Collaborative Research: Systematic Optimization in Wireless Multicasting

多播是将公共信息从一个源发送到多个目的地的网络，是多媒体分发、信息更新、群组会议等网络应用的核心组成部分。在中间终端对网络跟踪进行创新性编码，可以显著提高多播的吞吐量，优于传统的复制转发方法。由于无线介质的开放性质，无线链路的通信吞吐量取决于其传输功率和附近网络终端产生的干扰。本研究的目标是开发一个系统的框架，通过传输功率，速率和调度的联合优化最大化的一般组播utility function，在网络coding.Investigators模型的无线adhoc网络的拓扑图，其中包含点到点链路和点对多点超弧链路耦合链路吞吐量的能力。在最优网络编码的假设下，本文首先提出了一个迭代梯度导向的优化框架。网络效用最大化问题被转换为传输调度问题，该传输调度问题在其梯度方向上最大化近似效用，再加上连续更新近似效用及其瞬时梯度方向的导向矢量更新。然后，研究扩展的框架，多个组播会话的网络效用最大化。最后，研究开发分布式算法，以优化一个大规模的网络使用本地控制器的全局效用。 除了计划中的研究，调查人员还将尝试扩展算法到ad hoc网络与时变信道效用最大化需要有效利用的信道分集增益。

Research AbstractMulticasting, where common information is transmitted from a source to multiple destinations, is the core component of many network applications such as multimedia distribution, information update, group conferencing, etc. Creative encoding of network trac at the intermediate terminals can significantly improve the throughput of a multicast over conventional replicate-and-forward approaches. Due to the open nature of the wireless medium, communication throughput of a wireless link depends on its transmission power and on the interference generated by nearby network terminals. The goal of this research is to develop a systematic framework for maximizing a general multicast utility function via the joint optimization of transmission power, rate, and schedule, within the framework of network coding.The investigators model a wireless ad hoc network by means of a topology graph, which contains point-to-point links and point-to-multipoint hyperarc links with coupled link throughput capacities. Under the assumption of optimal network coding, the research first develops an iterative gradient-steering" optimization framework. A network utility maximization problem is converted to a transmission scheduling problem that maximizes an approximated utility in its gradient direction, coupled with a steering vector update that continuously updates the approximated utility and its instantaneous gradient direction. The research then extends the framework to utility maximization for a network with multiple multicast sessions. Finally, the research develops distributed algorithms to optimize a global utility of a large scale network using local controllers. In addition to the planned research, the investigators will also try to extend the algorithm to ad hoc networks with time varying channels where utility maximization requires efficient exploitation of the channel diversity gain.