Optimal UAV deployment in downlink non-orthogonal multiple access system:a two-user case

This paper investigates a unmanned aerial vehicle(UAV)deployment problem in a non-orthogonal multiple access(NOMA)system,where the UAV is deployed as an aerial mobile base station to transmit data to two ground users.An optimization problem is formulated by deploying the UAV for maximizing the sum rate of the two users.In order to solve the optimization problem,the feasible solution region is first reduced to a line segment between two users.Then,the optimization problem is simplified to a univariate problem,which can be solved by derivation under a certain situation,and the corresponding analytical solution is also provided.Moreover,a generalized algorithm,which considers 2 situations,is proposed to further determine the optimal UAV’s location.Specifically,four cases are discussed in the first situation.Extensive simulations are depicted to demonstrate effectiveness of the proposed algorithm and its superiority over the benchmarks in maximizing the two users’sum rate.

Demand Prediction of Ride-Hailing Pick-Up Location Using Ensemble Learning Methods

Ride-hailing and carpooling platforms have become a popular way to move around in urban cities. Based on the principle of matching riders with drivers, with Uber, Lyft and Didi having the largest market share. The challenge remains being able to optimally match rider demand with driver supply, reducing congestion and emissions associated with Vehicle clustering, deadheading, ultimately leading to surge pricing where providers raise the price of the trip in order to attract drivers into such zones. This sudden spike in rates is seen by many riders as disincentive on the service provided. In this paper, data mining techniques are applied to ultimately develop an ensemble learning model based on historical data from City of Chicago Transport provider’s dataset. The objective is to develop a dynamic model capable of predicting rider drop-off location using pick-up location data then subsequently using drop-off location data to predict pick-up points for effective driver deployment under multiple scenarios of privacy and information. Results show neural network algorithms perform best in generalizing pick-up and drop-off points when given only starting point information. Ensemble learning methods, Adaboost and Random forest algorithm are able to predict both drop-off and pick-up points with a MAE of one (1) community area knowing rider pick-up point and Census Tract information only and in reverse predict potential pick-up points using the Drop-off point as the new starting point.

Distributed cooperative deployment strategy in multi-UAVs assisted heterogeneous networks

Unmanned aerial vehicle base stations(UAV-BSs)can provide a fast network deployment scheme for heterogeneous networks.However,unmanned aerial vehicle(UAV)has limited capability and cannot assist the base station(BS)well.The ability of a UAV to assist the BSs is limited,and the cluster deployment relies on the leading UAV.The dispersive deployment of multiple UAVs(multi-UAVs)need a macro base station(MBS)to determine their positions to prevent collisions or interference.Therefore,a distributed cooperative deployment scheme is proposed for UAVs to solve this problem.The scheme can increase the ability of UAVs to assist users and reduce the pressure on BSs to deploy UAVs.Firstly,the randomly distributed users are pre-clustered.Then the placement problem was modeled as a circle expansion problem and a pre-clustering radius expansion algorithm was proposed.Under the constraint of users'data rates,it provides services for more users.Finally,the proposed algorithm was compared with the density-aware placement algorithm.The simulation results show that the proposed algorithm can provide services for more users and improve the coverage rate of users while ensuring the data rates.