Routing in Cognitive Radio Networks - A Survey

Harpreet Kaur Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 12, (Part - 2) December 2015, pp.182-194
www.ijera.com 182|P a g e
Routing in Cognitive Radio Networks - A Survey
Harpreet Kaur*, Nitin Gupta**
*(Department of Computer Science, NIT Hamirpur, Himachal Pradesh)
** (Department of Computer Science, NIT Hamirpur, Himachal Pradesh)
ABSTRACT
Cognitive Radio Networks (CRNs) have been emerged as a revolutionary solution to migrate the spectrum
scarcity problem in wireless networks. Due to increasing demand for additional spectrum resources, CRNs have
been receiving significant research to solve issues related with spectrum underutilization. This technology
brings efficient spectrum usage and effective interference avoidance, and also brings new challenges to routing
in multi-hop Cognitive Radio Networks. In CRN, unlicensed users or secondary users are able to use
underutilized licensed channels, but they have to leave the channel if any interference is caused to the primary or
licensed users. So CR technology allows sharing of licensed spectrum band in opportunistic and non-interfering
manner. Different routing protocols have been proposed recently based on different design goals under different
assumptions.
Keywords-About five key words in alphabetical order, separated by comma
I. Introduction
The continuously growing number of wireless
devices has resulted in growing congestion in
crowded ISM bands. Current wireless networks are
regulated by governmental agencies mainly
according to the fixed spectrum assignment. Licenses
are granted the rights for the use of various frequency
bands on a long term basis over vast geographical
areas. Due to the use of wireless technologies
operating in unlicensed bands, especially in ISM
band with the development of various applications in
different fields (e.g., sensor networks, mesh
networks, WLANs, personal area networks, body
area networks etc.) causes the overcrowding in this
band. On the other hand, the licensed band is used
very uneven and depends on the specific wireless
technologies, their market penetration, and the
commercial success of the operators to which the
frequencies have been assigned. According to studies
sponsored by the Federal Communications
Commission (FCC) of America, many allocated
spectrum blocks are used only for a brief periods of
time over a particular geographical area [10].The
utilization of assigned spectrum varies from 15% to
85%. So to address this problem, cognitive radio
(CR) is a promising technology to solve the spectrum
scarcity problem [11].Cognitive radio technology
offer the solution as a disruptive technology
innovation that will enable the future wireless world
.By efficiently utilizing the unused available
spectrum, CR technology improves the spectrum
efficiency.
To address this problem, the notion of Dynamic
Spectrum Access (DSA) has been used. In DSA, the
unlicensed users may use licensed spectrum bands
opportunistically in a dynamic manner. In CRNs,
SUs are allowed to opportunistically access any idle
frequency that is originally allocated to the Primary
(licensed) users (PUs) but currently not occupied.
The spare frequency bands are called spectrum holes,
which can be used by SUs under certain usage
constraints of preventing interference to primary
users(fig.1). There are several channels in every
spectrum hole. Different from multi-channel multi-
radio networks [5][6],cognitive radio networks
operate over wide spectrum with unpredictable
channel availability. They can tuned to any frequency
to any frequency band in that range with limited
delay[2][3].Cognitive radio transceivers have the
capability of changing their transmitter
parameters(operating spectrum, modulation,
transmission power and communication technology)
based on interactions with the surrounding spectral
environment. A cognitive radio has no prior
knowledge about the frequency channels to be used
due to spectrum dynamics. They can sense and after
sensing, channels that can be used for the
communication are assigned through spectrum
management without causing any interference or link
quality degradation to primary users. The
transmission of secondary users can be interrupted if
the primary user interrupted on the burrowed
channel. So cognitive nodes have to vacate channel
and switch to other available channel. So routing in
CRNs has to address many challenges such as
availability of different channels and radios on the
same node and synchronization between different
nodes on same channel.
Recently various routing protocols have been
proposed (e.g.,12,13,14,15,16,17,18,19,20,21,22,23,
24,25). Besides the main goal of protecting PU
transmissions, each protocol is designed with
RESEARCH ARTICLE OPEN ACCESS

different goals and based on different routing metric
e.g. maximizing available bandwidth, minimizing
end to end delay, minimizing interference,
minimizing hop count, maximizing spectral
opportunities etc. The performance of the protocol
can be measured with respect to its specific design
goal and by using different assumptions and
scenarios. To improve the performance of routing in
multi-hop CRN, a novel routing metric is essential to
minimize the impact of spectrum takeover by PUs.
In this paper, we conduct the extensive study of
routing schema used in multi-hop CRNs. The
contribution of the paper is divided as follows.
Section II discuss the routing scheme and defines the
different routing metrics. To better characterize the
unique features of cognitive radio networks, we
discuss different routing metrics used for designing
the routing protocols, basically divided as node
based, channel based and other metrics. Node based
metrics include the metrics calculated including the
node‘s features e.g. mobility, node energy etc.
Channel based metrics include the metrics calculated
as a result of channel characteristics like channel
flow, link cost, throughput etc. Other metrics are end
to end delay, probabilistic functions. Also it presents
the routing procedures and protocols proposed which
can find the optimal path efficiently employing the
proposed routing metrics. Section III discusses the
issues and designs related to route recovery and
maintenance procedures. The purpose of this work is
two-fold: first we aim at discussing the different
routing metrics, clearly highlighting the design
rationale. Section IV summarizes the different
routing metrics used by different routing protocols.
Different routing protocols proposed using these
metrics are elaborated.
Figure 1: CRN scenario
II. Routing Schema in Cognitive radio
Networks
Routing in CRNs is a joint decision of channel
selection and next hop node decision. Many research
work have been already made regarding the routing
issues and protocols in CRNs [4][7] based on
spectrum sensing, spectrum decision and spectrum
sharing. This section discusses the various steps of
routing performed in CRN. Fig. 2 shows the
taxonomy of the schema. We broadly categorize the
proposed routing procedures into three main classes
depending on the steps performed namely metric
computation and sensing, routing procedure and route
recovery and maintenance. We assign each step as a
label, for instance, the step metric computation and
sensing is assigned a label 1.
2.1. Metric Computation and Sensing
Traditional routing metrics (designed for both
wired and wireless environments) for link state or
distance vector are not well suited to be applied to
CRNs. The main reason is that there are frequent
dynamic changes in the CRN that may trigger a large
number of updates and lead to rapidly changing
routing tables. One of the main tasks of the cognitive
radio is to sense and determine whether a channel is
available or not. Each node senses spectrum, divide
the available band into channels, saves the list of
available channels and then chooses the channel
according to the priority depend upon the suitability
according to the link metrics and The dynamic
behavior of CRN inherits many characteristics of
wireless mobile networks like: nodes mobility, nodes
limited power and network life time along with some
of the constraints like interference, signal fading,
channel scarcity, conflict with licensed users. So a
number of routing metrics are proposed in different
routing protocols.
In the routing protocol STOP-RP [8], the author
involves all parameters that must be taken care for
cognitive environment. Route quality and spectrum

availability must be reflected in metric used for
multi-hop CRNs [8].
A good routing metric for CRNs has to assign
different weights to different channel based on
different factors and their probability that the
transmission will be interrupted by any PU activity or
SUs conflict. So it can estimate the future activity
and hence efficient routes can be find by minimizing
the interruption time and maintenance cost. Different
routing metrics are categorized as in fig. 3.
Figure 2: Routing Scheme for CRN
Metric Definition: A routing metric is a function
that assigns a weight (or cost) to any given path [31].
Optimal path between source and destination in a
connected network can be finding by considering
these metrics.
Metric: The routing metrics for cognitive radio
networks can be classified into three categories: a)
Channel based b) node based c) other metrics
2.1.1 Routing metrics for CRN
In this section, we present an overview of the
different routing metrics that are faced by CRNs and
targets the creation of routes for multi-hop CRNs.
Figure 3 summarizes the different routing metrics.
2.1.1.1. Channel Based
This section presents three types of Channel based
metrics:
1. Channel Stability
In CRN, channel stability or link stability is the
probability that the link will not experience any
breakdown due to variation of channel and nodes
could communicate with each other. It describes the
function of the route maintenance cost. The paper
[39] proposes a metric that combines link stability
with the switching delays. The paper [40] introduces
a CR metric describing the route stability. In [43], the
author proposes a CR metric based on path stability
and availability over time. The STOP-RD protocol
uses a routing metric that combines route stability
and end to end delay. It uses a metric for stability in
terms of link‘s available time. As in [59], the author
explained that how the spectrum dynamicity affects
the channel availability. If they have very low
dynamicity, it remains usable to SUs for many hours
to days. Moreover in the coolest path protocol [9],
total path cost is calculated which include the
maximum stability cost over the Path links or a
mixed cost between the maximum and
Routing
Schema for
CRN
Metrics
Computation
and Sensing
Routing
procedure
Route Repair
and
Maintenance

Figure 3:Routing metrics for CRN
accumulated cost along the path. Also it shows that
the accumulated cost achieves better performance in
terms of path switching ration and path longevity in
case of frequent PU activities. The work of [26]
defines thelink cost metric as a function of both the
link holding time and communication capacity and
reflects the usage pattern of primary user. The work
in [27] defines a new route stability metric that
reflects the cost that will be paid if the route needs to
be changed. In [64], link cost is calculated based on
route stability based metric. Also link cost is
calculated first and then get the cumulative routing
cost [8] as (1):
𝐶𝑖 = 𝑂𝑐𝑎 + 𝑂𝑝 +
𝑃 𝑘𝑡
𝑟 𝑖
1
1−𝑒 𝑝𝑡𝑖
.
1
𝑇 𝑙𝑖
(1)
Where Tli is the time duration during which a
spectrum band is available to the link li.
Cost of an end to end route can be calculated as (2):
𝐶 = 𝐶𝑖 + 𝑀. 𝐷𝑠𝑤𝑖𝑡𝑐 ℎ
𝑘
𝑖=1 (2)
Where k is the link number, M is the number of
spectrum band switches along the route and Dswitch is
the switch delay caused by a CR user switches
between two different bands.
2. Link Cost and Delay
Mainly a SU node incurs two types of delays
namely, a switch delay when it chooses to switch the
channel and back off delay when it waits for the PU
to release the channel if it chooses to remain in the
same channel. The paper[32] describes the routing
metric in which the author perform the asymptotic
analysis of the delay for CRNs by taking into account
the shortest path routing along with multipath routing
and network coding techniques. On the other hand,
sometimes channel switches along with load
balancing may help to minimize the channel
contention among SUs and hence lead to switch
delays minimization [14, 33].
In [42],the Opportunistic Link Transmission (OLT)
metric is proposed which captures three types of
delay as OLT = dtx + dq + daccess where dtx is link
transmission delay, dq is packet queuing delay at a
particular node and daccess is link access delay. Delayis
used to select the main route, which is the main route
whose RREQ packet arrives first in [45,46,47]
3. Common Control channel
In CRNs, there are mainly two types of channels:
common control channel (CCC) and data channel.

The SUs exchange control information like packets
such as, Route Request (RREQ) and Route Reply
(RREP) through CCC and send data through data
channels. As CRN works in the dynamic
environment, the availability of a static CCC is not
possible as SU have to vacate the channel once PU
appears. The papers [34,35,36,37]have adopted the
assumption of the availability of the static CCC,
which is not practical. Also actual route
characteristics are not defined by the use of CCC
[22].So as a result, it is necessary to design routing
schemes and protocols without depending on the
CCC for feasibility of the implementation. To solve
the deafness problem, the solution is to use a
common control channel (CCC) shared between all
nodes[48,49,50,51] for route initialization and
maintenance data.
4. Flow Detection
As explained in [20] there are mainly two types
of flow interferences along a route namely intra-flow
and inter-flow interferences. Intra –flow interference
occurs when the links belong to same data flow and
inter-flow interference occurs when the links belongs
to different data flows. The interferences can lead to
degradation of end to end performance. So flow
detection along with channel selection should be
taken into account while finding quality based routes.
5. Throughput
Dynamic spectrum networks enable fast
deployment of new wireless technologies. There are
many approaches which attained end to end
utilization with maximum throughput. SPEAR[30],a
high throughput multi-hop routing protocol in
dynamic environment, integrate spectrum discovery
with channel assignment by exploiting local spectrum
heterogeneity The paper [41] explains a routing
metric and a routing protocol to achieve higher
throughput efficiency, by allowing the CR users to
opportunistically transmit according to the spectrum
utility.
SAMER [13] tries to find a high throughput path
by opportunistically utilizing high throughput links
and guaranteed a path‘s long term stability. Both PU
and SU are considered by SAMER to quantify
channel availability. Each SU estimates the period of
time during which a channel is not used by U or SU
and can be used. So two neighboring nodes can
estimate different channel availabilities, the channel
availability for a particular link will be the smaller of
the two values. SAMER calculates the link matric
which is based on ETT [38], where ETT is one of the
popular routing metrics for traditional wireless mesh
networks. SAMER calculates the throughput based
metric for each link as a product of channel
availability, link bandwidth and loss rate.
In MRSA [28], final path is selected based on
available route bandwidth capacity metric. The path
with maximum bandwidth capacity is selected. A
similar approach is followed in SPEctrum Aware
Routing protocol (SPEAR) [30].
2.1.1.2. Node Based
1. Mobility
In multi-hop CRNs, each node is equipped with
a single cognitive transceiver with capabilities of
spectrum awareness and configurability. Mobility is
defined as the movement of the cognitive node over
the spectrum. The channel availability for data
transmission is dynamic in cognitive radio networks
which vary over time and space. As explained in
[25], there are more channel switches if the SU
movement increases and PUs are static. So to
smoothly perform the routing, the routing schemes
and algorithms should be aware of the mobility of the
nodes. Also energy efficiency of the nodes plays an
important factor as nodes have to monitor the
spectrum which consumes energy. Implementing
DSA techniques is very challenging due to high
fluctuation in channel availability as changes appear
due to primary user‘s activity and node
mobility[51].As shown in [27], the energy
consumption increases as a result of rerouting due to
unpredictable movement of nodes. The channel
access time decreases as mobility increases, which
lead to increase in number of channel switches. It
proposes a tree based algorithm along with channel
selection algorithm. The routing tree is maintained by
a static metric which calculates and assigns the
priorities based on the number of SUs already
assigned these channels.
To smoothly perform routing, the interrupted
node should promptly find an available channel to
continue its transmission to its next hop. If there is no
free channel, then path repair or rerouting happens.
2. Threshold Energy
When considering a network with mobile node
deployment, energy conservation is inevitable since
mobile nodes are often battery powered. In AODV
based routing protocols, flooding of RREQ control
messages cause energy consumption. The nodes have
to forward the request packets even though they may
not be chosen as part of the route later.[72]discuss the
different challenges of routing phases in which the
energy consumption factor plays an important part. In
protocols based on Global Positioning System (GPS)
[44], in which GPS is embedded at each node, route
is selected based on physical location of SU. So it
contributes in reducing the node‘s energy
consumption by controlling the flooding of the
routing control packets. In NDM protocol [56], routes
are selected for secondary users based on the
remaining energy at each node along the route. Total

remaining energy is calculated at all nodes and then
path is selected with maximum total remaining
energy.
3. PU Interference
There is a complex relation between PUs and
SUs systems. In dynamic environment, interference
is generated along a multi-hop path through
secondary users. The SUs have to leave the channel
when PU appears. So, optimal channel assignment
along with spectrum awareness should be taken into
account to minimize the interference. Random PU
activities cause diversity in PU behavior. Many
clustering mechanisms have been shown to reduce
the routing overhead as A SU member can associate
to new SU cluster head in case of interference. The
interference from primary users can easily be
detected by SUs with passive sensing techniques.
Routing in CRNs is classified on the basis of
contextual information used for taking the route
decisions which include the primary user activity and
repair management [63].
4. SU Interference
As node interference can be divided into two
parts: inter routing and intra routing. In intra routing
investigations, assumptions are adopted for routing
schemes which can provide best end to end network
performance. The paper [27] improves the
performance of CR-CR interference channels and
investigates how to coordinate multiple nodes using
different interference management techniques. It also
takes into account the SU interference. The paper
[68] describes a routing metric that minimizes the SU
interference to the PUs.
5. Resource Management
For decades, research has been done to maximize
the use of wireless resources in cognitive
environment. A cognitive network has a promise of
providing flexible and intelligent environment which
can improve resource management and network
performance. [60] proposes a cognitive resource
management technique that can allocate by learning
and adapting to specific radio transmission
environment. The mechanism applies the cross layer
approach and intelligently allocates resources
according to traffic demands, traffic priorities, link
capabilities, and link qualities.
6. Power Consumption
Some routing protocols are based on consumed
power to perform transmissions among nodes. In
CRNs, SUs have to continuously sense the presence
of PUs. So it is a challenging issue during dealing
with mobile devices as they have limited battery.
This applies in CRNs as well as in traditional ad-hoc
networks.
In [68],a dedicated interface called common link
control radio(CLCR) is used to sustain cognitive
radio related functions. The paper assumes a free
space propagation model for the transmission power
of nodes which increases with the distance. A power
aware routing protocol [21] is proposed which is
based on routing metric based on battery power
consumption. In minimum weight routing protocol
(MWRP)[48],the link weights are defined as the
transmission power required to reach the receiver
over a certain interface based on a free space
propagation model.
2.1.1.3. Other Metrics
1. End to End Delay
As with less hop distance in which nodes have
long transmission range, can lead to more SU
interference and performance decreases due to more
link failures. So hop count along with spectrum
awareness is necessary to improve end to end delay.
During transmission, channel unavailability at a
single node leads to link breakage is a serious issue to
be solved when end to end delay is considered. There
are many well defined procedures proposed to avoid
rerouting which is very costly as it increases end to
end delay. Channels available at different nodes are
of different bandwidth, propagation characteristics
and available for unequal times due to channel
heterogeneity. [63]. A cross layer framework is
proposed for spectrum assignment and routing [64].
Backup channel mechanism enables cooperative
channel switching which proves to be an efficient
solution for minimizing the end to end delay and the
problems created due to channel heterogeneity
[69].Neighboring nodes can switch to a single
channel in a cooperative manner. This enables a
transmitting node to find the same set of neighbors on
a different channel with the condition that the
neighbors can also tune to the same secondary
channel.
CRP [68] aims to minimize the end to end delay
without any interference to PUs and also allows a
level of performance degradation and provides PU
protection by selecting relay nodes. It calculates the
rebroadcast delay by calculating the cost function
based on local information. CRP is based on the cost
delay mapping and can be easily implemented via
minor modifications. In [67], effective transmission
time (ETT) is used. The metric captures the
transmission delays on a link by taking the expected
number of retransmissions into account. The work in
[65, 66] combines delay based on channel switching
time and multi-flow interference. The DORP protocol
[14, 70] includes the queuing delays. In SEARCH
[22], the selected path is one which minimizes the
end to end delay. The greedy location based metric
which includes the cost of the channel switching time

between paths on different channels along with the
path delay on each channel.
The packet transmission delay of the flow via a
node to the destination is reflected by the path cost
metric proposed in Urban-X[29].The work in [71]
gives a statistical model for end to end delay by
considering the impact of interference and dynamic
spectrum access, including spectrum access and
retransmission delay.
2. Probabilistic Functions
With reference to the metrics belonging to the
third class [1], the works propose to measure the
quality of route in terms of different probabilistic
functions. The expressions of the metrics account for
different metrics components like Link availability
probability block probability, Conditional block
probability, Expected link utilization, Expected link
interference for mobile networks. However, some are
described for static networks also.
In coolest path[62],for a SU link, a channel‘s
temperature is defined as the fraction of time during
which the channel is not available due to PU activity
in the neighborhood of any of the two SUs. Also
Coolest path is defined which provides three
definitions of the path temperature based on link
temperature:(i)accumulated temperature, sum of the
link temperatures of the path, (ii) highest
temperature, the maximum link temperature of the
links
In [61], the authors propose a probabilistic
metric that calculates the probability of PU
interference at a given SU for a given channel. The
metric can determine the most probable path which
satisfy a given bandwidth demand D in which N
nodes that operate on a maximum of M orthogonal
frequency bands.
3. Hop Count
Hop count is used as the main routing metric
which is used to select among the candidate paths. It
is a indicator of faster transmission delay as it
consume less networking resources because it passes
through lower number of nodes. For example
SAMER[13] takes a two tier routing approach which
balances between short term opportunistic gain(hop
count) and long term optimality(higher spectrum
availability).In SEARCH[22]hop count is a filtering
metric. It compares the hop count used in the original
route formation to the number of hops used in the
current path which differs as due to route
maintenance based on PU‘s activity, periodically. If
the difference is above threshold, there is a need of
new route formation.
The work in Multipath Routing and Spectrum
Access (MRSA)[28] uses hop count as a tie broker.
The new route is preferred only if it has low hop
count and final route is selected based on available
route bandwidth capacity metric. The work in Urban-
X [29] avoids routing loops so the routes are limited
to have maximum number of hopes. SPEAR [30] also
uses hop count metric as a tie broker. The hop count
in [45, 46, 47] is used as a filter for selecting
secondary users.
III. Route Procedure and Recovery
3.1. Route procedure
Most of the work on routing in cognitive radio
networks has been done on techniques and channel
assignments for distance minimization [8, 54, 55, 57,
60, 58, 64], while a link state protocol is required to
select the best possible channels at each nodes and
best route as well [52].Spectrum decision and routing
are based on local coordination, so on demand
routing and spectrum assignment scheme is used for
exchanging local information between the cognitive
nodes and multi-frequency scheduling. CAODV [52]
is a modified version of AODV, forwards packet
without requiring a dedicated control channel and
avoids active primary users regions during routing. In
DSDV, routing procedure is followed as:
1. Route Discovery: Initially when a node wants to
start transmission, it sends RREQ message on all
available channels to all its neighbors. Each
neighboring node which receives the route request
message then forwards to all its available channels
and can also save the information about the route
discovery for a specified time for route establishment
and maintenance purposes.
2. Route Reply: In this phase, route reply is sent back
either by destination or any node which has the route
entry for the destination node. Routes are established
and channels are selected based on link metric, flow
requirement and with the aim to increase network
capacity of wireless network by exploiting the
channel diversity. Channel assignment is done that
must satisfy the goal of improving the capacity of
network and also interference avoidance.
3.2. Route Recovery
Due to the dynamic spectrum and mobility, there
are frequent path breaks which led to frequent
channel switches. So route recovery is essential and
the routing schemes should be able to maintain routes
during different routing failures like channel

Metric Channel
Stability
Link cost and
delay
Common Control
Channel
Flow control Throughput
[33],[65]     
SAMER[13]     
[20]     
[25]     
[67]     
SEARCH[22]     
STOD-RP[24]     
DORP[70][14]     
Coolest path[9]     
[26]     
SMR[45]     
NDMR[46]     
NDM_AODV[47]     
[71]     
OLT[47]     
Gymkhana[43]     
[41]     
[64]     
[51]     
[42]     
SPEAR[30]     
URBAN-X[29]     
MRSA[28]     
Table 1: Channel based routing metrics used by routing protocols in CRN
switches, SU mobility etc. which can lead to
reduction in route maintenance cost. Route recovery
in cognitive radio networks is very challenging, as
SU has to leave channel due to PU appearance. This
local change at a particular node affects the end to
end delay and needs a well-defined route
management procedure [52]. The sudden appearance
of a PU in a given location may render a given
channel unusable in a given area, thus resulting in
unpredictable route failures, which may require
frequent path rerouting either in terms of nodes or
used channels. In this scenario, effective signaling
Metric Mobility Threshold
Energy
PU
Interference
SU
interference
Resource
Management
Power
Consumption
[36],[65]    
[51]   
[25]   
MWRP[48]      
[72]      
[27]      
STOD-RP[24]      
CRP[68]      
[60]      
NDM_AODV[47]      
[71]      
[34]      
[63]      
NDM[56]      
[44]      
[21]      
CRP[68]      
Table 2: Node based routing metrics used by routing protocols in CRN

procedures are required to restore ‗‗broken‖ paths
with minimal effect on the perceived quality. The
route discovery procedure is very similar to link state
routing algorithms where this newly introduced
weight is used.
A Spectrum-Tree base On-Demand routing
protocol (STOD-RP) is proposed in [24] which
simplifies the collaboration between spectrum
decision and route selection by establishing a
―spectrum-tree‖ in each spectrum band. The
formation of the spectrum-tree addresses the
cooperation between spectrum decision and route
selection in an efficient way. The routing algorithm
combines tree-based proactive routing and on-
demand route discovery. Moreover, a new route
metric which considers both CR user‘s QoS
requirements and PU activities is proposed. In
addition, their work provides a fast and efficient
spectrum-adaptive route recovery method for
resuming communication in multi-hop CRNs.
The work presented in [26] presents an algorithm
for handoff scheduling and routing in multi-hop
CRNs. One of the main contributions of this work is
the extension of the spectrum handoff to a multi-link
case. Following a classical approach, the problem of
minimizing latency for spectrum handoff across the
network is shown to be NP hard and a centralized and
a distributed heuristic algorithm has been developed.
The centralized algorithm is based on the
computation of the maximum non-conflict link set.
With this approach, the algorithm iteratively assigns
new channels to links. For route recovery and to
address the starvation problem, an aging based
prioritization scheme is utilized.
Routing protocol which chooses the best effort
route between the source-destination pair for multi-
hop communication is called single path routing.
Single path routing is not the best solution where the
network topology is highly dynamic like CRNAs and
where the network resources are limited. Multipath
routing is introduced as an alternative to single path
routing for its potential to address issues such as
route failure and recovery, and network congestion.
IV. Discussion
Table 1 summarizes the different channel based
routing metrics used for single hop and multi-hop
routing. Different routing protocols combine more
than one metric to achieve different goals or to break
ties when numbers of routes are equal under the
primary metric. Table 2 shows the node based routing
metrics adopted by different routing protocols. Also
it compares the different routing techniques based on
metrics. Table 3 summarizes the other metrics used
by routing protocols in CRN.
Metrics End to end delay Probabilistic Functions Hop count
[36],[65] 
SAMER[13]   
[51]   
[69]   
SEARCH[22]   
CAODV[20]   
Coolest path[9]   
[61]   
SMR[45]   
NDMR[46]   
NDM_AODV[47]   
[71]   
[1]   
SPEAR[30]   
URBAN-X[29]   
MRSA[48]   
CRP[68]   
Table 3: Other routing metrics used by routing protocols in CRN
V. Conclusion
Through available spectrum bands in CRNs with
multi-hop communication are different for each hop;
spectrum sensing information is required for
topology configuration in CRNs. The dynamic
spectrum changing pattern of devices enabled with
cognitive radio capabilities makes routing a
challenging issue. In this paper, we have discussed
the routing schema and different routing metrics used
in routing protocols to optimize performance and
minimize interference for CR users. Intrinsic
properties and performance of the routing protocols
for CRNs are presented. Finally we have presented

route management to provide efficient routing to CR
users in a dynamic environment.
The discussions provided in this survey strongly
advocate spectrum-aware communication protocols
that consider the spectrum management
functionalities. The cross-layer design requirement
necessitates a rethinking of the existing routing
protocols developed for CRNs. Many researchers are
currently engaged in developing the communication
technologies and protocols required for CRNs.
However, to ensure efficient spectrum-aware
communication, more research is needed along the
lines introduced in this survey.
REFERENCES
[1] 1.Y. Hou, Y. Shi, H. Sherali, Optimal
spectrum sharing for multi-hop software
defined radio networks, in: 26th IEEE
International Conference on Computer
Communications, INFOCOM 2007,
2007,pp. 1–9, doi:
10.1109/INFCOM.2007.9.
[2] E. Buracchini, The software radio concept,
IEEE Communication Magazine 38 (9)
(2000) 138–143.
[3] J. Mitola, Cognitive radio, An Integrated
Agent Architecture for Software Defined
Radio, PhD Dissertation Thesis, KTH,
Sweden.
[4] C. Cormio and K. Chowdhury, ―A survey on
mac protocols for cognitive radio networks,‖
Ad Hoc Networks, vol. 7, no. 7, p.
13151329, September 2009. [Online].
Available: http://dx.doi.org/10.1016/j.adhoc.
2009.01.002
[5] A. P. Subramanian, H. Gupta, S. R. Das, and
J. Cao, ―Minimum Interference Channel
Assignment in Multiradio Wireless Mesh
Networks,‖ IEEE Transactions on Mobile
Computing, vol. 7, no. 12, pp. 1459-1473,
Dec. 2008.
[6] B. Han, V. S. A. Kumar, M. V. Marathe, S.
Parthasarathy, and A. Srinivasan,
―Distributed Strategies for Channel
Allocation and Scheduling in Software-
Defined Radio Networks,‖ Proc. of IEEE
INFOCOM, pp. 1521-1529, 2009.
[7] W. Jeon and D. Jeong, ―An advanced quiet
period management protocol for cognitive
radio systems,‖ IEEE Trans. Veh. Technol.,
vol. 59, no. 3, p. 12421256.
[8] G.-M. Zhu, I. Akyildiz, and G.-S. Kuo,
―STOD-RP: A Spectrum-Tree Based On-
Demand Routing Protocol for Multi-Hop
Cognitive Radio Networks,‖ in IEEE
GLOBECOM 2008, Dec. 2008, pp. 1–5.
[9] X. Huang, D. Lu, P. Li, and Y. Fang,
―Coolest Path: Spectrum Mobility Aware
Routing Metrics in Cognitive Ad Hoc
Networks,‖ in Distributed Computing
Systems (ICDCS), 2011 31st International
Conference on,june 2011, pp. 182 –191.
[10] Xin, C. (2005). A novel layered graph model
for topology formation and routing in
dynamic spectrum access networks. IEEE
DySPAN, pp. 308–317, November 2005.
[11] I. Mitola, J. and J. Maguire, G.Q.,
―Cognitive radio: making software radios
more personal,‖ Personal Communications,
IEEE, vol. 6, pp. 13 –18, aug. 1999.
[12] K. C. How, M. Ma, and Y. Qin, ―Routing
and QoS provisioning in cognitive radio
networks‖, Elsevier Computer Networks,
vol. 55, no. 1, p. 330342, January 2011.
[13] I. Pefkianakis, S. H. Wong, and S. Lu,
―SAMER: Spectrum aware mesh routing in
cognitive radio networks‖, in Proc. Of IEEE
DySpan, 2008.
[14] Z. Yang, G. Cheng, W. Liu, W. Yuan, and
W. Cheng, ―Local coordination based
routing and spectrum assignment in multi-
hop cognitive radio networks‖, Mobile
Networks and Applications (Kluwer), vol.
13, no. 1-2, pp. 67–81, April 2008.
―Coolest Path: Spectrum mobility aware
routing metrics in cognitive ad hoc
networks‖, in Proc. of IEEE ICDCS, 2011.
[16] K. R. Chowdhury and I. F. Akyildiz, ―CRP:
A routing protocol for cognitive radio ad
hoc networks‖, IEEE Journalon Selected
Areas in Communication, vol. 29, no. 4,
April 2011.
[17] C. Xin, B. Xie, and C.-C. Shen, ―A novel
layered graph model for topology formation
and routing in dynamic spectrum access
networks‖, in Proc. of IEEE DySPAN, 2005.
[18] Q. Wang and H. Zheng, ―Route and
spectrum selection in dynamic spectrum
networks‖, in Proc. of IEEE CCNC, 2006.
[19] A. Sampath, L. Yang, L. Cao, H. Zheng, and
B. Y. Zhao, ―High throughput spectrum-
aware routing for cognitive radio networks‖,
in Proc. of IEEE Crowncom, 2008.
[20] A. S. Cacciapuoti, M. Caleffi, and L. Paura,
―Reactive routing for mobile cognitive radio
ad hoc networks‖, Elsevier Ad
HocNetworks, vol. 10, no. 5, pp. 803–815,
July 2012.
[21] L. Ding, T. Melodia, S. Batalama, and M. J.
Medley, ―ROSA: distributed joint routing
and dynamic spectrum allocation in
cognitive radio ad hoc networks‖, in Proc.
of ACM MSWiM,2009.
[22] K. Chowdhury and M. Felice, ―Search: A
routing protocol for mobile cognitive radio

ad-hoc networks‖, Elsevier
ComputerCommunications, vol. 32, no. 18,
pp. 1983–1997, December 2009.
[23] S. Deng, J. Chen, H. He, and W. Tang,
―Collaborative strategy for route and
spectrum selection in cognitive radio
networks‖, in Proc. of IEEE FGCN, 2007.
[24] G.-M. Zhu, I. F. Akyildiz, and G.-S. Kuo,
―STOD-RP: A spectrum-tree based on-
demand routing protocol for multihop
cognitive radio networks‖, in Proc. of
Globecom, 2008.
[25] I. Filippini, E. Ekici, and M. Cesana,
―Minimum maintenance cost routing in
cognitive radio networks‖, in Proc. of
IEEEMASS, 2009.
[26] W. Feng, J. Cao, C. Zhang, and C. Liu,
―Joint Optimization of Spectrum Handoff
Scheduling and Routing in Multi-hop Multi-
radio Cognitive Networks,‖ in Proceedings
of the 2009 29th IEEE International
Conference on Distributed Computing
Systems, ser. ICDCS ‘09. Washington, DC,
USA: IEEE Computer Society, 2009, pp.
85–92.
―Minimum Maintenance Cost Routing in
Cognitive Radio Networks,‖ in MASS,
2009, pp. 284–293.
[28] T. K. X. Wang and Y. Cho, ―A multipath
routing and spectrum access (MRSA)
framework for cognitive radio systems in
multi-radio mesh networks,‖ Proceedings of
the 2006 ACM workshop on Cognitive
Radio Networks, 2009.
[29] W. Kim, A. J. Kassler, M. D. Felice, and M.
Gerla, ―Cognitive Multi- Radio Mesh
Networks on ISM bands: A Cross-layer
Architecture,‖ in IPCCC, 2010, pp. 34–41.
B. Y. Zhao, ―High Throughput Spectrum-
aware Routing for Cognitive Radio
Networks,‖ in Proceedings of The Third
International Conference on Cognitive
Radio Oriented Wireless Networks and
Communications (CROWNCOM 2008),
2008.
[31] M. Lu and J. Wu, ―Opportunistic routing
algebra and its applications,‖ in Proc. 2009
IEEE Int. Conf. Comput. Commun., pp.
2374–2382.
[32] Z. Wang, Y. Sagduyu, J. Li, and J. Zhang,
―Capacity and delay scaling laws for
cognitive radio networks with routing and
network coding,‖ in Proc. 2010 Military
Commun. Conf., pp. 1375–1380.
[33] Ma, H., Zheng, L., Ma, X., & Luo, Y.
(2008). Spectrum aware routing for multi-
hop cognitive radio networks with a single
transceiver. In Proceedings of 3rd
international conference on cognitive radio
oriented wireless networks and
communications (CROWNCOM) (pp. 1–6).
Singapore.
[34] Huang, X. L.,Wang, G.,Hu, F.,&Kumar, S.
(2011). Stability-capacity-adaptive routing
for high-mobility multihop cognitive radio
networks. IEEE Transactions on Vehicular
Technology, 60(6), 2714–2729.
[35] Zhu, G. M., Akyildiz, I. F., & Kuo, G. S.
(2008). STOD-RP: A spectrum-tree based
on-demand routing protocol for multi-hop
cognitive radio networks. In Proceedings of
global telecommunications conference
(GLOBECOM) (pp. 1–5). New Orleans, LA.
[36] Chen-li, D., Guo-an, Z., Jin-yuan, G., & Zhi-
hua, B. (2009). A route tree-based channel
assignment algorithm in cognitive wireless
mesh networks. In Proceedings of
international conference on wireless
communications & signal processing
(WCSP) (pp. 1–5). Nanjing, China.
[37] Xin, C., Ma, L., & Shen, C. C. (2008). A
path-centric channel assignment framework
for cognitive radio wireless networks.
Mobile Networks and Applications, 13(5),
463–476.
[38] ——, ―CellSense: An Accurate Energy-
Efficient GSM Positioning System,‖
Vehicular Technology, IEEE Transactions
on, vol. 61, no. 1, pp.286–296, 2012.
B. Y. Zhao, ―High throughput spectrum-
aware routing for cognitive radio based ad-
hoc networks,‖ in Proc. 2008 Int. Conf.
Cognitive Radio Oriented WirelessNetw.
Commun.
―Minimum maintenance cost routing in
cognitive radio networks,‖ in Proc. 2009
IEEE Int. Conf. Mobile Adhoc Sensor Syst.,
pp. 284–293.
[41] L. Ding, T. Melodia, S. Batalama, and M. J.
Medley, ―Rosa: distributed joint routing and
dynamic spectrum allocation in cognitive
radio ad hoc networks,‖ in Proc. 2009 ACM
Int. Conf. Modeling, Analysis Simulation
Wireless Mobile Syst., pp. 13–20.
[42] S.-C. Lin and K.-C. Chen, ―Spectrum Aware
Opportunistic Routing in Cognitive Radio
Networks,‖ in GLOBECOM, 2010, pp. 1–6.
[43] F. Cuomo and A. Abbagnale, ―Gymkhana: a
connectivity-based routing scheme for
cognitive radio ad hoc networks,‖ in Proc.
2010 IEEE Int. Conf. Comput. Commun.
Workshops, pp. 1–5.

[44] Imielinski, T., & Navas, J. (1996). GPS-
based addressing and routing. IETF RFC
2009.
[45] S. ju Lee and M. Gerla, ―Split multipath
routing with maximally disjoint paths in ad
hoc networks,‖ in IEEE International
Conference on Communications (ICC‘02),
vol. 10, 2002, pp. 3201–3205.
[46] X. Li and L. Cuthbert, ―On-demand Node-
Disjoint Multipath Routing in Wireless Ad
hoc Network,‖ Local Computer Networks,
Annual IEEE Conference on, pp. 419–420,
2004.
[47] S. Ding and L. Liu, ―A Node-disjoint
Multipath Routing Protocol Based on
AODV,‖ International Symposium on
Distributed Computing and Applications to
Business, Engineering and Science, pp. 312–
316, 2010.
[48] M. H. C.W. Pyo, ―Minimum Weight
Routing based on A Common Link Control
Radio for Cognitive Wireless Ad Hoc
Networks,‖ Proceedings of the 2007
International Conference on Wireless
Communications and Mobile Computing
(IWCMC‘07), 2007.
[49] L. Ding, T. Melodia, S. Batalama, and J. D.
Matyjas, ―ROSA : Distributed Joint Routing
and Dynamic Spectrum Allocation in
Cognitive Radio Ad Hoc Networks,‖ in
Proceedings of the 12th
ACM international
conference on Modeling analysis and
simulation of wireless and mobile systems.
ACM, 2009, pp. 13–20. [Online]. Available:
http://ieeexplore.ieee.org/xpls/abs
all.jsp?arnumber=5508267
[50] M. Y. I. Beltagy and M. El-Dereni, ―A New
Routing Metric and Protocol for Multipath
Routing in Cognitive Networks,‖ IEEE
Wireless Communication and Networking
Confenrence (WCNC), 2011.
[51] H. Khalife, S. Ahuja, N. Malouch, and M.
Krunz, ―Probabilistic Path Selection in
Opportunistic Cognitive Radio Networks,‖
in GLOBECOM, 2008, pp. 4861–4865.
[52] J. So, N. Vaidya, Multi-channel MAC for ad
hoc networks: handling multi-channel
hidden terminals using a single transceiver,
in: ACM International Symposium on
Mobile Ad Hoc Networking and Computing
(MOBIHOC), May 2004, pp. 222–233.
[53] Akyildiz, I. F., Lee, W. Y., & Chowdhury,
K. R. (2009). CRAHNs: Cognitive radio ad
hoc networks. Ad Hoc Networks, 7(5), 810–
836.
B. Y. Zhao, ―High Throughput Spectrum-
aware Routing for Cognitive Radio based
Ad-hoc Networks,‖ in CrwownCom 2008,
2008.
[55] Chunsheng Xin · Liangping Ma · Chien-
Chung Shen, ―A Path-Centric Channel
Assignment Framework for Cognitive
RadioWireless Networks‖, Springer July
2008.
[56] S. Ding and L. Liu, ―A Node-disjoint
Multipath Routing Protocol Based on
AODV,‖ International Symposium on
Distributed Computing and Applications to
Business, Engineering and Science, pp. 312–
316, 2010.
[57] C.W. Pyo, M. Hasegawa, Minimum weight
routing based on a common link control
radio for cognitive wireless ad hoc
networks, in: Proceedings of the
International Conference on Wireless
Communications and Mobile Computing
(IWCMC), August 2007, pp. 399–404.
[58] H. Khalife, S.S. Ahuja, N. Malouch, M.
Krunz, Probabilistic path selection in
opportunistic cognitive radio networks, in:
Proceedings of the IEEE Globecom,
November 2008.
[59] Khalife, H., Malouch, N., & Fdida, S.
(2009). Multihop cognitive radio networks:
To route or not to route. IEEE Network,
23(4), 20–25.
[60] Hsien-Po Shiang and Mihaela van der
Schaar, ―Delay-Sensitive Resource
Management in Multi-hop Cognitive Radio
Networks‖.
[61] H. Khalife, S. Ahuja, N. Malouch, and M.
Krunz, ―Probabilistic Path Selection in
Opportunistic Cognitive Radio Networks,‖
in GLOBECOM, 2008, pp. 4861–4865.
―Coolest Path: Spectrum mobility aware
routing metrics in cognitive ad hoc
networks‖, in Proc. of IEEE ICDCS, 2011.
[63] I. F. Akyildiz, W.-Y. Lee, and K.
Chowdhury, ―CRAHNs: Cognitive Radio
Ad Hoc Networks,‖ Ad Hoc Net. J., vol. 7,
no. 5, July 2009.
[64] Zeeshan, M., Manzoor, M. F., & Qadir, J.
(2010). Backup channel and cooperative
channel switching on-demand routing
protocol for multi-hop cognitive radio ad
hoc networks (BCCCS). In Proceedings of
6th international conference on emerging
technologies (ICET) (pp. 394–399).
Islamabad, Pakistan.
[65] X. M. H. Ma, L. Zheng and Y. luo,
―Spectrum Aware Routing for Multi- Hop
Cognitive Radio Networks with a Single
Transceiver,‖ 3rd International Conference
on Cognitive Radio Oriented Wireless

Networks and Communications,
CrownCom, 2008.
[66] W. L. Geng Cheng andW. C. Yunzhao Li,
―Spectrum Aware On-Demand Routing in
Cognitive Radio Networks,‖ IEEE
International Symposium on New Frontiers
in Dynamic Spectrum Access Networks
(DySPAN 2007), 2007.
[67] H.-P. Shiang and M. van der Schaar,
―Distributed Resource Management in
Multihop Cognitive Radio Networks for
Delay-Sensitive Transmission,‖ IEEE
Transactions on Vehicular Technology, vol.
58, no. 2, pp. 941–953, Feb. 2009.
[68] K. R. Chowdhury and I. F. Akyildiz, ―CRP:
A routing protocol for cognitive radio ad
hoc networks‖, IEEE Journalon Selected
Areas in Communication, vol. 29, no. 4,
April 2011.
[69] M. Ma and D. Tsang, ―Impact of channel
heterogeneity on spectrum sharing in
cognitive radio networks,‖ in Proc. IEEE
ICC, 2008, pp.2377–2382.
[70] G. Cheng, Y. L. W. Liu, and W. Cheng,
―Joint on-demand routing and spectrum
assignment in cognitive radio networks,‖
IEEE International Conference on
Communications, ICC, 2007.
[71] P.-Y. Chen, S.-M. Cheng, W. C. Ao, and K.-
C. Chen, ―Multi-path routing with end-to-
end statistical QoS provisioning in underlay
cognitive radio networks,‖ in Computer
Communications Workshops (INFOCOM
WKSHPS), 2011 IEEE Conference on.
IEEE, 2011, pp. 7–12.
[72] Hou, L., Yeung, K. H., & Wong, K. Y.
(2011). A vision of energy-efficient routing
for cognitive radio ad hoc networks. In
Proceedings of 6th international symposium
on wireless and pervasive computing
(ISWPC) (pp. 1–4). Hong Kong, China.

Routing in Cognitive Radio Networks - A Survey

More Related Content

What's hot (18)

Viewers also liked (20)

Similar to Routing in Cognitive Radio Networks - A Survey (20)

Recently uploaded (20)

Routing in Cognitive Radio Networks - A Survey