Wireless Networking
Mobile networks have become highly complex systems. In order to better understand how network features affect performance and suggest additional improvements, it is crucial to examine them from an empirical perspective.
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The Open Radio Access Network (O-RAN) Alliance's most recent innovations are propelling the evolution of RAN deployments, moving away from conventionally closed and specialized hardware implementations and toward virtualized instances running over shared platforms and distinguished by open interfaces. Such progressive decoupling of radio software components from the hardware is paving the road for future efficient and cost-effective RAN deployments. However, there are still a lot of open challenges before O-RAN networks can be successfully deployed.
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Multipath Wireless Network
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Dataset of received signal strength indication (RSSI) collected from within an indoor office building.
This data set provides a comprehensive set of received signal strength indication (RSSI) readings from within an indoor office building from several perspectives throughout the building.
date/time of measurement start: 2007-08-17
date/time of measurement end: 2007-08-17
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N/A
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LTE Measurements on a 100m (triangular) grid on the University of Colorado Campus.
This data was collected at the University of Colorado Boulder. It contains careful point measurements, taken on a 100m equilateral triangular lattice, of the Verizon LTE network.
date/time of measurement start: 2011-06-01
date/time of measurement end: 2011-09-01
collection environment: The environment is the campus of the University of Colorado, Boulder, Colorado, USA.
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Dataset of RSS measurements collected at the University of Colorado Wide-Area Radio Testbed.
This data was collected by Caleb Phillips at the University of Colorado (CU). It contains RSS measurements (together with GPS data) collected using the CU Wide Area Radio Testbed (CU-WART), which involves seven 802.11 APs with phased array antennas mounted on university buildings.
date/time of measurement start: 2010-05-31
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Opening up of the CBRS band for the secondary users' transmissions poses challenges in the protection of incumbent radar users from co-channel interference. The use of Machine Learning algorithms for addressing these challenges requires representative real-world datasets.This dataset contains overlapping radar and LTE signals captures over-the-air in the shared CBRS band using an experimental testbed composed of software defined radios in RF anechoic chamber.
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