Font Size: a A A

Spectrum Shanging And Performance Analysis Of Un Manded Aerial Vehicle Network And Ground Network

Posted on:2020-01-21Degree:MasterType:Thesis
Country:ChinaCandidate:Z J GuoFull Text:PDF
GTID:2392330575456380Subject:Information and Communication Engineering
Abstract/Summary:PDF Full Text Request
In recent years,UAV-based applications have shown a spurt development and have been imp.lemented in many fields.One of the applications is that UAV serving as a base station in the air,providing wireless communication services for users.UAV has high mobility and economic convenience,which enables the multi-UAVs equipped with base stations to build a wireless communication network quickly.When serving as air base stations,UAVs can not only adjust the network capacity of the wireless cell dynamically,but also can quickly construct UAV-based mesh network in the scene of disaster relief and recovery to provide temporary communication for the disaster area.In order to improve the capacity of the UAV network,this paper studies the spectrum sharing and performance analysis of the UAV network and the ground network.The research contents are as follows.1)Propose the spectrum sharing model of the UAV network and the ground cellular network.Model the air-ground channel with Nakagami-m channel and LOS/NLOS channel respectively for spacious areas and urban areas.Using the theory of random geometry,the relationship between the coverage probability of the UAV network users and ground network users as well as the deployed parameters of UAVs under different channel conditions is analyzed.The correctness of the theoretical analysis is verified by Monte Carlo simulation.2)Using the probabilistic analysis results of integrated UAV network users and terrestrial network users,the Lagrangian multiplier method is used to calculate the optimal deployment density of UAVs with the goal of maximizing the UAV network capacity.Meanwhile,we verify the analysis with simulation.
Keywords/Search Tags:Unmanned Aerial Vehicle, Spectrum Sharing, Stochastic Geometry, Convex Optimization, Monte Carlo Simulation
PDF Full Text Request
Related items