Optimal Channel Reservation in Cooperative Cognitive Radio Networks

Jin Lai1, Ren Ping Liu2, Eryk Dutkiewicz1, Rein Vesilo1 1 Department of Electronic Engineering, Macquarie University, Sydney, Australia

2 CSIRO ICT Centre, Sydney, Australia Email: {jin.lai, eryk.dutkiewicz, rein.vesilo}@mq.edu.au; {ren.liu}@csiro.au

Abstract—This paper studies optimal channel reservation in cooperative cognitive radio networks (CRNs) where secondary users (SUs) have access to the combined spectrum pool of cooperating CRNs. Motivated by SU high forced termination in cooperative CRNs, we propose two channel reservation schemes, Fixed Channel Placement Reservation (FCPR) and Dynamic Channel Placement Reservation (DCPR), and theoretically analyze their performances using the Markov chain approach. Our numerical results, validated by simulation, indicate that for a given number of reserved channels, the DCPR algorithm achieves better user experience by reducing the forced termination probability. Based on this analysis, we propose two enhanced reservation algorithms: Algorithm A maximizes the overall capacity of CRNs to enable network operators to increase their revenue; Algorithm B minimizes the user experience cost function to provide better services.

Keywords-channel reservation; admission control; Markov Chain; cognitive radio network

I. INTRODUCTION Cognitive radio (CR) is envisaged as a novel technology to

greatly improve spectrum unitization by enabling opportunistic access to the licensed spectrum band by secondary users (SUs). According to [1] there are two approaches for dynamic spectrum sharing between primary networks and cognitive radio networks (CRNs): underlay spectrum sharing and overlay spectrum sharing. In underlay spectrum sharing, an SU spreads transmitted signals over a wide range of the frequency band while maintaining the interference to primary users (PUs) below the noise floor. In overlay spectrum sharing, PUs take precedence over SUs to utilize the licensed spectrum band. SUs are only allowed to opportunistically operate in the licensed spectrum band whenever it is left unused by PUs. Prior to accessing a specific frequency band, SUs or their proxies have to sense the available spectrum. The SU has to vacate the occupied channel immediately whenever a PU transmission on the same channel is detected. The interrupted SU may switch to an unused spectrum band if possible or be forced to terminate if no free channel can be found. This paper focuses on overlay spectrum sharing among infrastructure-based CRNs.

Since SUs are vulnerable to primary network traffic variations in a single CRN, separate admission control could lead to serious service degradation, particularly when relatively heavy traffic intensity occurs in the corresponding primary network. In the meanwhile, it is expected that in the future several CRNs with non-overlapping spectrum pools owned by

different network operators may coexist in the same geographical area. Therefore, a new CRN model that takes advantage of cooperation among such networks was proposed in [8] to improve SU completion probability and total supported SU traffic intensity. However, our results in Section II B indicate that SU forced termination probability limits the acceptable SUs' traffic intensity. Channel reservation can reduce SU forced termination probability and boost the acceptable SU traffic intensity. However additional issues, such as the handover cost, should also be taken into account in the performance models.

Channel reservation schemes, or guard channel policies, have been widely studied in wireless networks where mobile users have an exclusive right to use the available spectrum band. In [2] [3] channel reservation schemes were applied to reduce the dropping probability of handover calls by allowing them to use reserved channels. However, in the CRN scenario the features of reserved channels which can be used by either SUs or PUs are different from those of the aforementioned traditional wireless networks. [4] shows that the secondary system capacity can be improved significantly by suitably reserving a number of frequency slots for PUs' access. In [5-6] a channel reservation scheme for CR spectrum handover is proposed to reduce forced termination probability at a slight increase in blocking probability. In [7] the existing trade-off between blocking new SU sessions and dropping ongoing ones is investigated based on channel reservation schemes. However, channel reservation only in a single CRN is studied in [4-7]. In this paper we study channel reservation schemes among multiple cooperative CRNs.

In this paper we first examine issues of SU blocking and forced termination probabilities in cooperative CRNs. We then propose and analyze two channel reservation algorithms: Fixed Channel Placement Reservation (FCPR) and Dynamic Channel Placement Reservation (DCPR). Our Markov chain model results, validated by simulation, indicate that for a given number of reserved channels DCPR achieves better Quality of Experience (QoE) for SU by reducing the forced termination probability. Moreover, we propose two optimized channel reservation algorithms: The capacity-optimized channel reservation Algorithm A maximizes the total supported SU traffic volume; The QoE-optimized channel reservation Algorithm B seeks to minimize the negative impacts on user experience by making tradeoffs among SU blocking probability, forced termination probability and handover cost to provide SUs the best QoE.

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The remainder of this paper is organized as follows. In Section II the cooperative CRNs model and our motivation are presented and two channel reservation algorithms are proposed and evaluated. The two enhanced channel reservation schemes in cooperative CRNs are investigated and an optimized channel reservation scheme is derived in Section III. Finally Section IV concludes our paper.

II. CHANNEL RESERVATION ALGORITHMS

A. Cooperative CRNs Model A cooperative CRNs architecture is proposed in [8] to

improve performance experienced by SUs by allowing cooperation among CRNs as shown in Fig 1. SUs in this model may utilize any free channel in all the CRNs in the same geographical area. SU connection handover between different CRNs is supported in the system.

Figure 1. Cooperative CRNs model with Joint Admission Control

The entity, denoted in Fig 1 as JCAC (Joint Connection Admission Control), is introduced to make an admission decision upon a SU arrival. An incoming SU will be assigned to a CRN according to a predefined selection policy in JCAC. If the selected CRN does not have a free channel to accommodate this SU, the connection will be blocked. While a SU is holding a channel and a new PU requests to access this particular channel, the SU has to vacate the channel. If no free channel is available for the SU in the same CRN, the SU will attempt to switch over to a free channel in another CRN to continue the ongoing connection. If no free channel is available, the interrupted SU will be terminated. The completion of a SU connection will release the channel.

B. Issues in Cooperative CRNs Model When considering efficient operation between cooperative

CRNs, we are motivated by the fact that dropping an ongoing connection has far more significant impact on user experience than blocking a new connection request. In [8] we proposed a channel-aware selection scheme in cooperative CRNs and we observed that SU forced termination limits the total SU capacity. This can be seen in Fig.2 where the thresholds of SU blocking probability and forced termination probability are set to 6% and 1% respectively. The resulting maximum acceptable SU traffic intensity is determined by the SU forced termination

probability threshold. Therefore, we propose to apply channel reservation techniques to improve SU capacity while meeting the required QoS thresholds.

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SU

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Blocking Prob CH=10

Forced termination Prob CH=10Blocking Prob CH=15

Forced termination Prob CH=15

Blocking Prob CH=20

Forced termination Prob CH=20Blocking prob Threshold

Forced termination Prob Threshold

Figure 2. SU Blocking and Forced Termination probabilities

C. Channel Reservation Algorithms For a given number of reserved channels, we propose two

channel reservation algorithms to reduce SU forced termination probability in cooperative CRNs.

1) Fixed Channel Placement Reservation (FCPR) For this algorithm, a certain number of free channels are

reserved in each CRN. The number of reserved channels in each CRNs is independent on the numbers in other CRNs. For a SU connection request, CJAC first finds out the number of free channels in each CRN. If the number of free channels is higher than the number of reserved channels in a CRN, the CRN will become a potential option for the new connection and will be added into the candidate networks list (CNL). The full CNL can be obtained by interrogating all CRNs. If the CNL is empty, the incoming SU will be blocked due to no available network. If there is only one candidate in the list, the incoming SU will be assigned to that CRN. Otherwise one of the networks in the CNL will be selected randomly.

2) Dynamic Channel Placement Reservation (DCPR) The placement of reserved channels in DCPR can be

adjusted dynamically according to the constantly changing network status no matter where the reserved channels are located but CJAC always strives to maintain a number of free channels among cooperative CRNs above the specified threshold so as to accommodate handover connections interrupted by a PU’s sudden appearance. For an incoming SU, if the total number of free channels in all CRNs is less than the specified number of the reserved channels, then it will be blocked. Otherwise it will be randomly assigned to one of the CRNs which have at least one free channel.

D. Markov Analysis Model We consider a network scenario consisting of two CRNs

(note that our analysis can be readily extended to scenarios with more CRNs). We assume that 1C and 2C channels are available in CRN 1 and CRN 2. The arrival of PU1, PU2 and

SU follows a Poisson process with mean arrival rates 1puλ , 2puλ and suλ , respectively while the PU1, PU2 and SU

service times are exponentially distributed with mean 1/1 puμ , 2/1 puμ and puμ/1 . Hence SU traffic intensity is given

by ))/(( 21 pususu CC μλρ += .The total number of reserved channels is Nr . For the FCPR algorithm the channel reservation placement configuration is given by )2,1( NrNr satisfying NrNrNr =+ 21 .

A continuous-time Markov chain is developed to analyze the performance of cooperative CRNs using the two proposed channel reservation algorithms. Suppose that the system state can be referred to as )),)(,(( nmji where mi , represent the numbers of PU1 and PU2 in the system while nj , denote the numbers of SU in CRN 1 and CRN 2 respectively. Therefore, the state space Ω is defined by:

}0;0;0;0;0;0|)),)(,{((

222

111

CmnCmCnCjiCjCinmji≤+≤≤≤≤≤

≤+≤≤≤≤≤=Ω

The detailed description of the entire system can be found in [8]. In this paper we extend the model to include channel reservations. An example of the transition diagram is shown in Fig.3. In this case when a SU connection request is made the parameters θηω ,, which determine the probability of selecting a particular CRN will be computed taking into account the reservation algorithm used.

Figure 3. Example State Transition Diagram

By analyzing all the events and the corresponding state transition diagrams, transition rate matrix Q can be determined [9]. We then have 0=∏Q where

],...,,...,[ ))0,)(0,(()),)(,(())0,0)(0,0(( 21 nn CCnmji πππ=∏ represents the steady-state probability vector with the summation equal to one

Subsequently we define the following performance metrics:

1) SU Blocking Probability: According to the analysis of FCPR and DCPR, SU blocking probability blkP for FCPR and DCPR is given respectively by:

∑−≥+≥≥−≥+≥≥

=200100 21 NrCnm,n,m,NrCji,j,i

))n,m)(j,i((blk_FPRP π (1)

∑−+≥+++≥≥≥≥

=NrCCjinm,n,m,j,i

))n,m)(j,i((blk_DPRP210000

π (2)

2) SU Forced Termination Probability: Similar to [6] SU forced termination probability, defined as the conditional probability of terminated SUs rate under accepted SUs rate, is given by:

sublk

CnmnmCjipunmji

CnmCjijipunmji

ft PP

λ

λπλπ

)1(2121 ,0,0,

2)),)(,((,,0,0

1)),)(,((

−

+

=∑∑

=+>≥=+=+=+>≥ (3)

3) SU Network Handover Probability: Assume that all channels in one CRN are fully occupied by PUs and at least one SU while one or more channels are available in the other CRN. Then a new incoming PU will cause one SU in the former CRN to handover to the other CRN. Therefore, network handover probability is given by:

sublk

CnmnmCjipunmji

CnmCjijipunmji

hdvr PP

λ

λπλπ

)1(2121 ,0,0,

2)),)(,((,,0,0

1)),)(,((

−

+

=∑∑

=+>≥<+<+=+>≥ (4)

E. Numerical and Simulation Results Without loss of generality, we assume that PU1 traffic

intensity is heavier than PU2 traffic intensity with PU1 traffic intensity set to 0.9 and PU2 traffic intensity set to 0.1. The SU traffic intensity is set in the range from 0.1 to 1 with increments of 0.1. Three channels are assumed in each of the two CRNs. The total number of reserved channels equals two.

As is shown in Fig.4-5 for a given SU traffic intensity, SU forced termination probability drops significantly when reserving some channels for handover connections compared to the case without reservation, at the expense of an increase in SU blocking probability. Moreover, given SU traffic intensity and the total number of reserved channels, in DCPR SUs have a higher chance of being blocked, but a lower forced termination probability, while FCPR achieves a lower blocking probability at the cost of a higher forced termination probability. In addition, for FCPR the comparison between (2,0) and (0,2) indicates that when the reserved channels are placed in the CRN with a heavy PU traffic instead of that with a light PU traffic load a lower blocking probability results but at the expense of a higher forced termination probability.

A force termination is perceived negatively by the end user – more negatively than a call blocking [10]. By reducing the forced termination probability, DCPR improves the user experience effectively.

Comparison between the analytical and simulation results (with a 95% confidence interval level) in Fig. 4-5 illustrates an excellent agreement. It can be concluded that DCPR achieves more efficient reservation to reduce forced termination probability at the expense of higher blocking probability since the reserved channels can be placed dynamically.

Figure 4. SU Blocking Probability

Figure 5. SU Forced Termination Probability

III. OPTIMUM CHANNEL RESERVATION This section presents two enhanced channel reservation

schemes: one for maximizing overall capacity of CRNs and the other for minimizing the total system cost while guaranteeing the specified QoS requirements.

A. Capacity-Optimized Channel Reservation Maximizing overall capacity of CRNs is one of the major

objectives for network operators while the essential QoS requirements for networks are still guaranteed [4]. We propose a new scheme to optimize system capacity by finding out the optimal number Nr and placement of reserved channels. The optimization problem can be formulated as follows:

} ),( {Max placementNrfsu =ρ (5)

under the constraints of:

thresholdblkblk PP _≤ and threshold_ftft PP ≤ ; (6)

We firstly make the following propositions.

Proposition 1: For a given threshold_blkP , threshold_ftP and channel reservation placement )2,1( NrNr , assume 1Nr is fixed and 2Nr varies, the maximum acceptable SU traffic

intensity max_ftρ for threshold_blkP drops while the maximum

acceptable SU traffic intensity max_ftρ for threshold_ftP rises when 2Nr increases.

Proof: For a given SU traffic intensity suρ the increase of 2Nr indicates fewer available channels which definitely

results in blocking more incoming SUs, so when 2Nr increases )Nr,P(f threshold_blkmax_blk 2

1−=ρ decreases. Similarly since more reserved channels means less chance of being dropped, ),( 2_

1max_ NrPf thresholdftft

−=ρ increases with increasing 2Nr . The proposition is validated by Fig.4-5.

Proposition 2: For a given threshold_blkP , threshold_ftP , suρ and channel reservation placement )2,1( NrNr , if 1Nr is fixed and 2Nr varies, when 2Nr increases, the maximum capacity of CRNs max_suρ decreases monotonically or firstly increases and then decreases, i.e. there exists only one maximum value.

Proof: Since the maximum acceptable SU traffic intensity for a CRN is determined by ),(min max_ftmax_blkmax_su ρρρ = , if the initial placement of 02 =Nr produces

max_ftmax_blk ρρ < , then max_suρ is equal to max_blkρ .

Therefore, max_suρ drops all along. Otherwise, if the initial condition is max_max_ ftblk ρρ > , max_blkρ and max_ftρ will cross somewhere between the range according to proposition 1. After reaching the intersection if 2Nr continues to increase, then max_blkρ further drops which leads to the decrease of

max_suρ . This indicates that max_suρ will firstly increase and then decrease.

Based on the above propositions the search space of possible reservation settings is greatly reduced. The resulting algorithm for obtaining the optimum number and placement of reserved channels is given in Algorithms A.

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SU

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ckin

g P

roba

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No ReservationFCPR(0,2)FCPR(1,1)FCPR(2,0)DCPR(2)Simulation

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y

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FCPR(0,2) FCPR(1,1) FCPR(2,0) DCPR(2)Simulation

1: for i=0 to do2: for j=0 to do3: Calculate the maximum SU capacity CaF(i,j) by FPR(i,j)4: if i=0 and j=0 then continue;5: elseif j=0 and CaF(i,0)< CaF(i-1, 0) then break;6: elseif CaF (i,j)<CaF(i -1,j) then break;7: end if8: end for9: end for10: for k=0 to do11: Calculate the maximum SU capacity CaD(k) by DPR(i)12: if k 0 and CaD(k)<CaD(k-1) then break; end if13: end for14: Get the maximum value from Matrix Ca F and CaD

1C

≠

2C

21 CC +

Algorithm A: Capacity-Optimized Channel Reservation

B. QoE-Optimized Channel Reservation For a given SU traffic intensity, there may exist multiple

possible reservation settings which could satisfy the set QoS requirements. In this situation network operators may endeavor to provide better user experience which usually is measured by applying a set of QoE measures from users’ perspective. Meanwhile, both spectrum handover and network handover in cooperative CRNs which might not only increase the signaling overhead but also result in some network delay or packet loss will cause the degradation of QoE. Since spectrum handovers take place in the same network, the cost of spectrum handover can be neglected compared to network handover.

In this paper we propose a QoE-optimized channel reservation scheme by considering negative factors for users’ experience such as SU blocking probability, forced termination probability and handover cost while guaranteeing SU blocking probability and forced termination probability below the specified threshold levels. We define the following cost function:

hdvrftblktotal P*P*P*C γβα ++= (7)

where γβα ,, allow network operators to weigh adequately the cost parameters against each other, e.g. [10]. Therefore, for a given SU traffic intensity our objective is given by:

Nr)} ,({ suρtotalCMin (8)

under the following constraints:

threshold_blkblk PP ≤ and threshold_ftft PP ≤ ;

We firstly define the candidate reservation configuration set which satisfy the given SU traffic intensity and specified QoS requirements and can be obtain by using the Markov chain mode in Section II-C and Proposition 2 in Section III A as

});max(0;0;0|)DPR(or ),{(

max_21

221121

susuCCNrCNrCNrNrNrNr

ρρ ≤+≤≤≤≤≤≤=ℜ

Therefore, the optimal number and placement of reserved channels, which is given by }:),({ minarg* ℜ∈= rrCr sutotal ρ , can be obtained by using the following algorithm.

1: Compute the candidate reservation set2: If is null then3: SU traffic is overloaded and beyond the maximum capacity4: exit5: else6: Calculate the total cost of each candidate element7: Select the optimal one r* with the minimum cost8: end if9: //The algorithm can be re-calculated when SU traffic changes

ℜℜ

Algorithm B: QoE-Optimized Channel Reservation

This optimization algorithm can be computed offline based

on the Markov chain model for a range of SU traffic. In operation implementations, the optimum channel reservation

placement can be obtained by table lookup for a given SU traffic intensity.

C. Numerical Results In our analysis ten channels are assumed in each of the two

CRNs. The thresholds of SU blocking probability and forced termination probability are set to 3% and 0.5% while the weights of γβα ,, are 10, 100 and 2 respectively, taking into account the tradeoff among the performance measures.

Fig.6 shows the percentage increase in SU capacity that can be supported when using channel reservation compared to no reservation. We assume that when the supported SU traffic intensity is below 0.01 it can be neglected. For the region where both absence and presence of reservation can satisfy the specified QoS constraint, the results in Fig. 6 show a significant increase in SU capacity when using channel reservation. The increase can be over 70% for some settings of PU1 and PU2 traffic intensities. In addition, given SU, PU1 and PU2 traffic intensity 0.2, 0.6 and 0.2 respectively and specified QoS requirements, Fig.7 shows the possible feasible reservation settings among which the placements of FCPR (2, 1) can achieve the minimum total cost, offering SUs the best quality of experience.

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80

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SU

Cap

acity

Im

prov

emen

t %

Figure 6. Improvement of SU Capacity with Reservation in CRNs

Figure 7. Total System Cost of Candidate Channel Reservation Settings

(0,2) (1,1) (2,1) (3,0) (4,0) DCPR(1) DCPR(2)0

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0.9PU1=0.6, PU2=0.2, SU=0.2

Tot

al S

yste

m C

ost

IV. CONCLUSIONS In this paper we propose two channel reservation

algorithms to reduce SU forced termination probability and system cost. The system performance is analyzed with a Markov chain model, and our results are validated through extensive simulations. The results indicate that the Dynamic Placement Reservation (DCPR) achieves better user experience by reducing the forced termination probability. Moreover, two enhanced channel reservation algorithms are presented: Algorithms A maximizes the overall SU capacity to enable network operators to increase their revenue, and Algorithm B minimizes a user experience cost function to provide the best QoE for secondary users.

ACKNOWLEDGMENT The authors would like to acknowledge the support of

OPNET Technologies, Inc. for providing Opnet software to the simulations.

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