Nisar A Lala1, Altaf A Balkhi1, G. M. Mir1 and R. A. Simnani2

1College of Agricultural Engineering and Technology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir Srinagar, J and K, India

2Department of Electronics, Gandhi Memorial College Srinagar, J and K, India

 

Corresponding author Email: lalanisar_ae@rediffmail.com

ABSTRACT:

Cognitive radio is a future technology coined for increasing the utilization of otherwise under-utilized spectrum channels. Providing quality of service (QoS) to diverse flows as per their requirements is a very difficult job as there is no dedicated allocation of wireless channels in cognitive radio (CR) network. The paper selects few critical QoS parameters such as signal-strength, bandwidth and user-mobility that assess the influence on quality of communication between users using rule-based fuzzy inference system. The analytical results show the influence of those QoS parameters on the quality of communicating channels and open new issues in designing protocol structure for CR.

KEYWORDS:

Cognitive radio; quality of service (QoS); fuzzy logic

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Lala N. A, Balkhi A. A, Mir G. M, Simnani R. A. Quality of Service Provisioning in Cognitive Radio Network. Orient.J. Comp. Sci. and Technol;10(4)


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Lala N. A, Balkhi A. A, Mir G. M, Simnani R. A. Quality of Service Provisioning in Cognitive Radio Network. Orient. J. Comp. Sci. and Technol;10(4). Available from: http://www.computerscijournal.org/?p=7157


Introduction

The utilization report provided by Federal Communications Commission (FCC)1 spectrum task force indicates that utilization of the allocated spectrum varies between 15% to 85%. Mitola2,3 is considered to be the pioneer of the idea of cognitive radio (CR). CR4is a smart device having the capability to sense the entire spectrum in order to find the idle channels and utilizing these idle channels opportunistically for communication as and when required.

The important issues of CR are as under:

Spectrum Sensing

This function finds the idle spectrum to be used for its communication and the arrival of PU on the spectrum channels presently occupied by CR.

Spectrum management

Large chunks of idle spectrum are identified through spectrum sensing that needs to be categorized so that the best spectrum channelis selected for CR’s communication.

Spectrum mobility

Primary user has the priority to access the spectrum. Once PU appears on the channels occupied by CR, it needs to change the operating frequency instantly to minimize the interference.

Spectrum sharing

Once transmitting channels are selected, handshaking process is initiated between communicating users first,before actual communication starts.

Providing QoS guarantees to different flows as per their requirement in CR is a very difficult job. QoS5 is the ability of the system to provide minimum requisite service needed for satisfactory communication between users and has direct impact over CR performance. It is also defined by the degree of satisfaction of the CR users and their skill to treat different flows differently6,7. The definition of QoS changes with types of traffic and network. The QoS is a function of multiple contradictory parameters and those are not certain and exact. Application of fuzzy logic gives more effective solution when the available information is vague and imprecise. This paper proposes to use rule based fuzzy logic for assessing the influence of three critical parameters on QoS in a cognitive radio network. At the onset, fuzzy logic found applications in control system8,9 and then in telecommunication networks10-12. Fuzzy logic found more applications in cellular networks for admission control13and for routing14. It was extended to CR network for transmitting power control15 and foroptimization16. It found more applications for controlling the spectrum access17,18and spectrum handoff19-21 in cognitive radio. The authors of22,23have analysed impact of some QoS parameters.

Materials Aand Methods

An algorithm, using rule-based fuzzy inference system, is proposed in this paper to analyse the impact of three critical parameters namely signal-strength, bandwidth and user-mobility on the QoS in cognitive radio. We have used Hong and Rappaport traffic model24,25for user-mobility. The assumptions of the model are:

  • That all the users are uniformly distributed in that cell when a call is started.
  • The users, starting the call, have equal probabilities of moving in any direction but afterwards move in the same direction during the call.

The hand off requests 24,25arrive into the system as

formula1

Rn is the probability that the communication at least need one more hand off without blocking the service request.

Phh is the probability that the on-going communication need one more hand off.

Bo is the probability of blocking of new service requests.

Pf is the probability that handoff is not successful.

λo is the arrival rate of service requests in a cell.

Three inputsto fuzzy inference system are given as:

a)       input 1: signal-strength (in dBm)

b)       input 2: bandwidth (in KHz)

c)       input 3: user-mobility (in km/hour)

and the output of the fuzzy inference system is the quality of the communication channel in CR.Each inputis characterized by three fuzzy sub-sets, such as low, medium and high,as shown in (2) defined by universe of discourse in the range[0,1]

S(signal-strength) = S(bandwidth) = S(user-mobility) = {low, medium, high}(2)

and QoS is characterized by five fuzzy sub-sets, such as poor, average, good, very-good and excellent,as shown in(3),definedbyuniverse of   discoursein the range[0,1]

S(QoS)={poor, average, good, very-good,

excellent}(3)

The analysis of the proposed algorithm is being done using trapezoidal membership functions for inputs and the QoS,depicted in Figs. 1(a) and 1(b), and for normalized values in the range[0,1]. Table.a depicts the rules of the fuzzy inference system. The rules are designed to have the suitable values of the three contradictory inputs so as to satisfy the QoS requirement of the communicating users. For instance, rule 2demonstrates that if the signal-strength is low, unused bandwidth is low and user-mobility is medium, then the quality of the communicating channel is poor. The rule is translated into actual channel condition in which transmission loss of the communicating channel is very large, availability of unused bandwidth is limited and the user is moving with speeds that falls under middle category. As a result, the quality of the channel is not sufficient to satisfy QoS requirements of the communicating users. Rule 14demonstrates that if the signal-strength is medium, bandwidth is medium and user-mobility is medium, then the quality of the communicating channel is average. The rule is translated into actual channel condition in which transmission loss of the communication channel is in middle range, availability of unused bandwidth is sufficient and the user is moving with speeds that fall under middle category. As a result, the quality of the channel is just sufficient to satisfy QoS requirements of the communicating users.Also rule 26demonstrates that if the signal-strength is high, bandwidth is high and user-mobility is medium, then the quality of the communicating channel is excellent. The rule is translated into actual channel condition in which transmission loss of the communication channel is negligible, availability of unused bandwidth is very large and the user is moving with speeds that fall under middle category. As a result, the quality of the channel is more than sufficient so that the QoS requirement of the communicating users is easily satisfied.Fig.2 shows the decision of fuzzy inference system for inputs such as signal strength = 0.5 (middle value), bandwidth = 0.5 (middle value), user-mobility = 0 (stationary) and for these values the QoS comes out to be 0.5 (excellent).

Fig.1: Membership function plots of the inputs    a) signal-strength, bandwidth and user-mobility and   consequent b) QoS

Figure 1: Membership function plots of the inputs a)signal-strength, bandwidth and user-mobility and consequent b) QoS


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Table 1:   Rulesin fuzzy inference system

Table 1: Rulesin fuzzy inference system



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Figure 2: Decision of fuzzy inference system for QoS

Figure 2: Decision of fuzzy inference system for QoS 



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Results and Discussions

The proposed algorithm is analysed for static data transfers in Matlab’sfuzzy toolbox. Figs. 3(a), 3(b) and 3(c)demonstrate the effect of signal-strength, bandwidth and user-mobility on the quality of the communication between users while keeping the other two parameters constant at middle values (=0.5).The results indicate that with increase in the signal-strength there is increase in the quality of the communication of the users, while keeping bandwidth and user-mobility constant at middle values, as shown in Fig. 3(a). The further results indicate that with the increase in available unused bandwidth of a communication channel, there is also an increase in the quality of the communication of the users, while keeping signal-strength and user-mobility constant at middle values, as shown in Fig. 3(b). Fig. 3(c) indicates that with the increase inuser-mobility i.e. when the speed of the user increases, there is decrease in the quality of the communication of the users,while keeping bandwidth and signal-strength constant at middle values.

Fig. 3: Analytical results show effect of a) signal-strength  onQoS b) bandwidth on QoS c) user-mobility on QoS while keeping other two parameters constant at middle values (= 0.5)

Figure 3: Analytical results show effect of a) signal-strength onQoS b) bandwidth on QoS c) user-mobility on QoS while keeping other two parameters constant at middle values (= 0.5) 


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Figs. 4(a), 4(b) and 4(c)demonstrate the effect of bandwidth and signal-strength,user-mobilityand signal-strength, and user-mobilityand bandwidth on the quality of the communication between users while keeping the other parameter constant at middle value (=0.5).Decision surface results indicate that the quality of the communicating channels increases with increase in signal-strength and the availability of unused bandwidth, when the user is moving with constant medium speed, as shown in Fig. 4(a). The results indicate that the QoSin the communicating channel is the maximal, when the unused bandwidth and signal-strength,areat their maximal values. Decision surface results indicate that the quality of the communicating channel increases with increase in signal-strength and decrease in speed of the user while the available unused bandwidth is kept constant at middle value, as shown in Fig. 4(b). The results indicate that the QoSin the communicating channel is the maximal, when signal-strength is at its maximal value and user remains stationary or moves with very low speed.Decision surface results indicate that the quality of the communicating channel increases with increase in the availability of unused bandwidth and decrease in speed of the user, while the signal-strength is kept constant at middle value, as shown in Fig. 4(c). The results indicate that the QoS in the communicating channel is the maximal,when the availability of unused bandwidth is maximal and user remains stationary or moves with very low speed.

Fig. 4: Decision surface showing the effect of a) signal-strength and bandwidth on QoS b)  user-mobility and signal-   strength on QoS and c) user-mobility and bandwidth on QoS while keeping other parameter constant at    middle value (= 0.5)

Figure 4: Decision surface showing the effect of a) signal-strength and bandwidth on QoS b) user-mobility and signal- strength on QoS and c) user-mobility and bandwidth on QoS while keeping other parameter constant at middle value (= 0.5) 


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Conclusion

The paper proposed an algorithm using fuzzy inference system that assesses the impact of signal-strength, bandwidth and user-mobility on QoS in cognitive radio. The analysis of the proposed algorithm is being performed in Matlab’s fuzzy toolbox. The results indicate that with increase in the signal-strength and unused bandwidth, and decrease in user-mobility, there is increase in QoS of the communication between CR’s.

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