Integrated sensing and communication (ISAC) has emerged as a pivotal technology for advancing low-altitude wireless networks (LAWNs), serving as a critical enabler for next-generation communication systems. This paper investigates the system design for energy-saving uncrewed aerial vehicle (UAV) formations in dual-functional LAWNs, where a ground base station (GBS) simultaneously wirelessly controls multiple UAV formations and performs sensing tasks. To enhance flight endurance, we exploit the aerodynamic upwash effects and propose a distributed energy-saving formation framework based on the adapt-then-combine (ATC) diffusion least mean square (LMS) algorithm. Specifically, each UAV updates the local position estimate by invoking the LMS algorithm, followed by refining it through cooperative information exchange with neighbors. This enables an optimized aerodynamic structure that minimizes the formation’s overall energy consumption. To ensure control stability and fairness, we formulate a maximum linear quadratic regulator (LQR) minimization problem, which is subject to both the available power budget and the required sensing beam pattern gain. To address this non-convex problem, we develop a two-step approach by first deriving a closed-form expression of LQR as a function of arbitrary beamformers. Subsequently, an efficient iterative algorithm that integrates successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques is proposed to obtain a sub-optimal dual-functional beamforming solution. Extensive simulation results confirm that the ‘V’-shaped formation is the most energy-efficient configuration and demonstrate the superiority of our proposed design over benchmark schemes in improving control performance.

Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over LAWNs, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory tracking, while we analyze the communication reliability using the outage probability. Subsequently, we formulate an optimization problem to jointly determine control policy, transmit power allocation, and drone trajectory by accounting for the maximum travel distance and control input constraints. To address the resultant non-convex optimization problem, we first derive the closed-form expression of the outage probability under FBL transmission. Based on this, we reformulate the original problem as a quadratic programming (QP) problem, followed by developing an alternating optimization (AO) framework. Specifically, we employ the projected gradient descent (PGD) method and the successive convex approximation (SCA) technique to achieve computationally efficient sub-optimal solutions. Furthermore, we thoroughly analyze the convergence and computational complexity of the proposed algorithm. Extensive simulations and AirSim-based experiments are conducted to validate the superiority of our proposed approach compared to the baseline schemes in terms of control performance.

The rapid advancement of Internet of Things (IoT) networks has positioned unmanned aerial vehicles (UAV s) as critical enablers of next-generation wireless communication technologies. This paper focuses on orthogonal time frequency space (OTFS) modulation-assisted wireless control in UAV networks with finite blocklength (FBL) transmission. In particular, we in-vestigate the optimal power allocation that maximizes the fairness of control performance in terms of linear quadratic regulator (LQR) cost, subject to rate-LQR cost bounds and maximum available power budget constraints. To address the optimization problem, we first analyze the concave-convex property of the FBL rate function, followed by developing an efficient successive convex approximation (SCA)-based algorithm to obtain a sub-optimal solution. The convergence and computational complexity of the proposed algorithm are thoroughly analyzed. Simulation results validate the effectiveness of the proposed approach, offering promising insights for UAV-enabled wireless control systems.

The rapid advancement of Internet of Things (IoT) services and the evolution toward the sixth generation (6 G) have positioned unmanned aerial vehicles (UAVs) as critical enablers of low-altitude wireless networks (LAWNs). This work investigates the co-design of integrated sensing, communication, and control ($\mathbf {SC^{2}}$) for multi-UAV cooperative systems with finite blocklength (FBL) transmission. In particular, the UAVs continuously monitor the state of the field robots and transmit their observations to the robot controller to ensure stable control while cooperating to localize an unknown sensing target (ST). To this end, a weighted optimization problem is first formulated by jointly considering the control and localization performance in terms of the linear quadratic regulator (LQR) cost and the determinant of the Fisher information matrix (FIM), respectively. The resultant problem, optimizing resource allocations, the UAVs' deployment positions, and multi-user scheduling, is non-convex. To circumvent this challenge, we first derive a closed-form expression of the LQR cost with respect to other variables. Subsequently, the non-convex optimization problem is decomposed into a series of sub-problems by leveraging the alternating optimization (AO) approach, in which the difference of convex functions (DC) programming and projected gradient descent (PGD) method are employed to obtain an efficient near-optimal solution. Furthermore, the convergence and computational complexity of the proposed algorithm are thoroughly analyzed. Extensive simulation results are presented to validate the effectiveness of our proposed approach compared to the benchmark schemes and reveal the trade-off between control and sensing performance.

The forthcoming wireless network promises revolutionary advancements with significantly higher peak data rates, reduced latency, and vastly improved reliability. Among pivotal technologies, the design of novel multiple access schemes, particularly rate-splitting multiple access (RSMA), holds significant importance. In this article, we focus on the joint optimization of user scheduling, rate allocation, and beamforming for downlink multiple-input single-output communication networks under RSMA finite blocklength (FBL) transmission. The difficulty of the formulated optimization problem lies on the achievable rate function with FBL transmission and the joint design of user scheduling and beamforming. In order to solve the formulated problem, we first analyze the convexity and feasibility of the achievable rate function and further provide an efficient algorithm by cooperatively using strong Lagrangian duality, the difference of convex functions programming, the big-M method, and the alternating optimization algorithm for the joint optimization process. Numerical simulations validate the effectiveness of the proposed approach, offering promising insights for the future of 6G wireless networks.