Integrated sensing and communication (ISAC), as a rapidly advancing technique, introduces a fresh approach for achieving secure communication and intelligent sensing for future wireless networks. An ISAC framework empowered by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is explored in this paper, where a base station equipped with multiple antennas establishes wireless links to users each with a single antenna during the detection of a point target. The point target, regarded as an eavesdropper, trying to intercept users’ information. Cramér-Rao bound (CRB) serves as evaluation criterion to assess sensing accuracy of point eavesdropper, whereas the secrecy rate is employed to quantify the security level of the communication link. To optimize sensing-communication tradeoff, a joint optimization problem is constructed. To approach the formulated problem, a hybrid Block Coordinate Descent (BCD)-based algorithm is developed, which alternately updates the transmission beamforming and STAR-RIS phase shifts, using successive convex approximation (SCA) technique, penalty dual decomposition (PDD) framework and projected gradient method (PGM).

Simultaneously transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) can assist in achieving full-space signal coverage enhancement. Considering eavesdropping channels, a downlink system model with full coverage of STAR-IRS enabled unmanned aerial vehicle (UAV) secure communication is proposed. The aim is to attain the maximal value of the energy efficiency (EE) by exploring the joint resource allocation of the system. To solve this coupling problem, the lower and upper bounds of the sum-rate for legitimate and eavesdropping users are derived respectively, and Lagrange duality theory is employed to deal with the power control problem. Then the reflection/transmission amplitude splitting coefficient optimization of STAR-RIS using the Hybrid whale-bat (HWB) method in energy splitting (ES) mode are considered to fully exploit the performance gains brought by STAR-IRS deployment. Finally, the simulation verifies that the proposed joint design scheme can enormously promote the EE and safety performance of the system.

Human-centric Metaverse services requires novel communication and networking technologies to achieve seamless connectivity for Metaverse users. Reconfigurable intelligent surface (RIS) in 5G and beyond networks can provide highly reliable communication connections, superior user quality of service (QoS), seamless user connections, and extensive signal coverage for Metaverse. Deploying RIS in unmanned aerial vehicle (UAV) networks for Metaverse can enormously improve the signal propagation environment and human-centric communication experiences. Considering the channel uncertainty of the air-ground cascade communication link in Metaverse, an RIS-aided multi-UAV cross-layer network system is proposed. Under the cross-tier interference limitation and the rate outage probability constraint, the system EE improved by maximizing the minimal energy efficiency (EE) of UAV units. Different from the existing RIS schemes, which suffer from the significant channel acquisition cost or power consumption, this paper first proposes a topology design scheme of irregular RIS, which Metaverse user only connects a few RIS elements to obtain high EE. Secondly, with the imperfect cascade channel state information (CSI) error model, the rate outage probability constraint is approximated by Bernstein type inequality to enhance the seamless human-centric connectivity service. Hence a low complexity scheme is invoked to co-design the power control parameter at the UAV transmitter and RIS reflecting phase. Finally, affluent simulation curves verify that the irregular RIS controller deployment combined with low power loss topology design and low-complexity phase shift design contributes to improve human-centric QoS for Metaverse service.

Since the unmanned aerial vehicle (UAV) network and intelligent reflecting surface (IRS) technology can flexibly change wireless network links signal, the UAV-IRS network system is a potential solution to increase the communication performance gain. Motivated by the practicality of UAV-IRS networks, a non-orthogonal multiple access (NOMA) heterogeneous UAV communication system with simultaneous wireless information and power transfer (SWIPT) is considered, which consists of multiple UAV base stations (UBSs), a macro base station (MBS), and multiple IRSs for auxiliary communications. This paper pursues a goal to receive the system energy efficiency (EE) maximization by resource allocation and reflecting design of IRSs. Due to the strong coupling among multiple parameters in the original problem, this complex non-convex problem is decomposed into three stages. In the first stage, this paper decouples the problem into two subproblems of NOMA subchannel assignment and SIC decoding order to find the optimal solution separately. For the second stage, under the constraints of UAV’s maximum transmit power, users’ quality of service (QoS) requirements, user energy harvesting threshold and cross-layer interference constraints, a beamforming design based on Lagrangian duality is exploited. For the third stage, the power splitting (PS) factors and the reflecting phases of the IRS are jointly optimized using the penalty-SDR algorithm to approximate the suboptimal solution. Finally, the simulation curves exhibit the validity and excellent performance of the co-design scheme in improving the system EE.

As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified by simulations. Moreover, abundant simulation comparisons between the proposed scheme and other benchmark schemes demonstrate the validity and high performance gains of the proposed algorithm.