Due to their highly flexible deployment and agility features, unmanned aerial vehicles (UAVs) serving as aerial base stations are increasingly being used in challenging environments, including emergency communication, traffic offloading, and failures of existing communications infrastructure. A reliable and effective communication service requires 3D deployment and autonomous UAV trajectory optimization in each time slot. While most prior approaches focus on trajectory design that maximizes communication coverage or network throughput without considering fairness as well as computation time. This paper presents a multi-UAV trajectory control and fair communication (TCFC) scheme that maximizes ground user data rate and communication coverage while ensuring service fairness in a UAV-aided wireless communications system. The proposed TCFC scheme employs a two-stage learning approach. Firstly, it develops a gated recurrent unit-based link quality estimation model to assess each user’s link quality over time. Then, a federated multi-agent deep reinforcement learning (FedMADRL) algorithm is utilized to continuously adjust the trajectory of the UAVs, optimizing communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. The results show that the proposed TCFC scheme reduces computation time by 26.91% and provides comparable network performance with the baseline methods while improving DRL agents’ privacy.

Unmanned aerial vehicles (UAVs) are becoming increasingly popular as mobile base stations due to their flexible deployment and low-cost features, particularly for emergency communications, traffic offloading, and terrestrial communications infrastructure failures. This paper presents an autonomous trajectory control method for multiple UAVs equipped with base stations for UAV-enabled wireless communications. The objective of this work is to address the optimization challenge of maximizing both communication coverage and network throughput for ground users. The proposed multi-aerial base station trajectory control (MATC) scheme employs a two-stage learning approach. Initially, we developed a long short-term memory-based link quality estimation model to assess each user's link quality over time. The trajectory of the aerial base stations is then continuously adjusted through a centralized multi-agent deep reinforcement learning algorithm to optimize communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. Notably, the proposed approach operates solely on received signals from users, without requiring knowledge of their specific locations. The proposed MATC strategy achieves 97.41% communication coverage while maintaining satisfactory system throughput performance. Numerical results demonstrate that the proposed method significantly enhances both communication coverage and network throughput in comparison to the base line algorithms.

Unmanned aerial vehicles (UAVs) serve as airborne access points or base stations, delivering network services to the Internet of Things devices (IoTDs) in areas with compromised or absent infrastructure. However, urban obstacles like trees and high buildings can obstruct the connection between UAVs and IoTDs, leading to degraded communication performance. High altitudes can also result in significant path losses. To address these challenges, this paper introduces the deployment of reconfigurable intelligent surfaces (RISs) that smartly reflect signals to improve communication quality. It proposes a method to jointly optimize the 3D trajectory of the UAV and the phase shifts of the RIS to maximize communication coverage and ensure satisfactory average achievable data rates for RIS-assisted UAV-enabled wireless communications by considering mobile multi-user scenarios. In this paper, a multi-agent double-deep Q-network (MADDQN) algorithm is presented, which each agent dynamically adjusts either the positioning of the UAV or the phase shifts of the RIS. Agents learn to collaborate with each other by sharing the same reward to achieve a common goal. In the simulation, results demonstrate that the proposed method significantly outperforms baseline strategies in terms of improving communication coverage and average achievable data rates. The proposed method achieves 98.6% of a communication coverage score, while IoTDs are guaranteed to have acceptable achievable data rates.

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as helpful technologies to enhance UAV communication networks. However, due to the high mobility of UAVs, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. The simulation results showed that the proposed GRU model can effectively and accurately estimate the link quality of ground users in the RIS-assisted UAV-enabled wireless communication network.

Large-scale location estimation is crucial for many Artificial Intelligence Internet of Things ( IoT ) applications in the era of smart cities. This letter proposes a deep learning-based outdoor positioning scheme for large-scale wireless settings using fingerprinting techniques. We first developed a feature extraction technique using t -distributed stochastic neighbor embedding ( t -SNE) to extract dominant and distinguishable features while eliminating noises from the radio fingerprints. Afterward, we developed a deep learning-based coarse-fine localization framework to improve positioning performance significantly. Based on our numerical analysis, the proposed scheme reduces computation time by 64.41%, and the average positioning error is 38 cm. Therefore, the proposed approach significantly improved positioning accuracy and reduced computation time.

The Marine Internet of Things (MIoT) has experienced widespread adoption in the maritime industry; however, its communication infrastructure faces significant challenges due to a lack of terrestrial networks and the dynamic nature of oceanic environments. In response, low-Earth orbit (LEO) satellites have been suggested as an alternative solution. However, LEO satellites also face limitations, including coverage gaps due to the power constraints of MIoT devices and the curvature of the Earth, which impede continuous connectivity. To address these challenges, integrating unmanned aerial vehicles (UAVs) as aerial base stations between LEO satellites and MIoT devices has gained attention as an effective solution to enhance communication. Managing UAV mobility and ensuring reliable communication requires autonomous 3D trajectory optimization and dynamic resource allocation. This paper presents an integrated LEO and multi-UAV-enabled marine IoT network designed to maximize communication coverage, system throughput, and fairness. To achieve this, we formulate a joint UAV trajectory and resource allocation (UTCRA) optimization problem. To address the UTCRA problem, we propose a two-stage learning approach: 1) Gaussian mixture models (GMM)-based clustering is initially applied to dynamically cluster MIoT devices under different UAVs, ensuring a balanced distribution of user loads among UAVs. 2) A federated multi-agent deep deterministic policy gradient (FL-MADDPG) algorithm is employed to continuously optimize UAV trajectories and resource allocation, thereby enhancing communication efficiency. Simulation results demonstrate that FL-MADDPG with GMM significantly enhances communication coverage, average throughput, and fairness. It outperforms benchmark algorithms across all evaluated metrics, highlighting its strong potential as a method for next-generation marine IoT networks.