Environment-Aware Drone-Base-Station Placements in Modern Metropolitans

Unmanned aerial vehicles, i.e., drones, have recently caught attention for providing on-demand capacity to wireless networks as drone-base-stations (drone-BSs). Many studies assume simplified channel models based on average characteristics of the environment to estimate the placement of drone-BSs. However, especially in urban areas, positioning of drone-BSs with respect to intersections and roof-top heights of buildings can severely change the path loss characteristics. To address this issue, we adopt an ITU channel model utilizing more information about the environment, such as the shapes of the buildings. We optimize parameters of the selected ITU model, so that it can be used for altitudes both strictly lower and higher than building roof-tops. Using ray-tracing simulations as a benchmark, we compare the proposed model with a widely used simpler model. Results show that the proposed model can reduce the root-mean-squared error from 35 to 10 dB, which may have critical implications for drone-BS operations, such as planning for the required number of drone-BSs to cover outdoor urban users, as demonstrated with simulations.

[1]  Kathiravan Srinivasan,et al.  Intelligent deployment of UAVs in 5G heterogeneous communication environment for improved coverage , 2017, J. Netw. Comput. Appl..

[2]  Kandeepan Sithamparanathan,et al.  Optimal LAP Altitude for Maximum Coverage , 2014, IEEE Wireless Communications Letters.

[3]  Pavel Pechac,et al.  The UAV Low Elevation Propagation Channel in Urban Areas: Statistical Analysis and Time-Series Generator , 2013, IEEE Transactions on Antennas and Propagation.

[4]  Abdallah Khreishah,et al.  Providing wireless coverage to high-rise buildings using UAVs , 2017, 2017 IEEE International Conference on Communications (ICC).

[5]  Halim Yanikomeroglu,et al.  The New Frontier in RAN Heterogeneity: Multi-Tier Drone-Cells , 2016, IEEE Communications Magazine.

[6]  Mounir Ghogho,et al.  Drone Empowered Small Cellular Disaster Recovery Networks for Resilient Smart Cities , 2016, 2016 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops).

[7]  Rui Zhang,et al.  Placement Optimization of UAV-Mounted Mobile Base Stations , 2016, IEEE Communications Letters.

[8]  Walid Saad,et al.  Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs , 2015, IEEE Transactions on Wireless Communications.

[9]  Halim Yanikomeroglu,et al.  Efficient 3-D placement of an aerial base station in next generation cellular networks , 2016, 2016 IEEE International Conference on Communications (ICC).

[10]  Andrew R. Nix,et al.  Path Loss Models for Air-to-Ground Radio Channels in Urban Environments , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[11]  Halim Yanikomeroglu,et al.  Backhaul-aware robust 3D drone placement in 5G+ wireless networks , 2017, 2017 IEEE International Conference on Communications Workshops (ICC Workshops).