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Unmanned aerial vehicle (UAV) plays prominent role in enhancing backhaul connectivity and providing extended coverage areas due to its mobility and flexible deployment. To realize these objectives simultaneously, we present a new framework for positioning the UAV to maximize the small-cells backhaul network connectivity characterized by its Fiedler value, the second smallest eigenvalue of the Laplacian matrix representing the network graph, while maintaining particular signal-to-noise ratio constraint for each individual user equipment. Moreover, we show that the localization problem can be approximated by a low complexity convex semi-definite programming optimization problem. Finally, our extensive simulations verify the approximation validity and demonstrate the potential gain of UAV deployment. 

In this work, we utilizie unmanned aerial vehicles (UAVs) to enhance the connectivity of the backhaul of small-cells (SCs) network and achieve better coverage for those user equipment (UE) in deep fade and not attached to any SCs. Deploying UAVs as a backbone for communication systems has gained an increasing prominence, especially in emergency network breakdown as an efficient and reliable alternative to restore the network connectivity. Moreover, to meet the ever increasing demand on high date rate applications, larger bandwidth is needed which is realizable in mm-wave frequencies range from 20 to 60 GHz. In mm-wave frequencies range, transmitted signals suffer from deleterious path loss mainly due to direct path blockages and significant penetrations losses. We exploit the mobility of UAVs and optimize its position to cope with such impairments. To the best of our knowledge, this work is the first to optimize the UAVs’ locations to jointly increase the connectivity between SCs and improve coverage by overcoming mm-wave harmful pathloss.