Signal Processing in Large Systems: A New Paradigm
Signal Processing in Large Systems: A New Paradigm
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DOI：
10.1109/msp.2012.2207490
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发表时间：
2011-04
影响因子：
14.9
通讯作者：
Romain Couillet;M. Debbah
中科院分区：
文献类型：
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作者：
Romain Couillet;M. Debbah
长期以来，信号处理的检测和参数估计方法依赖于渐近统计量，即当一个总体的观测数n相对于总体规模n变大时，即n/ n→∞。现代技术和社会的进步现在要求研究有时极其庞大的人口，同时由于加速的系统动力学，需要快速的信号处理。这导致实际的n/ n比并不大，有时甚至小于1。因此，在过去十年中，经典信号处理方法发生了颠覆性的变化，主要是由大维随机矩阵理论领域推动的。然而，随机矩阵理论在信号处理应用中的早期工作是稀缺的和高度技术性的。本教程为随机矩阵理论的现代工具和由此衍生的信号处理方法提供了一个可访问的方法学介绍，重点是简单的说明性示例。
For a long time, detection and parameter estimation methods for signal processing have relied on asymptotic statistics as the number n of observations of a population grows large comparatively to the population size N, i.e., n/N → ∞. Modern technological and societal advances now demand the study of sometimes extremely large populations and simultaneously require fast signal processing due to accelerated system dynamics. This results in not-so-large practical ratios n/N, sometimes even smaller than one. A disruptive change in classical signal processing methods has therefore been initiated in the past ten years, mostly spurred by the field of large-dimensional random matrix theory. The early works in random matrix theory for signal processing applications are, however, scarce and highly technical. This tutorial provides an accessible methodological introduction to the modern tools of random matrix theory and to the signal processing methods derived from them, with an emphasis on simple illustrative examples.