Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances.

Leveraging the mobility and cost-effectiveness, unmanned aerial vehicles (UAVs) are deployed in Internet of Things (IoT) systems to efficiently collect data from IoT devices (IoTDs). However, due to the broadcast nature of UAV wireless communication channels, they are highly susceptible to eavesdropping attacks, resulting in information leakage. In this paper, we investigate a dual UAV-assisted IoT data collection system under the threat of multiple eavesdroppers. Specifically, the primary UAV is responsible for collecting data from ground IoT devices, while the jamming UAV generates jamming signals to interfere with eavesdroppers. We aim to minimize the age of information (AoI) of the IoTDs and the energy consumption of dual UAVs by jointly optimizing UAV trajectories and IoTD scheduling. Given the non-convex mixed-integer nature of this problem, traditional optimization methods struggle to deal with this without precise prior knowledge. Therefore, we propose a denoising diffusion probabilistic model-based twin delayed deep deterministic policy gradient (DDPM-TD3) algorithm. Specifically, we leverage the data modeling capability of the DDPM by integrating it with the actor network of TD3 to generate more rational actions. Simulation results indicate that DDPM-TD3 algorithm can effectively enhance the AoI performance and energy efficiency compared to several existing deep reinforcement learning benchmarks.

The Internet of Things (IoT) serves a vital function in supporting real-time decision-making across various applications by facilitating seamless data exchange between devices. However, as the IoT networks typically exchange data over wireless channels, the data transmission process is highly susceptible to malicious interference from jammers in the environment. Moreover, ensuring the freshness of the collected data of the decision center and managing the limited energy resources of IoT devices present significant challenges in the IoT networks. In this article, we consider a unmanned aerial vehicle (UAV)-assisted IoT network in the presence of a jammer, where the UAV is deployed to charge IoT devices through radio frequency (RF) energy transfer, and the IoT devices subsequently use the harvested energy to upload sensing data to the UAV using time division multiple access (TDMA). We aim to minimize both the secure Age of Information (AoI) of IoT devices and the energy consumption of the UAV by optimizing the UAV trajectory, IoT device scheduling, and proportion of data transmission duration. Given the nonconvex and dynamic nature of this optimization problem, we propose a diffusion model-enhanced twin delayed deep deterministic policy gradient (DM-TD3) algorithm to solve the problem. Specifically, considering the analytical and reasoning capabilities of the diffusion model, we integrate it into the actor network of TD3 to generate rational actions based on the observed state. Simulation results demonstrate the effectiveness of the proposed DM-TD3 algorithm compared to five benchmark approaches.

An intelligent reflecting surface (IRS)-enabled wireless power transfer (WPT) and data collection scheme for unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) network is investigated in this paper. Specifically, IoT devices (IoTDs) first harvest energy from the UAV and then upload the sensed data by applying time-division multiple access (TDMA), where an IRS is deployed to improve the transmission quality. We aim to minimize the age of information (AoI) and energy consumption of the UAV. For achieving this, we formulate an optimization problem by jointly optimizing the UAV trajectory, IRS phase shits, charging time allocation, and binary IoTD scheduling, which is a mixed-integer non-convex optimization problem. To address the issue, we first formulate our problem into a Markov decision process (MDP) and then propose an alternating optimization-double parameterized deep Q-network (AO-DPDQN) approach to solve the optimization problem. Specifically, an AO-based method is adopted to optimize the phase shifts of IRS to simplify the action space of MDP, and then double parameterized deep Q-network (DPDQN) is employed to optimize UAV trajectory, charging time allocation, and IoTD scheduling. Simulation results demonstrate the effectiveness and superiority of the proposed approach compared to various baselines.