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Recent Advances in Physical Layer Technologies for 5G-Enabled Internet of Things 2021

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Volume 2021 |Article ID 5548428 | https://doi.org/10.1155/2021/5548428

Junxia Li, Hui Zhao, Michael Johnson, "Secrecy Performance Analysis of a Cognitive Network for IoT over - Channels", Wireless Communications and Mobile Computing, vol. 2021, Article ID 5548428, 12 pages, 2021. https://doi.org/10.1155/2021/5548428

Secrecy Performance Analysis of a Cognitive Network for IoT over - Channels

Academic Editor: Xingwang Li
Received02 Mar 2021
Revised14 May 2021
Accepted01 Jun 2021
Published28 Jun 2021


With the development of Internet of Things (IoTs), devices are now connecting and communicating together on a heretofore unheard-of scale, forming huge heterogeneous networks of mobile IoT-enabled devices. For beyond 5G- (B5G-) enabled networks, this raises concerns in terms of spectral resource allocation and associated security. Cognitive radio is one effective solution to such a spectrum sharing issue which can be adopted to these B5G networks, which works on the principle of sharing spectrum between primary and secondary users. In this paper, we develop the confidentiality of cognitive radio network (CRNs) for IoT over - fading channels, with the information transmitted between secondary networks with multiple cooperative eavesdroppers, under the constraint of the maximum interference that the primary users can tolerate. All considered facilities use a single-antenna receiver. Of particular interest, the minimum limit values of secure outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC) are developed for this model in a concise form. Finally, the Monte Carlo simulations for the system are provided to support the theoretical analysis presented.

1. Introduction

With the recent roll-out of 5G technology globally, an ever-increasing number of intelligent devices are now joining the Internet including mobile IoT devices in social, industrial, healthcare, and smart-grid nature [1]. With this rapid rise in connectivity between such devices, associated security threats and challenges are also on the increase, becoming more pressing, and need to be resolved urgently.

Traditional network encryption mechanisms can resolve security problem through various encryption algorithms at the network layer and above. However, such encryption mechanisms can no longer provide perfect security for wireless communication networks due to the complexity and time-consuming nature of the problem. Fortunately, the influence of fading channel and noise actually provides the possibility for implementing physical layer security (PLS), which has been the subject of extensive research in the literature. On the basis of [2], Wyner first proposed a model to estimate the security of communication systems [3]. For the scenarios of active eavesdropping, Ai et al. provided another evaluation benchmark, namely, average secrecy capacity (ASC), over double-Rayleigh fading channels [4]. Referring to the classical Wyner eavesdropping model, SOP was given to study the security of the correlated Rician fading channels [5]. To minimize information leakage, a precoding scheme and the security of Rician fading channels were investigated by analyzing the SOP in [6]. Elsewhere, [7] studied the security capability of large-scale fading channels according to the probability of nonzero secrecy capacity (PNSC) and SOP.

The generalized fading channel can model the real transmission environment. By changing its parameters, it can represent many channel models. To account for this, a large section of the literature has studied the transmission performance and security of the generalized fading channels [815]. In [8], Lei et al. employed two mathematical forms to complete the derivation of the lower limit of SOP and strictly positive secrecy capacity (SPSC). Elsewhere, the ability of such a channel to resist active eavesdropping was investigated by deriving the ASC in [9]. Using a system model of decode-and-forward (DF) relay cooperation over generalized- channels, the exact and approximate theoretical expressions of SOP and ESC were evaluated in [10]. Using channels with the premise that the main link follows - distribution and the eavesdropping link was modelled as - distribution, the analytical expressions of ASC, SOP, and SPSC were derived in [11]. Sun et al. described the closed form of SOP and SPSC over other - shadowed fading channels in a concise form [12] and gave an approximate analysis through the method of moment matching. The authors of [13] analyzed the security of Fox’s -function fading channels by simulating the SOP and probability of nonzero secrecy capacity (PNZ). In real-world wireless communication networks (WCNs), the correlation between antennas cannot be ignored. Based on this, the security performance analysis of correlated systems over - fading channels [14] and - shadowed fading channels [15] has also been investigated.

More recently, nonorthogonal multiple access (NOMA) and ambient backscatter communication technology have attracted more and more attention due to the high spectral and energy efficiency for the Internet of Things. In order to investigate the reliability and security of the ambient backscatter NOMA systems considering hardware damage, the outage probability (OP) and the intercept probability (IP) were studied [16]. More practically, the ambient backscatter NOMA system under in-phase and quadrature-phase imbalance (IQI) was taken into account in [17], where the expressions for the OP and the IP are derived in closed exact analytical form [18] and the secure performance for the future beyond 5G (B5G) networks in the presence of nonlinear energy harvesters and imperfect CSI and IQI in terms of the closed form of OP and IP was studied.

Most recently, many scholars are interested in CRNs because they can make use of scarce spectrum resources without causing decoding errors to the primary user’s communication. Considering a multirelay network over Nakagami- fading channels, the authors of [19] studied the effect of three different relay schemes on the security capacity of the channel. In [20], the SOP of the single-input multiple-output (SIMO) underlay CRNs over Rayleigh fading channels with imperfect CSI were derived and analyzed. Park et al. [21] proposed a CRN model composed of a multirelay primary network and a direct link secondary network, where the outage performance of the two networks was analyzed. The secrecy outage performance of DF-based multihop relay CRN under different parameters was investigated in the presence of imperfect CSI in [22]. The authors in [23] studied the energy distribution of CRN by analyzing spectrum sharing. Based on an underlying CRN, the derivation and analysis of SOP and SPSC are described in [24]. Combined with machine learning, a resource allocation protocol for CRN has also been proposed, and the influence of channel parameters on spectrum efficiency is presented in [25]. For conditions where the secondary network cannot interfere with the communication of the primary network, the authors in [26] took PNSC and SOP as the benchmark for studying CRN over Rayleigh fading channels. Recently, security issues are studied for popular applications such as relaying system a direct connection [27] and NOMA system [28].

As a generalized channel, - fading can be equated with Rayleigh, one-sided Gaussian, Nakagami-, and Rician fading channel [29], which can be used to simulate many wireless communication scenarios, so it is of great value to explore transmission performance. Bhargav et al. [30] analyzed the security of the wiretap system over fading channels by deriving the SOP and SPSC of the considered system. The SOP was derived based on classical Wyner’s model over - distribution [31]. The authors in [32] studied the statistical properties of - distribution and obtained the probability density function (PDF) and cumulative distribution function (CDF) for multiple independent - variables. Utilizing DF relay scheme, the SOP and SPSC in a relay system over - channels were provided [33]. As an extension to [32], the authors of analyzed the secrecy outage performance of a SIMO wiretap system over - channels.

1.1. Motivation and Contribution

To date, there is negligible work presented in the literature on the CRN security assessment of multiple eavesdroppers over - fading channels. Motivated by the aforementioned discussions, this paper presents such an investigation into the secrecy outage performance of CRN under multiple eavesdroppers by deriving the SOP and SPSC.

The main contributions of this paper are summarized as follows: (i)The work presents a CRN security assessment of multiple eavesdroppers over - fading channels, considering multiple eavesdroppers in the cognitive radio network. It provides theoretical analysis of SOP and studies the influence of channel parameters and other parameters on the secrecy outage performance(ii)The paper also presents a derivation of SPSC for such a setup, from which it can be seen that SPSC is independent of the primary channel, and this conclusion is confirmed by simulation(iii)To further evaluate the security for the considered system, the asymptotic analysis of SOP in the high signal-to-noise ratios (SNRs) is derived in this paper. The simulation results indicate that the secrecy diversity order is equal to the main channel parameters and is not influenced by the other parameters

1.2. Organization

The rest of this paper is organized as follows. Section 2 illustrates the proposed system model. Section 3 presents the premise, including the PDFs and CDFs of the main, primary, and wiretap channel. The SOP is derived in Section 4, the asymptotic SOP is then considered in Section 5. Section 6 introduces the associated evaluation of the SPSC. In Section 7, numerical results are presented based on the Monte Carlo method to verify the theoretical analysis presented in the preceding sections. Finally, Section 8 provides the conclusion for this paper.

1.3. Notations

In this paper, is the modified Bessel function with order and we present as the Gamma function. denotes the Pochhammer symbol. is the denotation of the generalized Laguerre polynomial. presents upper incomplete Gamma function. is the Tricomi confluent hypergeometric function defined in [33] (Equation (9.211.4)). is the Gauss hypergeometric function of variable with parameters of , , and . In this paper, we represent the probability density function (PDF) in and the cumulative distribution function (CDF) in .

2. System Model

The system model is presented in Figure 1. It consists of one primary transmitter (), one primary receiver (), one secondary transmitter (), one secondary receiver (), and multiple eavesdroppers . An effective way to realize spectrum sharing is to use cognitive radio networks (CRN). There are three types of CRN: interweave, underlay, and overlay. The model in this paper adopts the underlying CRN. The system model and analysis method can also be applied to other wireless fading channels.

In this paper, we assume that the considered network functions in underlay mode, i.e., the secondary users (SUs), concurrently are entitled to use the resources of the primary network. In underlay mode, communication among the secondary networks of the CRN can be implemented, but it must be carried out under the limitation of guaranteeing the quantity of service (QoS) of the primary network. tries to transmit information to in the presence of multiple cooperative wiretappers, without reducing the communication quality of the primary network. Hence, the transmitter power of is written as where is the maximum interference power at and represents the peak transmit power of restricted by designed hardware. It is assumed that there are no direct links between and and that can only eavesdrop on the signal from . All links of the considered system are independent, nonidentity, frequency flat, and subject to - fading, with the coefficients of the channel unchanging during a transmission block.

Based on these assumptions, is the channel gain from to , ; the power gains can be denoted as a - random variable with channel parameters , assuming that all channel coefficients are integers; finally, is the number of wiretappers. Therefore, the received signals are where are the additive white Gaussian noise with zero mean value and variance of on , , and . From (2), the received instantaneous SNRs are

For convenience, we define and . Taking into account that multiple eavesdroppers collaborate with maximal ratio combining (MRC) technology, the total received instantaneous SNR at is

From this, one can get the total wiretapped channel power gain as

3. Statistical Characteristics of - Fading

Since all channels of the considered system experience the independent, nonidentity - fading, from [26] (Equation (10)), the - power probability density function (PDF) of the link from to SR or can be expressed as where , , and and denote the subscripts of the channel coefficient from to or , respectively; is the channel power gain of the link from to or , respectively; is the modified Bessel function; and is the Gamma function.

Utilizing ([34] Equation (8.445)), we substitute into (6), and after some algebraic manipulations, (6) can be rewritten as

According to the relation between the CDF and PDF, the CDF of the channel gain can now be derived as where denotes the lower incomplete Gamma function from ([34] Equation (8.350.1)).

In the considered system, all eavesdropping links, though independent, are not necessarily identical, and the cooperative eavesdroppers all apply MRC techniques, such that the total channel gain of all of the wiretap links is written as , where is the channel gain of the link from the transmitter to and is the number of eavesdroppers. Therefore, the PDF of is given by [33] (Equation (3)) where denotes the Pochhammer symbol [32], the series representation of the generalized Laguerre polynomial ([35] Equation ( The efficient in can be calculated as where ; is the number of eavesdroppers; is the average power gain of the th wiretap link; and are the channel coefficients of the th wiretap link. The parameters and must be carefully selected to guarantee the convergence of the series in (9). Specifically, when and , (9) will converge in any finite interval; if must be chosen as , to make certain the uniform convergence of (9) in any finite interval, where .

4. Analysis of Secrecy Outage Probability

According to information security theory, perfect secrecy connection can be guaranteed if the rate of encoding of the confidential data into code words is lower or equal to the secrecy capacity. Otherwise, the security of the information will be compromised. In this section, we focus on analyzing the SOP, which is an important performance metric of describing the security of the considered system; it denotes the maximum achievable rate. The secrecy capacity of CRN is where and are the instantaneous channel capacity of main and wiretap link (s), respectively.

For a CRN working in underlay mode, in order to guarantee the quality of the service for the primary network, the transmitter power of must be constrained by the maximum interference threshold, , that the primary user can tolerate and its maximum transmitter power, , as

From the definition of SOP, it can be denoted as

From expression (15), we can see that the SOP is composed of two components, with reference to SOP1 and SOP2. The remainder of this section will consider these 2 terms in greater detail.

4.1. SOP1 Analysis

When , the work mode is the same as for a normal communication system. According to probability theory, can be calculated as

Next, we derive an expression for , from (16): where , , and . Rearranging terms and using mathematical methods, we can obtain where and Taking account of the fact that , here, we derive the lower bound of as

Substituting (8) and (9) into (19) and utilizing [38] (Equation (, we derived the expression of as

Applying (8) to this equation, we get

From this, the lower bound of can be obtained by applying (20) and (21) as where

4.2. SOP2 Analysis

From (15), can be expressed as

Let , after some mathematical operations similar to those employed for , we obtain the following expression for SOP2: where . Substituting (8) into this equation, after some mathematical derivation, we can obtain as where

Then, substituting (7) into (25), can be given by where ; substituting (26) into (25), becomes

From this, we can substitute (28) and (7) into , and with the aid of binomial expansion and [34] (Equation (3.351.3)), can be shown to be where

is the upper incomplete Gamma function ([34] Equation (8.350.2)). Making use of the derivation result, the lower SOP can be obtained as

5. Analysis of Secrecy Outage Probability Asymptotic Secure Outage Probability

Although the expressions of SOP can help us perform numerical analysis on the secrecy outage performance of the considered system, asymptotic analysis can also be used to further evaluate the system performance. Therefore, we focus on the derivation of an asymptotic expression of SOP in this section and study the impact of the maximum transmit power of and the maximum interference that PU can tolerate on the secrecy communication with multiple eavesdroppers.

In the high-SNR region, the asymptotic SOP can be defined as where denotes the secrecy diversity order and represents higher order terms. The secrecy array gain is where

6. Probability of Strictly Positive Secrecy Capacity

In information theory, the absolute security of communication can be guaranteed only when the instantaneous secrecy capacity exceeds zero. Thus, SPSC is considered to be an important indicator for measuring the secure communication system, which is given by the formula

Substitutinginto (16), we can get where ; and and are the same as mentioned above. Then, can be obtained as

From this expression of , we can see that it does not rely on the primary channel gain but is only dependent on the gain of eavesdropping channel and main channel.

7. Numerical Results

In this section, the curves obtained by Monte Carlo simulation of SOP for the considered system are compared with the above mathematical analysis in order to consider the impact which the different related parameters have on the security of the cognitive networks. After verification, when the value of the variable reaches 50 times, it converges to a constant value. Infinite series does not affect the simulation results. The parameters utilised in this paper are set to and , and the other parameter settings are as shown in the relevant figure.

Figure 2 shows the curves of the SOP for different numbers of eavesdroppers. It can be seen that the analysis results are in agreement with the simulation curves across the entire range of SNRs. In addition, the approximate curve is the tangent line of the exact theoretical results. Moreover, we can also see that the SOP will increase as the number of eavesdroppers increases. This is due to the fact that all of the eavesdroppers cooperate with each other. The more eavesdroppers there are, the stronger the wiretapped signal strength, which means that the eavesdropper SNR increases, followed by an increase in the SOP.

Figures 3 and 4 provide the curves of SOP versus for different values of and . In order to observe the variation of SOP with more clearly, two groups of experiments with different parameters were carried out and the results are shown in Figure 3. It can be seen that SOP decreases with the increase of either or . Moreover, we can see that with this increase in the value oforwhich means the SNR at the receiver increases, the secrecy performance will improve.

Figures 5 and 6 show the influence of channel parameters on SOP. In Figure 6, we plot the different curves of SOP when and are varied. The blue curve is the difference between and ; the red curve shows the difference of SOP at and . It is worth noting that the two curves coincide together in the low region; when is very high, the difference is very small. When , the difference of SOP is a negative peak; the corresponding positive maximum appears at . In other words, at these two points, the channel parameters have the greatest impact on the channel security performance.

From Figure 7, it can be seen that the higher the value of , the better the security of the system, since can transmit more high power information. We can also see that there is a limitation for the SOP in the high . This is attributed to the fact that the maximum transmit power of is equal to 1, while , as suggested in the above sections.

Next, without loss of generality, we plot a set of curves with different parameters to observe the effect of on SOP. As we can see from Figure 8, the SOP for lower outperforms the ones for the higher . This is due to the fact that SOP is the probability that secrecy capacity remains below the output threshold . The lower the threshold is, the smaller the corresponding probability obtained. In particular, the red curve of is almost identical to the blue curve of .

Figure 9 shows the SPSC for different values of . From Figure 9, we can see that the SPSC decreases with the increasing values of . This can be explained by noting that the larger implies a stronger signal obtained by eavesdroppers; hence, the eavesdropper SNR increases which decreases the secrecy capacity and thereby increases the SOP. In addition, we can also observe that the SPSC does not change with the variation of as discussed in (38).

To sum up, the interesting conclusion can be obtained that the improvement of the confidentiality is manifested by a larger value of SPSC and a smaller value of SOP. Therefore, a smaller number of eavesdropping antennas, a larger , a larger , and a smaller can improve the confidentiality of the CRN model.

8. Conclusions

In this paper, we have investigated the security performance for CRNs which operate in underlay mode with 5G, beyond 5G, and Internet of Things (IoT) technologies, where all channels experience independent, but not necessarily identically, - fading. The exact and asymptotic theoretical expressions of SOP are derived for the considered system in the presence of multiple eavesdroppers. We also derive an equivalent expression for the SPSC of such a system. These resulting formulae show that the secrecy diversity order relies on the main channel parameter. This is corroborated with simulation results, which also prove this conclusion. Finally, Monte Carlo simulation results are presented to verify these analytical expressions and illustrate the influence which factors have on the secrecy performance versus the SNR ratio (s) of the channel (s).

Data Availability

Data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there is no conflict of interest about the publication of this paper.


  1. L. Xu, X. Yu, and T. A. Gulliver, “Intelligent outage probability prediction for mobile IoT networks based on an IGWO-Elman neural network,” IEEE Transactions on Vehicular Technology, vol. 70, no. 2, pp. 1365–1375, 2021. View at: Publisher Site | Google Scholar
  2. C. E. Shannon, “Communication theory of secrecy systems,” The Bell System Technical Journal, vol. 28, no. 4, pp. 656–715, 1949. View at: Publisher Site | Google Scholar
  3. A. D. Wyner, “The wire-tap channel,” The Bell System Technical Journal, vol. 54, no. 8, pp. 1355–1387, 1975. View at: Publisher Site | Google Scholar
  4. A. Mathur and H. Lei, “On physical layer security of double Rayleigh fading channels for vehicular communications,” IEEE Wireless Communications Letters, vol. 7, no. 6, pp. 1038–1041, 2018. View at: Google Scholar
  5. K. N. Le, “SOP under dual correlated Rician fading,” in 2018 24th Asia-Pacific Conference on Communications (APCC), pp. 68–72, Ningbo, China, 2018. View at: Google Scholar
  6. C. Liu and R. Malaney, “Location-based beamforming and physical layer security in Rician wiretap channels,” IEEE Transactions on Wireless Communications, vol. 15, no. 11, pp. 7847–7857, 2016. View at: Publisher Site | Google Scholar
  7. M. Ahmed and L. Bai, “Secrecy capacity of artificial noise aided secure communication in MIMO Rician channels,” IEEE Access, vol. 6, pp. 7921–7929, 2018. View at: Publisher Site | Google Scholar
  8. H. Lei, C. Gao, Y. Guo, and G. Pan, “On physical layer security over generalized gamma fading channels,” IEEE Communications Letters, vol. 19, no. 7, pp. 1257–1260, 2015. View at: Publisher Site | Google Scholar
  9. H. Lei, I. S. Ansari, G. Pan, B. Alomair, and M. Alouini, “Secrecy capacity analysis over α-μ fading channels,” IEEE Communications Letters, vol. 21, no. 6, pp. 1445–1448, 2017. View at: Publisher Site | Google Scholar
  10. H. Zhao, Z. Liu, L. Yang, and M. Alouini, “Secrecy analysis in DF relay over generalized- fading channels,” IEEE Transactions on Communications, vol. 67, no. 10, pp. 7168–7182, 2019. View at: Publisher Site | Google Scholar
  11. J. M. Moualeu and W. Hamouda, “Secrecy performance analysis over mixed α-μ and κ-μ fading channels,” in IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6, Barcelona, 2018. View at: Google Scholar
  12. J. Sun, X. Li, M. Huang, Y. Ding, J. Jin, and G. Pan, “Performance analysis of physical layer security over k-μ and k-μ shadowed fading channels,” IET Communications, vol. 12, no. 8, pp. 970–975, 2018. View at: Publisher Site | Google Scholar
  13. L. Kong, G. Kaddoum, and H. Chergui, “On physical layer security over Fox's H-function wiretap fading channels,” IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 6608–6621, 2019. View at: Publisher Site | Google Scholar
  14. M. K. Kundu, A. S. Sumona, A. S. M. Badrudduza, and S. Shabab, “Analysis of secrecy performance over correlated η-μ fading channels,” in IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON), pp. 100–103, Dhaka, Bangladesh, 2019. View at: Google Scholar
  15. J. Sun, H. Bie, X. Li, J. Zhang, G. Pan, and K. M. Rabie, “Secrecy performance analysis of SIMO systems over correlated kappa–mu shadowed fading channels,” IEEE Access, vol. 7, pp. 86090–86101, 2019. View at: Publisher Site | Google Scholar
  16. X. Li, M. Zhao, M. Zeng et al., “Hardware impaired ambient backscatter NOMA systems: reliability and security,” IEEE Transactions on Communications, vol. 69, no. 4, pp. 2723–2736, 2021. View at: Publisher Site | Google Scholar
  17. X. Li, M. Zhao, Y. Liu, L. Li, Z. Ding, and A. Nallanathan, “Secrecy analysis of ambient backscatter NOMA systems under I/Q imbalance,” IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 12286–12290, 2020. View at: Publisher Site | Google Scholar
  18. X. Li, H. Mengyan, Y. Liu, V. G. Menon, A. Paul, and Z. Ding, “I/Q imbalance aware nonlinear wireless-powered relaying of B5G networks: security and reliability analysis,” IEEE Transactions on Network Science and Engineering, 2020. View at: Publisher Site | Google Scholar
  19. H. Lei, H. Zhang, I. S. Ansari et al., “On secrecy outage of relay selection in underlay cognitive radio networks over Nakagami-m fading channels,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 614–627, 2017. View at: Publisher Site | Google Scholar
  20. H. Lei, J. Zhang, K. Park, I. S. Ansari, G. Pan, and M. Alouini, “Secrecy performance analysis of SIMO underlay cognitive radio systems with outdated CSI,” IET Communications, vol. 11, no. 12, pp. 1961–1969, 2017. View at: Publisher Site | Google Scholar
  21. J. Park, C. Jang, and J. H. Lee, “Outage analysis of underlay cognitive radio networks with multihop primary transmission,” IEEE Communications Letters, vol. 20, no. 4, pp. 800–803, 2016. View at: Publisher Site | Google Scholar
  22. K. Shim, N. T. Do, B. An, and S. Nam, “Outage performance of physical layer security for multi-hop underlay cognitive radio networks with imperfect channel state information,” in International Conference on Electronics, Information, and Communications (ICEIC), pp. 1–4, Da Nang, 2016. View at: Google Scholar
  23. L. Sboui, Z. Rezki, and M. Alouini, “Energy-efficient power allocation for underlay cognitive radio systems,” IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 3, pp. 273–283, 2015. View at: Publisher Site | Google Scholar
  24. C. Tang, G. Pan, and T. Li, “Secrecy outage analysis of underlay cognitive radio unit over Nakagami- fading channels,” IEEE Wireless Communications Letters, vol. 3, no. 6, pp. 609–612, 2014. View at: Publisher Site | Google Scholar
  25. W. Lee, “Resource allocation for multi-channel underlay cognitive radio network based on deep neural network,” IEEE Communications Letters, vol. 22, no. 9, pp. 1942–1945, 2018. View at: Publisher Site | Google Scholar
  26. H. Zhao, H. Liu, Y. Liu, C. Tang, and G. Pan, “Physical layer security of maximal ratio combining in underlay cognitive radio unit over Rayleigh fading channels,” in IEEE International Conference on Communication Software and Networks (ICCSN), pp. 201–205, Chengdu, 2015. View at: Google Scholar
  27. A. Pandey, S. Yadav, D. T. Do, and R. Kharel, “Secrecy performance of cooperative cognitive AF relaying networks with direct links over mixed Rayleigh and double-Rayleigh fading channels,” IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 15095–15112, 2020. View at: Publisher Site | Google Scholar
  28. M. V. Nguyen and D. Do, “Evaluating secrecy performance of cooperative NOMA networks under existence of relay link and direct link,” International Journal of Communication Systems, vol. 33, no. 6, article e4284, 2019. View at: Google Scholar
  29. M D, “The κμ distribution and the ημ distribution,” IEEE Antennas & Propagation Magazine, vol. 49, no. 1, pp. 68–81, 2007. View at: Google Scholar
  30. S. L. Cotton and D. E. Simmons, “Secrecy capacity analysis over κμ fading channels: theory and applications,” IEEE Transactions on Communications, vol. 64, no. 7, pp. 3011–3024, 2016. View at: Google Scholar
  31. J. M. Moualeu and W. Hamouda, “On the secrecy performance analysis of SIMO systems over κμ fading channels,” IEEE Communications Letters, vol. 21, no. 11, pp. 2544–2547, 2017. View at: Publisher Site | Google Scholar
  32. K. P. Peppas, “Sum of nonidentical squared k-μ variates and applications in the performance analysis of diversity receivers,” IEEE Transactions on Vehicular Technology, vol. 61, no. 1, pp. 413–419, 2012, Dhaka, Bangladesh, 2019, pp. 100-103. View at: Google Scholar
  33. A. Roy, P. Maji, G. Cherukuri, and S. Kundu, “PHY layer security for IOT in κ-μ fading channel,” in 9th International Conference on Communication Systems and Networks (COMSNETS), pp. 472–475, Bangalore, 2017. View at: Google Scholar
  34. I. Gradshteyn and I. Ryzik, Table of Integrals, Series, and Products, Academic, San Diego, CA, USA, 7th edition, 2007.
  35. Eq. (, https://functions.wolfram.com/.

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