Advanced Physical Layer Techniques for Wireless Communication Networks - A Factorization Approach

该研究项目致力于无线通信网络中高级物理层技术的潜在好处。在项目的前一部分中，我们分析了基于香农速率模型的网络效用最大化问题，用于描述网络中的无错误信息传输。这导致了分解方法，称为网络分解，它与调度密切相关，是首选的媒体接入技术。因此，所提出的方法固有地对应于相应网络的未知容量区域的内部近似。在这个更新项目中，我们依赖于一个概念上的新模型，基于不同的理解如何在网络编码通信网络中模拟无线广播优势。新的概念是基于抽象的广播功能的介绍，用于描述信息流从一个网络中的发送节点通过其传出链路。它已被证明，一个适当定义的广播功能和其各自的速率区域是密切相关的一个多拟阵公式已知的图论。因此，这个更新项目的目标是了解网络编码通信系统的多项式流模型在其最一般的概念和分析的联合优化的信息流，媒体访问控制机制，并在无线通信网络中的基础物理层参数同时建议的多项式流模型。与第一部分的项目，我们扩展我们的重点非香农速率广播模型，随机接入技术，物理层技术超越纯广播信道。

The research project addresses the potential benefits of Advanced Physical Layer Techniques in wireless communication networks. In the previous part of the project, we analyzed a Network Utility Maximization problem on the basis of Shannon Rate Models for describing error-free information transfer in a network. This led to decomposition approach, referred to as Network Factorization, which is tightly related to Scheduling as the Medium Access Technique of first choice. The proposed approach thus inherently corresponds to an inner approximation of the unknown capacity region of the respective network. In this renewal project we rely on a conceptionally new model based on a different understanding how to model the Wireless Broadcast Advantage in network coded communication networks. The new concept is based on the introduction of abstract Broadcast Functions for describing the information flow from a transmitting node in a network over its outgoing links. It has been shown that an appropriately defined broadcast function and its respective rate region is tightly related with a Polymatroid Formulation known from graph theory. The goal of this renewal project is thus to understand Polymatriod Flow Models for network coded communication systems in its most general conception and to analyze the joint optimization of the information flow, the Medium Access Control mechanism, and the underlying Physical Layer Parameters in a wireless communication network simultaenously based on the suggested polymatroid flow models. In contrast to the first part of the project we extend our focus on Non-Shannon Rate Broadcast Models, Random Access Techniques, and Physical Layer Techniques beyond pure broadcast channels.