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

该研究项目阐述了高级物理层技术在无线通信网络中的潜在优势。在本项目的前一部分中，我们在描述网络中无差错信息传输的香农速率模型的基础上，分析了网络效用最大化问题。这导致了分解方法，称为网络分解，它与作为首选媒体访问技术的调度密切相关。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近似。在这个更新项目中，我们依赖于一个概念上的新模型，该模型基于对如何在网络编码通信网络中建模无线广播优势的不同理解。该新概念基于引入抽象广播函数，用于描述来自网络中的发送节点通过其传出链路的信息流。结果表明，一个适当定义的广播函数及其相应的速率域与图论中已知的一个多项式密切相关。因此，本更新项目的目标是了解网络编码通信系统的多项式流量模型的最一般概念，并基于所建议的多项式流量模型同时分析无线通信网络中的信息流、介质访问控制机制和底层物理层参数的联合优化。与项目的第一部分不同，我们将重点放在非香农速率广播模型、随机接入技术和物理层技术上，而不仅仅是纯广播频道。

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.