Three-dimensional communication bidding for small base stations
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Three-Dimensional Deployment Optimization
We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this study focuses on enhancing the coverage of UAV-mounted 6G mobile base
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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 Multiple
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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
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Efficient three‐dimensional deployment of
UAVs can be used as flying base stations without an infrastructure to improve coverage, capacity, line-of-sight (LoS) connection, and rate performance in wireless communication. Furthermore, UAVs
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3-D Positioning and Resource Allocation for Multi-UAV Base Stations
Based on the proposed channel model, we formulate the joint optimization problem of UAV three-dimensional (3-D) positioning and resource allocation, by power allocation, user
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Efficient three‐dimensional deployment of multiple
UAVs can be used as flying base stations without an infrastructure to improve coverage, capacity, line-of-sight (LoS) connection, and rate performance in wireless
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3-D Positioning and Resource Allocation for Multi-UAV
N recent years, unmanned aerial vehicle (UAV)-assisted communication systems have at-tracted increasing attention for supporting the seamless coverage in the beyond fifth
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3D Deployment of Multiple UAV-Mounted Base Stations for UAV Communications
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
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3D deployment of UAV-mounted base stations for
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
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IEEE JOURNAL ON SELECTED AREAS IN
Abstract—The emerging concept of 3D networks, integrating terrestrial, aerial, and space layers, introduces a novel and complex structure characterized by stations relaying
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Three-Dimensional Deployment Optimization of UAVs Using
We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this study focuses on enhancing the
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3D Deployment of UAV-BSs for Effective Communication
To address this issue, we introduce a novel distributed Three-Dimensional (3D) deployment approach for UAV-based Base Stations (UAV-BSs) called 3D deployment for
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Adaptive 3D Placement of Multiple UAV-Mounted Base Stations
Uncrewed Aerial Vehicle-mounted Base Stations (UAV-BSs) have been envisioned as a promising solution to enable high-quality services in next-generation mobile networks.
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Can a 3D base station be used 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).
What is a 3D placement of unmanned aerial vehicle base station?
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
Can unmanned aerial vehicles be a base station for IoT?
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.
Can a fixed base station deliver a high-reliable and low-latency communication capacity?
However, achieving the ultra-reliable and low-latency communication capacity promised by 6G is not possible with fixed base stations alone. In particular, environments such as densely populated areas, disaster areas, rural areas, and hard-to-reach areas are among the scenarios where fixed infrastructures are inadequate.
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