Cellular networks are difficult to meet emergency rescue due to the destruction of base stations and infrastructure caused by natural disasters. Unmanned Ground Vehicles (UGVs) and other mobile communication devices encounter significant challenges when operating in disaster areas due to limited coverage and resources. To tackle this problem, this paper integrates Unmanned Aerial Vehicles (UAVs) into the emergency communication network and constructs an air-ground integration network architecture with UAV-UGV collaboration. Specifically, multi-UGV collaborate to collect disaster information, and multi-aerial intelligent reflecting surfaces with high maneuverability can effectively assist UGVs in transmitting the collected data to the remote control center. However, there is also a serious challenge to optimize the collaboration strategy between UGVs and UAVs. To address the concern, the collaboration between UAVs and UGVs is modeled as bipartite graph, where UAVs and UGVs belong to different sets of nodes, respectively. The problem is transformed into a matching game based on the bipartite graph. A stable Bidirectional Matching Game (BMG) algorithm is proposed, where matching players maximize the utility by adjusting the selection strategy. Extensive experimental results show that the proposed BMG algorithm outperforms other benchmark algorithms in terms of utility for both UAVs and UGVs.

Unmanned Aerial Vehicles (UAVs) can be deployed as aerial wireless base stations which dynamically cover the wireless communication networks for Ground Users (GUs). The most challenging problem is how to control multi-UAVs to achieve on-demand coverage of wireless communication networks while maintaining connectivity among them. In this paper, the cooperative trajectory optimization of UAVs is studied to maximize the communication efficiency in the dynamic deployment of UAVs for emergency communication scenarios. We transform the problem into a Markov game problem and propose a distributed trajectory optimization algorithm, Double-Stream Attention multi-agent Actor-Critic (DSAAC), based on Multi-Agent Deep Reinforcement Learning (MADRL). The throughput, safety distance, and power consumption of UAVs are comprehensively taken into account for designing a practical reward function. For complex emergency communication scenarios, we design a double data stream network structure that provides a capacity for the Actor network to process state changes. Thus, UAVs can sense the movement trends of the GUs as well as other UAVs. To establish effective cooperation strategies for UAVs, we develop a hierarchical multi-head attention encoder in the Critic network. This encoder can reduce the redundant information through the attention mechanism, which resolves the problem of the curse of dimensionality as the number of both UAVs and GUs increases. We construct a simulation environment for emergency networks with multi-UAVs and compare the effects of the different numbers of GUs and UAVs on algorithms. The DSAAC algorithm improves communication efficiency by 56.7%, throughput by 71.2%, energy saving by 19.8%, and reduces the number of crashes by 57.7%.

6G Internet of Things (IoT) is envisioned to provide large-scale network connections and high data transmission rates to satisfy the diverse needs of IoT nodes. The wireless powered communication network (WPCN) is the essential part of the future 6G IoT, which can provide nodes with reliable and efficient data and energy transmission. In complex environments, wireless power transmissions are inefficient due to transmission distance and obstacles. To address these concerns, we propose a novel quality of experience (QoE)-driven framework for aerial intelligent reflective surface (IRS)-assisted WPCN, which exploits the maneuverability of unmanned aerial vehicle (UAV) to improve the network performance. In the framework, we construct a nonlinear satisfaction function to quantify the QoE and design an adaptive reflective units configuration scheme based on the QoE to reduce resource consumption (e.g., energy) while satisfying the QoE requirements. The optimization problem of maximizing average throughput is formulated by jointly optimizing the aerial IRS flight trajectory, node association variable, time slot allocation ratio, and IRS phase. The existence of coupling between optimization variables and the nonconvexity lead to the difficulty of solving the optimization problem directly. To effectively solve the above optimization problem, the block coordinate descent (BCD) algorithm is utilized to decompose the optimization problem into four subproblems to be solved separately. Simulation results demonstrate that the proposed scheme can significantly enhance the throughput compared with other schemes.

A reliable driving trajectory prediction of surrounding vehicles is an essential reference for decision-making and safe driving of an autonomous vehicle. Although predicting short-term trajectories can be well achieved, it is still very challenging for long-term prediction of trajectories since the prediction space grows exponentially. In this paper, we propose a novel architecture for trajectory prediction from factored intention estimation (I2T), which decouples the trajectory prediction space into a high-level space for intention estimation and a low-level space for motion prediction. The long-term dependencies between intention cues and future motions during driving are naturally extended to the internal sharing mechanism of I2T, leading to improved performance. Furthermore, we design a Prioriformer model to serve as the backbone network for I2T so that it can accurately capture the long-term dependency couplings related to the task of intention estimation or motion prediction. Prioriformer model adopts a personalized normalization method, which facilitates learning latent representations of long-term features and avoids getting stuck on local optimum. A designed multi-scale fusion encoder extracts features from various receptive fields and then learns richer information from the representation subspaces. An efficient non-autoregressive decoder reduces the pressure in long-term prediction of trajectories while avoiding cumulative errors. Experiments on three real-world motion datasets show that I2T can significantly outperform the state-of-the-art.

The unmanned aerial vehicles (UAVs)-assisted intelligent traffic perception system can provide effective situation awareness. However, UAVs are required to be recharged before the energy is exhausted, which may cause task interruption. To address this concern, the charging UAV (CUAV) is employed to provide wireless charging for the mission UAVs (MUAVs). This paper studies the charging scheduling problem of the CUAV under the premise of optimizing the MUAVs deployment. We first model the MUAVs deployment problem considering the energy consumption and data transmission and establish the CUAV charging model. Then, the above problem is formulated as a multi-objective multi-agent stochastic game process to simplify the decisions-making of MUAVs and CUAV, based on which we propose the utility-based Pareto optimal deployment and charging algorithm, which reduces the computing complexity by equivalent utility of the MUAVs while using Kullback-Leibler divergence to constrain solutions. Next, to ensure the effectiveness of policy update, the multi-agent communication protocol is adopted to improve policy exploration efficiency. Simulation results show that the proposed algorithm outperforms existing works in terms of energy efficiency and charging by comparing with the Pareto front of different methods, endurance anxiety of the MUAVs, and charging utilization under different task modes.

Unmanned Aerial Vehicles (UAVs) can be employed as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground users (GUs). However, most existing works mainly focus on optimizing coverage and maximizing throughput, without considering the fairness of the GUs in communication services. This may result in certain GUs being underserviced by UAV-BSs in pursuit of maximum throughput. In this paper, we study the problem of UAV-assisted communication with the consideration of user fairness. We first design a Ratio Fair (RF) metric by weighting fairness and throughput to evaluate the tradeoff between fairness and communication efficiency when UAV-BSs serve GUs. The problem is formulated as a mixed-integer non-convex optimization problem based on the RF metric and we propose a UAV-Assisted Fair Communication (UAFC) algorithm based on multi-agent deep reinforcement learning to maximize the fair throughput of the system. The UAFC algorithm comprehensively considers fair throughput, UAV-BSs coverage, and flight status to design a reasonable reward function. In addition, the UAFC algorithm establishes an information sharing mechanism based on gated functions by sharing neural networks, which effectively reduces the distributed decision-making uncertainty of UAV-BSs. To reduce the impact of state dimension imbalance on the convergence of the algorithm, we design a new state decomposing and coupling actor network architecture. Simulation results show that the proposed UAFC algorithm increases fair throughput by 5.62%, 26.57% and fair index by 1.99%, 13.82% compared to the MATD3 and MADDPG algorithms, respectively. Meanwhile, UAFC can also meet energy consumption limitation and network connectivity requirement.