Throughput-optimal transmission scheduling in wireless networks has been a well considered problem in the literature, and the method for achieving optimality, MaxWeight scheduling, has been known for several decades. This algorithm achieves optimality by adaptively scheduling transmissions relative to each user’s stochastic traffic demands. To implement the method, users must report their queue backlogs to the network controller and must rapidly respond to the resulting resource allocations. However, many currently-deployed wireless systems are not able to perform these tasks and instead expect to occupy a fixed assignment of resources. To accommodate these limitations, adaptive scheduling algorithms need to interactively estimate these uncooperative users’ queue backlogs and make scheduling decisions to account for their predicted behavior. In this work, we address the problem of scheduling with uncooperative legacy systems by developing algorithms to accomplish these tasks. We begin by formulating the problem of inferring the uncooperative systems’ queue backlogs as a partially observable Markov decision process and proceed to show how our resulting learning algorithms can be successfully used in a queue-length-based scheduling policy. Our theoretical analysis characterizes the throughput-stability region of the network and is verified using simulation results.

中文翻译：

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非合作频谱共享网络中的吞吐量最大化

无线网络中的吞吐量优化传输调度一直是文献中考虑的问题，并且实现最优性的方法MaxWeight调度已经有几十年了。该算法通过根据每个用户的随机流量需求自适应地调度传输来实现最优性。要实现该方法，用户必须将其队列积压报告给网络控制器，并且必须快速响应所产生的资源分配。但是，许多当前部署的无线系统无法执行这些任务，而是期望占用资源的固定分配。为了适应这些限制，自适应调度算法需要交互式地估计这些不合作的用户的队列积压，并做出调度决定以解决他们的预测行为。在这项工作中，我们通过开发完成这些任务的算法来解决与不合作的遗留系统进行调度的问题。我们首先提出将不合作系统的队列积压推论为部分可观察到的马尔可夫决策过程的问题，然后继续展示如何将我们的学习算法成功用于基于队列长度的调度策略。我们的理论分析表征了网络的吞吐量稳定性区域，并通过仿真结果进行了验证。我们首先提出将不合作系统的队列积压推论为部分可观察到的马尔可夫决策过程的问题，然后继续展示如何将我们的学习算法成功用于基于队列长度的调度策略。我们的理论分析表征了网络的吞吐量稳定性区域，并通过仿真结果进行了验证。我们首先提出将不合作系统的队列积压推论为部分可观察到的马尔可夫决策过程的问题，然后继续展示如何将我们的学习算法成功用于基于队列长度的调度策略。我们的理论分析表征了网络的吞吐量稳定性区域，并通过仿真结果进行了验证。