SPECTRUM OCCUPANCY IN THE 2.36-2.4 GHZ BAND: MEASUREMENTS ANDANALYSIS

Ruben de Francisco∗ and Ashish Pandharipande†

∗IMEC / Holst Centre, Eindhoven, The NetherlandsEmail: ruben.defranciscomartin@imec-nl.nl

†Philips Research, Eindhoven, The NetherlandsEmail: ashish.p@philips.com

ABSTRACTIn this paper, we analyze spectrum occupancy of the 2.36-2.4GHz band. Interest in this band has risen recently, as sec-ondary allocation of this radio spectrum is being consideredfor body sensor network operation to offer wireless health-care services. We present spectrum measurements conductedin the Eindhoven area, in the Netherlands, where the 2.36-2.4GHz band is currently used for land mobile and amateur ra-dio services. Our objective is to show the spectrum usage inthis band, provide statistics for the received power of activetransmissions and obtain their occupation time.

1. INTRODUCTION

There has been increasing interest in dynamic spectrum shar-ing techniques with the aim of improving spectrum utiliza-tion, as a number of spectrum measurements [1], [2], [3]have shown that radio spectrum utilization in a number oflicensed bands is quite low. In this paper, we focus on the2.36-2.4 GHz band and analyze its spectrum occupancy. In-terest in this band has risen with FCC’s Notice for ProposedRulemaking [4] seeking comments on the secondary alloca-tion of this band for body sensor network operation to offerwireless healthcare services. Similar initiatives are takingplace in Europe, where ETSI recommends several bands foroperation of low power active medical implants (LP-AMI)in the 2.36-3.4 GHz range, including the mentioned 2.36-2.4GHz band [5].

Coexistence of such a proposed medical body sensor net-work with incumbent systems in this band could be achievedwith mechanisms like listen-before-talk protocols, location-based exclusion zones, etc. These mechanisms form the basisof cognitive radio technologies [6], [7] that facilitate coexis-tence with existing licensed systems while limiting harmfulinterference. In order to perform coexistence analysis, it isimportant to first understand the characteristics of incumbentsignal transmissions. Our objective in this paper is to ana-lyze the spectrum usage in the 2.36-2.4 GHz band by provid-ing statistics for the received power of active transmissionsand determining their occupation and idle times. Towardsthis end, we present spectrum measurements carried out inthe Eindhoven region, in the Netherlands, where this band iscurrently used for land mobile (primary service) and amateurradio services1 (secondary service) [8].

In Europe, each country has its own spectrum authorities,policies and frequency plannings [9]. The Conference of Eu-

1The authors would like to thank the Telecom Agentschap (Dutch RadioCommunication Agency) for providing very helpful information on spec-trum allocation in the Netherlands.

ropean Post and Telecommunications (CEPT) recommendsin [10] that the frequency band 2300-2330 MHz should pri-marily be used as a core band for airborne telemetry appli-cations and that the band 2330-2400 MHz should be used asan extension band where required. In Europe, aeronauticaltelemetry is currently used only in Cyprus and Greece in the2.3-2.4 GHz band [11].

In the United States, fixed, mobile and radiolocation(radar) services co-exist in the band 2360-2385 MHz. Be-tween 2385 and 2390 MHz, mobile and fixed services arepresent. Between 2390 and 2400 MHz, amateur radio ser-vices are allocated. As stipulated in [12], the use of theband 2360-2395 MHz by the mobile service is limited toaeronautical telemetering and associated telecommand oper-ations for flight testing of aircraft, missiles or major com-ponents thereof. The following three frequencies are sharedon a co-equal basis by Federal and non-Federal stations fortelemetering and associated telecommand operations of ex-pendable and reusable launch vehicles, whether or not suchoperations involve flight testing: 2364.5 MHz, 2370.5 MHz,and 2382.5 MHz.

For detailed frequency allocations for communicationservices in different ITU regions, the reader is referred to[9], [12], [13].

In this paper we present the following results obtainedfrom our spectrum measurements:• Spectrogram: Evolution of the power spectrum vs. fre-

quency and time.• Power Spectrum Statistics: Complementary cumulative

distribution function (CCDF) of received power.• Time Occupation and Idleness Statistics: Cumulative dis-

tribution function (CDF) of time occupancy, CDF of idletime, and average time occupancy.The remainder of the paper is organized as follows. Sec-

tion 2 presents the measurement setup used in this study, de-scribing the hardware and measurement parameters, as wellas the measurement location. The obtained spectrograms,power spectrum statistics, and time occupation and time idle-ness statistics are presented in Sections 3, 4, and 5, respec-tively. Finally, conclusions are drawn in Section 6.

2. MEASUREMENT SETUP

The following equipment was used to perform the presentspectrum occupation study:• Rohde & Schwarz ZVL6 spectrum analyzer• 2.4 GHz receive antenna• Laptop, connected to the spectrum analyzer via an Ether-

net cable

2010 European Wireless Conference

978-1-4244-6001-4/10/$26.00 ©2010 IEEE 231

−112.5 −112 −111.5 −111 −110.5 −110 −109.5 −109 −108.5 −108 −107.5 −107

Power[dBm]

Figure 1: Spectrogram measured between 9am and 9pm(top), and between 9pm and 9am (bottom).

• Data acquisition software

Appropriate spectrum analyzer measurement settingswere chosen in order to obtain good time and frequency res-olution. A lower value of the frequency resolution yields lessnoisy measurements (better sensitivity). A lower sweep timeprovides better time resolution, and thus more accurate timeoccupation statistics. The spectrum analyzer settings shownin Table 1 were used.

For the chosen spectrum analyzer parameters, we mea-sured the approximate noise level. This is an estimation ofthe noise power density in the 2355-2405 MHz band (it ac-counts for measurement noise), which was measured to bePn ≈−160 dBm/Hz.

In this spectrum occupation study, a semi-urban scenariowas considered. The spectrum analyzer was located inside anoffice in the High Tech Campus, Eindhoven, with the antennaplaced in front of a glass wall. The campus is located nearthe city center of Eindhoven and it is surrounded by fields

Table 1: Spectrum analyzer measurement parametersParameter ValueStart and Stopfrequency2

2355 MHz - 2405MHz

Resolutionbandwidth

30 kHz

Sweep time2 55msVideo band-width

30 kHz

Number ofdata points

1001

Referencelevel

-90 dBm

Span 50 dBPreamplifier ONAttenuation 0 dBFilter GaussianDetector Auto Peak

2355 2360 2365 2370 2375 2380 2385 2390 2395 2400 2405−180

−170

−160

−150

−140

−130

−120

Frequency [Mhz]

Pow

er S

pect

ral D

ensi

ty [d

Bm

/Hz]

AverageMaximumMinimumNoise power

Figure 2: Average/Maximum/Minimum Power spectral den-sity and noise level.

and trees. The building is a modern construction with largeglass facades.

3. SPECTROGRAM

The spectrograms shown in Figure 1 were obtained by sens-ing the band of interest continuously during 2 different days.On the first day, continuous measurements were taken be-tween 9am and 9pm hours. On the second day, measure-ments were taken between 9pm and 9am. In order to bet-ter identify the observed signals, we also show the adjacent5 MHz band on the lower and upper side, thus coveringthe range 2355-2405 MHz. Dark blue colors denote lowerreceived power, while red colors represent higher receivedpower.

Four narrowband transmissions can be easily identifiedwith moderate received power levels and bandwidths be-tween 50 and 60 kHz. Three of them operate during day time,

2Frequency spans of 2 MHz were chosen in some measurements, cen-tered at frequencies of interest, as discussed later. When 2 MHz span mea-surements are performed, the sweep time is set to 5 ms.

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Figure 3: CCDF of the received power in the range 2355-2405 MHz.

and have a more discontinuous traffic pattern, which may beused for amateur low-rate data transmission. These are thesignals centered around the frequencies 2362.48, 2376, and2386.83 MHz. The fourth one, centered at 2400.05 MHz,operates 24 hours continuously, which may be an unmannedamateur relay service (packet-radio or FM relay stations).

Three continuous wideband (day and night streaming)signals can be also identified around different frequencies,spanning about 3.7, 6.2 and 10 MHz, which may correspondto amateur TV (ATV) transmissions. The first one, withabout 3.7 MHz of bandwidth, may be an audio signal from anATV station. The other two signals, with about 6.2 MHz and10 MHz bandwidth, are possibly ATV signals with vestigialsideband modulation and double sideband modulation, re-spectively. Note that the wideband signals are received withmuch lower power than the narrowband signals discussedearlier.

During the 9pm-9am measurement, a radio operating inthe ISM band can also be clearly seen, centered at 2402 MHz.This is a Nordic radio present inside the building, whichtransmits with a bandwidth below 1 MHz. Note that the 9am-9pm and 9pm-9am measurements were not done on the same

Figure 4: CCDF of the received power around 2362.45 MHz.

day, which is why this radio can not be seen in the 9am-9pmspectrogram.

4. POWER SPECTRUM STATISTICS

In this section, we present the extracted statistics for the re-ceived power in a 3-hour measurement carried out between 9and 12am.

The power spectral density in the 2355-2405 MHz bandis shown in Figure 2. The figure shows the average powerspectral density, the maximum (max hold function in thespectrum analyzer) and minimum power density registeredat each frequency bin during the 3-hour measurement pe-riod and the estimated noise power density. Note that thisnoise power density is an approximation measured directlywith the spectrum analyzer. In fact, the average power spec-tral density at frequencies where no signals are present cor-responds indeed to the noise power density. The maximumvalues give an indication of how high the transmit power maybe at each frequency when there is a signal present. As in the24-hour measurements discussed earlier, we can also identifyhere clearly 3 narrowband signals and 2 wideband signals.The third wideband signal, in the range 2389.1-2399.1 MHz,

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Figure 5: CCDF of the received power around 2376 MHz.

cannot be easily indentified in this figure.Although the average, maximum and minimum received

power give important information on the sensed signals,we seek a more meaningful statistical measure for the re-ceived power. The CCDF of the received power is shownin Figure 3 (top). In order to identify the received sig-nals more clearly, we also plot the projection of the CCDFonto a 2D plane (bottom). The CCDF shows the proba-bility that the received power exceeds the value shown inthe abscissa (labeled as Received Power), at a given fre-quency. The CCDF is shown for all frequency bins, eachwith a bandwidth of 30 kHz. Hence, the received powercorresponds to the sensed power within the resolution band-width (RBW), which is related to the power spectral densityas PdBm = PdBm/Hz + 10log10 RBW . The plot clearly high-lights the frequencies at which signals are sensed at somepoint during the 3-hour measurement period.

In the remainder of this section, we show in detail thestatistics for the received power of the narrowband sensedsignals received with higher power, using a reduced spanof 2 MHz at the spectrum analyzer and a sweep time of 5

Figure 6: CCDF of the received power around 2386.83 MHz.

ms. These statistics were obtained from measurements car-ried out between 9 and 10am.

The CCDF of the sensed signals around 2362.48 MHz,2376 MHz, 2386.83 MHz, and 2400.05 MHz are shown inFigures 4, 5, 6, and 7, respectively.

The signal centered at 2362.48 MHz has an approximatebandwidth of 50 kHz. A second signal, with a bandwidthbelow 5 kHz can be seen around 2362.6 kHz.

The signal centered at 2376 has an approximate band-width of 60 kHz. The intermodulation effects of this signalcan be clearly observed in Figure 5, below and above the cen-ter frequency. Other narrowband signals, with lower receivedpower, can be observed at 2375.5 (BW < 60 kHz), 2376.26(BW < 5 kHz) and 2376.75 MHz (BW < 5 kHz).

The signal centered at 2386.83 MHz has an approxi-mate bandwidth of 60 kHz. The intermodulation effects ofthis signal can also be observed below and above the centerfrequency. Other narrowband signals, with lower receivedpower, can be observed at 2386.1 (BW < 50 kHz) and 2387MHz (BW < 5 kHz).

The signal centered at 2400.05 MHz has an approximate

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Figure 7: CCDF of the received power around 2400.05 MHz.

bandwidth of 50 kHz. Other narrowband signals, with lowerreceived power, can be observed in Figure 7 at 2399.4 MHzand 2400.1 MHz, both with bandwidths below 5 kHz. Asignal in the ISM band, centered around 2402 MHz is alsoreceived with high power.

5. STATISTICS OF TIME OCCUPATION AND IDLETIME

In a cognitive radio setting, secondary users have to be able todetect the presence of other radios that use the same spectrumband. In the remainder of this section, we show the timeoccupation and idle time statistics for the narrowband signalsreceived with higher power, namely those centered around2362.48 MHz, 2376 MHz, 2386.83 MHz, and 2400.05 MHz,as previously discussed. The extracted time occupation andidle time statistics are based on power detection, which is oneof the simplest spectrum sensing techniques. Other methods,such as cyclostationarity feature detection or wavelet-baseddetection, can alternatively be used to detect the presence ofother signals.

A power detection technique was used to divide portionsof the sensed spectrum as occupied or free, based on a powerthreshold:

Table 2: Average occupation timesCenter Average Average

Frequency Occupation OccupancyTime

[MHz] [ms] [%]2362.48 0.1450 0.42376.00 0.0589 0.12386.83 7.2115 16.72400.05 1.1277 2.2

• If the received power es below the power threshold, i.e.PRX < Pth , then the frequency bin is free (idle state). Onthe other hand,

• if PRX ≥ Pth, then the frequency bin is occupied.

Pth must be a few dB above the noise floor within the fre-quency bin, Pn. By inspection of the obtained power statis-tics, we find that a reasonable threshold is Pth = Pn + 10dB,where a margin of 10 dB above the noise level is used. Away to validate the threshold used is to make sure that infrequency ranges with no transmissions present the result-ing average occupancy is close to zero. Using the estimatedpower spectral density of the noise, −160 dBm/Hz, and giventhat the resolution bandwidth is set to 30 kHz, we have thatPn ≈ −160 + 10log(RBW ) ≈ −115 dBm. For a given fre-quency span, the number of effective frequency bins equalsthe number of data points, which in these measurements wasset to 1001.

The average occupation times obtained for the frequen-cies mentioned are summarized in Table 2. The highest av-erage occupancy is experienced at 2386.83 MHz, with thewireless medium occupied for almost 17% of the time. Fig-ure 8 shows the average occupancy in percentage of time,while Figure 9 shows the CDF of the time occupation in mil-liseconds at 2386.83 MHz. The average occupancy and theCDF of the time occupation at 2400.05 MHz are shown inFigure 11 and Figure 12, respectively.

In addition to an estimate of the CDF, the figure showsa lower bound on the CDF. This lower bound is a worstcase scenario from the point of view of spectrum occu-pancy. It takes into account the fact that the spectrum is notmeasured continuously, and that a sampling period approxi-mately equal to the chosen sweep time (5 ms) is used, whichvaries in practice. For instance, if the medium is detectedas being unoccupied in two consecutive time instants, tn andtn+1, the CDF lower bound will assume that in the worst casethe medium was busy during T < tn+1 − tn. The most rel-evant measure of the time occupancy is given however bythe CDF estimate. The estimate on the CDF assumes that ifthe medium in not occupied between tn and tn+1, the mediumwas free during T = tn+1−tn ms. The point at which the esti-mated CDF and the lower bound coincide provide an impor-tant time occupancy measure, since most signals will use themedium for a time duration below this threshold. This valuecan be used for the purpose of designing spectrum sensingalgorithms in the 2.36-2.4 GHz band. Looking closely atthe CDFs for the narrowband signals studied here, we ob-serve that in most cases (96% or more) the time occupancyof a given transmission is below 80 ms, which highlights theburstiness of those signals. The narrowband signals centeredaround 2362.48 MHz, 2376 MHz, and 2400.05 MHz yieldvalues even lower than 80 ms.

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Table 3: Average idle timesCenter Average Average

Frequency Idle IdlenessTime

[MHz] [%]2362.48 167.1 s 99.62376.00 31.26 s 99.92386.83 165.5 ms 83.32400.05 2.403 s 97.8

2385.5 2386 2386.5 2387 2387.5 23880

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Frequency [MHz]

Ave

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nt]

Figure 8: Average occupancy around 2386.83 MHz.

The average idle times obtained for the 4 narrowband sig-nals discussed above are summarized in Table 3. The lowestaverage idleness is experienced at 2386.83 MHz, with thewireless medium idle for approximately 165.5 ms in average.Figure 10 shows the CDF of the idle time in milliseconds at2386.83 MHz, while Figure 13 shows the CDF of the idletime at 2400.05 MHz in seconds. The obtained results showthat large idle periods are available accross all frequencies,which would easily make possible the operation of listen-before-talk protocols.

We point out that the obtained time occupation and idle-ness statistics are dependent on the detection algorithm used,the locations of the receiver and the locations of the trans-mitters where signals are originated. Hence, while the actualtime occupation sensed near the transmitter location may behigher, it will be perceived as lower at the receiver side dueto propagation losses and fading.

6. CONCLUDING REMARKS

Our measurement results show that large portions of the2.36-2.4 GHz band are indeed empty, with low or very lowpower levels sensed from existing systems at the receiver’slocation. Hence, on the basis of the available measurements,this band seems to be suitable for medical body sensor net-works. However, it should be noted that our measurementswere conducted only at a specific site. Since the statistics inthis frequency range may change quite locally, a more exten-sive measurement campaign is required to analyze spectrumoccupancy in this band across Europe.

In our measurements, carried out in a semi-urban sce-

0 50 100 150 200 250 300 350 4000.8

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Occupancy Time [ms]

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Figure 9: CDF of time occupancy around 2386.83 MHz.

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Figure 10: CDF of idle time around 2386.83 MHz.

nario, 3 continuous wideband (streaming) signals have beenidentified, received with very low power, spanning about 3.7,6.2 and 10 MHz, which may correspond to ATV transmis-sions. 4 narrowband transmissions have been identified withmoderate received power levels and bandwidths between 50and 60 kHz. Other narrowband signals were also identified,with bandwidths below 5 kHz. These signals may corre-spond to amateur voice or low-rate data transmission servicesand are difficult to detect when using large spectrum sensingbandwidths.

A simple power detection technique was used to divideportions of the sensed spectrum as occupied or free, basedon a power threshold. The results show that the narrowbandtraffic present is bursty, with time occupations below 80 msin most cases. This fact can be taken into account for design-ing spectrum sensing algorithms in the 2.36-2.4 GHz band.

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