摘要
The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs)in internet of things(IoT).This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment.We first define a measurement of communication performance of UAVto-SO in the target region which is regarded as the optimization objective.The state of one SO is active when it needs to transmit or receive the data;otherwise,silent.The switch of two different states is implemented with a certain probability that results in a dynamic communication environment.In the dynamic environment,the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other.To overcome these challenges in the deployment,we leverage a game-theoretic learning approach to solve the position-selected problem.This problem is modeled a stochastic game,which is proven that it is an exact potential game and exists the best Nash equilibria(NE).Furthermore,a distributed position optimization algorithm is proposed,which can converge to a pure-strategy NE.Numerical results demonstrate the excellent performance of our proposed algorithm.
The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs) in internet of things(IoT). This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment. We first define a measurement of communication performance of UAVto-SO in the target region which is regarded as the optimization objective. The state of one SO is active when it needs to transmit or receive the data; otherwise, silent. The switch of two different states is implemented with a certain probability that results in a dynamic communication environment. In the dynamic environment, the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other. To overcome these challenges in the deployment, we leverage a game-theoretic learning approach to solve the position-selected problem. This problem is modeled a stochastic game, which is proven that it is an exact potential game and exists the best Nash equilibria(NE). Furthermore, a distributed position optimization algorithm is proposed,which can converge to a pure-strategy NE. Numerical results demonstrate the excellent performance of our proposed algorithm.
基金
supported in part by the Natural Science Foundation of China under Grants 61801243, 61671144, and 61971238
by the China Postdoctoral Science Foundation under Grant 2019M651914
by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 18KJB510026
by the Foundation of Nanjing University of Posts and Telecommunications under Grant NY218124
