Channel Allocation in Cognitive Radio Networks using Evolutionary
Vinesh Kumar
, Sanjay K. Dhurandher
, Bhagyashri Tushir
and Mohammad S. Obaidat
School of Computer and Systems Sciences, Jawaharlal Nehru University, Delhi, India
CAITFS, Division of Information Technology, NSIT, University of Delhi, Delhi, India
Department of Computer Science & Information Science, Fordham University, Bronx, New York, U.S.A.
Signal to Interference Plus Noise Ratio, Channel, Probability, Markov Chain, Non-dominated Set of Solutions.
Cognitive radio technology provides a platform at which licensed and unlicensed user share the spectrum. In
spectrum sharing, interference plays an important role. Therefore, in this work, interference is considered as
a parameter for spectrum sharing between licensed and unlicensed users. The authors in this work proposed
a novel channel allocation technique using Non-dominated set of solutions according to following objectives:
maximum SINR, probability for maximum SINR and maximum free time of channels. The Non-dominated
set of solutions has been calculated using Naive and Slow method. The simulation analysis further shows that
the proposed technique outperforms the existing technique in terms of throughput and utilization by 65.47%
and 47.31% respectively.
Cognitive radio being an emerging technology, is
used for efficient utilization of the spectrum, by intro-
ducing cognitive users to search the data transmission
opportunities in the absence of primary or licensed
users. The architecture of cognitive radio was intro-
duced by J. Mitola in (Mitola, 2000). In addition to
this, the signal processing aspects of Cognitive ra-
dio was given by Simon Haykin in (Haykin, 2005).
Cognitive radio based on the concept of cognitive cy-
cle, has four main functions namely spectrum sens-
ing, spectrum sharing, spectrum decision and spec-
trum adaptation. In this cycle each function plays an
important role.
In spectrum sharing, both licensed and unlicensed
users share the spectrum. The coexistence of licensed
and unlicensed users is an important issue in spec-
trum sharing. In (Haykin, 2005; Akyildiz et al., 2006;
?; Masonta et al., 2013; Tragos et al., 2013; Dhu-
randher et al., 2009; Ahmed et al., 2014; Dhurand-
her et al., 2015), the researchers found that interfer-
ence and bandwidth play an important role in spec-
trum sharing in cognitive radio networks. In (Haykin,
2005), the author provides the fundamentals of cog-
nitive radio networks and also analyse the impact of
interference when primary and secondary users share
the spectrum. (Akyildiz et al., 2006) presented a de-
tailed survey of cognitive radio networks and also dis-
cussed the issues related to spectrum sharing in detail.
(Wang and Liu, 2011) discussed an overview of cog-
nitive radio networks and also studied spectrum sens-
ing and sharing in detail. They discussed various open
issues related to spectrum sharing in cognitive radio
networks and found that interference is an important
parameter in spectrum sharing. (Masonta et al., 2013)
focus on various aspects of channel and issues re-
lated to spectrum sharing in cognitive radio networks.
This paper provides a complete survey of spectrum
decisions. (Tragos et al., 2013) reviewed the various
techniques related to spectrum assignment in cogni-
tive radio networks. During this study, they described
the role of interference in spectrum assignment and
found that interference plays a central role in chan-
nel allocation in cognitive radio networks in (Tragos
et al., 2013). From the works of (Haykin, 2005; Aky-
ildiz et al., 2006; Wang and Liu, 2011; Masonta et al.,
2013; Tragos et al., 2013), it is clear that interference
plays a key role, when primary and secondary users
share the spectrum in any network. Keeping this in
mind, the proposed work focuses on minimizing the
The rest of the paper is organized as follows. In
Section-II, the related work and motivation towards
designing the proposed scheme has been discussed.
In Section-III, the system model has been discussed.
Kumar, V., Dhurandher, S., Tushir, B. and Obaidat, M.
Channel Allocation in Cognitive Radio Networks using Evolutionar y Technique.
DOI: 10.5220/0005939801060112
In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - Volume 6: WINSYS, pages 106-112
ISBN: 978-989-758-196-0
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Section-IV presents the simulations and analysis of
the result. Finally Section V concludes this work.
(Jiang et al., 2013) presented a scheme for chan-
nel allocation and reallocation in cognitive radio net-
works. They used a multidimensional Markov chain
and a multi antenna interface which was connected
with only one channel, that was also used for chan-
nel allocation. In this paper, the channel alloca-
tion behavior in server and non server based system
was studied and analysed. The researchers presented
an analytic model and defined the performance met-
rics namely blocking probability, dropping probabil-
ity and throughput for secondary users. From the
simulation analysis, it was found that the proposed
scheme improved the performance of cognitive radio
system. They considered multiple antennas with one
channel only.
(Bayhan and Alag
oz, 2014) presented a scheme
for best fit channel selection in cognitive radio net-
works. For this, a Markov model based scheme was
developed and used for theoretical analysis of best
fit channel selection. Also, the concept of spectrum
fragmentation was introduced. The performance of
proposed scheme over longest ideal time based chan-
nel selection scheme is crucial in terms of spectrum
utilization. From the simulation analysis, it was ob-
served that the proposed scheme preformed well and
provided significant results in practical situations. In
this work, only discrete state space is considered.
(Jalali et al., 2015) presented a dynamic chan-
nel access strategy for underlay cognitive radio net-
works using markov model. The researchers intro-
duced a partial channel occupancy (PCO) mode. The
PCO mode provides a partial occupied bandwidth to
secondary users, when secondary users co-exist with
primary users. They developed a continuous time
markov chain based model, that was used to evalu-
ate the performance of licensed and unlicensed net-
works. Furthermore, a cost against gain analysis were
presented and used to check the applicability of the
proposed technique for a given traffic scenario. The
proposed scheme was well supported by simulation
(Gelabert et al., 2010) presented a discrete time
Markov chain model for spectrum sharing between
primary and secondary users with imperfect sens-
ing. The researchers introduced the concept of spec-
trum awareness implementation approach. Using this
approach, the miss-detection and false alarm proba-
bilities were defined and discussed. With the help
of these probabilities, a discrete time markov chain
model was presented and derived. Based on the
Markov model, a scheme was presented and simu-
lated using a system level simulator. Through the
simulation analysis, the error in spectrum sensing was
analyzed and it was found that the spectrum sensing
could be improved by setting the value of interfer-
ence. The proposed scheme works only for central-
ized manner not distributed manner.
(Bedeer et al., 2014) presented an approach based
on multi objective optimization, that was used to in-
vestigate the optimal link adaptation of OFDM based
cognitive radio system. The researchers in this work
presented an algorithm in such a way that the through-
put of the system was maximized and the transmit
power was minimized with respect to licensed and un-
licensed users. The proposed algorithm was analysed
and simulated. From the simulation analysis, it was
found that the performance of the proposed algorithm
tends to an exhaustive search for the discrete optimal
allocations with a reduced computational effort. In
this work, an imperfect sensing was considered but
in imperfect sensing, the interference constraints may
get violated. The interference violation has not been
considered in this work.
(Qin et al., 2009) presented the multi objective op-
timization model using genetic algorithm. To imple-
ment the genetic algorithm, the chromosome is used
to identify the influence of evolving a radio. Using
this chromosome, Multi Objective Cognitive Radio
(MOCR) algorithm was proposed. The performance
of the algorithm was analyzed and simulated. The re-
sult shows that the proposed algorithm provides bet-
ter results. In this work, only routing constraints were
used to design the chromosome. Some other parame-
ters can be used to design chromosome.
(Suliman et al., 2009) presented the analysis of
cognitive radio networks with imperfect sensing. The
researchers developed two dimensional Markov chain
model with the help of false alarm probabilities and
missed detection probabilities. Using this model, the
behaviour of the network was analysed. In addition,
the balance equation from the Markov chain and pri-
mary user termination probabilities were also defined
and evaluated. From the simulation analysis, it was
observed that as per the changes in the arrival rate of
primary users, the probability of successful commu-
nication for secondary users decreased. In this work,
the state equation is defined only for few cases, which
may be extended for some other cases as well.
(Wen et al., 2012) presented a Max overall perfor-
mance algorithm using the concept of genetic algo-
rithm. They defined the Max Sum Bandwidth (MSB)
Channel Allocation in Cognitive Radio Networks using Evolutionary Technique
rule, Max Access Fair (MAF) rule and Max Overall
Performance (MOP) rule and using these rules and
Genetic algorithm, the fitness function was defined.
Applying these, an algorithm was proposed by the au-
thors. From the simulation analysis, it was found that
the proposed MOP algorithm gave a good overall per-
formance. The researchers considered bandwidth and
fairness as the metric and no other metric was consid-
(Wang et al., 2009) proposed a primary prioritized
markov approach for dynamic spectrum allocation.
They used continuous time markov chain to model
the interactions between primary and secondary users.
The researchers classified the proposed model into
two parts namely primary prioritized CTMC with-
out queue and primary prioritized CTMC with queue.
Each of them was further divided into two parts as sin-
gle user and multi user cases. Each prioritized based
model for dynamic spectrum access has been derived
and discussed in this work. Through simulation anal-
ysis, it was found that the proposed model provided
95% performance gain over a CSMA based random
access approach and also attained an optimal tradeoff
between spectrum utilization and fairness. The au-
thors did not consider the overhead of the network in
the proposed scheme.
(Mahdi et al., 2012) presented an adaptive dis-
crete PSO (ADPSO) algorithm using G.A and PSO.
They defined the transmission parameter adaptation
in cognitive radio networks and high data rate, less
power consumption were consider as the main objec-
tives. To design ADPSO algorithm, the researchers
focused on: 1) To reduce the time taken for con-
vergence when optimal set of parameters found. 2)
Overcoming the problem of local optimum in PSO
and G.A. The ADPSO algorithm was proposed and
for evaluating the proposed al-gorithm the multi car-
rier system was used. From simulation results, it was
found that the proposed algorithm performed well in
terms of convergence time and the algorithms was
also found to overcome the problem of local opti-
From the aforementioned study, it is observed
that the SINR/interference of a channel is one of
the important parameters toward channel allocation in
CRNs. For better communication, the channel uti-
lization and SINR are to be maximized according
to (Xiao et al., 2012; Kumar and Minz, 2015). To
overcome the limitations of the schemes discussed
earlier in (Jiang et al., 2013; Bayhan and Alag
2014; Jalali et al., 2015; Gelabert et al., 2010; Be-
deer et al., 2014; Qin et al., 2009; Suliman et al.,
2009; Wen et al., 2012; Wang et al., 2009; Mahdi
et al., 2012) and for better utilization of channels, the
authors of the work proposed in this paper were moti-
vated to design a Multi Objective based channel allo-
cation scheme in cognitive radio networks.
The proposed work in this paper is along the line
of previous research on channel allocation in cogni-
tive radio networks using Markov model by (Teotia
et al., 2015) and Multi Objective optimization Tech-
niques by (Kumar, 2015). Here, we presented Non-
dominated set of solutions based channel allocation
using multi objectives.
Let there be n channels or sub-bands in a frequency
band where each channel has n states and there ex-
ists SINR for each state at a time instant. Let the
states be denoted by C
, C
, C
, . . . C
at time in-
stants T
, T
, T
, . . . T
Let the state C
at time instance T
have SINR S
with probability P
. Let the primary user change the
state with probability P
(Teotia et al., 2015) presented a Markov model
based approach for channel allocation with the use of
a Markov chain to calculate the probability of SINR
at each state.
Using the concept of Markov chain in (?), the
three parameters namely time, SINR and probability
were calculated. These three parameters are shown in
Table 1.
Table 1: Parameter for Channel Allocation.
States Probability SINR Time
. . . .
. . . .
In (Kumar, 2015), using the parameters in Table 1,
a multi-objective function f (P, S, T ) is defined as:
f (P, S, T ) = W
where f
represents the function of probability, f
notes the function of SINR and f
denotes the func-
tion of time and W
, W
, W
denotes the corresponding
A multi-objective optimization problem using
Equation 1 has been defined in Equation 2 as:
Min f (K) = W
(k) +W
(k) +W
(k) (2)
subject to
1 K n
WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems
= 1
Where f
(k) = 1P, f
(k) = 1SINR, f
(k) = 1T
and n denotes the number of times.
In equation 2, f
, f
, f
are the three objectives ac-
cording to which channels are allocated to secondary
users. There exists a solution space for these objec-
tives which consists of the set of solutions. This set
of solutions contains a Non-dominated set of solu-
tions. There exist various techniques to find the Non-
dominated set of solutions in this solution set namely
Naive and Slow, Kung et. al. approach etc in (Deb,
2001). To calculate Non-dominated set of solutions
in this work the authors have used Naive and Slow al-
gorithm in (Deb, 2001; Kumar, 2015; ?). Using this
Non-dominated set of solutions, an algorithm for op-
timal allocation of channels to secondary users has
been proposed.
Algorithm 1: Channel allocation in cognitive radio
Input: States S = S
, S
, S
, S
. . . S
Probability of the states
P = P
, P
, P
, P
. . . P
SINR of channels SINR =
. . . SINR
Time T = T
, T
, T
, T
. . . T
Output: Channel allocation K
1 Obtain the SINR of each state.
2 for each State S
S, i = 1, 2, 3 . . . n do
3 Calculate the Probability of SINR with the
help of markov chain.
4 Obtain the time of each state
5 Obtain the solution set(K) that optimize
f (K) = W
(K) +W
(K) +W
6 k
= k
7 while |K| 1 do
8 Apply Naive and Slow algorithm to obtain
the non dominating set(K
9 k
= k (K
10 Assign channel that exist in K
to secondary
11 return C.A
Proposed Algorithm: The procedure of the pro-
posed technique is described in algorithm 1.
Let there exist M = {M
, M
, . . . , M
} chan-
nels, P = {P
, P
, . . . , P
} primary users and s =
, s
, . . . , s
} secondary users in a cognitive radio
network. Suppose at a given time instant t, N chan-
nels are used by primary users. Hence remaining
M N channels are free at that time t. The sec-
ondary users may use these free channels. There are s
secondary users competing for these channels. Now,
the secondary user base station initially senses the
channels according to the cognitive cycle and then
use these channels according to the non-dominated
set of solutions. Suppose the secondary user s
wishes to use the channel M
M , then the sec-
ondary user base station first considers SINR, proba-
bility and time at that channel. Using the SINR, prob-
ability and time, a multi-objective function is formu-
lated in such a way that SINR should be maximum
with maximum probability and maximum free time
of channels. A solution set has been obtained from
the multi-objective function, and Non-dominated set
of solutions has been calculated from this solution set
using Naive and Slow approach. Now the secondary
user base station assigns to be channel M
for com-
munication to be secondary users
based on the non-
dominated set of solutions. The channels that have
higher SINR and maximum free time is allocated to
secondary users. When the free channels are allocated
according to Non-dominated set of solutions, remove
the Non-dominated set of solutions from the solution
set. Now, again calculate the Non-dominated set of
solution from the remaining solution set and allocate
the free channels according to it. Apply the same pro-
cedure on the remaining solution set until the cardi-
nality of solution set is one. This procedure is carried
out for all secondary users. Thus, it is observed that
the channels that are allocated according to the pro-
posed scheme have less interference and better com-
munication. Hence, the proposed scheme provides an
optimal utilization of channels in cognitive radio net-
The performance of existing and proposed algorithm
is measured by comprehensive simulation study us-
ing OMNeT++ network simulator proposed by (Varga
and Hornig, 2008). The Cognitive radio network de-
veloped for simulation operates in a centralized man-
ner. In the network, base station is the central en-
tity which performs most of the actions and the sec-
ondary user base station makes decisions on assigning
the channels to the secondary users.
Each primary user is connected to its base station
via a channel. All the secondary users are connected
to one base station, and each primary user base
station is connected to secondary user base station.
Primary and secondary user communicate with each
other through base station. In the network at a given
Channel Allocation in Cognitive Radio Networks using Evolutionary Technique
time, PU can be in generating, receiving or in idle
state. At each iteration state of primary user, channel,
source and destination user are selected randomly.
Simulation Parameters: The performance of
both the algorithms is analysed in terms of packet
delivery ratio (PDR),throughput, end to end delay
(EED), packet flow (PF) and channel utilization by
altering the simulation time and number of channels
in the network. The PDR represents the ratio of the
number of delivered data packets received by the des-
tinations to the number of data packets generated by
the sources. The throughput of the network is calcu-
lated as average rate of data packets delivered over the
network and it is measured in bytes/second. The EED
is measured as the time taken in seconds by a data
packet to reach the destination. PF is the total number
of packets generated in the network per simulation.
Channel utilization when plotted against number of
channels is measured as average time in seconds for
which primary and secondary users uses the channels.
Channel utilization when plotted against simulation
time is calculated as percentage time in seconds for
which channels are used by primary and secondary
user per simulation time.
For the simulation of algorithms different topolo-
gies have been considered. Number of primary users,
secondary users and channels varies between 10 to 50,
20 to 100 and 10 to 50 respectively.
Figure 1: EED vs Simulation Time.
The variation of the existing and proposed algo-
rithm in terms of EED in shown in Figure 1. For
both the algorithms the EED increases with increase
in simulation time. As the simulation time of the net-
work increases, the packet flow in the network in-
creases. This in turn increases the processing time
for each packet hence increasing the EED. Also more
number of channels are allocated using proposed algo
hence its EED is more than existing algo. By chang-
ing the simulation time, the mean EED for existing
algo is observed to be 12.85s while for proposed algo
it is 20.01s. Thus the percentage increase in EED
when channel allocation is performed using proposed
algorithm is 56.32%.
From Figure 2 it is observed that PDR for both
the algorithms show non uniform behavior with in-
crease in simulation time. There is sharp increase in
the PDR of proposed algorithm when simulation time
is 1000s. This point represents the minimum inter-
ference and conflict experienced by destination nodes
which results in delivery of maximum packets. For
the existing algorithm mean PDR is 0.182 while for
proposed algorithm the mean PDR is 0.297. So dur-
ing channel allocation using proposed algo the PDR
is increased by 63.18%.
Figure 2: PDR vs Simulation Time.
Figure 3 depicts the variation of throughput
(bytes/sec) of both the algorithms with the simula-
tion time. Throughput for the algorithms increases
with increase in simulation time. In case of the
existing algorithm mean throughput is found to be
0.0168 bytes/sec and for proposed algorithm 0.0278
bytes/sec. Hence when channels are allocated us-
ing proposed algorithm, the improved throughput is
Figure 3: Throughput vs Simulation Time.
Figure 4 and figure 5 shows the relation between
channel utilization and number of channels and sim-
ulation time respectively. From the graphs it is seen
that the channel utilization of the existing and pro-
posed algorithm shows non uniform behaviour with
increases in number of channels and simulation time
respectively. With respect to number of channels and
compared to existing algo the mean utilization is im-
proved by 57.48s for proposed algo. Whereas with
respect to simulation time the mean utilization is im-
proved by 0.075s for proposed algo.
WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems
Figure 4: Channel Utilization vs Simulation Time.
Figure 5: Channel Utilization vs Number of Channels.
Figure 6 shows the variation of PF with simula-
tion time. For both the algorithms with increase in
simulation time the number of packets in the network
increases linearly. With increase in simulation time,
more time is available for users to generate and re-
ceive packets thus increasing the packet flow.
Figure 6: Packet Flow vs Simulation Time.
In this paper, a novel technique for channel as-
signment in cognitive radio network is designed in
accordance with the objectives: maximum SINR,
probability for maximum SINR and maximum free
time of channels. The objectives are achieved using
Non-dominated set of solutions. To calculate Non-
dominated set of solution Naive and Slow method is
used. It is observed that the proposed algorithm sur-
passes the existing algorithm in terms of channel uti-
lization and throughput.
In Future, other approaches can be used to opti-
mize the multi objective optimization problem. Also,
any other heuristic approach based algorithm for
channel allocation may be developed.
Ahmed, E., Gani, A., Abolfazli, S., Yao, L., and Khan,
S. (2014). Channel assignment algorithms in cog-
nitive radio networks: Taxonomy, open issues, and
challenges. Communications Surveys Tutorials, IEEE,
Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., and Mohanty,
S. (2006). Next generation/dynamic spectrum ac-
cess/cognitive radio wireless networks: a survey.
Computer Networks, 50(13):2127–2159.
Bayhan, S. and Alag
oz, F. (2014). A markovian approach
for best-fit channel selection in cognitive radio net-
works. Ad Hoc Networks, 12:165–177.
Bedeer, E., Dobre, O., Ahmed, M. H., Baddour, K. E., et al.
(2014). A multiobjective optimization approach for
optimal link adaptation of ofdm-based cognitive radio
systems with imperfect spectrum sensing. Wireless
Communications, IEEE Transactions on, 13(4):2339–
Coello, C. C., Lamont, G. B., and Van Veldhuizen, D. A.
(2007). Evolutionary algorithms for solving multi-
objective problems. Springer Science & Business Me-
Deb, K. (2001). Multi-objective optimization using evolu-
tionary algorithms. John Wiley & Sons.
Dhurandher, S. K., Misra, S., Ahlawat, S., Gupta, N.,
and Gupta, N. (2009). E2-scan: an extended credit
strategy-based energy-efficient security scheme for
wireless ad hoc networks. IET communications,
Dhurandher, S. K., Woungang, I., Obaidat, M., Kumar, K.,
Joshi, M., and Verma, M. (2015). A distributed adap-
tive admission control scheme for multimedia wireless
mesh networks. Systems Journal, IEEE, 9(2):595–
Gelabert, X., Sallent, O., P
erez-Romero, J., and Agust
R. (2010). Spectrum sharing in cognitive radio
networks with imperfect sensing: A discrete-time
markov model. Computer Networks, 54(14):2519–
Haykin, S. (2005). Cognitive radio: brain-empowered wire-
less communications. Selected Areas in Communica-
tions, IEEE Journal on, 23(2):201–220.
Jalali, E., Balapuwaduge, I. A. M., Li, F. Y., and Pla, V.
(2015). A dynamic channel access strategy for under-
lay cognitive radio networks: Markov modelling and
performance evaluation. Transactions on Emerging
Telecommunications Technologies.
Jiang, T., Wang, H., and Leng, S. (2013). Channel
allocation and reallocation for cognitive radio net-
Channel Allocation in Cognitive Radio Networks using Evolutionary Technique
works. Wireless Communications and Mobile Com-
puting, 13(12):1073–1081.
Kumar, V. (2015). Channel Allocation in Cognitive Radio
Networks. PhD thesis, Jawaharlal Nehru University,
New Delhi.
Kumar, V. and Minz, S. (2015). Ccra: channel criticality
based resource allocation in cognitive radio networks.
International Journal of Communication Systems.
Mahdi, A. H., Mohanan, J., Kalil, M., Mitschele-Thiel, A.,
et al. (2012). Adaptive discrete particle swarm op-
timization for cognitive radios. In Communications
(ICC), 2012 IEEE International Conference on, pages
Masonta, M. T., Mzyece, M., and Ntlatlapa, N. (2013).
Spectrum decision in cognitive radio networks: A sur-
vey. Communications Surveys & Tutorials, IEEE,
Mitola, J. (2000). Cognitive Radio—An Integrated Agent
Architecture for Software Defined Radio. PhD thesis,
Royal Institute of Technology (KTH).
Qin, H., Su, J., and Du, Y. (2009). Multiobjective evo-
lutionary optimization algorithm for cognitive radio
networks. In Information Engineering and Electronic
Commerce, 2009. IEEC’09. International Symposium
on, pages 164–168. IEEE.
Suliman, I., Lehtom
aki, J., Br
aysy, T., and Umebayashi, K.
(2009). Analysis of cognitive radio networks with im-
perfect sensing. In Personal, Indoor and Mobile Radio
Communications, 2009 IEEE 20th International Sym-
posium on, pages 1616–1620. IEEE.
Teotia, V., Dhurandher, S. K., Woungang, I., and Obaidat,
M. S. (2015). Markovian model based channel allo-
cation in cognitive radio networks. In IEEE Interna-
tional Conference on Data Science and Data Intensive
Systems. IEEE.
Tragos, E. Z., Zeadally, S., Fragkiadakis, A. G., and Siris,
V. A. (2013). Spectrum assignment in cognitive radio
networks: A comprehensive survey. Communications
Surveys and Tutorials, IEEE, 15(3):1108–1135.
Varga, A. and Hornig, R. (2008). An overview of the om-
net++ simulation environment. In Proceedings of the
1st International Conference on Simulation Tools and
Techniques for Communications, Networks and Sys-
tems & Workshops, Simutools ’08, pages 60:1–60:10,
ICST, Brussels, Belgium, Belgium. ICST (Institute for
Computer Sciences, Social-Informatics and Telecom-
munications Engineering).
Wang, B., Ji, Z., Liu, K., and Clancy, T. C. (2009). Primary-
prioritized markov approach for dynamic spectrum al-
location. Wireless Communications, IEEE Transac-
tions on, 8(4):1854–1865.
Wang, B. and Liu, K. R. (2011). Advances in cognitive
radio networks: A survey. Selected Topics in Signal
Processing, IEEE Journal of, 5(1):5–23.
Wen, K., Fu, L., and Li, X. (2012). Genetic algorithm based
spectrum allocation for cognitive radio networks. In
Advances in Computer, Communication, Control and
Automation, pages 693–700. Springer.
Xiao, X., Liu, S., Lu, K., and Wang, J. (2012). Max-
div: an optimal randomized spectrum access with
maximum diversity scheme for cognitive radio net-
works. International Journal of Communication Sys-
tems, 25(7):832–848.
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