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.

中文翻译：

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无线网络中调度实时流量的随机化的威力

在本文中，我们考虑了在冲突图干扰模型和单跳流量下无线网络中实时流量的调度问题。目标是保证每条链路至少有一定比例的数据包在它们的期限内被传递，这称为传递率。这个问题之前已经在限制性的基于帧的流量模型或贪婪的最大调度方案（如 LDF（最大赤字优先））下研究过，这些方案为一般流量模式提供了较差的交付率。在本文中，我们通过随机化每次可以传输的最大链路的选择来寻求不同的方法。我们在并置网络、多方网络和通用网络中设计随机策略，可以实现远高于 LDF 可实现的交付率。更多，我们的结果适用于作为正循环马尔可夫链演变的交通（到达和截止日期）过程。因此，与过去的工作相比，这项工作在效率和流量假设方面都有所改进。我们进一步展示了对各种流量模式和干扰图的广泛模拟结果，以说明我们的随机策略相对于 LDF 变体的收益。