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This short paper reports some initial experimental demonstrations of thetheoretical framework: the massive amount of data in the large-scale cognitiveradio network can be naturally modeled as (large) random matrices. Inparticular, using experimental data we will demonstrate that the empiricalspectral distribution of the large sample covariance matrix---a Hermitianrandom matrix---agree with its theoretical distribution (Marchenko-Pastur law).On the other hand, the eigenvalues of the large data matrix ---a non-Hermitianrandom matrix---are experimentally found to follow the single ring law, atheoretical result that has been discovered relatively recently. To our bestknowledge, our paper is the first such attempt, in the context of large-scalewireless network, to compare theoretical predictions with experimentalfindings.

Data Modeling with Large Random Matrices in a Cognitive Radio Network Testbed: Initial Experimental Demonstrations with 70 Nodes