Joint Optimization of Power and Location in Full-Duplex UAV Enabled Systems

Unmanned aerial vehicles (UAVs) can be used as aerial base stations (BSs) for future small cells. They can increase the spectral efficiency of the small cells due to their higher probability to have line-of-sight (LOS) connections and their mobility as a BS. In this article, in order to show the effectiveness of using full-duplex (FD) technology in UAV networks, we consider a UAV equipped with FD technology (FD-UAV) with imperfect self-interference cancelation as an aerial BS that serves both uplink (UL) and downlink (DL) users simultaneously in a small cell network. We aim to maximize DL sum-rate, whilst prescribing a certain quality of service for UL users, by optimizing the location of FD-UAV and available resources. The problem is nonconvex; so we propose an iterative method by exploiting the difference of convex functions programming to jointly optimize transmission power of users, FD-UAV location, and FD-UAV transmission power. Simulation results are illustrated to show the effectiveness of the proposed method for FD-UAV in comparison with ground BS, in both FD and half-duplex modes

Resource Allocation in Full-Duplex UAV Enabled Multi Small Cell Networks

Flying platforms such as Unmanned Aerial Vehicles (UAVs) are a promising solution for future small cell networks. UAVs can be used as aerial Base Stations (BSs) to enhance coverage, capacity and reliability of wireless networks. Also, with recent advances of Self Interference Cancellation (SIC) techniques in Full-Duplex (FD) systems, practical implementation of FD BSs is feasible. In this paper, we investigate the problem of resource allocation for multi-small cell networks with FD-UAVs as aerial BSs with imperfect SIC. We consider three different scenarios: a) maximizing the DL sum-rate, b) maximizing the UL sum-rate, and finally c) maximizing the sum of UL and DL sum-rates. The aforementioned problems result in non-convex optimization problems, therefore, successive convex approximation algorithms are developed by leveraging D.C. (Difference of Convex functions) programming to find sub-optimal solutions. Simulation results illustrated validity and effectiveness of the proposed radio resource management algorithms in comparison with ground BSs, in both FD mode and its half-duplex (HD) counterpart. The results also indicate those situations where using aerial BS is advantageous over ground BS and reveal how FD transmission enhances the network performance in comparison with HD one