spectrum occupancy

Spectrum Survey in Singapore: OccupancyMeasurements and Analyses

Md Habibul Islam†, Choo Leng Koh†, Ser Wah Oh†, Xianming Qing†, Yoke Yong Lai∗, Cavin Wang∗,Ying-Chang Liang†, Bee Eng Toh∗, Francois Chin†, Geok Leng Tan∗, and William Toh∗

†Institute for Infocomm Research (I2R), 21 Heng Mui Keng Terrace, Singapore 119613e-mail: {habibul,clkoh,swoh,qingxm,ycliang,chinfrancois}@i2r.a-star.edu.sg

∗Info-communication Development Authority (IDA) of Singapore, 8 Temasek Boulevard, Singapore 038988e-mail: {LAI Yoke Yong,Cavin WANG,TOH Bee Eng,TAN Geok Leng,William TOH}@ida.gov.sg

Invited Paper

Abstract— We study the 24-hour spectrum usage pattern inSingapore in the frequency bands ranging from 80 MHz to 5850MHz. The objectives are to find how the scarce radio spectrumallocated to different services is utilized in Singapore and identifythe bands that could be accessed for future opportunistic usedue to their low or no active utilization. The results from thespectrum measurements taken over 12 weekday periods revealthat a significant amount of spectrum in Singapore has very lowoccupancy all the time. The occupancy is quantified as the amountof spectrum detected above a certain received power threshold. Theoutcome of this study suggests that Singapore has a great potentialfor employing emerging spectrum sharing technology such as thecognitive radio technology to accommodate enormous demands forfuture wireless services. However, this study of spectrum survey ispreliminary in its nature and future long term studies need to beperformed to determine any potential secondary usage on thosechannels that have low or no active utilization.

I. INTRODUCTION

The emergence of new technologies and the phenomenalgrowth of wireless communication services have created an everincreasing demand for the radio frequency spectrum. The radiofrequency spectrum is a scarce natural resource and to meet thegrowing demands of all these new bandwidth hungry services,effective and efficient utilization of radio spectrum is essential.Here comes the issue of introducing a new and better spectrummanagement policy. The traditional policy of fixed spectrumallocation came into question by the shocking findings from anumber of measurement studies of spectrum utilization by theFCC Spectrum Policy task force [1]. These studies reveal that alarge portion of the allocated spectrum is used sporadically andgeographical variations in the utilization of assigned spectrumranges from 15% to 85% with a high variation in time [2].At the same time, it is observed in some other related studiesthat most of the spectrum, in most of the places is completelyunused most of the times. The results of these studies indicatethat the fixed spectrum allocation policy is no more a viableoption to meet the increasing demands for radio spectrum forfuture generation wireless services. Underutilization of scarceradio spectrum has given birth to the promising idea of allowingunused part of spectrum by the primary license holder to becomeavailable temporarily to secondary users.

In many countries, government policy initiatives are expand-ing unlicensed spectral bands facilitating dynamic spectrumaccess technologies such as cognitive radios and consideringhow to afford access to the white spaces in the televisionbands [3]. However, without proper understanding of the currentand projected spectrum usage pattern, all these initiatives andinvestments might not produce the expected results. Spectrummonitoring/spectrum survey is one of the essential tools ofspectrum management which provides the policy makers withthe necessary information about the frequency usage pattern ofdifferent services in different bands. Several broadband spectrumsurveys were conducted at San Francisco [4], Denver [5],and San Diego [6]. These survey reports show the maximum,minium and average measured power levels of received signalsin different bands. Although the maximum, minimum andaverage curves from these studies can be used to qualitativelyassess the relative density of channel occupancy on a band byband basis, these data cannot be used to infer the statisticalpercentage of time that channels are occupied. A series of morerecent studies by Shared Spectrum Company (SSC) [3], [7]–[12] provide the needed temporal spectrum use information andcould be used to estimate spectrum white space.

In this work, we conduct a similar spectrum survey inSingapore. The objective is to find the spectrum usage patternin frequencies from 80 MHz to 5.85 GHz and identify the bandsthat have low or no active utilization. The 24-hour measurementhas been taken at the roof top of Institute for InfocommResearch’s (I2R’s) building over 12 weekday periods. It shouldbe noted that compared to different cities in USA, where theprevious spectrum surveys were conducted, Singapore is uniquein the sense that spectrum usage in neighboring countries mightaffect the occupancy pattern in some bands in Singapore.

The rest of the paper is organized as follows. In Section II, weprovide the detail of the measurement equipment. A descriptionof data collection and data calibration methodology is givenin Section III. In Section IV, we describe our methodology toanalyze the data. Occupancy results of this survey are presentedin Section V. In Section VI, we make some notes regardingthe major observation of this study. Finally, in Section VII,

Fig. 1: Measurement systems.

we conclude our report and provide some recommendations forfuture study.

II. MEASUREMENT SYSTEMS

The measurement has been taken on the roof top (6th floor)of the Insitute for Infocomm Research (I2R) located at 21Heng Mui Keng Terrace, Singapore. The equipment used forthe measurement in this study consist of a BiConiLog (hybridbiconical and log periodic) directional antenna of model 3149made by ETS-Lindgren. The antenna has a frequency rangeof 80 MHz to 6 GHz and it accepts a maximum of 750 Wcontinuous power input at low frequency of operation. It ismounted on the roof top using vertical polarizaton. The antennais connected by a low loss cable to Agilent’s spectrum analyzerof model E4407B which has a operating frequency range of9 KHz to 40 GHz, measurement range from -150 dBm to30 dBm, ±1 dB overall amplitude accuracy and a maximumsensitivity of -150 dBm. The spectrum analyzer is connectedto a desktop computer by general purpose interface bus (GPIB)and these two equipment are kept in a cabinet with ventilaton.The LabVIEW8.2 software, installed in the desktop computer,is used for automatically setting the equipment for measurementand recording the captured data. Note that the antennas used inprevious spectrum surveys were both omni-directional, e.g. [3]and directional with automatic rotator, e.g. [7]. Moreover, theantennas in these studies were connected to pre-amps and filters.In our study, the directional antenna does not have any automaticrotator and no pre-amps and filters are used. Therefore, we rotatethe directional antenna manually to get the omni-directionaleffect of the signals and we rely on spectrum analyzer’s filter.The whole measurement system is shown in Fig. 1.

III. DATA CALIBRATION

The spectrum survey is performed over 12 weekday periodsacross a frequency range of 80 MHz to 5.85 GHz. To get theomnidirectional effect, the antenna is rotated manually each dayby 30o. The overall frequency range is divided into severalbands each having a frequency span of 60 MHz. Each 60MHz frequency band has 401 frequency points, i.e. the sepa-ration between two consecutive frequency points is 150 KHz.

Fig. 2: Antenna gain

For each frequency point, the raw data represent the receivedsignal power level measured at the spectrum analyzer for thatfrequency. For each 60 MHz frequency band, the measurementis carried out around every 13.8 minutes. Therefore, for eachfrequency point, we have 104 received data samples from 24-hour measurement and a total of 104 × 12 = 1248 samplesfor the whole observation period. During the measurement, theresolution bandwidth (RBW) and the video bandwidth (VBW)of the spectrum analyzer are set to 10 KHz and 100 KHz,respectively.

Due to antenna gains and cable losses, there is a differencebetween the power level at the spectrum analyzer input andthe power level at the antenna input. Antenna gains and cablelosses across different frequencies are shown in Fig. 2 andFig. 3, respectively. Let y, x, g, and l denote the power level atthe spectrum analyzer input, power level at the antenna input,antenna gain, and cable loss, respectiveley. Then, the calibrateddata x is obtained from the following relation

y (dBm) = x (dBm) + g (dB) + l (dB) . (1)

IV. METHODOLOGY FOR DATA ANALYSES

Spectrum occupancy is the most often used metric to deter-mine which frequency bands are occupied and which are free.One way to define the occupancy is the event that, during anobservation, the received signal strength at the measuring equip-ment is above a certain threshold. However, for opportunisticuse of an unoccupied licensed channel, choosing the thresholdis critical. Selecting the threshold too low would result in avery conservative occupancy estimate that decides that channelis in use due to the presence of ambient noise and make theopportunistic use limited even though the signal levels are toolow for the incumbent receivers to demodulate. On the otherhand, if the threshold is chosen too high, some channels maybe declared as unoccupied even though there may be someincumbent devices operating perfectly well at low power withhighly sensitive receivers. In the existing literature on spectrum

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survey, the threshold is often set a certain dB above the noiselevel of the measurement equipment. The drawbacks of thismethod is that if the equipment noise is more than the ambientnoise, we come up with a threshold too high to declare theobserved channel as unoccupied most of the time. Thus thismethod is prone to the sensitivity of the measuring equipment.The drawback of this method can be overcome by setting thethreshold a certain dB above the ambient noise. One of the ITUrecommendations [13, page 168] suggests setting the threshold10 dB above the ambient noise. Although the theoretical valueof the ambient noise can be calculated easily, the actual ambientnoise is hard to determine. Observing that the noise level at ourmeasurement equipment is a little higher than the theoreticalambient noise, in this work, we set the threshold as 6 dB abovethe minimum received signal power recorded in an observedband during 24 hours over 12 days.

V. SPECTRUM OCCUPANCY RESULTS

In this section, we present the band by band spectrumoccupancy results, where each of the spectrum occupancyplots consists of three subplots. The upper sub-plot depictsthe maximum received power at the antenna versus frequencymeasured during the whole 12-day period. For each frequencypoint, the maximum received power is determined by takingmaximum of all 1248 received samples of that frequency point.The middle sub-plot shows the spectrum occupancy versus timeand frequency. The occupancy is determined as follows: foreach frequency point over 104 time instances, the maximumvalue of 12 received samples is taken; if this maximum value isgreater than the threshold, then that frequency band is declaredas occupied and it is shown by a red dot in the middle-plot.Note that, the threshold for a particular band is derived from theminimum of the received power levels in all frequency pointsof that band. The lower sub-plot shows the duty cycle versusfrequency, where the duty cycle is defined as the fraction oftime the signal is above the threshold. The average duty cycle

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Fig. 4: Received power vs frequency.

of a band is defined as

average duty cycle =N

104 × Nf

where N is the total number of frequency points (based on themiddle sub-plot) with the maximum received signal exceedingthe threshold in a 24-hour period and Nf is the total number offrequency points in the observed band.

Before moving to our band by band spectrum occupancyanalyses, in Fig. 4, we show the received power versus frequencyplot for the whole frequency range of the measurement study(80 MHz to 5850 MHz). This plot show that in many bands,the maximum received power level is even below the equipmentnoise level which is around -103 dBm. Although, it indicatesthat overall spectrum utilization in the whole frequency rangeof our study might be very low, it does not give us a detailpicture of how spectrum is utilized in different bands allocatedto different services. Therefore, for a better view of the band byband occupancy pattern, we zoom into some selected bands.

Fig. 5 shows occupancy for 80 to 174 MHz band. This band ismainly allocated to FM radio, fixed/mobile (land), primary andsecondary radars, and maritime navigation [14]. Average dutycycle of this band is 34.84%. In Fig. 6 and Fig. 7, we show theoccupancy in the bands 174 to 230 MHz and 490 to 614 MHz.Both bands are allocated to broadcasting (analogue TV, digitalTV, HDTV, DAB) services [14] and as can be seen, they aremost heavily utilized bands observed in this study with averageduty cycle 49.05% and 52.35%, respectively. Notice that thesetwo bands have some spectrums where signals coming fromsome TV channels, e.g. ch6, ch9, ch10, ch11 and ch12, arevery weak. If we consider 174 to 230 MHz band, we can easilysee that two third of this band is unoccupied due to the weakreception of the signal broadcasting from those TV stations.Also note that two local TV channels namely Suria (ch12)and Central (ch5) provide some opportunities for secondaryusage since Suria is closed from 12 am to 9 am and Central isclosed from 12 am to 6 am. Another broadcasting band, whichis mainly allocated to TV band V (ch39 to ch60) is highly

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FM Radio Band II AeronauticalMobile/Radionav

Maritime, Amateur, PMR & Paging

threshold =-94.5 dBm

averageduty cycle= 0.3484

PMR

Fig. 5: 80 MHz to 174 MHz.

Broadcasting

Ch 5

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threshold =-98.9 dBm


Ch 6 DAB Ch 8 Ch 9 Ch 10 Ch 11 Ch 12


unoccupied. From Fig 8, we can see that average duty cycleof this band is 8.81% and from 720 to 790 MHz, this band hasalmost zero utilization.

Next, we observe the spectrum usage of the bands allocatedfor digital cellular services, GSM and 3G services. Fig. 9 showsthe spectrum usage of 824 to 890 MHz band allocated for digitalcellular services, e.g. trunked radio services and mobile dataservices [14]. Average duty cycle of this band is 27.24%. Fig. 10and Fig. 11 show the occupancy for the GSM900 and GSM1800bands in Singapore [14], respectively. These two bands haveaverage occupancy of 38.11% and 19.82%, respectively. Notethat, the occupancy figures in Fig. 10 and Fig. 11 between theuplink and downlink sides are not identical. Similar occupancypattern has been observed in the uplink and downlink bandsof 3G services as can be seen in Fig. 12. This result can beexplanined as follows. The control channels for GSM 900, GSM1800 and WCDMA are constantly being broadcasted by the basestations on the DL thus the DL for these frequencies seems fullyoccupied as they are always transmitting with relatively high

threshold = - 101.5 dBm


Broadcasting

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power. The uplink, on the other hand, for cellular systems isbased on active users communicating through the network. Ifthere is no active communication, there are still some periodicshort-pulse transmissions on the uplink for location updatingprocedures which are too short to be picked up by the analyzer.Also note that GSM 900 mobile stations have a higher transmitpower than on GSM 1800, which probably explains the higherpower picked up by the analyzer. From Fig. 12, it is observedthat 3G uplink is totally unoccupied. The reason behind thisoutcome is that WCDMA is a spread spectrum system where thesignal is modulated over a larger bandwidth to give a very lowoutput transmission power, which might not be detectable withthe analyzer and thus does not show any occupancy on the 3Guplink. Therefore, from the captured results, we cannot concludethat no 3G user is active during the measurement period.

Fig. 13 shows the spectrum usage by well-known unlicensedIndustrial, Scientific and Medical (ISM) band (2400 to 2500MHz) and wireless broadband access (WBA) (2500 to 2700MHz) services. Looking at Fig. 13, it appears that the whole

Digital Cellular Services Digital Cellular Services

Mobile Data, PMR & Trunking

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Fig. 10: 890 MHz to 960 MHz.

GSM 1800 GSM 1800

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Fig. 11: 1710 MHz to 1880 MHz.

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Fig. 12: 1880 MHz to 2400 MHz.

WBAISM

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Fig. 13: 2400 MHz to 2700 MHz.

band of 2400 to 2700 MHz is completely unused at thispoint. Similar occupancy pattern has been observed in thefixed satellite band from 3400 to 4200 MHz (Fig. 14) and theISM band from 5755 to 5875 MHz (Fig. 15). The occupancyestimates of these bands might not be the representatives of theactual occupancy. These results could be explained as follows: 1)WBA signals might not be detected at the measurement point ifthe access points are not close enough, 2) satellite signal powermight be much lower than the ambient noise when it reaches theground, and 3) short wavelength of ISM signals cannot penetratethrough walls.

VI. MAJOR OBSERVATIONS

In this section, we summarize the major observations fromthe occupancy results of this study and identify the channels forlong-term studies in order to provide the policy makers with thenecessary information for taking proper initiative to facilitatedynamic spectrum access technologies such as cognitive radio.A graphical presentation of the band by band average spectrumoccupancy as well as the average spectrum occupancy for the

Fixed-Satellite

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Fig. 14: 3400 MHz to 4200 MHz.

AeronauticalRadionavigation

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adiolocation ISM

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Fig. 15: 5000 MHz to 5850 MHz.

whole band of study are shown in Fig. 16. Note that due tospace constraint, we do not provide the occupancy plots for somebands that are shown in Fig. 16, where the average occupancyfor the whole 80 MHz to 5850 MHz band is determined asfollows. First, we determine the average spectrum usage (inMHz) of each band shown in Fig. 16 by mulitplying the averageoccupancy of each band by its corresponding bandwidth. Forexample, average spectrum usage of 80 to 174 MHz band is0.3484 × (174 − 80) = 32.7496 MHz. Then, summing thespectrum usage of all bands and dividing it by the total availabebandwidth 5850−80 = 5770 MHz, we get the average spectrumoccupancy of 4.54% for the whole frequency bands of study.Now using Fig. 16 and the results of Section IV, the followinguseful information can be extracted:

• Highest occupancy has been observed in the broadcastingbands and GSM900 bands.

• The bands allocated for GSM1800, radar, and digital cel-lular services (paging, mobile data, trunked radio) havemoderate occupancy.

0 25 50 75 100

The whole bandwith of study: 80−5850 MHzFM Radio, Aero, Fixed/Mobile, others: 80−174 MHz

TV, DAB: 174−230 MHzFixed/Mobile, PMR, Aero, others: 230−406 MHz

Mobile Data, PMR & Trunk: 406−490 MHzTV, DVB−T: 490−614 MHz

TV: 614−790 MHzMobile Data, PMR, Trunk, Fixed, others: 790−824 MHz

Digital Cellular, Mobile Data, PMR, Trunk: 824−890 MHzGSM900, RFID: 890−960 MHz

Aero Nav, Radar: 960−1429 MHzFixed/Mobile, DAB: 1429−1525 MHz

Mobile Sat, Met Sat, Aero Nav: 1525−1710 MHzGSM1800, Fixed: 1710−1880 MHz

DECT, TDD, 3G, Fixed/Mobile, WBA: 1880−2400 MHzISM, WBA: 2400−2700 MHz

Aero Nav, Radiolocation: 2700−3400 MHzFixed satellite: 3400−4200 MHz

Aero Nav, Fixed Sat: 4200−5000 MHzAero Nav, ISM, others: 5000−5850 MHz

Average occupancy (%)

Fig. 16: Band by band average spectrum occupancy in Singa-pore.

• Low occupancy has been observed in the bands allocatedfor fixed/mobile services, and primary and secondary radar.

• The bands allocated for aeronautical radionavigation, fixedsatellite, WBA and ISM appear to have no utilization.

• Frequencies above 1GHz are relatively underutilized exceptfor the cellular bands.

• In some bands, e.g. 174 to 230 MHz, frequency utilizationis normally higher during the day time compared to thenight time due to the end of transmission from some TVchannels during the night time.

• Although some frequencies in some bands are appeared tobe used, the duty cycle is quite low.

From the above observation, we can easily identify somechannels that might be of interest for future long-term studies.All the bands starting from 980 MHz to 5850 MHz except for thecellular bands are of particualr interest. Among them, satellite,WBA and ISM bands might require the measurement equipment,methodology and place to be changed to determine more accu-rate occupancy estimate. However, the bands which are allocatedfor aeronautical radionavigation, radiolocation, primary radar,and secondary radar can be identified as opportunity channels.In higher frequency bands, most of these services are allocatedwith the bands 980-1429 MHz, 2700-3400 MHz, 4200-5000MHz, and 5000-5850 MHz. In lower frequency bands, theseservices operate at the bands from 230 MHz to 490 MHz. Someother spectrum of interest might be 174-230 MHz and 614-790MHz bands. As we can see, 2/3 of the spectrum of 174-230 MHzband and almost all of 614-790 band are unoccupied most of thetimes and could be considered for opportunistic use. Finally, thespectrum allocated to Singapore TV channels Central and Suriaare not occupied during the night time and could be consideredfor other types of services during those periods.

VII. CONCLUSION

In this work, we studied the spectrum usage pattern inSingapore for the frequency bands ranging from 80 MHz to5850 MHz. Our measurement results suggest that except for

the frequency bands allocated for broadcasting and cell phones,most of the allocated frequencies are heavily underutilized. Theaverage occupancy for the whole range of frequency of thisstudy was found to be only 4.54%. However, the spectrum sur-vey measurements contained in this report cannot be solely usedto assess the feasibility of using alternative services or systemsin a band. To assess the feasibility of using alternative services,further occupancy studies need to be performed, especially inthose bands which are identified as less utilized. These longterm studies would enable us to indentify the seasonal trendsand potentially longer term trends in band by band spectrumutilization. For the case of bands where the signal were notdetected due to the low power transmission, the sensitivityof the receivers that operate in those bands should be takeninto account to determine the spectrum occupancy threshold toget more accurate occupancy estimate. In order to assess thevariations in spectrum usage in the environments with differentuser profiles, different population densities and different geo-graphic characteristics, parallel measurements should carried outin different places both indoor and outdoor over an extendedperiod of time. These future studies would help us to moreaccurately identify the frequency bands with low or no activeutilization and open the door for employing new spectrumsharing technologies such as cognitive radio technology.

REFERENCES

[1] Federal Communications Commission, Spectrum Policy Task Force, nov2002, rep. ET Docket no. 02-135.

[2] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “NeXt gen-eration/dynamic spectrum access/cognitive radio wireless networks: Asurvey,” Computer Networks Journal.

[3] M. A. McHenry, P. A. Tenhula, D. McClosky, D. A. Roberson, and C. S.Hood, “Chicago spectrum occupancy measurements & analysis and a long-term studies proposal,” in First International Workshop on Technology andPolicy for Accessing Spectrum (TAPAS’06), Boston, MA, Aug. 2006.

[4] F. H. Sanders, B. J. Ramsey, and V. S. Lawrance, “Broadband spectrumsurvey at San Francisco, CA,” NTIA, May 1995, NTIA Report 99-367.

[5] F. H. Sanders and V. S. Lawrance, “Broadband spectrum survey at Denver,Colorado,” NTIA, Sept. 1995, NTIA Report 95-321.

[6] F. H. Sanders, B. J. Ramsey, and V. S. Lawrance, “Broadband spectrumsurvey at San Diego, CA,” NTIA, Dec. 1996, NTIA Report 97-334.

[7] M. A. McHenry and K. Steadman, “Spectrum occupancy measurements,Location 1 of 6: Riverbend Park, Great Falls, Virginia, April 7, 2004,”Shared Spectrum Company, Aug. 2005.

[8] ——, “Spectrum occupancy measurements, Location 2 of 6: Tyson’sSquare, Center, Vienna, Virginia, April 9, 2004,” Shared Spectrum Com-pany, Aug. 2005.

[9] M. A. McHenry and S. Chunduri, “Spectrum occupancy measurements,Location 3 of 6: National Science Foundation Building Roof, April 16,2004, Revision 2,” Shared Spectrum Company, Aug. 2005.

[10] M. A. McHenry, D. McCloskey, and G. Lane Roberts, “Spectrum occu-pancy measurements, Location 4 of 6: Republican National Convention,New, York City, New York, August 30, 2004 - September 3, 2004,Revision 2,” Shared Spectrum Company, Aug. 2005.

[11] M. A. McHenry and K. Steadman, “Spectrum occupancy measurements,Location 5 of 6: National Radio Observatory (NRAO), Green Bank, WestVirginia, October 10 - 11, 2004, Revision 3,” Shared Spectrum Company,Aug. 2005.

[12] M. A. McHenry, D. McCloskey, and J. Bates, “Spectrum occupancymeasurements, Location 6 of 6: Shared Spectrum Building Roof, Vienna,Virginia, December 15 - 16, 2004,” Shared Spectrum Company, Aug. 2005.

[13] Radiocommunication Bureau, “HANDBOOK Spectrum Monitoring,” In-ternational Telecommunication Union (ITU), 2002.

[14] “Spectrum Management Handbook,” Info-communication DevelopmentAuthority (IDA) of Singapore, Feb. 2002, Issue 1 Rev 2.

spectrum occupancy

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