Unmanned aerial vehicle (UAV) terahertz communication has the advantages of both extremely high bandwidth and flexible deployment, which provides a good solution to meet the demanding communication requirements of 6G. However, the UAV platform's susceptibility to environmental influences such as wind and the time-varying nature of its communication links make it difficult to obtain perfect channel state information (CSI) , thus hindering the establishment of robust and stable secure communication links with ground users. In this research, an artificial noise-assisted robust optimization algorithm is proposed for secure downlink terahertz UAV communication with imperfect CSI. Specifically, with imperfect CSI at both users and eavesdroppers, artificial noise was introduced to enhance the secure communication. The objective was to minimize the total transmission power of the UAV by optimizing the beamforming vectors, artificial noise vectors, and UAV trajectory, under the constraints of worst-case user communication performance and worst-case eavesdropping rate. The problem, characterized as a non-convex polynomial with parameter uncertainties, was addressed by developing an alternating iterative algorithm based on semidefinite relaxation, S-procedure, and successive convex approximation. Equivalent transformations were used to convert the original fractional constraints with parameter uncertainties into linear matrix inequality constraints. Simulation results show that compared with benchmark algorithms, the proposed algorithm effectively reduces the total transmission power of the base station and achieves robust, energy-efficient secure communication under imperfect CSI conditions.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can improve their edge computing performance by taking the advantage of flexible deployment of UAVs and the ability to establish high-quality communication links between UAVs and ground users. The existing works on UAV-enabled MEC systems usually assume that the blocklength of the offloading transmission is long, and their results cannot be directly applied to MEC scenarios with strict requirements on computing delay. A UAV-enabled MEC system with ultra-reliable and low-latency communication (URLLC)-based task offloading is considered, in which a UAV carrying a computing server provides MEC service for multiple ground users and the users offload parts of their computing tasks to the UAV through URLLC. The UAV's deployment location, the offloading bandwidths of the users, and the computing central processing unit (CPU) frequencies of the UAV and users are jointly optimized to minimize the computation delay of the system. To solve the resulting non-convex optimization problem, the block coordinate descent method is used to decompose the problem into two subproblems that optimize the UAV's deployment location, and the offloading bandwidths and CPU frequencies, respectively, and the two subproblems are solved alternately. In solving the two subproblems, logarithmic functions are used to make nonlinear approximation to the expression of URLLC offloading rate to simplify the subproblems, and the two subproblems are both transformed into convex optimization problems by applying the successive convex approximation method. Simulation results show that the proposed algorithm can effectively balance the communication capability and computing capability of the system and reduce the system’s computing delay compared to other benchmark schemes.

In order to improve the spectrum utilization efficiency and the energy limitation of cognitive simultaneous wireless information and power transfer (SWIPT) network, a study is conducted on an intelligent reflecting surface (IRS) -assisted cognitive SWIPT network, where the primary user network shares its spectrum with the secondary user network in overlay mode, the secondary transmitter simultaneously transmits energy to the primary transmitter and information to the secondary receiver. An optimization algorithm for the throughput of the secondary user network is proposed, under the constraints of the maximum transmit power of the secondary user transmitter, the minimum throughput requirement of the primary user network, the available time slots, and the phase shifts of the IRS, and the beamforming vector of the secondary transmitter, the time slot allocation, and the phase shifts of the IRS are jointly optimized to maximize the throughput of the secondary user network. The optimization variables of the proposed problem are coupled with each other and the structure is highly non-convex, making it is difficult to solve directly. The proposed algorithm applies alternating optimization, semi-positive relaxation, and successive convex approximation techniques to transform the original problem into three subproblems for alternative solution. Simulation results show that the proposed algorithm can significantly improve the throughput of the secondary user network compared with the existing benchmark schemes.

Aerial Intelligent Reflecting Surfaces (IRS) by combining the advantages of aerial platforms and IRS, have the ability to flexibly control wireless channels. In mobile edge computing (MEC) systems, Internet of Things (IoT) devices offload computational tasks via wireless communication. Due to channel attenuation caused by obstacles and the broadcast nature of wireless channels, MEC systems face the risks of limited offloading rates and information leakage. A secure task offloading scheme is proposed for MEC systems assisted by aerial IRS to address these issues. In this system, IoT devices offloaded computational tasks to the MEC server within allocated time slots, while an eavesdropper attempted to intercept the information. The aerial IRS dynamically adjusted its reflection phase shifts and position to create secure channel conditions for task offloading. A joint optimization strategy was developed to optimize IRS phase shifts, time slot allocation, computation frequency, transmit power, and IRS position under energy and computation constraints. The original non-convex problem was decomposed using the block coordinate descent method, and a combination of the semidefinite relaxation and successive convex approximation methods was applied to convert it into a solvable convex problem. Simulation results show that the proposed scheme effectively improves the secure computation capability of the system.

An integrated sensing and communication system is expected to be a major application in 6G, but challenges remain in achieving efficient resource allocation and reducing interference between systems. Reconfigurable Intelligent Surfaces (RIS) and Rate-splitting Multiple Access (RSMA) are considered key technologies of 6G, offering potential solutions to these challenges. In this research, an RIS-assisted RSMA integrated sensing and communication system is proposed, introducing a framework for simultaneous communication and sensing using the same frequency spectrum. It explores rate-splitting and joint active-passive beamforming designs to enhance system performance and reduce interference while balancing communication and sensing needs. An optimization problem is formulated to maximize the minimum user rate under the constraint of an effective sensing power threshold. A joint optimization algorithm is proposed to solve this non-convex problem, utilizing a weighted minimum mean square error (WMMSE) approach to construct a rate-WMMSE relationship and employing block coordinate descent and successive convex approximation methods for variable decoupling and iterative solving. Simulation results show that the proposed algorithm achieves a significantly higher minimum user rate compared with other benchmark schemes under the same transmission power budget. This leads to a marked improvement in the fairness of user communications.