Functionalized glove sensors allow natural sweat analysis without requiring exercise or iontophoretic stimulation.

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.

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.

The integration of autonomous aerial vehicles (AAVs) and reconfigurable intelligent surfaces (RISs) in sixth-generation (6G) networks offers significant potential for enhancing integrated sensing and communication (ISAC) systems. Motivated by the need for real-time adaptability, spectrum efficiency, and intelligent wireless infrastructure, this article investigates a AAV-RIS-enabled ISAC framework capable of meeting the dual demands of sensing and communication in complex environments. A key challenge in such systems lies in dynamically balancing resource allocation, AAV trajectory planning, and interference management. To address this, we propose a joint optimization framework in which a AAV simultaneously conducts sensing operations and serves multiple ground users in obstructed environments. The formulated problem involves a nonconvex tradeoff between maximizing sensing signal-to-noise ratio (SNR) and communication throughput. To solve it, we develop a multiagent deep reinforcement learning (MADRL) solution. Three collaborative agents independently optimize AAV trajectories using deep deterministic policy gradient (DDPG), RIS phase shifts using twin delayed DDPG (TD3), and beamforming matrices using proximal policy optimization (PPO). These agents learn optimal policies under dynamic channel conditions and mobility constraints. Simulation results show that the proposed approach improves sensing SNR by 22% and communication rates by 20% compared to baseline methods. With equal weight factors for sensing and communication, the system achieves 95% of the maximum theoretical sensing SNR while ensuring a minimum communication rate of 1 bps/Hz, validating its effectiveness in real-time ISAC environments.