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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最优广播

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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:我们考虑在时变无线网络中的吞吐量最优广播问题,其具有底层定向的非循环(DAG)拓扑。已知的广播算法沿预先计算的生成树路线数据包。在大型无线网络中具有时变的连接性,最佳树木难以计算和维护。在本文中,我们提出了一种新的在线吞吐量 - 最优广播算法,它采用数据包调度和路由决策,避免了对跨越树的任何全局拓扑结构的需求。我们的算法利用System-Station信息的某些队列来进行传输决策,因此可以被认为是众所周知的背压策略的概括,这使得基于本地队列长度的点对点单播传输决策信息。从技术上讲,通过贪婪稳定队列来导出后压算法。但是,由于数据包重复,违反了工作 - 保护原理,并且类似的排队过程是非微不足道的,以在广播设置中定义。为了解决这一基本问题,我们确定了某些状态变量,其动态行为像虚拟队列。通过随机稳定这些虚拟队列,我们​​设计了一种吞吐量最佳广播策略。我们还导出了多变无线DAG的广播能力的新特征,并提出了高效算法来计算恰好或大致在各种假设下的容量。

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