Knowledge and Strategic Learning in Multi-user Communications

多用户无线通信系统形成了竞争环境，其中异构和自私的用户竞争有限的频谱资源。然而，最近主导多用户通信研究的技术并不适合异构环境，因为它们通常假设收发器具有类似的标准，并根据对竞争对手的完整或不了解被动地选择它们的动作。协议，实用程序等。这样的被动系统设计不利用用户？?聪明？并且可能导致低效的频谱使用。相比之下，本研究的特点，并构建多用户通信系统，其中用户进行积极的互动划分频谱。提出了一种新的多用户通信范式，用户之间的交互和他们所产生的性能不仅是由他们的能力，以适应他们的通信策略，但也由他们的能力，使最佳决策的信息交流的基础上，他们的知识，他们的竞争对手和环境。这项研究有两个主要的研究方向。首先，调查人员确定？知识的价值？这是当具有关于整个通信系统和竞争用户的不同知识量的用户和资源调节器交互时可以获得的性能界限。此外，战略用户应如何积极主动地积累知识，提高他们的效用也进行了研究。其次，调查人员构建的操作算法，可以接近这些性能界限，通过系统地获取有关其他用户的信息，并部署战略学习解决方案，使他们能够预测其他用户？响应，并最终优化其传输行为。什么？学习的价值观？其捕获需要各种信息开销和复杂性成本的各种战略学习技术的性能增益，也被量化。

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.