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Secrecy Analysis of Cognitive Radio Networks over Generalized Fading Channels
At present, the fifth generation (5G) communication networks are in the time of large-scale deployment principally because its characteristics consists of large bandwidth, fast response, and high stability. As a partner of 5G, the Internet of Things (IoT) involves billions of devices around the world, which can make the wireless communication environment more intelligent and convenient. However, the problem that cannot be ignored is the physical layer security of 5G-IoT networks. Based on this, we perform a security analysis of cognitive radio networks (CRN) for IoT, where the CRN is the single-input multiple-output (SIMO) model experiencing κ-μ shadowed fading with multiple eavesdroppers. To analyze the confidentiality of the system under consideration, we analyze the security performance for the considered IoT systems with the help of the derived secure outage probability (SOP) and probability of strictly positive secrecy capacity (SPSC). As a verification of the theoretical formula, Monte Carlo simulation is also provided. The results of great interest are the factors that can produce better security performance in high SNRs region which consist of smaller M, smaller , and larger N, and larger , smaller , and smaller .
On the fifth generation (5G) networks, Internet of Things (IoT) technology makes the data exchange between people and things and objects and objects more rapid and intelligent . However, the physical layer security (PLS) problem, that is, the confidential signals between IoT entities are easy to be intercepted and decoded by eavesdroppers . Therefore, how to improve the confidentiality performance of IoT systems is an urgent issue to be solved. There are two methods to deal with this problem; the first is based on the encryption and decryption algorithm of the network layer and above, and the other is to improve the security capacity of the channel by using the characteristics of the fading channel. In recent years, the latter, namely, physical layer security, has gradually become a hot topic in security research. Based on the channel capacity theory  and the classic Wyner’s eavesdropping model , Wei et al. in  described the optimal power of artificial noise to ensure the best antieavesdropping ability in the wiretap networks experiencing Rayleigh fading. The PLS of nonorthogonal multiple access (NOMA) networks  and relaying 5G networks  with Rayleigh channels were developed. The authors in  studied the factors that affect the security capability of Wyner’s model based on Rician channel, where analytical SOP were deduced and proved. The physical performance and security of nonideal IoT networks over Nakagami-m fading channels were investigated through two important metrics, namely, outage probability (OP) and intercept probability (IP) .
Different from the channels described above, the universality of generalized channels, that can be equivalent to other fading channels, has recently received considerable attention. [10–16]. On the premise of fully considering the actual unfavourable factors of the relaying wireless communication networks (WCNs) over Weibull channels, Li et al.  has completed the derivation of the exact formula of OP and the asymptote. The work in  developed the approximate expression of probability density function (PDF) and obtained the SOP analysis of the classical Wyner’s systems over generalized-K fading channels. Based on the proposed IoT relay networks, the authors of  explored the theoretical derivation and simulation analysis of OP and average symbol error probability (ASEP). In , when all the links in the wiretap networks suffered from κ-μ fading, Bhargav et al. derived the lower limit of SOP and the closed-form expression of SPSC. Under the condition of imperfect signal transmission, the authors in  obtained the theoretical formula of OP in considered model over α-μ fading with two different scenarios. Referring to Wyner’s classical wiretap model, Kong et al. derived and analyzed the SOP and the probability of nonzero secrecy capacity (PNZ) of Fox’s H-Function  channels and Fisher-Snedecor F  channels, respectively.
Recently, security issues are very important for popular applications such as Internet of vehicles , neural networks , and big data . As a developing strategy to solve the obstacles of spectrum scarcity and power allocation in IoT networks, CRN can significantly improve the transmission range and quality have gained great attention in recent literatures [20–26]. With the aid of artificial noise, the IP in the secondary network over Gaussian channel was investigated in . The cooperation of multiple secondary users will further enhance the capacity of CRN; based on this, two different protocols were proposed, and achievable ergodic secrecy rate (ESR) was used to analyze the security performance of the considered system . By employing a CRN network, which suffered from Rayleigh fading and had a legitimate receiver with multiple antennas, the authors of  provided the derivation of PNZ and SOP. In [23, 24], Lei et al. studied the security performance of CRNs undergoing Nakagami-m fading and generalized-K fading, respectively. The study of Zhang et al.  proposed a method to improve the security capacity of primary networks and secondary networks simultaneously and discussed the strategy of power allocation and band sharing. The authors in  studied the secrecy performance of nonorthogonal multiple access (NOMA) CRN over Nakagami-m fading channels, in which connection outage probability (COP), SOP, and effective secrecy throughput (EST) were obtained in a unified form.
More recently, the κ-μ shadowed model was first introduced by Paris in , and it is a generalized fading channel, which can be simplified as Rayleigh, Rician shadowed, one-side Gaussian, Nakagami-m, and κ-μ under suitable conditions. Moreover, the κ-μ shadowed fading can be applied to different systems including IoT links , satellite channels , and underwater links . According to the proposed mobile communication model with κ-μ shadowed fading, the authors of  studied the transmission performance by analyzing OP and ergodic channel capacity (ECC). The statistical characteristics of different shadowed κ-μ fading were derived in , where the closed-form PDF and cumulative distribution function (CDF) for envelope and signal-to-noise ratio (SNR) were obtained, respectively. The work in  investigated the outage performance of hexagonal network affected by κ-μ shadowed fading in term of analyzing the performance benchmark, namely, outage probability and rate. Sun et al. in  presented the derivation of SOP and SPSC for the systems over κ-μ shadowed distribution, in which legitimate users and eavesdroppers were both single antenna receivers. As continuations of , the security analyses of SIMO networks and decode-and-forward (DF) relaying networks were carried out in [35, 36].
So far, we have not discovered relative research based on CRN over κ-μ shadowed distribution in the open database, let alone the existence of multiple antennas and multieavesdroppers. Therefore, combined with the advantages of multiantenna technology [37, 38], this paper is explored. We first present a CRN for IoT, in which the subchannels undergo κ-μ shadowed fading. Then, we derive the mathematical expressions of SOP and SPSC under the condition of multiple eavesdroppers. Finally, Monte Carlo simulations are provided to compare with the mathematical analysis; moreover, some valuable conclusions are obtained. The work can provide theoretical basis for the security performance evaluation of WCNs (5G, Internet of vehicles, etc.).
The paper is arranged as follows; this section mainly introduces the background, related literatures, and motivation. In Section 2, we explain the CRN model for IoT over κ-μ shadowed fading channels, and the PDF and CDF of the channels with multiple eavesdropping terminals are also illustrated. SOP analysis is presented in Section 3. The theoretical derivation of SPSC is made in Section 4. Section 5 provides the numerical results and some interesting results. The conclusions of the paper appear in Section 6.
2. IOT System Model and Properties of Generalized Channels
2.1. IOT System Model
The work considers a four entities CRN model for IoT, as shown in Figure 1, where S is the data source of the secondary networks, while P is the sender of the primary networks, and D is the preset receiver, which uses multiple antennas to receive signals. There are many eavesdroppers equipped with multiple antennas . The secondary network is composed of S, D, and E. Underlay CRN makes spectrum sharing possible, but the cost is that the power of S will not affect the communication of the main networks. is interpreted as the channel coefficient of the link and the channel coefficients of main link and wiretap link are and , respectively. It should be noted that and are vectors because of multiantenna reception at D and E. In our model, we consider passive eavesdropping scenarios, where S does not know the channel state information (CSI) of the wiretap link, but grasps the CSI of the main link.
S will communicate with P, D, and E in the considered IoT networks at the same time. Suppose the secret signal from S is ; then, we havewhere is the output power of S, is the signal received by P, is the complex white Gaussian noise on the link from S to P, the statistical expectation of is zero, and the standard deviation is . and represent the received signal matrices of D and E, while and denote noise matrices, and both standard deviations are .
2.2. Properties of Generalized Channels
The generalized channels considered in our IoT model is κ-μ shadowed fading channel. This section describes its statistical properties, mainly including the PDFs and CDFs for SNRs in link , link , and link . Relying on , the PDF for κ-μ shadowed variable can be expressed aswhere the parameters used to determine the κ-μ shadowed variable are , , and . and are described as the instantaneous SNR and average SNR on κ-μ shadowed fading channels, denotes the Gamma function (equation (8.310.1) in ), and is defined as confluent hypergeometric function (equation (9.14.1) in ), respectively.
Because the expected receiver (D) has antennas and the maximum ratio combining (MRC) merging is adopted, the pdf for random variable (RV) at can be formulated as where means Pochammer calculation . From (4), we can deduce the CDF of RV at asfor eavesdropping channel with multiple eavesdroppers cooperation. Employing , we can obtain the PDF of the eavesdropping link as
3. SOP Analysis
For the IoT systems, we need clear benchmarks to measure its security performance . For the passive eavesdropping scenario considered in our model, SOPs are very suitable criteria, which can be understood as the probability that the difference value between the instantaneous capacity of the main link and the capacity of the eavesdropping link is less than a fixed threshold , and SOP can be written aswhere is the difference value between the instantaneous capacity of and .
Two constraints that must be considered in underlay CRN are (i) the power of S cannot exceed the maximum power value and (ii) the power of S should not exceed the threshold of anti-interference ability to the primary networks. Referring to (9) and , SOP in underlay CRN can be expressed aswhere X is the instantaneous SNR of the link of . Next, we will derive and in (9).
3.1. Calculation of
can be further written aswhere and , and we obtain
Then, making using of equation (3.381.4) in , after a more complex integral operation, we havewhere
From (4), is derived as
3.2. Calculation of
It can be seen from (9), is stated as
Referring to , we can rewrite aswhere
Replacing in (19) with (21), then, after the integral is completed by using equation (3.351.2) in , we obtain the derivation of aswhere is called the incomplete Gamma function (equation (8.350.2) in ).
4. SPSC Analysis
For passive eavesdropping scenarios, SPSC is often used to investigate the confidentiality of WCNs; therefore, we explore the SPSC of IoT networks in this section. According to the definition of SPSC in , we know that it can understand the probability that the difference value of instantaneous capacity between main channel and eavesdropping channel is not less than zero, which can be formulated as
Finally, by employing (24), we can complete the proof of SPSC.
5. Numerical Results
In this part, we perform the theoretical simulations of (9) and (24). As a verification of correctness, Monte Carlo simulations are also obtained for comparison with the analysis results. From all simulation diagrams (Figures 2–9), we can capture the following information. (i) All the analysis curves coincide well with the statistical simulation curves. (ii) All simulation results change with abscissa, namely, , and the change trend indicates that a large can improve the security of IoT systems. Moreover, matlab simulations show that the convergence condition of the analytical expression including infinite series is that the upper limit of the cycle is 55 times.
Figures 2 and 3 indicate how the SOP and SPSC change with when M takes different values. From Figure 2, we can see that when is fixed, the value of SOP will rise along with the rise of M. In Figure 3, the increase of M will lead to the decrease of SPSC. Moreover, the distances between curves represented by different M are larger. Therefore, we conclude that the increase of the number of eavesdroppers will significantly reduce the performance of IoT model.
The influence of the number of antennas on the safety performance is provided in Figures 4 and 5. We assume . According to simulation curves, a valuable discovery is that when the value of abscissa is greater than 9 dB; the increase of antenna number, i.e., N, will decrease the SOP and increase the SPSC. This means that, under the mechanism of high SNR, the security can be improved by increasing the number of receiving antennas. On the contrary, in the case of small SNR, the increase of the number of antennas will reduce the ability of security.
When the shape parameters and of the generalized channel change, the security analyses are shown in Figures 6 and 7. We suppose that and , where the subscript S, D, and E indicate that the links are , , and , respectively. Referring to Figures 6(a) and 7(a), we can see that the gradual increase of causes the increase of SOP and the decrease of SPSC with dB. However, the effect of increasing on the IoT systems is opposite to that of based on Figures 6(b) and 7(b).
means the threshold of anti-interference ability to the primary networks. Figure 8 describes its effect on SOP for IoT networks under consideration.Rin represents the threshold value of security capacity interruption, the impact of its change on system performance is illustrated in Figure 9. When IP changes from 5 dB to −7 dB, then to 15 dB and from 0.1 dB to 0.5 dB and then to 1 dB, the change trend of SOP is gradually decreasing. Therefore, we can find that smaller and smaller will increase the probability of system security interruption. Moreover, it is interesting that the impact of on security is more significant than that of .
is also an important parameter of κ-μ shadowed distribution, which represents the degree of shadow fading. The simulation results show that when the high SNR is established, the increase of will improve the SPSC and reduce the SOP, but the difference between the curves is very small. In other words, the impact of on the security performance is not obvious.
Good security performance requires as small as possible SOP and as large as possible SPSC. Through the above analysis of all simulations, we can obtain that larger M, larger , and smaller N will worsen the security of the system when the SNR is large. Meanwhile, we also know that larger , smaller , and smaller will enhance the security performance.
In this paper, we explore PHY layer security of Underlay CRNs for IoT networks over generalized fading channels on the basis of the derived SOP and SPSC. The CRN model with multiple antennas can be used in many different situations. The analytical formulas for SOP and SPSC are derived in a concise form. The accuracy of the analytical formulas is verified by Monte Carlo simulations. Moreover, we discuss the influence of the parameters including M, N, , , , and on the security of IoT networks.
The 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 are no conflicts of interest regarding the publication of this paper.
This work was supported in part by the National Natural Science Foundation of China under Grant 41174158 and Ministry of Land and Resources P.R.C. Special Project in the Public Interest under Grant 201311195-04.
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