Energy Efficient Localization of Primary Users for Avoiding
Interference in Cognitive Networks
by
Deepti Singhal, Garimella Ramamurthy, Manish Kumar Sharma
in
2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12,
2012, Coimbatore, INDIA
Report No: IIIT/TR/2012/-1
Centre for Communications
International Institute of Information Technology
Hyderabad - 500 032, INDIA
January 2012
2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA
Energy Efficient Localization of Primary Users for
Avoiding Interference in Cognitive Networks
Deepti Singhal, Manish Kumar Sharma, Rama Murthy Garimella,
International Institute of Information Technology, Hyderabad, India - 500 032,
[email protected], [email protected], [email protected]
Abstract—Cognitive Network is an intelligent network that
is aware of its surrounding environment, and adapts the
transmission or reception parameters of either a network or
a wireless node to achieve efficient communication without
interfering with primary users. For avoiding interference to
primary users, cognitive users must be aware of primary
users within the region of interest and disable themselves
whenever primary user is active. However, it is not possible
to identify the location of the passive listening primary users,
and causes hidden node problem in cognitive networks. This
paper applies leveling and sectoring based approach for
localization, and then disable region is identified considering
the communication range of both primary and cognitive user.
This will avoid the interference caused by hidden node
problem. The cognitive users within the disabled region are
sent to sleep mode until a free channel is detected in that
area by spectrum sensing module. This approach avoids
interference with primary user and also saves energy of
cognitive users. As this approach conserve energy of
cognitive users as well as simple to implement, it is suitable
for CWSN.
are the licensed users and hence have exclusive rights to
access the radio spectrum, whereas the cognitive users are
the unlicensed users that can opportunistically access the
free spectrum bands, without causing harmful interference to
primary users.
As Cognitive Users (CUs) are seen as the secondary users
to share the licensed band with the Primary Users (PUs),
they must avoid the interference to the PUs. For avoiding
interference to PUs, CUs must be aware of the PUs within the
region of interest. Hence CR networks or the opportunistic
networks require localizing the presence of PU within the
vicinity of the network. This motivates the study of precise
localization and tracking techniques for CR networks.
But there exists the hidden node problem. In hidden node
problem, two terminals can be within the range of another
terminal but out-of-range to each other. Two such terminals
are said to be ‘hidden’ from each other. In cognitive networks,
Index Terms - Cognitive Radio, Cognitive Wireless Sensor
a primary user and cognitive user can be hidden to each
Network, Localization.
other, and there could be another primary user which is
within the range of both, the primary and cognitive user. If
I. I NT RODUCT ION
this primary user is passive listening, it is not possible to
Traditionally wireless networks are running with fixed identify the presence of this passive listening user through
spectrum assignment policy regulated by government spectrum sensing. When a user is passive listening, it does
agencies. Spectrum is assigned to service providers on a long not acknowledge or respond to any request. In this situation,
term basis for large geographical regions. These spectrums hidden node problem arises in cognitive networks. As shown
can only be allowed to be used by licensed users, but
FCC measurements have indicated that 90 percent of the
time, many licensed frequency bands remain unused [1]. In
order to better utilize the licensed spectrum, the FCC has
launched a Secondary Markets Initiative [2], whose goal is
to remove regulatory barriers and facilitate the development
PU1
Rp
of secondary markets in spectrum usage rights among the
Wireless Radio Services. This introduces the concept of
dynamic spectrum allocation, which implicitly requires the
PU2
Rc
use of cognitive radios to improve spectral efficiency. The
Disable Region
CU1
inefficient usage of the existing spectrum can be improved
through opportunistic access to the licensed bands without
Fig. 1. Hidden Node Problem
interfering with the primary users. Cognitive Radio (CR) is
an intelligent wireless communication system that is aware
of its surrounding environment. It dynamically adapts the in figure 1, PU2 is passively listening, and we can’t identify
transmission or reception parameters of either a network or a the presence of PU2. If we disable CUs within the coverage
node to achieve efficient communication without interfering radius of PU1, i.e. Rp , only, and PU1 is transmitting data to
with primary users. In simple words, a cognitive radio network PU2, then CU1 can cause interference to this transmission.
consists of primary and secondary users. The primary users
978-1-4577-1583-9/ 12/ $26.00 © 2012 IEEE
2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA
Multiple solutions to the hidden node problem have been
provided, such as RTS/CTS handshake for WLAN [3], Busy
Tone Multiple Access (BTMA) for a centralized system [4]
and Dual Busy Tone Multiple Access (DBTMA) for ad hoc
networks [5]. However, these solutions are implemented for
networks where users are cooperative to each other, which is
not the case for cognitive networks. In paper [6], GPS (Global
Positioning System) enabled CUs cooperate to first estimate
the environment based on back propagation method and then
locate the position of PUs. In GPS technique, wireless node
calculates its position by carefully timing the signals sent by
the GPS satellites high above the Earth. But this solution
can only be used when GPS service is present and is very
expensive also.
In this paper, we use cooperative sensing to identify the
presence of PUs and then use the leveling and sectoring based
approach for localization. Finally, disable region is identified
considering the communication range of both primary and
cognitive user, which avoids the hidden node problem. The
cognitive users within the disabled region are sent to sleep
mode until a free channel is detected in that area by spectrum
sensing module. This avoids interference with the PUs and
sleep & wake-up procedure will conserve energy. This approach is also suitable to Cognitive Wireless Sensor Network
(CWSN) as battery life is a real constraint in these networks.
The rest of this paper is organized as follows. Section
II discusses the proposed localization algorithm. In Section
III, simulation results are presented. And finally, Section IV
summarizes the work done.
if their current level is higher than ‘H opC ount + 1’. The
nodes that are having their level equal to or less than the
‘H opC ount + 1’ value of the received packet then they don’t
update their current level value. In wireless communication
most of the time Line of sight path is not available, and
hence fading problems can occur. In such case hop-count
ratio-based technique is preferred.
If d is the distance between two levels and Rc is transmission range of a cognitive user then
Rc > 2 ∗ d
The total number of levels into the network is calculated
based on the above equation. After leveling, each CU has its
first coordinate as its level id, i.e. Li .
To differentiate between the nodes within the same level,
sensor network field is divided into equiangular regions
called sectors as [9]. Each sector is uniquely identified with
the help of sector ID. In order to inform their sector IDs
to CUs, the CRB sends Sector Broadcast Packets (SBP)
with directional antennas such that only nodes within a
sector receive this broadcast. SBPs contain sector ID and
information about the CRB. Sectors created with 30 degree
regions are shown in figure 2. Now, we assign the sector
WSN Field
Sector 4 Sector 3
Sector 5
Sector 2
Sector 6
Sector 1
Sector 7
II. P ROPOSE D A PPROACH
Proposed approach uses the leveling - sectoring based
localization [7], [8] where each CU is identified by two
coordinates, i.e. level id and sector id. First the entire
cognitive network area is divided into various levels as in
[9]. To divide sensor field into various levels, the Cognitive
Radio Base Station (CRB) sends packets containing level
ID for level 1 with minimum power level. All the nodes
that receive this signal set their levels as 1. Next the CRB
increases its signal power to reach the next level and sends
packet containing next level ID. All the nodes that receive
this signal, if have not already set their level ID, set their
level to 2. This process continues until the CRB has sent
signals corresponding to all levels. The number of levels into
which the network gets divided is equal to the number of
different power levels at which the CRB has transmitted the
signal. Leveling can also be done using hop-count ratio-based
technique. In this technique, hop-count of all the nodes
is initialized to infinity. First sink broadcast packets with
hop-count field set as zero. Nodes that directly receive these
packets set their level as 1. These updated packets are again
broadcast after incrementing hop-count field by 1. Nodes that
receive these packets update their level to ‘H opC ount + 1’,
(1)
BaseStation
Clusters
Sector 12
Sector 8
Sector 11
Sector 9 Sector 10
Fig. 2.
Sector Representation
number, i.e. θj , as the second coordinate to cognitive
users. Since the region was already divided into levels, the
Sub−Sector
Fig. 3.
Sub-Sector Representation
sectoring created a number of sub-sectors as shown in figure 3.
Now, the cognitive users know their location in terms
of (Li , θj ). These cognitive users now do the cooperative
sensing to identify the presence of PUs [10], [11]. In
cooperative sensing, multiple cognitive users cooperate
to reach an optimal global decision by exchanging and
combining individual sensing information. This will increase
the detection probability and reduce the detection time [4],
2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA
[12]–[16]. Cognitive users send their individual sensing
information to the CRB. Once the presence of PU is detected,
CRB calculates the relative position of PU in terms of (Li , θj )
based on the signal strength detected by CUs.
According to Friis transmission equation, given two antennas and the power input to the transmitting antenna, Pt , the
ratio of power available at the output of the receiving antenna,
Pr , is given by
2
Pr = G t G r
λ
Pt
(2)
4πR
Where Gt and Gr are the antenna gain of the transmitting and
receiving antennas, respectively, λ is the wavelength, and R is
the distance between transmitter and receiver. Thus distance
R is
s
P t Gt Gr λ2
(3)
R=
Pr (4π)2
As the distance is inversely related to the received power,
CRB selects the CR with maximum received power. Thus,
CRB identifies the closest CU, and assigns the same subsector location to the PU.
2) Add the Li , level id of the PU, in the disable level id
list.
3) Add (DisableLevelC nt − 1) levels from (Li − 1)
to
(Li − (DisableLevelC nt − 1)) in the disable level
id
list.
4) Add (DisableLevelC nt − 1) levels from (Li + 1) to
(Li + (DisableLevelC nt − 1)) in the disable level id
list.
Second step is to find the sectors that fall within the disable
region. Steps to find the sector ids are:
1) Calculate the distance of the sub-sector midpoint from
the CRB
(6)
DistanceF romBS = ((Li − 1) ∗ d) +
d2
2) If CRB is inside the disable region, i.e.
DistanceF romBS <= Rd , then all the sectors
should be disabled. This scenario is shown in figure 5.
Otherwise follow the steps from 3 - 7.
After getting the location information of PU in terms of
(Li , θj ), radius for disable region is calculated with below
equation:
Rd = Rc + R p
(4)
Where Rc is the coverage radius of cognitive user and Rp
is the coverage radius of primary user. The disabling region
is shown in figure 4.
Fig. 5.
R d
R
3) If CRB is outside the disable region, i.e.
DistanceF romBS > Rd , then a tangent can be drawn
from the base station position as shown in figure 6.
opposite
According to Pythagoras Theorem, sin θ = hypotenuse
,
and θ can be calculated as
c
R p
Enabled CU
θ = sin−
Disabled CU
PU
Fig. 4.
Scenario When Base Station is inside the disable region
1
opposite
hypotenuse
(7)
Region to be disabled
Now, algorithm calculates the list of sub-sectors that are
coming in this region and sends a message to disable all the
CUs in those sub-sectors.
Assume that the level id and sector id of the PU are (Li , θj ).
First step is to find the levels that fall within the disable region.
Steps to find the level ids are:
1) Calculate the number of levels to be disabled
(DisableLevelC nt) at one side (up or down) with
DisableLevelC nt =
Rd
d
+1
(5)
4) Calculate the number of sectors to be disabled
(DisableSectorC nt) at one side (left or right) with
DisableSectorC nt =
θ
SectoringAngle + 1
(8)
5) Add θj , the sector id of the PU, in the disable sector id
list.
6) Add (DisableSectorC nt − 1) left neighbor sectors
in the disable sector id list.
7) Add (DisableSectorC nt − 1) right neighbor sectors
in the disable sector id list.
2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA
Error% by varying PU%
100
"VaryingPU.txt" u 1:2
90
80
70
opposite
Error%
60
hypotenuse
50
40
theta
adjacent
30
20
10
Fig. 6.
0
Scenario When Base Station is outside the disable region
2
4
6
8
10
12
14
16
18
20
PU%
Fig. 7.
Now the list of all levels and sectors that fall in the
disable region is known, and base station broadcast this
list to all cognitive users. If CU’s level id and sector
id, both are present in the broadcasted list, CU put itself
to sleep until a wake-up message is sent from the base station.
This approach disables the transmissions from all those
users that are in the disable region, but it also disables
some CUs that are outside the disable region and not
causing interference to the PU. Future research can be done
to improve the performance of the approach. Next Section
discusses the simulation results of the approach discussed here.
Error percentage by varying number of primary users
level error percentage is almost constant. The enabled CU
count within the disable region for this scenario is always zero.
Second scenario evaluates the performance of proposed
approach by varying number of CUs in same network. In this
scenario, number of PUs are fixed and taken as 10. Figure 8
Error% by varying number of CUs
100
"VaryingCU.txt" u 1:2
90
80
70
III. S IMUL AT ION R E SULT S
Error%
60
In this section, we evaluate the performance of our proposed
algorithm by means of simulation using MATLAB. The algorithm will be evaluated in a number of different scenarios.
In simulation, the distance between two levels, d, is taken
as 5 units and the sectoring angle, θ, as 30 degree. Wrong
enabled CU that should be disabled and the error percentage
of disabled CU count with respect to actual disabled CU count
is calculated to evaluate the performance of the approach. The
Error for disabled CU count is calculated with:
q
2
(#C U DisableSim − #C U DisableActual)
Error =
(9)
where #C U DisableSim is the number of CU that are disabled using the proposed approach, and #C U DisableActual
is the number of CU that should be actually disabled. Error%
is calculated with:
50
40
30
20
10
0
100
200
300
400
500
600
700
800
900
1000
#CU
Fig. 8.
Error percentage by varying number of CUs
shows that the error percentage is almost constant for all the
cases, that means the performance of the algorithm is same
for sparse and dense cognitive networks. The enabled CU
count within the disable region for this scenario is also zero.
In next scenario, performance is evaluated by varying the
network area. For this scenario, number of cognitive users and
Error% =
∗ 100
(10) primary users are taken as 1000 and 10 respectively. Figure
9 shows the error percentage for this scenario. This graph
shows that if network area is increasing for same number of
In first scenario, the performance is evaluated by varying primary users, then the error percentage is decreasing. The
the number of primary users in the network. For this enabled CU count within the disable region for this scenario
scenario, number of cognitive users and grid size are taken is also zero for all the grid sizes.
as 1000 and 2000 respectively. Figure 7 shows the error
percentage for the scenario. According to this graph it is
From figure 7, 8 & 9, it is clear that the performance of
clear that as the number of PU are increasing in the network; proposed algorithm is same if cognitive users are increasing
the error percentage is also increasing. But after certain in network. But if the increase in primary users is high,
Error
#T otalC U
2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA
available. Our algorithm locates the PU based on leveling
and sectoring approach, and then disable region is identified
to avoid interference to the primary user. Section III has
covered the simulation results for the proposed algorithm,
which shows that the proposed approach provide acceptable
results. Specially, the count for wrongly enabled CUs is 0
for all the scenarios, which shows that the interference to the
primary users is prevented, and thus this approach can be
implemented in cognitive networks.
Error% by varying Network Area
100
"VaryingNetworkArea.txt" u 1:2
90
80
70
Error%
60
50
40
30
20
10
R E FE RE NCE S
0
1000
1200
1400
1600
1800
2000
Grid Size
Fig. 9.
Error percentage by varying network area
then the error percentage is increasing. If a network is full
of primary users than there is no meaning in implementing
cognitive network in that area. And thus proposed approach
can be implemented to the practical scenarios.
Finally, performance is evaluated by varying the sectoring
angle as shown in figure 10. For this scenario, number of
cognitive users and primary users are taken as 1000 and
10 respectively. This graph shows that for same number of
Error% by varying Sectoring Angle
100
"VaryingSectorAngle.txt" u 1:2
90
80
70
Error%
60
50
40
30
20
10
0
10
20
30
40
50
60
Sectoring Angle
Fig. 10.
Error percentage by varying Sectoring Angle
primary and cognitive users in a network area, as the sectoring
angle is increasing, the error percentage is increasing linearly.
But selection of the sectoring angle depends upon the type
of antenna used at the base station and other implementation
constraints. For this reason, in all other simulation scenarios,
30 degree sectoring angle is selected. For all the angles in
this scenario, enabled CU count within the disable region is 0.
IV. C ONCL USION
In this paper, we have proposed the Energy Efficient
Localization of Primary Users for Avoiding Interference in
Cognitive Networks. This approach can be implemented in
networks where GPS like localization techniques are not
[1] FCC Spectrum Policy Task Force. Fcc report of the spectrum efficiency
working group. Nov. 2002.
[2] Secondary markets initiative. http://wireless.fcc.gov/licensing/secondarymarkets/.
[3] Wireless lan medium access control (mac) and physical layer (phy)
specifications. ANSI/IEEE Std 802.11:1999 (E) Part 11, 1999.
[4] F. Tobagi and L. Kleinrock. Packet switching in radio channels:
Part ii–the hidden terminal problem in carrier sense multiple-access
and the busy-tone solution. Communications, IEEE Transactions
on,
23(12):1417 – 1433, December 1975.
[5] Z.J. Haas and Jing Deng. Dual busy tone multiple access (dbtma)a multiple access control scheme for ad hoc networks.
Communications, IEEE Transactions on, 50(6):975 –985, June 2002.
[6] Lin Liu, Zhengyi Li, and Chi Zhou. Backpropagation-based cooperative
localization of primary user for avoiding hidden-node problem in cognitive networks. International Journal of Digital Multimedia Broadcasting,
page 9, 2010.
[7] G. Lakshmi Phani, K. Venkat Sayeesh, K. Vinod Kumar, and
G. Rama Murthy. Erfla: Energy efficient combined routing, fusion,
localization algorithm in cognitive wsn. In Wireless And Optical Communications Networks (WOCN), 2010 Seventh International Conference
On, pages 1 –5, 2010.
[8] M.K. Sharma, A. Singal, P.G. Vijay, and R. Murthy. Minimal energy
consumption in the localization of a sensor network.
In
Sustainable Energy Technologies (ICSET), 2010 IEEE International
Conference on,
pages 1 –4, 2010.
[9] A. Mirza and R.M. Garimella. Pascal: Power aware sectoring based
clustering algorithm for wireless sensor networks. In Information
Networking, 2009. ICOIN 2009. International Conference on, pages
1
–6, 2009.
[10] A. Ghasemi and E.S. Sousa. Collaborative spectrum sensing for
opportunistic access in fading environments. In New Frontiers in
Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005
First IEEE International Symposium on, pages 131 –136, 2005.
[11] G. Ganesan and Y. Li. Cooperative spectrum sensing in cognitive radio
networks.
In New Frontiers in Dynamic Spectrum Access
Networks,
2005. DySPAN 2005. 2005 First IEEE International Symposium on,
pages 137 –143, 2005.
[12] G. Ganesan and Li Ye. Cooperative spectrum sensing in cognitive
radio, part ii: Multiuser networks. Wireless Communications, IEEE
Transactions on, 6(6):2214 –2222, 2007.
[13] G. Ganesan and Ye Li. Cooperative spectrum sensing in cognitive radio,
part i: Two user networks. Wireless Communications, IEEE Transactions
on, 6(6):2204 –2213, 2007.
[14] J.N. Laneman, D.N.C. Tse, and G.W. Wornell. Cooperative diversity in
wireless networks: Efficient protocols and outage behavior. Information
Theory, IEEE Transactions on, 50(12):3062 – 3080, 2004.
[15] Z.J. Haas and Jing Deng. Dual busy tone multiple access (dbtma)a
multiple access control scheme for ad hoc networks.
Communications, IEEE Transactions on, 50(6):975 –985, June 2002.
[16] G. Ganesan and Ye Li. Agility improvement through cooperative
diversity in cognitive radio. In Global Telecommunications Conference,
2005. GLOBECOM ’05. IEEE, volume 5, pages 5 pp. –2509, 2005.