doc.: ieee 802.22-06/0187-01-0000 submissionyonghong zeng, insitute for infocomm researchslide 1...

Yonghong Zeng, Insitute for Infocomm Research

Slide 1

doc.: IEEE 802.22-06/0187-01-0000

Submission

Covariance based sensing algorithms for detection of DTV and wireless microphone

signals

IEEE P802.22 Wireless RANs Date: 2006-11-10

Name Company Address Phone email Yonghong Zeng Institute for

Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613

65-68748211 [email protected]

Ying-Chang Liang Institute for Infocomm Research

21 Heng Mui Keng Terrace, Singapore 119613

65-68748225 [email protected]

Authors:

Notice: This document has been prepared to assist IEEE 802.22. 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 802.22.

Patent Policy and Procedures: The contributor is familiar with the IEEE 802 Patent Policy and Procedures http://standards.ieee.org/guides/bylaws/sb-bylaws.pdf 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 Chair Carl 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 802.22 Working Group. If you have questions, contact the IEEE Patent Committee Administrator at [email protected].>


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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


Slide 3

doc.: IEEE 802.22-06/0187-01-0000

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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


Slide 4

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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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Flow-chart of the energy with minimum eigenvalue (EME) detection


Decision: if the energy >r*minimum eign,signal exists;Otherwise, signal not exists.


Compute the average energy and minimum eigenvalue of the covariance matrix




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Flow-chart of the covariance absolute value (CAV) detection


Decision: if T1 >r*T2,signal exists;Otherwise, signal not exists.


Compute the absolute sum of the matrix, T1,

and the absolute sum of diagonal elements, T2




Slide 7

doc.: IEEE 802.22-06/0187-01-0000

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Flow-chart of the Covariance Frobenius norm (CFN) detection


Decision: if T3 >r*T4,signal exists;Otherwise, signal not exists.


Compute the sum of powers of the matrix elements, T3, and the

sum of powers of diagonal elements, T4




Slide 8

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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)


Slide 9

doc.: IEEE 802.22-06/0187-01-0000

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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)


Slide 10

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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 5.381119 MHz (the same as the captured DTV signal). The sampling rate is 21.524476 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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Slide 11

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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)

EME MME

0.497 0.497 0.496 0.483 0.108 0.081 0.086

EG-xdB: energy detection with xdB noise uncertainty.


Slide 12

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Probability of detection (wireless microphone signals, sensing time 9.30 ms)


Slide 13

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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.


Slide 14

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The filter used for signals and noises(amplitude of frequency response)


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doc.: IEEE 802.22-06/0187-01-0000

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Probability of false alarm (filtered noise, sensing time 18.60 ms)

EG-2dB

EG-1.5dB

EG-1dB

EG-0.5dB


CAV MME

0.499 0.497 0.495 0.487 0.102 0.103 0.105

EG-xdB: energy detection with xdB noise uncertainty.


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Probability of detection (WAS-311/48/01)


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Slide 22

doc.: IEEE 802.22-06/0187-01-0000

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Average probability of detection atSNR = -18 dB and sensing time 18.60 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


CAV MME

0.575 0.576 0.595 0.611 1.000 0.871 0.877


Slide 23

doc.: IEEE 802.22-06/0187-01-0000

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Average probability of detection atSNR = -20 dB and sensing time 60 ms

(average over the 11 DTV signals)

EG-2dB

EG-1.5dB

EG-1dB

EG-0.5dB


CAV MME

0.551 0.560 0.570 0.610 1.000 0.883 0.896


Slide 24

doc.: IEEE 802.22-06/0187-01-0000

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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.


Slide 25

doc.: IEEE 802.22-06/0187-01-0000

Submission

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)


Slide 26

doc.: IEEE 802.22-06/0187-01-0000

Submission

References 1. A. Sahai and D. Cabric, “Spectrum sensing: fundamental limits and practical challenges,”

in Dyspan 2005 (available at: www.eecs.berkeley.edu/ sahai), 2005.∼

2. Steve Shellhammer et al., “Spectrum sensing simulation model”, http://grouper.ieee.org/groups/802/22/Meeting_documents/2006_July/22-06-0028-07-0000-Spectrum-Sensing-Simulation-Model.doc, July 2006.

3. Suhas Mathur et al., “Initial signal processing of captured DTV signals for evaluation of detection algorithms”, http://grouper.ieee.org/groups/802/22/Meeting_documents/2006_Oct/22-06-0158-05-0000-Intial-Signal-Processing-for-DTV-Signal-Files.doc, Oct. 2006.

4. I.M. Johnstone, “On the distribution of the largest eigenvalue in principle components analysis,” The Annals of Statistics, vol. 29, no. 2, pp. 295—327, 2001.

5. Victor Tawil, “51 captured DTV signal”, http://grouper.ieee.org/groups/802/22/Meeting_documents/2006_May/Informal_Documents, May 2006.

6. Yonghong Zeng and Ying-Chang Liang, “Eigenvalue based sensing algorithms”, http://grouper.ieee.org/groups/802/22/Meeting_documents/2006_July/22-06-0118-00-0000_I2R-sensing.doc

7. Yonghong Zeng and Ying-Chang Liang, “Performance of eigenvalue based sensing algorithms for detection of DTV and wireless microphone signals”, http://grouper.ieee.org/groups/802/22/Meeting_documents/2006_Sept/22-06-0186-00-0000_I2R-sensing-2.doc

doc.: ieee 802.22-06/0187-01-0000 submissionyonghong zeng, insitute for infocomm researchslide 1...

Documents

covariance matrix sample

sample covariance matrix

matrix elements

statement ieee standards

infocomm researchslide

submissionyonghong zeng

t2 sample

t4 sample