论文标题
截止日期是否有效控制蜂窝下行链路系统中的实时流量？
Is Deadline Oblivious Scheduling Efficient for Controlling Real-Time Traffic in Cellular Downlink Systems?
论文作者
论文摘要
5G网络（例如虚拟现实）在5G网络中的带宽关键潜伏流量的出现激发了人们对使用硬质系线流量的无线资源分配问题的兴趣。试图解决此问题带来了两个挑战：（i）基站（BS）APRIORI不知道流量的到达和渠道状态，因此需要在线做出分配决策。 （ii）试图最大化奖励的无线资源分配算法可能是不公平的，从而为某些用户带来了不可接受的服务。我们将问题建模为在线凸优化问题。我们提出了一种正式的截止日期（DO）算法，并表明它大约是3.6竞争力。此外，我们通过模拟显示，我们的算法非常仔细地跟踪先知离线解决方案，极大地超过了几种现有算法。在第二部分中，我们对分配施加了随机约束，要求保证每个用户可以实现一定的及时吞吐量（在一段时间内在截止日期内交付的流量量）。我们建议为该设置的长期公平截止日期遗忘（LFDO）算法。我们将Lyapunov框架与对在线算法的分析相结合，以表明LFDO保留了DO的高性能，同时满足了长期随机约束。
The emergence of bandwidth-intensive latency-critical traffic in 5G Networks, such as Virtual Reality, has motivated interest in wireless resource allocation problems for flows with hard-deadlines. Attempting to solve this problem brings about two challenges: (i) The flow arrival and the channel state are not known to the Base Station (BS) apriori, thus, the allocation decisions need to be made online. (ii) Wireless resource allocation algorithms that attempt to maximize a reward will likely be unfair, causing unacceptable service for some users. We model the problem as an online convex optimization problem. We propose a primal-dual Deadline-Oblivious (DO) algorithm, and show it is approximately 3.6-competitive. Furthermore, we show via simulations that our algorithm tracks the prescient offline solution very closely, significantly outperforming several existing algorithms. In the second part, we impose a stochastic constraint on the allocation, requiring a guarantee that each user achieves a certain timely throughput (amount of traffic delivered within the deadline over a period of time). We propose the Long-term Fair Deadline Oblivious (LFDO) algorithm for that setup. We combine the Lyapunov framework with analysis of online algorithms, to show that LFDO retains the high-performance of DO, while satisfying the long-term stochastic constraints.
