linhhoang-ex/uav-bs-placement-drl
This repository is the implementation of the deep reinforcement learning (DRL) framework for multi-UAV 3D placement optimization proposed in the paper Adaptive 3D Placement of
This repository is the implementation of the deep reinforcement learning (DRL) framework for multi-UAV 3D placement optimization proposed in the paper Adaptive 3D Placement of
Our work investigates a model to find the efficient placements of multiple UAVs and optimize bandwidth between UAVs and GBS to enhance network performance by maximizing
We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this study focuses on enhancing the
In this paper, we propose a novel heuristic algorithm based three-dimensional (3-D) UAV deployment scheme while guaranteeing the connectivity of the UAV network in both
Our work investigates a model to find the efficient placements of multiple UAVs and optimize bandwidth between UAVs and GBS to
This repository is the implementation of the deep reinforcement learning (DRL) framework for multi-UAV 3D placement optimization proposed in
For the first time, this paper focuses on the problem of modifying the axis-Z location coordinates in three-dimensional (3D) target location. A novel algorithm is proposed by establishing the
1. Introduction, the enhancement of wireless network performance is concerned with meeting the increasing communication demands. For wireless communication systems,
Recently, unmanned aerial vehicles (UAVs) have been reported a lot as aerial base stations (BSs) to assist wireless communication in Internet of Things (IoT). However, most
Compared with the existing works, the constraints of the required quality of service (QoS) and the service ability of each UAV are considered, which makes the problem more challenging. A...
Compared with the existing works, the constraints of the required quality of service (QoS) and the service ability of each UAV are considered, which makes the problem more challenging. A...
Abstract In this paper, we propose to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users in outdoor environments with obstacles. In particular, the
We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this
Since the users'' location and traffic demand vary over time, we dynamically group users into clusters using a K-means-based algorithm and adjust the 3D location of UAV-BSs using an
PDF version includes complete article with source references. Suitable for printing and offline reading.
A 3D placement of unmanned aerial vehicle base station based on multi-population genetic algorithm for maximizing users with different QoS requirements. Paper presented at: 2018 IEEE 18th International Conference on Communication Technology (ICCT). IEEE Efficient 3-D placement of an aerial base station in next generation cellular networks.
IEEE Efficient 3-D placement of an aerial base station in next generation cellular networks. Paper presented at: 2016 IEEE International Conference on Communications (ICC).
Recently, unmanned aerial vehicles (UAVs) have been reported a lot as aerial base stations (BSs) to assist wireless communication in Internet of Things (IoT). However, most results for UAV deployment require uniform access requirements and obstacle-free environment.
Additionally, at their new locations, these behaviors are adjusted to facilitate accurate coverage estimation from the base stations they serve. In the deployment optimization of UAVs, the placement of base stations is determined using received signal strength data obtained through the ray-tracing-based channel modeling technique.