doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 1 Simulations for wireless microphone detection by eigenvalue and covariance based methods IEEE P Wireless RANs Date: Authors: Notice: This document has been prepared to assist IEEE It is offered as a basis for discussion and is not binding on the contributing individual(s) or organization(s). The material in this document is subject to change in form and content after further study. The contributor(s) reserve(s) the right to add, amend or withdraw material contained herein. Release: The contributor grants a free, irrevocable license to the IEEE to incorporate material contained in this contribution, and any modifications thereof, in the creation of an IEEE Standards publication; to copyright in the IEEE’s name any IEEE Standards publication even though it may include portions of this contribution; and at the IEEE’s sole discretion to permit others to reproduce in whole or in part the resulting IEEE Standards publication. The contributor also acknowledges and accepts that this contribution may be made public by IEEE Patent Policy and Procedures: The contributor is familiar with the IEEE 802 Patent Policy and Procedures including the statement "IEEE standards may include the known use of patent(s), including patent applications, provided the IEEE receives assurance from the patent holder or applicant with respect to patents essential for compliance with both mandatory and optional portions of the standard." Early disclosure to the Working Group of patent information that might be relevant to the standard is essential to reduce the possibility for delays in the development process and increase the likelihood that the draft publication will be approved for publication. Please notify the Chairhttp://standards.ieee.org/guides/bylaws/sb-bylaws.pdf Carl R. StevensonCarl R. Stevenson as early as possible, in written or electronic form, if patented technology (or technology under patent application) might be incorporated into a draft standard being developed within the IEEE Working Group. If you have questions, contact the IEEE Patent Committee Administrator at >

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 2 Microphone signals Type1: simulated soft speaker, signal at origin Type2: simulated soft speaker, signal at other frequency Type3: silent wireless microphone speaker, signal at origin Type4: silent wireless microphone speaker, signal at other frequency Type5: simulated loud speaker, signal at origin Type6: simulated loud speaker, signal at other frequency The power spectral density (PSD) of the signals are shown below, where the unit for x-axis is MHz

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 3 Signal type 1

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 4 Signal type 2

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 5 Signal type 3

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 6 Signal type 4

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 7 Signal type 5

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 8 Signal type 6

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 9 Settings and procedures for the simulations Generate signals at baseband and sample the signal with sampling rate 12 MHz Filter the signal with a low-pass filter with 6 MHz bandwidth Pass the signal through a multipath simulator (Rayleigh fading with 5 taps (equal power profile)) Generate white noise (sampling rate 12 MHz) Filter the white noise with the low-pass filter with 6 MHz bandwidth Add the signal and scaled noise Down-sample (decimate) the signal by a factor 2 Use the eigenvalue and covariance based methods for detection (L=10)

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 10 Notations Pd: probability of detection Pfa: probability of false alarm MME: maximum-minimum eigenvalue detection EME: energy to minimum eigenvalue detection EG-0dB: energy detection with 0 dB noise uncertainty (no noise uncertainty, ideal case) CAV: covariance absolute value detection CFN: covariance Frobenius norm detection In the following, Pfa=0.1 for all cases

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 11 Pd for eigenvalue based methods (sensing time 4ms, Pfa=0.1): type 1 and 2

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 12 Pd for eigenvalue based methods (sensing time 4ms, Pfa=0.1): type 3 and 4

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 13 Pd for eigenvalue based methods (sensing time 4ms, Pfa=0.1): type 5 and 6

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 14 Pd for eigenvalue based methods (sensing time 10ms, Pfa=0.1): type 2

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 15 Pd for eigenvalue based methods (sensing time 10ms, Pfa=0.1): type 4

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 16 Pd for eigenvalue based methods (sensing time 10ms, Pfa=0.1): type 6

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 17 Pd for covariance based methods (sensing time 4ms, Pfa=0.1): type 2

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 18 Pd for covariance based methods (sensing time 4ms, Pfa=0.1): type 4

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 19 Pd for covariance based methods (sensing time 4ms, Pfa=0.1): type 6

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 20 Pd for covariance based methods (sensing time 10ms, Pfa=0.1): type 2

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 21 Pd for covariance based methods (sensing time 10ms, Pfa=0.1): type 4

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 22 Pd for covariance based methods (sensing time 10ms, Pfa=0.1): type 6

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 23 Summary method4ms10ms MME-21.0dB-23.1dB EME-16.4dB-18.4dB CAV CFN EG-0dB The following table gives the required SNR to meet Pd=0.9 and Pfa=0.1 (average on the 6 types of signals)

doc.: IEEE /0325r0 Submission July 2007 Yonghong Zeng, Insitute for Infocomm ResearchSlide 24 References 1.Steve Shellhammer et al., “Spectrum sensing simulation model”, Spectrum-Sensing-Simulation-Model.doc, July Spectrum-Sensing-Simulation-Model.doc 2.Chris Clanton, Mark Kenkel and Yang Tang, “Wireless Microphone Signal Simulation Method,” IEEE /0124r0, March Yonghong Zeng and Ying-Chang Liang, “Maximum-minimum eigenvalue detection for cognitive radio”, IEEE PIMRC, Yonghong Zeng and Ying-Chang Liang, “Covariance based signal detections for cognitive radio”, IEEE DySpan, Yonghong Zeng and Ying-Chang Liang, “Eigenvalue based sensing algorithms”, _I2R-sensing.doc _I2R-sensing.doc 6.Yonghong Zeng and Ying-Chang Liang, “Covariance based sensing algorithms for detection of DTV and wireless microphone signals”, _I2R-sensing-2.ppt _I2R-sensing-2.ppt 7.Yonghong Zeng and Ying-Chang Liang, “Updates on the covariance based and eigenvalue based sensing algorithms”, _I2R-update.ppt _I2R-update.ppt