Maintaining stable and efficient communication links for high-speed unmanned aerial vehicles (UAVs) in millimeter-wave (mmWave) communication systems remains a challenge due to beam misalignment caused by UAV mobility. Existing beam-tracking methods often struggle to provide accurate tracking under dynamic conditions, leading to frequent communication disruptions. To address this issue, we propose an enhanced Interactive Multiple Model (IMM) algorithm integrated with a Random Forest Model (RF-EIMM) to improve the accuracy and robustness of UAV trajectory predictions across diverse motion patterns. Furthermore, we propose an adaptive beamwidth optimization strategy that dynamically adjusts the beamwidth in real time, reducing the beam switching frequency, and minimizing the power consumption of the antenna array. Experimental results demonstrate that our approach significantly improves beam alignment accuracy, mitigates misalignment caused by UAV mobility, and outperforms existing methods in terms of spectral efficiency and beamforming gain.

The spatial correlation between the main and eavesdropping channels impacts the effectiveness of beamforming (BF) technology in securing the Internet of Things (IoT) communication system. This study proposes an IRS-assisted secure BF scheme (IRS-SBS) for a multiuser IoT system under imperfect channel state information (CSI). The security issue, constrained by channel spatial correlation, is formulated as a nonconvex optimization problem with probabilistic constraints. To address this, we introduce a novel actor-critic algorithm combined with a generative diffusion model (AC-GDM). This approach jointly optimizes the base station (BS) precoding matrix and the intelligent reflecting surface (IRS) phase shift matrix, subject to constraints on transmit power and phase shifts. The AC-GDM algorithm utilizes a denoising process to recover the optimal BF solution from the Gaussian noise inherent in the multiuser wireless channel environment. Simulation results demonstrate that the minimum achievable secrecy rate of the IRS-SBS outperforms the artificial noise (AN) scheme and the BF scheme by approximately 1.9576 and 2.3596 bps/Hz, respectively. These results validate the effectiveness of IRS-SBS in significantly mitigating the impact of channel spatial correlation on the security of IoT communication systems.

Given that the locations of base stations (BSs) remain fixed after installation, direct data forwarding to remote user equipment (UE) becomes challenging. Unmanned aerial vehicles (UAVs) offer a hopeful solution as mobile relays for next generation wireless communications to realize data forwarding with the flexible and cost-effective deployment. However, the limited onboard energy of UAVs and slow progress in energy storage technology pose significant challenges to achieving energy-efficient communication. Therefore, in this article, we investigate a wireless communication network utilizing a UAV as a high-altitude relay for data forwarding, and formulate a UAV relay deployment optimization problem (URDOP) to minimize the energy consumption of data forwarding and UAV hovering by optimizing UAV deployment, including the locations and number of UAV hover points. Given that the URDOP is a mixed-integer programming problem, conventional gradient-based approaches face limitations. To address this, we propose a self-adaptive differential evolution with a variable population size (SaDEVPS) algorithm to solve the URDOP. The performance of proposed SaDEVPS is verified through simulations, and the results show that it can successfully decrease the energy consumption of system when compared to other benchmark algorithms.

Unmanned aerial vehicles (UAVs) are usually resource constrained, and have the limited communication and energy storage capacity. Collaborative beamforming (CB) in UAV networks based on a virtual node antenna array (VNAA) can enhance the signal-to-noise-ratio (SNR) and energy efficiency of a single UAV node. The UAV nodes can move to better locations for constructing the VNAA to achieve a maximum SNR of CB. However, this will result in an extra motion energy consumption. In this paper, we formulate a joint optimization problem to simultaneously optimize the received SNR and motion energy consumption of UAVs for CB. Then, a mended particle swarm optimization with weed optimization mechanism (PSOWOM) algorithm is proposed to solve the formulated joint optimization problem. Simulation results verify the effectiveness of the proposed algorithm.