Abstracts:This paper investigates optimal trajectory design for an unmanned aerial vehicle (UAV) enabled two-way relaying, where a fixed-wing UAV employs an amplify-and-forward protocol to assist data exchange between two ground users. With the aim of maximizing the system energy efficiency (EE), an optimization problem corresponding to the UAV's trajectory design is formulated, where the UAV's initial/final speed and location constraints in addition to the acceleration constraint of the UAV are considered. The initial optimization problem is intractable due to its non-concave objective function and nonconvex constraints. To this end, slack variables are introduced, and then the successive convex approximation (SCA) method and the Dinkelbach's algorithm are applied to transform it into a convex optimization problem, which is solved by a proposed iterative algorithm. Simulation results show that the proposed iterative algorithm converges quite quickly, and with the trajectory design, the two-way UAV relaying is much more superior than the compared benchmark schemes in terms of EE.

Abstracts:Hybrid precoding is a crucial technique for massive multiple-input multiple-output (MIMO) systems owing to its capability to offer an adequate beamforming gain while reducing the hardware cost. However, the nonconvex objective functions and constraints pose great challenges to hybrid precoders design. The conventional precoding method that contains a two-step process including channel estimation and precoding design based on such estimate is not necessarily optimal to tackle this problem. In this article, a transformer-empowered approach waiving high-dimensional channel estimation is proposed to design precoders with the goal of simplifying the complicated hybrid precoding problem into the optimization of neural network structure. Specifically, the proposed approach learns channel sensing from uplink pilots and then operates downlink hybrid precoding depended on interleaved-polymerization-transformer-based analog precoding network (IPTAP-Net) which decomposes on a peruser basis and conventional linear digital precoding algorithm to reduce computational complexity in multi-user systems. Simulations show that the proposed methodology acquires remarkable performance improvement and strong robustness, as compared to state-of-the-art hybrid precoding schemes. Furthermore, proposed approach develops a generalizable talent for manifold multi-user cells.

Abstracts:With the rapid development of autonomous mobility technologies, drones are now widely used in many applications, including military domain. Particularly in battlefield conditions, designing a deep reinforcement learning (DRL)-based autonomous control algorithm presents significant challenges due to the need for real-time and adjustable nonlinear trajectory planning. Therefore, this paper introduces a novel situation-aware DRL-based autonomous nonlinear drone mobility control algorithm tailored for cyber-physical loitering munition applications. The proposed DRL-based drone mobility control algorithm is crafted with a focus on real-time situation-aware operations, enabling it to navigate through many obstacles encountered on the battlefield efficiently. For efficient observation and intuitive fast understanding of time-varying real-time situations, this paper presents an algorithm that works on a cyber-physical virtual battlefield environment using Unity. In detail, our proposed DRL-based nonlinear drone mobility control algorithm utilizes situation-aware sensing components that are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Thus, this approach is obviously beneficial for avoiding obstacles in complex and unpredictable battlefields. Our visualization- based performance evaluation shows that the proposed algorithm outperforms other mobility control algorithms, with an average performance nearly twice as high when the obstacle density is 50%. This superiority is further evidenced by the detailed trajectory planning presented.