This paper investigates the joint optimization of trajectory planning and resource allocation for a high-altitude platform stations synthetic aperture radar (HAPs-SAR) system. To support real-time sensing and conserve the limited energy budget of the HAPs, the proposed framework assumes that the acquired radar data are transmitted in real time to a ground base station for SAR image reconstruction. A dynamic trajectory model is developed, and the power consumption associated with radar sensing, data transmission, and circular flight is comprehensively analyzed. In addition, solar energy harvesting is considered to enhance system sustainability. An energy-aware mixed-integer nonlinear programming (MINLP) problem is formulated to maximize radar beam coverage while satisfying operational constraints. To solve this challenging problem, a sub-optimal successive convex approximation (SCA)-based framework is proposed, incorporating iterative optimization and finite search. Simulation results validate the convergence of the proposed algorithm and demonstrate its effectiveness in balancing SAR performance, communication reliability, and energy efficiency. A final SAR imaging simulation on a 9-target lattice scenario further confirms the practical feasibility of the proposed solution.

Integrated sensing and communication (ISAC) has evolved as a critical paradigm to enhance the dual functions concurrently. However, ISAC may encounter performance limitations, due to undesired channel conditions, small target size, and security threats. In this paper, we investigate intelligent reconfigurable surface (IRS)-aided bi-static ISAC networks, where the IRS is mounted directly on the target surface, and analyze the signal enhancing and suppressing effects of the target-mounted IRS, respectively. First, we maximize the sensing signal-to-noise ratio (SNR) while satisfying the users’ communication requirements by jointly optimizing the transmit beamforming and IRS reflection. To solve this optimization problem, an alternating optimization algorithm is employed to decouple the optimization variables, followed by the application of successive convex approximation and penalty dual decomposition to solve the subproblems. Second, we consider two threatening scenarios where two adversarial base stations (BSs) intend to capture the information reflected by the target. In the first scenario where the adversarial receiving BS attempts to exploit the reflected ISAC signal, we minimize its received power via optimizing the transmit beamforming and the IRS reflection alternately. In the second scenario where the adversarial transmitting BS emits a dedicated signal to detect the target, we focus on optimizing the IRS reflection. Simulation results are presented to show the effectiveness of the proposed schemes.

In this paper, we investigate a secure transmission problem in a simultaneously transmitting and reflecting re-configurable intelligent surface (STAR-RIS) assisted integrated sensing and communication (ISAC) system. The STAR-RIS is exploited to establish line-of-sight links for sensing the target and transmitting the confidential signal to users via non-orthogonal multiple access. Specifically, the sum secrecy rate is maximized by jointly optimizing the transmit beamforming, artificial jamming and STAR-RIS's passive transmission and reflection beamformings, while ensuring the minimum beam-pattern gain required by the target is met. To deal with the non-convex problem, we divide it into two subproblems and leverage successive convex approximation to transform them into convex ones. Then, an alternating optimization algorithm is proposed to solve the problem iteratively. Simulation results demonstrate the effectiveness of the proposed scheme and the potential of STAR-RIS to enhance the security of ISAC.

We study a semi-passive intelligent reflecting surface (IRS) enabled ISAC, where IRS is employed to assist the secure communication and perform target sensing. Specifically, we model two types of targets, namely point targets and extended targets. The direction-of-arrival (DoA) of the former and the complete target response matrix of the latter should be estimated. We derive the Cramér-Rao bound (CRB) as the performance metric of target estimation. To achieve the performance tradeoff, we design a weighted optimization problem that balances maximizing the secrecy rate and minimizing the CRB, via jointly optimizing the transmit beamforming and phase shifts of IRS. Then, we employ the alternating optimization, successive convex approximation and semi-definite relaxation to tackle the non-convex problems for the two target cases. Simulation results show the effectiveness of the proposed schemes.

Utilizing unmanned aerial vehicles (UAVs) as aerial platforms to provide both sensing and communication services is envisioned as a promising paradigm, due to their inherent flexibility and maneuverability. In this paper, we propose a UAV-enabled sensing-assisted communication scheme for vehicular networks using the integrated sensing and communication (ISAC) technique. Specifically, we consider the geometry of vehicles as extended targets with multiple resolvable scatters and adjust beamwidth to cover the entire vehicle in the ISAC duration. Based on the reflected signals, the UAV can predict the state of vehicle, which is then exploited to generate tailored beams to effectively track the vehicle. To address the asymmetric sensing and communication requirements, a three-stage ISAC scheme with dynamic sensing duration and frequency is proposed according to the communication/sensing performance in real time. The initial state of vehicle is estimated in the first stage, followed by the use of ISAC wide beams in the second stage to achieve the vehicle coverage, employing an extended Kalman filtering (EKF) approach for state tracking and prediction. In the third stage, the UAV selectively transmits either an ISAC beam or a communication-only beam based on monitored sensing and communication performance metrics. Finally, simulation results are provided to evaluate the efficacy of the proposed scheme as compared to other benchmarks and also shed light on the tradeoff between communication and sensing.

Integrated sensing and communication (ISAC) has evolved into an influential technique to ameliorate energy and spectrum scarcity via co-designing these two functionalities. However, the target can be a potential eavesdropper aiming at wiretapping the information transmitted to the communication user. This paper studies a semi-passive intelligent reflecting surface (IRS) enabled ISAC system, where the IRS is employed to assist the secure communication and simultaneously perform the target sensing based on the echo signals received by the dedicated sensor at the IRS. Specifically, we model two types of targets, namely point targets and extended targets. The direction-of-arrival (DoA) of the former and the complete target response matrix of the latter should be estimated. Under this configuration, we derive the Cramér-Rao bound (CRB) as the performance metric of target estimation. To achieve an optimal performance trade-off, we formulate a weighted optimization problem that balances maximizing the secrecy rate and minimizing the CRB, via jointly optimizing the transmit beamforming and the phase shifts of IRS. Then, we employ the alternating optimization, successive convex approximation and semi-definite relaxation to tackle the proposed non-convex problems for the two target cases. Simulation results show the effectiveness of the proposed schemes compared with benchmarks.

Integrated sensing and communication (ISAC) has attracted plenty of attentions as an up-and-coming approach to address the spectrum congestion via sharing the same hardware platform. However, including communication information in the sensing waveform will raise the risk of eavesdropping by the sensing targets as the eavesdroppers. In this paper, we investigate a secure transmission problem in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted ISAC system, which is segmented into a sensing region and a communication region. The STAR-RIS is deployed to build line-of-sight (LoS) links for sensing the target as well as transmitting the confidential signal to users via non-orthogonal multiple access. Specifically, the sum secrecy rate is maximized by jointly optimizing the transmit beamforming, artificial jamming and STAR-RIS’s passive transmission and reflection beamformings, while ensuring the minimum beampattern gain required by the target. To deal with the non-convex problem, we divide it into two subproblems and leverage successive convex approximation to transform them into convex ones. Then, an alternating optimization algorithm is proposed to solve the problem in an iterative manner. Simulation results demonstrate the validity of the proposed scheme and the potential of STAR-RIS to enhance the security of ISAC.

In this paper, we propose a secure transmission scheme for aerial intelligent reflecting surface (IRS) assisted wireless networks. A multi-antenna access point (AP) serves multiple legitimate users in the presence of multiple eavesdroppers, whose precise positions are unknown. An IRS is carried by the unmanned aerial vehicle (UAV) to help establish virtual line-of-sight links between the AP and ground users, as well as ensuring the secure transmission. The hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS are jointly optimized to maximize the worst-case sum secrecy rate, subject to the minimum rate requirement of legitimate users. The non-convex optimization problem is decomposed into three subproblems, each of which is transformed into a convex one by utilizing successive convex approximation. An alternating optimization algorithm is applied to tackle the subproblems iteratively. Simulation results validate the effectiveness of the proposed scheme and the security enhancement by the joint optimization.

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.

In this paper, an integrated intelligent reflecting surface (IRS)-unmanned aerial vehicle (UAV) communication scheme is proposed where the IRS is mounted on the UAV as a mobile relay between the base station (BS) and the ground user. We present two schemes to maximize the spectrum efficiency (SE) and the energy efficiency (EE) of the system by jointly optimizing the active beamforming, passive beamforming and UAV trajectory. First, to tackle the SE maximization problem, we divide it into three sub-problems to optimize the variables iteratively. For the active and passive beamforming, the closed-form solutions can be directly derived. The suboptimal trajectory design can be obtained by utilizing the successive convex approximation (SCA). Furthermore, considering the limited on-broad energy of UAV, a scheme to maximize the EE is proposed. The optimal active beamforming and the passive beamforming can be similarly obtained. For the non-convex fractional programing of trajectory optimization, it can be solved via the Dinkelbach’s method. Numerical results demonstrate that the effectiveness of the proposed algorithms.

Despite the wide utilization of unmanned aerial vehicles (UAVs), UAV communications are susceptible to eavesdropping due to air-ground line-of-sight channels. Intelligent reflecting surface (IRS) is capable of reconfiguring the propagation environment, and thus is an attractive solution for integrating with UAV to facilitate the security in wireless networks. In this paper, we investigate the secure transmission design for an IRS-assisted UAV network in the presence of an eavesdropper. With the aim at maximizing the average secrecy rate, the trajectory of UAV, the transmit beamforming, and the phase shift of IRS are jointly optimized. To address this sophisticated problem, we decompose it into three sub-problems and resort to an iterative algorithm to solve them alternately. First, we derive the closed-form solution to the active beamforming. Then, with the optimal transmit beamforming, the passive beamforming optimization problem of fractional programming is transformed into corresponding parametric sub-problems. Moreover, the successive convex approximation is applied to deal with the non-convex UAV trajectory optimization problem by reformulating a convex problem which serves as a lower bound for the original one. Simulation results validate the effectiveness of the proposed scheme and the performance improvement achieved by the joint trajectory and beamforming design.

Unmanned aerial vehicles (UAVs) have been widely employed in wireless communications, and the performance can be enhanced with the assistance of intelligent reflecting surface (IRS). However, the finite energy of UAVs greatly limits the endurance and becomes a bottleneck for IRS-UAV communications. In this paper, an integrated IRS-UAV communication scheme is proposed where the IRS is mounted on the UAV to connect the base station and the ground user. We present two schemes to maximize the spectrum efficiency (SE) and the energy efficiency (EE) of the system by jointly optimizing the active beamforming, passive beamforming and UAV trajectory. First, to tackle the SE maximization problem, we divide it into three subproblems to optimize the variables iteratively. For the active and passive beamforming, the closed-form solutions can be directly derived. The suboptimal trajectory design can be obtained by utilizing the successive convex approximation. Furthermore, considering the limited on- broad energy of UAV, a scheme to maximize the EE is proposed. The optimal active beamforming and the passive beamforming can be similarly obtained. For the non-convex fractional programming of trajectory optimization, it can be solved via the Dinkelbach’s method. Numerical results demonstrate that the proposed algorithms are effective for the IRS-UAV networks to maximize the SE and EE, respectively.

Unmanned aerial vehicles (UAVs) have been used in millimeter-wave (mmWave) networks as relays to assist remote or blocked communication nodes. In this paper, we perform secrecy analysis for UAV-based mmWave relaying networks, where a cooperative jamming scheme is proposed via utilizing the destination and an external UAV to cooperatively disrupt the eavesdroppers at the two stages of relaying, respectively. Considering the probability of line-of-sight (LoS) between the UAV and ground nodes, the three-dimensional (3D) antenna gain, and the Nakagami-m small-scale fading model, closed-form SOP of the network is obtained by employing the Gauss-Chebyshev quadrature. Simulation results are presented to validate the theoretical expressions of SOP and to show the effectiveness of the proposed scheme.

Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. In this paper, we perform a cooperative dual-UAV enabled secure data collection scenario and propose two schemes to ensure the security. The worst-case average secrecy rate is first maximized with the propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of UAVs are jointly optimized. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency. Based on the Dinkelbach method, we transform the fractional objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution. Finally, numerical results are provided to evaluate the effectiveness of the proposed schemes.

Employing unmanned aerial vehicles (UAVs) in millimeter-wave (mmWave) networks as relays has emerged as an appealing solution to assist remote or blocked communication nodes. In this case, the network security becomes a great challenge due to the presence of malicious eavesdroppers. In this paper, we perform a secrecy analysis for a UAV-based mmWave relaying network. We first investigate the relaying scheme without jamming where the UAV decodes and forwards the information from the source to the destination with malicious eavesdropping. Furthermore, to enhance the secrecy performance, we propose a cooperative jamming scheme via utilizing the destination and an external UAV to cooperatively disrupt the eavesdroppers at the two stages of relaying, respectively. Using the probability of line-of-sight (LoS) between the UAV and ground nodes, the three-dimensional (3D) antenna gain, and the Nakagami-m small-scale fading model, the secrecy outage probability (SOP) of the two schemes with and without jamming is analyzed. Closed-form expressions for the SOP of the two schemes are obtained by employing the Gauss-Chebyshev quadrature. Simulation results are presented to validate the theoretical expressions of SOP and to show the effectiveness of the proposed schemes.

Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. However, its line-of-sight makes UAV communication vulnerable to malicious eavesdroppers. In this paper, we propose two cooperative dual-UAV enabled secure data collection schemes to ensure security, with the practical propulsion energy consumption considered. We first maximize the worst-case average secrecy rate with the average propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of the two UAVs are jointly optimized. To solve the non-convex multivariable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency of UAV data collection, which is a fractional and mixed integer nonlinear programming problem. Based on the Dinkelbach method, we transform the objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution to secrecy energy efficiency maximization. Numerical results show that the average secrecy rate is maximized in the first scheme with propulsion limitation, while in the second scheme, the secrecy energy efficiency is maximized with the optimal velocity to save propulsion power and improve secrecy rate simultaneously.

Unmanned aerial vehicles (UAVs) are playing an important role in wireless networks, due to their cost effectiveness and flexible deployment. Particularly, integrating UAVs into existing cellular networks has great potential to provide high-rate and ultra-reliable communications. In this paper, we investigate the uplink transmission in a cellular network from a UAV using non-orthogonal multiple access (NOMA) and from ground users to base stations (BSs). Specifically, we aim to maximize the sum rate of uplink from UAV to BSs in a specific band as well as from the UAV's co-channel users to their associated BSs via optimizing the precoding vectors at the multi-antenna UAV. To mitigate the interference, we apply successive interference cancellation (SIC) not only to the UAV-connected BSs, but also to the BSs associated with ground users in the same band. The precoding optimization problem with constraints on the SIC decoding and the transmission rate requirements is formulated, which is non-convex. Thus, we introduce auxiliary variables and apply approximations based on the first-order Taylor expansion to convert it into a second-order cone programming. Accordingly, an iterative algorithm is designed to obtain the solution to the problem with low complexity. Numerical results are presented to demonstrate the effectiveness of our proposed scheme.

In this paper, we investigate the uplink transmission in a cellular network from a UAV and ground users to ground base stations (BSs). Specifically, we aim to maximize the sum rate of the uplink from the UAV to ground BSs in a specific idle frequency band as well as from the co-channel users to their associated BSs by optimizing precoding vectors at UAV. To mitigate the interference, we apply successive interference cancellation (SIC) not only to the BSs connected with UAV using non-orthogonal multiple access (NOMA) for transmission, but also to other BSs communicating with ground users in the same band. The precoding optimization problem with constraints on the SIC decoding and the uplink transmission rate is formulated, which is non-convex and intractable. Thus, we introduce auxiliary variables and apply first-order approximations based on Taylor expansion to convert it into a second-order cone programming. An iterative algorithm is proposed with low complexity to calculate the solution to the non-convex precoding optimization problem. Numerical results demonstrate the effectiveness of our proposed scheme.

The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. The sum rate is maximized by jointly optimizing the UAV trajectory and the NOMA precoding. To solve the optimization, we decompose it into two steps. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained using two schemes with different constraints. The first scheme intends to cancel the interference from the BS to the UAV-served user, while the second one restricts the interference to a given threshold. In both schemes, the non-convex optimization problems are converted into tractable ones. An iterative algorithm is designed. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.