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The paper has two parts. The first one deals with how to use large randommatrices as building blocks to model the massive data arising from the massive(or large-scale) MIMO system. As a result, we apply this model for distributedspectrum sensing and network monitoring. The part boils down to the streaming,distributed massive data, for which a new algorithm is obtained and itsperformance is derived using the central limit theorem that is recentlyobtained in the literature. The second part deals with the large-scale testbedusing software-defined radios (particularly USRP) that takes us more than fouryears to develop this 70-node network testbed. To demonstrate the power of thesoftware defined radio, we reconfigure our testbed quickly into a testbed formassive MIMO. The massive data of this testbed is of central interest in thispaper. It is for the first time for us to model the experimental data arisingfrom this testbed. To our best knowledge, we are not aware of other similarwork.

Massive MIMO as a Big Data System: Random Matrix Models and Testbed