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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 1 Covariance based sensing algorithms for detection of DTV and wireless microphone signals 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 IEEEs name any IEEE Standards publication even though it may include portions of this contribution; and at the IEEEs 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 >

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 2 Abstract Sensing algorithms using properties of the sample covariance matrix are presented The methods can be used without knowledge of the signal, the channel and noise power Simulation results based on the captured DTV signals and wireless microphone signals are presented Comparisons with the energy detection are given

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 3 Principle of the algorithms The statistics of signal is different from that of noise The difference is characterized by the eigenvalue distributions or non-diagonal elements of the covariance matrix

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 4 Flow-chart of the maximum-minimum eigenvalue (MME) detection Transform the sample covariance matrix Decision: if the maximum eign >r*minimum eign, signal exists; Otherwise, signal not exists. Choose a smoothing factor and the threshold r Compute the maximum eigenvalue and minimum eigenvalue of the covariance matrix Sample and filter the signals Compute the sample covariance matrix

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 5 Flow-chart of the energy with minimum eigenvalue (EME) detection Transform the sample covariance matrix Decision: if the energy >r*minimum eign, signal exists; Otherwise, signal not exists. Choose a smoothing factor and the threshold r Compute the average energy and minimum eigenvalue of the covariance matrix Sample and filter the signals Compute the sample covariance matrix

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 6 Flow-chart of the covariance absolute value (CAV) detection Transform the sample covariance matrix Decision: if T1 >r*T2, signal exists; Otherwise, signal not exists. Choose a smoothing factor and the threshold r Compute the absolute sum of the matrix, T1, and the absolute sum of diagonal elements, T2 Sample and filter the signals Compute the sample covariance matrix

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 7 Flow-chart of the Covariance Frobenius norm (CFN) detection Transform the sample covariance matrix Decision: if T3 >r*T4, signal exists; Otherwise, signal not exists. Choose a smoothing factor and the threshold r Compute the sum of powers of the matrix elements, T3, and the sum of powers of diagonal elements, T4 Sample and filter the signals Compute the sample covariance matrix

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 8 Advantages of the algorithms No signal information is needed (compared to coherent detection) Robust to multipath propagation (compared to coherent detection) No synchronization is needed (compared to coherent detection) No noise uncertainty problem (compared to energy detection) Good performance (can be better than the ideal energy detection without noise uncertainty)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 9 Advantages of the algorithms Same detection method for all signals (DTV, wireless microphone, …) Same threshold for all signals (the thresholds is independent on the signal and noise power)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 10 Simulations for wireless microphone signals FM modulated wireless microphone signal (200 KHz bandwidth) The source signal is generated as evenly distributed real number in (-1,1). We assume that the signal has been down converted to the IF with central frequency MHz (the same as the captured DTV signal). The sampling rate is MHz (the same as the captured DTV signal). The passband filter with bandwidth 6 MHz is the raised cosine filter with 89 tapes. The signal and white noise are passed through the same filter. Sensing time is 9.30 mili seconds (ms). The smoothing factor is chosen as L=10. The threshold is set based on the required Pfa=0.1 (using random matrix theory) and fixed for all signals. The threshold is not related to noise power.

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 11 Probability of false alarm (filtered noise, sensing time 9.30 ms) EG- 2dB EG- 1.5dB EG- 1dB EG- 0.5dB EG-0dB (no uncertainty) EMEMME EG-xdB: energy detection with xdB noise uncertainty.

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 12 Probability of detection (wireless microphone signals, sensing time 9.30 ms)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 13 Simulations for captured DTV signals Based on the Spectrum sensing simulation model. The captured DTV signals are passed through a raised cosine filter (bandwidth 6 MHz, rolling factor ½, 89 tapes). White noises are added and passed through the same filter to obtain the various SNR levels. The smoothing factor is chosen as L=16. The threshold is set based on the required Pfa=0.1 (using random matrix theory) and fixed for all signals. The threshold is not related to noise power.

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 14 The filter used for signals and noises (amplitude of frequency response)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 15 Probability of false alarm (filtered noise, sensing time ms) EG- 2dB EG- 1.5dB EG- 1dB EG- 0.5dB EG-0dB (no uncertainty) CAVMME EG-xdB: energy detection with xdB noise uncertainty.

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 16 Probability of detection (WAS-311/48/01)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 17 Probability of detection (WAS-311/36/01)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 18 Probability of detection (WAS-006/34/01)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 19 Probability of detection (WAS-051/35/01)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 20 Probability of detection (WAS-032/48/01)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 21 Probability of detection (WAS-049/34/01)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 22 Average probability of detection at SNR = -18 dB and sensing time ms 11 of the 12 DTV signals in the proposed subset of captures (by Victor) were tested. (signal WAS-047/36/01 not found). The average probability of detection at SNR = -18 dB is as follows. EG- 2dB EG- 1.5dB EG- 1dB EG- 0.5dB EG-0dB (no uncertainty) CAVMME

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 23 Average probability of detection at SNR = -20 dB and sensing time 60 ms (average over the 11 DTV signals) EG- 2dB EG- 1.5dB EG- 1dB EG- 0.5dB EG-0dB (no uncertainty) CAVMME

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 24 The computational complexity Filtering the received signals: (K+1)N multiplications and additions, where K is the order of filter and N is the number of samples (if K is large, FFT can be used to reduce the complexity); Computing the covariance matrix of the received signal: LN multiplications and additions, where L is the smoothing factor; Transforming the covariance matrix: needs 2L^3 multiplications and additions; Others: at most L^2 multiplications and additions; Total: (K+L+1)N+2L^3+L^2 multiplications and additions.

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 25 Conclusions The covariance based detections do not need any information on signal, the channel, the noise level and SNR Same detection method for all signals (DTV, wireless microphone, …) Same threshold for all signals (the thresholds is independent on the signal and noise power) Performance is comparable to ideal energy detection (can be better than if over- sampled)

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doc.: IEEE / SubmissionYonghong Zeng, Insitute for Infocomm ResearchSlide 26 References 1.A. Sahai and D. Cabric, Spectrum sensing: fundamental limits and practical challenges, in Dyspan 2005 (available at: sahai), Steve Shellhammer et al., Spectrum sensing simulation model, Spectrum-Sensing-Simulation-Model.doc, July Spectrum-Sensing-Simulation-Model.doc 3.Suhas Mathur et al., Initial signal processing of captured DTV signals for evaluation of detection algorithms, Intial-Signal-Processing-for-DTV-Signal-Files.doc, Oct I.M. Johnstone, On the distribution of the largest eigenvalue in principle components analysis, The Annals of Statistics, vol. 29, no. 2, pp , Victor Tawil, 51 captured DTV signal, May Yonghong Zeng and Ying-Chang Liang, Eigenvalue based sensing algorithms, 0000_I2R-sensing.doc 0000_I2R-sensing.doc 7.Yonghong Zeng and Ying-Chang Liang, Performance of eigenvalue based sensing algorithms for detection of DTV and wireless microphone signals, 0000_I2R-sensing-2.doc 0000_I2R-sensing-2.doc

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