Unmanned aerial vehicles (UAVs) can be employed as aerial base stations to support communication for the ground users (GUs). However, the aerial-to-ground (A2G) channel link is dominated by line-of-sight (LoS) due to the high flying altitude, which is easily wiretapped by the ground eavesdroppers (GEs). In this case, a single UAV has limited maneuvering capacity to obtain the desired secure rate in the presence of multiple eavesdroppers. In this paper, we propose a cooperative jamming approach by letting UAV jammers help the UAV transmitter defend against GEs. To be specific, the UAV transmitter sends the confidential information to GUs, and the UAV jammers send the artificial noise signals to the GEs by 3D beamforming. We propose a multi-agent deep reinforcement learning (MADRL) approach, i.e., multi-agent deep deterministic policy gradient (MADDPG) to maximize the secure capacity by jointly optimizing the trajectory of UAVs, the transmit power from UAV transmitter and the jamming power from the UAV jammers. The MADDPG algorithm adopts centralized training and distributed execution. The simulation results show the MADRL method can realize the joint trajectory design of UAVs and achieve good performance. To improve the learning efficiency and convergence, we further propose a continuous action attention MADDPG (CAA-MADDPG) method, where the agent learns to pay attention to the actions and observations of other agents that are more relevant with it. From the simulation results, the rewards performance of CAA-MADDPG is better than the MADDPG without attention.

The emerging backscatter communication technology is recognized as a promising solution to the battery problem of Internet of Things (IoT) devices. For example, the wireless sensor network with backscatter communication technology can monitor the environment in remote areas without battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To tackle this challenge, we propose a multi-UAV-aided data collection scenario where the unmanned aerial vehicle (UAV) can fly close to the backscatter sensor node (BSN) to activate it and then collects the data. We aim to minimize the total flight time of the rechargeable UAVs when the collection mission is finished. During the data collection process, the UAVs can return to the charging station to recharge itself when the energy of UAV is not sufficient to complete the mission. To reduce the complexity of the task, we first use the Gaussian mixture model clustering method to divide the BSNs into multiple clusters. Then we consider the deterministic boundary and ambiguous boundary for the UAV flying regions, respectively. For the deterministic boundary scenario, we propose a single-agent deep option learning (SADOL) algorithm, where each UAV cannot fly beyond the deterministic boundary. For the ambiguous boundary scenario, we propose a multiagent deep option learning (MADOL) algorithm to enable the UAVs to cooperatively learn the ambiguous BSNs assignment. In the simulation, we compare the proposed algorithms with multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) algorithms, which proves the proposed algorithms can achieve better performance.

Unmanned aerial vehicle (UAV) technology is recognized as a promising solution to area coverage problems (ACPs) and has been extensively studied recently. In this paper, we study the 3D irregular terrain surface coverage problem with a hierarchical UAV swarm. We first build the 3D model of a random irregular terrain and propose a geometric way to project the 3D terrain surface into many weighted 2D patches. Then we develop a two-level hierarchical UAV swarm architecture, including the low-level follower UAVs (FUAVs) and the high-level leader UAVs (LUAVs). For FUAVs, we design a coverage trajectory algorithm to carry out specific coverage tasks within patches based on the star communication topology. For LUAVs, we propose a swarm deep Q-learning (SDQN) reinforcement learning algorithm to select patches. Moreover, an observation history model based on convolutional neural networks (CNNs) and the mean embedding method is integrated into SDQN to address the communication limitation problems of LUAVs. The numerical results show that FUAVs can cover the entire area of each patch with little redundancies, and the total coverage time of the SDQN is less than that of existing methods, which demonstrates the effectiveness of the proposed algorithms.

This study explores the use of Large Language Models (LLMs), specifically GPT-4, in analysing classroom dialogue—a key task for teaching diagnosis and quality improvement. Traditional qualitative methods are both knowledge- and labour-intensive. This research investigates the potential of LLMs to streamline and enhance this process. Using datasets from middle school mathematics and Chinese classes, classroom dialogues were manually coded by experts and then analysed with a customised GPT-4 model. The study compares manual annotations with GPT-4 outputs to evaluate efficacy. Metrics include time efficiency, inter-coder agreement, and reliability between human coders and GPT-4. Results show significant time savings and high coding consistency between the model and human coders, with minor discrepancies. These findings highlight the strong potential of LLMs in teaching evaluation and facilitation.

In this letter, we investigate a multi-tag backscatter communication system in the presence of an eavesdropper. In order to strengthen transmission security, the optimal tag is chosen among N tags. We consider a practical backscatter communication scenario, where channel correlation between the forward and backscatter links may exist and we study its impact on the system’s security. Specifically, we derive an analytical expression for the secrecy outage probability (SOP). Moreover, in order to gain more insights for the system design, we obtain an asymptotic closed-form expression for the SOP over correlated Rayleigh fading channels. Finally, simulations are performed to validate the theoretical analysis.