Secrecy Sum Rate Research Articles

A Robust Scheme for RIS-Assisted UAV Secure Communication in IoT

Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAV) have been extensively studied on the Internet of Things (IoT) systems to improve communication performance. In this paper, we aimed to counter simultaneous jamming and eavesdropping attacks by jointly designing an active beamforming vector at the base station (BS) and reflect phase shifts at the RIS. Specifically, considering imperfect angular channel state information (CSI), the sum secrecy rate maximization problem in the worst case could be formulated, which is NP-hard and non-convex. To address this problem, we improved the robust enhanced signal-to-leakage-and-noise ratio (E-SLNR) beamforming to reduce the computational complexity and mitigate the impact of interference, eavesdropping and jamming. Furthermore, a genetic algorithm with a tabu search (GA-TS) method was proposed to efficiently obtain an approximate optimal solution. The simulation results demonstrated that the proposed GA-TS method converged faster with better results than conventional GA, while the proposed robust scheme could achieve higher sum secrecy rates than the zero-forcing (ZF) and SLNR schemes.

Enhanced Secure Communication via Novel Double-Faced Active RIS

Although the reconfigurable intelligent surface (RIS) technology is envisioned promising to enhance communication from all aspects, including physical-layer security, increasing concerns have lately been cast onto its defects—the severe “double-fading” loss and its confined-to-half-space coverage. Diverse novel RIS architectures have recently emerged to partially overcome these shortcomings, yet perfect solution is still absent. This paper proposes a novel double-faced active (DFA)-RIS structure to surmount the above two prominent defects simultaneously. Furthermore, we utilize the DFA-RIS to promote secrecy performance via jointly designing access point (AP)’s beamforming and DFA-RIS configuration towards maximizing sum secrecy rate (SR). The optimization problem is highly challenging due to the constraints deriving from the DFA-RIS architecture, especially the presence of power splitting parameters. By leveraging majorization–minimization (MM) and penalty dual decomposition (PDD) methods, we develop an efficient solution that updates all variables via convex optimization techniques. Our proposed solution is significant and general as it is applicable to all other cutting-the-edge RIS architectures to maximize sum SR, which has not yet been thoroughly worked out. Numerical results verify the convergence and effectiveness of our proposed algorithm and demonstrate that our proposed DFA-RIS architecture outperforms all other state-of-the-art RIS techniques to enhance communication security.

Robust Beamforming Design for RIS-Aided NOMA Secure Networks With Transceiver Hardware Impairments

Integrating reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) provides a promising solution to support large-scale secure communications. However, reducing the impact of transceiver hardware impairments (HWI) on the performance of RIS-aided NOMA secure networks remains a challenge. In this paper, we propose a robust transmission scheme for RIS-aided NOMA secure networks with transceiver HWI under two proposed eavesdropping scenarios and two artificial noise methods. A closed-form expression for the distortion noise power caused by transceiver HWI in NOMA networks is derived to quantify transceiver HWI. The sum secrecy rate of NOMA users with imperfect successive interference cancellation (SIC) is maximized under distortion noise for more robust security. Further, to tackle the resulting non-convex problem, we decompose it into two sub-problems. Both of them are converted into convex problems using the multi-dimensional quadratic transform (MDQT) method, semi-definite relaxation (SDR) technique, and the successive convex approximation (SCA) algorithm, and then employing the alternate optimization (AO) algorithm solve them. Numerical results demonstrate that the proposed scheme has superior security performance over orthogonal multiple access (OMA) networks, time division multiple access (TDMA) networks, and traditional NOMA networks, achieving more robust performance than the conventional scheme that ignores HWI and imperfect SIC.

Secure Transmission Design for Aerial IRS Assisted Wireless Networks

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.

Hybrid UAV-Enabled Secure Offloading via Deep Reinforcement Learning

In this letter, we consider a secure offloading system consisting of a unmanned aerial vehicle (UAV)-mounted edge server, ground user equipments (UEs) and a malicious eavesdropper UAV. With the aim of maximizing secrecy sum-rate, we propose an adaptation of a helper UAV to switch the mode between jamming and relaying. We jointly optimize the helper UAV’s trajectory and mode and UEs’ offloading decision under energy budget constraints and operational limitations of nodes. The proposed algorithm is developed based on a deep deterministic policy gradient (DDPG)-based method, whose superior performances are verified via numerical results, as compared to other benchmark schemes.

Sensing and Secure NOMA-Assisted mMTC Wireless Networks

Throughout this study, a novel network model for massive machine-type communications (mMTC) is proposed using a compressive sensing (CS) algorithm and a non-orthogonal multiple access (NOMA) scheme. Further, physical-layer security (PLS) is applied in this network to provide secure communication. We first assume that all the legitimate nodes operate in full-duplex mode; then, an artificial noise (AN) signal is emitted while receiving the signal from the head node to confuse eavesdroppers (Eve). A convex optimization tool is used to detect the active number of nodes in the proposed network using a sparsity-aware maximum a posteriori (S-MAP) detection algorithm. The sensing-aided secrecy sum rate of the proposed network is analyzed and compared with the sum rate of the network without sensing, and the closed-form expression of the secrecy outage probability of the proposed mMTC network is derived. Finally, our numerical results demonstrate the impact of an active sensing algorithm in the proposed mMTC network; improvement in the secrecy outage of the proposed network is achieved through increasing the distance of the Eve node.

Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

In this paper, the application of Non-Orthogonal Multiple Access (NOMA) is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential eavesdropper. To support secure transmissions from legitimate users, two NOMA Secrecy Sum Rate Transmit BeamForming (NOMA-SSR-TBF) schemes are proposed to maximise the SSR of a Base Station (BS) with sufficient and insufficient transmit power. For BS with sufficient transmit power, an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate transmissions. In addition, for BS with insufficient transmit power, a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate transmissions. More specifically, iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial jamming. Experimental results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works, such as the maximized artificial jamming power scheme, the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

IRS-Assisted Physical Layer Security in MIMO-NOMA Networks

In this letter, we propose deploying the intelligent reflecting surface (IRS) to enhance the physical layer security in non-orthogonal multiple access (NOMA). The secrecy sum-rate of IRS-assisted multiple-input multiple-output (MIMO) NOMA is maximized in the presence of an eavesdropper. Because of the discrete unit-modulus constraint and the nonconvex property, the secrecy sum-rate maximization problem is hard to solve. We reformulate the original problem into an equivalent parameterized optimization using rotation matrices and apply the particle swarm algorithm for global optimization. Simulation results verify that the performance of the proposed algorithm is close to the secrecy capacity of the channel, realized by exhaustive search, and outperforms other methods like alternating optimization and zero-forcing in terms of complexity and achievable rates.

Joint Beamforming Design for Secure RIS-Assisted IoT Networks

This article studies secure communication in an Internet of Things (IoT) network, where the confidential signal is sent by an active refracting reconfigurable intelligent surface (RIS)-based transmitter, and a passive reflective RIS is utilized to improve the secrecy performance of users in the presence of multiple eavesdroppers. Specifically, we aim to maximize the weighted sum secrecy rate by jointly designing the power allocation, transmit beamforming (BF) of the refracting RIS, and the phase shifts of the reflective RIS. To solve the nonconvex optimization problem, we propose a linearization method to approximate the objective function into a linear form. Then, an alternating optimization (AO) scheme is proposed to jointly optimize the power allocation factors, BF vector, and phase shifts, where the first one is found using the Lagrange dual method, while the latter two are obtained by utilizing the penalty dual decomposition method. Moreover, considering the demands of green and secure communications, by applying Dinkelbach’s method, we extend our proposed scheme to solving a secrecy energy maximization problem. Finally, simulation results demonstrate the effectiveness of the proposed design.

Secrecy Sum-Rate Enhancement for NOMA-VLC System With Pseudo User

Non-orthogonal multiple access (NOMA) enabled visible light communication (VLC) system is vulnerable to malicious eavesdropping, mainly because of the inherent broadcast nature of VLC technology. To this end, this letter proposes a novel concept called pseudo user (PU), which obstructs the successive interference cancellation (SIC) procedures of the eavesdropper. The minimum-feasible (MF) power allocation policy toward the PU is obtained under different power allocation strategies. Then, the secrecy sum-rate and user fairness along with the sum-rate are evaluated under both perfect SIC (pSIC) and imperfect SIC (ipSIC) scenarios. Results show that compared with the benchmark scheme without PU, the secrecy sum-rate and user fairness are both greatly enhanced with slight sum-rate loss. Furthermore, this letter proves that with much lower computations, the proposed scheme yields close-to-optimum secrecy sum-rate and user fairness performance provided by the exhaustive search algorithm in the pSIC scenario.

On Secure CDRT With NOMA and Physical-Layer Network Coding

This paper proposes a new scheme to enhance the secrecy performance of non-orthogonal multiple access (NOMA)-based coordinated direct relay transmission (CDRT) systems with an untrusted relay. The physical-layer network coding (PNC) and the NOMA schemes are combined to improve spectrum efficiency. Furthermore, inter-user interference and friendly jamming signals are utilized to suppress the eavesdropping ability of the untrusted relay without compromising the acceptance quality of legitimate users. Specifically, the far user in the first slot and the near user in the second slot act as jammers that generate jamming signals to ensure secure transmissions of confidential messages. We investigate the secrecy performance of the NOMA-based CDRT systems with the PNC scheme and derive the closed-form expression for the ergodic secrecy sum rate. The asymptotic analysis at a high signal-to-noise ratio is performed to obtain more insights. Finally, simulation results are presented to demonstrate the proposed scheme’s effectiveness and the theoretical analysis’s correctness.

Secrecy rate maximization for 6G cognitive satellite-UAV networks

To cover remote areas where terrestrial cellular networks may not be available, non-terrestrial infrastructures such as satellites and unmanned aerial vehicles (UAVs) can be utilized in the upcoming sixth-generation (6G) era. Considering the spectrum scarcity problem, satellites and UAVs need to share the spectrum to save costs, leading to a cognitive satellite-UAV network. Due to the openness of both satellite links and UAV links, communication security has become a major concern in cognitive satellite-UAV networks. In this paper, we safeguard a cognitive satellite-UAV network from a physical layer security (PLS) perspective. Using only the slowly-varying large-scale channel state information (CSI), we jointly allocate the transmission power and subchannels to maximize the secrecy sum rate of UAV users. The optimization problem is a mixed integer nonlinear programming (MINLP) problem with coupling constraints. We propose a heuristic algorithm which relaxes the coupling constraints by the penalty method and obtains a sub-optimal low-complexity solution by utilizing random matrix theory, the max-min optimization tool, and the bipartite graph matching algorithm. The simulation results corroborate the superiority of our proposed scheme.

Secure Transmission Over Multiple Access Wiretap Channel by Cross-Time Interference Injection

Due to the openness of wireless communication, information transmitted in the multiple access channel is vulnerable to eavesdropping by illegitimate users. Classic artificial noise-assisted schemes can improve security but are not suitable for power-constrained users. To address this problem, we propose a cross-time interference injection scheme in this paper, which improves security by introducing cross-time self-interference and inter-user interference, and does not require artificial noise. The proposed scheme exploits the properties of the Hadamard matrix and the wireless channels, so that the eavesdropping channel suffers from stronger interference than the legitimate channel. We analyze the security performance when both the legitimate receiver and the eavesdropper exploit the successive interference cancellation (SIC) scheme, including the secrecy sum rate, the secrecy capacity, and the secrecy outage probability. We further investigate the effect of the number of eavesdropper’s antennas, the number of legitimate users, and the number of time slots used by the scheme on the security performance, and obtain the corresponding relationship between these parameters when achieving a positive secrecy rate. Finally, simulation results corroborate our theoretical analysis.

IRS-Assisted Physical Layer Security for 5G Enabled Industrial Internet of Things

5G is a key enabler of Industrial Internet of Things (IIoT) that provides seamless connectivity between machines, sensors and computing servers. Security and privacy are major concerns for 5G enabled IIoT. Physical Layer Security (PLS) is a promising technique that can enhance the security of 5G enabled IIoT. In this paper, we present an IRS-assisted PLS scheme for 5G enabled IIoT that improves the weighted secrecy sum-rate (WSSR) of the industrial wireless network, where eavesdroppers are present around the facility. The key idea is to jointly optimize active and passive beamforming vectors to increase the secrecy rate at the user. To maximize WSSR, we use the stable matching algorithm that optimally assigns IRSs for secure data sharing between industrial units. Simulation results show that the proposed scheme enhances the WSSR performance by 40% and minimum secrecy rate by 25% as compared to the random and maximum weight matching schemes, respectively.

Adaptive secure transmission for two‐way energy harvesting relay networks with two‐sided eavesdropping

AbstractIn this paper, for energy harvesting (EH) two‐way relay (TWR) networks with two‐sided eavesdroppers, the problem of secure information transmission is investigated. Based on this system model, a novel full‐duplex (FD) relaying jamming scheme is proposed to achieve simultaneously information reception, EH and jamming signal transmission at the cooperative relay. Particularly, the entire transmission phase and received signal at relay node are divided into two different time durations and three different parts by time allocation (TA) and power splitting (PS), respectively. For the aim of maximizing the sum secrecy rate, adaptive TA factor and PS factor are jointly optimized. We derive the non‐convex optimization function. To solve this highly non‐convex optimization problem, a heuristic approach based on particle swarm optimization (PSO) is proposed. Numerical results verify the superior sum secrecy rate of the proposed scheme. Besides, the impact of system parameters on the output performance is analyzed.

Security Optimization of Cooperative NOMA Networks With Friendly Jamming

We investigate physical layer security of a downlink cooperative non-orthogonal multiple access system with an untrusted relay. A friendly jammer (FJ) is employed in order to improve the secrecy sum rate. Aiming at maximizing the secrecy sum rate by optimizing the power allocation at both the source and the FJ, an optimization problem is formulated and solved iteratively by using alternating optimization method. Specifically, the problem is decoupled into two sub-problems, each of which is optimally solved. Moreover, the secrecy sum rate of the proposed system with imperfect channel state information is studied. Finally, simulation results validate the effectiveness of the proposed FJ based schemes.

Robust and Secure Resource Allocation for ISAC Systems: A Novel Optimization Framework for Variable-Length Snapshots

In this paper, we investigate the robust resource allocation design for secure communication in an integrated sensing and communication (ISAC) system. A multi-antenna dual-functional radar-communication (DFRC) base station (BS) serves multiple single-antenna legitimate users and senses for targets simultaneously, where already identified targets are treated as potential single-antenna eavesdroppers. The DFRC BS scans a sector with a sequence of dedicated beams, and the ISAC system takes a snapshot of the environment during the transmission of each beam. Based on the sensing information, the DFRC BS can acquire the channel state information (CSI) of the potential eavesdroppers. Different from existing works that focused on the resource allocation design for a single snapshot, in this paper, we propose a novel optimization framework that jointly optimizes the communication and sensing resources over a sequence of snapshots with adjustable durations. Besides, artificial noise (AN) is exploited by the BS for joint sensing and physical layer security provisioning. To this end, we jointly optimize the duration of each snapshot, the beamforming vector, and the covariance matrix of the AN for maximization of the system sum secrecy rate over a sequence of snapshots while guaranteeing a minimum required average achievable rate and a maximum information leakage constraint for each legitimate user. The resource allocation algorithm design is formulated as a non-convex optimization problem, where we account for the imperfect CSI of both the legitimate users and the potential eavesdroppers. To make the problem tractable, we derive a bound for the uncertainty region of the potential eavesdroppers’ small-scale fading based on a safe approximation, which facilitates the development of a block coordinate descent-based iterative algorithm for obtaining an efficient suboptimal solution. Simulation results illustrate that the proposed scheme can significantly enhance the physical layer security of ISAC systems compared to three baseline schemes. Moreover, compared to the conventional multi-stage approach for ISAC system design, the proposed approach based on variable-length snapshots not only facilitates a highly-directional offline sensing beam design but also allows us to flexibly prioritize communication or sensing depending on the application scenario.

Secure Precoding Optimization for NOMA-Aided Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is an up-and-coming technique for future 6G networks. However, the communication message carried by the detection waveform will face the risk of being eavesdropped, which leads to the challenge of wireless security for ISAC networks. In this paper, we leverage non-orthogonal multiple access (NOMA) to support more users for the ISAC network, with the precoding well designed to guarantee the security. Specifically, we formulate a joint precoding optimization problem to maximize the sum secrecy rate for multiple users via artificial jamming, where the superimposed signal for NOMA users can be concurrently employed for the target detection. Since the optimization problem is non-convex, it is transformed into a convex one based on successive convex approximation (SCA), where the Taylor’s approximation and second-order cone (SOC) constraint are further applied. Then, we propose an iterative algorithm, through which the original optimization problem can be solved effectively. Simulation results show that the proposed secure NOMA-ISAC scheme can guarantee the secure transmission while ensuring the sensing performance.

Gaussian Multiuser Wiretap Channels in the Presence of a Jammer-Aided Eavesdropper.

This paper considers secure communication in the presence of an eavesdropper and a malicious jammer. The jammer is assumed to be oblivious of the communication signals emitted by the legitimate transmitter(s) but can employ any jamming strategy subject to a given power constraint and shares her jamming signal with the eavesdropper. Four such models are considered: (i) the Gaussian point-to-point wiretap channel; (ii) the Gaussian multiple-access wiretap channel; (iii) the Gaussian broadcast wiretap channel; and (iv) the Gaussian symmetric interference wiretap channel. The use of pre-shared randomness between the legitimate users is not allowed in our models. Inner and outer bounds are derived for these four models. For (i), the secrecy capacity is obtained. For (ii) and (iv) under a degraded setup, the optimal secrecy sum-rate is characterized. Finally, for (iii), ranges of model parameter values for which the inner and outer bounds coincide are identified.

Deep Reinforcement Learning for RIS-Aided Multiuser Full-Duplex Secure Communications With Hardware Impairments

In this article, we investigate a reconfigurable intelligent surface (RIS)-aided multiuser full-duplex secure communication system with hardware impairments at transceivers and RIS, where multiple eavesdroppers overhear the two-way transmitted signals simultaneously, and a RIS is applied to enhance the secrecy performance. Aiming at maximizing the sum secrecy rate (SSR), a joint optimization problem of the transmit beamforming at the base station (BS) and the reflecting beamforming at the RIS is formulated under the transmit power constraint of the BS and the unit modulus constraint of the phase shifters. As the environment is time varying and the system is high dimensional, this nonconvex optimization problem is mathematically intractable. A deep reinforcement learning (DRL)-based algorithm is explored to obtain the satisfactory solution by repeatedly interacting with and learning from the dynamic environment. Extensive simulation results illustrate that the DRL-based secure beamforming algorithm is proved to be significantly effective in improving the SSR. It is also found that the performance of the DRL-based method can be greatly improved and the convergence speed of the neural network can be accelerated with appropriate neural network parameters.