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978-1-4244-7173-7/10/$26.00 2010 IEEE

Head Exposure to Cellular Telephones:A System-Level Study

Hayat Abdulla and Renny E. Badra

Departamento de Electrnica y Circuitos

Universidad Simon BolivarCaracas, Venezuela

[email protected], [email protected]

AbstractThis work is aimed at quantifying the effects of a

number of system-level parameters and conditions on the amount

of non-ionizing radiation dissipated in the head of cell phone

users in the 850 MHz band. The three major cellular technologies

of today, namely, GSM, CDMA2000 1X and UMTS, are

evaluated. Five randomly chosen commercial products from each

technology are investigated in terms of their Specific Absorption

Rate (SAR) and maximum transmit power, as reported by the US

Federal Communications Commission (FCC). A statistical system

model for urban and suburban cells is used to calculate, with the

help of the uplink power budget and the corresponding powercontrol mechanisms, the statistics of the phone transmit power

via Monte Carlo simulations. A linear model relating SAR and

transmitted power is then used to obtain SAR statistics over the

entire cell. Results show some minor differences among the

technologies considered, and also between urban and suburban

environments, but more importantly, they show a strong

correlation between SAR levels and the percentage of cell area

that is covered, which in turn depends on key system design

parameters such as cell density, cell tower height, and the use of

certain RF techniques.

Keywords-component; Specific Absorption Rate; Non-ionizing

radiation; GSM; CDMA2000 1X; UMTS.

I. INTRODUCTION

Public concern regarding the potential harmful effects ofelectromagnetic emissions from cell phones on the head ofvoice service users has triggered in the past two decades a largenumber of research efforts, with results that can be qualified asinconclusive. While some studies indicate no relationshipbetween cell phone use and the incidence of brain tumors,some others suggest there is a slight to moderate statisticalcorrelation between the two. It can be stated that the questionof potential health risks associated to using cell phones close tothe head is still open.

Some of these studies have identified three factors aspotentially influential with regard to the possible health risksassociated to cell phone use: the level of radiation, the durationof the exposure and the age of the user. While the two latterfactors are entirely governed by consumer habits, the first one(level of emissions) can be effectively controlled bymanufacturers and service providers. Regulatory agencies have

strongly enforced the compliance of standards for maximumlevel of emissions by cell phone manufacturers. However, littleattention has been paid to the potential benefits derived fromactions and measures that can be implemented by cellularoperators in order to reduce the level of emissions on the headof cell phone users, which is precisely the subject of this effort.

This work is aimed at quantifying the influence of systemdesign parameters and strategies on the level of head radiationon cellular voice services of three major cellular technologies.

In this article, Sect. II establishes a key relationship betweentransmit power and head emissions. Sect. III describes the radiopropagation model for the cellular channel, while Sect. IVdiscusses the cellular uplink budget and Sect. VI presents theoverall simulation strategy. Sect. VII contains the main results,and Sect. VIII states the conclusions from the study.

II. SARAND TRANSMIT POWER

The Specific Absorption Rate (SAR) is the most commonmeasure of the level of exposure of organic tissue to non-ionizing electromagnetic radiation [1]. It is defined as theamount of power dissipated per unit mass, and its units arewatts per kilogram (W/kg). The effect of non-ionizing radiation

over organic tissue is heat. Experimentally, SAR is obtained bytaking an average of the power dissipated in heat over a certaintissue mass (typically 1 or 10 grams). Human dummies calledphantoms filled with liquids emulating the electrical propertiesof the body part under study are used to perform SARmeasurements, following tightly established procedures. In theUS, the Federal Communication Commission (FCC) sets theparameters and procedures for SAR measurements, and alsopublishes the results for all commercially available cell phones.Head SAR measurements are given a PASS grade if they fallbelow the 1.6 W/kg. limit set by the American NationalStandards Institute (ANSI) for head radiation over generalpopulation users [2,3]. SAR measurements for every model ofcell phone are obtained at maximum transmit power, and are

tested under a number of different scenarios and conditions.For a given product to be authorized for commercial use, all ofits SAR results are required to pass [3].

Heat produced from electromagnetic radiation is the resultof electric currents induced in the organic tissue by the electric


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and magnetic fields resulting from the close presence of the cellphone antenna. Thus, there is a direct relationship between thepower emitted by the device and the heat that it produces in theneighboring tissue, as measured by its SAR. Furthermore, itcan be stated that such relationship is strictly linear, that is

SAR = PtK

SAR, (1)

where SAR is the device measured SAR under a certain set of

conditions, is the device average transmit power at thetime of the measurement, andKSAR is a proportionality constantthat depends on the test conditions and the device itself. Oncean experimental SAR measurement and its correspondingtransmit power are available, the parameter KSAR for thatproduct under such conditions is obtained from (1). As thephone moves through the cell, its transmit power changes as aresult of the corresponding power control mechanisms.However, the linear model in (1) makes it possible to obtain anestimate of the SAR for that phone in any location in the cell,once the transmit power for such location is calculated viasystem-level modeling and simulation.

III. PROPAGATION MODEL

The uplink cellular propagation model employed in allsimulations accounts for signal power loss incurred betweenthe transmit and receive antennas, that is, excluding all cellularsystem equipment. It is comprised of five terms,

LP= L

D+ L

Sh+ L

S+ L

Pn+ L

B(2)

whereLDis the distance-dependent propagation loss, LSh is theshadowing factor, LS is the small-scale fading component, LPnis the building/car penetration loss andLB is the power loss dueto interaction with the users body. All five terms in (1) aregiven in dB. A brief description of each of them is presented

next.The distance-dependent propagation loss LD is estimated

using the Walfish-Ikegami (W-I) model proposed by COST-231 [4]. Such model considers two propagation scenarios: line-of-sight (LOS) and obstructed or non-line-of-sight (NLOS).The LOS model is essentially an adjusted free-spacepropagation model, while the NLOS model considers, inaddition to free-space losses, those caused by energy dispersionand diffraction from buildings and obstructions. The W-Imodel has been found to be rather accurate in medium to largeurban cells, for distances between 20 and 5000 meters. Table Ishows the parameters employed in this work for the distance-dependent W-I model.

Before describing the signal fading and loss components ofthe channel model, it should be remarked that loss (as well asfading) margins cannot be directly used in this type of study,which is not concerned with estimating the maximum allowablepropagation loss, as usual in regular link budget analysis;instead, this study requires the statistical estimation of theactual propagation loss, which in turn necessitates the use ofstatistical models and Monte Carlo trials, as will be explained.

The shadowing factorLSh accounts for large-scale variationsof signal strength due to obstructions located in the vicinity of

the mobile transmitter. It is accurately described by a randomvariable (RV) with a log-normal probability distribution [4],that is, the distribution of the RV expressed in dB is normal. Inthis study, the mean and standard deviation of such RV havebeen set to 0 dB and 8 dB, respectively, in agreement withwidely used models [5].

TABLE. 1. PARAMETERS OF THE W-I PROPAGATION MODEL

Parameter Urban Suburban

Frequency 850 MHz

Cell Radius 1-2 km 2.5 5 km

Mobile station antenna height 1.5 m

Base station antenna height 30 m 20 m

Angle between LOS path and street 90

Average building height 24 m 9 m

Inter-building distance 15 m 15 m

Street width 30 m

The small-scale fading component LS is aimed at modelingsignal variations caused by multipath propagation over shortdistances. The amplitude of this fading factor is modeled by aRice RV in LOS siuations, and a Rayleigh RV in NLOSscenarios [4]. In the case of the Rice RV, the ratio between theamplitude of the specular component and the RV standarddeviation is set to 2. The mean of both Rice and Rayleigh RVsare set to 1, so that the amplitude of the faded signal could beobtained by simply multiplying the incoming signal amplitudby the corresponding RV. The small-scale fading componentLSis obtained by taking 20 log () of the corresponding RV.

It should be noted that bothLSh andLSare RVs that may takeon either possitive or negative values (in dB), which impliesthat such shadowing factors may actually represent a gaininstead of a loss. Such gain (or loss) must be interpreted as

relative to the average power of the signal for that particularpath distance.

The building penetration power loss has been reported to beabout 10 dB, with additional losses as a function of the distanceto the nearest window [6]. On the other hand, if the user isinside a vehicle, the mean penetration loss is estimated to bearound 8 dB [7]. Accordingly, in this work the penetration loss(building or vehicle) is modeled as a uniform RV taking valuesbetween 0 and 12 dB in NLOS situations, and is set to zero forLOS trajectories.

As seen, three of the terms in the proposed propagationmodel (2) require the determination of the type of propagationtrajectory (LOS/NLOS) incurred by the signal. A two-tier

statistical model for this binary RV has been used in this study,which is in agreement with previous models [5]. It postulatesthat as mobile units move closer to the cell tower, theprobability of having a LOS propagation path increases.Accordingly, the probability of NLOS for distances above acertain threshold is set to one, and then it decreases linearly asthe path distance drops below such threshold, becoming zero inthe vicinity of the cell tower. The threshold distance is set tohalf the cell radius in all simulations, which results in 75% ofNLOS locations in all cells, regardless of their size.


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Finally, the RF power loss caused by electromagneticinteraction with the human body of the userLB is set to aconstant value of 3 dB [8].

IV. LINKBUDGET ANALYSIS

Link budget analysis seeks to determine the minimum

instantaneous transmit powerPmin that would be required inorder to close the uplink, that is, the RF link from the MobileStation (MS) to the Base Station (BS). The link budgetequation expressed in dBm and dB is

Pmin

= Rsen + Lp + Lii

Gii

, (3)

whereRsen is the receiver sensitivity, Lp is the propagation lossdefined in (2),Li is the i-th system loss factor and Gi is the i-thsystem gain factor. Receiver sensitivity is calculated using

Rsen = 174dBm

Hz+10log(R) + NF+

Eb

No

req

, (4)

whereR is the compressed voice information bit rate in bits persecond, NFis the receiver noise figure in dB, excluding cablesand connectors, and (Eb/No)req is the minimum requiredinformation bit energy to noise ratio, also in dB. Theinformation bit rate corresponds to the uncoded digital voicestream (9.6 kbps for 1X and 12.2 kbps for UMTS).

Equation (4) can be equivalently written as:

Rsen = 174dBm

Hz+10log(B) + NF+ SNRreq , (5)

where B is the signal bandwidth in Hz and SNRreq is therequired signal-to-noise ratio in dB. For convenience, (5) isoften used instead of (4) for systems based in Time-DivisionMultiple Access (TDMA) such as the GSM system in ourstudy, which has a nominal signal bandwidth of 200 kHz.

The system loss factors Li considered in (3) for the GSMsystem are the other-cell co-channel interference factor and thebase station cable losses. In the CDMA 1X and UMTS systemsthe loss factors are the cell load factor (a measure of co-channelinterference, [8]), the base station cable losses, and the other-channel power allocation factor, 10log(), where

=P

other

Ptotal

, (6)

with Potherbeing the power allocated by the MS transmitter to

overhead code channels different from the code channelcarrying the voice bit stream, and Ptotal being the total MStransmit power.

The gain factors Giconsidered in (3) for the GSM systemare the base station antenna gain, and the BS antenna diversitygain. For the CDMA 1X and the UMTS systems the gainfactors included in (3) are the base station antenna gain and thesoft handover gain, which is defined as the amount of transmitpower that the mobile station is able to save due to soft

handover (SHO) in progress. In both CDMA systems the effectof antenna diversity gain is included in the estimated(Eb/No)req, so it is not explicitly considered as a part of the linkbudget itself.

BS antenna gain is obtained by measuring the angle ofarrival of the direct path between MS and BS, and reading thecorresponding gain from the horizontal radiation pattern of theselected antenna. Antenna data has been extracted from a

commercial product in the 850 MHz band [9], and is the samefor all three technologies.

About one-third of the mobiles in any given CDMA cell aretypically in SHO situation. The maximum value of SHO gainhas been estimated to be in the range of 2 to 4 dB, for both 1Xand UMTS, although its exact value depends on numerousfactors [8, 10]. The variability of this parameter has made itdifficult for researchers to come up with a statistical model forits behavior. In this study, an empirical SHO gain probabilitydistribution obtained for the 1X system is employed [11],which is given in Table II. The behavior of the random variablewithin the limits of each SHO gain interval in Table II isassumed to be uniform. From this distribution, the average

SHO gain is found to be about 1.3 dB. For simplicity, in thisstudy SHO gain is applied only to mobiles that wouldotherwise be out of coverage, which is a conservative scenario,since CDMA mobiles will sometimes go to SHO even whencoverage is guaranteed by a single cell connection.

TABLE II. DISTRIBUTION FORSHO GAIN [11].

Interval of SHO Gain values Probability (%)

0 dB < SHO gain < 1 dB 60.2





The full list of parameters used in (3), as employed in thesimulations, are given in Table III for each of the technologiesunder study.

TABLE III. LINK BUDGET PARAMETERS.

Parameter GSM CDMA1X UMTS

(Eb/No)req N/A 3.0 dB 5.0 dB

SNRreq 9.0 dB N/A N/A

Receiver Noise Figure 5.0 dB

Receiver Sensitivity -107.0 dBm -126.6 dBm -123.1 dBm

BS Antenna Type Sectorized, 90 beamwidthBS Antenna Max Gain 15.1 dBi

BS Cable losses 2 dB

Co-channel interference 2.0 dB N/A N/A

Uplink Load Factor N/A 3.0 dB (50% load)

10log() [eq. (6)] N/A 1.5 dB 1.8 dB

MS ant. gain + cable loss 0 dB

BS ant. diversity gain 3.0 dB Included in (Eb/No)req


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V. MONTE CARLO SIMULATIONS AND POWERCONTROL

The methodology employed in this work establishes thateach investigated cell is assumed to be hexagonal, with the cellradius as one of the controlled parameters. The spatialdistribution of the cell locations to be simulated is determinedby a rectangular grid that is scaled in space so that there is anapproximate number of 26,000 simulation points within eachcell, regardless of its radius. Simulation locations which are

closer than 20 meters from the cell center are discarded,following restrictions of the W-I propagation model. TenMonte Carlo trials are performed for each cell location, andtheir results are individually recorded for statistical purposes.This simulation set-up results in approximately 260,000 MonteCarlo trials uniformly spread over each tested cell.

Once the minimum required MS instantaneous transmitpowerPmin for each of the Monte Carlo trials is obtained usingthe statistical models presented in Sections III and IV, thecorresponding MS transmit powerPt is determined using thepower control algorithms that are inherent to each technologyunder study. Power control is accomplished in all threetechnologies by means of a closed-loop mechanism [4]. Whilethe power control algorithm for both CDMA systems (1X andUMTS) is based on fast base station feedback of channelconditions (in the order of one-thousand commands persecond), the power control in GSM, on the other hand, isrelatively slow and subject to significant transient delays. Inorder to employ a common set of conditions for all threesystems, voice users are assumed to be static or low-speedusers (pedestrians), so that any transient aspects of the behaviorof the respective power control algorithm are assumed to havedied out, and therefore are not considered.

Table IV presents the parameters associated to the powercontrol mechanisms of the three technologies under study, asobtained from the standards and the technical data sheet of thephone models considered in the study. For pedestrian users, the

effect of the closed-loop power control algorithms can beassumed to simply adjust the actual transmit powerPt of thecell phone to the power step that is immediately above theminimum required MS instantaneous transmit power Pmin.When Pmin, including SHO gain if applicable, exceeds themaximum transmit power given in Table IV, the cell phone isconsidered to be out of coverage, and the corresponding MonteCarlo trial is excluded from the calculation of power and SARstatistics.

TABLE IV. POWER CONTROL PARAMETERS.

Parameter GSM 1X UMTS

Maximum Power 33 dBm 25 dBm 23 dBm

Minimum Power 5 dBm -50 dBm -50 dBmGranularity 2 dB 1 dB 1 dB

Finally, in order to obtain the statistics of the SAR using(1), the average transmit power needs to be calculatedfrom the instantaneous transmit powerPt available from eachMonte Carlo trial. The average transmit power in all threetechnologies is reduced by the so-called voice activity factorv,which can be defined as the ratio of average transmit power topeak transmit power. Table V shows the assumed values forv.

In the GSM system, v is also affected by the transmission ofsilence descriptor frames and the Slow Associated ControlChannel (SACCH). In both CDMA systems, v is affected bythe overhead code channels (pilot, etc). Additionally, in GSMsystems the average transmit power is further reduced by theTDMA duty cycle factor, which accounts for the fact that onlyone out of eight time slots is actually used by the mobile stationtransmitter. Thus, the TDMA duty cycle factor in GSM reducesthe average transmit power by1/8, or 9.0 dB.

TABLE V. VOICE ACTIVITY FACTORS EMPLOYED IN THE STUDY.

Voice Activity Factor v GSM 1X UMTS

Percent 70% 67% 67%

VI. RESULTS AND ANALYSIS

Five cell phone models for each of the technologies understudy were randomly selected. All of the telephones aredesigned to operate in the cellular (850 MHz) band, althoughsome may also operate in other bands. Two parameters wereextracted from the SAR test reports published by the FCC [12]:the maximum SAR for 850 MHz measurements, and the

average transmit power at which such SAR value was obtained.The proportionality constant KSAR is then obtained for eachmodel using (1). Parameters for the fifteen randomly chosenphone models are shown in Table VI. In the case of GSMphones, average power has been corrected to account for theTDMA duty cycle factor.

TABLE VI. CELL PHONE PARAMETERS.

GSM Model1 Model2 Model3 Model4 Model5 Average

SAR[w/kg]

0.840 0.776 0.660 1.24 0.476 0.798

[dBm] 23.97 23.22 22.92 22.97 20.06 22.42

KSAR [kg-1] 3.368 3.698 3.409 6.259 5.912 4.529

1X Model1 Model2 Model3 Model4 Model5 Average

SAR[w/kg]

1.160 0.721 1.090 1.130 0.728 0.996

[dBm] 23.30 24.87 25.07 25.20 25.64 24.82

KSAR [kg-1] 5.426 2.349 3.392 3.413 1.988 3.314

UMTS Model1 Model2 Model3 Model4 Model5 Average

SAR[w/kg]

1.06 0.876 1.00 1.08 0.706 0.944

[dBm] 23.00 22.05 23.70 23.60 22.94 23.06

KSAR [kg

-1

] 5.313 5.464 4.266 4.714 3.588 4.669

Although the random sample size is relatively small, itappears as if CDMA1X phones do better in terms ofKSAR thanGSM or UMTS phones; that is, on the average, thetransmission power in 1X phones translates into less of SARthan in other technologies (about 25% less). Nevertheless,larger sample of models should be used in order to corroboratethis statement.


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Monte Carlo simulations were divided into two sets. In thefirst set of tests, cell sizes are adjusted in order to provide thesame quality of coverage (90% of cell area are is covered) forall three technologies. In the second set of trials, all cell radiiare fixed (2 km. for urban cells and 5 km. for suburban cells).Two performance indicators are defined: the average SAR andthe fraction of simulation points with SAR higher than 0.2w/Kg, which is one-tenth of the limit set by the InternationalCommission for Non-Ionizing Radiation Protection (ICNIRP)for head radiation over general population users [1]. Thisfraction is called F and is expressed as a percentage of allsimulation points within coverage for each cell. Monte Carlotrials that result in the cell phone being out of coverage areexcluded from both indicators.

Tables VII and VIII display the results for the first set ofsimulations (cell area coverage fixed to 90%). In these tests,urban cell radii were set to 1150, 1750 and 1260 meters, andsuburban cell radii were set to 2625 m, 3980 m and 2860meters (for GSM, 1X and UMTS, respectively).

TABLE VII. RESULTS FOR FIXED 90% AREA COVERAGE, URBAN CELLS.


AverageSAR[w/kg]

0.073 0.080 0.074 0.136 0.128 0.098

F[%] 13 13 13 19 19 15.4


AverageSAR[w/kg]

0.125 0.068 0.098 0.124 0.072 0.098

F[%] 19 11 16 19 13 15.6


Average

SAR[w/kg]

0.098 0.101 0.079 0.087 0.066 0.086

F[%] 16 16 13 13 11 13.8

TABLE VIII. RESULTS FOR FIXED 90% AREA COVERAGE, SUBURBAN CELLS.


AverageSAR[w/kg]

0.073 0.080 0.073 0.135 0.127 0.097

F[%] 13 13 13 18 18 15.0


AverageSAR[w/kg]

0.125 0.068 0.098 0.125 0.072 0.098

F[%] 19 11 16 19 13 15.6


AverageSAR[w/kg]

0.097 0.099 0.078 0.087 0.065 0.085

F[%] 16 16 13 13 11 13.8

It can be seen from Tables VII and VIII that average SARlevels in all cases hover around 0.01 watt/kg, which is about5% of the maximum ICNIRP limit of 2.0 watt/kg. It is alsoobserved that for the same phone, both the average SAR and theFfraction of phones over 0.2 watt/Kg. are almost identical forboth types of environment (urban, suburban), given thatcoverage is the same (90%). It can also be seen that, on theaverage, all three technologies perform similarly in terms of thetwo selected indicators, with a slight advantage for UMTS,which exhibits a lower average SAR andFby about 10%.

Tables IX and X show the results for the second set ofsimulations, carried out for cell radius fixed to 2 km in urbancells and 5 km in suburban cells. In these tests, urban cellcoverage was found to be 70%, 86% and 75%, and suburbancell coverage was found to be 66%, 84% and 70% (for GSM,1X and UMTS, respectively). All of these coverage reliabilityvalues are significantly lower than the value used in the first setof simulations (90%)

TABLE IX. RESULTS FOR FIXED 2 KM CELL RADIUS, URBAN CELLS.


AverageSAR[w/kg]

0.131 0.143 0.132 0.243 0.229 0.176

F[%] 26 26 26 34 34 29.2


AverageSAR[w/kg]

0.160 0.081 `0.117 0.138 0.080 0.115

F[%] 25 13 20 21 15 18.8


Average

SAR[w/kg]

0.162 0.166 0.130 0.144 0.109 0.142

F[%] 29 29 24 24 20 25.2

TABLE X. RESULTS FOR FIXED 5 KM CELL RADIUS, SUBURBAN CELLS.


AverageSAR[w/kg]

0.141 0.155 0.143 0.262 0.248 0.190

F[%] 28 28 28 37 37 31.6


AverageSAR[w/kg]

0.176 0.090 0.130 0.153 0.089 0.127

F[%] 28 15 22 24 17 21.1


AverageSAR[w/kg]

0.174 0.179 0.140 0.155 0.118 0.153

F[%] 31 31 27 27 22 27.6


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From Tables IX and X it can be observed that that averageSAR and Fvalues are substantially higher than those obtainedfor 90% cell area coverage. Actually, it can be seen that therelative increase in these two indicators is proportional to thedecrease in coverage reliability. At a constant cell radius,CDMA1X is the technology with lower head exposure levels.UMTS SAR levels are about 20% higher than those of 1X, andGSM levels are about 50% higher than those of 1X. This isconsistent with the coverage levels obtained for each case, with1X being the system with higher cell area coverage, and GSMbeing the system with lower coverage, as mentioned before.

One additional indicator that is often used to measureperformance in terms of transmit power is the fraction ofsimulation points in which the cell phone uses its maximumpower. This is not directly a measure of exposure, which isquantified by SAR rather that transmit power, but it is certainlyrelated. Table XI shows such results for all three technologies(results for all five cell phone models in each technology havebeen averaged).

TABLE XI. FRACTION OF MONTE CARLO TRIALS IN WHICH CELL PHONESTRANSMIT AT MAXIMUM POWER.

Fixed 90% coverage Fixed cell radius

Urban Suburban Urban (2 km) Suburban (5 km)

GSM 4% 4% 8% 9%

1X 3% 3% 4% 4%

UMTS 3% 3% 6% 7%

From Table XI, it is clear that cells with the same areacoverage exhibit approximately the same fraction of mobiles atmaximum power, regardless of the type of environment (urbanor suburban). At fixed radius, however, 1X cell phones tend to

transmit at maximum power less frequently than their UMTSand GSM counterparts, which is consistent with the resultspresented previously.

VII. CONCLUSIONS

In this work, the problem of quantifying the level of heademissions from cellular phones operating under the three majortechnologies, as a function of system parameters, has benanalyzed. In order to achieve this goal, a novel methodologythat is based on linearly relating transmit power to SAR, andemploys experimental test results reported for a number ofcommercial devices, has been designed and implemented.

It can be concluded from the results found that the SAR

levels of commercial phones, as they are averaged throughoutall possible locations in typical urban and suburban cells,depend strongly on the cell area coverage reliability providedby the system. As the percentage of cell area coveragedecreases, that is, as the reverse link budget gets tighter, phonesin increasingly more cell locations are driven by the powercontrol mechanisms to higher transmit power levels, thusincreasing their SAR levels. In other words, improving thereverse link budget design margins translates not only intohigher coverage reliability but also into lower transmit power

levels and lower head emissions. This means that theimplementation by cellular operators of system designstrategies and techniques aimed at enhancing the uplinkcoverage, such as increased cell density, higher cell towers, theuse of cell tower amplifiers, cell antenna diversity, microcellsand active repeaters, tend to have a possitive impact in theaverage emission levels on the head of cellular voice users.

All other system factors evaluated seem to have a lesser

impact on the level of emissions. For instance, there are nomajor differences between the results for urban and suburbancells, as long as the cell coverage reliability levels are similar.When comparing technologies, UMTS shows a slightadvantage in terms of average emissions at constant coveragereliability, whereas CDMA2000 1X displays the lower averageSAR at constant cell radius.

When comparing different cell phone models, all otherfactors kept constant, one can expect significant differences inaverage SAR levels: up to almost a 2:1 ratio within the sametechnology. This fact emphasizes how important it is forconsumers to review the manufacturers specificationsregarding SAR, which are often available only through the

Internet.

REFERENCES

[1] International Commission for Non-Ionizing Radiation Protection(ICNIRP), Guidelines for Limiting Exposure to Time-Varying Electric,Magnetic, and Electromagnetic Fields (up to 300 Ghz) Health Phys.,vol. 74, no. 4, 1998.

[2] American National Standards Institute (ANSI), "Safety Levels withRespect to Human Exposure to Radio Frequency Electromagnetic Fields,3 kHz to 300 GHz," ANSI/IEEE C95.1-1992 (previously issued as IEEEC95.1-1991). Copyright 1992 by the Institute of Electrical andElectronics Engineers, Inc. (IEEE), New York.

[3] Federal Communications Commission (FCC), Office of Engineering andTechnology, Evaluating Compliance with FCC Guidelines for HumanExposure to Radiofrequency Electromagnetic Fields. OET Bulletin 65,1st Edition, 1997.

[4] Rappaport, T. Wireless Communications. Principles and Practice. 2/E.Prentice. 2001.

[5] ETSI Rec TR 125 996. UMTS Spatial Channel Model for MIMOsimulations. Versin 9. 2010.

[6] H. Okamoto, K. Kitao, and S. Ichitsubo. Outdoor-to-IndoorPropagation Loss Prediction in 800-MHz to 8-GHz Band for an UrbanArea. IEEE Trans. Veh. Techn., Vol. 58, No. 3, March 2009, pp 1059-67.

[7] Kostanic, I. Hall, C. McCarthy, J. Measurements of the vehiclepenetration loss characteristics at 800 MHz, 48th IEEE VehicularTechnology Conference, May 1998, pp. 1-4.

[8] [12]. Holma H. and Toskala A., W-CDMA for UMTS: Radio Access forThird Generation Mobile Communications. John Willey and Sons, 1/E,2000.

[9] Amphenol Antel. Product Catalogs. Antenna Catalog.www.antelinc.com. Consulted on 10/10/2007.

[10] J. S. Lee, L. E. Miller. CDMA Systems Engineering Handbook, ArtechHouse, 1E, 1998.

[11] Wang C. C., Huang J. F., Propagation Path Loss Characterization for an870 MHz Cellular CDMA System in Taipei City. Int. Symp.Microwave Antenna, Propagation and EMC Technologies for WirelessCommunications, 2007.

[12] Federal Communications Commission (FCC), Office of Engineering andTechnology, Equipment Authorization.://fjallfoss.fcc.gov/oetcf/eas/reports/. Consulted on 23/02/2008.

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