Efficient Algorithms for Air-to-Ground Channel Reconstruction in UAV-Aided Communications

This paper develops an efficient algorithm to learn and reconstruct from a small measurement samples an air-to-ground radio map with fine- grained propagation details so as to predict the signal strength between a wireless equipped UAV and arbitrary ground users, and ultimately the optimal position of the UAV as a mobile relay. In this paper, a joint data clustering and parameter estimation algorithm is developed to learn an multi-segment propagation model from energy measurements that may contain large observation noise. To reduce the reconstruction complexity, we propose to learn a hidden multi-class virtual obstacle model to help efficiently predict the air-to-ground channel. Numerical results demonstrate that the channel prediction error is significantly reduced, and meanwhile, the radio map reconstruction time is reduced to 1/300.

[1]  David Gesbert,et al.  3D City Map Reconstruction from UAV-Based Radio Measurements , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[2]  Walid Saad,et al.  Optimal transport theory for power-efficient deployment of unmanned aerial vehicles , 2016, 2016 IEEE International Conference on Communications (ICC).

[3]  Mark A Beach,et al.  A 3-D integrated macro and microcellular propagation model, based on the use of photogrammetric terrain and building data , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

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

[5]  Georgios B. Giannakis,et al.  Learning Power Spectrum Maps From Quantized Power Measurements , 2016, IEEE Transactions on Signal Processing.

[6]  Yasamin Mostofi,et al.  Channel learning and communication-aware motion planning in mobile networks , 2010, Proceedings of the 2010 American Control Conference.

[7]  D.W. Bliss,et al.  Path-Loss Characteristics of Urban Wireless Channels , 2009, IEEE Transactions on Antennas and Propagation.

[8]  David Gesbert,et al.  Joint user grouping and beamforming for low complexity massive MIMO systems , 2016, 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[9]  Feng Jiang,et al.  Optimization of UAV Heading for the Ground-to-Air Uplink , 2011, IEEE Journal on Selected Areas in Communications.

[10]  David Gesbert,et al.  Optimal positioning of flying relays for wireless networks: A LOS map approach , 2017, 2017 IEEE International Conference on Communications (ICC).

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

[12]  Abbas Jamalipour,et al.  Modeling air-to-ground path loss for low altitude platforms in urban environments , 2014, 2014 IEEE Global Communications Conference.

[13]  David Gesbert,et al.  Learning radio maps for UAV-aided wireless networks: A segmented regression approach , 2017, 2017 IEEE International Conference on Communications (ICC).

[14]  Jose F. Monserrat,et al.  Map-Based Channel Model for Urban Macrocell Propagation Scenarios , 2015 .

[15]  Eric W. Frew,et al.  Real-time estimation of wireless ground-to-air communication parameters , 2012, 2012 International Conference on Computing, Networking and Communications (ICNC).

[16]  Mehdi Bennis,et al.  Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis , 2014, GLOBECOM 2014.