On the Use of Particle Swarm Optimization Techniques for Channel Assignments in Cognitive Radio...

Shawkat AliCQUniversity, Australia

Noureddine AbbadeniKing Saud University, Saudi Arabia

Mohamed BatoucheUniversity of Constantine, Algeria

Multidisciplinary Computational Intelligence Techniques:Applications in Business, Engineering, and Medicine

Multidisciplinary computational intelligence techniques: applications in business, engineering, and medicine / Shawkat Ali, Noureddine Abbadeni, and Mohamed Batouche, editors. p. cm. Summary: “This book explores the complex world of computational intelligence, which utilizes computational methodolo-gies such as fuzzy logic systems, neural networks, and evolutionary computation for the purpose of managing and using data effectively to address complicated real-world problems”-- Provided by publisher. Includes bibliographical references and index. ISBN 978-1-4666-1830-5 (hardcover) -- ISBN 978-1-4666-1831-2 (ebook) -- ISBN 978-1-4666-1832-9 (print & perpetual access) 1. Computational intelligence. 2. Evolutionary computation. I. Ali, Shawkat, 1969- II. Abbadeni, Noureddine, 1970- III. Batouche, Mohamed, 1964- Q342.M856 2012 006.3--dc23 2012003207

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Chapter 12

Hisham M. AbdelsalamCairo University, Egypt

Haitham S. HamzaCairo University, Egypt

Abdoulraham M. Al-ShaarCairo University, Egypt

Abdelbaset S. HamzaUniversity of Nebraska-Lincoln, USA

On the Use of Particle Swarm Optimization Techniques

for Channel Assignments in Cognitive Radio Networks

ABSTRACT

Efficient utilization of open spectrum in cognitive radio networks requires appropriate allocation of idle spectrum frequency bands (not used by licensed users) among coexisting cognitive radios (secondary users) while minimizing interference among all users. This problem is referred to as the spectrum al-location or the channel assignment problem in cognitive radio networks, and is shown to be NP-hard. Accordingly, different optimization techniques based on evolutionary algorithms were needed in order to solve the channel assignment problem. This chapter investigates the use of particular swarm optimization (PSO) techniques to solve the channel assignment problem in cognitive radio networks. In particular, the authors study the definitiveness of using the native PSO algorithm and the Improved Binary PSO (IBPSO) algorithm to solve the assignment problem. In addition, the performance of these algorithms is compared to that of a fine-tuned genetic algorithm (GA) for this particular problem. Three utilization functions, namely, Mean-Reward, Max-Min-Reward, and Max-Proportional-Fair, are used to evaluate the effectiveness of three optimization algorithms. Extensive simulation results show that PSO and IBPSO algorithms outperform that fine-tuned GA. More interestingly, the native PSO algorithm outperforms both the GA and the IBPSO algorithms in terms of solution speed and quality.

DOI: 10.4018/978-1-4666-1830-5.ch012

203

On the Use of Particle Swarm Optimization Techniques for Channel Assignments

INTRODUCTION

Administrative spectrum management approach is the currently used approach by the regulators to allocate spectrum to different wireless services. Efficient utilization of open spectrum in cognitive radio networks requires appropriate allocation of idle channels (spectrum frequency bands not used by licensed users) among coexisting cogni-tive radios (secondary unlicensed users) while minimizing interference (Zhao, Peng, Zheng, & Shang, 2009). In this approach, secondary users access the spectrum opportunistically by identify-ing spectrum availability (spectrum white holes) instantaneously and without interfering with the primary users. For a secondary user, to be capable of overlaying its transmission successfully, cogni-tion capabilities are needed, such as being aware for the surrounding, learning and understanding the variations and activities, and accordingly, adjusts operating parameters to operate efficiently.

The problem of assigning channels (frequency bands) among primary and secondary users while minimizing interference among all users is known as the resource allocation or the channel assign-ment problem in cognitive radio networks. This problem is shown to be NP-hard in the literature (Peng, Zheng, & Zhao, 2006). Accordingly, sev-eral heuristics were proposed to solve the channel assignment problem using game theory (Nie & Comaniciu, 2006), pricing and auction mecha-nisms (Huang, Berry, & Honig, 2006; Kloeck, Jaekel, & Jondral, 2005), local bargaining (Cao & Zheng, 2005), and vertex labeling (Peng, etl al, 2006;Zheng & Peng, 2005).Recently, evolutionary algorithms are used to address the resource alloca-tion problem. In particular, in (Zhao, Peng, Zheng, & Shang, 2009), three evolutionary algorithms were performed including genetic algorithm (GA), quantum genetic algorithm (QGA), and particle swarm optimization (PSO) techniques.

This chapter investigates the quality of the PSO techniques, in terms of solution speed and qual-ity, in solving the channel assignment problem in

cognitive radio networks. In particular, the chapter studies the definitiveness of using the native PSO algorithm proposed by Kennedy et al. (Kennedy & Eberhart, 1997) and the Improved Binary PSO (IBPSO) algorithm (Yuan, Nie, Su, Wang, & Yuan, 2009) to solve the assignment problem. In addition, the performance of these algorithms is compared to that of a fine-tuned genetic algorithm (GA) for this particular problem. Three utilization functions, namely, Mean-Reward, Max-Min-Reward, and Max-Proportional-Fair, are used to evaluate the effectiveness of three optimization algorithms. Extensive simulation results show that, PSO and IBPSO algorithms outperform that fine-tuned GA. More interestingly, the native PSO algorithm outperforms both the GA and the IBPSO algorithms in terms of solution speed and quality.

The remainder of this chapter is organized as follows. The following section presents the system model and defines the problem statement. The PSO and the IBPSO algorithms are then presented, fol-lowed by simulation results and analysis. Finally, future research and conclusions are given.

SYSTEM MODEL AND PROBLEM STATEMENT

To better understand the problem addressed in this paper, we consider the first commercial application of CR in TV white space (interleaved spectrum).

Figure 1 shows a sample setup for a cognitive radio network. A typical cognitive network con-sists of a set of primary users X each is assigned a channel selected from a pool of M orthogonal, non-overlapping spectrum bands that differ in bandwidth and transmission range. Each channel of them is associated with a protection area with protection radius dP (x,m). However, it is assumed here that dP (x,m) is the same for all channels for simplicity in analysis. There are N coexisting secondary users that are planned to utilize these idle channels occupied by primary users in order to provide their services. A secondary can be a

204


wireless access point (or transmission link). It is assumed that each secondary user can utilize multiple channels at one time, but limited to the radio interface constraint.

Each secondary user keeps a list of available channels that it can use without interfering with neighboring primary users (Figure 1). The spec-trum access problem becomes a channel allocation problem. A secondary user’s transmission is bounded by a minimal, and maximal transmission powers that are user-specific. These boundaries have corresponding transmission ranges of and, respectively. Also, the transmission of secondary user can not overlap with the transmission of the primary user who uses the same channel. There-fore, each secondary user can adjust its transmis-sion range by tuning its transmit power on chan-nels to avoid interference with primary users.

Different secondary users are assigned dif-ferent available spectrum based on its location, radio interface, and requirements. Each secondary

user should be aware of its position with respect to the surrounding primary users, three different scenarios exist (Figure 2):

a. The secondary user exists within the protec-tion range of the primary user, then it cannot use the channel occupied by this primary user.

b. The secondary user is located outside the protection range of the primary user, but its dS dmin, it still cannot use the channel occupied by this primary user.

c. The secondary user exists outside the pro-tection range of the primary user, and dS, dmin, then this secondary user can operate on this channel, with a transmission range of dS (n,m) as long as it is less than the dmax.

The following are the key components in the used model (Peng, Zheng, & Zhao, 2006):

Figure 1. Structure of a simple cognitive radio network

205


• Channel availability: L l l

n m n m N M� � | � ,

, ,={ }

×∫ 0 1 is a matrix repre-

senting per user available spectrum: ln m.

= 1 if and only if channel m is available to user n.

• Channel reward: B ={bn,m}N*M, a ma-trix representing the channel reward: Bn,m represents the maximum reward that can be acquired by user n using channel m.

• Interference constraint: Let C C C

n k m n k m N N M= { }{ }

× ×� | ,

, , , , , ,∫ 0 1 ,a ma-

trix represents the interference constraints among secondary users. Cn,k,m =1, if us-ers n and k would interfere if they use chan-nel m simultaneously. Cn,k,m = 1 – ln,m.

• Conflict free channel assignment: LetA a a

n m n m N M= ( ){ }

×, ,| ,∫ 0 1 , a matrix

that represents the assignment: an m,

= 1 if channel m is assigned to user n.

• User Reward: R

= ={ }=

−

×∑� � ��B a b

n m

M

n m n mN

0

1

1, ,

. �represents

the reward vector that each user gets for a given channel assignment. In our context,

the reward is the coverage area of the sec-ondary user which is proportional to d

S2

(n,m).

The objective of the spectrum allocation problem in open spectrum systems is to find a channel allocation that maximizes a utilization function U(R).

To define U(R), two spectrum design factors can be considered, namely, spectrum utilization and fairness. Different combinations of these two factors result in different definitions for the U(R) function. We consider three utilization functions, namely, Mean-Reward (MR), Max-Min-Reward (MMR), and Max-Proportional-Fair (MPF). The following equations reproduced from (Peng, Zheng, & Zhao, 2006)to describe these three utilization functions:

UN

BN

a bMSR

n

N

nn

N

m

M

n m n m= =

=

−

=

−

=

−

∑ ∑∑1 1

0

1

0

1

0

1

� �.��, ,

(1)

U B a bMMR n N n n N

m

M

n m n m= =

≤ ≤ ≤ ≤=

−

∑min ., ,0 0

0

1

min (2)

Figure 2. Secondary-primary position’s scenarios

206


U EMPFn

N

m

M

= + −

⋅=

−

=

−

∏ ∑0

1

1

11

1 4a bn m n m, ,

NN

(3)

A baseline reward of 1E - 4 is used in order to prevent the case of having auser with no channels (user starvation).

OPTIMIZATION ALGORITHMS FOR CHANNEL ASSIGNMENT

This section gives the formulation of the GA, PSO, and the IBPSO algorithms for solving the channel assignment problem under interference constraints in cognitive radio networks.

Genetic Algorithm (GA)

In this study, the genetic algorithm (GA) reported in (Zhao, Peng, Zheng, & Shang, 2009)is imple-mented in order to solve the problem under the three utilization functions. However, according to a previous study (Hamza & Elghoneimy, 2010) the selection of the GA parameters considerably affects the quality of the presented solution. Thus, we conclude that, in order to study the impact of the network parameters, we need to fine-tune the GA for each utilization function to ensure that the used GA will yield the best possible solution for each utilization function. The result of this study is summarized in Table 1. Algorithm 1 shows the procedures of solving the spectrum allocation

problem in cognitive radio networks employing the genetic algorithm.

Particle Swarm Optimization (PSO) Algorithm

In (Kennedy & Eberhart, 1997), a discrete binary version of the PSO was introduced. This algorithm uses the concept of the velocity as a probability that a bit takes either logical 010, or 000. In this algorithm, a particle’s velocity is continuous. The update function of particle’s velocity is given in Equation 4:

v wv C pb C gb xijt

i it t

it+ = + ( )+ −( )1

1 1 1 1�α α (4)

where w is an inertia weight, α1 and α2 are random numbers uniformly distributed between 0 and 1, C1 and C2 are cognitive and social parameters, pbti

local best at iteration t, gbt global best at iteration t and xti is position for variable i at it-eration t.

A special sigmoid function ( ( ( )))sig vi t is used in order to calculate the vusing the velocity cal-culated above.

v sig v te

it

i v ti

+−

= ( )( ) =+

1 1

1�

� ( ) (5)

Finally, a particle’s position is updated ac-cording to:

Table 1. GA fine-tuned parameters under different utilization functions

Literature Selection (Zhao, et. al, 2009) MR MMR MPF

Population Size 20 20 200 500

Crossover Probability 0.80 0.85 0.85 0.85

Mutation Probability 0.01 0.1 0.1 0.1

Crossover Method Two-points Uniform Single point Single point

Selection Scheme Roulette Wheel Tournament(Size=5) Random Random

207


�,

.x

if r v

otherwiseit i j i

t+

+<1

11

0

(6)

where rij are uniformly random numbers between 0 and 1. The following algorithm shows the proce-dures of solving the spectrum allocation problem in cognitive radio networks employing particle swarm optimization technique.

Algorithm 2 shows the generic procedures of solving the spectrum allocation problem in cog-nitive radio networks using the Binary Particle Swarm Optimization algorithm.

Improved Binary PSO (IBPSO) Algorithm

The Improved Binary PSO (IBPSO) (Yuan, Nie, Su, Wang, & Yuan, 2009) relies on the basic idea of local best position and global best position that is originally proposed by J. Kennedy and R. Eberhart in (Kennedy & Eberhart, 1997). In IBPSO algorithm, an individual is a bit string which starts it strip from a random point in the search space and tries to become nearer to the global best position and previous best position of itself. However, unlike the original PSO, IBPSO

employs logical operators in the equations used to update both, velocity and position according to Equation (4) and (5), respectively.

v pbest x gbest xidt

idt

idt

dt

idt+ = ⊗ ⊕( )+ ⊗ ⊕( )1

1 2� � � �ω ω

(7)

x x vit

it

it+ = ⊕1 (8)

where i, t, and d represents the particle’s num-ber, number of iteration, and solution dimension (number of variables), respectively. w1 and w2 are two random binary integer numbers uniformly distributed in the range of [0, 1].

The IBPSO relies on bitwise logical operators where the solution is guided to local best and global best values with uniform probability to keep the stochastic nature of the algorithm. Through finding the solution x matched with local best and global best. New x depends on improving the old x with the velocity that depends on local and global best solutions. Analyzing the operation of this algorithm, we find that the solu-tion stuck in local optimal with low probability ofgetting out of this local optimal solution.

Algorithm 1.

1: begin2: Define utilization function U(R).

3: Given the matrices L, B, and C, set the solution length as d = lmm

M

n

N

n,=

=

=

− ∑∑ 0

0

0

1

and set L1 = ( , ) ,n m ln m

| ={ }1 such that elements in L1 are arranged increasingly in n and m.4: Generate an initial population with random chromosomes. 5: Define parameters based on Table 1. 6: while (t <max number of iterations) do

7: For all chromosomes, map the jth element in L1 for 1<j<L1. For all m, search all (n, k)that satisfies Cn k m, ,=1 , and check if

a an m k m, ,= =1 , randomly set one of them to 0.

8: Evaluate each chromosome according to the objective function U(R).9: Perform the desired selection and crossover scheme. 10: end while11: Find the current best solution. 12: end

208


Algorithm 3 shows the generic procedures of solving the spectrum allocation problem in cogni-tive radio networks using the IBPSO algorithm.

SIMULATION RESULTS AND ANALYSIS

This section presents and analyzes the simulation results of the performance of the GA, PSO, and the IBPSO algorithms with respect to the MR, MMR, and MPF utility functions under consideration. In order to ensure consistency and stability, the results reported in this study represent the aver-age of 50 experiments. Experimental simulation reported in this paper is performed using MATLAB (running on a 2:66 GHz Core 2 Duo processor PC with 2 GB RAM with a 32 bits register). Fig-ures 3, 4, and 5 show box plots that demonstrate the performance of the three algorithms under investigation under MR, MMR, and MPF utility functions, respectively.

Figure 3 depicts the box plot of the time elapsed to reach the optimum solution using different three algorithms under the MR utilization function. As we can see, GA present the worst performance

compared to other algorithms where it possesses the largest median. Moreover, its median is larger than the max of other algorithms. On the other hand, the original PSO outperforms all al-gorithms as its median is less than the medians of all other algorithms (0:052sec). Numerical results of the three algorithms under the MR utilization function are tabulated in Table 2.

Although the solution space of the problem is small, the IBPSO algorithm using small number of particles stuck in local optima and fails to find the optimum solution found by other algorithms even with large number of iterations. Therefore, a large number of particles (20000 particles) is used while in other PSO algorithms the number of particles is only, 20 particles.

The proposed enhanced PSO solves this problem by using the mutation operation which expands the exploration of the search space, and therefore outperformed the IBPSO.

The box plots of the time elapsed to reach the optimum solution using different three algorithms under the MMR, and MPF utilization functions are shown in Figure 4 and Figure 5, respectively. Table 3 and Table 4 contain results of MMR and MPF utilization functions, respectively. Again,

Algorithm 2.

1: begin2: Define utilization function U(R).3: Given the matrices L, B, and C, set the solution length as d = l

mm

M

n

N

n,=

=

=

− ∑∑ 0

0

0

1and set L1 = ( , ) ,n m l

n m| ={ }1 such that elements in L1

are arranged increasingly in n and m.4: Generate an initial population with random particles. 5: Define parameters (number of particles, maximum number of iterations, and Pmut).6: while (t <max number of iterations) do7: For all particles, map the jth element in L1 for 1 < j < L1. For all m, search all (n; k) that satisfies Cn,k,m= 1, and check if an,m= ak,m= 1, randomly set one of them to 0. 8: Evaluate each particle’s position according to the objective function U(R).9: If a particle’s current position is better than its previous best position, update it. 10: Determine the best particle (according to the particle’s previous best positions). 11: Update particles’ velocities according to Equation 4. 12: Calculate v

it+1 for particle i using Equation 5.

13: Move particles to their new positions according to Equation 6. 14: end while15: Find the current best solution. 16: end

209


the GA presents the worst performance among other algorithms. For the IBPSO, the interquar-tile Distance is significantly reduced since the algorithm employs a large number of particles (20000), however it is median is larger than both original PSO and the proposed enhanced PSO.

A common observation from these three figures is that the GA presents the worst performance among all algorithms. The reason of this perfor-mance is that in GA, several operations are per-

formed such as: selection, mutation, and crossover. Also, several parameters are involved in the GA and hence, it takes long time per iteration.

What also worth note is that the proposed enhanced PSO performance improves as the solution space gets more complicated where its performance becomes comparable to the original PSO. Comparing the original and the proposed enhanced PSO algorithms, the interquartile dis-

Algorithm 3.

1: begin2: Define utilization function U(R).3: Given the matrices L, B, and C, set the solution length as d = l

mm

M

n

N

n,=

=

=

− ∑∑ 0

0

0

1

and set L1 = ( , ) ,n m ln m

| ={ }1 such that elements in L1 are arranged increasingly in n and m.4: Generate an initial population with random chromosomes. 5: Define parameters based on Table 1. 6: while (t <max number of iterations) do7: For all chromosomes, map the jth element in L1 for 1 < j < L1. For all m, search all (n, k) that satisfies Cn,k,m= 1, and check if an,m= ak,m= 1, randomly set one of them to 0. 8: Evaluate each chromosome according to the objective function U(R).9: If a particle’s current position is better than its previous best position, update it. 10: Determine the best particle (according to the particle’s previous best positions). 11: Update particles’ velocities. 12: Move particles to their new positions. 13: end while14: Find the current best solution. 15: end

Figure 3. Comparison of the GA, PSO, and IBPSO algorithms under the MR

210


tance of the proposed algorithm is less than the one of the original PSO.

Another aspect that can be used in order to evaluate the effectiveness of the investigated algorithms, is the reward obtained by users, ver-sus the iterations. For this purpose, the objective value versus the iterations is plotted for different algorithms, under different utilization functions. For this study, a fixed number iterations and populations (2000, and 20, respectively) are used for the comparison purpose.

Figure 6 depicts the performance of three al-gorithms under the MR utilization function. Due to the simplicity of the objective function, the three algorithms present comparable performance.

From the figure, PSO and GA algorithms, are almost identical, and outperform other algorithms. On the other hand, the enhanced IBPSO, main-tained a steady increasing rate, however, it requires a larger number of iterations, compared to the GA and the PSO algorithms, in order to find the best achievable solution.

Figure 4. Comparison of the GA, PSO, and IBPSO algorithms under the MMR

Figure 5. Comparison of the GA, PSO, and IBPSO algorithms under the MPF

211


In spite of the simplicity of this objective func-tion, it can be noticed that the IBPSO presents the worst solution among the three algorithms. For the IBPSO, the lack of the searching diversity, mentioned before, reflects on the solution found as it stuck at a certain, suboptimal, solution with no improvement noticed along the iterations.

Figure 6 plots the objective value versus it-erations of the three algorithms under the MMR utilization function. It is shown that both PSO and enhanced IBPSO reached the best possible value, and hence, outperforming other two algorithms.

For the PSO, and the enhanced IBPSO, a steady increasing rate towards the best solution is maintained. However, the enhanced IBPSO outperforms the PSO, since it achieves the best possible solution in a less number of iterations. For the GA, a steady increasing rate is observed, but a much more worse solution is found compared to the PSO, and the enhanced IBPSO. It also can be seen that, the IBPSO presents the worst per-formance among the three algorithms.

Similar to the MMR performance, both PSO, and enhanced PSO out performs the GA and the IBPSO algorithm in the study under MPF utiliza-tion function (Figure 8). For the PSO, it achieves a better solution at early phases (since it is a fully directed search), however, at late phases the solu-tion is improved with a small rate. On the other hand, for the enhanced IBPSO it presents a stable increasing rate towards the best achievable value compared to the PSO. This performance is due to the fact that the enhanced IBPSO possesses a

higher searching diversity due to the mutation capability.

For the GA, a similar performance to the en-hanced IBSPO, with respect to the increasing rate, is noticed. However, the GA finds a worse solu-tion compared to the PSO and the enhanced IB-PSO.

FUTURE RESEARCH DIRECTIONS

This chapter discussed how Computational Intel-ligence Techniques (namely; Genetic Algorithms and Particle Swarm Optimization) are used to tackle a problem that received attention in recent years from both the practical point of view and the computational point of view.

With the rapid expansion of wireless devices in the last decade, efficient spectrum utilization of the limited spectrum resources became the

Table 2. Results of different algorithms under the MR function

GA PSO IBPSO

Minimum 0.13 0.022 0.036

Maximum 0.623 0.095 0.199

Mean 0.316 0.055 0.111

Median 0.296 0.052 0.102

Std Deviation 0.11 0.018 0.036

Variance 0.012 0 0.001

Table 3. Results of different algorithms under the MMR function

GA PSO IBPSO

Minimum 0.525 0.007 0.375

Maximum 8.385 1.163 0.704

Mean 1.891 0.26 0.447

Median 1.609 0.076 0.387

Std Deviation 1.569 0.309 0.12

Variance 2.462 0.096 0.014

Table 4. Results of different algorithms under the MPF function

GA PSO IBPSO

Minimum 4.717 0.014 3.186

Maximum 29.352 8.092 8.236

Mean 12.226 1.119 4.657

Median 11.402 0.155 4.909

Std Deviation 5.629 1.722 1.026

Variance 31.696 2.968 1.054

212


focus of network management practitioners. The concept of cognitive radio (focus of this chapter) was proposed to deal with this issue, more specifically; the spectrum allocation or the channel assignment problem in cognitive radio

networks. This problem was shown to be NP-hard. Extensive simulation results presented in the chapter show that, in general, Particle Swarm Optimization Algorithms outperform fine-tuned Genetic Algorithms.

Figure 6. Reward versus iterations under MSR utilization function

Figure 7. Reward versus iterations under MMR utilization function

213


This study can be extended on two main axes: (1) to include other key parameters of cognitive radio networks and a judicious selection of pa-rameters is performed in order to better utilize the spectrum of the cognitive networks; and (2) to investigate and fine-tune PSO algorithms for more efficient and effective solutions.

CONCLUSION

In this paper, we investigate the spectrum alloca-tion problem in cognitive radio networks under varying network parameters. A fine-tuned generic algorithm (GA) is used to solve two utilization functions, namely, the Mean-Reward (MR), and the Max-Proportional-Fair (MPF), under different values for the primary user’s protection range. Extensive simulation results show that a network with primary users of large protection areas de-grades the utilization of the spectrum under both MR and MPF utility functions.

REFERENCES

Cao, L., & Zheng, H. (2005). Distributed spectrum allocation via local bargaining. In IEEE SECON 2005 Proceedings (Vol. 5, pp. 119–127). China: IEEE Communications.

Hamza, A., & Elghoneimy, M. (2010). On the ef-fectiveness of using genetic algorithm for spectrum allocation in cognitive radio networks. In High-Capacity Optical Networks and Enabling Tech-nologies (HONET), 2010 (pp. 183–189). Cairo, Egypt: IEEE. doi:10.1109/HONET.2010.5715770

Huang, J., Berry, R., & Honig, M. (2006). Auc-tion-based spectrum sharing. Mobile Networks and Applications, 11(3), 405–418. doi:10.1007/s11036-006-5192-y

Kennedy, J., & Eberhart, R. (1997). A discrete binary version of the particle swarm algorithm. In 1997 IEEE International Conference on Systems, Man, and Cybernetics, Computational Cyber-netics and Simulation (Vol. 5, pp. 4104-4108). Orlando, FL: IEEE.

Figure 8. Reward versus iterations under MPF utilization function

214


Kloeck, C., Jaekel, H., & Jondral, F. (2005). Dy-namic and local combined pricing, allocation and billing system with cognitive radios. In 2005 First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2005 (pp. 73-81). Baltimore, MD: IEEE.

Nie, N., & Comaniciu, C. (2006). Adaptive channel allocation spectrum etiquette for cognitive radio networks. Mobile Networks and Applications, 11(6), 779–797. doi:10.1007/s11036-006-0049-y

Peng, C., Zheng, H., & Zhao, B. (2006). Utili-zation and fairness in spectrum assignment for opportunistic spectrum access. Mobile Networks and Applications, 11(4), 555–576. doi:10.1007/s11036-006-7322-y

Settles, M. (2005). An introduction to particle swarm optimization (pp. 1–8). Moscow, Idaho: University of Idaho.

Umarani, R., & Selvi, V. (2010). Particle swarm optimization-evolution, overview and applica-tions. International Journal of Engineering Sci-ence, 2(7), 2802–2806.

Yuan, X., Nie, H., Su, A., Wang, L., & Yuan, Y. (2009). An improved binary particle swarm op-timization for unit commitment problem. Expert Systems with Applications, 36(4), 8049–8055. doi:10.1016/j.eswa.2008.10.047

Zhao, Z., Peng, Z., Zheng, S., & Shang, J. (2009). Cognitive radio spectrum allocation using evolu-tionary algorithms. IEEE Transactions on Wireless Communications, 8(9), 4421–4425. doi:10.1109/TWC.2009.080939

Zheng, H., & Peng, C. (2005). Collaboration and fairness in opportunistic spectrum access. In 2005 IEEE International Conference on Communica-tions, ICC 2005 (Vol. 5, pp. 3132-3136). Beijing, China: IEEE.

KEY TERMS AND DEFINITIONS

Channel Allocation: A radio resource man-agement process for allocating bandwidth and communication channels to base stations, access points and terminal equipments.

Cognitive Radio (CR): A form of wireless communication in which a transceiver can intel-ligently detect which communication channels are in use and which are not, and automatically changes itstransmission or reception parameters to communicate efficiently, while avoiding inter-ference with licensed or licensed exempt users.

Evolutionary Algorithm (EA): A subset of evolutionary computation, a generic population-based meta-heuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection.

Genetic Algorithm (GA): A search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solu-tions to optimization and search problems.

Particle Swarm Optimization (PSO): A computational method that optimizes a problem byiteratively trying to improve a candidate solu-tion (particles) with regard to a given measure of quality by having a population of particles and moving these particles around in the search-space according to simple mathematical formulae over the particle’s position and velocity.

Radio Frequency (RF): A rate of oscillation in the range of about 3 kHz to 300 GHz, which corresponds to the frequency of radio waves, and the alternating currents which carry radio signals

Unlicensed Band Cognitive Radio: A form of CR that can only utilize unlicensed parts of radio frequency spectrum.

On the Use of Particle Swarm Optimization Techniques for Channel Assignments in Cognitive Radio...

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