SPECTRUM SENSING FUNCTION FOR COGNITIVE RADIO APPLICATIONS

• US 20090102981A1
• Filed: 12/23/2008
• Published: 04/23/2009
• Est. Priority Date: 07/12/2007
• Status: Active Grant

• Current Assignee: BAE Systems Information and Electronic Systems Integration Incorporated (BAE Systems Plc)
• Original Assignee: BAE Systems Information and Electronic Systems Integration Incorporated (BAE Systems Plc)
• Inventors: Mody, Apurva N.
• Granted Patent: US 8,154,666 B2
• Time in Patent Office: Days
• US Class Current: 348/732
• CPC Class Codes: H04K 3/22 including jamming detection and monitoring
• CPC Class Codes: H04K 3/45 characterized by including monitoring of the target or target signal, e.g. in reactive jammers or follower jammers for example by means of an alternation of jamming phases and monitoring phases, called "look-through mode"
• CPC Class Codes: H04K 3/822 by detecting the presence of a surveillance, interception or detection
• CPC Class Codes: H04L 27/2647 Arrangements specific to the receiver only equalisation H04L27/01
• CPC Class Codes: H04L 5/0007 the frequencies being orthogonal, e.g. OFDM(A), DMT
• CPC Class Codes: H04W 16/14 Spectrum sharing arrangements between different networks

Abstract

A method and system are disclosed to detect a broad class of signals including Advanced Television Systems Committee (ATSC) digital television (DTV) and wireless microphone signals. This signal detection method performs in Gaussian noise, employing Higher Order Statistics (HOS). Signals are processed in time and frequency domains as well as by real and imaginary components. The spectrum sensing employed also supports Denial of Service (DoS) signal classification. The method can include parameters that may be tailored to adjust the probability of detection and false alarm.

Claims

1. A method for implementation of a Spectrum Sensing Function wherein Higher Order Statistics (HOS) are applied to segments of received signals in time and frequency domains comprising the steps of:

moving to a particular portion of a frequency spectrum;

applying band pass filter;

applying low noise amplifier;

collecting waveforms in said portion of a frequency spectrum;

downconverting said collected waveforms;

applying a low pass filter;

converting to focus on a spectrum of interest;

sampling to adjust a sampling rate;

applying an analog to digital conversion;

applying serial to parallel conversion to convert a stream of samples;

applying a Fast Fourier Transform (FFT);

detecting at least one signal using higher order statistics;

classifying a segment as belonging to Class Signal or Class Noise; and

identifying said at least one signal.

2. The method of claim 1, wherein said frequency spectrum is a channel.

3. The method of claim 1, wherein said downconverting is to base-band.

4. The method of claim 1, wherein said downconverting is to an intermediate frequency (IF) band.

5. The method of claim 1 wherein said converting is up.

6. The method of claim 1 wherein said converting is down.

7. The method of claim 1 wherein said sampling is up.

8. The method of claim 1 wherein said sampling is down.

9. The method of claim 1 wherein said applying serial to parallel conversion converts said stream of samples to segments.

10. The method in claim 1 wherein each SAID segment is processed individually.

11. The method in claim 1 wherein all SAID segments are concatenated into one large block and processed collectively.

12. The method of claim 1 wherein said applying serial to parallel conversion converts said stream of samples to vectors.

13. The method of claim 1, wherein time and frequency domain components are processed separately.

14. The method of claim 1, comprising pre-processing selected from the group consisting of:

filtering, noise whitening, down-conversion, up-conversion, frequency shift, frequency translation, re-sampling, down-sampling, up-sampling, applying a Fast Fourier Transform (FFT), signal conditioning wherein said pre-processing is applied to said collected waveform before computing higher order statistics (HOS).

15. The method of claim 1, wherein real and imaginary components are processed separately.

16. The method of claim 1, wherein Higher Order Statistics (HOS) processing is selected from the group consisting of:

singular higher order cumulants, power spectral density, bi-spectrum, tri-spectrum, and a poly-spectrum.

17. The method of claim 1, wherein multiple parallel stages are used to perform steps to convert, adjust, second low pass filter, sample, and signal detection to perform Spectrum Sensing in multiple bands simultaneously.

21. The method of claim 17 wherein said received signal comprises a wireless microphone beaconing signal.

18. A method for signal identification comprising the steps of:

moving to a particular portion of a frequency spectrum;

applying a band pass filter;

collecting waveforms present in said spectrum;

downconverting said collected waveforms;

applying an analog to digital conversion;

first filtering down-converted signal through an image rejection first Low Pass (LP) filter, wherein an image of said downconverted signal is suppressed;

upconverting said first filtered signal, wherein a characteristic frequency component of said signal would be shifted closer to 0 Hertz frequency;

second filtering said upconverted signal;

downsampling said second filtered signal;

converting samples of said downsampled signal from serial to parallel;

collecting said samples;

storing said samples in a buffer;

applying a Fast Fourier Transform (FFT);

determining higher order moments and cumulants of real and imaginary portions of said stored samples;

calculating signal probability; and

classifying received signal.

19. The method of claim 18 wherein a standardizing source for said received signal is selected from the group consisting of Advanced Television Systems Committee (ATSC), Digital Television (DTV), National Television Systems Committee (NTSC), and Digital Video Broadcasting (DVB).

20. The method of claim 18 wherein said received signal comprises a wireless microphone signal.

22. The method of claim 18 wherein said received signal comprises a co-existing beaconing signal.

23. The method of claim 18 wherein said characteristic frequency component of said signal is the video carrier of a television signal.

24. The method of claim 18 wherein said characteristic frequency component of said signal is the audio carrier of a television signal.

25. The method of claim 18, wherein a demodulating stage Fast Fourier Transform (FFT) detects a wireless microphone signal.

26. The method of claim 18 wherein said Fast Fourier Transform (FFT) stage used for data demodulation is used to implement a spectrum sensing function.

27. The method of claim 18 wherein said Fast Fourier Transforms (FFTs) used for data demodulation and to implement a spectrum sensing function have equal sizes.

28. The method of claim 18 wherein said Fast Fourier Transforms (FFTs) used for data demodulation and to implement a spectrum sensing function have un-equal sizes.

29. The method of claim 18 wherein said collected waveform of said downconverting step is collected at Radio Frequency (RF) stage.

30. The method of claim 18 wherein said collected waveform of said downconverting step is collected at Intermediate Frequency (IF) stage.

31. The method of claim 18 wherein said first low pass filter of said first filtering step has a bandwidth of 8 MHz.

32. The method of claim 18 wherein said first filtered signal is upconverted by approximately 2.69 MHz.

33. The method of claim 18 wherein said second low pass filter of said second filtering step has a bandwidth equal to N_FFT/((T_sensing)Z).

34. The method of claim 18 wherein said downsampling said second filtered signal is by a factor of floor (Fs/(Second Filter Bandwidth)).

35. The method of claim 18 wherein said converting step is carried out using a first-in first-out (FIFO) buffer and a Fast Fourier Transform (FFT) of length N_FFT.

36. The method of claim 18 further comprising:

adjusting a fine threshold parameter (γ

); and

reclassifying said received signal.

37. The method of claim 18 comprising:

choosing a probability step parameter (δ

) equal to one-half the inverse of a number of moments and cumulants of order greater than two available for computation of real and imaginary parts of each segment of said received signal.

38. The method of claim 18 comprising:

choosing all cumulants greater than two for computation of signal detection probabilities.

39. The method of claim 18 comprising:

choosing a subset of cumulants for computation of signal detection probabilities.

40. The method of claim 18, wherein said frequency spectrum is a channel.

41. The method of claim 18 wherein said downconverting is to base-band.

42. The method of claim 18 wherein said downconverting is to an intermediate frequency (IF) band.

43. The method of claim 18 wherein Advanced Television Systems Committee (ATSC) digital television (DTV) signals are detected near ATSC-DTV pilot tones.

44. The method of claim 18 wherein a wireless microphone beaconing signal is detected in a vicinity of Advanced Television Systems Committee (ATSC) digital television (DTV) pilot tones.

45. The method of claim 18 wherein Digital Video Broadcasting (DVB) digital television (DTV) signals are detected in a vicinity of pilot tones.

46. The method of claim 18, wherein multiple parallel stages are used to perform steps to convert, adjust, second low pass filter, sample, and signal detection to perform Spectrum Sensing in multiple bands simultaneously.

47. A wireless system for Spectrum Sensing and signal identification wherein Higher Order Statistics (HOS) are applied to segments of received signals in time and frequency domains comprising:

signal detection and identification classification modules configured to perform the steps of;

moving to a particular portion of a frequency spectrum;

applying a band pass filter;

collecting waveforms present in said spectrum;

downconverting said collected waveforms;

applying an analog to digital conversion;

first filtering down-converted signal through an image rejection first Low Pass (LP) filter, wherein an image of said downconverted signal is suppressed;

upconverting said first filtered signal, wherein a video carrier would be shifted closer to 0 Hertz frequency;

second filtering said upconverted signal;

downsampling said second filtered signal;

converting samples of said downsampled signal from serial to parallel;

collecting said samples;

storing said samples in a buffer;

applying a Fast Fourier Transform (FFT);

determining higher order moments and cumulants of real and imaginary portions of said stored samples;

calculating signal probability; and

classifying said received signal.

48. A computer-readable medium having computer-readable signals stored thereon that define instructions that, as a result of being executed by a computer, instruct said computer to perform a method for signal identification wherein Higher Order Statistics (HOS) are applied to segments of received signals in time and frequency domains comprising the steps of:

receiving a signal;

detecting at least one signal; and

identifying said at least one signal.

49. A computer-readable medium having computer-readable signals stored thereon that define instructions that, as a result of being executed by a computer, instruct said computer to perform a method for signal identification wherein Higher Order Statistics (HOS) are applied to segments of received signals in time and frequency domains comprising the steps of:

receiving a signal;

detecting at least one signal; and

classifying said at least one signal.

50. A method for classifying a Denial of Service (DoS) signal comprising the steps of:

determining bit error rate degradation of a received signal;

performing signal or noise detection on said received signal using higher order statistics (HOS);

detecting time and frequency domain components of said received signal;

identifying Gaussianity whereby said DoS signal is classified from results of said detecting step.