Indoor cognitive radio operation within the broadcast TV protection contour

Although the broadcast television (TV) spectrum is currently open for unlicensed operation in the USA, a considerably large geographic area still remains excluded from the unlicensed operation due to potential interference to the licensed users. However, it might be possible to reuse primary spectrum within the protection contour if the frequency reuse occurs inside a building that shields radio signals and reduces interference to the primary system. Interference to outdoor licensed users from the indoor operations can be minimized if the unlicensed users adjust their transmit power according to their locations in the building. This paper presents an analysis and effectiveness evaluation of a novel cognitive radio (CR) system which enables CRs to access the licensed spectrum inside a building in the area within the protection contour. The system utilizes an indoor sensor network for (i) interference sensing, (ii) CR transmit power control, to limit the interference to the outdoor primary receiving antennas. Power control model of the indoor system has been developed to estimate safe transmit power for the indoor users. Two cases have been considered; single-user single-sensor (single indoor user and single sensor), and multi-user multi-sensor. Based on the power control model, a power control algorithm has been developed and its effectiveness is assessed through simulations. The algorithm is effective in realistic propagation scenarios, e.g. when internal partition walls and multipath fading are present. The outage probabilities in these propagation scenarios are found and the procedure of determining the transmit powers for CRs is presented.     

Spectrum efficient power allocation schemes for OFDM cognitive radio with statistical interference constraints

In this paper, we study the power allocation problem for an orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) system. In a departure from the conventional power allocation schemes available in the literature for OFDM-based CR, we propose power allocation schemes that are augmented with spectral shaping. Active interference cancellation (AIC) is an effective spectral shaping technique for OFDM-based systems. Therefore, in particular, we propose AIC-based optimal and suboptimal power allocation schemes that aim to maximize the downlink transmission capacity of an OFDM-based CR system operating opportunistically within the licensed primary users (PUs) radio spectrum in an overlay approach. Since the CR transmitter may not have the perfect knowledge about the instantaneous channel quality between itself and the active PUs, the interference constraints imposed by each of the PUs are met in a statistical sense. We also study an optimal power allocation scheme that is augmented with raised cosine (RC) windowing-based spectral shaping. For a given power budget at the CR transmitter and the prescribed statistical interference constraints by the PUs, we demonstrate that although the on-the-run computational complexity of the proposed AIC-based optimal power allocation scheme is relatively higher, it may yield better transmission rate for the CR user compared to the RC windowing-based power allocation scheme. Further, the AIC-based suboptimal scheme has the least on-the-run computational complexity, and still may deliver performance that is comparable to that of the RC windowing-based power allocation scheme. The presented simulation results also show that both the AIC-based as well as the RC windowing-based power allocation schemes lead to significantly higher transmission rates for the CR user compared to the conventional (without any spectral shaping) optimal power allocation scheme.     

5G-based wideband cognitive radio system design with cooperative spectrum sensing

Since the 5G bandwidth is very large, there are a large number of non-continuous idle spectrum in 5G communication. In this paper, we have designed transmitter and receiver of a 5G-based wideband cognitive radio (CR) system with cooperative spectrum sensing, in order to improve transmission performance and avoid interference signals. Each CR user marks the spectrum availability for getting the sub-basis function through doing Inverse Fast Fourier Transform (IFFT) with the product of spectrum marker vector and random phase vector. The cooperative spectrum sensing can be realized by cascading the sub-basis functions of all the users. Multiple access of the CR system is also proposed to access much non-continuous idle spectrum. The simulation results have shown that the proposed CR system can avoid the interference effectively and outperform the spread spectrum system obviously.     

A single-channel single-winner auction model for homogeneous channel allocation in CRNs

Cognitive radio (CR) is a novel intelligent technology which enables opportunistic access to temporarily unused licensed frequency bands. A key functionality of CR is to distribute free channels efficiently amongst Secondary Users (SUs) boosting spectrum usage to assist the escalating wireless applications world wide. In this context, this paper introduces a channel allocation mechanism which enables SUs (CR enabled unlicensed users) to dynamically access unused spectrum bands to fulfill their spectrum needs. We model the channel allocation problem as a sealed-bid single-sided auction which primarily aims at maximizing the overall spectrum utilization. Market based spectrum auctions in CR networks motivate licensed users to participate and lease their under utilized radio resources to gain monetary benefits. Sequential bidding is applied to this model for auctioning homogeneous channels, which reduces communication overhead. Bid submission takes into account two major CR constraints, namely, dynamics in spectrum opportunities and differences in channel availability time, which on incorporation provide disruption free data transmission to the SUs. We reduce resource wastage in this model by performing multiple auction rounds. Application of second price auction determines winning bidders and their respective payments to auctioneer. The design of our auction mechanism is supported with the proofs of truthfulness and individually rational properties. Furthermore, experimental results indicate that our model outperforms an existing auction method. Spectrum utilization values show 22 to 75% improvement in our model with changing number of SUs, and 23 to 93% improvement in our model with changing number of channels.     

On spectrum sensing in cognitive radio CDMA networks with beamforming

In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA) networks is analyzed in the presence of receive beamforming at the base stations (BSs). More precisely, we analyze, through simulations, the performance achievable by a CR user, with and without spectrum sensing, in a three-cell scenario. Uplink communications are considered. Three different schemes for spectrum sensing with beamforming are presented, together with a scheme without spectrum sensing. CR users belong to a cognitive radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users. Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The impact of beamforming on the system performance is analyzed, considering various metrics. In particular, we evaluate the performance of the proposed systems in terms of outage probability, blocking probability, and average data rate of CR users. The results obtained clearly indicate that significant performance improvements can be obtained by CR users with the help of beamforming. The impact of several system parameters on the performance of the three considered spectrum sensing schemes with beamforming is analyzed. Our results, in terms of probability of outage, show that the relative improvement brought by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%) than in the presence of spectrum sensing (reduction of 42%).     

Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.     

An energy efficient scheme by exploiting multi-hop D2D communications for 5G networks

The multi-hop Device-to-Device (M-D2D) communication has a potential to serve as a promising technology for upcoming 5G networks. The prominent reason is that the M-D2D communication has the potential to improve coverage, enhanced spectrum efficiency, better link quality, and energy-efficient communication. One of the major challenges for M-D2D communication is the mitigation of interference between the cellular user (CUs) and M-D2D users. Considering this mutual interference constraint, this work investigates the problem of optimal matching of M-D2D links and CUs to form spectrum-sharing partners to maximize overall sum rates of the cell under QoS and energy efficiency (EE) constraints. In this paper, we investigate the interference management for multi-hop (more than one-hop) D2D communication scenarios where we propose a channel assignment scheme along with a power allocation scheme. The proposed channel assignment scheme is based on the Hungarian method in which the channel assignment for M-D2D pairs is done by minimum interference value. The power allocation scheme is based on Binary Particle swarm optimization (BPSO). This scheme calculates the specific power values for all the individual M-D2D links. We have done a comprehensive simulation and the result portrays that our proposed scheme performs better compared to the previous work mentioned in the literature. The results clearly indicate that the proposed scheme enhances the EE of up to 13% by producing the optimal assignment of channels and power for the CUs and M-D2D users.     

A spectrum handoff scheme for optimal network selection in Cognitive Radio vehicular networks: A game theoretic auction theory approach

The recent strides in vehicular networks have emerged as a convergence of multi radio access networks having different user preferences, multiple application requirements and multiple device types. In future Cognitive Radio (CR) vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. Hence, it becomes a challenge for CR vehicular node to select the optimal network for the spectrum handoff decision. A game theoretic auction theory approach is interdisciplinary effective approach to select the optimal network for spectrum handoff. The competition between different CR vehicular node and access networks can be formulated as multi-bidder bidding to provide its services to CR vehicular node. The game theory is the branch of applied mathematics which make intelligent decision to select the optimal alternative from predetermined alternatives. Hence, this paper investigates a spectrum handoff scheme for optimal network selection using game theoretic auction theory approach in CR vehicular networks. The paper has also proposed a new cost function based multiple attribute decision making method which outperforms other existing methods. Numerical results revel that the proposed scheme is effective for spectrum handoff for optimal network selection among multiple available networks.     

AMI通信网络中微功率无线通信的性能测试技术及系统研究

微功率无线通信是高级量测体系(AMI)的主要通信方式之一。各厂家的通信模块性能不一,测试技术尚未形成统一的规范。设计了微功率无线通信的测试系统,由射频性能测试系统和协议一致性分析系统组成。系统采用屏蔽测试箱、多功能电磁波小室、综合测试分析仪、误码分析仪、标准协议信号源等模块组成,屏蔽箱对800MHz频率以下的无线电信号有超过70dB 的抑制,可以提供相对纯净的无线电暗室环境,减少外界无线电波的干扰。测试频率范围为30MHz~1GHz,提供66个测试频点,测试频率误差小于2ppm,误码测试精度小于0.001%,功率测试精度（闭环）为5%（0.2dB）。实现了在实验室情况下对微功率无线网络性能的全面测试和评估,具备很好可操作性、便利性和可复现性。     

Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.     

Cognitive radio network with secrecy and interference constraints

In this paper, we investigate the physical-layer security of a secure communication in single-input multiple-output (SIMO) cognitive radio networks (CRNs) in the presence of two eavesdroppers. In particular, both primary user (PU) and secondary user (SU) share the same spectrum, but they face with different eavesdroppers who are equipped with multiple antennas. In order to protect the PU communication from the interference of the SU and the risks of eavesdropping, the SU must have a reasonable adaptive transmission power which is set on the basis of channel state information, interference and security constraints of the PU. Accordingly, an upper bound and lower bound for the SU transmission power are derived. Furthermore, a power allocation policy, which is calculated on the convex combination of the upper and lower bound of the SU transmission power, is proposed. On this basis, we investigate the impact of the PU transmission power and channel mean gains on the security and system performance of the SU. Closed-form expressions for the outage probability, probability of non-zero secrecy capacity, and secrecy outage probability are obtained. Interestingly, our results show that the strong channel mean gain of the PU transmitter to the PU’s eavesdropper in the primary network can enhance the SU performance.     

Impact of the mobile terminal scheme on millimeter-wave radio over fiber systems based on photonic heterodyning techniques

The use of communication networks relying on millimeter-wave (mm-wave) wireless links promises a great capacity enhancement as well as improved security. However, given the high-directivity of mm-wave links, coverage requirements are difficult to meet unless the network is assisted by an infrastructure. Given its low-cost, power-efficiency, and high capacity, radio over fiber has emerged as a strong candidate for the implementation of such infrastructure. Among the different generation techniques, photonic heterodyning has attracted considerable attention due to its capacity to generate radio frequency (RF) signals in the entire microwave/mm-wave range without requiring broadband electro-optical modulator. However, the RF signals generated using these techniques suffer from significant phase noise, a major impairment that degrades the system performance. In this paper we study two approaches to overcome this limitation: (1) the use of optical sideband injection locking (OSBIL) to generate tones with highly correlated phase noise and (2) heterodyning independent lasers in combination with a mobile terminal (MT) that is insensitive to the phase of the RF signal. A qualitative comparison between the two techniques in terms of MT sensitivity to the RF phase noise, the power sensitivity, and base station power efficiency leads to the conclusion that OSBIL is more suitable for networks with medium-range node separation, whereas heterodyning of independent laser with phase-insensitive MT is a cost-efficient solution for networks where nodes are closer to each other.     

Optimal satisfaction degree in energy harvesting cognitive radio networks

A cognitive radio(CR) network with energy harvesting(EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model(HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree(WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user(SU) and the interference to the primary user(PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming(MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution(DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service(Qos). Numerical results are given to verify our analysis.     

Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

Spectrum sensing is an important function in radio frequency spectrum management and cognitive radio networks. Spectrum sensing is used by one wireless system (e.g., a secondary user) to detect the presence of a wireless service with higher priority (e.g., a primary user) with which it has to coexist in the radio frequency spectrum. If the wireless signal is detected, the second user system releases the given frequency to maintain the principle of not interfering. This paper proposes a machine learning implementation of spectrum sensing using the entropy measure as a feature vector. In the training phase, the information about the activity of the wireless service with higher priority is gathered, and the model is formed. In the classification phase, the wireless system compares the current sensing report to the created model to calculate the posterior probability and classify the sensing report into either the presence or absence of wireless service with higher priority. This paper proposes the novel application of the Fluctuation Dispersion Entropy (FDE) measure recently introduced in the research community as a feature vector to build the model and implement the classification. An improved implementation of the FDE (IFDE) is used to enhance the robustness to noise. IFDE is further enhanced with an adaptive method (AIFDE) to automatically select the hyper-parameter introduced in IFDE. Then, this paper combines the machine learning approach with the entropy measure approach, which are both recent developments in spectrum sensing research. The approach is compared to similar approaches in literature and the classical energy detection method using a generated radar signal data set with different conditions of SNR(dB) and fading conditions. The results show that the proposed approach is able to outperform the approaches from literature based on other entropy measures or the Energy Detector (ED) in a consistent way across different levels of SNR and fading conditions.     

无线多径信道中基于时间反演的物理层安全传输机制

宽带无线通信用户大多处在复杂的环境中,其时变多径传播和开放特性将严重影响通信系统的性能.针对物理层安全研究中的窃听信道问题,提出了一种适用于宽带无线多径信道的联合时间反演技术和发端人工噪声的物理层安全传输机制.首先,在一个典型窃听信道模型中采用时间反演技术,利用其时空聚焦性来提高信息在传输过程中的安全性;其次,由于时间反演的时空聚焦性,信息在聚焦点附近容易被窃听,通过在发送端加入人工噪声来扰乱窃听用户对保密信息的窃听,由于合法用户采用零空间人工噪声法,人工噪声对合法用户没有影响.理论分析和仿真结果表明,与已有物理层安全机制相比,所提机制可以有效地提高系统的保密信干噪比和可达保密速率,降低合法用户的误比特率,系统的保密性能得到提升.     

Physical layer security using beamforming techniques for 5G and beyond networks: A systematic review

It has been predicted that by the year 2030, 5G and beyond 5G (B5G) networks are expected to provide hundreds of trillions of gigabytes of data for various emerging applications such as augmented, mixed, and virtual reality (AR/MR/VR), wireless computer-brain interfaces (WCBI), connected robotics and autonomous systems. Most of these applications share data with each other using an open channel, i.e., the Internet. The open and broadcast nature of wireless channel makes the communication susceptible to various types of attacks (e.g., eavesdropping, jamming). Thus, there is a strong requirement to enhance the secrecy of wireless channel to maintain the privacy and confidentiality of transmitted data. Physical layer security (PLS) has evolved as a novel concept and robust alternative to cryptography-based techniques, which have a number of drawbacks and practical issues for 5G and beyond networks. Beamforming is an energy-efficient PLS technique, that involves steering of the transmitted signal in a particular direction, while considering that an intruding user attempts to decode the transmitted data. Motivated from these points, this article summarizes various beamforming based PLS techniques for secure data transmission in 5G and B5G networks. We investigate the eight most promising techniques for beamforming in PLS: Non-Orthogonal Multiple Access (NOMA), Full-Duplex Networks, Massive Multiple-Input Multiple-Output (MIMO), Cognitive Radio (CR) Network, Relay Network, Simultaneous Wireless Information and Power Transfer (SWIPT), UAV Communication Networks and Space Information Networks, and Heterogeneous Networks. Moreover, various physical layer threats and countermeasures associated with 5G and B5G networks are subsequently covered. Lastly, we provide insights to the readers about constraints and challenges for the usage of beamforming-based PLS techniques in various upcoming future applications.     

Network capacity of cognitive radio relay network

After successful dynamic spectrum access, cognitive radio (CR) must be able to relay the message/packets to the destination node by utilizing existing primary system(s) (PS) and/or cooperative/cognitive radio nodes in the cognitive radio network. In this paper, we pioneer the exploration of the fundamental behaviors of interference between CRs and PS in such a relay network via network coding. Interference on PS’s network capacity is shown to be unavoidable and unbounded in the one-hop relay network. Extending to the tandem structure, interference is unbounded but avoidable by appropriate constraints. In cooperative relay network, interference is bounded and avoidable. Moreover, parallel cooperative relay network can accommodate more CR transmission pairs. Such an analysis can be generalized to arbitrary networks. We derive that interference is avoidable when at least one route from CR’s source to the sink bypasses the bottlenecks of PS. Then under the constraint of no interference to PS, we derive CR’s maximum network capacity in such a network. Link allocation to achieve the maximum network capacity can be formulated and solved as a linear programming problem. Consequently, given any network topology, we can determine whether CR’s interference is avoidable, and maximize CR’s network capacity without interfering PS’s network capacity. Simulation results on randomly generated network topologies show that CR’s network capacity achieves on average 1.3 times of PS’s network capacity with interference avoidance constraint, and demonstrates spectrum efficiency at networking throughput and high availability.     

Chance-constrained resource allocation for cognitive wireless-powered backscatter communication networks

With the rapid development of the Internet of Things (IoT) and the increasing number of wireless nodes, the problems of scare spectrum and energy supply of nodes have become main issues. To achieve green IoT techniques and resolve the challenge of wireless power supply, wireless-powered backscatter communication as a promising transmission paradigm has been concerned by many scholars. In wireless-powered backscatter communication networks, the passive backscatter nodes can harvest the ambient radio frequency signals for the devices’ wireless charging and also reflect some information signals to the information receiver in a low-power-consumption way. To balance the relationship between the amount of energy harvesting and the amount of information rate, resource allocation is a key technique in wireless-powered backscatter communication networks. However, most of the current resource allocation algorithms assume available perfect channel state information and limited spectrum resource, it is impractical for actual backscatter systems due to the impact of channel delays, the nonlinearity of hardware circuits and quantization errors that may increase the possibility of outage probability. To this end, we investigate a robust resource allocation problem to improve system robustness and spectrum efficiency in a cognitive wireless-powered backscatter communication network, where secondary transmitters can work at the backscattering transmission mode and the harvest-then-transmit mode by a time division multiple access manner. The total throughput of the secondary users is maximized by jointly optimizing the transmission time, the transmit power, and the reflection coefficients of secondary transmitters under the constraints on the throughput outage probability of the users. To tackle the non-convex problem, we design a robust resource allocation algorithm to obtain the optimal solution by using the proper variable substitution method and Lagrange dual theory. Simulation results verify the effectiveness of the proposed algorithm in terms of lower outage probabilities.     

Towards 5G: Performance evaluation of 60 GHz UWB OFDM communications under both channel and RF impairments

Detailed analysis on the impact of RF and channel impairments on the performance of Ultra-Wideband (UWB) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems based on the IEEE 802.15.3c standard, for high data-rate applications using the 60 GHz millimetre frequency band is presented in this paper. This frequency band, due to the large available bandwidth is very attractive for future and 5G wireless communication systems. The usage of OFDM at millimetre-wave (mmWaves) frequencies is severely affected by non-linearities of the Radio Frequency (RF) front-ends. The impact of impairments is evaluated, in terms of some of the most important key performance indicators, including spectral efficiency, power efficiency, required coding overhead and system complexity, Out-Of-Band Emissions (OOBEs), Bit Error Rate (BER) target and Peak Signal-to-Noise Ratio (PSNR). Additionally, joint distortion effects of coexisting Phase-Noise (PN), mixer IQ imbalances and Power Amplifier (PA) non-linearities, on the performance degradation of a mmWave radio transceiver, combined with various multipath fading channels, are investigated. Subsequently, the power efficiency of the system is evaluated by estimating values of the PA Output-Power-Backoff (OBO) needed to meet the requirements for the Transmit Spectrum Mask (TSM) and BER target. Finally, a comparison of the system overall performance between uncoded and coded OFDM systems combined with Quadrature Amplitude Modulations (16 and 64 QAM) and its maximum operable range are evaluated by transmitting a Full HD uncompressed video frame under five different RF impairment conditions over a typical LOS kiosk 60 GHz IEEE channel model.     

Physical layer security in mixed Rf/FSO system under multiple eavesdroppers collusion and non-collusion

Secure transmission in wireless networks is a big critical issue due to the broadcast nature of the wireless propagation environment. In this paper, the physical layer security performance in a mixed radio frequency (RF)/free space optical (FSO) system under multiple eavesdroppers is investigated. The RF links and FSO link within the system are assumed to respectively undergo Nakagami-m and Gamma–Gamma fading distributions. The two practical eavesdroppers scenarios considered includes: Colluding and Non-colluding in which their channel state information is unavailable at the source. The closed-form expressions for the lower bound security outage probability and the strictly positive secrecy capacity under both scenarios are derived by utilizing the system end-to-end cumulative distribution function and eavesdroppers’ probability density function. The results show that the increase in the number of eavesdroppers under both scenarios profoundly degrades the system secrecy performance. Moreover, it is demonstrated that both the atmospheric turbulence and pointing errors affect the concerned system secrecy and the impact of RF fading parameters is also presented. The accuracy of the numerical results obtained is validated by Montel-Carol simulations.