CIF: Small: Theory and Codes for Intermittent and Sparse Communication

对传感器网络和用于大量应用的其它无线通信网络基础设施的日益增长的兴趣提出了重要的工程挑战。 特别是，需要在恶劣环境中快速可靠地传输数据，通常使用非常稀缺的能源和带宽资源。 与传统网络相比，许多新兴应用中通信的一个显着特征是通信的固有稀疏性，即参与节点之间的通信是高度间歇性的。 例如，传感器节点可以每隔几天发送几百比特的信息。 在这种情况下，不仅存在由于噪声引起的不确定性，而且还存在定时不确定性。 由于每次都需要与接收器同步，因此这种传输简档产生了独特的挑战。 因此，对于短的传输，传统的同步方法可以消耗不成比例的份额的可用resources.This研究开发和分析模型异步通信适合这样的应用程序，从基本的单用户信道和进行更广泛的多用户和网络的情况。 调查研究与这些模型相关的基本限制和权衡，并追求在这种环境中实现资源高效通信的架构，协议和实用代码。 因为这样的模型明确地在通信中合并了先前同步的缺乏，所以感兴趣的相关联的编码方案确保在存在这样的定时不确定性的情况下的可靠通信。 该研究使用这样的模型来评估不同候选稀疏通信架构的相对价值，包括量化同步和通信子系统分离的传统架构的低效率，而不是同步和通信相结合的方法。

Growing interest in sensor networks and other wireless communication network infrastructure for a host of applications presents important engineering challenges. In particular, there is a need to deliver data quickly and reliably in harsh environments, using often very scarce energy and bandwidth resources. Compared with traditional networks, a distinguishing feature of communication in many emerging applications is the inherent sparseness of communication---that is,communication between participating nodes is highly intermittent. For example, a sensor node may send a few hundred bits of information every few days. In such cases, there is not only uncertainty due to noise to contend with, but timing uncertainty as well. Such transmission profiles create unique challenges, due to the requirement to synchronize with the receiver each time. As a result, for short transmissions, traditional synchronization approaches can consume a disproportionate share of the available resources.This research develops and analyzes models for asynchronous communication suitable for such applications, starting with basic single-user channels and proceeding to broader multiuser and network scenarios. The investigation studies fundamental limits and trade-offs associated with such models, and pursues architectures,protocols, and practical codes for resource-efficient communication in such settings. Because such models explicitly incorporate the lack of prior synchronization in the communication, the associated coding schemes of interest ensure reliable communication in the presence of such timing uncertainty. The research uses such models to assess the relative value of different candidate sparse communication architectures, including quantifying the inefficiency of traditional architectures in which synchronization and communication subsystems are separate, over an approach in which synchronization and communication are combined.