论文标题
关于在无线网络中安排实时流量的随机化功能
On the Power of Randomization for Scheduling Real-Time Traffic in Wireless Networks
论文作者
论文摘要
在本文中，我们考虑了在冲突式干扰模型和单跳流量下安排无线网络中实时流量的问题。目的是确保在其截止日期内至少将每个链接的一定一部分分组交付，这被称为交付比率。在基于限制性框架的流量模型或LDF（最大缺乏第一）等贪婪的最大调度方案之前，已经对此问题进行了研究，这些计划为一般流量模式提供了较差的交付比率。在本文中，我们通过随机化可以在每次传输的最大链接的选择中随机化采取不同的方法。我们在共处的网络，多目标网络和通用网络中设计随机策略，这些策略可以达到远比LDF所能达到的交付比率要高得多。此外，我们的结果适用于流量（到达和截止日期）过程，这些过程将作为积极的经常性马尔可夫链而发展。因此，与过去的工作相比，这项工作在效率和交通假设方面都是一种改进。我们进一步介绍了各种交通模式和干扰图的广泛仿真结果，以说明我们对LDF变体的随机策略的收益。
In this paper, we consider the problem of scheduling real-time traffic in wireless networks under a conflict-graph interference model and single-hop traffic. The objective is to guarantee that at least a certain fraction of packets of each link are delivered within their deadlines, which is referred to as delivery ratio. This problem has been studied before under restrictive frame-based traffic models, or greedy maximal scheduling schemes like LDF (Largest-Deficit First) that provide poor delivery ratio for general traffic patterns. In this paper, we pursue a different approach through randomization over the choice of maximal links that can transmit at each time. We design randomized policies in collocated networks, multi-partite networks, and general networks, that can achieve delivery ratios much higher than what is achievable by LDF. Further, our results apply to traffic (arrival and deadline) processes that evolve as positive recurrent Markov Chains. Hence, this work is an improvement with respect to both efficiency and traffic assumptions compared to the past work. We further present extensive simulation results over various traffic patterns and interference graphs to illustrate the gains of our randomized policies over LDF variants.
