Multi hop distributed coordination in

International Journal of Computer Networks & Communications (IJCNC) Vol.7, No.2, March 2015
DOI : 10.5121/ijcnc.2015.7211 137
MULTI-HOP DISTRIBUTED COORDINATION IN
COGNITIVE RADIO NETWORKS
Abdallah Ijjeh
Department of Electrical and Electronic Engineering, Al-Balqa` Applied University / Al-
Huson University College, Irbid, Jordan
ABSTRACT
Control Channel Dilemma (CCD) is still an open problem for Cognitive Radio Network (CRN). In this
article, we investigate and present a novel solution of Distributed Coordination in CRN to mitigate the
necessity for a common control channel. This scheme is based on the fact that a group of nearby nodes
almost detects the same frequency spectrum. That will allow every group of close nodes to choose a node
called coordinator, which in its turn chooses a reliable control channel for the group, then the reaming
nodes become slaves. Furthermore, the nodes that belong to two or more groups have an important role in
maintaining the network connectivity since they act as bridges to relay data between different groups. The
proposed approach carries potentials to provide robust operation under network dynamics conditions.
Finally, several simulation experiments are presented to validate the proposed approach.
KEYWORDS
Cognitive Radio Networks, Distributed Coordination, Control Channel Dilemma, Spectrum Heterogeneity,
Coordinator, Slave, Dual-slave
1.INTRODUCTION
In recent years Cognitive Radios (CRs) have emerged as a promising technology to enhance
spectrum utilization by applying opportunistic on-demand spectrum access techniques [1] [2].
Traditionally, the radio spectrum is statically licensed and split into different zones for different
organizations. Spectrum measurements carried out by FCC and other organizations have shown
significant temporal and geographical variations in the utilization of the licensed spectrum,
ranging from 15 to 85 percent [3] [4] [5]. These measurements motivated the necessity for a more
efficient and agile spectrum utilization and allocation policies. Consequently, the FCC has been
revising its regulations to allow for more efficient spectrum utilization. CR technology stands as a
good candidate since the CR’s are well known for their cognitive capability and reconfigurability
[4].
One of the main challenges to enable an efficient CRN operation in multi-hop non-centralized
environment is to find a reliable mechanism for exchanging control information. Typically, to
obtain such a mechanism, one can use a licensed common control channel (CCC) for control
information exchange [6]. Different MAC protocols [1] [4] [7] were designed for CRNs assuming
the existence of such a channel. While this approach may look simple, it is contradictory to the
opportunistic nature of CRNs and can cause a single point of failure. Furthermore, under high
traffic load the CCC may go under performance bottleneck [8]. To handle this issue, many
solutions have been proposed, none of which are totally satisfactory [7].

138
The most intuitive way to design medium access control MAC protocol for CRNs is by assuming
a predetermined non-dedicated CCC that is known to all users. This control channel can be
implemented either as a channel in an unlicensed band or as an ultra-wideband (UWB) channel
[6]. Unfortunately, both solutions of non-dedicated schemes have several draw backs that make
their practicality under question. Specifically, using UWB leads to a small control transmission
range, which might jeopardize CRN connectivity. On the other hand, single point of failure and
bottleneck performance may arise when using a fixed CCC can cause a single point of failure.
Even worse, in a relatively big network area, a CCC may not be available to all CR users because
different users may see different spectrum holes [7].
A more elaborate way to solve the CC dilemma can be obtained by considering the fact that
neighbouring CR’s typically have a similar view of spectrum availability. Therefore, coordination
mechanism based on clustering concept can be used to enable reliable coordination between CR
users [7]. Each cluster dynamically selects a local CCC for exchanging control information.
However, by applying such an approach in multi-hop CRNs environment, the network is limited
by several deployment challenges, such as, determining the cluster, ensuring connectivity sizes.
The third way to solve the control channel dilemma is to use Hopping-Based Control Channel
(HBCC) [6]. According to this approach, CR users hop across all available channels according to
a predefined hopping sequence. During this process, a CR different CR users exchange control
information to choose the optimal channel to use for data transmission. Once, they successfully
exchange control information, they stop hopping and start data transmission. This process will be
repeated every time communication between CR’s is required. However, the synchronization
process remains the main difficulty in applying this approach [9].
Previous works have been done to tackle the CC dilemma [10] [11] [7] [12]. For instance in [13]
the authors proposed an approach in which to set up coordination groups. In that approach the
users need to obtain some information about neighbours, especially their spectrum availability.
This is done by channel scanning and beacon broadcast. After the discovery process each node
will have the necessary information about its neighbours and their available channels, based on
that the CR user will select the control channel. In [3] the authors proposed a resource-aware
rendez-vous algorithm based on a spectrum hole management scheme. In their approach they
proposed an algorithm that can establish the control channel between CRs and the base station
even when there is available direct channel between them. That can be done simply by adding a
relay function that can ensure more reliable communication services in the CR networks.
In this paper, we propose a new distributed coordination scheme that takes spectrum
heterogeneity and the challenges presented earlier into consideration. CR’s in our scheme are
self-organize into groups and coordinate using locally available common channels. This approach
significantly improves connectivity scalability, and reduces deployment costs. This approach is
dedicated to solve control channel dilemma (CCD). As a first step, we propose a virtual group-
based coordination scheme and develop distributed group setup and maintenance algorithms
where CR’s chooses coordination channels adaptively. In the second step we introduced the
concept of dual slave and triple slave users to increase system reliability.
After this introduction, the rest of this paper is organized as follows; in section II we present
hypothesis and the basic assumptions about the network and the available spectrum properties,
namely, spectrum heterogeneity principle and its impact on the coordination between CR users. In
Section III, we present the general concept of distributed coordination and an algorithm to
perform distributed coordination virtual groups and describe the implementation using legacy
MAC protocols and a modified MAC protocol in Section III. In Section IV, we discuss the
simulation setup and results and compare with previous solutions to control channel dilemma.

139
2. PRELIMINARIES
In this section we present the basic assumptions regarding the CRN and the spectrum availability
conditions.
2.1. Network model
We consider network consisting both primary users (PR’s) and secondary users (CR’s). The
spectrum is divided into N - non-overlapping orthogonal channels. Secondary users are allowed
to use licensed spectrum if they don’t interfere primary users [13]. Moreover, those secondary
users are assumed to be static or quasi-static and each user can accurately measure its available
channels without affecting the licensed networks PR’s that use the same spectrum bands.
Secondary users are also assumed to be capable of communicating by choosing the same channel
[14]. We also assume that each CR user has a multi-radio device and uses a single half-duplex
transceiver, and can either transmit or receive on one channel at a time [6].
2.2. Spectrum Heterogeneity effects
As mentioned earlier, available spectrum is not always completely utilized by PR’s, hence, CR’s
can use the holes in the spectrum for establishing connection. However, because different CR’s
have different channel availability list, it will be very difficult or even impossible to find a
common control channel that covers all the geographical are of the CRN.
To show that, we have done a simulation for a CRN deployed in an area with heterogeneous
spectrum availability. The results we obtained show that while CR users do share significant
spectrum with local neighbours, a common channel is rarely available to all users in the network
(figure 1).
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
P
idle
ProbabilityOfGlobalControlChannel
N
ch
=5
N
ch
=10
N
ch
=20
Nch
=40
N
ch
=80
Figure 1, Probability of dynamic global control channel versus Pidle
In figure 1 we can notice the difficulty of finding dynamic global common control channel for
nodes distributed in L x L grid area. Even if this channel does exist, different nodes will need
global information to know it which violates our assumption of a non-centralized ad hoc network.

140
3. DISTRIBUTED COORDINATION MODEL
As discussed in the previous section, spectrum heterogeneity reduces the feasibility of using a
single common control channel. However, the fact that nearby users share similar spectrum
availability motivates us to propose a distributed coordination scheme. In this section, we present
the general concept behind our scheme and a detailed algorithm to generate distributed groups.
This scheme is completely distributed (i.e. it only requires local information). That will eliminate
the necessity for global synchronization; which will minimize the overhead. Furthermore, robust-
efficient coordination and better adaptability for spectrum heterogeneity will be achieved.
3.1. General Concept
In our distributed coordination scheme, CR’s are self-organized into local coordination virtual
groups based on spectrum availability. Nodes on each group form a single-hop or two-hp network
and use a common coordination channel and have one coordinator node. Assuming users
maintain connections with each other through local control channel, only members in the same
group can directly communicate with each other. Network connectivity is maintained by users at
group boundaries that have access to multiple control channels. These “bridge” nodes or “slaves”
relay data between groups and connect users in neighbour regions with different spectrum
availabilities.
3.2. Distributed Coordination Algorithm
To set up the common channel in a given neighbourhood, and to achieve balanced power
consumption scenario every node will have equal probability of becoming group coordinator.
This can be realized by setting a timer of a uniform distribution period.
Figure2, different kinds of nodes in the CRN according to their functionality
The algorithm is described by the following steps:
I. The first CR user with its timer to be expired broadcasts an alert message to its
neighbouring CR users, indicating that it will switch to a new control channel after to
seconds. This coordinator CR user instructs other neighbouring CR users to switch to a
new control channel for Tsw seconds in a timestamp message.

141
II. All CR users that received the alert message resynchronize their timers according to to
and follow the coordinator. We call such CR users slaves.
III. If a slave CR user receives multiple alert messages that indicate the same next control
channel selection, it will chose the strongest one, but will keep track for other control
channels in case of interruption form the PR’s in that cluster
IV. If a slave CR user receives multiple alert messages that indicate different control
channels, it adopts all the control channel selections for the time indicating in the
received messages. This border CR user is called to as a dual-slave CR user or triple-
slave …etc. depending on the number of alert messages they received. Those multi-slave
CR users have the same common control channels as other neighbouring CR users that
belong to different neighbouring clusters. Hence, the can function as bridges between
different clusters to maintain connectivity.
V. If a CR user losses synchronization for any reason, it conducts channel scanning to find
an appropriate CC. Once identifying that CC, it requests a timestamp message to reset its
own timer.
VI. Every time the cluster life time Tsw expires, the nodes in that cluster repeat the same
procedure from step I.
One can notice that the coordinator and the slave CR users from a virtual cluster. Thus, there are
no problems of inter-cluster communications and interference. This scheme flexible and allow for
easy adaptation for topology changes. Moreover, it requires simple synchronization requirements
among neighbouring CR users.
4. PERFORMANCE EVALUATION
In this section we present some simulation results to evaluate the performance of the new
proposed coordination control channel scheme. All simulations were carried out using C
language.
In the following experiments, we consider Nuser the number of users in a CRN coexists with a
number of PRN’s in 1000×1000 meter2
region. The area is divided into grids each of 10 ×10
meter2
area as shown in figure 3.
Figure 3, CR’s spatial distribution in the grid at the beginning of simulation

142
For balanced nodes distribution overall the area we assume same number of CR’s in each grid.
We assume Nchannel non-overlapping channels. The average idle duration for each channel is μi
and λi for average busy duration. The switching time period (the duration of occupying control
channel by coordinator) is set to be Tsw. the time needed to transmit single packet is . All nodes
are assumed to move in random direction, random step size of maximum value D << 1000 meters
(the covered region dimensions). Each CR user has Nctrl transceivers for control channel and Nt for
data transmission, where Nctrl << Nt and transmission radius of R meters.
Figure 4 shows the relation between switching time period and probability of control channel
availability (PCCA) for different Pidle probabilities for the network, for both single hop and two hop
systems the performance is almost the same.
0 20 40 60 80 100 120 140 160 180 200
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
Tsw
Probabilityofcontrolchannel
Two hop groups
One hop groups
Figure 4, probability of control channel availability versus switching time period, for Pidle = 50%
Figure 5 shows the relation between switching time period and PCCA for different Pidle
probabilities. As expected higher the PCCA decreases as the cluster life time Tsw increases until it
saturates to Pidle eventually.
Figure 5, probability of control channel availability versus switching time period

143
Figure 6, shows the PCCA availability verses Pidle for different transmission radius, when
transmission radius increases the PCCA increases because increasing the transmission radius will
increase the searching region, hence, there is a higher probability to find a control channel.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
ProbabilityOfControlChannelAvailability
P
idle
R=100m
R=200m
R=300m
R=500m
Figure 6, probability of control channel availability verses Pidle
Figure 7, shows the probability of control channel availability verses Pidle for single hop and two
hop systems. As we can notice, the two hop system provides with a slightly higher probability to
find a control channel since it increase the range of searching for that channel.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
P
idle
ProbabilityofControlChannelAvailability
One Hop Groups
Two Hop Groups
Figure 7, probability of control channel availability verses Pidle
Figure 8, shows the relation between number of groups versus transmission radius for single-hop
system and two-hop systems. It is obvious that a small transmission radius leads to a small group
size; hence, more groups are necessary to cover the entire area.

144
100 200 300 400 500 600 700 800 900 1000
0
10
20
30
40
50
60
70
R
NumberofCoordinators
One Hop Groups
Two Hop Groups
Figure 8, number of groups versus transmission radius
Figure 9, shows the number of nodes of different types for single hop and two hop system at
R=300m, Pidle=50% for 100 CR users. As we can notice, high number of dual-slaves and triple-
slaves is obtained. That has a significant role in maintaining the connectivity between
neighboring clusters and eventually in the entire area of the CRN.
Coordinator Slave Dual Slave Triple Slave Quad Slave
0
5
10
15
20
25
30
35
NumberofNodesofDifferentTypes
Single Hop Groups
Dual Hop Groups
Figure 9, number of nodes of different types for single hop and two hop system at R=300m, Pidle=50% for
100 CR users.

145
4. CONCLUSIONS
In this paper we proposed a distributed coordination scheme without relying on the availability of
a prespecified (licensed or unlicensed) fixed control channel. Therefore, the security and the
single point of failure issues, caused by using a fixed control channel, are significantly mitigated.
The simulation results have shown efficiency of this approach to handle the control channel
dilemma locally at the cluster level. Moreover, the bridge nodes (dual-slaves and triple-slaves)
have significant role in maintaining the connectivity and increasing the throughput.
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Author
Abdullah Ijjeh has received his BSc in Electrical Engineering from Kuwait University in
1981. He received his M.Sc. in communication engineering from Yarmouk University, Jordan
in 1988. He received his PhD in communication engineering from Polytechnic Institute, St.
Petersburg, Russia 1995. Currently he is working at the Department of communication
engineering at Al-Balqa` Applied University, Jordan. His research interests are wireless and
fibre optics communication systems.

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