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

This paper has two parts. The first one deals with how to use large random matrices as building blocks to model the massive data arising from the massive (or large-scale) multiple-input, multiple-output (MIMO) system. As a result, we apply this model for distributed spectrum sensing and network monitoring. The part boils down to the streaming, distributed massive data, for which a new algorithm is obtained and its performance is derived using the central limit theorem that is recently obtained in the literature. The second part deals with the large-scale testbed using software-defined radios (particularly, universal software radio peripheral) that takes us more than four years to develop this 70-node network testbed. To demonstrate the power of the software-defined radio, we reconfigure our testbed quickly into a testbed for massive MIMO. The massive data of this testbed are of central interest in this paper. For the first time, we have modeled the experimental data arising from this testbed. To our best knowledge, there is no other similar work.