Movable antennas (MAs) have received increasing attention in wireless communications due to their capability of position adjustment to reconfigure wireless channels. However, moving MAs results in non-negligible delay, which may decrease the effective data transmission time. To reduce the movement delay, this paper investigates a new MA trajectory optimization problem. In particular, given the desired destination positions of multiple MAs, we aim to jointly optimize their associations with the initial MA positions and the corresponding movement trajectories within a two-dimensional (2D) region. The goal is to minimize the overall movement delay for all MAs subject to inter-MA minimum distance constraints and practical motor-induced moving direction constraints. However, this problem is a continuous-time mixed-integer linear programming (MILP) problem that is challenging to solve. To tackle this challenge, we first consider a special case with a one-dimensional (1D) MA array and derive the optimal trajectories for MAs in closed-form. Then, we consider another special case without the moving direction constraints and propose a two-stage optimization algorithm that sequentially optimizes the MAs’ position associations and trajectories. This algorithm first relaxes the inter-MA distance constraints and optimally solves the resulting delay minimization problem, followed by successive convex approximation (SCA) to adjust the obtained MA association and trajectory solutions. Furthermore, we extend this two-stage algorithm to the general scenario with the moving direction constraints by introducing the Manhattan distance and combining the A* and conflict-based search (CBS) algorithms. Simulation results are provided to show the effectiveness of our proposed trajectory optimization methods in reducing the movement delay as well as draw useful insights for practical design.

The coming industrial internet of things (IIoT) era is anticipated to see the proliferations of goal-oriented applications in real-time wireless control systems. In such systems, a low processing latency is required to guarantee the timely transmission of control information. To achieve this goal, this paper proposes a new integrated sensing, computing, communication, and control$(\text{ISC}^{3})$architecture, where semantic communications are adopted to make sensible semantic inference (SI) for the control information over time. In particular, we introduce a new criterion, i.e., mutual information (MI), for control performance evaluation by drawing from the field of wireless communications. We calculate the MI by designing a semantic feature extractor (SFE) module at the transmitter (Tx) to identify the semantic correlation among its sensed control information over time, thereby adjusting the frequency of its control information transmission. Meanwhile, a semantic feature reconstructor (SFR) module is employed at the receiver (Rx) to predict the current control information based on its previously received information if there is no control information transmission from the Tx. Finally, on-site experimental results are provided, showing that our proposed scheme can significantly reduce the communication overhead while improving the control performance significantly.

By providing cost-efficient flexibility beyond wired control systems, real-time wireless control systems (WCSs) are pivotal in industrial internet of things (IIoT), which can enable massive emerging applications in IIoT, e.g., autonomous driving, remote medical, teleoperation, etc. To guarantee good control performance over wireless networks, ultra-reliable and low-latency communications (URLLCs) are required from communication perspective. However, this would result in frequent high-rate data transmission, leading to extremely high resource consumption or even network congestion, which impedes the application of WCSs in IIoT. This paper proposes a novel co-design method for communication and control in WCSs, which offers a new strategy to address the challenges introduced by URLLC. Specifically, we first formulate an optimization problem aiming to minimize the communication and control cost by jointly optimizing transmission trigger, communication power, and Lyapunov cost, which, however, is NP-hard. To address this challenge, we employ a two-stage strategy by first defining a new metric, i.e, state-to-error ratio (SER), as a trigger condition to evaluate control performance. Based on this metric, we analyze the relationship between SER and signal-to-noise-ratio (SNR) and show their hidden consistency in evaluating both communication and control performance, thus facilitating our communication and control co-design. Subsequently, we establish a closed-form relationship between the control convergence rate and communication reliability and thereby obtain the optimal transmit power to ensure the overall system performance. Finally, simulation results are provided to demonstrate the efficacy of our proposed method.

In this paper, we propose a time-division integrated sensing, communication and control (ISCC) scheme designed to dynamically enhance communication and sensing capabilities on the UAV platform. The UAV is dispatched to track a randomly moving target for capturing and transmitting sensing data to the base station via wireless communication. The goal is to leverage the ISCC framework for maximizing the cumulative sensing mutual information while guaranteeing successful data transmission by optimizing the allocation of the communication and sensing time slots together with the UAV’s control scheme. The formulated problem cannot be straightforwardly solved by off-the-shelf optimization algorithms due to the time-varying environment. To tackle this challenge, a constrained soft actor-critic (C-SAC) algorithm is developed, which dynamically switches between maximizing rewards and minimizing constraint violations to ensure robust performance in changing environments while maintaining the simplicity and efficiency of unconstrained policy optimization. Simulation results demonstrate that the proposed C-SAC algorithm outperforms dual-variable-based methods in handling the constrained problems, while extensive Monte Carlo tests confirm the robustness of the ISCC policy trained by the proposed algorithm, which adapts to varying target speeds and achieves higher cumulative mutual information compared to the point-mass UAV models.

In this article, a joint design of communication sensing and control (JDCSC) scheme is developed which focuses on a scenario where a cellular-connected unmanned aerial vehicles (UAV) senses a moving target. The goal is to maximize the sensing mutual information via jointly optimizing the transmit power, the trajectory of the UAV and the task completion time, while meeting the onboard energy, the communication service quality, and the UAV flight safety constraints. In particular, UAV dynamics are considered, which are usually ignored in the existing design and inferior communication and sensing quality of service might be resulted. The formulated problem is dynamic optimization problem, which is difficult to be solved. The control parameterization method and exact penalty function scheme are utilized to transform the problem into a static nonlinear program which can be solved by gradient-based methods. The effectiveness of the JDCSC approach is verified by carrying out some numerical examples.

This paper studies the three-dimensional (3D) trajectory optimization problem for unmanned aerial vehicle (UAV) aided wireless communication. Existing works mainly rely on the kinematic equations for UAV’s mobility modeling, while its dynamic equations are usually missing. As a result, the planned UAV trajectories are piece-wise line segments in general, which may be difficult to implement in practice. By leveraging the concept of state-space model, a control-based UAV trajectory design is proposed in this paper, which takes into account both of the UAV’s kinematic equations and the dynamic equations. Consequently, smooth trajectories that are amenable to practical implementation can be obtained. Moreover, the UAV’s controller design is achieved along with the trajectory optimization, where practical roll angle and pitch angle constraints are considered. Furthermore, a new energy consumption model is derived for quad-rotor UAVs, which is based on the voltage and current flows of the electric motors and thus captures both the consumed energy for motion and the energy conversion efficiency of the motors. Numerical results are provided to validate the derived energy consumption model and show the effectiveness of our proposed algorithms.