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Clustering and Reinforcement Learning based
Routing for Cognitive Radio Networks
Yasir Saleema,b,c , Kok-Lim Alvin Yaub, Hafizal Mohamadc, Nordin Ramlic, Mubashir Husain
Rehmanid, Qiang Nie
aInstitute Mines-Telecom, Telecom SudParis, France
bSunway University, Selangor, Malaysia.
cMIMOS Berhad, Kuala Lumpur, Malaysia.
dCOMSATS Institute of Information Technology, Wah Cantt, Pakistan.
eLancaster University, Lancashire, United Kingdom.
Abstract
Cognitive radio network (CRN) is afuture generation wireless communication system which allows secondary
users (SUs) to use the underutilized or unused spectrum, known as white spaces, in licensed spectrum with minimum
interference to the primary users (PUs). However, the dynamic conditions of CRNs (such as PUs’ activities and
channel availability) make the routing challenging as compared to traditional wireless networks. In this tutorial, we
focus on solving the routing problem in CRNs with the help of clustering mechanism. Cluster-based routing in
CRNs enhances network scalability by reducing the flooding of routing overheads, as well as network stability by
reducing the effects of dynamicity of channel availability. Additionally, reinforcement learning (RL), an artificial
intelligence approach, is applied as a tool to further enhance network performance. We present SMART, which is a
cluster-based routing scheme designed for CRN, and evaluate its performance via simulations in order to show the
effectiveness of cluster-based routing in CRNs using RL.
Index Terms
Cognitive radio, clustering, routing, reinforcement learning, cluster-based routing
I. INTRODUCTION
Cognitive radio network (CRN) is afuture generation wireless communication system which solves the problem
of spectrum scarcity caused by static channel assignment policy in the past. CRN solves this problem by allowing
secondary users (SUs) or unlicensed users to explore and exploit underutilized licensed channels, known as white
spaces which are owned by primary users (PUs) or licensed users for improving the overall channels utilization.
Whenever a PU re-appears on the operating channel of a SU, the SU must switch to another available channel or
wait for the PU’s transmission to cease.
With the emergence of CRN applications such as cognitive radio sensor networks and cognitive vehicular net-
works, multi-hop routing for wide area coverage is becoming an essential. Multi-hop routing in CRN is challenging
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due to several reasons. Firstly, CRN is characterized by the dynamicity of channel availability (or white spaces)
due to different levels of PUs’ activities. Secondly, the broadcasting of routing control messages over the distinctive
available channels causes higher routing overhead and limits network scalability. Thirdly, the dynamicity of channel
availability can cause the lack of common control channel (CCC) for exchanging control information in routing.
Routing protocols for traditional wireless networks that maintain end-to-end paths, e.g., ad-hoc on-demand
distance vector (AODV) routing protocol, are not preferable for CRNs because they do not consider the challenges
of multi-hop routing in CRNs and highly increase the network overhead by flooding the routing messages constantly.
Hence, such protocols cannot be directly applied in CRNs. Therefore, routing protocols for CRNs must address
the challenges of CRNs by considering spectrum awareness in order to establish stable routes, so that SUs can
perform data communication for long duration without having much disruptions from PUs, as well as with minimal
interference to PUs. Furthermore, there is only limited cluster-based routing schemes proposed in the literature in
the context of CRNs.
In this tutorial, we solve the routing problem in CRNs with the help of clustering mechanism and reinforcement
learning (RL), an artificial intelligence approach, which is our main contribution. Cluster-based routing for CRNs
enhances network scalability by reducing the flooding of routing overheads as well as network stability by reducing
the effects of the dynamicity of channel availability. RL is a tool that further enhances network performance through
observing and learning the environment. We have proposed a cluster-based routing scheme using RL, which is known
as SMART and is designed for CRNs, in order to fulfill the requirement on the minimum number of common
channels in a cluster through cluster maintenance (i.e., cluster merging and splitting) which enhances network
stability, as well as enhances network performance. Since, cluster-based routing has not been well investigated
before in the context of CRNs, this is the focus of our article.
The organization of this article is as follows. In Section II and III, we present an overview of clustering and cluster-
based routing for CRNs, respectively, by highlighting their advantages and importance for solving the problem of
multi-hop routing in CRNs. In Section IV, we present an overview of RL. Subsequently, in Section V, we present
SMART (SpectruM-Aware clusteR-based rouTing), which is a cluster-based routing scheme that applies RL for
CRNs. In Section VI, we evaluate the performance of SMART. Finally, we conclude in Section VII.
II. CLUSTERING IN CRNS
Clustering, a topology management mechanism, provides network stability and scalability by organizing the nodes
into logical groups called clusters. The cluster structure provides a suitable network model to support cooperative
tasks which are very important for CR operations (e.g., routing and channel sensing). Fig. 1 shows a cluster
structure in which nodes are grouped into three clusters. Each cluster consists of four types of nodes: clusterhead,
member node, relay node and gateway node. The clusterhead is the central process for cooperative tasks within the
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cluster. Each member node is associated with a clusterhead. Clusterhead and member nodes communicate between
themselves using a common channel known as operating channel. This communication is known as intra-cluster
communications. The operating channel is available to all nodes in a cluster. A relay node is a member node that
provides connection to another member node which is located out of the transmission range of clusterhead. A
gateway is also a member node which can hear from neighboring cluster(s). It provides two-hop, or even more,
inter-cluster communications and is located at the boundary of a cluster.
Fig. 1. Cluster structure.
Cluster size represents the number of nodes in a cluster and it affects various performance metrics. Larger cluster
size minimizes routing overhead since the flooding of routing overheads only involves clusterheads and gateway
nodes along a backbone, as well as reduces error probability in the final decision of channel availability since it
is based on channel sensing outcomes collected from higher number of nodes in a cluster. Smaller cluster size
(or higher number of clusters in a network) maximizes the number of common channels, and hence connectivity
among nodes in a cluster, because physically close nodes are more likely to have a similar list of available channels.
Since clusters may use different operating channels, the contention and interference levels in the network can be
reduced, and this subsequently improves routing and network performances. Higher number of common channels
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in a cluster minimizes the occurrence of re-clustering due to improved connectivity between nodes within a cluster.
While achieving larger cluster size may seem to be more favourable in traditional wireless networks in order to
improve scalability, the same cannot be said for CRNs. Since smaller cluster size maximizes the number of common
channels in a cluster, it enhances the connectivity among member nodes and clusterhead in a cluster. This improves
stability and addresses the challenge of dynamicity of channel availability in CRNs [1].
III. CLU ST ER -BAS ED ROUTING IN CRNS
Routing protocols can be cluster-based which runs over the clustered network. In the literature, there have been a
larger number of separate investigations into clustering [2], [3] and routing [4], [5]. While, there is only a perfunctory
attempt to investigate cluster-based routing schemes for CRNs. Readers are referred to surveys on clustering [6],
[7] and routing algorithms [8] in CRNs for a comprehensive review of the literature.
Cluster-based routing is preferred in CRN for the following reasons. Firstly, it provides network stability by
reducing the effects of dynamic channel availability since any changes to channel availability affect the network at
the cluster level, so only local updates are required instead of whole network reconfiguration. Secondly, it provides
network scalability as routing control messages, such as route request (RREQ) and route reply (RREP), are only
exchanged among some nodes, particularly clusterheads and gateway nodes. As clusterheads and gateway nodes
share a similar operating channel, and gateway nodes are aware of operating channel of neighboring clusters, this
facilitates broadcasting using a single transceiver as it is no longer required to broadcast in the distinctive available
channels used by neighboring nodes in non-clustered networks. Thirdly, it reduces the need of a common control
channel for exchanging control information in routing since an operating channel is used which is available to all
nodes in a cluster. Fourthly, it supports cooperative tasks and improves channel sensing outcomes. For example, a
clusterhead collects channel sensing outcomes from its member nodes and subsequently makes a final decision on
channel availability. This improves the accuracy of channel availability decision as compared to the decision made
based on the outcome of a single node.
IV. REINFOR CE ME NT LEARNING:ATOOL T O ENHANCE NET WORK PERFORMANCE
RL [9] is an artificial intelligence approach that enables an agent or decision maker to observe its state and
reward, learn, and then perform an action in order to improve the state and reward in the next time instant. In
RL model, the action affects (improves or deteriorate) the state and reward which affects the next choice of action
by an agent. With the passage of time, an agent estimates the reward for each state-action pair, which constitutes
knowledge; and subsequently carries out a proper action at next time instant given a particular state to maximize
accumulated rewards. The important representations for the agent in RL model include state, action and reward.
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•State represents the decision-making factors which is observed in the operating environment by an agent. It
can affect the reward (or network performance).
•Action represents the action of an agent which helps an agent to learn about the optimal actions. It can affect
the state (or operating environment) and reward (or network performance).
•Reward represents the positive or negative consequence on operating environment caused by the agent’s action
in previous time instant in the form of network performance.
Q-routing has been applied in routing which is a prominent RL scheme. In Q-routing model, the state represents
the destination node, action represents the next-hop neighbor node of the decision making node that relays data
towards destination node, and the reward represents network performance (e.g., throughput). Each link of a route
is associated with a cost (e.g., delay) and a node computes Q-value for each state-action pair (or destination and
next-hop neighbor node pair) in order to estimate the cost required for transmitting the data towards the destination
node along the route.
There are two main advantages of applying RL to routing in CRNs. Firstly, rather than considering each factor
which affects the network performance, RL models the network performance that covers various factors in the
operating environment or network conditions affecting the network performance (i.e., the channel utilization level
by PUs and channel quality); hence, it is a simple modeling approach. Secondly, prior knowledge of the operating
environment or network conditions is not necessary; and so a SU can learn about the operating environment on the
fly as time goes by. Hence, the application of RL to cluster-based routing in CRNs can improve both routing and
clustering performances and it is very novel. Since CRN is characterized by the dynamicity of channel availability
due to PUs’ activities, cluster maintenance is imperative in CRNs to adapt the cluster structure and cluster size. RL
reduces the effects of dynamic channel availabilities by observing, learning and taking the optimal or near-optimal
actions that minimizes cluster maintenance.
V. SMART
We present SMART for overcoming the challenges of multi-hop routing in CRNs through cluster-based routing
and RL. In SMART, clustering aims to form clusters that fulfill the requirements on the number of common channels
in a cluster and allow nodes to forward routing control messages efficiently without the need of broadcasting on all
the available channels; while RL aims to find a route that increases the usage of white spaces for maximizing SUs’
network performance. Moreover, in order to overcome the dynamicity of channel availability, SMART provides
extension to clustering through cluster merging and splitting. Subsequently, SMART adjusts cluster size as time
goes by, so that a cluster fulfils the requirement on cluster size for improving scalability, as well as the number of
common channels in a cluster for improving stability. SMART estimates the OFF-state probability of a channel at
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next time instant [10], and uses this estimation to rank and select the operating channels in clustering and routes
in routing.
A. Clustering
There exists significant amount of work on cluster formation and gateway node selection in CRNs, therefore
readers are recommended to refer the existing work [11], [12], [13] for cluster formation and gateway node selection.
However, cluster maintenance (i.e., cluster merging and splitting), and cluster-based routing using RL are novel and
have not been investigated before. Therefore, we mainly present them in this paper which is our main contribution.
Cluster maintenance adjusts the cluster size in order to reduce dynamic effects of the network and it consists of
cluster merging and splitting. These are best explained with illustrations as presented in Fig. 2. In this figure, the
labels of the nodes are revised after each clustering event for better understanding. We assume that a threshold for
minimum number of common channels is 2 in both cluster merging and splitting. Cluster merging combines two
clusters into one and is possible when two clusters satisfy the threshold for minimum number of common channels.
Fig. 2(a) presents initial clusters formed after cluster formation in which gateway node 1 in cluster 2 discovers that
the set of common channels between clusters 1 and 2 is two and it satisfies the threshold value. Thus, it informs
both clusterheads in clusters 1 and 2 about the potential cluster merging. Suppose, both clusterheads agree to merge
and subsequently, gateway node 1 in cluster 2 becomes the new clusterhead as presented in Fig. 2(b). The existing
clusterheads in Fig. 2(a) relinquish their roles and become member nodes of the new clusterhead, and then inform
their respective member nodes to join the new clusterhead. Member nodes which are in the transmission range of
new clusterhead in Fig. 2(a) join the new clusterhead. However, member nodes which are not in the transmission
range of new clusterhead request their previous clusterheads to provide connection towards the new clusterhead,
and so the relinquished clusterheads become relay nodes for such member nodes as presented in Fig. 2(b). Finally,
the new clusterhead selects operating channel of the new cluster, and subsequently, gateway nodes are selected to
provide inter-cluster communication for the newly merged cluster.
Cluster splitting splits one cluster into two and it is performed when a clusterhead realizes that its cluster cannot
satisfy a threshold for minimum number of common channels. Fig. 2(c) shows clusters after cluster splitting is
performed on Fig. 2(b). Suppose, common channels 3 and 4 of cluster 2 in Fig. 2(b) are re-occupied by PUs. So,
clusterhead of cluster 2 in Fig. 2(b) initiates cluster splitting. Since the clusterhead is aware of a list of available
channels for all nodes in its cluster, it counts the number of nodes in each available channel and ranks these channels
based on maximum node degree. Subsequently, the clusterhead selects the highest ranked channels and identifies
nodes which have such channels available. In Fig. 2(b), such channels are available to four nodes in cluster 2, so
the clusterhead forms one cluster comprised of these nodes as cluster 2, presented in Fig. 2(c). For the remaining
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Fig. 2. An illustration of cluster maintenance: (a) initial clusters formed after cluster formation; (b) new clusters after cluster merging is
performed on clusters 1 and 2 in initial clusters; (c) new clusters after cluster splitting is performed on cluster 2 due to re-appearance of
PUs’ on common channels 3 and 4 of cluster 2 in Fig. 2(b).
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nodes, the clusterhead identifies the common channels among these nodes and creates another cluster consists of
them, which is presented as cluster 3 in Fig. 2(c). Finally, clusterheads and gateway nodes for the newly split
clusters are selected.
B. Cluster-based Routing using Reinforcement Learning
In this section, we present cluster-based routing based on Q-routing, a RL model, which is performed on the
clustered network. A source clusterhead estimates the Q-value for each neighbor node to reach the destination
node and subsequently, it updates the routing table of Q-values. The traditional Q-value equation [14] is modified
to incorporate the OFF-state probability of the bottleneck channel along a route. The bottleneck channel is the
channel having the least OFF-state probability for the next time instant along a route towards the destination node,
connecting two clusters via a SU neighbor node. The Q-value equation can be generally described below:
Qnext
src (dst, nbr)←(1 −α)×Qcurrent
src (dst, nbr)+α×min chanP r obcurrent
src,nbr , Qcurrent
nbr,max(dst) (1)
where 0≤α≤1is the learning rate, src is source clusterhead, nbr is the SU neighbor node of the source
clusterhead, dst is the destination node, chanP robcurrent
src,nbr is the OFF-state probability of the operating channel
between source clusterhead and its SU neighbor node, Qcurrent
nbr,max(dst)is the OFF-state probability of the bottleneck
channel along a route from a SU neighbor node of the source clusterhead’s neighbor node to the SU destination node.
The minimum value among chanP robcurrent
src,nbr and Qcurrent
nbr,max(dst)represents the channel availability probability of
bottleneck channel along the route.
Fig. 3 presents an example of cluster-based routing using RL in which clusterhead 1 wants to send data packets
to SU destination node BS. Initially, the clusterhead 1 initiates RREQ towards SU destination node BS in order to
discover a route. The procedure of RREQ propagation is traditional, so we are not going into its details. When a
SU destination node BS receives two RREQ messages from clustersheads 2 and 4, it generates RREP messages and
sends them back to clusterhead 1 using the reverse route in which RREQ messages traversed. When clusterhead 4
receives RREP message from SU destination node BS via its gateway node, it updates the Q-value with channel
OFF-state probability of the link between cluster 4 and SU destination node BS. Subsequently, clusterhead 4 embeds
this Q-value in RREP and forwards it towards clusterhead 3. When clusterhead 3 receives RREP from clusterhead
4, it compares the OFF-state probability of a channel provided in the RREP with the OFF-state probability of a
channel along the link between clusters 3 and 4, and finds that its link channel has lower OFF-state probability.
Therefore, it updates the Q-value and forwards it to SU source node by embedding it in RREP message. When SU
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source node receives RREP from clusterhead 3, it updates its routing table of Q-values. Similar procedure runs on
clusterhead 2 to process RREP. Finally, there are two entries in routing table of Q-values at SU source node. The
SU source node selects clusterhead 3 as its next-hop SU node because it provides the highest Q-value for the route
leading to destination node BS. It is important to note that the lower route is selected because it is more stable,
although it is longer compared to the alternative (upper) route.
Fig. 3. Cluster-based routing example.
VI. PERFORMANCE EVALUATIO N
The performance of SMART is evaluated in the network simulator QualNet 6.1, which is incorporated with CR
functionality. The total number of SUs is 10 and channels is 5. The SU learning rate αis set to 0.5 for maintaining
a balance between the estimated and recent value. Since a cluster must have at least two common channels (i.e.,
master and backup channels), therefore the threshold for the minimum number of common channels is set to 2.
Whenever a master channel is re-occupied by PUs’ activities, all member nodes and the clusterhead in a cluster
switch to a backup channel. The simulation time for each run is 550s and a total of 100 simulation runs, each
with random topology, were performed for each measurement. Each result shown in a graph is an average value
for the values gathered in 100 runs. We assume a perfect channel sensing because the main focus of our work is
on network layer. The ON-OFF transitions of PU activity follows a Poisson model with exponential distribution
with rates λk
ON,j and λk
OF F,j for ON and OFF periods, respectively.
The network performance of SMART is compared with clustered and non-clustered schemes. The clustered
scheme is known as SMART-NO-MNT (SMART no maintenance) that operates similar to SMART, however it does
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Fig. 4. Evaluation results: a) SU-PU interference ratio; b) route discovery frequency; c) end-to-end delay.
not have the functionality of cluster maintenance (i.e., cluster merging and splitting). The non-clustered scheme,
called SA-AODV (spectrum-aware AODV), is a variant of AODV routing protocol designed for CR environment
which has been used for comparison in the literature [15]. SA-AODV is spectrum-aware and operates on multi-
channel environment. It selects a random channel from the list of available channels for operation. There are
two performance metrics of SMART, specifically, SU-PU interference ratio and route discovery frequency. SU-PU
interference ratio is the ratio of the total number of SU-PU interfered packets to the total number of transmitted
packets by a SU source node. Route discovery frequency is the number of route discovery (or RREQ messages)
initiated by a SU source node.
Fig. 4(a) presents SU-PU interference ratio.Fig. 4(b) shows route discovery frequency and Fig. 4(c) illustrates
end-to-end delay by varying the number of SUs. SMART achieves significantly lower SU-PU interference ratio as
well as route discovery frequency. This is because SMART is a cluster-based routing that adopts cluster maintenance
(i.e., cluster merging and splitting) and RL. The cluster maintenance mechanisms reduce the dynamic effects of
network caused by PUs’ activities, and RL helps in the right selection of SU next-hop node in routing by learning
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from the environment and previous actions. Therefore, the selected routes are stable, having lower chances of PUs’
re-appearance. However, SMART achieves higher end-to-end delay. This is because higher number of SUs increases
the number of clusters and cluster size in the network, resulting in more frequent cluster maintenance. SMART-NO-
MNT causes higher route discovery frequency than SMART due to the lack of cluster maintenance mechanisms.
Therefore, there is a higher chance that clusters are lack of inter-cluster connection with increasing number of PUs,
and so the SU source node initiates higher number of re-routing, and hence higher number of RREQ messages
are sent in order to discover a route. Additionally, SMART-NO-MNT drops higher number of packets due to the
lack of inter-cluster connection, and therefore, with lower number of transmissions, the SU-PU interference and
end-to-end delay are naturally lower. SA-AODV causes higher SU-PU interference and route discovery frequency
due to the lack of stability achieved by clustering, as well as the benefit of RL for learning from the environment
and previous actions. Moreover, since SA-AODV is a non-clustered scheme, it does not incur delays caused by
clustering, contributing to lower end-to-end delay. The results show the effectiveness and feasibility of cluster-based
routing and the application of RL to routing for CRNs.
VII. CONCLUSION
In this article, we focus on routing problem in CRN caused by an intrinsic characteristic of cognitive radio,
specifically dynamic channel availability. The problem is addressed by clustering mechanisms, particularly cluster
merging and splitting, and an artificial intelligence approach, specifically RL. Clustering and RL solves the routing
problem in CRN and improves network scalability and stability. We also propose SMART, which is a cluster-based
routing scheme for CRNs and evaluate it through simulations. One of the main goals of CRN is to minimize SUs’
interference to PUs. The simulation results confirm that cluster-based routing minimizes SUs’ interference to PUs,
as well as selects more stable routes and achieves significantly lower route discovery frequency.
ACK NOWLEDG EM EN T
This work was supported by the Ministry of Education Malaysia under Fundamental Research Grant Scheme
(FRGS) FRGS/1/2014/ICT03/SYUC/02/2. Kok-Lim Alvin Yau and Qiang Ni were also funded under the Small
Grant Scheme (Sunway-Lancaster), grant agreement number SGSSL-FST-CSNS-0114-05 and PVM1204. The work
of Qiang Ni was also supported by the U.K. EPSRC under Grant EP/K011693/1 and by the European FP7 CROWN
project under Grant number PIRSES-GA-2013-610524.
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