A Dynamic Spectrum Access Scheme forUnlicensed Systems Coexisting with Primary

OFDMA SystemsHai Ngoc Pham1,2, Pal Grønsund1,2,3, Paal E. Engelstad1,2,3, Ole Grøndalen3

1Department of Informatics, University of Oslo, NorwayEmail: {hainp,paalrgr,paalee}@ifi.uio.no

2Simula Research Laboratory, Oslo, NorwayEmail: {hainp,paalrgr,paalee}@simula.no

3Telenor R&I, Oslo, NorwayEmail:{pal.gronsund,paal.engelstad,ole.grondalen}@telenor.com

Abstract—In this paper, we consider Mobile WiMAX and LTEas the future technologies for primary OFDMA systems and studyDynamic Spectrum Access (DSA) for unlicensed systems coex-isting with primary OFDMA systems. We propose an effectiveDSA scheme that allows an unlicensed system to statistically andopportunistically access the whole spectrum bandwidth duringthe last consecutive symbols of each primary OFDMA subframe.There is a tradeoff in gaining high normalized throughput byenlarging the access duration in each subframe and keeping lowinterference caused to the primary system by shorten the accessperiod. Then, we model that tradeoff by deriving the optimizationproblem that maximizes the normalized throughput of theunlicensed system while guaranteeing the harmful interferenceconstraints set by the primary system. We develop and conductour simulations in our simulator modified from the powerfulWiMAX simulator developed by the WiMAX Forum in Ns-2.

I. INTRODUCTION

The licensed frequency bands have gradually become ascare resource due to the growing demand and usage of wire-less systems as well as the fixed allocation policy. However,the utilization of such spectrum bands is low. The FCC’s Spec-trum Policy Task Force reports in [1] that at any given time andlocation, only 15% - 85% of the spectrum is utilized. Thus,DSA technique [2] is envisioned as an emerging technology toimprove the spectrum utilization. The DSA technology allowsa secondary (unlicensed) system to utilize a licensed spectrumwhen it is not occupied by its owned primary (licensed)system. Such utilization however must guarantee not causingdegradation in service to the primary system by protecting theprimary system from interference.

There has been recent research on dynamic spectrum accessand allocation schemes to enhance the efficiency of spec-trum utilization. Weiss et.al in [3] discuss spectrum poolingapproach to overlay a unlicensed system on an existing li-censed system without sacrificing the primary transmissionquality or requiring any changes to the licensed system. In[4], Gerihofer et.al consider the coexistence of Bluetooth

and WLAN in terms of reusing sparsely occupied frequencybands in the time domain by exploiting idle periods betweenbursty transmissions. Willkomm et.al in [5] characterize thebehavior of primary users in the cellular spectrum in order tounderstand if and when spectrum is available, which can beused to enable DSA in cellular bands. In [6], Bernardo et.alpropose dynamic spectrum assignment algorithms for multicellOFDMA systems that allow licensed spectrum holders torelease spectrum bands for other secondary markets to lease.

In the present paper, on the other hand, by observing thecapacity usage and the statistics of spectrum occupancy ofa primary OFDMA system as partly reported in our pre-vious work in [7], we argue that a secondary system canstatistically and opportunistically exploit the statistics of theavailable primary spectrum resource in order to improve thespectrum utilization in a coexisting network of both primaryand secondary systems. In particular, we propose an efficientDSA scheme, namely DSA-α scheme, that allows a secondarysystem to access the whole spectrum bandwidth during the lastconsecutive symbols of each primary OFDMA subframe. Thisaccess duration in the time domain is modeled as the accessratio α on the maximum OFDM symbols of an OFDMAsubframe. We show that the longer the access duration or thehigher α, the higher normalized throughput could be gainedby the DSA-α secondary system. However, by accessing theprimary spectrum in each subframe, the secondary systemwill cause interference to the primary system, which is mono-tonically increasing with the increasing of α. Hence, in thispaper we model that tradeoff by proposing an optimizationproblem that maximizes the normalized throughput gained bythe DSA-α secondary system while guaranteeing the harmfulinterference constraints set by the primary system.

The rest of this paper is organized as follows. Section IIpresents the primary OFDMA system model and our modifiedNs2-WiMAX simulator basing on the WiMAX Forum simu-lator. Next, Section III describes our proposed DSA-α schemeand its optimization problem. The simulation results are thenpresented in Section IV. Finally, conclusion and future work

are drawn in Section V.

II. SYSTEM MODEL AND THE NS2-WIMAX SIMULATOR

A. System Overview

Fig. 1 shows the system model of our proposed dynamicspectrum access scheme, namely DSA-α scheme, where thesecondary system operates coexisting with the primary systemon the primary spectrum by statistically and opportunisticallyaccessing the available statistics of the primary spectrumresource. In this paper, we consider the primary OFDMAsystem as a mobile WiMAX primary system.

Primary BS(Mobile WiMAX / OFDMA)

PU PU

Secondary BS

SU SU

Fig. 1. The coexistent of primary and secondary systems

B. The Ns2-WiMAX Simulator of Primary OFDMA Systems

To simulate primary OFDMA and mobile WiMAX systems,we use the network simulator ns-2.1.5 [8] with an implementa-tion of the IEEE 802.16e-2005 [9] amendment by the WiMAXForum [10]. In the simulator, all the general WiMAX standardparameters are configured as in [11]. The channel bandwidthis 10 MHz. The downlink (DL) and uplink (UL) permutationtypes are PUSC and the DL/UL ratio is 2/1 in the TDDscheme used by the simulator. The modulation scheme 64-QAM, 3/4 rate is currently configured as the same for everyprimary user. The OFDMA channel model is a COST-HATA-231 bulk path loss component combining with a Clarke-Gansimplementation of the Rayleigh Fading model [12].

In this simulator, the primary base station allocates spectrumresource to its serviced primary users by a complex schedulerthat maintains important information such as the QoS for eachflow, DL queue status for each flow, UL bandwidth grant andthe channel state information for each mobile station. The cur-rent release of the simulator supports scheduling configurationas either vertical striping or horizontal striping.

In this paper, vertical striping is configured for the schedulerso that the whole spectrum bandwidth can be allocated to theprimary users in the time domain. This technique is consideredmore reasonable for a secondary system to access the availableprimary spectrum without knowing the complex details of howthe primary OFDMA system logically indexes the subcarriersinto the subchannels in each OFDMA subframe. To charac-terize and model the statistics of the spectrum distribution ineach OFDMA frame of the primary system, we implement ouradd-on functionalities in the simulator to capture the neededstatistics information of the whole system.

III. OUR NEW DYNAMIC SPECTRUM ACCESS SCHEME

A. Simulation Statistics of the Primary ODMA System

We simulate the underlying primary system in our modifiedNs2-WiMAX simulator in order to characterize the primaryspectrum occupancy over the data traffic simulation. Mathe-matically, the statistics model of each OFDMA resource blocki, which is defined as the whole consecutive subchannels at thesymbol i in an OFDMA subframe, at time τ can be modeledas a probability density function pdf(i, τ).

Fig. 2 shows a statistics example of the cumulative oc-cupancy distribution in the OFDMA DL-subframe during 20seconds simulation. The simulation is conducted for 1 primarybase station and 20 primary users with CBR over UDP traffic.This statistics of the primary spectrum occupancy in the time

0

5

10

15

20

25

300 5 10 15 20 25 30

OFDM Symbols

log−dl−id−bs1−udp20−tcp0.t: Occupancy Distribution − DL Subframes

Sub

Cha

nnel

s

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Fig. 2. Occupancy distribution in the DL-subframe

domain indicates that the majority of the primary spectrumresource in the DL-subframe from the symbol 16, for example,to the end of the subframe is not fully exploited. Hence, thereis a great potential for secondary systems to operate on theprimary spectrum by exploiting this statistics model.

B. DSA-α: A New Dynamic Spectrum Access Scheme

From the statistics of the primary spectrum distribution,we observe that the spectrum occupancy in each primarysubframe is distributed decreasingly from the first OFDMAsymbol to the last symbol. Hence, statistically, a secondarysystem should access the primary spectrum during the lastconsecutive symbols of each subframe. Fig. 3 illustrates ourDSA-α scheme, which allows a secondary system to access awhole consecutive resource blocks during the last symbols ofthe primary OFDMA subframe. We model this access periodas the access ratio α on the maximum OFMD symbols ofthe subframe. Let Nsym and nα denote the maximum OFDMsymbols in the OFDMA subframe and the access period(number of symbols), respectively, hence α = nα

Nsym.

In our scheme, it is assumed that either the DSA-α sec-ondary system is scheduled precisely or it implements perfectspectrum sensing so that it can access each subframe accu-rately at the access point shown in Fig. 3. Detailed discussionon such scheduling and sensing mechanisms is considered as

access ratio α %

access period

DSA access point

OFDMA Subframe - Vertical Striping

Scheduling

Sα consecutive

resource blocksN

sub

Sub

chan

nels

Nsym OFDM Symbols

nα OFDM symbols

Fig. 3. Illustration of our DSA-α scheme in an OFDMA subframe

the future work. In the next subsections, we present the opti-mization problem that maximizes the normalized throughputgained by the DSA-α scheme while guaranteeing the harmfulinterference constraints set by the primary system.

1) Expected Normalized Throughput of the DSA-α Scheme:From the statistics model of the primary spectrum usage,we can derive the cumulative probability distribution for theoccupancy of the Sα consecutive OFDMA resource blocks ofthe duration ratio α, which can be exploited by our proposedDSA-α scheme, in the time domain τ ∈ [0, T ] as:

cdff (α ≤ α, τ) =1

αNsym

∑i∈Sα

pdf(i, τ) (1)

Hence, we can easily formulate the expected access prob-ability (pdsa(α)) that the secondary system can opportunisti-cally exploit the available primary spectrum by our DSA-αscheme in the time domain as follows:

pdsa(α) =1T

∫ T

0

(1 − cdff (α ≤ α, τ)) dτ

=1T

∫ T

0

(1 − 1

αNsym

∑i∈Sα

pdf(i, τ)

)dτ (2)

In our model, the spectrum utilization gain that a secondarysystem could achieve from exploiting the primary spectrumstatistics (2) through our DSA-α scheme can be evaluated asthe normalized throughput Tdsa(α). This normalized through-put implicates the expectation from the secondary systemthat the longer the access period in each primary subframe(by increasing the ratio α), the higher spectrum utilization.Furthermore, the higher the chance to access the spectrum(by increasing the statistics pdsa(α)), the higher spectrumutilization. Thus, we can formulate Tdsa(α) as follows:

Tdsa(α) =nα.pdsa(α) (3)

=αNsym

T

∫ T

0

(1 − 1

αNsym

∑i∈Sα

pdf(i, τ)

)dτ

2) Expected Interference caused by the DSA-α Scheme:However, by opportunistically and statistically accessing theprimary spectrum, the secondary system will cause inter-ference to the primary system, since the DSA-α schemeallows the secondary users (SUs) to access Sα consecutiveOFDMA resource blocks in every primary OFDMA subframe.In each single subframe, at the point in time τ , the co-channelinterference power level at the i-th resource block of the whole

subchannels and 1 OFDMA symbol caused by a SU k to aprimary user (PU) j can be calculated following [13] as:

Ik,jCCI(i, τ) = pdf(i, τ)

[|hk,j |2.P k,j

loss.PkTX

](4)

where P kTX is the transmission power of the k-th SU, P k,j

loss

is the path loss power from the k-th SU to the j-th PU, and|hk,j | is the channel gain.

Denote KSU as the number of the SUs accessing α portionof each OFDMA subframe. Thus, the expected interferenceintroduced by the DSA-α secondary system to the j-th PU ina subframe at time τ can be derived as:

E[Ijdsa(α, τ)] =

KSU∑k=1

∑i∈Sα

Ik,jCCI(i, τ) (5)

Given a harmful interference threshold I for each PU ineach subframe, the following constraint can be set by theprimary system to avoid harmful interference at any time:

E[Idsa(α, τ)] � max{E[Ijdsa(α, τ)]} ≤ I , ∀τ ∈ [0, T ] (6)

In addition, the harmful interference probability ph thatthe interference produced by the DSA-α secondary systemexceeds I (violating (6)) at time τ can also be modeled as:

ph(E[Idsa(α, τ)] > I) ={

1, if: E[Idsa(α, τ)] > I

0, otherwise (7)

Then, the expected probability that the secondary systemmay harmfully interfere the primary system during the wholesimulation time T can be estimated as following:

ph(E[Idsa(α)] > I) =1T

∫ T

0

ph(E[Idsa(α, τ)] > I)dτ (8)

Thus, the primary system can also avoid harmful interfer-ence by setting up a limit for harmful interference occurrenceby introducing a harmful probability threshold ph, which re-quires the DSA-α system to guarantee the following condition:

ph(E[Idsa(α)] > I) ≤ ph (9)

It can be seen from the proposed interference models thatthe higher the access ratio α, the higher the expected interfer-ence (5), which means the higher the chance it may becomeharmful interference to the primary system by violating eitherthe constraint (6) or (9). Thus, obviously, there is a tradeoffin gaining the normalized throughput (3) as high as possibleby increasing α, while guaranteeing the harmful interferenceconstraints (6) and/or (9) by reducing α.

3) Maximization of the Normalized Throughput by the DSA-α Scheme: One of the main objectives in dynamic spectrumaccess is to allow a secondary system to gain as muchspectrum resource utilization as possible, while keeping the in-terference to the primary system as low as possible. Hence, inour scheme, we maximize the normalized throughput (3) whileguaranteeing the harmful interference constraints (6) and/or (9)set by the primary system. We formulate such maximization

of spectrum utilization as the following optimization problemin our DSA-α scheme:

α∗ = arg maxα

Tdsa(α) (10)

= arg maxα

αNsym

T

∫ T

0

(1 − 1

αNsym

∑i∈Sα

pdf(i, τ)

)dτ

subject to:c1 : E[Idsa(α, τ)] ≤ I , ∀τ ∈ [0, T ], and/or (10a)

c2 : ph(E[Idsa(α)] > I) ≤ p (10b)

The physical meaning of the optimization (10) is twofold.First of all, it provides a comprehensive mathematical tool fora secondary system to gain the optimal normalized throughputby deploying our DSA-α scheme, while avoiding harmfulinterference to the primary system. Second of all, it provides apowerful economical tool for the primary system who wants tolease any α portion of its licensed spectrum in each OFDMAsubframe by adjusting the interference threshold I (hard-interference-constraint) and/or the probability threshold p(soft-interference-constraint). The hard-interference-constraint(10)a restricts the DSA-α secondary system not causingharmful interference at any time, while the soft-interference-constraint (10)b eases that restriction by accepting harmfulinterference to be occurred sometimes under the limitation ofthe probability threshold p. Obviously, by increasing thoseinterference thresholds, the primary system could statisticallylease more of its available licensed spectrum resource.

IV. PERFORMANCE EVALUATION

This section validates the performance of our proposedDSA-α scheme via simulations conducted in our modifiedWiMAX simulator in the network simulator Ns-2.1.5. In allsimulations, the primary system data traffic is generated as thedownlink (DL) traffic in the period of T = 20 seconds. Forthe CBR traffic over UDP, the packet size is 1500 bytes andthe rate is 1 Mbps.

First of all, the expected access probability (2) to accessthe primary OFDMA DL-subframe is evaluated as shown inFig. 4. The primary system consists of 1 primary base stationproviding data service in different scenarios to 5, 10, 15, 20,25, and 30 CBR/UDP primary users (PUs). These results reflexwell the statistics of the occupancy distribution of the primaryspectrum. The expected access probability or the opportunityfor the DSA-α secondary system decreases when this systemwants to exploit higher portion of the primary spectrum.However, it is also observed that sometimes this expectedaccess probability increases when the access ratio α increases,for example in the cases of 25 and 30 primary users andα ∈ (65, 75)%. This is due to the vertical scheduling algorithmimplemented in the simulator that sometimes allocates the databursts from the next symbol before finishing allocating all thesubchannels from the previous symbol in the primary DL-subframe (see symbols 6 to 10 in Fig. 2). It is also seen thatthe heavier the primary traffic, the less opportunity for theDSA-α secondary system.

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0.3

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0.5

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0.7

0.8

0.9

1

DSA−α access ratio: α (%)

Exp

ecte

d A

cces

s P

roba

bilit

y

5 CBR/UDP PUs10 CBR/UDP PUs15 CBR/UDP PUs20 CBR/UDP PUs25 CBR/UDP PUs30 CBR/UDP PUs

Fig. 4. Expected Access Probability in DL-Subframe: CBR/UDP Traffic

The normalized throughput (3) gained by the DSA-α sec-ondary system in the primary DL-subframe for different simu-lation scenarios is evaluated and shown in Fig. 5. It is seen that

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2

4

6

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14

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20

DSA−α access ratio: α (%)

Nor

mal

ized

DS

A T

hrou

ghpu

t

5 CBR/UDP PUs10 CBR/UDP PUs15 CBR/UDP PUs20 CBR/UDP PUs25 CBR/UDP PUs30 CBR/UDP PUs

Fig. 5. Normalized Throughput in DL-Subframe: CBR/UDP Traffic

this normalized throughput is monotonically increasing whenthe secondary system accesses more portion of the spectrumresource in each primary DL-subframe. However, this normal-ized throughput becomes saturated when the access ratio α ishigher than 50% of the maximum OFDM symbols in eachDL-subframe. This is reasonable since the vertical stripingscheduling allocates the spectrum resource to the primary usersfrom the first OFDMA symbol until the last symbol and hencethe portion of the spectrum at the last symbols is mostly under-utilized. This confirms again our motivation and validates theformulation of our DSA-α scheme.

Fig. 6 illustrates the numerical calculation of the maximumexpected interference model proposed in (5) during the sim-ulated data traffic time. The DSA-α network consists of onlyone SU with the transmission power PTX = 2 W [13]. Tosimplify the numerical calculation, the channel gain and thepath loss are set to be 1. These results validate our interferencemodel that the expected interference monotonically increaseswhen α inceases. Thus, when the primary system sets I for thehard-interference-constraint (10)a, the system can determine

0 10 20 30 40 50 60 70 80 90 100−10

−8

−6

−4

−2

0

2

4

6

8

DSA−α access ratio: α (%)

Max

imum

Exp

ecte

d In

terf

eren

ce (

dB)

5 CBR/UDP PUs10 CBR/UDP PUs15 CBR/UDP PUs20 CBR/UDP PUs25 CBR/UDP PUs30 CBR/UDP PUs

Fig. 6. Maximum Expected Interference in DL-Subframe: CBR/UDP Traffic

the maximum access ratio at which the constraint (10)a is stillsatisfied and then the optimization (10) can be solved. For thefuture work, the detailed simulation of both a primary systemand a DSA-α secondary system will be implemented to eval-uate the actual expected interference and to compare it withthe proposed model. In addition, the simulation and evaluationof the soft-interference-constraint (10)b are considered as thefuture work as well.

We also validate the performance of our DSA-α schemein scenarios of having primary WiMAX users with FTPtraffic over TCP using Best Effort (BE) WiMAX profile withunlimited rate. Fig. 7 illustrates the normalized throughput (3)gained by the DSA-α secondary system. The trend in thisgraph again reflexes the effectiveness and the performance ofour scheme. It is also observed that with FTP/TCP traffic, thereis less opportunity for spectrum utilization by the secondarysystem than in the case of CBR/UDP traffic. This is due tothe fact that FTP/TCP PUs have unlimited BE profile, whereFTP traffic uses as much capacity as possible.

0 10 20 30 40 50 60 70 80 90 1000

1

2

3

4

5

6

7

8

9

10

DSA−α access ratio: α (%)

Nor

mal

ized

DS

A T

hrou

ghpu

t

1 FTP/TCP PUs2 FTP/TCP PUs3 FTP/TCP PUs

Fig. 7. Normalized Throughput in DL-Subframe: FTP/TCP Traffic

V. CONCLUSION

In this paper, we have proposed a dynamic spectrum accessscheme (DSA-α scheme) that allows a secondary system tostatistically and opportunistically access the whole spectrum

bandwidth during the last consecutive symbols of each primaryOFDMA subframe. To characterize the spectrum occupancydistribution of a primary OFDMA system, we have conductedthe simulations in our simulator modified from the WiMAXsimulator developed by the WiMAX Forum in Ns-2. Then,we have formulated the normalized throughput gained by thesecondary system and its expected interference models to theprimary system. We have shown that there is a tradeoff ingaining high normalized throughput by enlarging the accessduration in each subframe and keeping the expected interfer-ence lower by shorten the access period. Thus, this tradeoffhas been modeled by deriving the optimization problem thatmaximizes the normalized throughput, while guaranteeingthe hard-interference-constraint and/or the soft-interference-constraint set by the primary system.

For the future work, we will implement the completesimulation for the coexistence of a DSA-α secondary systemand a primary OFDMA system in our simulator to simulate theactual expected interference and compare it with the proposedmodels. This work expects the realization of a schedulingmethod for the secondary system to precisely access theprimary spectrum. Spectrum sensing can also be implementedto allow the secondary system knowing exactly when toaccess the primary spectrum. Last but not least, improving theprimary system to tolerate higher interference, which allowsthe DSA-α secondary system accessing higher portion of eachsubframe symbols, is also raised as the future work.

REFERENCES

[1] FCC, “Spectrum policy task force report,” Tech. Rep. ET Docket No.02-155, Nov. 2002.

[2] Q. Zhao and B. M. Sadler, “A survey of dynamic spectrum access,”IEEE Signal Process. Mag., vol. 24, no. 3, pp. 79–89, May 2007.

[3] T. Weiss and F. Jondral, “Spectrum pooling: an innovative strategy forthe enhancement of spectrum efficiency,” IEEE Comms. Magz., vol. 42,no. 3, pp. S8–14, Mar 2004.

[4] S. Geirhofer, L. Tong, and B. Sadler, “Dynamic spectrum access inthe time domain: Modeling and exploiting white space,” IEEE Comms.Magz., vol. 45, no. 5, pp. 66–72, May 2007.

[5] D. Willkomm, S. Machiraju, J. Bolot, and A. Wolisz, “Primary userbehavior in cellular networks and implications for dynamic spectrumaccess,” IEEE Comms. Magz., vol. 47, no. 3, pp. 88–95, March 2009.

[6] F. Bernardo, R. Agusti, J. Perez-Romero, and O. Sallent, “Dynamicspectrum assignment in multicell ofdma networks enabling a secondaryspectrum usage,” Wirel. Commun. Mob. Comput., 2009.

[7] P. Grønsund, H. N. Pham, and P. E. Engelstad, “Towards dynamicspectrum access in primary ofdma systems,” 20th Personal, Indoor andMobile Radio Communications Symposium (PIMRC), Sept. 2009.

[8] The network simulator ns-2. [Online]. Available:http://www.isi.edu/nsnam/ns/

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