A sub-space method to detect multiple wireless microphone signals in TV band white space

Authors:
Harpreet S. Dhillon;Jeong-O Jeong;Dinesh Datla;Michael Benonis;R. Michael Buehrer;Jeffrey H. Reed
Affiliations:
Wireless Networking and Communications Group (WNCG), Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, USA 78712;Wireless@Virginia Tech, Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, USA 24061;Wireless@Virginia Tech, Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, USA 24061;Wireless@Virginia Tech, Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, USA 24061;Wireless@Virginia Tech, Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, USA 24061;Wireless@Virginia Tech, Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, USA 24061
Venue:
Analog Integrated Circuits and Signal Processing
Year:
2011

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Abstract

The main hurdle in the realization of dynamic spectrum access systems from the physical layer perspective is the reliable sensing of low power licensed users. One such scenario shows up in the unlicensed use of the TV bands where the TV band devices are required to sense extremely low power wireless microphones (WMs). The lack of technical standards among various wireless manufacturers and the resemblance of certain WM signals to narrow-band interference signals, such as spurious emissions, further aggravate the problem. Due to these uncertainties, it is extremely difficult to abstract the features of WM signals and hence develop robust sensing algorithms. To partly counter these challenges, we develop a two-stage sub-space algorithm that detects multiple narrow-band analog frequency-modulated signals generated by WMs. The performance of the algorithm is verified by using the real WM signals experimentally captured under low SNR conditions. The problem of differentiating between the WM and other narrow-band signals is left as future work.