UAV Communication System Capacity Optimization Based On Unified And Segmented Channel Models

With continuous technology advancement and cost deduction,unmanned aerial vehicles(UAVs)or drones have been more widely used in various applications.UAV-assisted terrestrial communications(in which UAVs are employed as the aerial communication platform)and cellular-connected UAV communications(in which UAVs are considered as the aerial users)have become two widely investigated paradigms for integrating UAVs into future wireless communication networks.How to optimize the capacity of the UAV communications system is one of the most important problem.In particular,different from the conventional terrestrial communication links,under different scenarios,it is necessary to use different channel models(unify and segmented channel models)to model the UAV-to-ground communication links,respectively.As a result,how to realize the capacity optimization of UAV communication systems under different channel models is an urgent problem.This paper aims to study the capacity optimization problem of the UAV communication systems based on the different channel models,two scenarios are considered,namely cellular-connected UAV communication system based on unify channel model and UAV-assisted wireless communication system based on segment channel model,respectively.The first research content investigates a new scenario of spectrum sharing between UAV and terrestrial wireless communication based on unify channel model,in which a cognitive/secondary UAV transmitter communicates with a ground secondary receiver(SR),in the presence of a number of primary terrestrial communication links that operate over the same frequency band.This research content exploits the UAV's mobility in three-dimensional(3D)space to improve its cognitive communication performance while controlling the co-channel interference at the primary receivers(PRs),such that the received interference power at each PR is below a prescribed threshold termed as interference temperature(IT).First,this research content considers the quasi-stationary UAV scenario,where the UAV is placed at a static location during each communication period of interest.In this case,this research content jointly optimizes the UAV's 3D placement and power control to maximize the SR's achievable rate,subject to the UAV's altitude and transmit power constraints,as well as a set of IT constraints at the PRs to protect their communications.Next,this research content considers the mobile UAV scenario,in which the UAV is dispatched to fly from an initial location to a final location within a given task period.This research content proposes an efficient algorithm to maximize the SR's average achievable rate over this period by jointly optimizing the UAV's 3D trajectory and power control,subject to the additional constraints on UAV's maximum flying speed and initial/final locations.Finally,numerical results are provided to evaluate the performance of the proposed designs for different scenarios,as compared to various benchmark schemes.From the numerical result,it is shown that in the quasi-stationary scenario the UAV should be placed at its minimum altitude while in the mobile scenario the UAV should adjust its altitude along with horizontal trajectory,so as to maximize the SR's achievable rate in both scenarios.The second research content considers a UAV-assisted wireless communication system based on segmented channel model,in which multiple users on the ground send independent messages to a UAV via NOMA transmission.This research content aims to design the UAV's online dynamic maneuver(in real time)for maximizing the sum-rate throughput of all ground users over a finite time horizon.Different from conventional offline designs considering static user locations under unify(known)channel models,this research content considers a more challenging scenario with mobile users and segmented channel model,in which due to the random user's movement and complex channel environment,the UAV is not able to acquire the whole channel state information(CSI)before communication,but via some proper localization technique and channel estimation algorithm,the UAV causally knows CSI.Under this setup,this research content first proposes a new approach for UAV dynamic maneuver design based on reinforcement learning(RL)via Q-learning,in which at each time slot,the UAV can use the obtained real-time sum-rate of all ground users as the reward function to update the Q-table.Next,in order to further speed up the convergence and increase the throughput,this research content presents an enhanced RL-based approach by additionally exploiting expert knowledge of well-established wireless channel models to initialize the Q-table values.Numerical results show that the proposed RL-based and enhanced RL-based approaches significantly improve the sum-rate throughput,and the enhanced RL-based approach considerably speeds up the learning process owing to the proposed Q-table initialization.Specially,the performance of the enhanced RL-based algorithm is highly related to the average pre-trained channel model,the enhanced RL-based algorithm with the channel model closer to the practical scenario can achieve better performance.