This article investigates a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage rate by optimizing the three-dimensional (3D) positions of UAVs, user clustering, and frequency band. .
This article investigates a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage rate by optimizing the three-dimensional (3D) positions of UAVs, user clustering, and frequency band. .
In this context, this study focuses on enhancing the coverage of UAV-mounted 6G mobile base stations. The number and placement optimization of UAV-mounted 6G mobile base stations, deployed to support terrestrial base stations during periods of increased population density in a given area, are. .
enhancement [5]–[7], communication relaying [8]–[10], and data broad-cast/collection [11]–[13]. Compared to conventional terrestrial communications with typically fixed infrastructures, UAV-assisted systems offer new degrees of freedom in the spati l domain to further improve communication. .
GitHub - linhhoang-ex/uav-bs-placement-drl: Source code of the paper "Adaptive 3D Placement of Multiple UAV-Mounted Base Stations in 6G Airborne Small Cells With Deep Reinforcement Learning," in IEEE Transactions on Networking, Apr. 2025. feat (uav controller): add functionality to deal with uav. .
Recently, unmanned aerial vehicles (UAVs) have attracted lots of attention because of their high mobility and low cost. This article investigates a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage. .
To extend the coverage of traditional terrestrial communication networks and serve more diverse application scenarios, employing unmanned aerial vehicles (UAV) as aerial base stations has emerged as a viable solution. However, due to the mobility of users and the dynamic nature of UAV base stations. .
The algorithm effectively integrates dynamic weight adaptation, multi-time scale op-timization, and bidirectional information exchange, enhancing the adaptability and efficiency of UAV-assisted base station deployment in dynamic environments. The AMB-TD3 algorithm achieves a signal coverage rate of.
