1. Compressed Sensing Approaches to Spectrum Sensing in Cognitive Radio Networks
Peng Zhang
Cognitive Radio Institute
Tennessee Technological University
9/18/2018

2. Acknowledgement Center for Manufacturing Research
National Science Foundation
Office of Naval Research
Army Research Office
Prof Robert Qiu, Dr. Nan Guo and my labmates

3. Objectives & Contact What is compressed sensing (CS)
How can CS help in communications
How to use CS in your research
How can CS help in spectrum sensing for cognitive radio networks (CRN)
Lab webpage:
This presentation can be downloaded at
Introduce CS with few mathematical stuffs

4. Outline Brief Introduction to CS CS and Spectrum Sensing for CRN
How CS Comes
How to use CS
Applications
UWB communications
Imaging
Radar
Your research
CS and Spectrum Sensing for CRN
Brief introduction to CRN
Brief introduction to spectrum sensing and challenges
CS based spectrum sensing
Conclusions

5. Outline Brief Introduction to CS CS and Spectrum Sensing for CRN
How CS Comes
How to use CS
Applications
UWB communications
Imaging
Radar
Your research
CS and Spectrum Sensing for CRN
Brief introduction to CRN
Brief introduction to spectrum sensing and challenges
CS based spectrum sensing
Conclusions

6. How CS Comes Curse of sampling
Nyquist and Shannon’s sampling statement
Sampling rate must be dense enough
Twice as much as signal’s bandwidth
Consequences of this curse
Large number of sensors
Denser sampling
Deluge of data: How to acquire, store, process efficiently?

7. How CS Comes Hints from digital compression/de-compression
Example: Image compression
10 MB image -> 0.1 MB JPG image
The image has a SPARSE structure in another domain
E.g., Wavelet Transform

8. How CS Comes The flow of compression Dense sampling at Nyquist rate
Sparse samples after compression
Decompress/Reconstruct data

9. How CS Comes Question: Can we get the compressed samples directly?
The breakthrough: CS
The flow of sensing the “compressed samples”
Compressed sensing at much lower rate
Decompress/Reconstruct data

10. Outline Brief Introduction to CS CS and Spectrum Sensing for CRN
How CS Comes
How to use CS
Applications
UWB communications
Imaging
Radar
Your research
CS and Spectrum Sensing for CRN
Brief introduction to CRN
Brief introduction to spectrum sensing and challenges
CS based spectrum sensing
Conclusions

11. How to Use CS How to get the ‘compressed samples’?
Sparse representation: K-sparse signal
Sampling method
Dense sampling at Nyquist rate
Random sampling at much lower rate

12. How to Use CS
Can original signal be reconstructed from such randomness?
Yes because
Random samples preserves all original information with high probability

13. How to Use CS The math behind random sampling
Sparse representation with dictionary
Random sampling with sampling matrix

14. How to Use CS The requirement of randomness
Sampling matrix must meet the restricted isometry property (RIP)
Sampling matrix must be incoherent with dictionary matrix
Fortunately, the following matrices are good with high probability
Random Gaussian
Random binary
All information of original signals is compressed in the random samples!

15. How to Use CS How to reconstruct signal from random samples?
This can be solved by convex programming tools
The solvers:
Basis pursuit (BP)
Matching pursuit (MP)
Orthogonal matching pursuit (OMP) …

16. How to Use CS Example Signal is sparse in Fourier domain
Random sampling in time domain
30 random samples << 1000 Nyquist rate samples
Perfect recovery from random samples

17. Outline Brief Introduction to CS CS and Spectrum Sensing for CRN
How CS Comes
How to use CS
Applications
UWB communications
Imaging
Radar
Your research
CS and Spectrum Sensing for CRN
Brief introduction to CRN
Brief introduction to spectrum sensing and challenges
CS based spectrum sensing
Conclusions

18. Applications Summary of the success of CS Applications
Sparse representation
Random sampling
Reconstruction
Applications
UWB communications
Imaging
Radar

19. Applications UWB communications
UWB has salient property in radio propagation
Robust against multi-path fading

20. Applications UWB challenges Curse of sampling rate
Ultra-wide in frequency domain
Ultra-short in time domain

21. Applications How CS is used in UWB communications
UWB signal is sparse in time domain
Random sampling possible using incoherent filter
Reconstruction possible using digital modules
The CS based UWB system using 50 Msps sampling rate 1 ……

22. Applications Radar Radar also uses UWB pulses to sense targets
Transmit one single UWB pulse
Receive several reflected pulses, or the channel
Use the channel for target detection
Radar also has the curse
The narrower UWB pulse, the better detection performance, the HIGHER sampling rate

23. Applications How CS is used in Radar system
Reflected pulses are sparse in time domain
Random sampling possible using PN generator
Reconstruction possible using digital modules
The CS based Radar sensing system model

24. Applications
Illustrative sensing result

25. Applications Single pixel camera Image is sparse in wavelet domain
Random sampling possible using DMD array
Separate single pixel measurement
Reconstruction possible using digital module

26. Applications
Illustrative result

27. Your Research? Steps to use CS in your research Understand CS
Analysis your research problems
Determine sparse representations
Determine random sampling methods
Use reconstruction solvers
Check the result!

28. Outline Brief Introduction to CS CS and Spectrum Sensing for CRN
How CS Comes
How to use CS
Applications
UWB communications
Imaging
Radar
Your research
CS and Spectrum Sensing for CRN
Brief introduction to CRN
Brief introduction to spectrum sensing and challenges
CS based spectrum sensing
Conclusions

29. Brief Introduction to CRN
Spectrum usage, the pessimistic view
Lack of unused spectrum
300 GHz already depleted

30. Brief Introduction to CRN
Challenges for current radios
Spectrum, the most expensive thing
A $4.6 billion auction for 22 MHz C-band
Increasing demands
HDTV, 3G/4G network, WiFi, etc..
Is spectrum really depleted?
Today, spectrum might be the most expensive thing. In US spectrum auction in 2008, 22 MHz C band was bid for $4.6 billion.
On the other side, the demands for new radios are still booming. HDTV, 3G/4G cellphone network, WiFi… More and more people are getting into the radio world. So the natrual question arise: is spectrum really depleted?

31. Brief Introduction to CRN
Spectrum usage, the optimistic view
Inefficient use of spectrum by “primary users” (PU)
The utilization: 15% ~ 85%
Time, frequency and space dependent
The “Spectrum Hole”
There is “room” for “secondary users” (SU)
Power
Time
Frequency
Spectrum in use by Primary user
Spectrum Hole
If we observe the use of spectrum in detail, we have reasons to be optimistic. The spectrum is not efficiently used. From the FCC report and field measurement results, the utilization ranges from 15% ~ 85%. Also, it’s time and place dependent. The utilization in midnight or rural area is quite low. There are still rooms available, as we called, the changing “spectrum hole”. If a radio knows when and where the “spectrum hole” exists, why not use it for communications?

32. Brief Introduction to CRN
The solution: Cognitive Radio Networks
Spectrum sensing
Sense the “spectrum hole” intelligently
Unlicensed use for SU
Minimum interference
Software defined radio (SDR)
Fully reconfigurable
Spectrum management
Dynamic spectrum access
It is on the way:
FCC approved the use of CR network in the TV band white space
Intelligence
SDR Network
CRN is the solution to use “spectrum holes” for communications. The key part is spectrum sensing…
Every node in the network is intelligent. It can choose the optimum radio technology in real-time.
DSA: the network can manage use of spectrum dynamically according to the need.
There were many debates on CRN, but it’s on the way, given the success of spectrum sensing.

33. Brief Introduction to CRN
This is the 10th year for CRN research
FCC approves CRN use in Digital TV (802.22)
Academic players
TTU
Virginia Tech …
Business players
Spectrum Bridge
Google
CRN trials
Wilmington, NC
Claudville, VA

34. Outline Brief Introduction to CS CS and Spectrum Sensing for CRN
How CS Comes
How to use CS
Applications
UWB communications
Imaging
Radar
Your research
CS and Spectrum Sensing for CRN
Brief introduction to CRN
Brief introduction to spectrum sensing and challenges
CS based spectrum sensing
Conclusions

35. Spectrum Sensing Spectrum sensing: A question must be answered
When and where will there be spectrum wholes?
Two directions
Narrowband sensing
Nyquist rate sampling is affordable
Wideband sensing
Curse of sampling:
Nyquist rate sampling is not affordable
Spectrum Holes
5GHz Bandwidth

36. Spectrum Sensing Two approaches
Cooperative PU
PU registration database
SU fetch information from database to know when and where there will be spectrum wholes
Un-cooperative PU
SU must sense the radio channels without database
We are interested in the wideband sensing, Un-cooperative PU case

37. Spectrum Sensing Overcoming the curse of sampling
Narrowband based wideband sensing
Separate the wideband into a number of sub-bands
Sense each sub-band simultaneously with lots of narrowband filters
Complex RF design
Sense sub-bands one by one, “shift-and-sense”
Long delay
Wideband sensing
Sense the whole wideband, all at once
Shorter delay
Simpler RF design
Use CS to reduce sampling rate

38. Outline Brief Introduction to CS CS and Spectrum Sensing for CRN
How CS Comes
How to use CS
Applications
UWB communications
Imaging
Radar
Your research
CS and Spectrum Sensing for CRN
Brief introduction to CRN
Brief introduction to spectrum sensing and challenges
CS based spectrum sensing
Conclusions

39. CS Based Spectrum Sensing
Can CS be used to overcome the curse of wideband sensing?
The basic premise of CRN: The spectrum is too sparse
CS is possible
Signal is sparse in frequency domain
Random sampling possible with analog-to-information convertor(AIC) and other analog methods
Reconstruction possible using digital modules

40. CS Based Spectrum Sensing: Xampling
The first implemented CS based wideband sensing
Reconstruct 2 GHz spectrum with 280 MHz sampling rate
Demo and video:

41. CS Based Spectrum Sensing: Xampling
The first implemented CS based wideband sensing

42. CS Based Spectrum Sensing
However, this implementation has limitations
A very sparse spectrum is required (5% occupancy)
Signal strength must be much stronger than thermal noise
Reconstruction algorithm is not done in real-time
New algorithms needed for
More dense occupancy spectrum
Less signal strength
Real-time algorithm

43. CS Based Spectrum Sensing
Our most recent research alleviates the above problems by modifying the reconstruction algorithm
Detailed will be published soon
Recovered by Our Algorithm
Original Noisy Spectrum
Recovered by Traditional Algorithm

44. Outline Brief Introduction to CS CS and Spectrum Sensing for CRN
How CS Comes
How to use CS
Applications
UWB communications
Imaging
Radar
Your research
CS and Spectrum Sensing for CRN
Brief introduction to CRN
Brief introduction to spectrum sensing and challenges
CS based spectrum sensing
Conclusions

45. Conclusions CS basics CS applications in several fields
How to apply CS to your research
CS wideband sensing for CRN
References:
CS related pictures are from:
R. Baraniuk, J. Romberg, M. Wakin, Tutorials on Compressive Sampling

46. Thank You! http://www.wired.com/magazine/2010/02/ff_algorithm/all/1