Resource And Interference Management In Wireless Communications With Unmanned Aerial Vehicles

Nowadays,unmanned aerial vehicles(UAVs)have become an integral part of wireless net-works as well as a key enabler of the 5G and future massive Internet of things wireless networks.UAVs can be deployed as aerial base stations to improve network connectivity and coverage of cellular networks.As aerial base stations,UAVs comes with numerous benefits to enhance network performance in terms of coverage,connectivity,and spectrum efficiency.UAV-mounted base stations can be used to provide high quality network con-nectivity and extend coverage in wireless cellular networks.Moreover,UAVs can operate as flying mobile terminals within a cellular network to enable several applications ranging from real-time video streaming to item delivery.However,there are still many challenging issues such is energy and interference manage-ment in designing architectures and deployment of wireless communications with UAV net-works.Taking advantage of the rational behaviors,environmental dynamics,and the various preferences of the nodes in wireless networks,game theory has recently been adopted as an effective tool for analyzing and modelling the UAV-aided networks.Therefore,this dis-sertation seeks to investigate resource and interference management problem for wireless communications with UAV networks using game theory techniques.Firstly,we study and optimize energy efficiency for two cooperating UAVs acting as aerial base stations using Nash bargaining game.Secondly,we address the problem of interference caused by massive cellular-connected UAVs to the serving ground base stations using mean field game(MFG).Thirdly,we investigate the interference and energy constraints in dense unmanned aerial base stations using the advanced MFG.Generally,the main features of our work are:1.The optimization of energy consumption is a critical issue to ensure high availabil-ity,acceptable performance,and an economically viable UAV-BSs deployment.We propose Nash bargaining game theory approach for energy-efficiency optimization in UAV-assisted networks.We address the issue of adaptive beaconing period scheduling for UAV-BSs using Nash bargaining solution,which provide optimal beaconing period duration to optimize energy consumption.Extensive simulations are conducted to val-idate the usefulness of our proposed method.The numerical results show our proposed technique performs better than non-cooperative and always beaconing strategies in term of energy efficiency.2.UAVs can operate as flying mobile terminals within a cellular network to enable sev-eral applications ranging from real-time video streaming to item delivery.The ability of UAVs to establish line-of-sight connectivity to cellular base stations(BSs)when applied as users comes with benefits and difficulties.Although it enables high-speed data access for the UAV,but also it can lead to substantial inter-cell mutual interference among the UAVs and ground network.The problem for uplink interference manage-ment distributed power control in a cellular-connected UAVs network is addressed in this thesis.The problem is modeled by a stochastic dynamic game among UAVs,where each UAV aim at maximizing energy efficiency and minimizing inference level caused to the ground network.Each UAV to ground BS communication is character-ized by a state given by the available transmit energy and the individual interference state whose evolutions are governed by certain dynamics.The stochastic game is for-mulated and approximated by using MFG for simplicity.Our results indicate that this approach can lead to better performance in terms of energy efficiency and interference at the resulting equilibrium.3.Unmanned aerial access networks(AANs)can dynamically adjust their positions,re-sponding to changing locations of users in order to maximize their coverage and im-prove the quality of service.However,the dynamic repositioning of AANs is con-strained by limited onboard energy and mutual interference especial when the deploy-ment of UAVs is dense.Finding optimal speed and placement of AANs to provide sufficient signal strength to users and reduce energy consumption and minimize in-terference remains to be major challenges.Therefore,we propose a distributed joint power and velocity control technique for dense AANs.The proposed distributed con-trol method considers two states,the location of an AAN and the remaining battery energy.The control problem is formulated as a differential game and then extended to MFG considering a dense network.The interference introduced from AANs to ground users is derived using mean-field approximation method.The cost function is designed by combining the performance of the AAN-to-ground user downlink com-munication performance,transmit power,and velocity of UAVs.A finite difference algorithm based on the Lax-Friedrichs method and Lagrange relaxation is then de-veloped to solve the corresponding MFG.The algorithm output illustrate the optimal power and velocity controls and corresponding mean field distribution of ANNs over a predefined period of time.Moreover,the numerical results are provided to demon-strate the effectiveness of our proposed algorithm.