Stochastic geometry-based modelling of mobile uav relay networks under realistic fading

F De Saint Moulin, C Wiame, C Oestges… - 2021 IEEE 93rd …, 2021 - ieeexplore.ieee.org
2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021ieeexplore.ieee.org
We consider a relay network based on Unmanned Aerial Vehicles (UAV). Terrestrial Base
Stations (TBS) and UAV Relay Nodes (RN) are modelled using two Homogeneous Poisson
Point Processes (HPPP). UAVs can hover at a fixed position or move following specific
displacement schemes. The Coverage Probability (CP) of a typical user equipment (UE) is
derived, either when it communicates via a direct link (from the TBS to the UE) or via a relay
link (from the TBS to the UE through a UAV RN). Every link can be in Line-of-Sight (LoS) or …
We consider a relay network based on Unmanned Aerial Vehicles (UAV). Terrestrial Base Stations (TBS) and UAV Relay Nodes (RN) are modelled using two Homogeneous Poisson Point Processes (HPPP). UAVs can hover at a fixed position or move following specific displacement schemes. The Coverage Probability (CP) of a typical user equipment (UE) is derived, either when it communicates via a direct link (from the TBS to the UE) or via a relay link (from the TBS to the UE through a UAV RN). Every link can be in Line-of-Sight (LoS) or Non Line-of-Sight (NLoS), and suffers from Rician fading with distance-dependent parameters. This coverage is calculated by means of both stochastic geometry (SG) and Monte-Carlo (MC) simulations. The benefits of the use of UAV as RNs are analysed depending on their altitude, density, and mobility scheme.
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