Spectrum sensing function for cognitive radio applications

• US 8,154,666 B2
• Filed: 12/23/2008
• Issued: 04/10/2012
• 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
• Primary Examiner(s): Vu, Huy
• Assistant Examiner(s): Renner, Brandon
• Application Number:
  US12/342,485
• Publication Number:
  US 20090102981A1
• Time in Patent Office: 1,204 Days
• Field of Search: 370/252, 370/328, 370/29, 370/390, 375316-352
• 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 waveforms in time and frequency domains comprising the steps of:

selecting a particular portion of a frequency spectrum;

applying a first band pass filter which is configured to exclude regions of the frequency spectrum that are outside of the selected portion;

applying a low noise amplifier;

collecting waveforms from said portion of said frequency spectrum;

downconverting said collected waveforms;

applying an analog to digital conversion to said waveforms at a first sampling rate;

applying a second filter to said waveforms;

up or down converting said waveforms so as to shift a characteristic frequency component of said waveforms to a specified detection frequency;

applying a third filter which is configured to pass only frequencies near the specified detection frequency;

resampling said waveforms so as to adjust the sampling rate;

applying serial to parallel conversion to convert the digitized waveforms to a stream of time domain segments, each time domain segment including a plurality of time domain samples;

applying a Fast Fourier Transform (FFT) to each time domain segment so as to obtain a corresponding frequency domain segment, each of the frequency domain segments including a plurality of frequency domain samples;

processing both the time domain segments and the frequency domain segments using higher order statistics;

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

for at least one segment that is classified as Class Signal, identifying at least one signal within said segment.

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

3. The method of claim 1, wherein said downconverting is to baseband.

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

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

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

7. The method of claim 1 wherein said resampling adjusts the sampling rate to a higher sampling rate.

8. The method of claim 1 wherein said resampling adjusts the sampling rate to a lower sampling rate.

9. The method in claim 1 wherein each said segment is processed individually.

10. The method in claim 1 wherein a plurality of said segments are concatenated into one large block and processed collectively.

11. The method of claim 1 wherein the time domain samples and the corresponding frequency domain samples are vectors having real and imaginary components.

12. The method of claim 1, wherein time domain segments and the corresponding frequency domain segments are processed separately.

13. 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).

14. The method of claim 11, wherein the real and imaginary components are processed separately.

15. 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.

16. The method of claim 1, wherein multiple parallel stages are used to perform steps to up or down convert, adjust, apply a third filter, resample, apply serial to parallel conversion, apply FFT, and process segments using high order statistics so as to perform Spectrum Sensing of multiple characteristic frequency components of said waveforms simultaneously.

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

selecting a particular portion of a frequency spectrum;

applying a band pass filter which is configured to exclude regions of the frequency spectrum that are outside of the selected portion;

applying a low-noise amplifier;

collecting waveforms present in said particular portion of said spectrum;

downconverting said collected waveforms;

applying an analog to digital conversion to said waveforms at a first sampling rate;

first filtering said down-converted waveforms using an image rejection filter, wherein an image of said downconverted waveforms is suppressed;

up or down converting said first filtered waveforms, wherein a characteristic frequency component of said waveforms is shifted closer to 0 Hertz frequency;

second filtering said up or down converted waveforms;

downsampling said second filtered waveforms to reduce its sampling rate;

converting said downsampled waveforms from serial to parallel by dividing said downsampled waveforms into a plurality of time domain segments, each of said time domain segment including a plurality of time domain samples, each of said time domain samples having a real part and an imaginary part;

collecting said segments;

storing said segments in a buffer;

applying a Fast Fourier Transform (FFT) to each of said segments so as to obtain a corresponding frequency domain segment, each of the frequency domain segments including a plurality of frequency domain samples;

determining higher order moments and cumulants of real and imaginary portions of said frequency domain segments;

for each frequency domain segment, calculating a signal probability; and

if the signal probability indicates that a signal has been received, classifying the received signal as Class signal.

18. The method of claim 17 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).

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

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

21. The method of claim 17 wherein said received signal comprises a co-existing beaconing signal.

22. The method of claim 17 wherein said characteristic frequency component of said waveforms is the video carrier of a television signal.

23. The method of claim 17 wherein said characteristic frequency component of said waveforms is the audio carrier of a television signal.

24. The method of claim 17, wherein a demodulating stage Fast Fourier Transform (FFT) detects a wireless microphone signal.

25. The method of claim 17 wherein said Fast Fourier Transform (FFT) is applied by a Fast Fourier Transform stage which is used for data demodulation and is also used to implement a spectrum sensing function.

26. The method of claim 17 wherein said Fast Fourier Transforms (FFTs) have equal sizes.

27. The method of claim 17 wherein said Fast Fourier Transforms (FFTs) have un-equal sizes.

28. The method of claim 17 wherein said collected waveforms of said downconverting step are collected at a Radio Frequency (RF) stage.

29. The method of claim 17 wherein said collected waveforms of said downconverting step are collected at an Intermediate Frequency (IF) stage.

30. The method of claim 17 wherein said first filter of said first filtering step has a bandwidth of 8 MHz.

31. The method of claim 17 wherein said first filtered waveforms are upconverted by approximately 2.69 MHz.

32. The method of claim 17 wherein said second filtering of said up or down converted waveforms is applied by a low pass filter having a bandwidth equal to N_FFT /((T_sensing) Z).

33. The method of claim 17 wherein said downsampling said second filtered waveforms is by a factor of floor (Fs/(Second Filter Bandwidth)).

34. The method of claim 17 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.

35. The method of claim 17 further comprising:

adjusting a fine threshold parameter (γ

); and

reclassifying said received signal.

36. The method of claim 17, wherein said particular portion of said frequency spectrum is a channel.

37. The method of claim 17 wherein said downconverting is to baseband.

38. The method of claim 17 wherein said downconverting is to an intermediate frequency (IF) band.

39. The method of claim 17 wherein Advanced Television Systems Committee (ATSC) digital television (DTV) signals are detected near ATSC-DTV pilot tones.

40. The method of claim 17 wherein a wireless microphone beaconing signal is detected in a vicinity of Advanced Television Systems Committee (ATSC) digital television (DTV) pilot tones.

41. The method of claim 17 wherein Digital Video Broadcasting (DVB) digital television (DTV) signals are detected in a vicinity of pilot tones.

42. The method of claim 17, wherein multiple parallel stages are used to perform the steps of up or down converting, second filtering, downsampling, converting from serial to parallel, applying an FFT, determining higher order moments and cumulants, calculating, and classifying so as to perform spectrum sensing simultaneously at a plurality of characteristic frequency components of said waveforms.

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

a band pass filter that is configured to select waveforms from a particular portion of a frequency spectrum and exclude from the selected waveforms regions of the frequency spectrum that are outside of the selected portion;

a low noise amplifier that is configured to amplify the waveforms;

a waveform collector that is configured to collect the waveforms;

a downconverter that is configured to downconvert said collected waveforms in frequency;

an analog-to-digital converter that is configured to convert the downconverted waveforms to digital waveforms;

an image rejection first Low Pass (LP) filter, that is configured to suppress an image of said downconverted waveforms;

an up/down converter that is configured to up or down convert said first filtered waveforms, wherein a video carrier would be shifted closer to 0 Hertz frequency;

a second filter that is configured to filter said converted waveforms;

a downsampler that is configured to downsample said second filtered waveforms so as to reduce the sampling rate;

a serial-to-parallel converter that is configured to convert said downsampled waveforms from serial to parallel by dividing said downsampled waveforms into a plurality of time domain segments, each time domain segment including a plurality of time domain samples, each time domain sample being a vector having a real part and an imaginary part;

a buffer that is configured to store said time domain segments;

a Fast Fourier Transform module that is configured to apply a Fast Fourier Transform (FFT) to each of the stored time domain segments;

a high order statistical analysis analyzer that is configured to determine higher order moments and cumulants of real and imaginary portions of said samples;

a signal probability calculator that is configured to calculate signal probability; and

a classifier that is configured to classify said received waveforms as being Class noise or Class signal.