This paper proposes a novel flexible coupler antenna array that incorporates additional degrees of freedom (DoF) in radiation pattern reconfiguration to achieve strong mechanical beamforming gains and enhanced communication coverage with low hardware cost. Particularly, passive couplers move around a fixed active antenna so that the induced currents on the passive elements can be reshaped to achieve radiation pattern reconfiguration. A new form of mechanical beamforming can be obtained by moving only the passive couplers while keeping the active antenna stationary. In addition, the flexible coupler antenna can slide along a rail toward users, thereby enhancing communication coverage. To fully exploit the potential of the flexible coupler array, we formulate a two-timescale sum-rate maximization problem with statistical channel state information (CSI). The active antenna position is optimized based on scattering cluster-core statistics in the slow timescale, while mechanical beamforming is optimized based on multipath channel statistics in the fast timescale, subject to movement and energy constraints. To address the coupling between timescales and the high cost of extensive channel sampling, we develop a digital agent framework that leverages an electromagnetic (EM) map to generate statistical channel information for different user and antenna positions. Then, a deep neural network is trained to learn a slow-fast performance (SFP) surrogate, which is fine-tuned with a small number of real measurements and then applied for position optimization at the slow timescale using projected gradient ascent. Mechanical beamforming at the fast timescale is obtained by selecting per-antenna radiation patterns from a predefined dictionary via a convex relaxation. Simulation results demonstrate that the proposed flexible coupler array significantly improves system throughput, and the digital agent-assisted algorithm achieves satisfactory performance with greatly reduced online computational complexity.

In this paper, we propose a digital agent (DA)-assisted resource management scheme for enhanced user quality of experience (QoE) in integrated sensing and communication (ISAC) networks. Particularly, user QoE is a comprehensive metric that integrates quality of service (QoS), user behavioral dynamics, and environmental complexity. The novel DA module includes a user status prediction model, a QoS factor selection model, and a QoE fitting model, which analyzes historical user status data to construct and update user-specific QoE models. Users are clustered into different groups based on their QoE models. A Cramér-Rao bound (CRB) model is utilized to quantify the impact of allocated communication resources on sensing accuracy. A joint optimization problem of communication and computing resource management is formulated to maximize long-term user QoE while satisfying CRB and resource constraints. A two-layer data-model-driven algorithm is developed to solve the formulated problem, where the top layer utilizes an advanced deep reinforcement learning algorithm to make group-level decisions, and the bottom layer uses convex optimization techniques to make user-level decisions. Simulation results based on a real-world dataset demonstrate that the proposed DA-assisted resource management scheme outperforms benchmark schemes in terms of user QoE.

Six-dimensional movable antenna (6DMA) has been identified as a new disruptive technology for future wireless systems to support a large number of users with only a few antennas. However, the intricate relationships between the signal carrier wavelength and the transceiver region size lead to inaccuracies in traditional far-field 6DMA channel model, causing discrepancies between the model predictions and the hybrid-field channel characteristics in practical 6DMA systems, where users might be in the far-field region relative to the antennas on the same 6DMA surface, while simultaneously being in the near-field region relative to different 6DMA surfaces. Moreover, due to the high-dimensional channel and the coupled position and rotation constraints, the estimation of the 6DMA channel and the joint design of the 6DMA positions and rotations and the transmit beamforming at the base station (BS) incur extremely high computational complexity. To address these issues, we propose an efficient hybrid-field generalized 6DMA channel model, which accounts for planar-wave propagation within individual 6DMA surfaces and spherical-wave propagation among different 6DMA surfaces. Furthermore, by leveraging directional sparsity, we propose a low-overhead channel estimation algorithm that efficiently constructs a complete channel map for all potential antenna position-rotation pairs while limiting the training overhead incurred by antenna movement. In addition, we propose a low-complexity design leveraging deep reinforcement learning (DRL), which facilitates the joint design of the 6DMA positions, rotations, and beamforming in a unified manner. Numerical results demonstrate the superiority of the proposed hybrid-field channel model, which achieves sum rates closely approaching that of the near-field channel model. The results also show that the proposed channel estimation algorithm can accurately recover the channel with lower computational complexity than traditional channel estimation algorithm. Moreover, the 6DMA system enhanced by the proposed DRL algorithm significantly outperforms existing flexible antenna systems, especially in the near-field region.

In this paper, we propose a novel digital twin (DT)assisted joint communication and control scheme, in which robots can achieve much better collaboration for search and rescue (SAR) tasks in disaster-affected areas. Particularly, the design of the scheme is decomposed into two sub-developments at different timescales. First, a communication-aware robust control policy is developed for each robot in a small timescale based on nonlinear model predictive control and control barrier function constraints, which mitigates the impact of device-todevice communication delay on task execution. Second, a controlaware radio spectrum resource allocation strategy is developed at the edge server in a large timescale to improve total control task effectiveness of robots. This is achieved by a DT-assisted deep reinforcement learning (DRL) algorithm, where DTs of robots are utilized to emulate potential robot movements for DRL state augmentation. Simulation results demonstrate that the proposed scheme outperforms benchmarks for SAR tasks. A simulation demo can be found at: https://youtu.be/9Salnh9EIVg.

Since vehicles from multiple roads frequently merge at intersections, it formulates a typical traffic bottleneck of modern transportation systems. Proper vehicle coordination and motion plan at road intersections are of importance to guarantee safety as well as improving the traffic throughput, fuel efficiency and so on. In this paper, we try to present a general dedicated intersection coordination framework for autonomous vehicles, where both high- and low-level planners are appropriately designed and integrated. In the high-level planner, two different strategies are formulated to coordinate the autonomous vehicles to generatereference trajectoriesandfeasible “tunnels”, respectively. Especially, a novel space-time-block based resource allocation scheme is presented to describe the feasible tunnels. Furthermore, to avoid collisions with unexpected obstacles such as pedestrians, bicycles or other vehicles with human drivers, a low-level planner is designed to generate practical trajectories based on the solutions from the high-level planner, according to their local on-board observations. Simulations and practical experiments are carried out, to show that our proposed coordination framework can achieve obvious performance advantages in various traffic metrics, including the throughput, fairness in driving maneuvers and driving comfort, etc. We also find that the high-level planner is effective in eliminating possibledeadlocksamong autonomous vehicles, which is rarely discussed in existing investigations.

Since multiple roads merge at intersections, intersections are areas with high incidence of traffic jams. Proper vehicle coordination at intersections is of great importance for modern intelligent transportation systems (ITS). In this paper, we try to give an optimal scheduling framework in the presence of uncertainties in communications, control, and sensing, based on the model predictive control (MPC) control model. Based on a typical V2I communication model, we introduced the impact of CCH length and the number of vehicles on 802.11p communication. Due to the limitation in communications and computing resources, we give an improved age of information (AoI) based resource allocation method, to maximize the traffic throughput at intersections. Numerical results are provided and evaluated.

Proper coordination systems for autonomous vehicles (AVs) in complex road structures such as road intersections are of great importance. However, considering the fact that not only AVs but also human-driven vehicles (HVs) are involved at intersections, existing coordination strategies are limited in reliability and flexibility. In order to guarantee the vehicle safety in the mixed vehicle intersections, we propose a double-level coordination framework. In the centralized node, a high-level planner based on appropriate space-time resource allocation is used to provide feasible tunnels to AVs, to achieve high traffic throughput. Furthermore, on each AV, a low-level planner replans a collision-free practical trajectory to avoid HVs. Sensing, localization and control uncertainties are all considered in the framework to give a proper replanning interval in low-level planner, for improving the robustness of the system. Numerical results are provided and validate our analysis.