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

Acknowledgement Center for Manufacturing Research National Science Foundation Office of Naval Research Army Research Office Prof Robert Qiu, Dr. Nan Guo and my labmates http://iweb.tntech.edu/rqiu

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) Email: pzhang21@tntech.edu Lab webpage: http://iweb.tntech.edu/rqiu This presentation can be downloaded at http://iweb.tntech.edu/rqiu/publications.htm#other_pres Introduce CS with few mathematical stuffs http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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? http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

How CS Comes The flow of compression Dense sampling at Nyquist rate Sparse samples after compression Decompress/Reconstruct data http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

How to Use CS Can original signal be reconstructed from such randomness? Yes because Random samples preserves all original information with high probability http://iweb.tntech.edu/rqiu

How to Use CS The math behind random sampling Sparse representation with dictionary Random sampling with sampling matrix http://iweb.tntech.edu/rqiu

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! http://iweb.tntech.edu/rqiu

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) … http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

Applications Summary of the success of CS Applications Sparse representation Random sampling Reconstruction Applications UWB communications Imaging Radar http://iweb.tntech.edu/rqiu

Applications UWB communications UWB has salient property in radio propagation Robust against multi-path fading http://www.radartutorial.eu/18.explanations/ex53.en.html http://www-emt.tu-ilmenau.de/ukolos/uwb_communication.php http://iweb.tntech.edu/rqiu

Applications UWB challenges Curse of sampling rate Ultra-wide in frequency domain Ultra-short in time domain http://iweb.tntech.edu/rqiu

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 …… http://iweb.tntech.edu/rqiu

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 http://www.interfacebus.com/Electronic_Dictionary_Radar_Terms_E.html http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

Applications Illustrative sensing result http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

Applications Illustrative result http://iweb.tntech.edu/rqiu

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! http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

Brief Introduction to CRN Spectrum usage, the pessimistic view Lack of unused spectrum 300 GHz already depleted http://iweb.tntech.edu/rqiu

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?

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? http://bwrc.eecs.berkeley.edu/Research/Cognitive/home.htm

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.

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 http://www.spectrumbridge.com http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

CS Based Spectrum Sensing: Xampling The first implemented CS based wideband sensing Reconstruct 2 GHz spectrum with 280 MHz sampling rate Demo and video: http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/hardware.html http://iweb.tntech.edu/rqiu

CS Based Spectrum Sensing: Xampling The first implemented CS based wideband sensing http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://iweb.tntech.edu/rqiu

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 http://dsp.rice.edu/cs http://webee.technion.ac.il/Sites/People/YoninaEldar/Info/hardware.html http://iweb.tntech.edu/rqiu/publications.htm http://iweb.tntech.edu/rqiu

Thank You! http://www.wired.com/magazine/2010/02/ff_algorithm/all/1 http://iweb.tntech.edu/rqiu