2012 IEEE Wireless Communications and Networking Conference: Services, Applications, and Business

An Effective Use of Spectrum Usage Estimation forIEEE 802.22 Networks
Saptarshi Debroy, Shameek Bhattacharjee and Mainak Chatterjee

Kevin Kwiat

Department of Electrical Engineering & Computer Science
University of Central Florida
Orlando, Florida 32816
Email: {saptarsh, shameek, mainak}@eecs.ucf.edu

Air Force Research Laboratory
Information Directorate
Rome, NY 13441
Email: kevin.kwiat@rl.af.mil

Abstract—IEEE 802.22 networks consist of base stations andconsumer premise equipments (CPEs) where the base stationin each cell opportunistically accesses and allocates (uplink anddownlink) channels to all the CPEs in its cell. Information on
white space (unused primary channels) availability is reported by
the CPEs to the base stations. Thus, a base station’s effectivenessto allocate channels are based on its ability to gauge the spectrum
usage at various locations.
In this paper, we propose a channel usage estimation framework where a base station uses the spectrum reports fromother neighboring base stations to estimate the spectrum usage
scenario at any arbitrary location within its cell. Our estimation
framework is based on Shepard’s interpolation technique forirregular points. We propose a channel allocation scheme thatminimizes interference among CPEs and maximizes white space
utilization. Through simulation experiments, we demonstrate theaccuracy of the estimation technique, utilization of the available
spectrum, and efficiency of allocation scheme. We also showthat our scheme achieves very low false positives and no false
negatives. Finally, we show that the optimal number of basestations that need to be consulted is in accordance with Shepard’s
bounds1 .

I. I NTRODUCTIONIEEE 802.22 based wireless regional area network(WRAN) [2] is a cognitive radio technology [3] that operates
on the underutilized and unused sub-900 MHz bands usedprimarily for TV services. The core components of an IEEE
802.22 network are the base stations (BS) and the consumer
premise equipments (CPE). Secondary nodes (BS and CPE)
opportunistically access unused or underutilized license bandsof primary users. As soon as secondary nodes detect activity
from a primary user on a band, they relinquish that band within
the channel move time.
Secondary nodes monitor the activity of primary users on
a constant basis using spectrum sensing mechanisms, therebybuilding a knowledge of spectrum usage called the spectrum
or channel usage report. This report empowers the secondary
nodes to better adapt their transmission parameters to thecurrent conditions. Channel allocation to CPEs is performedby the BS, which is assumed to have an accurate estimationof spectrum usage report of all CPEs in its range. Hence,
efficient channel allocation and sustenance of high data rates
1 Approved for Public Release; Distribution Unlimited: 88ABW-20113831,
11JUL11978-1-4673-0437-5/12/$31.00 ©2012 IEEE

with low interference in WRAN largely rests upon the BS’s
ability to accurately estimate spectrum usage and allocate
unused bandwidth to the CPEs. Though the IEEE 802.22
draft [1] mentions that BSs estimate the spectrum usage at
CPE locations by allowing the CPEs to share parameters suchas antennae pattern, height, effective isotropic radiated power(EIRP) etc., the actual spectrum usage estimation methodology
as well as channel allocation process are unspecified.
Most of the existing work in IEEE 802.22 networks arefocused on self-coexistence [6], signal detection or spectrum
sensing [8], inter-cell and control channel assignment [4];however, to the best of our knowledge, the existing literatureoffers little in terms of investigating spectrum usage estimationin WRANs or using predicted primary occupancy for efficient
resource allocation.
In this paper, we propose and evaluate a novel spectrum
usage estimation methodology and use it to efficiently allocate
data channels to CPEs. We specifically analyze the problem
of a BS estimating the spectrum usage at any arbitrary CPE
location. To achieve this, the BS opportunistically shares raw
spectrum usage reports with selective neighboring BSs andfuses the spectrum data using a modified version of wellknown Shepard’s interpolation technique for irregular data sets
in a multi-dimensional space [7]. We extend this estimation
framework to an efficient intra-cell channel allocation schemethat minimizes interference among CPEs, maximizes unused
channel utilization and increases spatial reuse of such freechannels.
We evaluate our framework along three dimensions: accuracy of our proposed estimation technique, utilization ofunused channels, and effectiveness of the proposed allocation
scheme. We observe that on an average the estimation technique yields 6% false positive and no false negatives. We also
derive bounds on the optimal number of neighboring BSs to
be consulted in order to reach a desired level of estimation accuracy. We show how the proposed channel allocation scheme
ensures at least 57% free channel utilization. We also showthat in a potentially extreme and highly improbable scenario
with all the CPEs in a cell requesting channel allocation at thesame time, the proposed scheme successfully allocates 42% of
all the CPEs with a highest possible being 64%.

3239

II. I NFORMATION G ATHERING AND S HARINGIn this section, we define the general model of WRAN. We
delineate the sequence of steps taken by a BS and a CPE toestablish communication, which facilitates in CPEs providing
the BS with spectrum usage reports. We also discuss themethodology by which BSs exchange such reports amongst
each other. This information exchange drives our spectrum
estimation and allocation in subsequent sections.

BS2

BS3

BS1

p
BS

7
r

A. General WRAN model

BS

BS4

6

We consider an IEEE 802.22 network that is divided into
cells, each having a single BS. A BS can communicate with theCPEs in its cell only through free channels in the spectrum andwith its neighboring BSs using pre-defined control channels.
The main objective of a BS is to find out free channels in the
spectrum and allocate the CPEs a pair of uplink and downlink
channels. All the cells are prone to co-channel and adjacentchannel interference. The IEEE 802.22 draft [1] requires all
the devices in the network to be installed at fixed locations
and the BS to be aware of the location of all the CPEs underit.
The challenges of channel allocation in IEEE 802.22 networks in comparison to other conventional networks (cellular)
is the absence of pre-defined control channels between BS and
CPEs and the very concept that channels to be allocated by the
BS are temporally perishable. Therefore a BS has to have two
separate units, a cognitive radio enabled unit responsible forsense-transmit-listen and another solely in charge of allocating
channels to the CPEs and communicating with other BSs.

BS5

Fig. 1. All the base stations that fall under the circle drawn centering 𝑝 withsearch radius 𝑟

C. Opportunistic cooperation among base stations

the CPEs from sending their entire spectrum usage reports/
binary decision vectors to the BS because of high payload ofsuch data and increased chances of collision. Instead we allow
other BSs within a particular range of the CPE to share theirraw spectrum usage reports with the BS in question throughtheir pre-defined control channels. And here we somewhat
diverge from conventional cooperative spectrum sensing. In
conventional cooperative spectrum sensing, cooperation i.e.,
sharing of decision vectors (signifying occupancy of channels)
among cognitive radio enabled nodes takes place at all times
involving all the sensing nodes. Such exhaustive and continuous sharing is unsuitable for an IEEE 802.22 network as it will
result unnecessary flooding of spectrum data. Thus we needan on-demand and opportunistic cooperative spectrum sharing
technique among selected BSs.
In our proposed scheme. such sharing is triggered by thereception of any channel allocation request and that is whenthe BS unit responsible for channel allocation takes over.
Selection of participating BSs or data points 2 depends on
the search radius specified. Intuitively a bigger search radius
will incorporate more BSs resulting in enhanced accuracy of
estimation but at a price of increased complexity. More on
optimal choice of search radius is discussed in Section III and
Section V. It is to be mentioned that the proposed opportunistic
spectrum collaboration retains the best of both centralized anddistributed cooperation techniques.
Fig. 1 shows an IEEE 802.22 network with seven different
cells and each having a single BS. The figure shows 𝐵𝑆2 ,𝐵𝑆3 and 𝐵𝑆4 share their spectrum usage with 𝐵𝑆7 as theycome under the sharing range from 𝑝 within radius 𝑟.
Once BS successfully receives spectrum usage reports fromneighboring BSs, it estimates of spectrum usage scenario at
the CPE which we discuss in the next section.

The cognitive radio enabled unit of a BS can only tune
itself to a particular channel for a stipulated amount of time
before moving to the next channel. This motivated us to relieve

2 We use the cognitive base stations as the data gathering points, hence wealso refer to them as ‘data points’ in future mathematical formulation

B. Sense, transmit and listen
Initial communication between the BS and a CPE takes
place through beacon broadcast by the BS and subsequent
allocation request by the CPE. A BS employs a sensetransmit-listen approach to facilitate communication. Throughits cognitive radio device, the BS tunes itself to a particularchannel to find out existence of primary. If free, BS transmits
a beacon and listens for a while for allocation requests from
CPEs. Details of this sense-transmit-listen policy is governedby the MAC design which is outside the scope of this paper.
Only those CPEs can listen to beacons which tune themselvesto that particular channel at that time provided that channel
is not occupied by the a primary. Therefore, if there are 𝐶
CPEs in the systems currently looking to get allocated with 𝐾
total number of channels in the spectrum, then the probabilityof successful rendezvous between the BS and a CPE is 𝐾𝐶2 .
Although the realistic chances of more than one CPE tuned
to the same free channel and responding to a beacon from the
BS is still not very well gauged, we adopt a CSMA/CA likeapproach where the CPEs sense the channel before sending
their allocation request to avoid collision.

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III. E STIMATION OF S PECTRUM U SAGE AT CPE
In this section we discuss the interpolation technique usedby the BS to estimate the spectrum usage at any arbitrary CPE
location in the cell.
Estimating the spectrum usage at any arbitrary point from agiven set of points is non-trivial. In essence, we seek to definea continuous two-dimensional interpolation function which
passes through all the given irregularly-spaced data points.
Let us consider 𝑁 cognitive radio enabled BSs and coordinates of the 𝑖th BS be (𝑥𝑖 , 𝑦𝑖 ). Also, this BS records
some data value of 𝑧𝑖 . The data value can correspond to
one of the many radio parameters like SNR, duty cycle, or
detected energy for a particular channel that the BS is sensing.
Now, given 𝑁 such triplets (𝑥𝑖 , 𝑦𝑖 , 𝑧𝑖 ), we seek to find a
two dimensional interpolation function 𝑓 (𝑥, 𝑦) = 𝑧 that will
be continuous and differentiable, passing through all the data
points i.e., 𝑓 (𝑥𝑖 , 𝑦𝑖 ) = 𝑧𝑖 , and conforming to real life values.
We start with a basic approach to interpolate values using
weighed averages. Let 𝑒𝑖 be the value of the detected energy
from the spectrum usage report of 𝐵𝑆𝑖 . If 𝑑𝑖 is the Euclidean
distance between 𝐵𝑆𝑖 and any arbitrary CPE 𝑐, then theinterpolation function considering all the BSs can be defined
as:
⎧ 𝑁
∑



(𝑑𝑖 )−𝑘 𝑒𝑖



⎨ 𝑖=1
𝑖𝑓 𝑑𝑖 ∕= 0 𝑓 𝑜𝑟 𝑎𝑙𝑙 𝐵𝑆𝑖 (𝑘 > 0)𝑁𝐹1 (𝑐) =∑(𝑑𝑖 )−𝑘




⎩ 𝑖=1
𝑒𝑖
𝑖𝑓 𝑑𝑖 = 0 𝑓 𝑜𝑟 𝑠𝑜𝑚𝑒 𝐵𝑆𝑖(1)
Although this technique of finding expected value at anarbitrary point is easy to compute, it overlooks some key
aspects like the number, locations, and the relative positions of
the known data points with respect to point where the value isto be determined. As far as number of data points is concerned
it is intuitive that as the number of data points increases, more
will be accuracy. However, it is infeasible to use the spectrum
value of every BS in the network for estimation of spectrum
data at any CPE in the network regardless of its location.
In this regard, we make use of the Shepard’s [7] method tofind a computationally reasonable interpolation function for
irregularly spaced data points in a two dimensional region.
A. Selection of points
As we mentioned earlier, one of the key issues of successful
approximation is to choose nearby data points prudently. We
only select those data points which are sufficiently close to a
CPE because the effect of those values on that CPE will be
more than the distant points.
Let us consider a collection of data points near CPE 𝑐with the search radius being 𝑟. Choice of this search radius determines the number of data points to be selected
for interpolation and thus should be chosen to select suchdata points that have the most influence on point 𝑐. Let
𝑅𝑐 = {𝐵𝑆𝑖 ∣𝑑𝑖 ≤ 𝑟} be the set all data points within the

search radius. where 𝑑𝑖 is the distance of 𝐵𝑆𝑖 from CPE 𝑐.
We also define the set 𝑅𝑐𝑛 = {𝐵𝑆1 , 𝐵𝑆2 , ......., 𝐵𝑆𝑛 } suchthat 0 ≤ 𝑑1 ≤ 𝑑2 ≤ ....... ≤ 𝑑𝑛 , which denotes the BSs in an
ascending order of their distances from 𝑐. A weighing function
dependent on the search radius is defined as [7]:⎧ 1
𝑖𝑓 0 < 𝑑 ≤ 3𝑟
⎨ 𝑑
27 𝑑
2
𝑝𝑖 =
( − 1) 𝑖𝑓 3𝑟 < 𝑑 ≤ 𝑟⎩ 4𝑟 𝑟0
𝑖𝑓 𝑟 < 𝑑Thus considering the effect of distance of the data points,
the interpolation function can be modified as:⎧ ∑(𝑝𝑖 )2 𝑒𝑖




𝑛
∈𝑅𝑐
⎨ 𝐵𝑆𝑖∑
𝑖𝑓 𝑑𝑖 ∕= 0 𝑓 𝑜𝑟 𝑎𝑙𝑙 𝐵𝑆𝑖
𝐹2 (𝑐) =(𝑝𝑖 )2𝑛
⎩ 𝐵𝑆𝑖 ∈𝑅𝑐𝑒𝑖
𝑖𝑓 𝑑𝑖 = 0 𝑓 𝑜𝑟 𝑠𝑜𝑚𝑒 𝐵𝑆𝑖(2)However, this interpolation function does not reflect theeffect of direction of those data points i.e., the relative angle
they make with each other. In the following section we will
try to incorporate the directional effects.
B. Direction of data points
We seek to incorporate the directional effect of the data
points by considering all the possible set of angles thateach data point makes with all other data points. Thus the
directional weighting term for each selected data point 𝐵𝑆𝑖
near 𝑐 can be defined as∑(𝑝𝑗 )[1 − 𝑐𝑜𝑠∠𝐵𝑆𝑖 𝑐 𝐵𝑆𝑗 ]𝑎𝑖 =𝐵𝑆𝑗 ∈𝑅𝑐𝑛∑(𝑝𝑗 )∀𝑗 ∕= 𝑖𝐵𝑆𝑗 ∈𝑅𝑐𝑛

Now, considering all the effects of number, distances, anddirections of data points on point 𝑐, we define the weighing
factor as 𝑤𝑖 = (𝑝𝑖 )2 (1 + 𝑎𝑖 ). It is to be noted that in the directional weighing term 𝑎𝑖 , the distance weighting factor 𝑝𝑗 is
included in the numerator and the denominator because points
near 𝑐 should be more important in shadowing than distantpoints. Thus, the final interpolation function considering the
distance and direction factors is:⎧ ∑𝑤 𝑖 𝑒𝑖∈𝑅𝑐𝑛⎨ 𝐵𝑆𝑖∑
𝑖𝑓 𝑑𝑖 ∕= 0 𝑓 𝑜𝑟 𝑎𝑙𝑙 𝐵𝑆𝑖(3)𝐹3 (𝑐) =
𝑤𝑖



𝑛
𝐵𝑆
∈𝑅

𝑖
𝑐
⎩
𝑒𝑖
𝑖𝑓 𝑑𝑖 = 0 𝑓 𝑜𝑟 𝑠𝑜𝑚𝑒 𝐵𝑆𝑖
The interpolation function 𝐹3 (𝑐) meets all the requirements(i.e., defined at every point, continuous and differentiable)
which we need to compute the spectrum usage scenario at
CPE 𝑐.
Once the received noise from the primaries is estimated for
all the channels, BS predicts the occupancy matrix (binary
decision vectors) of 𝑐 by comparing the estimated value
with a predefined threshold. The predicted occupancy matrix
determines the potentially available channels at 𝑐.

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IV. A LLOCATION OF CHANNEL AMONG CPE S
In this section we describe the technique that a BS applies
to allocate channels to the CPEs.
Once the potentially available channels at 𝑐 is determined,
the BS initiates channel allocation process. Successful channel
allocation to a CPE involves allocating a pair of channels,
downlink and uplink. Only those channels can be allocated as
downlinks and uplinks which are free both at the CPE and at
BS, otherwise it will cause harmful interference with primarytransmission. We could have provisions for false negatives
where BS estimates a channel to be free but actually not,but our extensive simulation results show that the estimation
technique is false-negative proof.
Whenever two channels are allocated to a CPE, they areadded to the sets of allocated downlink and uplink channels.
This helps the BS to ensure spatial reuse of the channels within
its cell. The BS is thus responsible to ensure that those channels are only allocated to CPEs which are sufficiently apart tocause co-channel interference. Adjacent-channel interferenceis also taken care of by BS.
If CPE 𝑐 wants to relinquish its channels, they are removed
from their respective sets. There may be several reasons forchannel relinquishment. Session termination by the CPE 𝑐 orchange in spectrum usage scenario in either BS or CPE side
because of arrival of primary incumbent. In that case thesechannels cannot be used any longer until they are freed byprimary incumbent, and thus the CPE in question has to bereallocated within the channel move time or kept starving.
Ideal Case: In an ideal scenario of Fig. 2 three CPEs 𝑝, 𝑞 and𝑟 reside under 𝐵𝑆1 and 𝐵𝑆2 . Now it does not matter whichchannels are allocated to CPEs 𝑞 and 𝑟 with respect to 𝑝 as
the former two are under different BSs. Fig. 2 shows that 𝑞 is
given 𝑐ℎ3 and 𝑐ℎ4 whereas 𝑟 is given 𝑐ℎ1 and 𝑐ℎ2 which are
the same as that of 𝑝. It is of importance what channels areallocated to CPEs 𝑞 and 𝑟 with respect to each other as they
reside in the same cell and within their mutual interference
region.

ch 3
ch 1

q
ch 4
ch 1
r

ch 2

BS 1

BS 2

p

p

BS

BS1

BS

BS1

2

(a) Node 𝑝 receives beacons fromboth BSs.
ch2(b) Node 𝑝 responds to 𝐵𝑆1

1
p

ch

BS

BS

2

1

2

(c) 𝐵𝑆1 allocates uplink and downlink to node 𝑝
Fig. 3.

A case where 𝑝 resides in the overlapping region of cell 𝑖 and 𝑗

V. S IMULATION MODEL AND RESULTS
We conduct extensive simulations in C and MATLAB tovalidate the performance of the proposed channel allocation
technique. We use a grid size of 100x100 square units and toemulate primary behavior, we use the model and the range ofvalues from the real-world spectrum data archive of RWTH
Mobnets [5]. We consider a basic path-loss model and assume
50-400 channels of bandwidth 2MHz-250kHz each.
Fig. 4 shows the estimated received signal strength fromprimaries (noise from primary incumbent) for a typical IEEE
802.22 cell at a particular distance from primary transmitter.
The transmitter located at (30,45) is assumed to transmit at
1mW. This power profile is estimated by an IEEE 802.22 BS
which resides in the middle of the cell. All the points in theregion can be thought of as potential CPE locations. Throughsuch RSS estimation, the BS predicts channel occupancy inits cell and computes the free channels at a particular CPE.

ch 2

Fig. 2. Channels allocated to 𝑝 and 𝑞 are (𝑐ℎ1 , 𝑐ℎ2 ), and (𝑐ℎ3 , 𝑐ℎ4 )respectively. CPE 𝑟 under 𝐵𝑆2 at a considerable distance apart from 𝑝 isallocated the same channels as 𝑝.

CPE in cell overlapped region: When a CPE lies in theoverlapping region of two or more cells, then it receives
beacons from all the corresponding BSs. In such cases, theCPE can only respond to any one of the BSs. Fig 3 describes
such a situation where CPE 𝑝 lies in the overlapping region of
the cells of 𝐵𝑆1 and 𝐵𝑆2 . 𝑝 chooses to associate with 𝐵𝑆1

3242

Power spectral density (dBm/100kHz)p

and sends a channel allocation request. Channel allocation tonode 𝑝 by 𝐵𝑆1 takes place in the same technique discussed.
Fig. 3 describes the steps taken by 𝐵𝑆1 to allocate CPE 𝑝channels 𝑐ℎ1 and 𝑐ℎ2 as uplink and downlink respectively. It
is to be noted that the response from node 𝑝 will also reach
𝐵𝑆2 , but 𝐵𝑆2 will ignore that as the response message from𝑝 carries the unique identifier of 𝐵𝑆1 and not of 𝐵𝑆2 .

−40
−50
−60
−70
−80
60

60

Tx

50

50

40

40
30

Y−coordinates

Fig. 4.

30

X−coordinates

Estimated RSS for an IEEE 802.22 cell

20
15
10

30
25
20
15
10

5

5

0
0.4

0

0.5

0.55

0.6

0.65

0.7

0.75

0.8

Probability of white space in TV spectrum

Number of channels allocated

25

0.45

250

False negatives
False positives

35

5

10

15

20

25

30

35

200

150

100

50

0
50

40

Number of selected BSs for interpolation

100

150

200

250

300

350

400

Total number of channels in the spectrum(a) Nature of occurances of false (b) Accuracy of estimation withpositives in estimation
varying number of selected BSs
Fig. 5.

1

Prob. of white space =0.6

Utilization of free channels

40

Total channels =100
Total channels =200
Total channels =400

30

Number of occurances

Occurances of false postitives

35

Prob. of wh. sp.=0.4
Prob. of wh. sp.=0.6
Prob. of wh. sp.=0.8

0.8

0.6

0.4

0.2

0

50

100

200

400

Total number of channels in the spectrum(a) Number of channel allocated (b) Utilization of available chanfrom total available channels
nels for different probailities ofwhite space

Accuracy of spectrum usage estimation
Fig. 6.
110
100
90

Number of allocated CPEs

In Fig. 5(a), we show how occurrences of false positives
in estimation vary with the probability of getting white space.
We vary the total number of available channels (bandwidthof each channel) in order to determine its effect on theoccurrences of false positives. We observe that as there are
more channels available for the secondary nodes to use, theconservative approach of the estimation technique becomes
more predominant. However, we see that even with 80%
probability of getting free channels the ratio of instances offalse positives to total number of channels is less than 0.1.
Fig. 5(b) depicts the accuracy of the estimation techniquein terms of instances of both false positives and false negatives with varying number of neighboring BSs consulted forthe interpolation purpose. We see a sharp increase in bothinstances when more than 15 BSs are selected. This finding
is in accordance with Shepard’s [7] claim about the existenceof upper and lower bounds on the number of data-points to
be used for most accurate estimation. This upper bound on
number of selected BSs helps us to choose optimal searchradius depending on the density and topology of an IEEE
802.22 cell.
Figures 6(a) and 6(b) collectively portray how the available
channels in the spectrum are utilized by the proposed allocation technique. In Fig. 6(a), a line with 45 ∘ slope signifies
100% utilization which occurs when there are more channels
available to allocate to the CPEs. However even with 50
channels with probability of 0.6, we see that 17 free channels
are allocated resulting 57% (17/[50x0.6]) utilization.
Nature of free channel utilization with varying numberof available channels in the spectrum is better understoodin Fig. 6(b) where we vary total number of channels inthe spectrum and probability of getting white space thereby
indirectly varying number of free channels to utilize. We
observe that even with a very low probability of white space
0.4 (with respect to TV spectrum), the proposed allocation
scheme ensures more than 50% utilization of free spectrum
in most occasions. The total number of allocable CPEs are
kept at 255 for all the scenarios above which is the maximum
number of CPEs that an IEEE 802.22 cell can have [1].
In Fig. 7, we show the efficiency of the proposed allocation
technique by calculating the number of CPEs being allocateda pair of uplink and downlink channels. We observe that with
400 channels and a probability of getting white space of 0.8,
we allocate 106 out of possible 160 CPEs.

Utilization of available channels in the spectrum
Total channels =100
Total channels =200
Total channels =400

80
70
60
50
40
30
20
10
50

Fig. 7.

100
150
200
Total number of CPEs in the cell

250

Number of allocated CPEs for different number of channels

VI. C ONCLUSIONSIn this paper, we proposed a spectrum usage estimation
technique and channel allocation scheme for IEEE 802.22
networks. We modified Shepard’s interpolation technique for
a BS to fuse information gathered from other neighboring BSs
and predict the spectrum usage scenario at any location in its
cell. We showed how a BS can make wise uplink and downlink
channel allocations based on channel occupancy vector. We
conducted simulation experiments to demonstate the effciencyof the proposed techniques in terms of accuracy of estimation,utilization of free spectrum, and effectiveness of the allocation
technique.

3243

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