CSPA: Channel Selection and Parameter Adaptation scheme based on genetic algorithm for cognitive radio Ad Hoc networks
© Aslam and Lee; licensee Springer. 2012
Received: 3 April 2012
Accepted: 23 September 2012
Published: 21 November 2012
The cognitive radio (CR) is evolved as the potential technology to solve the problem of spectrum scarcity and to meet the stringent requirements of upcoming wireless services. CR has two distinct features, the spectrum sensing and the parameter adaptation. The former feature helps the CR to find the vacant spectrum slots/channels in the radio band while the latter mechanism allows it to adjust the operating parameters (e.g. frequency band, modulation and power, etc.) accordingly. The primary user (PU) activity has serious effects on the overall performance of the cognitive radio network (CRN).The CR should vacate the channel if it detects the arrival of the primary user (PU) in order to avoid the interference. The channel eviction/switching phenomenon severely degrade the quality of service (QoS) of the CR user and it is perhaps the key challenge for the CRN. In this paper, we propose the dynamic channel selection and parameter adaptation (CSPA) scheme based on the genetic algorithm to provide better QoS for the CR by selecting a best channel in terms of the quality, the power and the PU activity. The CSPA deals with the problem of channel switchings and it provides better QoS to the CR user. Simulation results prove that CSPA outperforms the existing schemes in terms of channel switchings, average service time, power and throughput.
KeywordsCognitive radio Genetic algorithm QoS Channel characteristics PU activity
The radio spectrum is the most precious resource in the wireless communication. Each wireless transmitter-receiver pair performs communication by occupying a specific portion of the radio spectrum. According to the latest research figures, the usage of the allocated spectrum band is very limited and it is around 15% to 25%[1, 2]. On the other side of the picture, the current allocation state of the spectrum is overcrowded and there is a small portion of spectrum left available for new emerging wireless services. These factors incline the researchers to devise new ways for the efficient utilization of the existing spectrum. The most feasible and promising solution is the CRN.
The CR is an intelligent adaptive and opportunistic radio, which can increase the spectrum efficiency by dynamic selection of the unused portion in the radio band using the cognitive sensor (CS) and quick reconfiguration of the transmission-reception parameters using software defined radio (SDR). The CR utilizes the idle channels during the available time depending upon the information collected through spectrum scanning or spectrum sensing. In the current modeling of CRN, if the PU arrives on a the channel, then the CR immediately shifts to another channel or remains silent to avoid the interference with the PU. The overall performance of the CRN directly depends upon the spectrum sensing, the PU activity and the variations in the quality of the channel during course of transmission.
The channel selection is an important area in the CRAHNs. It enables the CR to choose the best channel among the pool of sensed channels. The current research in the area of channel selection relies on the latest observations of the channel state as a base knowledge for the selection mechanism. However, a channel that is to be utilized by the PU cannot be used for the transmission of the CR. Therefore, when the PU arrives, the CR should suspend its communication, and it should look for other channel to restart its communication[4–6]. Such periodic suspensions in the transmission of the CR user can cause severe degradation in their QoS. To deal with such disruptive scenarios, a new model is presented in[7–11]. In these schemes, each CR predicts the future arrival of the PU on different channels and selects the best channels for its communication depending upon the least PU activity. The purpose of these schemes is to minimize the interference with the PUs. However, other factors are also important for the selection of the optimal channel such as the channel condition and the QoS requirement of the CR. A robust channel selection scheme should accommodate all these factors for the performance evaluation of the CRN. Moreover, these factors are contradictory in nature and demand an intelligent and an effective solution.
The genetic algorithm (GA) seems to be the best solution owing to its multi-objective optimization capability. It can be employed to solve the problems which have large search space and contradictory objectives. In the latest research models[13, 14], the authors describe the cognitive decision making process (DMP) using the GA to solve the multi-objective optimization problem in the CRN. However, their schemes only consider the channel condition as the prime factor and lacks in terms of the PU activity and CR QoS requirements.
Similar GA based schemes presented in[15–19] perform the channel selection by considering the power (PWR), modulation (MOD), bit error rate (BER), bandwidth (B) and frequency as the basic genes. In these schemes, the objective function converges to the optimal value and then termination condition is achieved based on the desired criteria. Although theses schemes provide the detailed analysis and implementation of the channel selection scheme to optimize the factors such as data rate (DR), PWR and BER but these schemes also don’t consider the PU activity and CR QoS parameters.
Another GA based parameter adaptation scheme is presented in. It covers the detailed description of various GA parameters and provides a comprehensive simulation model for the parameter reconfiguration. The authors select the three basic objective functions such as minimize BER, minimize power and maximize throughput and provide the simulation results for two different objective function’s weight scenarios termed as emergency and low power. Although simulation results show better comparisons in terms of throughput, BER and power like[16–18], yet all of these schemes lack in accommodating the effects of the PU activity on the performance of the CRN.
We formulate the problem of channel selection and parameter adaptation in CRAHNs using GA by considering all the relevant parameters (BER, PWR, DR, etc.) and other important factors (Channel condition, the PU activity and the CR QoS requirement) from the perspective of both the PU and the CR. We provide an analysis of existing GA based schemes and discuss the challenges and issues involved in the design of such schemes. This is a valuable contribution for future research in this direction.
We propose a method to model the arrival activity of the PU and also formulate the mathematical equation to compute the transmission opportunity index (TOI). The TOI represents the future channel availability to the CR without interruptions from the PU.
Finally, we compare our proposed scheme with the existing random channel selection schemes in terms of the channel switchings, the service time, throughput and the power consumption.
The rest of paper is organized as follows. Section Problem formulation describes the problem formulation of the CSPA scheme. The GA, its applications and its modeling with respect CRAHNs is presented in Section Genetic algorithm. Section Experimental results explains the experimental setup and simulation results of our proposed scheme. Finally, Section Conclusion draws the conclusion and sets the future research direction for the design of the channel selection scheme in CRAHNs.
Each CR user n= 1, 2, 3… N periodically senses the radio environment to gather the statistics about the PUs which are operating in the vicinity under the PU base station as shown in Figure 2.
For spectrum sensing, the CR user employs the cyclo-stationary feature detection (CSFD) scheme as shown in Figure2. We prefer CSFD sensing scheme for its two basic advantages as compared to the other transmitter based spectrum sensing schemes. Firstly, it can detect the presence of the PU transmitter reliably. Secondly, it can discriminate the waveform of different wireless technologies. In our case, reliable detection (low probability of false alarm and miss detection) is extremely important because each CR user needs to maintain the history of channel based on its sensing results. And, also discrimination property is desirable in our scheme because we are considering the CR network operating with multiple PU networks. The CSFD can easily differentiate between the PU signals of different technologies. For example, it can easily classify the signal of the CDMA and the GSM technology. This discrimination property helps the CR to quickly adapt its parameters according to the desired technology. The entire working of CSFD can be represented by the following equations presented in[21–23] and depicted in Figure3. The threshold value of ? indicates about the presence or absence of the PU.
where, v represents the evaluated frequency and it represents the spectral components with separation factorα.
- iii)Each CR user maintains the channel history from the sensing results obtained from the CSFD scheme.(5)
The H matrix forms the history patterns for the PUs on the k number of channels. The size of history matrix is k*s. The increase in the size increases the reliability but the system becomes more complex. We select the normal values for both k and s to show the outcomes and advantages achieved through CSPA.
Where η represents TOI and Ψ indicates the history pattern of the PU on the channel for Ts number of time slots and it is calculated using equation (5). The value of summation varies from 0 to 1 while the TOI (η) varies from 0.3697 to 1. The variables Ts and s can be used interchangeably and these are used together to improve clarity.
♦Minimization of switching overhead.
♦ Minimization of the interference.
♦ Minimization of energy by reducing channel switchings.
♦ Better QoS to CR user.
- iv)The CSPA selects the best channel in terms of the CR QoS requirement, the condition of channel and the activity of the PU. The CRN can be characterized with three basic entities i.e. the PU, the CR and the channel. Each entity has unique features and the nature of these features strongly affect the overall performance of the CRN. The CR user has unique requirements from the channel depending upon its QoS parameters. However, the PU has prime concern from CR that it should operate under tolerable interference limit. The Figure5 depicts the main entities involve in the channel selection of CRN.
This section covers the background and detailed description of the genetic algorithm. Moreover, we also provide the gene and chromosome structure along with the explanation of the objective functions of CSPA algorithm in this section.
Application and background
The GA is a biologically inspired heuristic search technique based on the phenomenon of natural genetics. The GA maintains the population of individuals that characterize the candidate solutions to the given problem. Each individual chromosome in the population is evaluated to find its fitness level (how much it is close to the optimal solution) from the given objective functions. The chromosomes consist of different genes and these genes can be represented through the binary numbers [0,1].The complexity of chromosome is directly depends upon the number of genes and the bits/gene.
The GA performs well for large search space problems because it can work on a population in parallel instead of processing a single solution at a time. The parallel processing allows the GAs to explore several parts of the solution at same time and also many real world problems require simultaneous optimization of several objective functions. Hence, these algorithms become suitable to solve these problems. However, the objective functions may have conflict in their objectives. These contradictory requirements of different objective functions give rise to a set of possible solutions call pareto optimal solutions instead of a single possible solution. The main reason for the multiple possible solutions is that no single solution can be thought as the best one than the other solution in the optimal set. The working of GA can be described by the algorithm 1.
Algorithm 1: Basic steps in the GAs
Initialize the population of chromosomes.
Compute the fitness level of each chromosome to rank them.
Select the best chromosomes in terms of their fitness.
Perform the crossover operation on selected chromosomes.
Perform the mutation function on selected chromosomes.
If stopping criteria is achieved terminate the GA otherwise move to step 2 of the algorithm.
Formation of New Generation
Selection: The chromosomes are selected in such a way to have the better level of fitness in the current available population.
Crossover: The crossover is an operator to share information between chromosomes and to create new individuals from the incoming generation.
Mutation: The new created individual will be mutated at a definite point in the chromosome.
Implementation of the GA in CRN
Encoding of chromosomes
Frequency band (FB)
Bit error rate (BER)
Data rate (DR)
Interference to primary user (ITPU)
Transmission opportunity index (TOI)
- viii)Bit level representation of chromosome The seven genes constitute the structure of chromosome. The 30-bit chromosome represents the complete solution of CSPA scheme using 30 bits. The arrangement of all genes in the chromosome is shown in Figure7.
Selection of chromosome
The selection rate depends upon the complexity and convergence time and we choose the selection rate equals to 1/2.
The next step after forming the new generation is the mutation process. Mutation alters a binary bit of 1 to 0 or vice versa in the child chromosomes.
The mutation rate indicates that how many chromosomes out of the total chromosomes undergo the mutation process. For example, the mutation rate of 0.03 indicates that 3 chromosomes out of total chromosomes undergo the mutation process.
where, w = w1, w2, w3 ⋯ w m is a weight vector. We consider the four transmission modes depending upon the weight given to each objective function.
Formulation of objective functions
In equation (14), the d r represents the desired data rate and d c indicates the data rate available on channel. The power optimization function is depicted in the equation (15) where P indicates the power required for the transmission on the given channel and Pmax is the maximum power available to the CR user. The in equation (16) represents the average BER of all the channels and is the minimum value of the BER among the available channels. The factor I shows the ITPU on the given channel while Imax is the maximum interference limit to the PU. Similarly η is the future opportunity for the CR on the given channel while ηmax represents the maximum value of the opportunity index. Depending upon the weightage given to each objective function we define the four transmission modes as shown in the Additional file2: Table S2.
This section describes experimental results along with the detailed explanation of different simulation parameters. Moreover, it explains the significance of our simulation result in terms of channel switching, service time and power consumption.
We consider the CRAHNs where each CR has capability of accurate sensing and history management. Although the given results are true for more generic cases yet we show our outcomes for specific values of the GA parameters. Additional file3: Table S3 specifies the basic GA parameters that we have considered for our implementation.
The value of the crossover rate is 0.5 and it indicates that the six chromosomes are selected to form the next generation. The mutation rate is selected based upon the existing work in the channel selection process. The 200 iterations are performed to get the desired solution (chromosome).
The CR user requirements
The CSPA selects the channel according to the requirement of the CR users, the PU activity and the QoS parameters of CR. For the current case the desired QoS parameters of the CR are given in Additional file4: Table S4. The CR user needs 265 kbps with maximum tolerable BER of 10-5 to follow its transmission.
In Figure12 (a), the comparison is shown on the basis of channel switchings. To transmit a data file of size 2 Kb, by using the CSPA the CR needs to perform channel switching only 7 times while the random channel selection scheme requires 39 switchings which is almost 5.5 times higher as compared to CSPA scheme. The performance of CSPA is even much better for video application that needs to transmit large amount of data as CSPA shows better performance for large data files. Hence, the performance of the CSPA scheme is much better than the traditional random channel selection schemes in terms of the channel switchings.
In Figure12 (b), the comparison is shown on the basis of the time taken to transmit a data of the CR user. For the transmission of 2 Kb data file, the CR takes only 84.5 milliseconds (ms) while the random channel selection scheme requires 149.5 ms which is twice higher as compared to CSPA scheme. Moreover, the performance gain of the CSPA is even more significant for large data files. Although the GA consumes time in the selection of the best channel however GA selection time is considerably less as compared to the saving time.
The given simulation results makes CSPA suitable for power sensitive applications like cognitive radio sensor networks.
The channel selection and parameter adaptation are the most critical functions for CR user in CRAHNs. An optimal channel selection scheme would help to provide the better QoS to the CR users and operate inconsistence with the PU without creating any unpleasant effects on the ongoing communication of the PU. The both communication parties can perform their tasks without interference to each other. On the other hand the selection of channel on random basis without considering the PU activity would result in the higher number of channel switchings which cause the delay in the service time and wastage of the precious power. From the PU perspective, it is also desirable that the CR causes lease collisions and operates under tolerable interference range.
In this article, we proposed a channel selection and parameter adaptation scheme and formulate the problem for CRAHNs using the GA by considering all the important factors including the channel characteristics, the PU arrival activity and the QoS requirement of the CR users.
We also propose a method to model the activity of the PU and also formulate the mathematical equations to compute the transmission opportunity index which indicates the duration for the future availability of channel to the CR without interruptions from the PU. We add this information in the chromosome to incorporate the effect of the PU activity inside GA based decision making process for selecting the optimal channel. The solution is evolved through GA which performs the genetic operation and then CR user adapts their parameters according to the selected channel. We also define four transmission modes depending upon the weight given to the respective objective function. The four modes are defined and their effectiveness is discussed according to their applications in the real implementation.
Simulation results show that including the PU activity within the gene structure helps to significantly reduce the number of channel switchings, minimizes the delay in the service time and saves the transmission power. The CSPA scheme shows a significant improvement in saving the transmission power that makes it suitable for power sensitive application such as cognitive radio sensor network.
In future, we are planning to develop a similar model using particle swarm optimization and show the comparison with the genetic algorithm in terms of their convergence time. Moreover, we are also developing a model to incorporate the channel usage patterns of other CR users in the CRAHNs.
This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the Convergence-ITRC (Convergence Information Technology Research Center) support program (NIPA-2012-C6150-1101-0003) supervised by the NIPA (National IT Industry Promotion Agency) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012009449).
- FCC: Second Report and Order And Memorandum Opinion and Order, ET Docket No. 08–260. 2008.Google Scholar
- Akyildiz IF, Lee W, Vuran MC, Mohanty S: NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. J. Comput. Netw 2006, 50(13):2127-2159. 10.1016/j.comnet.2006.05.001View ArticleMATHGoogle Scholar
- Akyildiz IF, Lee WY, Chowdhury K: CRAHNs: cognitive radio ad hoc networks. J. Ad Hoc Netw. 2009, 7(5):810-836. 10.1016/j.adhoc.2009.01.001View ArticleGoogle Scholar
- Nie N, Comaniciu C: Adaptive channel allocation spectrum etiquette for cognitive radio networks. in IEEE DySPAN 2005, 269-278.Google Scholar
- Zheng H, Cao L: Device-centric spectrum management. in IEEE DySPAN 2005, 56-65.Google Scholar
- Saleem A, Kyung-Geun L: Fair, efficient and power optimized spectrum sharing scheme for cognitive radio network. EURASIP J. Wirel. Commun. Netw 2011. 10.1186/1687-1499-2011-201Google Scholar
- Yang L, Cao L, Zheng H: Proactive channel access in dynamic spectrum networks. J. Phy Commu 2008, 1(2):103-111. 10.1016/j.phycom.2008.05.001View ArticleGoogle Scholar
- Marko H, Sofie P, Aarne M: Performance improvement with predictive channel selection for cognitive radios. in IEEE CogART Workshop 2008, 1-5.Google Scholar
- Ki Won S, Seong-Lyun K, Jens Z: Temporal spectrum sharing based on primary user activity prediction. IEEE Trans. Wirel. Commun. 2010, 9(12):3848-3855.View ArticleGoogle Scholar
- Vamsi Krishna T, Ping W, Dusit N: Channel status prediction for cognitive radio networks. J. Wirel. Commun. Mob. Comput. (WCMC) 2010, 10: 1-13. 10.1002/wcm.909View ArticleGoogle Scholar
- Christian James Rieser T: Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking. Dissertation, Blacksburg, Virginia; 2004.Google Scholar
- Rondeaut W, Le B, Maldonado D, Scaperoth D, Bostian C: Cognitive radio formulation and implementation. in IEEE CrownCom 2006, 1-10.Google Scholar
- Newman TR, Barker BA, Wyglinski AM, Arvin A: Cognitive radio implementation for wireless multicarrier transceivers. Wiley Wirel. Commun. Mob. Comput. 2006, 6: 1-14. 10.1002/wcm.239View ArticleGoogle Scholar
- Qin H, Su J, Du Y: Multiobjective evolutionary optimization algorithm for cognitive radio networks. in IEEE IEEC’09 2009, 164-168.Google Scholar
- Jae Moung K, Sung Hwan S, Ning H, Guanbo Z, Young Min K, Joo Kwan L: Cognitive radio software testbed using dual optimization in genetic algorithm. in IEEE CrowCom 2008, 1-6.Google Scholar
- Chantaraskul S, Moessner K: Implementation of a genetic algorithm-based decision making framework for oppertunistic radio. IET Commun 2010, 4: 495-506. 10.1049/iet-com.2009.0479View ArticleGoogle Scholar
- Si C, Newman TR, Wyglinski EAM: Genetic algorithm-based optimization for cognitive radio networks. in Proc. 2010 IEEE Sarnoff Symposium 2010, 1-6.Google Scholar
- Siddique T, Azam A: Spectrum Optimization in Cognitive Radio Networks Using Genetic Algorithm. Blekinge Institute of Technology, Sweden; 2010.Google Scholar
- Newman TR, Rakesh R, Wyglinski AM, Evans JB, Minden GJ: Population adaptation for genetic algorithm-based cognitive radios. in IEEE CrownCom 2007, 279-284.Google Scholar
- Gardner W, Brown W: Spectral correlation of modulated signal. Part I—analog modulation. IEEE Trans. Commun 1987, COM-35: 584-594.View ArticleMATHGoogle Scholar
- Gardner WA, Brown W, Chen CK: Spectral correlation of modulated signal. Part II—digital modulation. IEEE Trans. Commun. 1987, COM-35: 595-601.View ArticleMATHGoogle Scholar
- Hong I, Sohn SH, Lee JK, Kim JM: DFS algorithm with ranking based on genetic algorithm in UHF portable system. in IEEE ISCIT 2009, 454-459.Google Scholar
- You C, Kwon H, Heo J: Cooperative TV spectrum sensing in cognitive radio for Wi-Fi networks. IEEE Trans. Consum. Electron. 2011, 57: 62-67.View ArticleGoogle Scholar
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