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This repository contains our raw datasets from channel measurements performed at the University of Utah campus. In addition, we have included a document that explains the setup and methodology used to collect this data, as well as a very brief discussion of results. 
File organization:
* documentation/ - Contains a .docx with the description of the setup and evaluation.
* data/ - HDF5 files containing both metadata and raw IQ samples for
each location at which data was collected. Notice we collected data at 14 
different client locations. See map in the attached docx (skipped locations 12 and 16).
We deployed 5 different receivers at 5 different rooftops. Due to resource constraints,
one set of files contains data from 4 different locations whereas another set 
contains information from the single remaining location.

We have developed a set of python scripts that allow us to parse and analyze the data.
Although not included here, they can be found in our public repository: https://github.com/renew-wireless/RENEWLab
You can find the top script here.

For more information on the POWDER-RENEW project please visit the POWDER website.
The RENEW part of the project focuses on the deployment of an open-source massive MIMO system.
Please visit our website for more information.

 

Massive MIMO is one of the key technologies in 5G wireless broadband, capable of delivering substantial improvements in capacity of next-generation wireless networks. However, due to its inherent complexity, its operation, reconfiguration, and enhancement present significant challenges and risks. In this paper we present RENEW, a fully programmable and observable massive MIMO network. We present the architectural design for full programmability at every layer of the wireless stack, from the radio hardware, including PHY and MAC layer configurations, all the way up to the network core functionality using network function virtualization. We also present mechanisms to enable observability at every layer of the stack. These include various indicators in the radio and core access network, hence enabling effective monitoring, troubleshooting, and performance evaluation of the network at large.