In recent years, Unmanned Aerial Vehicles (UAVs) have been used in fields such as architecture, business delivery, military and civilian theaters, and many others. With increased applications comes the increased demand for advanced algorithms for resource allocation and energy management. As is well known, game theory and machine learning are two powerful tools already widely used in the wireless communication field and there are numerous surveys of game theory and machine learning usage in wireless communication. Existing surveys however focus either on game theory or machine learning and due to this fact, the current article surveys both game-theoretic and machine learning algorithms for use by UAVs in Wireless Communication Networks (U-WCNs). We also discuss how to combine game theory and machine learning for solving problems in U-WCNs and identify several future research directions.
翻译:近年来,无人驾驶航空飞行器(无人驾驶飞行器)被用于建筑、商业交付、军事和民用剧院以及其他许多领域。随着应用的增加,对资源分配和能源管理先进算法的需求也随之增加。众所周知,游戏理论和机器学习是无线通信领域已经广泛使用的两种强有力的工具,对无线通信中的游戏理论和机器学习使用进行了大量调查。然而,现有的调查要么侧重于游戏理论,要么侧重于机器学习,由于这一事实,目前的文章调查了无线通信网络(U-WCNs)中无人驾驶飞行器使用的游戏理论和机器学习算法。我们还讨论如何将游戏理论和机器学习结合起来,解决无线通信中的问题,并确定未来若干研究方向。