Delay, Feedback, and Interaction

信息论是传播的战略理论：提供架构指导和基本界限。对于通信，有三个核心服务质量参数：差错概率、速率和端到端延迟。香农的开创性容量定理建立了速率的界限，即允许的错误概率为零，而可接受的端到端延迟为无穷大。然而，对于许多应用，如远程医疗、远程控制(例如无人机中的无线飞行)，甚至视频会议，短暂的延迟也是至关重要的。不幸的是，事实证明，经典的面向块码的信息论方法在涉及端到端延迟的权衡方面具有误导性，特别是在涉及反馈的情况下。本研究旨在纠正这种情况，从而更深入地了解信息流的性质及其沟通需求。具有这些基本的体系结构结果将不仅有助于指导下一代通信系统的设计，而且对于管理无线频谱的监管政策的设置也是至关重要的，因为交互和非交互应用必须在无线环境中有效地共存。该研究做出了三个核心贡献：(A)通过利用我们新的幻觉界限和不确定性聚焦界限技术来表征在具有和没有反馈的通信系统中的时延和错误概率之间的权衡，(C)在交互控制和分布式估计的背景下，通过采用一种新的方法来研究源-通道分离定理，从而加深我们对交互控制和分布式估计背景下的信息流的理解。该理论CS模型通过将一个通信问题显式地归结为另一个通信问题来显示问题级的等价性。

Information theory is the strategic theory of communication: providing architectural guidance and fundamental bounds. For communication, there are three core quality of service parameters: probability of error, rate, and end-to-end delay. Shannon's seminal capacity theorems establish bounds on rate as the tolerated probability of error goes to zero while the acceptable end-to-end delay goes to infinity. However, for many applications like telemedicine, remote control (e.g. fly-by-wireless in UAVs), and even video-conferencing, short delays are also critical. Unfortunately, it turns out that the classical block-code oriented approaches to information theory are misleading regarding the tradeoffs involving end-to-end delay, especially when feedback is involved. This research aims to remedy that situation and thereby get a deeper understanding of the nature of information flows and their communication requirements. Having these fundamental architectural results will help guide not only the design of next generation communication systems, but is also critical for the setting of regulatory policy governing wireless spectrum since both interactive and non-interactive applications must coexist efficiently in the wireless context.This research makes three core contributions: (a) Characterizing the tradeoff between delay and probability of error in communication systems with and without feedback in the context of both soft and hard latency constraints by leveraging our new hallucination bound and uncertainty focusing bound techniques, (b) Understanding how to exploit noisy and limited feedback in the above contexts, (c) Deepening our understanding of information flows in the context of interactive control and distributed estimation by taking a new approach to source-channel separation theorems based on the theoretical CS model of showing problem-level equivalences through explicit reductions of one communication problem to another.