Adaptive Routing in Cognitive Radio Mobile
1st David Hrabcak
Department of Electronic
and Multimedia Communication
Technical university of Kosice
2nd Martin Matis
Department of Electronic
and Multimedia Communication
Technical university of Kosice
3rd Lubomir Dobos
Department of Electronic
and Multimedia Communication
Technical university of Kosice
Abstract—In this paper, Adaptive Routing algorithm for Cog-
nitive Radio Mobile Ad-Hoc Network (CR-MANET) is proposed.
Cognitive Radio (CR) is technology that is widely considered as a
promising technology to solve the problems in wireless networks
resulting from the limited available spectrum and the inefﬁciency
in the spectrum usage. It is an enabling technology that allows
unlicensed (secondary) users to exploit the spectrum allocated to
licensed (primary) users in an opportunistic manner. While in
classical CR networks the central network entity such as a base-
station in cellular networks is responsible for spectrum and rout-
ing management, in CR-MANET networks this responsibility lies
on end-user devices. Therefore, routing protocols in CR-MANET
need to be robust and must adaptively cooperate with spectrum
sensing methods. The proposed Adaptive Routing in CR-MANET
is therefore composed of function blocks that are able to perform
spectrum sensing, intelligent channel management and adaptive
Index Terms—Cognitive Radio, Mobile Ad-Hoc Networks,
Adaptive routing, Routing method
The importance of Cognitive Radio (CR) technology comes
from the insufﬁcient use of the spectrum for radio commu-
nication. In 2002, the US Federal Communication Commis-
sion (FCC) reported that the radio spectrum was 20% to
85% under-utilized . Therefore the change of spectrum
management was necessary. The licensing of the wireless
spectrum is currently undertaken on a long-term basis over vast
geographical regions. CR is technology, that allows unlicensed
(secondary) users to exploit the spectrum allocated to licensed
(primary) users. The main idea of CR is, that secondary user
(SU) is able to exploit unused licensed spectrum, but needs
to vacate the band once the primary user (PU) is detected.It
is strictly prohibited for SU to interfere with PU.It is strictly
prohibited for SU to interfere with PU. Therefore, SU needs to
constantly perform spectrum sensing to ﬁnd available spectrum
(spectrum holes) for communication.
Composing of routing protocols and techniques impose
unique challenges due to the high ﬂuctuation in the avail-
able spectrum as well as diverse quality-of-service (QoS)
requirements. Especially in CR-MANET due to the distributed
multi-hop architecture where nodes are mobile, autonomous
and the whole network is decentralized without the central
network entity such as a base-station in cellular networks.
Based on those assumptions and the difﬁculty of the problem
is necessary, that every node needs to make routing decisions
based on some intelligent or more sophisticated method, that
uses partial or basic spectrum sensing information rather than
using predictive or modelling methods .
In general, different intelligent methods can be implemented
on each device in CR-MANET, like game theory, fuzzy
logic, neural networks, biological algorithms among others.
All sufﬁcient information collected from spectrum sensing
is computed by one of the mentioned intelligent methods
for spectrum management. Based on this intelligent and au-
tonomous decision of every node, the used routing technique
can provide efﬁcient message transfer between a couple of
nodes (source and destination) without interference.
Therefore we decided to create adaptive routing method for
CR-MANET, that is composed of the function blocks that are
able to perform spectrum sensing, intelligent channel manage-
ment, and adaptive routing. Collected data from spectrum sens-
ing are calculated by the intelligent method based on Fuzzy
logic. This method provides intelligent channel assignment and
link metric for adaptive routing of communication.
II. ADA PT IV E ROUTING FOR CR-MANET
The main focus of this work is proposing Adaptive Routing
for CR-MANET (AR-CRM). The innovation of AR-CRM is
composing of function blocks consist of spectrum sensing,
intelligent channel management and adaptive routing. The
advantage of this routing method is, that function blocks
are separate from each other, but they collaborate to pro-
vide the best and efﬁcient routing without PU interference.
For each block is therefore possible to implement different
methods for spectrum sensing, intelligent methods and also
A. Overview of AR-CRM
The structure of proposed AR-CRM is depicted in Fig.1.
The following two terms results from the AR-CRM structure:
•Node - autonomous nodes in MANET and CR-MANET
individually perform spectrum sensing, intelligent spec-
trum management in their surroundings and selecting of
the optimal source path. All actions of function blocks
Intelligent method for
Fig. 1. Classiﬁcation of routing methods for CR-MANET
captioned in the blue Node area (Fig.1) of the structure
are therefore performed autonomously by each node.
•Network - the term network represents the distribution of
network topology information among nodes. The nodes
periodically send their information about network topol-
ogy to the neighbours and also provide them information
about the spectrum sensing. Distribution of topology and
spectrum sensing information is, therefore, collaborative
This structure contains function block responsible for topol-
ogy distribution, which means that routing scheme is based on
proactive routing. Local information about network and links
are exchanged among neighbours, which help nodes build their
own knowledge about network topology. All nodes perform
they own local spectrum sensing through Spectrum Sensing
function block. Data collected from this block are exchanged
between neighbours through topology distribution block to
help nodes acquire best possible information about spectrum.
Intelligent Method for Spectrum Management is block that
autonomously performs intelligent spectrum management on
each node, based on data collected from spectrum sensing
block and from neighbours. The output of this intelligent
method is the best possible channel and metric for all links
of the node. The Routing function block is responsible for
collecting topology information from Topology Distribution
function block and for building the topology of the network
for given node. This topology is merged with the output from
intelligent spectrum management. Therefore, the node is able
to ﬁnd optimal source path to the destination without PU
interference and with best possible QoS.
B. Topology distribution
Topology distribution is function block responsible for
distribution of information about network topology. Every
node builds its own internal topology in order to have actual
knowledge about the network. Distribution of information
about topology is exchanged among neighbour nodes in form
of Topology Distribution Message (TDM). Structure of TDM
is possible to see in Fig.2.
TDM is actually an adjacency matrix and is symmetric.
Values below diagonal (red color) represent metric for given
link generated by intelligent method. Values above diagonal
(blue color) represents channel assigned for a given link. The
ﬁrst row and column represent ID of nodes in the matrix.
Column and row marked as IDN represent ID of the node that
Time IDN I D2 ID3 ... I xD
IDN I NF 7 2 ... 0
ID2 53 INF 0 ... 0
ID3 21 0 INF ... 0
... ... ... ... ...
I xD 0 0 0 ... INF
Fig. 2. Structure of TDM message.
generates TDM. Another consecutive IDs represents nodes,
that node generating TDM is aware of.
TDM is periodically exchanged among neighbour nodes.
Given node has network knowledge about 2nhops with every
single exchange of TDM with neighbour nodes, where nis a
number of exchanges. Therefore, TDM will grow until every
node in the network will be discovered. After that, TDM
could change its size, for example when some node leaves
or join network.
C. Spectrum Sensing
Spectrum sensing is the process that every node au-
tonomously provide in order to discover PU activity and
also retrieve important parameters needed for the intelligent
method. In our paper, we do not propose any spectrum sensing
method. There are many spectrum sensing methods, that is
possible to implement for this function block. Examples of
these methods are matched ﬁlter detection, energy detection,
and feature detection .
More important for the AR-CRM is an exchange of spec-
trum sensing information among nodes. This is an important
feature of CR because the exchange of this information could
help nodes that are not able to perform spectrum sensing due to
multi-path fading, or they are located in a shadowing area .
Therefore, the Spectrum Sensing Message (SSM) is ex-
changed among neighbour nodes. Structure of SSM is possible
to see in Fig.3. The ﬁrst column represents ID of neighbour
nodes. The second column represents PU activity and the third
column represents channel on the link to given neighbour
node. Other columns could be used to carry other important
parameters for the intelligent method. Difference between
TDM and SSM is, that SSM carries information only about
neighbour nodes while SSM carries information about all
nodes in the network. After the SSM is generated, it is
exchanged by Topology Distribution function block.
D. Intelligent spectrum management method
One of the suitable intelligent methods for spectrum man-
agement must be implemented on each device due to the
decentralized type of network. Each device creates its own
IDN P U
Channel Parameter 1 Parameter 2 ... Parameter N
ID1 0 7 X X X
ID2 0 4 X X X
I xD 1 13 X X X
Fig. 3. Structure of SSM message.
Fig. 4. Example of interference in CR-MANET based on CSMA/CA.
decision for every wireless connection with neighbour node
based on spectrum sensing data.
Fuzzy logic is used for our proposal of AR-CRM method as
suitable, fast and responsible intelligent method for calculation
of each wireless link weight shared with the neighbour node.
This intelligent mechanism provides decisions between every
couple of nodes and assigns optimal communication channel
from the range of available channels.
1) Inputs parameters for Fuzzy logic: Important parameters
are obtained directly or are calculated from spectrum sensing
process and are considered as QoS of the link. Every couple of
nodes (A,B) need to bargain about best possible communica-
tion channel between them. This process is autonomous based
on Fuzzy logic and contains data collected from spectrum
sensing. Asymmetric trafﬁc is a common part of CR-MANET.
For this reason, TDD (time division duplex) is considered.
Providing of this trafﬁc is based on CSMA/CA. Based on this
is possible to assume, that black wireless links are multiplexed
by nodes A and B (Fig.4). Those links will not interfere
with the selected channel between A and B. The interference
may occur between the selected channel on the considered
link (A,B) and green links, which are not time synchronized
by node A or B. The Fuzzy logic mechanism is therefore
responsible for minimizing interference.
The ﬁrst obtained parameter is RSS I (Received Signal
Strength Indicator) and represents received strength of the
signal on devices antenna This information is possible to
obtain directly from device wireless card.
Next parameter for Fuzzy logic is SIR (Signal-to-
Interference Ratio), which is represented by the ratio of
received signal strength on selected device and sum of all
received signal strength on interfered devices based on Eq.(1):
SI R =RSSIl
where αiis the inﬂuence factor from the range [0,1], that
represents how strong is given channel inﬂuenced by the
The last input parameter is trafﬁc on the given link between
A and B (Fig.4). It is not always useful to select some path
which consists of the lowest number of hops when the actual
trafﬁc on the links is very high. We assume that other devices
may communicate through the network. For this reason, we
consider that every couple of nodes have some trafﬁc on the
link from the range [0-100]. Zero represents link without trafﬁc
while value 100 represents fully busy link. This parameter
represents the ratio of data bit-rates (bps) and supported bit-
rates (bps) on given communication channel by Eq.(2). The
result is transformed to the percentage.
T raf f ic =Data bit −rates/s
Supported bit −rates/s ∗100 (2)
2) Fuzzy logic integration to adaptive routing protocol:
For integration of intelligent method (Fuzzy logic) in Matlab
environment is the Fuzzy logic designer used. There are
deﬁned functions for input parameters, which are represented
by suitable curves. Function block for rules deﬁnes relations
among input parameters. Finally, resulting value is created and
perceived as QoS parameter for the communication link.
3) Channel assigning process: Every couple of node (e.g.
A and B) uses fuzzy logic for calculation of resulting value
for each possible communication channel from input values.
Based on this process, the set of best possible channels from
fuzzy logic is created for each link and couple of nodes, which
wants to set the best channel. After that, a couple of nodes
bargain about best channels based on their set of best possible
channels and tries to select optimal channel for both of them.
If it is not possible for a couple of nodes to make a deal,
the node with lower identiﬁcation (MAC address) selects the
best channel and this channel must be accepted by neighbour
node. On the Fig.5 is possible to see a structure of intelligent
method block with input and output parameters.
Routing function block is responsible for the generation of
the internal network topology of the node based on information
from periodically distributed TDM messages. The internal
network topology of the node has the same structure as TDM.
Internal topology is also periodically updated by informa-
tion from the intelligent method, i.e channels, and metrics
for neighbour links. In periodical intervals routing function
block create a copy of internal topology, which is actually
TDM message. Than TDM is distributed through Topology
Distribution function block.
Another important feature of Routing function block is op-
timal path ﬁnding. Topology Distribution ensures that Routing
block has actual knowledge about the network. The intelligent
method implemented on every node provide best possible
channels for every link and also evaluates those links with
metric (Fig.5). Since internal topology has a form of the
adjacency matrix, Routing function block create a graph of
the network, where vertices are nodes, and links are edges.
Metrics assigned to every link are used as the weights of edges.
Fig. 5. Structure of intelligent method block.
Based on this graph is possible to ﬁnd the optimal path from
source to the destination node based on shortest path algorithm
(e.g. Dijkstra algorithm). Metrics and channels produced by
Intelligent method ensure that chosen path is shortest in term
of QoS. The chosen path can cross area of PU activity or
avoids this area. It depends on parameters on the links and
III. SIMULATIONS AND RES ULTS
The Routing Method AR-CRM was fully implemented in
Matlab software. The main focus of simulations was oriented
on the optimal path selection by AR-CRM with the compar-
ison of path selection by DSR routing protocol , modiﬁed
to work in the CR-MANET environment (DSR-CR).
For simulation scenario, the network of 50 nodes with the
radio range of 100 meters on 500x500 meters wide area was
used. The number of available channels was set to 13 for AR-
CRM, like in Wi-Fi. PU activity area was also placed in the
simulated area with different radius from 25 to 300 meters and
PU operates on the channel 7. In case of DSR-CR, we assume
that it considers all channels as one, same for SU and PU.
The ﬁrst simulation was focused on the comparison of
the Average QoS on the selected path from source to the
destination node by AR-CRM and DSR-CR. On Fig.6 is
possible to see, that in all simulations with a different radius
of PU activity area, AR-CRM selects the path with better QoS.
Selected path by AR-CRM and DSR-CR are depicted on Fig.7.
AR-CRM was able to cross PU activity area since intelligent
method selects non-interfered channels. DSR-CR selects the
path that avoids PU activity area because PU affects all links
in activity area and DSR-CR considers this area as unavailable.
From a radius of 225 meters, DSR-CR did not ﬁnd a path due
to the big PU area.
Average QoS on selected path based on radius of PU activity area
Radius of PU activity area [m]
DSR-CR A R-CRM
Fig. 6. Average QoS on selected path based on radius of PU activity area.
The second simulation was oriented on the comparison of
Trafﬁc parameter on the selected path by AR-CRM and DSR-
CR. In Fig.7 is possible to see, that selected path by AR-CRN
is better in term of average trafﬁc, maximal and minimal trafﬁc
on some segment of the path.
IV. CONCLUSION AND FUTURE WORK
Adaptive Routing for CR-MANET is innovative routing
method that is composed of function blocks, that provides
spectrum sensing, intelligent spectrum management and ef-
fective routing. After comparison of AR-CRM and DSR-CR
Optimal paths for AR-CRM and DSR- CR between
nodes 77 and 47 with PU activity
Fig. 7. Optimal paths selected by DSR-CR and AR-CRM.
DSR-CR AR-CR M
Traﬃc on path [%]
Exis"ng traﬃc on selected op"mal paths
max min average
Fig. 8. Existing trafﬁc on selected optimal paths.
is possible to conclude, that incorporating of spectrum sensing
and intelligent method for spectrum management is beneﬁcial
for optimal path selection in the CR-MANET. In the future,
we want to implement other CR-MANET protocols for com-
parison with AR-CRM and provide more detailed analyses.
This work was performed in the framework of research
project VEGA 1/0075/15, funded by the ministry of education
of Slovak Republic, COST Action CA15127 (RECODIS) and
COST Action CA15104 (IRACON).
 M. T. Quach, “Cross-layer framework for interference avoidance in
cognitive radio ad-hoc networks,” (Doctoral dissertation, Universit de
 F. R. Yu & H. Tang, “Cognitive radio mobile ad hoc networks,”(Vol.
507). New York: Springer, 2011.
 I. F. Akyildiz, W. Y.Lee & K. R. Chowdhury, “ CRAHNs: Cognitive
radio ad hoc networks,” AD hoc networks, 7(5), 810-836, 2009.
 B. Wang & K. R. Liu, “Advances in cognitive radio networks: A
survey,”IEEE Journal of selected topics in signal processing, 5.1: 5-23,
 Mathworks.com, “Fuzzy Logic Toolbox,” Retrieved from https://www.
mathworks.com/products/fuzzy-logic.html, (2017, February 5).
 D. B. Johnson, D. A. Maltz & J. Broch, “DSR: The dynamic source
routing protocol for multi-hop wireless ad hoc networks,”Ad hoc net-
working, 5, 139-172, 2001.