A Compressive-Sensing Approach to Distributed White-Space Sensing

A Compressive-Sensing Approach to Distributed White-Space Sensing

Internship Description

‚ÄčThe used portions of the UHF spectrum, popularly referred to as white spaces, represent a new frontier for wireless networks, offering the potential for substantial bandwidth and long transmission ranges. These white spaces include, but not limited to, 180 MHz of available bandwidth from channel 21 (512 MHz) to 51 (698 MHz), with the exception of channel 37. On November 4, 2008, the FCC issued a historic ruling permitting the use of unlicensed devices in these white spaces [10]. In its ruling the FCC imposed an important requirement that white space wireless device must not interfere with incumbents, including TV broadcasts and wireless microphone transmissions. This landmark ruling was a result of extensive tests performend by the FCC on white space hardware prototypes that were submitted by Adaptrum, Microsoft, Phillips and Motorola. These prototypes demonstrated feasible solutions for an accurate ang agile sensing of incumbent signals. Most of the prior research in UHF white spaces has focused on accurately detecting the presence of incumbent RF signals [1, 2, 3].  In  this project the goal is to complement existing spectral sensing techniques for white space identification by pursuing a compressive sensing (CS) based approach [4, 5, 6]. The fact that the number of free channels can be small, compared to the total number of possible channels, suggests that this spectral sensing problem can be modeled as a sparse recovery / detection problem. The expected benefit of the application of CS is improving accuracy and reducing computational complexity. In addition, the structure of the measurement matrix can be utilized for further performance enhancement and computational complexity reduction [7], thus resulting in a more robust and fast spectral sensing technique.

Faculty Name

Field of Study

‚ÄčElectrical Engineering