtained figure (AC/DC, cooling, cables / 3) the consumption related to each sector is calculated. As mentioned in the previous part the energy consumption of the base band unit and fans are constant and independent from load, therefore, the energy consumption will be the same for all sites and independent from their output power. At the last stage the energy consumption of three parts are added together to obtain the total energy consumption of the site.

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Figure 4.1: Site (RBS) energy consumption modeling.

Cell Pmax

(40W)

Pmax x Load

Cell Output Power

Cell load ratio

(from Simulator)

Site RU Energy Consumption

Number of cells / site

Cell Output

Power Number of

cells / site

Site AC/DC-Cooling-cables Energy

Consumption

Site CBU-Fans-ASC/TMA

Energy Consumption

Site Energy

Consumption

5 10 15 20 25 30 35 400

20

40

60

80


Output power [w]

[ %

No

rma

lized t

o P

RB

S,F

L ]

5 10 15 20 25 30 35 40

0

20

40

60

80


Output power [w]

[ %

No

rma

lized t

o P

RB

S,F

L ]

5 10 15 20 25 30 35 40

0

20

40

60

80


Output power [w]

[ %

No

rma

lize

d t

o P

RB

S,F

L ]

AC/DC-Cooling-cables Energy Consumption / number of cells in the site

5 10 15 20 25 30 35 400

20

40

60

80

100CBU + fans + ASC/TMA + Iub

Output power [w]

[% N

orm

aliz

ed

to

PR

BS

,FL ]

Site CBU-Fans-ASC/TMA

Energy Consumption

5 10 15 20 25 30 35 400

20

40

60

80

100Radio Unit 40W

Output power [w]

[ %

Norm

alized t

o P

RU

,FL]

RU 40W Energy Consumption

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The simulation result of the energy consumption of the RBS on the network with SISO configuration is shown in Figure 4.2. The average energy consumption of RBS over 19 sites is simulated over different average load scenarios. The vertical axis is normalized to PRBS,FL,Sim1, the input power of the fully loaded site (with SISO configuration) in the simulator.

5 10 15 20 25 30 35 400

20

40

60

80

100Average Energy consumption of RBS

Average outputpower [w]

[ %

Norm

aliz

ed t

o P

RB

S,F

L,S

im1 ]

Figure 4.2: Average energy consumption of RBS over load in SISO configuration.

4.2 Energy Consumption of Sites With the MIMO Configuration In this work, energy consumption of the RBS is studied in two different types of MIMO configuration and with applying different levels of sleep modes. In one configuration, the sites have four 10 W radio units and in the other configuration the sites have two 20 W radio units per each sector cell. An example of how different sleep modes are used in the cells is depicted in Figure 4.3. In Figure 4.3 as an example we assume that the output power of one cell is 16 W. In 1x40W (SISO) configuration where there is just one radio unit per cell, there will be no sleep mode (except going to Omni configuration, which is not considered in this report). However when we have MIMO configurations then different sleep modes are studied for saving energy. In MIMO 4x10W configuration, without sleep mode, all radio units are switched on and transmit almost 4W, while in fact just 2 radio units could be enough for handling this traffic and others can be switched off. One radio unit transmits with full load (10W) and the other one can handle the remaining 6 W. Alternatively, each of the two RUs can work with 8W or some other distribution of power, which is not further considered in this report. Thus we can save energy by turning off two radio units. When the configuration is 2x20 W then instead of using two active radio units which handle almost around 8 W each, one

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radio unit is enough to handle the whole traffic and the other one will be switched off.

Figure 4.3: Example of using different sleep modes in two different MIMO configurations. Figure 4.4 illustrates the algorithm which is used for modeling the energy consumption of RUs in RBS with MIMO configuration, with and without sleep modes. Let PRUmax represent the maximum RU output power, and N represent the number of RUs working with their maximum output power. As explained by the example above when the sleep mode is on, according to the output power the number of radio units (N) needed to work with maximum output power (PRUmax) is calculated. The number (N) is multiplied by the energy consumption of one RU at full load (from Figure 2.5, Figure 2.6 and Figure 2.7 depending on which kind of RU is used) and added to the energy consumption of the remaining output power, to give the energy consumption by RUs in the cell. In the normal case (no sleep mode) the output power of the cell is divided to the number of antenna branches (RUs per cell) and the related energy consumption of each branch is added together to give the energy consumption of the RUs in the cell.

SISO

1x40W

Cell load=16W

4x10W

2x20W

MIMO 2x20W-No

Sleep mode

Cell load=2x8W

MIMO 4x10W-No

sleep mode

Cell load=4x4W

MIMO 4x10W-

Sleep mode on

Cell load=1x10W+6W

MIMO 2x20W-

Sleep mode on

Cell load=16W

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The energy consumption of the RU in each RBS is calculated by adding the energy consumption of the RUs in that RBS. Energy consumption of AC/DC, cooling, and cables and also CBU and fans is the same as that of the SISO configuration. By adding the three parts, the RBS energy consumption for each case is obtained.

Figure 4.4: Algorithm of site RU energy consumption modeling with and without applying sleep mode. 4.2.1 Energy Consumption in Sites With 4x10W Configuration

The energy consumption of RBS using sleep modes is compared with no sleep mode. The sites are MIMO configured with four radio units per each cell with maximum output power of 10 W. Figure 4.5 shows the energy consumption of RBS as the load increases, for two cases with sleep mode on and normal case (no sleep mode is used). The vertical axis is normalized to PRBS,FL,Sim2 the input power of the fully loaded site (with MIMO 4x10W configuration) in the simulator.

Cell Output Power / Number of antennas per cell

N = floor ( Cell Output Power / PRUmax ) & R = mod ( Cell Output Power, PRU max )

0 5 10 15 20 25 30 35 400

50

100

150

200

250

300Radio Unit 40w

O utput power[w]

En

erg

y c

onsu

mpti

on[w

]

RU Energy Consumption model

YES NO

Antenna branch

Output Power

Sleep mode on?

RU energy consumption of the RBS-no sleep mode

RU energy consumption of the RBS- sleep mode on

Cell Output Power

N & R

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Consider the case when sleep mode is on. It is clear that when the output power of the cell is less than 10 W, then the cell has only one active radio unit. It is seen clearly that as the output power of the cell increases to 20 W, 30 W, and 40 W, the number of active radio units is increased to 2, 3 and 4 respectively.

5 10 15 20 25 30 35 400

10

20

30

40

50

60

70

80

90

100Average Energy consumption of RBS 4x10W


[ %

Norm

aliz

ed t

o P

RB

S,F

L,S

im2 ]

No Sleep mode

Sleep mode on

Figure 4.5: Average energy consumption of RBS over load in MIMO 4x10W.

Note that the energy consumption, when using sleep mode, increases in sloped steps. For each RBS, the energy consumption increases in a step-wise manner as each RU is switched on in each step. The curve showing the case of sleep mode on is sloped as a result of using the average energy consumption of RBS in our network. When for example simulator generates 10 UEs per cell (average number of UEs/cell equal to 10) and distributes them across the network, it may happen that some cells have 9 UEs or some have 12 UEs for example. Therefore the output power and the energy consumption will not be the same for all cells. The energy consumption when using sleep mode reaches the consumption of no sleep mode in full load when we have 40W as output power. The constant part (energy consumption at no load) in this figure for the curve when no sleep mode is used is greater than that of the curve with using sleep mode, since they have four active radio units, only one active radio unit respectively. 4.2.2 Energy Consumption in Sites With 2x20W Configuration

In this MIMO configuration sites we have two radio units per each cell with a maximum output power of 20 W. The energy consumption of RBS with sleep mode on and without sleep mode is compared in Figure 4.6. For the output power less than 20 W, cells can use just one active radio unit with the other one switched off. When

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the output power increases above 20 W, then the other radio unit is switched on and a cell uses two active radio units to handle the traffic. The vertical axis is normalized to PRBS,FL,Sim3 the input power of the fully loaded site (with MIMO 2x20W configuration) in the simulator. Same as the MIMO configuration 4x10W, the curve shows that the energy consumption when sleep mode is on increases in sloped steps due to using the average energy consumption of RBS. At full load this curve reaches the curve which shows the energy consumption of the RBS when no sleep mode is used.

5 10 15 20 25 30 35 400

10

20

30

40

50

60

70

80

90

100Average Energy consumption of RBS 2x20W


[ %

Norm

aliz

ed t

o P

RB

S,F

L,S

im3 ]

no sleep mode

Sleep mode on

Figure 4.6: Average energy consumption of RBS over load in MIMO 2x20W.

4.3 Conclusion

The evaluation shows that using sleep modes results in less energy consumption compared to when no sleep mode is used in both configurations (4x10W and 2x20W). At full load energy consumption is same in two cases (using sleep mode and without it). It is clear that with sleep mode on, in each RBS, at medium load a saving of roughly 25% in 4x10W and 10% in 2x20W configuration is achievable and the saving increases at lower loads.

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5 Traffic Scenario The main goal with this chapter is to describe how we use a real traffic data to study how much energy can be saved by applying the different sleep modes illustrated in the previous chapters. A snapshot of a real network traffic is considered and prepared to be used in the simulator. The traffic data has been gathered from few hundred cells in a European city during 22 days in 2009 at RNC level over 15 minute intervals and include Circuit Switched (CS) and Packet Switched (PS) data. Figure 5.1 shows the average throughput (CS+PS) of the network over all cells versus time (days).

Figure 5.1: Average throughput of the network over time in HSPA system.

This traffic data is from an HSPA system; however the simulator implements for an LTE systems. The maximum throughput for HSPA systems is 7.2 Mbps and it is 15 Mbps for LTE system modeled in the simulator (the LTE system does not capture performance enhancing features such as spatial multiplexing, frequency-dependent scheduling etc). Therefore a scaling of 15/7.2 will map the HSPA performance to the LTE performance. Figure 5.2 shows the mapped data traffic to the LTE performance. Note that the average data traffic is higher for LTE compared to HSPA due to the higher capacity of LTE. As these two figures show, the network traffic exhibits a day and night pattern. The throughput is higher during the day and it is lower during the night. They indicate that some energy could be saved by turning off radio units during the time when the load of the cells is low (e.g. during the nights).

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Figure 5.2: Average throughput of the network over time in LTE system.

Figure 5.3 shows the cumulative distribution of the cells’ throughput in the network. As it is clear from the figure in more than 81% of the cases the cells had a throughput less than 2 Mbps (which is only 0.13 of the total capacity). Hence, by employing energy saving methods, a significant amount of energy could be saved in the network.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

10

20

30

40

50

60

70

80

90

100CDF of cells load

Throughput [Mbps]

[%]

Figure 5.3: Distribution of throughput for cells of the network.

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Figure 5.4 illustrates the algorithm which maps the data traffic in throughput [Mbps] to the output power [W]. By increasing the number of UEs per cell in the simulator, the resulting throughput and load ratio will be calculated and the load ratio versus throughput is obtained (as seen in Figure 5.5). The load ratio is increasing non-linearly over the throughput, because the inter-cell interference increases when the load increases.

Figure 5.4: Algorithm of mapping the throughput to the output power.

Throughput

[Mbps]

Mapping

throughput to load ratio

Load ratio

[%]

Output Power = PMAX x Load Ratio

Mapping load ratio to output power

Output power

[W]

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Figure 5.5: Average load ratio variation over throughput.

As explained in chapter 5 (equation 5.1), to get the output power it is enough to multiply the load ratio by Pmax (40 W). In Figure 5.6 the average output power of the network is shown over time.

Figure 5.6: Average output power of the network over time in LTE network.

Figure 5.7 shows the distribution of the cells’ output power in the network. In more than 80% of the cases the cells in the network had an output power of less than 2 W. Therefore in MIMO configuration by putting some RUs in sleep mode, a significant amount of saving can be obtained.

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0 5 10 15 20 25 30 35 400

20

40

60

80

100CDF of cells output power

output power [W]

[%]

Figure 5.7: Distribution of output power for cells in the network.

Based on the measurements presented in this chapter, many cells exhibit a day and night load pattern, and in the most cases (80%), cells were loaded by only 0.13 of the capacity. There are a few cells with a very high load. These observations indicate the potential for enhancing energy efficiency.

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6 Simulation Results 6.1 Introduction In this chapter the results of simulations is shown and we see how much energy saving can be expected by using the proposed sleep modes in chapter 5. The simulation results with the real traffic scenario (from chapter 5) is shown and analyzed. The results are classified in two categories:

1. Estimation of energy saving with measured traffic data (in the European city during 22 days)

2. Estimation of energy saving in different scales of measured traffic data (with the traffic of the European city during 22 days, scaled up or down)

6.2 Estimation of Energy Saving with Measured Traffic Data The traffic data of the European city (chapter 5) has been gathered from few hundred cells during 22 days over 15 min intervals. Figure 6.1 reveals the cumulative number of active RUs which were needed during this period. Figure 6.1 shows the cumulative number of active RUs a) in 4x10W configuration and b) in 2x20W configuration. In both configurations in more than 98% of cases, less than two active RUs were needed to handle the traffic load (and at least 3 or 1 can be turned off respectively). Therefore it is obvious that a significant amount of saving is expected to be achieved by using sleep modes methods.

0 1 2 3 40

0.2

0.4

0.6

0.8

1Cumulative number of active RUs-4x10W

Number of active RUs/cell

[%]

0 1 20

0.2

0.4

0.6

0.8

1Cumulative number of active RUs-2x20W


[%

]

a) b)

Figure 6.1: Cumulative number of active RUs during the 22days. a) Sites with 4x10W configuration b) Sites with 2x20W configuration.

By applying the method in Figure 4.1 and Figure 4.4 to this traffic data the RBS energy consumption at each time in two types (using sleep modes and without sleep mode) for both of the MIMO configurations (4x10W and 2x20W) is obtained. The

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method in Figure 6.2 shows how energy consumption in each RBS is calculated with applying sleep modes and with out sleep mode.

Figure 6.2: Algorithm of calculating energy saving.

6.2.1 Energy Saving of Sites with the MIMO 4x10W Configuration

Figure 6.3 shows the total network energy consumption during 22 days with applying sleep modes and without sleep modes when the configuration is 4x10W.

0 2 4 6 8 10 12 14 16 18 20 220

50

100

150

200Total energy consumption of the network - 4x10W

time [days]

Energ

y c

onsum

ption [

KW

]

No sleep mode

Sleep mode on

Figure 6.3: Total energy consumption of the network.

Algorithms in figure

4.4 and 4.1

Site energy consumption with

sleep mode on

Site energy consumption with sleep mode off

Cell output power

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The figure above (6.3) shows that the energy consumption of the network is in average 170 KW while when applying the different sleep modes the energy consumption could reduce to 74 KW. A saving around 96 KW could be achieved by applying the sleep modes during these 22 days which corresponds to about 56.5% saving (the total energy consumption of the network during 22 days is 88480 KWh in other words around 37 KWh/RBS/day which with applying sleep modes decreases to 38400 KWh and 16 KWh/RBS/day; a saving around 21 KWh/RBS/day is obtained). The network consists of sites with one to five sectors. Figures 6.4 to 6.8 show the distribution of RBS energy consumption in the network during 22 days for 1-sector sites to 5-sector sites respectively; with sleep mode and without sleep mode (in sites with 4x10W configuration). From these figures, it is clearly seen that:

• The energy consumption of the 1-sector radio base station ranges from roughly 620 W to 678 W without sleep mode and from 224 W to 373 W with applying sleep modes(Figure 6.4);

• The energy consumption of the 2-sector radio base station ranges from roughly 1096 W to 1210 W without sleep mode and from 303 W to 709 W with applying sleep modes (Figure 6.5);

• The energy consumption of the 3-sector radio base station ranges from 1571 W to 1853 W without sleep mode and from 382 W to 1334 W when sleep mode is on (Figure 6.6);


• The energy consumption of the 5-sector radio base station ranges from 2521 W to 2821 W without sleep mode and from 843 W to 1610 W when sleep mode is on (Figure 6.8).

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0 500 1000 1500 2000 2500 30000

10

20

30

40

50

60

70

80

90

100CDF: RBS energy consumption of 1-sector sites - 4x10W

Energy consumption [W]

[%]

No sleep mode

Sleep mode on

Figure 6.4: Distribution of RBS energy consumption in the network in 1-sector sites.

0 500 1000 1500 2000 2500 30000

10

20

30

40

50

60

70

80

90



[%]

No sleep mode

Sleep mode on


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0 500 1000 1500 2000 2500 30000

10

20

30

40

50

60

70

80

90



[%]

No sleep mode

Sleep mode on


0 500 1000 1500 2000 2500 30000

10

20

30

40

50

60

70

80

90



[%]

No sleep mode

Sleep mode on


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0 500 1000 1500 2000 2500 30000

10

20

30

40

50

60

70

80

90



[%]

No sleep mode

Sleep mode on

Figure 6.8: Distribution of RBS energy consumption in the network in 5-sector sites. Figure 6.9 summarizes the information in the Figures 6.4 to 6.8 as the distribution of RBS energy consumption for the whole network (including all the 5 types of sites mentioned above) during 22 days. The minimum and maximum of RBS energy consumption in 4x10W configuration without sleep mode are around 620 W and 2821 W respectively; while they are 224 W and 1610 W respectively when sleep mode is used.

0 500 1000 1500 2000 2500 30000

10

20

30

40

50

60

70

80

90

100CDF: RBS energy consumption-4x10W


[%]

No sleep mode

Sleep mode on

Figure 6.9: Distribution of RBS energy consumption in the network.

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200 400 600 800 1000 1200 1400 1600 18000

10

20

30

40

50

60

70

80

90

100CDF: RBS energy saving - 4x10W

Energy saving [W]

[%]

Figure 6.10: Distribution of RBS energy saving in the network.

Figure 6.10 shows the distribution of energy saving (Figure 6.1) in radio base stations in the network during 22 days in 4x10W configuration. The smallest energy saving in a radio base station is 297W and the largest energy saving is 1685W. The results of this section show that the average energy saving in 4x10W configuration with using sleep mode is around 21 KWh/RBS/day (56% saving). The minimum RBS energy consumed during these 22 days is by a 1-sector site and corresponds to 620W, which can decrease to 224W by applying sleep mode. The maximum RBS energy consumption corresponds to a 5-sector site about 2821W which using sleep mode could be reduced to 1620W.

6.2.2 Energy Saving of Sites with the MIMO 2x20W Configuration Figure 6.11 shows the average RBS energy consumption during 22 days with applying sleep modes and without sleep modes when the configuration is 2x20W.

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0 2 4 6 8 10 12 14 16 18 20 220

20

40

60

80

100

120

140

160

180

200Total energy consumption of the network - 2x20W

time [days]

Energ

y c

onsum

ption [

KW

]

No sleep mode

Sleep mode on

Figure 6.11: Total energy consumption of the network.

The figure above (6.11) implies that the energy consumption of the network is in average 100 KW while with applying the different sleep modes the energy consumption could reduce to 70 KW. A saving around 30 KW could be achieved by applying the sleep modes during these 22 days which corresponds to about 30% saving (the total energy consumption of the network during 22 days is 52070 KWh in other words 22 KWh/RBS/day which with applying sleep modes decreases to 36556 KWh and 15.5 KWh/RBS/day; a saving around 6.5 KWh/RBS/day is obtained). Figures 6.12 to 6.16 show the distribution of RBS energy consumption in the network during 22 days for 1-sector sites to 5-sector sites respectively; with sleep mode and without sleep mode (in MIMO 2x20W configuration). From these figures, it is clearly seen that:

• The energy consumption of the 1-sector radio base station ranges from roughly 400 W to 457 W without sleep mode and from 224 W to 364 W with applying sleep modes(Figure 6.12);

• The energy consumption of the 2-sector radio base station ranges from roughly 655 W to 769 W without sleep mode and from 303 W to 587 W with applying sleep modes (Figure 6.13);


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• The energy consumption of the 5-sector radio base station ranges from 1420 W to 1718 W without sleep mode and from 810 W to 1351 W when sleep mode is on (Figure 6.16).

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Figure 6.17 summarizes the information in the Figures 6.12 to 6.16 as the distribution of RBS energy consumption for all sites in the network (including all the 5 types of sites mentioned above) during 22 days. The minimum and maximum of RBS energy consumption in 2x20W configuration without sleep mode are around 400 W and 1444 W respectively; while they are 224 W and 1003 W respectively when sleep mode is used.

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Figure 6.17: Distribution of RBS energy consumption in the network during 22 days.

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Figure 6.18: Distribution of RBS energy saving in the network during 22 days.

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Figure 6.18 shows the distribution of energy saving (Figure 6.1) in radio base stations in the network during 22 days in 2x20W configuration. The smallest energy saving in a radio base station is 88W and the largest energy saving is 617W. The results of this section show that the average energy saving in 2x20W configuration with using sleep mode is around 6.5 KWh/RBS/day (30% saving). The minimum RBS energy consumed during these 22 days is by a 1-sector site and corresponds to around 400W which can decrease to 224W by applying sleep mode. The maximum RBS energy consumption corresponds to a 5-sector site about 1718W which using sleep mode could be reduced to 1351W.

7.3 Estimation of Energy Saving in Different Scales of Measured Traffic Data This section discusses the variation of saving when the traffic changes. For studying this subject the data traffic from the European city has been scaled up and down. In this context and to see how the number of needed active RUs changes when the traffic load changes, Figures 6.19 and 6.20 show the cumulative number of active RUs which would be needed if the traffic load for instance had been 400% (4 times the measured traffic load used before), or 700% (7 times the measured traffic load used before) during 22 days, both for 4x10W and for 2x20W configurations. In both figures it can be seen that the number of active RUs per cell will increase with load. Therefore the amount of saving by using sleep modes is expected to decrease.

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Figure 6.20: Cumulative number of active RUs over different scaled traffic in 2x20W configuration. Energy consumption of each RBS per day with and without sleep mode is shown in Figure 6.21 and 6.22 in two different configurations 4x10W and 2x20W respectively while the traffic has been scaled up (more than 100%) or down (less than 100%). It can be seen that in these two configurations with applying sleep modes we can have saving and when the traffic increases the saving decreases. From Figure 6.21 for 4x10W configuration the following information can be seen:

• When traffic scales by 10%, 100%, 200% and 300% the energy consumption of each RBS in average is around 37 KWh/RBS/day in no sleep mode and reduces to roughly 16 KWh/RBS/day when the sleep mode is on; an amount of saving around 21 KWh/RBS/day (56%) is obtained.

• When traffic scales by 400% the energy consumption of each RBS in average is around 39 KWh/RBS/day in no sleep mode and reduces to roughly 19 KWh/RBS/day when the sleep mode is on; an amount of saving around 20 KWh/RBS/day (51%) is obtained.

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• When traffic scales above 900% the energy consumption of each RBS in average is around 52 KWh/RBS/day in no sleep mode and reduces to roughly 49 KWh/RBS/day when the sleep mode is applied; an amount of saving around 3 KWh/RBS/day (5%) is obtained.

From Figure 6.22 for 2x20W configuration the following information can be seen:

• When traffic scales by 10%, 100%, 200% and 300% the energy consumption of each RBS in average is around 22 KWh/RBS/day in no sleep mode and reduces to roughly 15.5 KWh/RBS/day when the sleep mode is on; an amount of saving around 6.5 KWh/RBS/day (30%) is obtained.

• When traffic scales by 400% the energy consumption of each RBS in average is around 23 KWh/RBS/day in no sleep mode and reduces to roughly 17 KWh/RBS/day when the sleep mode is on; an amount of saving around 6 KWh/RBS/day (26%) is obtained.

• When traffic scales above 800% the energy consumption of each RBS in average is around 36 KWh/RBS/day in no sleep mode and reduces to roughly 35 KWh/RBS/day when the sleep mode is applied; an amount of saving around 1 KWh/RBS/day (3%) is obtained.

Figures 6.21 and 6.22 show that the energy consumption and saving in the range 10%-300% of measured traffic data is same (56%), and starts to decrease for higher traffic loads since greater number of RUs are needed (Figure 6.19 and 6.20).

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Figure 6.22: Energy consumption of RBS per day over different scaled traffic in 2x20W configuration. It is seen that by increasing the traffic load to 1500% of the measured data traffic (without modifying the network topology), the saving decreases from 56% to 5% in 4x10W configuration and from 30% to 3% in 2x20W configuration; however, in real mobile networks when the traffic increases operators will add new RBSs and capacity to handle the traffic, so it is likely that there will always be energy to be saved.

6.4 Conclusion Looking at cumulative number of active RUs in both configurations (4x10W and 2x20W) revealed that a significant amount of saving was expected to be obtained with applying sleep mode. The results from this section show that it is feasible to decrease energy consumption by applying sleep modes. The methods in Figure 4.1 and 4.4 were applied to the European traffic data and the results showed that the average RBS energy saving of around 21 KWh/RBS/day (56% saving) in 4x10W configuration and 6.5 KWh/RBS/day (30% saving) in 2x20W configuration is achieved during 22 days with applying sleep mode. The following results are obtained for RBS energy consumption:

• when no sleep mode is used: − The minimum energy consumption of RBS related to 1-sector sites

are around 620W in 4x10W and 400W in 2x20W configuration respectively

− The maximum energy consumption of RBS related to 5-sector sites are around 2821W in 4x10W and 1718W in 2x20W configuration respectively

• when sleep mode is used:

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− The minimum energy consumption of RBS related to 1-sector sites are around 224W in both configurations

− The maximum energy consumption of RBS related to 5-sector sites are around 1610W in 4x10W and 1351W in 2x20W configuration.

The distribution of RBS energy consumption in different site types (1-sector sites, 2-sector sites etc.) was studied and the results show that the sites with one sector were the lower consumers and five sector sites were the higher ones. Estimating energy saving in different scales of measured data showed that when the traffic load increases, greater numbers of RUs per cell will be needed to handle the traffic and saving will decrease. When the load is 10-300% of the measured data traffic, with sleep modes a saving of 21KWh/RBS/day (i.e. 56%) in the 4x10W configuration and a saving of 6.5 KWh/RBS/day (i.e. 30%) in 2x20W configuration are obtained. For this range of load we have the same saving since in most of the time the network uses less than 2 active RUs to handle the traffic. The saving decreases to 1 KWh/RBS/day (i.e. 5%) in 4x10W and 1 KWh/RBS/day (i.e. 3%) in 2x20W when the data traffic is scaled by 1500% of the measured data. However, an increase by 1500% is rather unlikely since the operator would in these circumstances add more RBSs to handle the traffic increase. As such, we can conclude that the implementing of the presented sleep modes can provide a considerable reduction in the energy consumption of the LTE mobile networks.

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7 Summary 7.1 Discussion and Conclusion In this thesis, an energy saving feature (referred to as sleep modes) which activates RUs as a function of load has been studied and the corresponding savings have been evaluated. Energy consumption of different components of the radio base station was modeled. The energy consumption of radio base stations has been modeled in two types of MIMO (4x10W and 2x20W) configuration sites over different load scenarios. The results show that the implementation of the presented technique, using sleep modes in radio base stations can provide a considerable reduction in the energy consumption. The energy saving potential of putting the radio units in sleep mode was investigated with a traffic load scenario measured during 22 days. This traffic data has been gathered from few hundred cells from a European city. The results over this period, indicate a possibility of an energy saving of 21KWh/RBS/day (56% saving) in 4x10W configuration and 6.5 KWh/RBS/day (30% saving) in 2x20W configuration. The minimum RBS energy saving is 297W and 88W in 4x10W and 2x20W configuration respectively; the maximum energy saving is 1685W and 617W in 4x10W and 2x20W configuration respectively. Estimated energy consumption in different scales of measured data traffic shows that with a drastically increase in the traffic load, the saving will decrease. However, in real mobile networks when the traffic demand increases, operators will add new RBSs and capacity to handle the traffic, so there will always be low-loaded RBSs and there will always be energy to be saved by applying sleep modes. When spatial multiplexing and spatial diversity are used in the cells the throughput which is received by the mobile will be higher. Since spatial multiplexing and spatial diversity are not modelled in our simulator, therefore our energy saving estimation results might be somewhat pessimistic (i.e. more energy can be saved than what we estimate). In summary we can conclude that applying sleep mode method on RBS cells can effectively reduce the energy consumption of mobile networks without any degradation of the network performance.

7.2 Further Work

The following steps can be considered as further work in this area:

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• Research activities are required in the area of energy efficiency metrics. This involves monitoring energy efficiency in the network, and identifying the cells which are least energy efficient. This is crucial in order to inform the operator about potential base station upgrade as well as to identify those base stations that can be put into sleep mode during low load. To do this first we need to find which metrics are most suitable for measuring energy efficiency. There are different metrics that are used to measure energy efficiency. In general there is no common view on energy efficiency metrics and a wide range of different metrics are used by vendors and operators. Today RBS energy efficiency is typically measured in terms of energy [J / RBS] or average power per RBS [W / RBS]. In order to take variations in traffic, climate etc into consideration the average power should be based on a yearly average value. Energy consumption of a network is measured as the total consumed electrical energy [kWh] measured over a given time unit, typically one day, one week, or one year. Thus, energy consumption should be expressed in the unit [kWh/day] rather than as an average power [W].

• Extending the scenario from hexagonal network to real network scenarios (e.g. hierarchical, multi-carrier networks).

• Implement the algorithms for minimizing energy consumption in networks in order to have a smart energy efficient network. This includes three parts: detection, compensation and verification. Detection is about recognizing the cells which can go to sleep mode. Further, we need to handle the coverage of the sleeping cells, which is corresponding to compensation part. During verification we should compare the energy consumption of the network before and after optimization, and consider the impact on coverage, downlink and uplink quality, energy consumption in the surrounding cells which is corresponding to the verification part.

• Adaptive deployment: during low traffic hours some cells can be turned off while antennas in neighboring cells are tilted upwards to cover the area of the sleeping cells. Adaptive deployment method can also be used between the different RATs. Studying the impact on Key Performance Indicators (KPIs) like coverage, downlink and uplink quality should be considered.

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References

[1] Ericsson white paper, “Sustainable energy use in mobile communications”

[2] Fred Richter, Albrecht J. Fehske, and Gerhard P. Fettweis,“Energy Efficiency Aspects of Base Station Deployment Strategies for Cellular Networks”, 70th IEEE Vehicular Technology Conference, Anchorage, Alaska, USA, 20-30 September 2009

[3] Marco Ajmone Marsan, Michela Meo, “Energy Efficient Management of two Cellular Access Networks” GreenMetrics 2009 Workshop (In conjunction with ACM SIGMETRICS/Performance 2009) 15 June 2009, (Seattle, WA, USA)

[4] R. Kumar and L. Mieritz, “Conceptualizing ‘Green IT’ and data centre power and cooling issues,” Gartner, Research paper G00150322, September 2007.

[5] Lars Ahlin, Jens Zander, Ben Slimane, “Principles of wireless communications” Studentlitteratur, 1997

[6] 3GPP TS 36.942, ‘Universal Terrestrial Radio Access (UTRA); Radio Frequency (RF) system scenarios’ V8.1.0

[7] 3GPP TS 36.133, ‘Evolved Universal Terrestrial Radio Access (E-UTRA); Requirements for support of radio resource management’ V8.4.0

[8] A. Simonsson and A.Furuskär, ‘Uplink Power Control in LTE-Overview and Performance’, Vehicular Technology Conference (VTC-Fall), 2008

[9] 3GPP TS 36.213, ‘Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures’ V8.4.0

[10] 3GPP TR 36.814, ‘Further Advancements for E-UTRA Physical Layer Aspects’, V0.4.1(2009-02)

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Energy Efficiency in Wireless Networks

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