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Throughput-Optimal Broadcast in Wireless Networks with Dynamic Topology

机译:具有动态拓扑的无线网络中的吞吐量优化广播

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We consider the problem of throughput-optimal broadcasting in a time-varying wireless network with an underlying Directed Acyclic (DAG) topology. Known broadcast algorithms route packets along pre-computed spanning trees. In large wireless networks with time-varying connectivities, the optimal trees are difficult to compute and maintain. In this paper, we propose a new online throughput-optimal broadcast algorithm, which takes packet-by-packet scheduling and routing decisions, obviating the need for any global topological structures, such as spanning trees. Our algorithm utilizes certain queue like system-state information for making transmission decisions and hence, may be thought of as a generalization of the well-known back pressure policy, which makes point-to-point unicast transmission decisions based on the local queue-length information. Technically, the back-pressure algorithm is derived by greedily stabilizing the queues. However, because of packet-duplications, the work-conservation principle is violated, and an analogous queueing process is non-trivial to define in the broadcast setting. To address this fundamental issue, we identify certain state variables whose dynamics behave like virtual queues. By stochastically stabilizing these virtual queues, we devise a throughput-optimal broadcast policy. We also derive new characterizations of the broadcast capacity of time-varying wireless DAGs and propose efficient algorithms to compute the capacity either exactly or approximately under various assumptions.
机译:我们考虑具有基础的有向非循环(DAG)拓扑的时变无线网络中吞吐量优化广播的问题。已知的广播算法沿着预先计算的生成树路由分组。在具有随时间变化的连接性的大型无线网络中,最佳树很难计算和维护。在本文中,我们提出了一种新的在线吞吐量最佳广播算法,该算法采用逐包调度和路由决策,从而消除了对任何全局拓扑结构(例如生成树)的需求。我们的算法利用某些队列(例如系统状态信息)来做出传输决策,因此可以认为是众所周知的反压策略的概括,该策略基于本地队列长度来进行点对点单播传输决策信息。从技术上讲,背压算法是通过贪婪地稳定队列而得出的。但是,由于数据包重复,因此违反了工作保存原则,并且在广播设置中定义类似的排队过程并非易事。为了解决这个基本问题,我们确定某些状态变量,其动态行为类似于虚拟队列。通过随机稳定这些虚拟队列,我们​​设计了一种吞吐量最佳的广播策略。我们还推导了时变无线DAG的广播容量的新特征,并提出了有效的算法来在各种假设下准确地或近似地计算容量。

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