CIF: Small: Fundamental Limits of Layered Wireless Networks

这项研究的目标是确定何时以及如何以无缝和频谱高效的方式在共享和/或未经许可的频段上分层多个无线网络的基本信息理论限制。无线网络无处不在：WiFi、蓝牙、无绳电话填充越来越密集的未经许可的频段。蜂窝、卫星、军事和第一响应者网络占用独家许可频段，这些频段正逐渐让位于动态的二次频谱共享许可形式。对于这两种类型的许可，以智能、高效和可扩展的方式在相同的有限频谱资源中共存的多个无线网络是至关重要的。现在是时候重新审视如何对无线网络进行最佳分层，达到远远超出当今将正交接入和将干扰作为噪声处理的“干扰限制”解决方案的范围。这项研究从经典的信息理论单层、同质网络转移到现实中的遗忘约束，即不同层应该如何以分层方式交互。了解如何最佳地分层网络是消除当前频谱效率低下的重要一步。研究了分布式和集中式分层网络，其目标是：1)建立分层网络的结构化框架；2)推导出确定性分层网络及其相关的高斯分层网络的容量区域；3)通过考虑分层网络的广义自由度、每个层中节点数目的吞吐量扩展规律以及吞吐量随层数的扩展规律，通过渐近度量来理解分层网络。

The goal of the research is to determine the fundamental, information theoretic limits of when and how to layer multiple wireless networks on shared and/or unlicensed bands in a seamless and spectrally efficient manner.Wireless networks are everywhere: WiFi, Bluetooth, cordless phones populate increasingly dense unlicensed frequency bands. Cellular, satellite, military and first-responder networks occupy exclusively licensed bands that are gradually giving way to dynamic, secondary spectrum sharing forms of licensing. For both types of licensing, it is crucial that multiple wireless networks co-exist in the same finite spectral resources in an intelligent, efficient and scalable manner.The time is ripe for a fresh look at how to optimally layer wireless networks, reaching far-beyond today's ``interference-limited'' solutions which combine orthogonal access and the treatment of interference as noise. The research moves away from classical information theoretic single-layer, homogeneous networks to include realistic oblivion constraints on how different layers should interact in a hierarchical fashion. Understanding how to best layer networks constitutes a major step towards eliminating current spectral inefficiencies. The fundamental bounds on the performance of layered networks developed will reveal when incentives for networks to adopt a layered policy exist, which is expected to significantly impact the design of future networks.Both distributed and centralized layered networks are investigated, with goals to:1) develop a structured framework for layered networks, 2) derive capacity regions for deterministic layered networks and associated Gaussian layered networks to within a constant gap, 3) understand layered networks through asymptotic metrics by considering the generalized degrees of freedom of the layered network, the throughput scaling laws with number of nodes in each layer, and the throughput scaling law with number of layers.