Multi-Node SDR Testbed for Automated Data Collection in Dense Multi-User Environments

Presenter

Jordan B. Hendricks

Group Members

Jariel R. Rodriguez

Campus

UMass Boston

Sponsor

Michael Rahaim, Department of Engineering, UMass Boston

Schedule

Session 1, 10:30 AM - 11:15 AM [Schedule by Time][Poster Grid for Time/Location]

Location

Poster Board A1, Campus Center Auditorium, Row 1 (A1-A20) [Poster Location Map]

Abstract

In the age of technology, wireless device density continues to increase. Furthermore, communications and localization are often considered jointly in modern wireless systems. As novel techniques are considered for dense multi-user environments, researchers need easy-to-use tools to evaluate and compare performance. Our project extends our lab’s existing multi-node wireless testbed infrastructure to enable data collection associated with both data communications and indoor positioning. The initial testbed architecture enables wireless network performance analysis across a set of centrally controlled Raspberry Pi microcontrollers. Our work enhances this testbed with data collection capabilities for RSSI power measurements and SDR-based channel state information readings. These measurements enable testing for a variety of localization techniques where frequency characteristics and signal strength can be used to map the environment. Since accurate testing requires large amounts of data, automated data collection is essential. Accordingly, we developed automated scripts where RSSI is measured repeatedly across distributed nodes. Measurements for channel state information are added to create another dimension of accuracy. Once a map is defined, we can apply machine learning techniques to predict locations of test nodes using similar measurements. For example, if we know the characteristics in one corner of the room, and a device is recognized with similar characteristics, a prediction can be made. Indoor positioning is impactful with asset tracking, security, directional communications, and much more. Given the unique characteristics of different environments, a repeatable, autonomous, and centralized data collection tool offers a foundation for testing and comparing novel localization methodologies that can be user-defined.

Keywords

Wireless Communications, Data Collection, Localization

Research Area

Engineering

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