Hybrid spectrum access with relay assisting both primary and secondary networks under imperfect spectrum sensing

This paper proposes a novel hybrid interweave-underlay spectrum access for a cognitive amplify-and-forward relay network where the relay forwards the signals of both the primary and secondary networks. In particular, the secondary network (SN) opportunistically operates in interweave spectrum access mode when the primary network (PN) is sensed to be inactive and switches to underlay spectrum access mode if the SN detects that the PN is active. A continuous-time Markov chain approach is utilized to model the state transitions of the system. This enables us to obtain the probability of each state in the Markov chain. Based on these probabilities and taking into account the impact of imperfect spectrum sensing of the SN, the probability of each operation mode of the hybrid scheme is obtained. To assess the performance of the PN and SN, we derive analytical expressions for the outage probability, outage capacity, and symbol error rate over Nakagami-m fading channels. Furthermore, we present comparisons between the performance of underlay cognitive cooperative radio networks (CCRNs) and the performance of the considered hybrid interweave-underlay CCRN in order to reveal the advantages of the proposed hybrid spectrum access scheme. Eventually, with the assistance of the secondary relay, performance improvements for the PN are illustrated by means of selected numerical results.


Introduction
In recent years, the growing demand of mobile multimedia services has led to a serious shortage of radio frequency spectrum. However, measurement campaigns have shown that most of the allocated spectrum bands are underutilized [1]. To alleviate the severe scarcity of spectrum resources, a promising technique, called cognitive radio, has been developed. This technology allows secondary users (SUs) to access the licensed spectrum of the primary users (PUs) by utilizing interweave, underlay, or overlay spectrum access techniques [2][3][4]. In particular, in the interweave spectrum access, the SU periodically monitors the radio spectrum to detect occupancy in the different parts of the spectrum and then opportunistically utilizes the spectrum holes for its communication [4]. A *Correspondence: thi.my.chinh.chu@bth.se 1 Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden Full list of author information is available at the end of the article drawback of this scheme is the limitation on access time which leads to a reduction in the effectiveness of spectrum utilization compared to other spectrum access schemes. However, an interweave cognitive radio network (CRN) may achieve high performance since the transmit power of the secondary transmitter is not bounded by the interference power constraint of the primary receiver. In the overlay spectrum access, the SU is only allowed to access frequency bands if the secondary network (SN) uses interference cancelation techniques to mitigate interference to the primary network (PN) [2]. In the underlay spectrum access, the PU and SU can concurrently operate in the same licensed spectrum, but the secondary transmitter must adjust its transmit power to meet the interference power constraint of the primary receiver [3]. Due to this interference power constraint, the SUs have to reduce their transmit powers which degrades the performance of the SN. To reveal the performance of CRNs using different spectrum access schemes, comparisons of the outage probability for CRNs using interweave, underlay, and overlay techniques were presented in [5].
Inspired by the inherent benefits of the overlay and underlay spectrum access, hybrid overlay-underlay CRNs have been proposed in [6,7]. Specifically, Oh and Choi [6] derived the optimal switching rate between spectrum access modes in order to maximize throughput of a hybrid overlay-underlay CRN. A power allocation strategy for a hybrid overlay-underlay CRN was proposed in [7] to maximize the channel capacity of the CRN. The works of [8,9] focused on incorporating cooperative communications into hybrid overlay-underlay CRNs to benefit from both the cognitive radio and cooperative communication techniques such as efficient spectrum utilization and improved link reliability, respectively. In [10], a novel hybrid scheme which combines the interweave and underlay schemes for a single hop CRN was introduced. Specifically, an M/M/1 queuing model with Poisson traffic arrival is utilized in [10] to analyze the average service rate of video services for a hybrid interweave-underlay CRN. In [11], the spectrum access of a hybrid interweaveunderlay cognitive cooperative radio network (CCRN) was modeled as a continuous-time Markov chain (CTMC) to obtain the steady state probabilities of the interweave and underlay modes. Nevertheless, the relay in [11] only forwards the signals of the secondary network but does not support the primary network. Recently, in [12], an amplify-and-forward (AF) relay has been employed to forward the signals of both the primary and secondary networks which provides a performance gain for the PN as well as an improved spectrum utilization. However, the SUs of the CCRN in [12] utilize the underlay spectrum access under the interference constraint of the PUs all the time regardless of the activity of the PUs. To the best of our knowledge, no work has deployed hybrid interweaveunderlay spectrum access for CCRNs where the relay forwards the signals of the PNs and SNs in which the effect of imperfect spectrum sensing is considered.
In this paper, we examine a hybrid interweave-underlay spectrum access scheme for CCRNs. The motivation for this kind of hybrid spectrum access scheme is to inherent benefits of both the underlay and interweave spectrum access schemes. Specifically, the CCRN basically operates in the underlay spectrum access to obtain high spectrum efficiency, i.e., if the CCRN senses that it is concurrently active with the PN, the SUs must operate in the underlay spectrum access. In this operation mode, the SUs must control their transmit powers to satisfy the interference power constraint. However, if the PN is sensed to be inactive, the SUs opportunistically operate in the interweave mode without facing the interference power constraint imposed by the primary receiver to improve the system performance. In order to benefit from the advantages of relaying communication, in our proposed scheme, the secondary relay forwards the signals of the PN and CCRN. This is different from overlay CCRNs where the SUs have knowledge of the codebook of the PU and utilize this information to decode the message of primary users [2]. In our system, the secondary relay simply amplifies the signals of the PN and CCRN and broadcasts the resulting signals. It is noted that, in our study, the characteristics of traffic patterns of the PUs and SUs are taken into account when modeling the spectrum access. Further, the impact of imperfect spectrum sensing of the SUs are also considered when analyzing the system performance of the PN and SN. It is assumed that the PN and SN are subject to Nakagami-m fading. The Nakagami-m fading model is used here as it comprises a wide variety of fading channels as special cases by setting the fading severity parameter m to particular values. For example, m = 0.5 represents one-sided Gaussian fading and m = 1 represents Rayleigh fading. The Nakagami-m fading model also closely approximates the Nakagami-q (Hoyt) and the Nakagami-n (Rice) models.
Our proposed novel hybrid interweave-underlay spectrum access for a CCRN where the relay forwards the signals of both the primary and secondary networks aims at two goals, i.e., improving spectrum efficiency and enhancing performance for both the PN and SN. Major contributions of this paper can be summarized as follows: • We develop a CTMC to model the spectrum access of a hybrid interweave-underlay CCRN wherein the secondary AF relay assists primary and secondary communications. • On this basis, the equilibrium probability of each operation mode of the hybrid interweave-underlay CCRN over Nakagami-m fading is derived. • The effect of imperfect spectrum sensing in terms of false alarm and missed detection of the CCRN on the system performance is also investigated. • We further develop an analytical framework to assess the performance of the secondary and primary networks in terms of outage probability, outage capacity, and symbol error rate. • We also make performance comparisons between the proposed hybrid CCRN and the conventional underlay CCRN to reveal the superior performance of the hybrid CCRN. • Finally, through the provided numerical results, essential insights into the impact of network parameters on system performance are revealed.
The rest of this paper is organized as follows. Section 2 describes the system model, constructs the CTMC, and calculates the probabilities of operation modes in the PN and SN. Sections 3, 4, and 5 derive the expressions for outage probability, outage capacity, and symbol error rate (SER) for the PN and SN, respectively. Selected numerical results are provided in Section 6. Finally, conclusions are given in Section 7.
Notation: We use the following notations in the paper. (n) and (n, x) are the gamma function ( [13], Eq. (8.310.1)) and incomplete gamma function ( [13], Eq. (8.350.2)), respectively. The nth order modified Bessel function of the second kind is denoted as K n (·) ( [13], Eq. (8.432.1)) and U(a, b; x) is the confluent hypergeometric function ( [13], Eq. (9.211.4)). Moreover, 2 F 1 (α, β; γ ; z) is the Gauss hypergeometric function ( [13], Eq. (9.100)). Further, E{·} stands for the expectation operator and C k n = k! /[ n! (k − n)! ] denotes the binomial coefficient. Finally, the probability density function (PDF) and the cumulative distribution function (CDF) of a random variable (RV) X are denoted as f X (·) and F X (·), respectively. For Nakagamim fading with fading severity parameter m i , channel mean power i , and α i = m i / i , the PDF and CDF of the channel power gain X i are expressed as [14]

System and channel model
We consider a cognitive AF relay network consisting of a secondary transmitter SU TX , AF relay SU R , secondary receiver SU RX , primary transmitter PU TX , and primary receiver PU RX (see Fig. 1 Let the arrival traffics of PU TX and SU TX be modeled as Poisson processes with arrival rates λ p and λ s , respectively. Further, the departure traffics of PU TX and SU TX are also Poisson processes with departure rates μ p and μ s , respectively. Since relaying transmission is deployed, departure traffics of PU TX and SU TX become arrival traffics of SU R with rates μ p and μ s , respectively. Finally, the departure traffics at SU R resulting from the arrival traffics of PU TX and SU TX are also Poisson processes with rates μ p r and μ s r , respectively. As a result, when the PN and SN are concurrently active, the arrival of the system is λ p + λ s . Further, the arrival and departure rates of SU R are μ p + μ s and μ s r + μ p r , respectively. In this system, the communication occurs over two time slots. In the first time slot, PU TX and/or SU TX transmit their signals to the relay. The relay SU R amplifies and then forwards the signals to the destinations in the second time slot, i.e., the event that both PU TX and SU R or both SU TX and SU R simultaneously transmit signals never occurs. Thus, we can model the spectrum access of the system as the seven-state transition diagram shown in Fig. 2. In Fig. 2, State 0 represents the event that all terminals, PU TX , SU TX , and SU R , are inactive. Furthermore, States P, S, and PS correspond to the events that only PU TX , only SU TX , and both PU TX and SU TX are active, respectively. Finally, States R P , R S , and R stand for the events that SU R forwards the signal of only the PU TX , of only the SU TX , and of both the PU TX and SU TX , respectively.
Given the state transition diagram of the CTMC shown in Fig. 2, we construct a linear equation system consisting of the flow-balance equations and the normalized equation. This enables us to obtain the steady state probability of each state. In this equation system, a flowbalance equation represents the law that the arrival rate of any state is always equal to its departure rate. Furthermore, the normalized equation expresses the fact that the sum of all steady state probabilities is always equal to one. As a result, the linear equation system is given as follows: μ s P S = μ s r P R S (μ s + μ p )P PS = μ s r + μ p r P R μ p P P = μ p r P R P λ p P 0 = μ p P P (λ s +λ p )P 0 = (μ p +μ s )P PS P 0 + P S + P R S + P R + P R P + P P + P PS = 1 where P 0 , P P , P S , P PS , P R , P R S , and P R P are the probabilities that the system resides in State 0, State P, State S, State PS, State R, State R S , and State R P , respectively. Since there exist seven system states, it is sufficient to select six independent balance equations and the normalized equation from (3) to obtain the seven steady state probabilities. Therefore, we can rewrite the set of the first six flowbalance equations and the normalized equation as Ap = b where p and b are vectors defined as b = (0000001) T and p = P 0 P S P R S P R P R P P P P PS T . Furthermore, A is a 7 × 7 matrix constructed as Then, the vector p, consisting of the seven steady state probabilities, is obtained as Let p p and p s be the probabilities that the PN and the CCRN are active, respectively. Then, p p and p s are calculated as p p = P P P R P + P PS P R (6) p s = P S P R S + P PS P R When considering the impact of imperfect spectrum sensing at the SUs, the missed detection and false alarm probabilities must be taken into account. The missed detection probability, p m , is the probability that the SUs consider the licensed spectrum as being vacant although it is occupied by the PU. False alarm probability, p f , is the probability that the SUs consider the licensed spectrum as occupied by the PU even though the PU is inactive. Then, the detection probability and no false alarm probability can be calculated from the missed detection and false alarm probabilities as (1 − p m ) and (1 − p f ), respectively. Taking into account imperfect spectrum sensing, the following scenarios of the PN and CCRN can occur depending on the states of the terminals in the networks.

Scenario 1: The secondary network operates in underlay mode
In this scenario, the SUs sense that the spectrum band is occupied by the PN. Therefore, the SUs must operate in underlay mode under the interference power constraint Q imposed by the PU RX , i.e., SU TX transmits the signal x (1) s with average power P (1) s = Q/|h 7 | 2 where h 7 is the channel coefficient from SU TX to PU RX . Further, the amplifying gain G 1 at SU R is selected to guarantee that the interference from the CCRN to the primary receiver does not go beyond Q, i.e., There are two cases that Scenario 1 occurs.
• Case 1 : The PN and SN are active and the SN correctly senses the active state of the PN. The probability of this event is The communication in Case 1 is described as follows.
In the first time slot, PU TX and SU TX broadcast their signals to the relay and to their respective receivers, PU RX and SU RX . Since the PN and SN concurrently share the same spectrum, SU TX must control its transmit power subject to the interference power threshold Q. As a result, the received signal y (1,1) 1p at PU RX , the received signal y (1,1) 1s at SU RX , and the received signal y (1,1) r at SU R in the first time slot are, respectively, given by where x p is the transmit signal at PU TX with average power P p . Further, n p , n s , and n r denote the additive white Gaussian noise (AWGN) with zero mean and variance N 0 at PU RX , SU RX , and SU R , respectively. As for the received signal at the relay, the term h 4 x (1) s in (12) can be considered as interference from the SN to the PN and the term h 1 x p can be considered as the interference from the PN to the SN. Usually, these terms are much larger than the noise term n r , such that n r in (12) can be neglected as suggested in [15]. In the second time slot, SU R amplifies the received signal y (1,1) r with the gain G 1 and then forwards the primary and secondary signals. Consequently, the received signal y (1,1) 2p at PU RX and the received signal y (1,1) 2s at SU RX in the second time slot are, respectively, given by Assume that PU RX and SU RX deploy selection combining (SC) to select the best signal among the direct link and relaying link. From (10) and (13), the instantaneous signal-to-interference-plus-noise ratio (SINR) of the PN is obtained as where γ (1,1) 1p and γ (1,1) 2p are, respectively, the instantaneous SINRs of the direct link and relaying link of the PN, i.e., Here, β 1 = Q/P p , β 2 = N 0 /P p , and X l = |h l | 2 , l = 1, 2, . . . , 8. From (11) and (14), the instantaneous SINR of the direct link γ (1,1) 1s and relaying link γ (1,1) 2s of the SN are, respectively, given by Thus, the instantaneous SINR of the SN is obtained as • Case 2 : The PN is inactive but the SN senses that the PN is active. The probability of this event is Though the PN is inactive, due to the false alarm, SU TX senses the PN as active and still controls its transmit power P (1) s subject to Q. As a result, the received signal y (1,2) 1s at SU RX , and the received signal y (1,2) r at SU R in the first time slot are, respectively, given by In the second time slot, SU R amplifies the received signal with the gain G 1 and then forwards the resulting signal. Hence, the received signal at SU RX in the second time slot is given by Considering the instantaneous signal-to-noise ratio as a particular case of SINR where no interference exists, the SINR of the CCRN is obtained from (23) and (24) as where γ (1,2) 1s and γ (1,2) 2s are, respectively, the instantaneous SINRs of the direct link and the relaying link of the SN, i.e.,

Scenario 2: The secondary network operates in interweave mode
In this scenario, the SUs sense that the PN is inactive. Thus, the SUs operate in interweave mode and the SN does not consider the interference power constraint Q of the PN. Then, SU TX transmits the signal x (2) s with average power P s . Further, the gain factor G 2 at SU R is selected to guarantee that the transmit powers at SU TX and SU R are the same, i.e, G 2 is selected as There are two cases that cause Scenario 2 to happen.
• Case 1 : Only the SN is active and the inactive state of the PN is correctly sensed. The probability of this event is Since the SUs sense that the PN is inactive, the SN operates in interweave mode without suffering from the interference power constraint of PU RX . Furthermore, there is no interference incurred by the PN to the SN. As a result, the received signal y (2,1) r at SU R and the received signal y (2,1) 1s at SU RX in the first time slot are, respectively, given by In the second time slot, SU R amplifies the received signal y (2,1) r with the gain G 2 and then forwards the resulting signal to SU RX . Thus, the received signal y (2,1) 2s at SU RX in the second time slot can be expressed as Defining β 3 = P s /N 0 , the instantaneous SINRs of the direct link γ (2,1) 1s and relaying link γ (2,1) 2s of the SN are, respectively, obtained from (31) and (32) as Thus, the instantaneous SINR of the SN is obtained as • Case 2 : Both the PN and SN are active but the SN senses that the PN is inactive. With the incorrect sensing outcome, the SN operates in interweave mode though the PN is active. The probability of this event is Then, the PU TX and SU TX transmit the signals x p and x (2) s to the relay and to their respective receivers. As a result, the received signal y (2,2) 1p at PU RX , the received signal y (2,2) 1s at SU RX , and the received signal y (2,2) r at SU R in the first time slot are, respectively, given by s in (39) represents interference from the SN to the PN while the term h 1 x p can be considered as the interference from the PN to the SN. Usually, these terms are much larger than the noise term n r , such that n r in (39) can be neglected [15]. As a result, the received signals, y (2,2) 2p at PU RX and y (2,2) 2s at SU RX , in the second time slot are, respectively, given by y (2,2) From (37) and (40), the instantaneous SINR of the PN is obtained as where γ (2,2) 1p and γ (2,2) 2p are, respectively, the instantaneous SINRs of the direct and relaying links of the PN: From (38) and (41), the instantaneous SINR of the SN is obtained as where γ (2,2) 1s and γ (2,2) 2s are, respectively, the instantaneous SINRs of the direct link and the relaying link of the SN, i.e.,

Scenario 3: Only the PN is active
In this scenario, the SN does not transmit and the probability of this case does not depend on the sensing outcome, i.e., Hence, the received signal y (3,1) r at SU R and the received signal y (3,1) 1p at PU RX in the first time slot are, respectively, given by In the second time slot, SU R amplifies the received signal with factor G 2 3 = 1/|h 1 | 2 to guarantee that the transmit powers of PU TX and SU R are the same. Then, SU R forwards the resulting signal to PU RX . As a result, the received signal y (3,1) 2p at PU RX in the second time slot is given by From (50) and (51), the instantaneous SINR of the PN is given by where γ (3,1) 1p and γ (3,1) 2p are, respectively, the instantaneous SINRs of the direct link and the relaying link of the PN, i.e.,

Outage probability
In order to evaluate the performance of the PN and CCRN for a given metric B, we need to first formulate the performance B(S i ) for each operation mode S i of the hybrid scheme. Then, the expectation B of B can be calculated as [16] where S is the state space containing all operation modes, S i is the ith operation mode, and p i is the probability that the system operates in the ith mode. Equation (55) will be utilized in the sequel to analyze the performance of the PN and SN in terms of outage probability, outage capacity, and symbol error rate.

Outage probability of the PN
Outage probability is the probability that the instantaneous SINR falls below a predefined threshold γ th . From (55), the outage probability of the PN, P p out , can be formulated as p,out is the outage probability of the PN in Scenario i-Case j, i.e., P respectively, given as follows: Proof See Appendix 1.

Outage capacity analysis
The outage capacity C corresponding to outage probability % can be approximated as [18] where erfc −1 [·] is the inverse complementary error function. Furthermore, E{C} and E{C 2 } are the first and second moment of the channel capacity, respectively. Based on the Shannon capacity theorem, the rth moment of the channel capacity is given by

Outage capacity of the CCRN
Similarly, outage capacity, C s, , in (bits/s/Hz) of the SN is derived from (55) where C s, is the outage capacity in Scenario i-Case j of the SN. Substituting (70), (73), (74), and (78) into (80), the rth moment of the channel capacity of the CCRN in Scenario i-Case j are obtained. In particular, the rth moments of the channel capacity of the CCRN in Scenario 1-Case 1 is given by Then, the rth moment of the channel capacity of the CCRN in Scenario 1-Case 2 is given by κ jl T 0, j + k, β 1 α 7 β 2 α 4 , l, 0, 0, 0, 1, 0, 1, r − 1, 1 Finally, the rth moment of the channel capacity of the CCRN in Scenario 2-Case 2 is derived as

Symbol error rate analysis
According to [19], with modulation parameters α and β, the SER is expressed in terms of the CDF of the instantaneous SINR as

Symbol error rate of the CCRN
According to (55), the SER for the secondary network is given by where P s,e is the SER of the CCRN in Scenario i-Case j. Substituting (70) into (90), we obtain P (1,1) s,e of the CCRN in Scenario 1-Case 1 as in (96).

Numerical results
In this section, we present numerical results to evaluate the performance for the PN and the CCRN for various network parameters. In particular, the numerical results are provided to quantify the considered metrics and to illustrate the performance improvement obtained when applying the proposed relaying-assisted hybrid spectrum access scheme.
Let us recall that d 1 , d 2 , d 3 , d 4 , d 5 , d 6 , d 7 , and d 8 denote the normalized distances of the links PU TX → SU R , SU R → PU RX , PU TX → PU RX , SU TX → SU R , SU R → SU RX , SU TX → SU RX , SU TX → PU RX , and PU TX → SU RX , respectively. Assume that the considered system operates in a suburban environment such that all channel mean powers are attenuated with the link distances according to the exponential decaying path loss model with path loss exponent of 4. The SINR threshold used for calculating the outage probability is selected as γ th = 3 dB. Further, the expected outage threshold is chosen as = 0.1 % when calculating the outage capacity.
First, we will illustrate the impact of imperfect spectrum sensing at the SUs, the fading severity parameters, and the communication link distances d 1 , d 2 , and d 3 of the PN on the system performance in terms of outage probability, outage capacity, and SER of the PN. To reveal the superior performance of the proposed scheme, we then provide a comparison of the performance of the PN with and without the use of the secondary relay. Assume that the arrival processes of the PN and CCRN follow a Poisson distribution with arrival rates λ p = λ s = 85 packets/sec. Furthermore, the departure processes of the PN and secondary network at SU TX and PU TX are also modeled as Poisson distribution with rate μ s = μ p = 100 packets/sec. Moreover, departure rates at SU R for the traffics from the primary transmitter and secondary transmitter are set as μ s r = μ p r = 100 packets/s. In addition, the socalled transmit signal-to-noise ratio (SNR) of the CCRN in the interweave mode and the interference-power-to-noise ratio in the underlay mode are selected as P s /N 0 = 10 dB and Q/N 0 = 5 dB, respectively. Finally, the distances, d 4 , As can be seen from Fig. 3a-c, Case 1 provides the lowest outage probability and SER, and the highest outage capacity as compared to Cases 2 and 3 because Case 1 has more favorable fading conditions. Furthermore, Case 3 considers different fading severity parameters on different channels. Specifically, Rayleigh fading is present on the interference channels from the secondary transmitter to the primary receiver and from the primary transmitter to the secondary receiver, i.e., m 7 = m 8 = 1, while the communication channels inside the PN and the CCRN have the some line-of-sight links with m i = 2, i = 1, 2, . . . , 6. As Case 3 induces more severe fading on the interference links compared to Case 2, while communication links in both cases have the same fading severity parameter, a performance improvement in the PN is observed for Case 3. Finally, imperfect spectrum sensing of the SUs degrades the performance of the PN, i.e, the outage probability and SER increase and the outage capacity decreases as the false alarm and missed detection probabilities of the SUs increase. Figure 4a-c presents comparisons for the outage probability, outage capacity, and SER of the PN with and without the assistance of the secondary relay. For these examples, we assume that the SUs can perform spectrum sensing perfectly. Moreover, the fading severity parameters of all As can be seen from Fig. 4a-c, with the communication link distances d 1 = d 2 = 0.4 and d 3 = 0.6, Case 4 provides lower outage probability and SER, and higher outage capacity as compared to Case 5. This is because all channel mean powers are attenuated with the link distances according to an exponentially decaying function. Thus, the average channel power gains of the communication channels of the PN in Case 4 are higher than those of Case 5. It can also be observed from these figures that the proposed relaying scheme where the relay assists the communication of both the PN and SN can improve the performance of the PN. In particular, with the help of the secondary relay, the outage probability, outage capacity, and SER of the PN always outperform those results obtained for the PN without the assistance of the secondary relay in both Case 4 and Case 5.
Next, we investigate the effect of the arrival rate of the PN and imperfect spectrum sensing of the SUs on the outage probability, outage capacity, and SER of the CCRN. Then, we demonstrate the influence of the transmit SNR P s /N 0 in interweave mode and the interference-power-tonoise ratio Q/N 0 of PU RX in underlay mode on the performance of the CCRN. In addition, a comparison of the performance of the hybrid interweave-underlay CCRN with that of the conventional underlay CCRN is presented.
In these examples, we select the arrival rate of SU TX as λ s = 85 packets/s, and the departure rates of PU TX , SU TX , and SU R as μ p = μ s = μ p r = μ s r = 100 packets/s. The transmit SNR of the PN is selected as P p /N 0 = 10 dB.   In these examples, we select the communication link distances of the CCRN as d 4 = d 5 = 0.5 and d 6 = 0.7. Furthermore, we fix the transmit SNR in the interweave mode as P s /N 0 = 10 dB. As expected, when the arrival rate of the PN decreases from Case 7 to Case 6, the performance of the CCRN improves significantly, i.e., the outage probability and SER decrease and the outage capacity increases. This is attributed to the fact that as λ p decreases, the idle periods of the PN increases. Thus, the probability that the CCRN operates in the interweave mode increases. It can also be observed that false alarm degrades the performance of the SN since it reduces the opportunity for the SN to operate in the interweave mode. Finally, missed detection increases the chance for the SN to operate in the interweave mode which increases the performance of the SN. However, when missed detection occurs, the SUs assume that the licensed spectrum is vacant although it is occupied by the PUs which degrades the performance of the PN as can be seen in Fig. 3. Figure 6a-c shows performance comparisons between the hybrid interweave-underlay CCRN and the conventional underlay CCRN in terms of outage probability, outage capacity, and SER. In these examples, it is assumed that the SUs can perform perfect spectrum sensing. Further, we fix the transmit SNR of the CCRN and arrival rate of the PN as P s /N 0 = 10 dB and λ p = 85 packets/s, respectively. Then, we investigate the effect of the normalized distances in the CCRN, i.e., d 4 from SU TX to SU R , d 5 from SU R to SU RX , and d 6 from SU TX to SU RX , for the following cases: • Case 8: d 4 = d 6 = 0.4 and d 6 = 0.7 • Case 9: d 4 = d 5 = 0.5 and d 6 = 0.8 As expected, when d 4 , d 5 , and d 6 increase from Case 8 to Case 9, the outage probability and SER of the hybrid CCRN and conventional CCRN increase. However, the outage capacity decreases according to the increase of d 4 , d 5 , and d 6 since the transmit signals are attenuated with distance according to the exponential decaying path loss model. Finally, for both of the examined cases in Fig. 6, the performance of the proposed hybrid CCRN always outperforms that of the respective underlay CCRN.

Conclusions
In this paper, we have utilized a continuous-time Markov chain to model the spectrum access of the PU and SUs of a hybrid interweave-underlay CCRN wherein an AF relay assists both the PN and CCRN. Based on this Markov model, we have obtained the steady state probability of each state which then is utilized to calculate the probability of each scenario of the PN and CCRN. Considering mutual interference between the PN and CCRN, we have derived expressions for the outage probability, Fig. 6 Performance comparisons of the CCRN when using the underlay and hybrid schemes: a outage probability, b outage capacity, c symbol error rate outage capacity, and SER for both networks under imperfect spectrum sensing. Numerical results have been provided to illustrate the effect of the primary arrival rates, the fading severity parameters, the communication and interference link distances, and the interference power threshold of PU RX on the performance of the PN and CCRN. Through the selected examples, it can be observed that the PN can achieve a performance improvement with the assistance of the secondary relay to forward its signals. Finally, for the selected numerical results, the performance of the CCRN applying the propose hybrid interweave-underlay spectrum access always outperforms that of the conventional underlay CCRN.