预测网络控制与MaxWeight的可调次优性
Predictive Network Control and Throughput Sub-Optimality of MaxWeight
摘要：
我们提出了一种新的控制策略，称为预测网络控制（PNC）控制无线通信网络（在数据包级别），基于模型预测控制（MPC）的范例。与使用提前一步预测的常见近视策略相比，PNC预测系统在扩展范围内的未来行为，从而促进性能增益。我们定义了一个先进的系统模型，在该模型中，我们使用马尔可夫链与伯努利试验相结合，以模拟网络的随机组件。此外，我们介绍了算法，并提出了两个详细的仿真例子，这表明一般的改进性能和增益的稳定区域相比，标准的政策。
英文摘要：
We present a novel control policy, called Predictive Network Control (PNC) to control wireless communication networks (on packet level), based on paradigms of Model Predictive Control (MPC). In contrast to common myopic policies, who use one step ahead prediction, PNC predicts the future behavior of the system for an extended horizon, thus facilitating performance gains. We define an advanced system model in which we use a Markov chain in combination with a Bernoulli trial to model the stochastic components of the network. Furthermore we introduce the algorithm and present two detailed simulation examples, which show general improved performance and a gain in stability region compared to the standard policy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
