Analysis and Simulation of Interference to Vehicle...

1664 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 4, MAY 2011

Analysis and Simulation of Interference toVehicle-Equipped Digital Receivers From Cellular

Mobile Terminals Operating in Adjacent FrequenciesTheodore (Ted) S. Rappaport, Fellow, IEEE, Stefano DiPierro, Member, IEEE, and Riza Akturan, Member, IEEE

Abstract—This paper provides novel analysis methods anddetailed simulations for determining the impact of out-of-bandemissions (OOBE) of adjacent band wireless services on mobilereceive-only wireless services. Using the interference from theadjacent cellular bands on an operational digital wireless re-ceive-only system as an example, we demonstrate the impact ofthe interference from adjacent cellular band transmitters anddetermine the performance impact such as outage on satellitereceivers in realistic roadway conditions. This paper presents newmethods for determining appropriate OOBE spectral masks foruse by new cellular subscriber transmitters. This paper considersrealistic propagation and traffic conditions in five cities through-out the United States and offers approaches that the engineeringcommunity may use to determine interference conditions betweennew cellular and fixed broadband mobile services in adjacentspectrum bands to existing receive-only mobile receiver systems,such as satellite radio.

Index Terms—Adjacent spectrum interference, cellular radio,interference, mobile communication, mobile satellite, power con-trol, receivers, simulation, vehicle-to-vehicle communication.

I. INTRODUCTION

TO DATE, the literature has not offered a careful studyof interference that mobile cellular terminals emit into

other mobile digital receive-only terminals. The novel topicof RF interference between nearby cars traveling along high-ways is virtually unexplored but will become important asfuturistic wireless vehicular systems emerge. This work findsthe needed interference masks for adjacent cellphone transmit-ters that interfere with incumbent receive-only satellite radioreceivers as cars travel in typical rush-hour highway condi-tions. Federal communications regulatory agencies such asthe Federal Communications Commission (FCC), as well aswireless telecommunications companies, must have ways toanalyze the impact of adjacent channel interference betweensuch diverse systems as digital mobile broadcast receivers (such

Manuscript received July 12, 2010; revised December 2, 2010; acceptedFebruary 4, 2011. Date of publication March 3, 2011; date of current versionMay 16, 2011. The work of T. S. Rappaport was supported by Sirius XMSatellite Radio Inc. The review of this paper was arranged by Editor L.-L. Yang.

T. S. Rappaport is with the Wireless Networking and CommunicationsGroup, The University of Texas at Austin, Austin, TX 78712 USA and alsowith TELISITE Corporation, Austin, TX 78703 USA (e-mail: [email protected]).

S. DiPierro and R. Akturan are with Sirius XM Satellite RadioInc., New York, NY 10020 USA (e-mail: [email protected];[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TVT.2011.2122311

as terrestrial or satellite digital radio) and cellular voice anddata networks. This paper provides a detailed analysis andsimulation methodology that may be used to determine theimpact of adjacent band interference between different mobilewireless systems and demonstrates an approach that considersrealistic roadway conditions, realistic propagation models, andcellular system behavior. The resulting analysis and simulationtechnique yield statistical data for the percentage of mobiledigital receivers on the road that suffer link margin degradationor performance outage levels as a function of the out-of-bandemissions (OOBE) mask transmitted by the adjacent bandcellular mobile transmitters.

II. ADJACENT MOBILE CHANNEL

INTERFERENCE PROBLEM—AN EXAMPLE

By way of example, consider the wireless communicationsservices (WCS), which is a fixed and mobile cellular servicethat has been allocated spectrum adjacent to satellite digitalaudio radio service (SDARS). Fig. 1 shows the spectrum al-locations for SDARS and WCS systems in the United States.SDARS is allocated the spectrum between 2320 and 2345 MHz.WCS is assigned to the spectrum in the lower adjacent 2305- to2320-MHz band and the upper adjacent 2345- to 2360-MHzband, and wireless carriers are in the early stages of rolling outWCS commercial service.

The SDARS systems are broadcast systems designed forsimplex transmissions (i.e., receive only). Because of the fixedand rigid requirements of satellite transmitter power levels,receiver antenna heights, and vehicle placements optimized forthe best satellite downlink reception by the majority of users,broadcast systems, in general, operate in a “noise-limited”regime, where the coverage areas provided by the broadcasterare limited by the signal power propagated to the listener andthe listener’s noise level at the receiver. The noise limitationcould further be impacted by the increase in the noise floor dueto external interference. SDARS primary broadcast stations arelocated in space, over 30 000 km above ground, making thelink margin particularly sensitive on Earth. Cellular systems,on the other hand, are operated in an interference-limitedregime and are relatively unrestricted by their license fromadding new base stations and adding more subscribers overtime within a geographic area, as customers are added to thenetwork. Satellite broadcast systems are designed to maximizecoverage from just one or a few transmitters, although terrestrialrepeaters may be used to augment coverage problems. In the

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RAPPAPORT et al.: INTERFERENCE TO VEHICLE-EQUIPPED DIGITAL RECEIVERS FROM MOBILE TERMINALS 1665

Fig. 1. Spectrum allocations (2.3 GHz) in the Unites States.

Unites States, there are a few hundred repeaters used to provideSDARS coverage where foliage or urban clutter blocks themobile satellite signals, although these repeaters cover less than1% of the continental United States (CONUS).

Satellite systems are typically designed to exceed 99.9%average link availability [1]. However, since the U.S. SDARSsystems operate on a subscription basis and only have receive-only service capability, they needed to be designed beyond anaverage outage probability to account for the severe shadowfading experienced by mobile receivers. Thus, SDARS systemdesigns implemented their satellite transmitters and vehicle-mounted receivers to meet a design target that exceeds 99%availability throughout the entire CONUS region under certainworst-case (and not average) extended empirical roadside shad-owing (EERS) propagation model assumptions [2]. In otherwords, the SDARS systems have been designed so that, in theworst-case conditions, at most only 1 out of 100 users will havean outage, or any user, at worst case, will lose 1 min out of100 min of listening. Typical satellite signal power levels areweak when compared with modern terrestrial mobile and fixedwireless systems. For example, the average low-band satellitepower level received in Miami, FL, is −101 dBm per 4 MHz,and it is −99 dBm in clear sky in the Northern Virginia/DCarea, which is equivalent to −107 and −105 dBm over a 1-MHzbandwidth, respectively. In contrast, the received signal levelsof typical third-generation or fourth generation (3G or 4G)Worldwide Interoperability for Microwave Access (WiMAX)transmissions are −75 or −80 dBm over a 4-MHz bandwidthat a cell edge.

The thermal noise level of the protected SDARS spectrum,without adjacent cellular interference, is −113 dBm over a4-MHz bandwidth, which is found by

Pn = kTB = (1.38x10−23) × (97) × (4x106)

= 5x10−15 W = −143 dBW =−113 dBm

4 MHz(1)

where T = 97 K is due to Tsky = 50 K and a low-noise Trx

receiver front-end of 45 K, as SDARS receivers have front-endnoise figures of less than 1 dB.

Without interference protection, the out-of-band interferencefrom neighboring mobile transmitters would increase the noiselevel of the interfered mobile receivers, thereby eroding thenarrow link margin of the satellite system. The purpose ofthis paper is to determine suitable OOBE masks on adjacent

band mobile transmitters using simulation and analysis so that,in real-world conditions, the quality of the interfered servicefor the subscriber population base may be ascertained. Themethodologies presented here may be generalized for otheradjacent-band mobile systems.

As mentioned earlier, in contrast with satellite systems, cel-lular systems are designed to operate in an interference-limited,rather than noise-limited, environment since many base stationsand subscriber stations transmit on the same frequencies withina geographic region. The received signal levels at subscriberunits within a cellular coverage area vary by several orders ofmagnitude but are always at much stronger power levels thanthe signals received by satellites. This wide dynamic rangeof signals is due to the large variations in distances betweensubscribers and a base station and the resulting propagationpath loss. As shown in [3, Fig. 4.17], the path loss (e.g., theamount of power lost in propagation from the base station to asubscriber unit) typically varies from 70 up to 140 dB (a signaldynamic range of over 70 dB or seven orders of magnitude inpower) within a 10-km cell.

Even indoor wireless networks with coverage distances ofonly a couple of hundred meters have large dynamic ranges,as shown in [3, Fig. 4.27], where the propagation path lossover a distance of 40 m inside a building increases from 30 to100 dB. Robust signal variations tolerated by cellular systemsallow cellular operators to install new base stations wheneverlink margins become saturated with interference or whenevernew subscribers require more capacity. Given the large dynamicrange of received signal levels in a cellular system, it is evidentfrom cellular system design that subscriber units will oftenenjoy carrier-to-noise ratios of 30–50 dB from a serving basestation, which is a much greater link margin than can beachieved in satellite systems. These tremendous link marginsallow cellular systems to tolerate greater interference.

While satellite radio broadcast systems provide signal levelsthat are no greater than −95 dBm on the ground in most casesover a 4-MHz bandwidth, cellular systems typically operate atminimum useable signal levels of −80 dBm or greater over a4-MHz bandwidth, certainly with much higher link marginsthan satellite systems, as they are predesigned to anticipatefuture self-interference from more users over time. Typically,cellular systems operate at 20–30 dB above the thermal noisefloor and use adaptive modulation on both forward and reverselinks for agility in signaling data rates to adapt to changinginterference and coverage levels.


From the interfered service standpoint, two issues are ofprimary concern to maintain protection of its service fromadjacent mobile transmitters. One issue is front-end overload,which can be mitigated by a guard band between adjacentservices. The overload can be measured by determining theadjacent signal level required to “block” or “mute” the receiver.This blocking is caused by nonlinear receiver saturation and isdefined by a desired-to-undesired (D/U) power ratio. However,just as important as the guard band frequency gap or the D/Uvalues for adjacent bands is the particular OOBE mask thatis specified by the federal regulatory agency for all adjacentcellular band users. The OOBE mask determines the adjacentfrequency interference power from each mobile transmitterthat leaks into the protected receiver spectrum. This additiveinterference may be modeled as Gaussian noise that adds to theinterfered receiver’s noise floor. This paper attempts to quantifyhow interference from mobile cellular devices with variousOOBE spectrum masks impair incumbent satellite digital re-ceivers in realistic highway conditions, and our methodologymay be applied to all types of adjacent mobile services in anytype of propagation environment.

Referring to Fig. 1, the OOBE from an A block mobiletransmitter that leaks into the C block of spectrum or a B blocksubscriber transmitter that leaks into a D block of spectrumwill have less system impact on other cellular subscribersthan would a C or D block subscriber that transmits out-of-band interference or overloads a nearby SDARS receiver. Thisfollows since the frequency separation between the upper WCSbands is greater than the frequency gap between the C and Dblocks and the SDARS band. Furthermore, for an impactedreceiver that receives interference, the OOBE emissions of anA or B block subscriber may or may not be worse than a Cor D block subscriber, regardless of an established guard bandbetween the two services, since it is the specific spectrum maskof each subscriber unit that will dictate OOBE emissions.

For mobile cellphone coverage, each base station is generallylimited in coverage distance by the mobile-to-base (uplink,or reverse link) link budget, since the mobile and portablesubscriber devices use smaller transmitter power levels thanthe base stations. To help limit the cellular network’s owninterference and to retain as much battery life as possible ineach subscriber handset, power control is employed in thecellular subscriber devices [4]. Power control is required for theproper management of cochannel interference within a cellularsystem [3], [5]. With power control, the base station is able todynamically cause the transmission power and the data rate ofeach subscriber to be ratcheted up or down, depending on theparticular location of the subscriber to its serving base stationand the quality of the forward and reverse links.

III. OUT-OF-BAND EMISSIONS MASKS FOR A SIMPLE

VEHICLE-TO-VEHICLE INTERFERENCE MODEL

To meet the original out-of-band interference protection pro-vided by the FCC 1997 rules for satellite radio in the UnitedStates [6], the adjacent mobile and fixed cellular service userswere originally required to ensure that their out-of-band trans-

missions remain at or below −125 dBm/MHz when received bya SDARS receiver.1 In a congested highway situation, a typicaldistance between a vehicle with an adjacent band mobile phoneuser and a vehicle with a satellite radio listener could be assmall as 3 m, i.e., the length of a normal sized car and roughlythe width of a normal roadway lane. Given the free-space prop-agation path loss of 49 dB at 2300 MHz over a 3-m separationdistance between an adjacent band service subscriber transmit-ter and a satellite radio receiver, the 1997 specified OOBE at themobile transmitter (just leaving the antenna and before propa-gation) would be −125 dBm/MHz + 49 dB = −76 dBm/MHz,which is equal to −106 dBW/MHz at the transmitter. Nowlet us assume an additional increase in propagation path lossdue to the attenuation of the subscriber transmitter signalthrough the vehicle body or due to path loss between peoplein the transmitter’s car. If we assume an additional 10 dBof path loss, the maximum level of tolerable transmittedOOBE at a 3-m distance would be equal to −96 dBW/MHzwithin the SDARS band. Measurements performed by severalparties in recent years indicate that there can be car body lossdue to human diffraction and car body penetration [7]–[9].This simple two-car canonical example results in a requiredtransmitter OOBE mask of 96 + 10 log P (with P in Wattsper MHz) for suitable SDARS reception, which is 14 dB lessstringent than the FCC 1997 ruling, thereby indicating that theOOBE mask can be reduced from the original value of 110 +10 log P [6]. However, this analysis suggests a required maskthat is 41 dB more stringent than the recently proposed OOBEmask of 55 + 10 log P for the adjacent band mobile terminals[4]. While this is a simple canonical example, it should beclear that the only effective way to fully understand the impactand sensitivities of communication system performance is toconsider the impact of many mobile transmitters at varyingdistances, with different propagation effects. Thus, it is onlythrough a combination of analysis (for accurate abstraction ofcomplex satellite and cellular system issues) and Monte Carlosimulation (for highway traffic and propagation variations) thattrue interference effects can be predicted, as described in thefollowing section.

IV. SIMULATION OF WIRELESS COMMUNICATIONS

SERVICE (WCS) OUT-OF-BAND INTERFERENCE IMPACT

ON SATELLITE DIGITAL AUDIO RADIO SERVICE (SDARS)

To determine realistic interference levels and the resultingdegradation to mobile digital receivers, we developed an ex-tensive simulation environment based on Matlab that exploitedanalysis where tractable. The simulator allows an engineer toinput important propagation parameters, traffic parameters, andtransmitter and receiver parameters to determine real-worldaffects and resulting reception outages caused by the adjacent

1This is because of the 1-dB noise floor increase criteria used by the FCC toestablish the 2.3-GHz coordination rules in 1997, and the recently proposedWiMAX Forum coordination plan with satellite systems at 3.5 GHz for asatellite radio receiver having an interference free noise floor of −113 dBm over4 MHz (see Sirius XM Ex Parte submitted to the FCC in WT Docket No. 07-293, February 27, 2008). A 1-dB noise floor increase occurs at −119 dBm/MHzwhen a WCS OOBE interference level is −125 dBm/MHz.


band mobile stations that radiate out-of-band interference. Theuse of simulation, when combined with analysis for suitableabstraction, is arguably the only viable method to determine theimpact of OOBE for adjacent mobile services and the listeningpublic in realistic highway, multi-vehicle environments.

The simulator considers the random location of the interfer-ing mobile stations and the interfered receivers on the roadway,the likelihood of whether the mobile stations are transmittingand if the interfered receivers are turned on, the duty cycleof transmitting devices, whether they are from the differentspectrum blocks (shown in Fig. 1) or whether they are usingmobile station power control, and the particular transmissionpower and power control level for each transmitting subscriberstation. The simulator also allows the user to specify differentvehicle-to-vehicle path loss models that govern the propagationof out-of-band interference from the interfering mobile stationsto each of the interfered receivers, as well as whether theproposed interference protection mask, or some different user-specified mask, is used on each of the interfering mobile trans-mitters. This allows us to compare the impact of transmitterOOBE on the interfered receivers. The simulator also usesthe results of analysis based on the satellite look-angle dataand link margin data based on the EERS propagation modelin [2] to determine accurate SDARS outage probabilities fora wide range of different latitudes and longitudes within theUnited States. We also use analysis to abstract the impact ofcellular system power control on the transmission power ofeach mobile transmitter and then simulate the resulting powercontrol statistics on the user population.

A. Highway Traffic, Roadway, and SubscriberAdoption Rate Models

The simulation must properly model the impact of adjacentservice out-of-band interference from all mobile transmittersand the resulting degradation to mobile SDARS reception qual-ity; thus, it is necessary to first consider a realistic highway en-vironment. Our simulator allows the engineer to enter roadwaylength (in miles), the number of highway lanes on the road,the average speed of each vehicle, and the traffic volume ofvehicular traffic as measured in cars per hour. Our simulatorthen generates random locations of specific vehicles travelingthroughout a highway segment.

In the simulation, we assume that each lane of the highwayhas a uniform distribution of highway traffic, so that users maybe randomly located in any of the highway lanes, as found intypical urban and suburban highways during rush hour. Theselection of roadway length is a variable parameter and canbe adjusted to describe the roadway being modeled. For thesimulations, we chose five typical U.S. cities with varyinglatitudes and longitudes (New York City; Jackson, MS; Denver,CO; Miami, and Charlotte, NC) and selected popular highwayswithin each of those cities in satellite coverage areas. Specificroad segments were selected for the analysis using the GoogleMaps traffic feature, which uses past conditions to predict trafficas a function of day and time.2 Peak-hour traffic times were

2See http://maps.google.com.

determined, and road segments that show typical peak-hourtraffic speeds were identified.3 The average daily traffic (ADT)volume over the length of each segment was then determinedfrom MapInfo Dynamap/Traffic Counts.4 The number oflanes and segment length was determined by measuring aerialphotographs and maps in Google Earth Pro. All of the segmentsselected are only covered by satellite (and not by terrestrialrepeaters), with the exception of the Miami segment, which hasa very weak SDARS terrestrial signal repeater below −94 dBm.

Each of the selected road segments has a corresponding dailytraffic volume (in cars per day) distributed over a 24-h period.We make a reasonable assumption that 60% of the daily trafficflow occurs during the four peak hours (two peak morning hoursof 7–9 am and two peak evening hours of 4–6 pm) during theday and determine a typical rush hour traffic volume (in cars perhour) by multiplying the overall daily volume by 60% and thendividing by 4. Such data may be found in the public domainfrom Google, AAA, and MapQuest.

At any given time during any one of the four peak travelhours, the gross number of vehicles on a stretch of highwayis determined by multiplying the traffic volume (in vehiclesper hour) by the roadway length (in miles) and then dividingby the average speed of travel (in miles per hour) during rushhour, i.e.,

Number of Vehicles =Traffic VolumeVehicle Speed

× Roadway Length.

(2)

Of the total number of vehicles on a roadway segment, acertain proportion will be satellite radio subscribers and anotherportion will be adjacent cellular system subscribers. The simu-lator allows one to input the user density of the interfered andinterfering system users. These data are entered as a customerpenetration rate, in terms of percentage of total vehicles, anddictate the user density of both the interfering system usersand the interfered system receivers on the simulated highway.For the work presented here, a satellite radio penetration rateof 34% is used, as this is believed to be a realistic customerpenetration rate for the year 2015—five years from now [10].We assume that the adjacent interfering cellular system has amarket penetration that is a moderate 5%, which we believe is afair and conservative estimate for a new WCS cellular system.Since interference levels will be directly proportional to thecellular subscriber penetration rate, a conservative estimate of5% penetration rate will not overstate and will likely under-state the potential adjacent band system subscriber OOBE inthe simulations. The corresponding numbers of satellite-radio-equipped vehicles and interfering-service-equipped vehicles arefound in the simulator by multiplying the total number ofvehicles by the respective services’ penetration rates.

3Peak hour speeds are shown on the maps as green (> 50 mi/h), yellow(25–50 mi/h), and red (< 25 mi/h).

4Dynamap/Traffic Counts, 1992–2002 MapInfo Corporation, provides two-way ADT count data on U.S. interstates and highways nationwide and on majorand residential roads in major metropolitan areas (where counts have been takenby government agencies). Enhanced coverage is provided in metropolitan areasusing additional local city and county resources.


B. Modeling the Frequency Band of InterferingMobile Transmitters

As shown in Fig. 1, U.S. satellite radio services are of-fered on the lower and upper halves of its 25-MHz SDARSspectrum allocation. The adjacent cellular band that wouldcause interference is allocated frequency blocks that are bothabove and below the interfered spectrum, and thus, we makethe assumption that there will be sufficient frequency spacingsuch that not all the interfering transmitters will affect all theinterfered receivers. That is, we make the up-front assumptionthat the interfering system subscriber transmitters operatingon frequencies below the interfered spectrum will offer nointerference to the satellite radio listeners using the upperhalf of the interfered spectrum, and similarly, we assume thatthe interfering subscriber transmitters operating on frequenciesabove the interfered spectrum will not interfere with satelliteradio listeners using the lower half of the interfered spectrum.This is a conservative modeling approach and produces simula-tions slightly more favorable to the interfering cellular serviceproviders, as one would expect occasional overload and OOBEto occur from nearby interfering transmitters on the highway.However, we intentionally choose to neglect this situationbecause high-quality satellite radio receivers are employed,and the simulation is greatly simplified. Our analysis makesa reasonable assumption that there is an equal distributionof interfering system users in the upper and lower blockssurrounding the interfered spectrum and an equal distribution ofvehicles equipped with either low-band or high-band receivers.

To account for the various band allocations of the interferedand interfering system users, our simulator specifically focuseson modeling just the interference and link degradation that willbe experienced by high band listeners in the interfered spectrumallocation of 2332.5–2345 MHz, and we therefore only con-sider potential interfering cellular subscriber transmitters thatuse the high end of the adjacent spectrum band (e.g., subscribersin the A, B, and D block allocations above 2345 MHz are theonly potential interfering users considered in the simulationspresented here). Since the simulation results focus on the highband of the interfered spectrum, we use the satellite link marginand satellite look-angle data to determine listener degradationfor the high-band interfered system. Alternatively, the simulatormay just as easily be used to model low-band interfered listenerdegradation and low-band interfering spectrum subscribers inthe A, B, and C blocks below 2320 MHz, and we wouldanticipate similar results. In the simulator, we specify a “bandfactor” that divides the total number of vehicles with interferingterminals and the total number of vehicles equipped with theinterfered receivers to determine only the high-side users. Thus,the total numbers of interfering vehicles and satellite radiovehicles are each divided by 2 in the simulations presented here.By using the band factor and assuming equal distributions of theinterfering and interfered vehicles throughout their respectivespectrum allocations, we conduct simulations for link erosionand statistical outages of availability. The simulation resultspresented here give interference and reception degradation sta-tistics specifically for satellite radio listeners, but our approachmay be generalized for all types of adjacent-spectrum mobilesystems.

Once the customer penetration data and the roadway con-ditions are entered, we assume that any car is equally likelyto be an interfering system user as any other car, and any caris equally likely to be an interfered receiver-equipped car asany other user. With all power levels, highway factors, andlocations determined, we activate the simulator to iterativelygenerate random positions for all interfered receiver-equippedvehicles and all interfering mobile-equipped vehicles withinthe bounds set by the roadway segment length, number oflanes, and lane width. We can set the number of iterations inthe simulator and use a nominal value of 100 iterations persimulation run. For each iteration, approximately 1000 differentvehicle locations are generated, depending on the traffic densityand the length of the particular highway segment, as given in(2), and these vehicle locations are randomly assigned to beeither an interfering or interfered receiver vehicle based on thepenetration rates of each service. Since there are 100 iterationsfor each simulation, each simulation run typically generatesabout 1 000 000 vehicle locations, from which interfering andinterfered users are randomly determined based on the specifiedpenetration rates.

C. Usage Activity, Duty Cycle, and Colocation Modeling

When considered over all iterations, approximately 1 000 000vehicle locations are generated by the simulator over a 2-Dplane earth for a particular roadway, and each of these vehiclelocations is tagged as either not having interfering transmittersand not having the interfered receivers (the large majority ofthe cases) or as having either the interfering transmitter or theinterfered receiver equipment. Since not all of the interferingor interfered system users will have their equipment turned onwhile driving, we determine which of the vehicles have activeequipment by randomly applying a listening factor in the caseof satellite radio and an activity factor in the case of interferingdevices. This is done randomly across all of the locations ofthe interfering and interfered vehicles. These listening/activityfactors may be entered by the user. In the simulation resultspresented here, we used a listening factor of 85% for satelliteradio and an activity factor of 13% for the interfering system.For the interfering system users, this means that one out ofevery eight equipped vehicles will contain an active transmitter,or that every equipped vehicle will use their transmitter forslightly less than 8 min out of every hour. We determinedthe interference activity factor for the interfering transmittersfrom Table I, which was published in a WiMAX Forum study[11]. The last line of Table I shows that the overall peak busyhour activity would be 1/7.9, or 13%, which means that at anyinstant of time, only 1 of 7.9 mobiles would be transmitting ata time, or thought another way, each mobile would be in usefor about 8 min per busy hour. The activity factor is also knownas the holding time, or dwell time, and is typically computedbased on statistics compiled during a cellular network’s busiesthour. In recent years, smart phones and web browsing have ledto more bursty transmissions but longer activity periods thanconventional cell phone calls. Based on the random vehiclelocations for the interfering system equipped vehicles, theinterference activity factor is used to determine which of those


TABLE IWiMAX FORUM BUSY HOUR ACTIVITY FACTOR ESTIMATES (FROM [11])

vehicles actually have a transmitting subscriber terminal duringthe simulation so that only those cell phones that are actuallytransmitting (and not in idle mode) are used by the simulationfor interference analysis.

The simulator also allows the engineer to specify the dutycycle of each transmitter. The duty cycle adjusts the total aver-age radiated power of a subscriber transmitter over a small timeinterval (typically on the order of milliseconds, as specified bythe particular air interface standard). The duty cycle is used inthe simulator as a scaling factor that reduces the transmitterpower from its assigned transmission power. This is done toaccurately account for the average interference power that willbe reduced by the duty cycle. The duty cycle values usedin the simulations presented here range from 6.25% to 38%,where 38% is the upper limit specified in the proposed FCCrules [4].

Similarly, the simulator allows the user to specify an in-terfered receiver’s listening factor that dictates the percentageof receivers that are on and being listened to. If a vehiclelocation is randomly chosen to represent an equipped vehicle,then the listening factor is used to determine which of thosevehicles actually have their receiver turned on in the car. Theactivity factor is used to eliminate those receivers that areturned off so that only equipped vehicles that are being listenedto are considered as being interfered with in the interferenceanalysis. For the simulations presented here, we assume that85% of the equipped vehicles are being listened to at any pointin time.

In the simulator, we took care to avoid having an interferingtransmitter within the same vehicle as an interfered receiver,since the interference level would likely be extremely strong insuch a case and could unfairly lead to an overexaggeration ofmobile interference. It is reasonable to assume that an interfer-ing user would not be listening to the radio while making a call,but it is possible that, in real-world applications, the interferingusers and the listeners of the interfered receivers could be invehicles next to each other or behind each other on a highway.To be fair in the simulation, we do not count the interferingtransmitter as active when the simulator produces a locationthat has both an operational interfering station and an operatinginterfered receiver station within 3 m of one another. Instead,we adjust the effective transmitter separation distance for that

particular interfering/interfered device pairing by moving thetransmitter away from the receiver to a new location that is 3 maway from the specific receiver, and computing the interferencebased on a 3-m separation. This ensures that proper customerpenetration rates are considered for both services while not pe-nalizing the interfering service for the case when an interferingmobile user is also a subscriber to the interfered service. Thesimulator measures the instances when this collocation occurs,and as subsequently shown, it is very rare (with likelihoods onthe order of a couple hundredths of 1%).

D. Vehicle-to-Vehicle Propagation Models

Once the simulator has generated a large data set of theinterfering and interfered equipped vehicles that are in oper-ation, the simulator computes the straight-line (line-of-sight)distances between every operating mobile transmitter and everyoperating interfered receiver’s vehicle, and it applies one ofa wide range of user-selectable distance-dependent statisticalpropagation models to determine the OOBE received by thereceivers due to interfering mobile transmissions. The choice ofa proper propagation model used to describe transmissions froman interfering transmitter to an interfered receiver is criticalfor accurately determining the interference effects of adjacentservice transmissions.

Based on published papers from the Netherlands; CarnegieMellon University, Pittsburgh, PA; and the Intelligent VehicleHighway Systems community, as published in [12]–[17], wefind that many measurements and models have been proposedfor vehicle-to-vehicle communication systems in the 900-MHzto 6-GHz range. Most of the propagation path loss models inthe literature and the models used in the simulation follow theform given in [3, eqn. (4.69a)] as

PL(in decibels)=[Free-Space Loss at 1 m]+10n log (d)+Xσ

(3)

where n is the path lost exponent, and Xσ is a mean dif-fraction loss parameter that is Gaussian distributed (in deci-bels). Carnegie Mellon proposed a path loss model having alog-distance path loss exponent of n = 1.8 and a log-normalshadowing of 5.5 dB. Other propagation models propose a


free-space path loss model of n = 1.8 to n = 2.0, and one studyfound that n = 3.1 but indicated the high value of n is likelydue to a single attenuation factor related to car body loss.

We note that some measurements and models consider theimpact of car body loss, and others do not. It is reasonable toexpect that in some instances an interfering subscriber woulduse her cell phone within a vehicle, and as a result, therewould likely be some car body loss and human body lossas the interference signal was radiated from the vehicle. Thisbody loss would cause the interference signal received at theinterfered receiver to be reduced when compared with modelsthat do not consider any type of attenuation in addition to free-space propagation. We note that this loss would be random dueto a large number of specific variables, such as the orientationof people in a vehicle, the number of cars on the road, theorientation of antennas, etc. Thus, it is reasonable to assumethat the car-to-car propagation loss would be log-normallydistributed.

Based on the cited data, as well as other 2.3-GHz mea-surement results provided by various parties in [7]–[9], wepropose several different vehicle-to-vehicle propagation pathloss models. We employ the concept of a mean attenuationfactor, a diffraction parameter that models the cumulative ef-fects of car body loss, human body loss, and blockage betweenvehicles. Based on reported measurements, we assume lossfactors of 10 and 16 dB and consider path loss exponents ofn = 2.0 and 2.18. We also assume log-normal shadowing dueto variations in the channel and consider standard deviations ofthe shadowing to be 0 (no shadowing variation), 2, and 4 dB.We note that the loss factors add attenuation above the distant-dependent path loss model, and the log-normal shadowingallows our simulator to generate realistic random effects thatwill impact interference levels received at the interfered re-ceivers. We use Xσ to represent a random shadowing loss thatis assigned a mean value of either 10 or 16 dB and is log-normally distributed (normal in decibels about the mean value),and the free-space loss at 1 m is 40 dB. In our simulations,Xσ is a Gaussian random variable having values in decibels.The simulator allows the user to consider either a free-spacepath loss exponent value of n = 2.0, as well as a path lossexponent value of n = 2.18. Standard deviations for the log-normal shadowing component are selected as 0 (no shadowing),2, or 4 dB for the path loss.

The simulation applies the path loss model to all of theinterfering transmitters and generates a random path loss valuefor every interfering transmitter. Using the distance betweenevery interfering vehicle and the interfered vehicles, the simula-tion computes the resulting received interference power at eachreceiver based on the particular interference protection maskthat is specified in the simulator. We make the fair assumptionthat each transmitter voltage signal is independent from eachother, or at least uncorrelated and of zero mean, such that theinterference powers from multiple transmitters are added in alinear fashion at a particular receiver.

Based upon the simulated levels of interference at eachreceiver over a large number of iterations, the simulator pro-duces statistics that determine reception quality degradationat each interfered receiver based on outages (availability of

service) as well as the satellite link margin reduction due toadjacent spectrum mobile interference [e.g., decrease in signalto interference and noise ratio (SINR)].

E. Modeling of Power Control in Simulation

To determine the power levels of each of the operational in-terfering transmitters, it is necessary to consider power control,as this will help reduce the out-of-band interference to inter-fered receivers [4]. Without the specific known base station lo-cations of every mobile transmitter, it is impossible to preciselypredict the exact impact of power control, but a remarkablygood estimate may be determined by considering the coverageof a nominal interfering system’s cellular base station and thendetermining the statistical proximity of a uniform distributionof users within the base station coverage region. This techniquehas been used successfully in the cellular industry to properlypredict and design capacity estimates and outage probabili-ties [3].

Using the concentric cell geometry of a typical base station,as shown in Fig. 2, we can determine from the analysis whatthe coverage of a typical interfering system forward link wouldbe. The forward link path loss may be used as a proxy forthe reverse link path loss, as the average path loss values(taken as either spatial or temporal average) on the forwardand reverse links are reciprocal (will be the same on average).Thus, without knowing the precise details of feedback usedfor power control, we may estimate the transmitting signallevels of interfering system subscribers in a statistical senseby considering the base station to mobile radio channel andapplying statistical methods. While the WiMAX standard andthe other cellular systems specify power control typically in3-dB steps ranging from 0-dB power attenuation (no powercontrol) to 30-dB power attenuation (maximum power control),the following section shows that a close approximation may befound by using fewer step attenuations.

F. Interfering Power Control Analysis

In Fig. 2, three contours around an interfering system basestation are shown, as follows.

1) R3 defines a boundary at the edge of the cell coverage,which is considered the practical maximum distance foran interfering mobile to be controlled by the serving basestation. Subscribers that fall within the concentric circlezone between R2 and the outer edge of the cell R3 willtransmit with power = PHIGH.

2) The area between R1 and R2 defines a concentric circlezone where WCS mobiles transmit with reduced power =PMED.

3) The area between the base station and R1 defines a con-centric circle zone where mobiles transmit with the mostreduced power = PLOW, representing the maximum re-duction in transmitter power.

The concentric circle zones identified by the radial bound-aries desribed by R1, R2, and R3 define three regions, as shownin Fig. 2: A1, A2, and A3


Fig. 2. Cellular geometry and analysis of WCS transmit power distribution.

If we assume a uniform spatial distribution of transmitterswithin the cell coverage region, then the percentage of thetransmitters that operate at each of the three power levels(PctHIGH, PctMED, and PctLOW) can be given by the ratio ofthe areas of each of the three regions to the total coverage areaas follows:

PctHIGH =A3

(A1 + A2 + A3)=

π(R3)2 − π(R2)2

π(R3)2

= 1 − (R2)2

(R3)2(4a)

PctMED =A2

(A1 + A2 + A3)=

π(R2)2 − π(R1)2

π(R3)2

=(R2)2 − (R1)2

(R3)2(4b)

PctLOW =A1

(A1 + A2 + A3)=

π(R1)2

π(R3)2=

(R1)2

(R3)2. (4c)

It is clear that if one uses more than three concentric zones todetermine the power control levels, there is much more simu-lation complexity. Furthermore, if one were to expand Fig. 2to consider a greater number of concentric circle zones fordifferent power control levels, the concentric circle zone havingthe greatest amount of attenuation (e.g., the greatest degree ofpower control) will occur in the region closest to the servingbase station (since the path loss is smallest in this zone). Fig. 2shows that in the concentric zone closest to the base station,the area is much smaller than the area of the zones that arefurther away from the base station. Thus, for a uniform spatialdistribution of users, the percentage of subscribers will be muchsmaller within the closest concentric zone than in the zones that

are further away from the base station. Hence, the percentage ofsubscribers that undergo maximum power attenuation will bemuch smaller than the percentage of subscribers that undergomoderate, or no, power control.

If we assume that the maximum coverage boundary of thecell site R3 is determined by the thermal noise floor (e.g.,consider a new deployment at the very early stage of its growth,not yet in its interference-limited regime), then we can solvefor the cell site coverage distance by considering a realisticpropagation path loss based on realistic urban and suburban cel-lular systems using a well-accepted mean path loss exponent ofn = 2.7 or 27 dB loss per decade of distance (see [3, Fig. 4.17]).

Assuming a green field cellular system installation with alink budget that requires an average signal-to-noise ratio (S/N)of 30 dB above the thermal noise floor to be received by aninterfering subscriber terminal at the edge of the cell, and abandwidth B = 2.5 MHz (D Block subscriber), a base stationeffective isotropic radiated power (EIRP) of 750 W, λ of0.128 m, and unity gain subscriber antennas, we can solvefor the cell edge distance R3 under the constraint of having areceived subscriber signal power level of

Pr = 1000 × kT0B = −80 dBm. (5)

Note that this link calculation is for the initial installationof a mobile cellular service, before capacity demands requirethe cell coverage radius (e.g., base station power) to shrink.It can readily be shown that since our analysis is based onproportional areas related to propagation path loss, which isreciprocal between the base station and the mobile subscribers,our approach to modeling the effect of power control can beapplied to any size cell, as determined by a particular base


station power level and propagation path loss model that isdependent on distance.

Using [3], we note that the average power received by acellular subscriber is given by a log-distance path loss model

Pr (in dBm) = EIRP (in dBm)

−[

Free-Space Path Lossclose in (in decibels)

+ 10n log(

d

dclose in

) ](6)

where the close-in far field free-space reference distance istypically dclose in = 100 m for cellular systems. The free-spacepath loss for a transmitter at 2340 MHz is

Lfree−space =λ2

[(4πd)2]= 39.8 dB ∼= 40 dB (7)

at a distance of 1 m from the radiating antenna and is 79.6 dBat a distance of 100 m from the radiating antenna. To solvefor d, we equate the terms and let n = 2.7, i.e.,

10n log10

(d

dclose in

)= EIRP (in dBm)

− 79.6 dB − Pr (in dBm). (8)

For the case of Pr(dBm) = −80 dBm at the cell edge and givenan EIRP = 750 W = 58.75 dBm, dclose in = 100 m, and n =2.7, we find the coverage boundary of the cell to be

log10(R3/100 m) =59.1527

(9a)

R3 = 100 × 1059.1527 m = 15 514 m ∼= 15.5 km. (9b)

If we make a reasonable assumption that the interfering trans-mitters will support two power attenuation steps of 6 and 12 dB,then the subscribers may reduce their transmitted poweraccording to

PMED =PHIGH − 6 dB (10a)

PLOW =PHIGH − 12 dB. (10b)

To determine the concentric zones in which subscribers radiatereduced transmission power levels under power control, weneed to find the cell radii in Fig. 2 that provide for strongersubscriber received power levels of −80 dBm + 6 dB and−80 dB + 12 dB so that the power control may ratchet down thesubscriber’s transmitted power to be the same as those operatingin the farthest region from the cell. By using the link equationto solve for Pr(dBm) values of −74 and −68 dBm, we find thatR2 and R1 are calculated as follows:

R2 = 100 × 1052.927 m = 9, 301 m ∼= 9.3 km (11a)

R1 = 100 × 1046.927 m = 5, 458 m ∼= 5.5 km. (11b)

Using these boundaries and assuming a uniform distributionof WCS users within the coverage area, the percentage ofinterfering system users that are transmitting at full power, 6-dB

backoff, and 12-dB backoff is determined by the ratios of con-centric circle coverage areas. Using the foregoing values of R1,R2, and R3, we can calculate the transmit power distributionof interfering system subscribers within a cell site (PctHIGH,PctMED, and PctLOW) to be

PctHIGH = 1 − (9.3)2

(15.5)2= 64% (12a)

PctMED =(9.3)2 − (5.5)2

(15.5)2= 23% (12b)

PctLOW =(5.5)2

(15.5)2= 13%. (12c)

The preceding analysis shows that only 13% of the totalsubscribers would experience power attenuations of 12 dBfrom the maximum mobile transmit power and that 64% ofthe total subscribers would have no attenuation due to powercontrol. If the concentric geometry were expanded to includeadditional power control levels of, say 15-dB backoff, and upto 30-dB backoff, then the concentric circles closest to thebase station would have much smaller areas and consequentlymuch fewer subscribers. For example, when one considersFig. 2 and the preceding analysis, a 30-dB power attenuationlevel would require us to solve for the distance from the basestation at which the received power is −80 dBm + 30 dB, or−50 dBm. Following the preceding analysis, we find that the30-dB backoff cell coverage zone would have a radius of 120 m.One can quickly see by inspection that the number of sub-scribers within a 120-m radius from the base station would beminiscule. This is seen by computing the percentage of usersthat would experience 30-dB backoff from (4c), i.e.,

Pct30dB atten =(0.12)2

(15.5)2= 0.006%. (13)

Less than a 100th of 1% of the subscribers would be attenuatedby 30 dB under power control.

Based on reports from the field and personal experience,the authors believe that it is fair to simulate power controlsettings for mobile transmitters at levels of 0, 6, and 12 dBbelow maximum power. It is rare to see power reductionsfar below 12 dB in actual operating large cell systems, andgiven the need for meaningful web browsing and voice qualityrequired in WiMAX, long-term evolution, or high speed packetaccess (HSPA) systems, one would rarely see power attenua-tions greater than 12 dB in an interference-limited environment.Thus, the choices of 0-, 6-, and 12-dB power backoff arebelieved to be fair and reasonable for an accurate representationof power control for an interfering device. Nevertheless, thesimulator has been built in a flexible manner so that other powercontrol levels may be set. This can be done be performing thepreceding analysis and inserting the percentage of users thatwould experience each of the three different power controllevels. Additional power control levels are easily added bymodifying (4) and Fig. 2.

Since the locations of vehicles on the roadway are statis-tically generated, the percentage of transmitters that undergopower control may also be statistically generated as if theywere uniformly distributed throughout the coverage areas ofmany cell sites along the highway. When iterated over all of


the locations of all interfering and interfered vehicles along thehighway, the impact of power control is properly accountedfor, since a proportion of transmitters are allocated their lowertransmitter powers due to power control, based on the precedinganalysis. Specifically, for the simulation results presented here,we assume that active transmitters are proportioned such that64% of the transmitters are transmitting with full subscribertransmit power levels, 23% of the transmitters are transmittingat 6 dB below their maximum power level, and 13% of thetransmitters are transmitting at 12 dB below their maximumpower level. The step of implementing the effect of power con-trol (e.g., adjusting the transmit power level of each subscribertransmitter) is performed on eligible transmitters that remainafter “turning off” the transmitters that are inactive based onthe specified activity factor.

G. Interfering Spectrum Block Allocation andOOBE Spectral Mask Considerations

The simulator allows the user to specify attenuation valuesthat are applied to the transmitter spectrum masks to take intoaccount the adjacent channel interference rules that requireinterference components at the interfered receivers to produceless interference as the frequency separation increases. Themodel used in the simulator allows the user to select differentlevels of attenuation to be applied to the interfering transmittersat different frequencies away from the spectrum band center.

For example, to study the example OOBE mask in [4] inconnection with Fig. 1, the simulator must implement the55 + 10 log P spectral mask for a 0.25-W interfering systemsubscriber transmitter using a 2.5-MHz channel in D block.To do this, we compute the attenuation required by the mobilestation as 55 + 10 log(0.25 W) dB. This equates to 55 − 6 dB,which is 49 dB of attenuation applied to all D block transmit-ters. For all transmitters assigned to be in the first adjacentchannel (e.g., the A-upper block), the simulator implements61 − 6 dB or 55 dB of attenuation level to implement the exam-ple 61 + 10 log P OOBE mask for the case of 0.25-W transmit-ters [4]. The simulator applies 67 − 6 = 61 dB of attenuationfor the B upper block transmitters to implement the example67 + 10 log P mask with 0.25-W transmitters. If all the cellularcarriers occupying the adjacent channel bands use subscriberequipment that have identical spectral mask characteristics,then it is reasonable to assume that identical equipment in theupper band (A block) would cause less interference into theinterfered band as compared with D block transmitters, sincethe spectral emissions from the interfering system subscribersare further attenuated over the additional 5-MHz separation asrequired by the example OOBE mask (e.g., 61 + 10 log P at5 MHz above the interfered satellite radio spectrum’s upperband edge). Even less interference power will be received at theinterfered receivers from the upper band B block transmissions,since the upper band B block transmitters are separated fromthe interfered band edge by 10 MHz (where the example maskis 67 + 10 log P ) [4].

The simulator allows the user to specify attenuation levels toimplement the interference mask and allows the user to specifyother OOBE protection masks, as well. For the purposes of

determining whether a transmitter is from the D, A, or B block,the simulator randomly assigns one of the three required OOBEattenuation levels [4] to each active transmitter, in essenceassigning each transmitter a random spectrum “channel.”

H. Path Loss and Interference Power Receivedat Satellite Receivers

As discussed in Section IV-D, the simulator computes thedistances between all interfered and interfering vehicles andthen calculates the random path loss for the interfering signalsat each interfered receiver based on a distant-dependent pathloss model with a mean diffraction parameter. The cumulativeinterference seen at each satellite receiver is then calculated bysumming up all interference levels from all transmitters. Thesimulator limits the maximum distance of eligible interferencesources to a settable maximum, which is nominally 200 m,to speed up the run time of the simulator, as no appreciableinterference will be received from a transmitter that is 200 mor more away from a receiver. Similarly, as described inSection IV-C, the simulator does not allow any interference tobe caused by transmitters that are closer than 3 m. The receivedpower from each transmitter is reduced by the OOBE maskassignment, as described in Section IV-G. For each iteration,the simulator computes and stores the aggregate power fromeach of the transmitters as seen at each receiver, as well as thetotal OOBE interference level to each of the interfered receiver.The overall statistics of interference power levels are computedover many iterations.

I. Interference and Outage Calculationsat the Interfered Receiver

The simulator uses SDARS satellite look angles, satelliteantenna patterns, and designed link margins to determine thesignal levels at different locations on Earth. The simulatorincorporates the actual designs employed by the U.S. SDARSservices to exceed 99% availability under worst-case EERSmodel assumptions [2].

The EERS model is a highly regarded mobile satellite propa-gation model and is used here to determine the link outage per-formance and the signal-to-noise ratios on Earth for the baselinecase with no WCS transmitters present. The same EERS modelis then used to quantify the degradation, in terms of outagestatistics, for each interfered receiver as the interference isintroduced. That is to say, for every simulated receiver, themobile adjacent band interference is treated as thermal noise,and the satellite link margin in the simulation is reduced by theadditional amount of additive interference received within thereceiver band. The EERS model is then used by the simulatorto determine the resulting new outage likelihood for everysimulated receiver based on the particular interference level ateach receiver. In addition, the actual increase in noise floor (indecibels) caused by the interference at each receiver is alsocaptured by the simulator.

The EERS model is represented in the simulator code in0.5-dB SINR steps. We note that before evaluating the inter-ference at each interfered receiver, only those receivers deemed


to be “on,” as specified by the listening factor, are consideredby the simulator. A number of satellite receivers are randomlyeliminated from consideration, as determined by the “listening”rate (nominally 85%, which means that 15% of the interferedreceivers are not considered). For each active receiver, interfer-ence terms (in linear power units) are then summed together,yielding an aggregate interference power that is added to theinterfered receiver’s baseline noise floor of −113 dBm/4 MHz.The resulting noise floor due to both thermal noise and adjacentservice interference is then used with the EERS model todetermine new worst-case outage probabilities.

V. SIMULATION RESULTS

As described in Section IV, the power control “bands” usedin the simulation are based on a statistical distribution ofinterfering vehicle locations in a base station coverage area.The power control levels are settable and distributed randomlyto the interfering system users, in proportion to the cellularcoverage zones. All the transmitters, active and inactive, receivea random power level assignment for each iteration. Only activetransmitters are used to compute interference. The receivedpower at each interfered receiver is calculated by subtractingthe total car-to-car path loss as computed from the propagationmodels in Section IV-D from the transmitted power of each sim-ulated mobile transmitter. The engineer can vary the path lossparameters to assess performance using different assumptions.

In the simulator, baseline outage statistics are first computedfor today’s SDARS satellite system without the effects of anyOOBE. The baseline is found using analysis, based on existingsatellite link margins in conjunction with the EERS model,and is corroborated by today’s SDARS real-world performance.The simulator is able to recreate the analysis results (to verifyproper operation) and then is used to determine the link margindegradation due to OOBE interference by summing the inter-ference of simulated transmitters, treating the interference asadditive noise, and then recomputing the signal availability andoutage based on the EERS model to each of the receivers usingthe reduced link margin. Tallies are kept to compare overallavailability statistics with the baseline performance. Thus, thesimulator produces a new quality-of-service estimate (e.g.,outage percentage or availability percentage) that can be usedto compare the interfered system’s listener performance undervarying degrees of interference and different adjacent-bandmobile transmitter spectrum masks. The simulator also keepstallies of the number and percentage of simulated receivers thatexperience link margin degradations (e.g., SINR degradations)of greater than 1 dB, greater than 2 dB, and greater than 3 dBfrom today’s baseline.

A. Simulation of Interference and Outages to the ExampleInterfered System Subscribers in Charlotte, NC

Consider the simulated rush hour traffic on a 6.75-mi portionof Interstate 85, west of Charlotte, with traffic parameters asshown in Table II.

Using the traffic volume calculation technique in Section IV,we calculate a peak hourly volume of 6900 vehicles on the

TABLE IITRAFFIC PROFILE FOR CHARLOTTE, NC

highway. Using the simulator, with the satellite link parametersshown in Table III, we found that the baseline availability is99.74% for a 99% worst-case availability criterion. In otherwords, without adjacent band interference, only 0.26% of theSDARS receivers in the Charlotte will experience a worst-caseoutage more than 1% of the time. A total of 1 489 900 vehiclelocations were simulated over 100 iterations, where each iter-ation was conducted for the case of 47 transmitters and 317 re-ceivers being independently placed on the stretch of highway.

The satellite geometry, link performance, and overall QoS forCharlotte are shown in Table III.

Table IV shows the simulation results for Charlotte withadjacent channel mobile transmitters for the case of two differ-ent propagation models. One model assumes a free-space pathloss exponent of n = 2, and a 10-dB attenuation factor, with ashadowing standard deviation of 4 dB, whereas another modelassumes a path loss exponent of n = 2.18, a 16-dB attenuationfactor, and a 4-dB standard deviation. Table IV compares theresults of several different OOBE masks that could be assignedto transmitters by federal regulators, including a recently pro-posed FCC 55-dB mask [4].

It can be seen from Table II that with no interference (e.g.,before the interfering service is put into effect), the baselineworst-case outage is 0.3% (100%–99.7%). That is, only 0.3%of today’s subscribers will ever dip below the 99% worst-caseoriginal SDARS design criterion for outages. This is better thanthe design goal of less than 1% of all users experiencing outage.However, Table IV shows that a 55 + 10 log P mask wouldallow greater than 1% outage due to interference, dependingon which of the two vehicle-to-vehicle propagation models areused.

We note that SDARS designed and built its system with thegoal of having better than 99% worst-case availability statisticsthroughout the Unites States. That is to say, these systems weredesigned for users to experience a worst-case coverage situationwhere no more than 1% of its listeners have an outage in normalconditions. Alternatively, the system was designed such that, atthe worst case, a listener would experience 1 min of outageduring a 100-min listening period. The simulation results inTable IV show that, in Charlotte the percentage of users whowill experience outages increases from the present day 0.3% upto 4.95%. The results in [18] show similar outcomes for Miami.

We note from [19] that in heavily populated cities suchas Charlotte, Miami, and New York City, the 55-dB exam-ple mask would cause 4.95%, 10.66%, and 7.72% of thereceiver population to dip below the 99% worst-case avail-ability design threshold, respectively, when a free-space prop-agation loss model is used between vehicles, having a 10-dB


TABLE IIISATELLITE PARAMETERS FOR CHARLOTTE, NC

TABLE IVSIMULATION RESULTS OF INTERFERENCE FROM MOBILE TRANSMITTERS IN CHARLOTTE, NC

diffraction loss. With the higher loss (n = 2.18) propagationmodel between vehicles, the same cities have 1.88%, 3.71%,and 3.06% of the listeners breach the 99% availability designgoal. When considering the percentage of listeners that suffermore than a 3-dB drop in link based on interference, wesee that 5.24%, 10.66%, and 6.23% of the receivers in thenamed cities are adversely affected, and with the higher losspropagation model between vehicles, 2.06%, 3.71%, and 2.33%of the listeners have their service rendered unusable. The resultsshow that the propagation model used for vehicle-to-vehicletransmissions has a primary impact on the resulting interferencestatistics. The complete source code of our simulation anddetailed simulation results for the aforementioned five cities areprovided in [19].

VI. CONCLUSION

This paper has analyzed the potential interference to digitalmobile receivers to deduce the levels of OOBE from adjacentinterfering mobile service subscribers in the interfered servicebands. In doing this work, we determined reasonable guidelinesfrom which suitable adjacent-channel spectrum masks couldbe developed, and found that the particular vehicle-to-vehiclepropagation model greatly impacts the outage statistics of a mo-bile receive-only system. The simulations here exploit analysiswhere possible and provide methods for finding mobile-to-mobile interference levels and resulting outages using a knownbaseline (preinterference conditions) and assumes linear in-creases in noise floor at the simulated receivers of an interferedsystem. The simulation provides insight into the exact degreeof outage statistics as the noise floors are raised by variousadjacent band transmitters. It can be seen from Table IV thatin Charlotte, the OOBE mask of 55 + 10 log P will cause largerthan 1% outage among the interfered system receivers, whereasan OOBE mask of 75 + 10 log P would reduce the outagepercentage to 0.69% or below. We note that the simulation andthe analysis approach given here may be extended to includenonlinear effects such as receiver blocking. Practical solutions

to remedy the interference problems for a large population ofusers are to limit the transmitter OOBE levels at 75 + 10 log Por more.

REFERENCES

[1] T. Pratt and C. W. Bostian, Satellite Communications, 1st ed. Hoboken,NJ: Wiley, 1986, p. 119.

[2] J. Goldhirsh and W. J. Vogel, Handbook of Propagation Effects for Ve-hicular and Personal Mobile Satellite Systems. Baltimore, MD: JohnsHopkins Univ. Press, Dec. 1998, ch. 3.3.

[3] T. S. Rappaport, Wireless Communications: Principles and Practice,2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2002, pp. 141–145,pp. 477–484, and eqn. 9.37.

[4] FCC Public Notice, WT Docket No. 07-293; IB Docket No. 95-91;GEN Docket No. 90-357; RM No. 8610, Apr. 2, 2010.

[5] J. Andrews, A. Ghosh, and R. Muhamed, Fundamentals of WiMAX: Un-derstanding Broadband Wireless Networking. Upper Saddle River, NJ:Pearson Prentice-Hall, 2007.

[6] FCC, Establishment of Rules and Policies for the Digital Audio RadioSatellite Service in the 2310-2360 MHz Frequency Band, Mar. 3, 1997. IBDocket No. 95-91; GEN Docket No. 90-357; RM No. 8610.

[7] Sirius XM Ex Parte Comments, Feb. 27, 2008. Submitted to the FCC WTDocket No. 07-293.

[8] WCS Coalition ExParte Comments, May 9, 2008. Submitted to the FCCWT Docket No. 07-293.

[9] WCS Coalition ExParte Comments, Aug. 1, 2008. Submitted to the FCCWT Docket No. 07-293.

[10] Sirius XM Marketing Projections, Apr. 2010.[11] D. Gray, “A comparative analysis of mobile WiMAX deployment alter-

natives in the access network,” The WiMAX Forum, May 2007, p. 14.Table 3.

[12] L. Cheng, B. Henty, D. D. Stancil, B. Fan, and P. Mudalige, “A fullymobile, GPS enabled, vehicle-to-vehicle measurement platform for char-acterization of the 5.9 GHz DSRC channel,” in Proc. IEEE AntennasPropagation Soc. Int. Symp., Honolulu, HI, Jun. 2007, pp. 2005–2008.

[13] J. Turkka and M. Renfors, “Path loss measurements for a non-line-of-sightmobile-to-mobile environment,” in Proc. Int. Conf. ITS Telecommun.,Phuket, Thailand, Oct. 2008, pp. 274–278.

[14] J. S. Davis, II and J. P. M. G. Linnartz, “Vehicle to vehicle RF propa-gation measurements,” in Proc. Asilomar Conf. Signals, Syst., Comput.,Oct. 1994, pp. 470–474.

[15] A. Paier, J. Karedal, N. Czink, C. Dumard, T. Zemen, F. Tufvesson,A. F. Molisch, and C. F. Mecklenbräuker, “Characterization of vehicle-to-vehicle radio channels from measurements at 5.2 GHz,” Wireless Pers.Commun., vol. 50, no. 1, pp. 19–32, Jul. 2009.

[16] T. Tank and J. P. M. G. Linnartz, “Statistical characterization of Ricianmultipath effects in a mobile-mobile communication channel,” Int. J.Wireless Inf. Netw., vol. 2, no. 1, pp. 17–26, Jan. 1995.


[17] A. Paier, J. Karedal, N. Czink, H. Hofstetter, C. Dumard, T. Zemen,F. Tufvesson, A. F. Molisch, and C. F. Mecklenbräuker, “Car-to-car radiochannel measurements at 5 GHz: Pathloss, power-delay profile, and delay-Doppler spectrum,” in Proc. IEEE Int. Symp. Wireless Commun. Syst.,Oct. 2007, pp. 224–228.

[18] T. S. Rappaport, S. DiPierro, and R. Akturan, “Analysis and simulationof adjacent service interference to vehicle-equipped satellite digital radioreceivers from cellular mobile terminals,” in Proc. IEEE Veh. Technol.Conf., Sep. 2010, pp. 1–5.

[19] T. S. Rappaport, “Technical analysis of the impact of adjacent ser-vice interference to the Sirius XM satellite digital audio radio services(SDARS),” as part of Sirius XM Supplemental Comments submitted tothe FCC in WT Docket No. 07-293; IB Docket No. 95-91; GEN DocketNo. 90-357; RM No. 8610, Apr. 29, 2010.

Theodore (Ted) S. Rappaport (S’82–M’84–S’85–M’87–SM’91–F’98) is the William and BettyeNowlin Chair and the founding director of theWireless Networking and Communications Group(WNCG) at The University of Texas at Austin,which is a center he founded in 2002. In 1990,he founded one of world’s first wireless academicresearch centers—the Mobile and Portable RadioResearch Group (MPRG)—while with the faculty ofVirginia Tech University, Blacksburg. He is servinghis first elected term on the IEEE Vehicular Tech-

nology Society (VTS) Board of Governors (BoG) and serves on the IEEEVTS Fellows selection committee and the Propagation Committee, wherehe has introduced the concept of a semantic web-archive for propagationmeasurements and models for IEEE members. He served as an elected memberof the BoG of the IEEE Communications Society from 2007 to 2009. Hisresearch focuses on wireless communication system design, RF propagationand antennas, RFIC design, and broadband wireless systems at millimeter andsub-THz frequencies. He has authored, co-authored, or co-edited approximately20 books, including the best-selling text Wireless Communications: Principlesand Practice, which has been translated into six languages, and he is listed byISI as one of the world’s most highly cited authors in the communications field.He has published over 250 papers, advised more than 100 students, and has over100 U.S. and international patents issued or pending. He is an active consultantto industry, having worked with over 30 major corporations in his career, andis an Outstanding Electrical and Computer Engineering (OECE) alumnus fromPurdue University.

Stefano DiPierro (M’91) is a Systems AnalysisManager with Sirius XM Satellite Radio, where hefocuses on architecture analysis and advanced tech-nology projects for satellite and wireless systems.His work has included modeling, simulation, andanalysis of enhancements to the Sirius XM transmis-sion network architecture, engineering support fornew services, interference mitigation, and exploringthe application of emerging wireless networks to theSirius XM system. Prior to joining Sirius XM, he wasan Associate with Booz, Allen and Hamilton. There,

he worked in the U.S. Army communications research community, providingsystems engineering in the areas of satellite and wireless communications, high-bandwidth on-the-move communications, sensor data networks, remote assettracking, and geo-location.

Riza Akturan (M’01) received the B.S. degree fromthe Istanbul Technical University, Istanbul, Turkey.He received the M.S. and Ph.D. degrees in electricalengineering from the University of Texas at Austin.During his doctoral work, he focused on the quality-of-service optimization of mobile satellite communi-cation systems and participated in design activitieswith Motorola/Iridium and Globalstar.

In 1996, he joined Globalstar as a Senior SystemScientist, conducted research in RF propagation andsignal processing, and participated in the CDMA

air-interface design. He was promoted to Manager of System Design andValidation in 1998 and managed projects in the deployment of Globalstar’slow-earth orbit satellite communication system. In 2000, he joined US-Wirelessas the Director of Network Development. His team developed cellular basestation smart antenna logic for position location finding in E-911 serviceimplementation. In 2001, he joined Sirius Satellite Radio (now Sirius XM) asDirector of System Engineering. He was responsible from system design andanalysis of various uplink, satellite, ground repeater, and receiver segments.He was promoted to the Senior Director position in 2007 with additionalresponsibilities in regulatory and next-generation system design areas, andcurrently, he is focused on physical layer link design activities. He has two U.S.patents and publications in propagation, cellular and satellite communicationsystems, and antenna and hardware design subjects.

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