Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can be assembled in the air-ground integrated sensing and communication (ISAC) to significantly enhance the coverage and sensing performance. However, the near-field effect should be further considered with higher carrier frequency and increasing number of STAR-RIS elements. In this paper, we propose a STAR-RIS enabled air-ground near-field ISAC scheme, where an unmanned aerial vehicle (UAV) is deployed as the mobile base station (BS) and the semi-passive STAR-RIS architecture is adopted to alleviate the severe path loss. Specifically, we maximize the weighted sum rate to guarantee both the communication and sensing functionalities by jointly modifying the beamforming vectors at the BS, the reflection/transmission matrices of the STAR-RIS and, the hovering location of the UAV to well match the near-field effect, which is non-convex with coupled variables. To address this challenge, we first decompose the problem into three subproblems via block coordinate descent. Then, the semidefinite relaxation and successive convex approximation are leveraged to recast these subproblems into convex ones. Finally, we develop an alternating algorithm with low complexity to iteratively solve them. Simulation results are shown to demonstrate the superiority and validity of the proposed scheme.

Integrated sensing and communication (ISAC) can ensure the secure transmission through sensing the eavesdroppers. However, the information obtained by a single base station (BS) is difficult to accurately track the moving eavesdroppers. In this paper, we investigate the sensing-assisted secure communication, where multiple BSs cooperatively sense an unmanned aerial vehicle (UAV) target, also regarded as an aerial eavesdropper. We propose a two-stage scheme to ensure the secure transmission. In the first stage, we estimate the current location and velocity of the UAV through fusing the sensing information from these BSs, to further predict the location in the next time slot. Meanwhile, the prediction variance is derived to bound the errors. In the second stage, we tackle the robust optimization with the prediction errors. Considering the tradeoff between the security and sensing performance, the weighted sum of secrecy rate and radar mutual information rate is maximized via jointly designing the user scheduling and beamforming, which is non-convex. Thus, we decompose it into two subproblems, where the scheduling is obtained via the branch and bound algorithm and the beamforming vectors are optimized by the successive convex approximation. In the end, we design a robust algorithm to address the original problem. Simulation results are shown to prove the efficiency of the proposed scheme.

Intelligent reflecting surface (IRS) can reconfigure the channel conditions, while the passive beamforming gain is limited by the severe double path-loss effect. Fortunately, active IRS (AIRS) is emerging to tackle obstacles by simultaneously adjusting the phases and amplitudes. In this paper, we propose an AIRS-assisted unmanned aerial vehicle (UAV)-relaying scheme, where the AIRS is equipped on the UAV to reflect the signal from the ground base station (GBS) to users via non-orthogonal multiple access. We jointly adjust transmit beamforming, reflection matrix and UAV trajectory to maximize the average sum rate, which is non-convex. Thus, the problem is decomposed into three subproblems via block coordinate descent. The beamforming optimization is solved through semidefinite relaxation. Then, the reflection matrix of AIRS and UAV trajectory are jointly optimized via successive convex approximation. Finally, we design an iterative algorithm to effectively tackle the original problem. Simulation results are shown to verify the performance of the designed scheme.

Intelligent reflecting surface (IRS) can reconfigure the channel conditions, while the passive beamforming gain is limited by the severe double path-loss effect. Fortunately, active IRS (AIRS) is emerging to tackle obstacles by simultaneously adjusting the phase and amplitude of each reflection element. In this paper, we propose an AIRS-assisted unmanned aerial vehicle (UAV)-relaying scheme, where the AIRS is equipped on the UAV to reflect the signal from the ground base station (GBS) to users via non-orthogonal multiple access. We jointly adjust beamforming vectors at the GBS, reflection matrix of the AIRS and UAV trajectory to maximize the average sum rate. However, the problem is non-convex. Thus, it is decomposed into three subproblems via block coordinate descent. The beamforming optimization at the GBS is transformed into a standard semidefinite program through semidefinite relaxation. Then, the reflection matrix of AIRS and UAV trajectory subproblems are solved through successive convex approximation. Ultimately, we design an iterative algorithm to effectively tackle the original problem. Simulation results are shown to verify the performance of designed scheme.

Combining non-orthogonal multiple access (NO-MA) and unmanned aerial vehicles (UAVs) can achieve better performance for wireless networks. In this paper, we propose an effective scheme for NOMA-UAV network with multiple clusters. Due to the limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the non-convex sub-problems can be transformed into convex ones by successive convex approximation. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.

Combining non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAVs) could achieve better performance for wireless networks. However, effective resource allocation for quality of service (QoS) provision among all users still remains as a great challenge for multi-cluster NOMA-UAV networks. In this paper, we propose a NOMA-UAV scheme, where a UAV is deployed as the mobile base station to serve ground users. To meet the QoS requirements of all users with limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the power and location optimizations are also non-convex, which can be transformed into convex ones by successive convex approximation. The duration optimization is a linear programming which can be solved directly. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.