Knowledge and Strategic Learning in Multi-user Communications

多用户无线通信系统形成竞争环境，异构和自利的用户争夺有限的频谱资源。然而，最近主导多用户通信研究的技术不太适合异构环境，因为它们通常假设收发器具有相似的标准，并根据对竞争对手的完全了解或不了解而被动地选择其操作？协议、实用程序等。这种被动的系统设计没有利用用户吗？ ？聪明？并可能导致频谱使用效率低下。相比之下，这项研究描述并构建了多用户通信系统，其中用户进行主动交互以划分频谱。提出了一种新的多用户通信范式，其中用户之间的交互及其最终性能不仅取决于他们调整通信策略的能力，还取决于他们根据对竞争对手和环境的了解做出信息交换最佳决策的能力。该研究有两个主要研究方向。首先，研究人员确定“知识的价值”，即当对整个通信系统和竞争用户具有不同知识量的用户和资源调节器进行交互时，可以达到的性能界限。此外，还研究了战略用户应如何主动积累知识并提高其效用。其次，研究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，构建可以接近这些性能界限的操作算法。响应并最终优化其传输行为。 “学习的价值”也被量化，它捕获了需要各种信息开销和复杂性成本的各种战略学习技术的绩效收益。

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.