Interference management for cellular-connected UAVs: A deep reinforcement learning approach
In this paper, an interference-aware path planning scheme for a network of cellular-
connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at …
connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at …
Deep reinforcement learning for interference-aware path planning of cellular-connected UAVs
In this paper, an interference-aware path planning scheme for a network of cellular-
connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV acts as a …
connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV acts as a …
Three dimensional path planning using Grey wolf optimizer for UAVs
Robot path planning is essential to identify the most feasible path between a start point and
goal point by avoiding any collision in the given environment. This task is an NP-hard …
goal point by avoiding any collision in the given environment. This task is an NP-hard …
Seamless and energy-efficient maritime coverage in coordinated 6G space–air–sea non-terrestrial networks
Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial
systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of …
systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of …
Resource management in UAV-assisted wireless networks: An optimization perspective
Wireless networks are expected to provide connectivity to an increasing number of users
with heterogeneous requirements. Future wireless networks will integrate aerial and …
with heterogeneous requirements. Future wireless networks will integrate aerial and …
RGSO-UAV: Reverse Glowworm Swarm Optimization inspired UAV path-planning in a 3D dynamic environment
A Chowdhury, D De - Ad Hoc Networks, 2023 - Elsevier
Three-dimensional path planning for UAVs is a very complex, NP-hard optimization
problem. It is an effort to resolve the best feasible trajectory between the source and the …
problem. It is an effort to resolve the best feasible trajectory between the source and the …
Optimal positioning of flying base stations and transmission power allocation in NOMA networks
M Nikooroo, Z Becvar - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) are considered as
an efficient tool to enhance the capacity of future mobile networks and to facilitate the …
an efficient tool to enhance the capacity of future mobile networks and to facilitate the …
WiFi networks on drones
A Guillen-Perez, R Sanchez-Iborra… - … : ICTs for a …, 2016 - ieeexplore.ieee.org
The huge growth in the number of connected wireless devices leads to an increasing
demand for network connectivity. In this context, aerial networks may play an important role …
demand for network connectivity. In this context, aerial networks may play an important role …
Cellular-connected UAVs over 5G: Deep reinforcement learning for interference management
In this paper, an interference-aware path planning scheme for a network of cellular-
connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at …
connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at …
Intelligent and fuzzy UAV transportation applications in aviation 4.0
In the last decade, Aviation 4.0 has attracted lots of researchers' attention and with huge
scientific progress, it has become one of the most important issues that researchers have …
scientific progress, it has become one of the most important issues that researchers have …