Knowledge and Strategic Learning in Multi-user Communications

多用户通信中的知识和策略学习

• 批准号: 0830556
• 负责人: Mihaela van der Schaar
• 金额: $ 25.57万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830556&HistoricalAwards=false
• 关键词: Knowledge; Strategic; Learning; Multi; user

Multi-user wireless communications systems form competitive environments, where heterogeneous and self-interested users compete for the limited spectrum resources. However, the techniques that have recently dominated multi-user communication research are not well suited for heterogeneous environments, because they usually assume transceivers that have similar standards and passively select their actions based on either complete or no knowledge about the competitors? protocols, utilities etc. Such passive system designs do not take advantage of the users? ?smartness? and may lead to inefficient spectrum usage. In contrast, this research characterizes and constructs multi-user communications systems, where users engage in proactive interactions for dividing the spectrum. A new multi-user communication paradigm is proposed, where the interaction between users and their resulting performance is driven not only by their ability to adapt their communication strategies, but also by their ability to make optimal decisions about information exchanges based on their knowledge about their competitors and the environment. The research has two main research thrusts. First, the investigators determine ?the value of knowledge?, which are the performance bounds that can be attained, when users and resource moderators with different amounts of knowledge about the entire communication system and the competing users interact. Moreover, how strategic users should proactively accumulate knowledge and improve their utility is also investigated. Second, the investigators construct operational algorithms that can approach these performance bounds, by systematically acquiring information about other users and deploying strategic learning solutions that enable them to forecast the other users? responses and, ultimately, to optimize their transmission actions. The ?values of learning?, which capture the performance gains for various strategic learning techniques requiring various information overheads and complexity costs, are also quantified.

多用户无线通信系统形成了竞争环境，在该环境中，异质和自私的用户竞争有限的频谱资源。但是，最近在多用户通信研究中统治的技术不适合异质环境，因为它们通常假设收发器具有相似的标准，并且基于对竞争对手的完整或不了解的知识，可以被动地选择其行为？协议，实用程序等。这种被动系统设计不利用用户？聪明？并可能导致频谱使用效率低下。相比之下，这项研究表征并构建了多用户通信系统，在该系统中，用户从事积极的互动以划分频谱。提出了一种新的多用户沟通范式，在这种情况下，用户及其由此产生的绩效之间的相互作用不仅取决于他们适应其沟通策略的能力，而且还取决于他们根据他们的知识来对信息交流做出最佳决策的能力竞争对手和环境。该研究有两个主要的研究作用。首先，调查人员确定知识的价值？，当用户和资源主持人对整个通信系统和竞争用户互动的用户和资源主持人相互作用时，可以达到哪些绩效界限。此外，还研究了战略用户应如何积极积累知识并改善其实用性的方式。其次，调查人员通过系统地获取有关其他用户的信息并部署使他们能够预测其他用户的战略学习解决方案来构建可以接近这些性能界限的操作算法？响应，最终是为了优化其传输动作。学习的价值观，哪个捕获各种战略学习技术的绩效提高，需要各种信息开销和复杂性成本。