Resource Allocation in Multi-Small Cell Networks With Full-Duplex UAV PROJECT TITLE : Resource Allocation in Full-Duplex UAV Enabled Multi-Small Cell Networks ABSTRACT: Flying platforms, such as unmanned aerial vehicles (UAVs), offer the potential to be an effective solution for the development of future small cell networks. The coverage, capacity, and reliability of wireless networks can all be improved by using unmanned aerial vehicles (UAVs) as aerial base stations (BSs). Additionally, the recent developments of self interference cancellation (SIC) techniques in full-duplex (FD) systems have made it possible to implement FD BSs in a practical setting. In this paper, we investigate the problem of resource allocation for multi-small cell networks that use FD-UAVs as aerial BSs with imperfect SIC. Specifically, we focus on networks that have FD-UAVs as aerial BSs. We take into consideration three distinct possibilities: a) maximizing the DL sum-rate; b) maximizing the UL sum-rate; and finally, c) maximizing the sum of the DL and UL sum-rates. The problems described above lead to non-convex optimization issues; consequently, successive convex approximation algorithms are developed by making use of D.C. (Difference of Convex functions) programming in order to locate sub-optimal solutions. The results of the simulation demonstrated the validity and efficiency of the proposed algorithms for radio resource management in comparison with ground BSs, in both full-duplex (FD) mode and its half-duplex (HD) counterpart. The findings also highlight the circumstances in which employing aerial base stations (BS) is preferable to employing ground-based base stations (BS), and they demonstrate how FD transmission improves the performance of a network in comparison to HD transmission. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Extraction of Resources-Aware Features for Mobile Edge Computing Optimal trajectory planning and reinforcement learning-based collision avoidance in UAV communication networks