时变衰落信道下多载波干扰网络的分布式功率控制算法-跟踪性能分析与优化
Distributive Power Control Algorithm for Multicarrier Interference Network over Time-Varying Fading Channels - Tracking Performance Analysis and Optimization
摘要：
在过去的几年里，干扰受限网络上的分布式功率控制受到了越来越多的关注。分布式解决方案（如迭代注水，梯度投影等）在\emph{quasi-static}信道下进行了深入研究。然而，由于这种分布式解决方案涉及迭代更新和显式消息传递，因此假设无线信道在迭代期间保持不变是不现实的。不幸的是，这些分布式解决方案在\emph{time-varying}信道下的行为通常是未知的。本文研究了有限状态马尔可夫信道（FSMC）模型下$K$对多载波干扰网络中的分布式尺度梯度投影算法（DSGPA）。我们将分析拟议DSGPA的\emph{convergence property}和\emph{tracking performance}。我们的分析表明，建议的DSGPA收敛到一个极限区域，而不是一个单一的点下的FSMC模型。我们还表明，跟踪误差的增长顺序是由$\mathcal{O}\(1 \big/ \bar{N}\)$，其中$\bar{N}$是FSMC的\emph{average sojourn time}。在此基础上，我们将通过马尔可夫决策过程模型推导出\emph{tracking error optimal scaling matrices}。我们将表明，跟踪误差的最佳缩放矩阵可以实现分布式在每个发射机。数值结果表明，所提出的DSGPA优于三个基线计划，如梯度投影算法与恒定步长的性能。
英文摘要：
Distributed power control over interference limited network has received an increasing intensity of interest over the past few years. Distributed solutions (like the iterative water-filling, gradient projection, etc.) have been intensively investigated under \emph{quasi-static} channels. However, as such distributed solutions involve iterative updating and explicit message passing, it is unrealistic to assume that the wireless channel remains unchanged during the iterations. Unfortunately, the behavior of those distributed solutions under \emph{time-varying} channels is in general unknown. In this paper, we shall investigate the distributed scaled gradient projection algorithm (DSGPA) in a $K$ pairs multicarrier interference network under a finite-state Markov channel (FSMC) model. We shall analyze the \emph{convergence property} as well as \emph{tracking performance} of the proposed DSGPA. Our analysis shows that the proposed DSGPA converges to a limit region rather than a single point under the FSMC model. We also show that the order of growth of the tracking errors is given by $\mathcal{O}\(1 \big/ \bar{N}\)$, where $\bar{N}$ is the \emph{average sojourn time} of the FSMC. Based on the analysis, we shall derive the \emph{tracking error optimal scaling matrices} via Markov decision process modeling. We shall show that the tracking error optimal scaling matrices can be implemented distributively at each transmitter. The numerical results show the superior performance of the proposed DSGPA over three baseline schemes, such as the gradient projection algorithm with a constant stepsize.
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