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Acquiring and Learning User Information in the Context of Cognitive Device Management

机译:在认知设备管理的上下文中获取和学习用户信息

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One of the key features of emerging and future wireless systems is the continuous increase in the number and diversity of available radio access technologies. A great number of research efforts in the past decade have focused on ways to more efficiently exploit the various wireless access technologies. Among the recent trends in this direction are cognitive wireless networks and systems, targeting to tackle the complexity of the diversified radio environment. Eventually, the goal of these evolving systems is to provide personalised services to users at any time, at any place. Clearly in order to really improve the experience of all users, even technology agnostic ones, functionality is required, on both the network and the user-device side, for providing the "always best connection" in a transparent manner. This paper focuses on mechanisms for acquiring and learning information on user preferences, requirements and constraints as part of a cognitive device management system that enables optimal device configuration taking into account in addition to user requirements, environment characteristics and experience established through learning mechanisms. In this context, the paper presents a scheme for learning and estimating user preferences based on Bayesian networks.
机译:新兴和未来无线系统的关键特征之一是可用无线电接入技术的数量和多样性的持续增加。过去十年的大量研究努力集中在更有效地利用各种无线接入技术的方法上。在该方向的最近趋势中是认知无线网络和系统,瞄准解决多元化无线电环境的复杂性。最终,这些不断发展的系统的目标是在任何时候向用户提供个性化服务。显然,为了真正提高所有用户的经验,即使是技术不可知的,在网络和用户设备侧都需要功能,用于以透明的方式提供“始终最佳连接”。本文侧重于获取和学习关于用户偏好,要求和约束的机制,作为认知设备管理系统的一部分,除了用户要求,环境特征和通过学习机制建立的经验之外还考虑到最佳设备配置。在这种情况下,本文提出了一种基于贝叶斯网络的学习和估算用户偏好的方案。

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