Spectrum Sharing Radar: Coexistence via Xampling

We present a Xampling-based technology enabling interference-free operation of radar and communication systems over a common spectrum. Our system uses a recently developed cognitive radio (CRo) to sense the spectrum at low sampling and processing rates. The Xampling-based cognitive radar (CRr) then transmits and receives in the available disjoint narrow bands. Our main contribution is the unification and adaptation of two previous ideas—CRo and CRr—to address spectrum sharing. Hardware implementation shows robust performance at SNRs up to –5 dB.

[1]  Yonina C. Eldar Sampling Theory: Beyond Bandlimited Systems , 2015 .

[2]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[3]  Fulvio Gini,et al.  Spectrum sensing and sharing for cognitive radars , 2016 .

[4]  Samuel Cheng,et al.  Compressive image sampling with side information , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[5]  Shannon D. Blunt,et al.  Optimizing sparse allocation for radar spectrum sharing , 2016, 2016 IEEE Radar Conference (RadarConf).

[6]  Junzhou Huang,et al.  Learning with structured sparsity , 2009, ICML '09.

[7]  Hao He,et al.  Waveform design with stopband and correlation constraints for cognitive radar , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[8]  Yonina C. Eldar,et al.  Sub-Nyquist Cyclostationary Detection for Cognitive Radio , 2016, IEEE Transactions on Signal Processing.

[9]  Shannon D. Blunt,et al.  Analysis of symbol-design strategies for intrapulse radar-embedded communications , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Yonina C. Eldar,et al.  Towards sub-nyquist cognitive radar , 2016, 2016 IEEE Radar Conference (RadarConf).

[11]  Junzhou Huang,et al.  Learning with dynamic group sparsity , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Bhaskar D. Rao,et al.  Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning , 2011, IEEE Journal of Selected Topics in Signal Processing.

[13]  Jian Li,et al.  Spectrally Constrained Waveform Design [sp Tips&Tricks] , 2014, IEEE Signal Processing Magazine.

[14]  Yonina C. Eldar,et al.  Sub-Nyquist Sampling for Power Spectrum Sensing in Cognitive Radios: A Unified Approach , 2013, IEEE Transactions on Signal Processing.

[15]  A. Aubry,et al.  A new radar waveform design algorithm with improved feasibility for spectral coexistence , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Braham Himed,et al.  IEEE 802.22 passive radars: multistatic detection and velocity profiler , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Augusto Aubry,et al.  Radar waveform design in a spectrally crowded environment via nonconvex quadratic optimization , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Urbashi Mitra,et al.  Radar waveform design in spectrum sharing environment: Coexistence and cognition , 2015, 2015 IEEE Radar Conference (RadarCon).

[19]  R. Gold,et al.  Optimal binary sequences for spread spectrum multiplexing (Corresp.) , 1967, IEEE Trans. Inf. Theory.

[20]  Yonina C. Eldar,et al.  Xampling: Signal Acquisition and Processing in Union of Subspaces , 2009, IEEE Transactions on Signal Processing.

[21]  Yonina C. Eldar,et al.  Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals , 2007, IEEE Transactions on Signal Processing.

[22]  Yonina C. Eldar,et al.  Performance of time delay estimation in a cognitive radar , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[23]  Yonina C. Eldar,et al.  Xampling: Analog to digital at sub-Nyquist rates , 2009, IET Circuits Devices Syst..

[24]  Symeon Chatzinotas,et al.  Application of Compressive Sensing in Cognitive Radio Communications: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[25]  Garry M. Jacyna,et al.  A high-level overview of fundamental limits studies for the DARPA SSPARC program , 2016, 2016 IEEE Radar Conference (RadarConf).

[26]  Yonina C. Eldar,et al.  Spectrum Sharing Solution for Automotive Radar , 2017, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).

[27]  Yonina C. Eldar,et al.  Analog to Digital Cognitive Radio , 2017 .

[28]  B. Himed,et al.  Radar-centric design of waveforms with disjoint spectral support , 2012, 2012 IEEE Radar Conference.

[29]  M. Lindenfeld Sparse frequency transmit-and-receive waveform design , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[30]  John Y. N. Cho,et al.  The Threat to Weather Radars by Wireless Technology , 2016 .

[31]  Namrata Vaswani,et al.  Recursive Recovery of Sparse Signal Sequences From Compressive Measurements: A Review , 2016, IEEE Transactions on Signal Processing.

[32]  Prabahan Basu,et al.  Performance bounds on cooperative radar and communication systems operation , 2016, 2016 IEEE Radar Conference (RadarConf).

[33]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[34]  Yonina C. Eldar,et al.  Cognitive sub-Nyquist hardware prototype of a collocated MIMO radar , 2016, 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa).

[35]  Hong Sun,et al.  Bayesian compressive sensing for cluster structured sparse signals , 2012, Signal Process..

[36]  Kenneth E. Barner,et al.  Iterative algorithms for compressed sensing with partially known support , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[37]  Michael P. Fitz,et al.  Towards simultaneous radar and spectral sensing , 2014, 2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN).

[38]  J. R. Guerci,et al.  Joint design and operation of shared spectrum access for radar and communications , 2015, 2015 IEEE Radar Conference (RadarCon).

[39]  Yonina C. Eldar,et al.  Sub-Nyquist Radar via Doppler Focusing , 2012, IEEE Transactions on Signal Processing.

[40]  Yonina C. Eldar,et al.  Expected RIP: Conditioning of The modulated wideband converter , 2009, 2009 IEEE Information Theory Workshop.

[41]  Fulvio Gini,et al.  White space Passive Coherent Location system based on IEEE 802.22 , 2015, 2015 16th International Radar Symposium (IRS).

[42]  Eylem Ekici,et al.  Optimal spectrum utilization in joint automotive radar and communication networks , 2016, 2016 14th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

[43]  H. Landau Necessary density conditions for sampling and interpolation of certain entire functions , 1967 .

[44]  Wei Lu,et al.  Modified-CS: Modifying compressive sensing for problems with partially known support , 2009, 2009 IEEE International Symposium on Information Theory.

[45]  Louis L. Scharf,et al.  Matched subspace detectors , 1994, IEEE Trans. Signal Process..

[46]  Yilong Lu,et al.  Designing single/multiple sparse frequency waveforms with sidelobe constraint , 2011 .

[47]  Karl Gerlach,et al.  Thinned spectrum ultrawideband waveforms using stepped-frequency polyphase codes , 1998 .

[48]  T. Blumensath,et al.  Theory and Applications , 2011 .

[49]  Wei Lu,et al.  Regularized Modified BPDN for Noisy Sparse Reconstruction With Partial Erroneous Support and Signal Value Knowledge , 2010, IEEE Transactions on Signal Processing.

[50]  Zhi Chen,et al.  Support knowledge-aided sparse Bayesian learning for compressed sensing , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[51]  Joseph Mitola,et al.  Accelerating 5G QoE via public-private spectrum sharing , 2014, IEEE Communications Magazine.

[52]  Yonina C. Eldar,et al.  From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals , 2009, IEEE Journal of Selected Topics in Signal Processing.

[53]  Athina Petropulu,et al.  MIMO radar and communication spectrum sharing with clutter mitigation , 2016, 2016 IEEE Radar Conference (RadarConf).

[54]  Yonina C. Eldar,et al.  Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery , 2010, IEEE Transactions on Signal Processing.

[55]  Chita R. Das,et al.  Performance Analysis of Communications & Radar Coexistence in a Covert UWB OSA System , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[56]  Yonina C. Eldar,et al.  Sub-Nyquist Sampling: Bridging Theory and Practice , 2011, ArXiv.

[57]  Yonina C. Eldar,et al.  Sub-Nyquist channel estimation over IEEE 802.11ad link , 2017, 2017 International Conference on Sampling Theory and Applications (SampTA).

[58]  Sumit Roy,et al.  Spectrum sharing between a surveillance radar and secondary Wi-Fi networks , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[59]  Braham Himed,et al.  ComRadE: Cognitive Passive Tracking in Symbiotic IEEE 802.22 Systems , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[60]  Huaiyi Wang,et al.  On spectrum sharing between communications and air traffic control radar systems , 2015, 2015 IEEE Radar Conference (RadarCon).

[61]  Janne J. Lehtomäki,et al.  On opportunistic spectrum access in radar bands: Lessons learned from measurement of weather radar signals , 2016, IEEE Wireless Communications.

[62]  Athina P. Petropulu,et al.  Optimum Co-Design for Spectrum Sharing between Matrix Completion Based MIMO Radars and a MIMO Communication System , 2015, IEEE Transactions on Signal Processing.

[63]  Mark E. Davis Foliage Penetration Radar: Detection and characterisation of objects under trees , 2011 .

[64]  C. Nunn,et al.  Spectrally-compliant waveforms for wideband radar , 2012, IEEE Aerospace and Electronic Systems Magazine.

[65]  Yonina C. Eldar,et al.  Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware , 2017, IEEE Signal Processing Magazine.

[66]  Marc Heddebaut,et al.  Millimeter-wave communicating-radars for enhanced vehicle-to-vehicle communications , 2010 .

[67]  Robert W. Heath,et al.  Investigating the IEEE 802.11ad Standard for Millimeter Wave Automotive Radar , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[68]  Shannon D. Blunt,et al.  Radar Spectrum Engineering and Management: Technical and Regulatory Issues , 2015, Proceedings of the IEEE.

[69]  Bryan Paul,et al.  Inner Bounds on Performance of Radar and Communications Co-Existence , 2016, IEEE Transactions on Signal Processing.

[70]  Bhaskar D. Rao,et al.  Recovery of block sparse signals using the framework of block sparse Bayesian learning , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[71]  Hongbin Li,et al.  Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals , 2013, IEEE Transactions on Signal Processing.

[72]  Daniela Tuninetti,et al.  Let's share CommRad: Effect of radar interference on an uncoded data communication system , 2016, 2016 IEEE Radar Conference (RadarConf).

[73]  James D. Taylor Ultra-wideband Radar Technology , 2000 .

[74]  Christian Sturm,et al.  Waveform Design and Signal Processing Aspects for Fusion of Wireless Communications and Radar Sensing , 2011, Proceedings of the IEEE.

[75]  F. Gini,et al.  Design of a cognitive radar for operation in spectrally dense environments , 2013, 2013 IEEE Radar Conference (RadarCon13).

[76]  Aaron D. Lanterman,et al.  Gaussian multiple access channels for radar and communications spectrum sharing , 2016, 2016 IEEE Radar Conference (RadarConf).

[77]  Hao He,et al.  Waveform Design for Active Sensing Systems: A Computational Approach , 2012 .

[78]  Jeffrey H. Reed,et al.  On the Co-Existence of TD-LTE and Radar Over 3.5 GHz Band: An Experimental Study , 2016, IEEE Wireless Communications Letters.

[79]  Jeffrey H. Reed,et al.  Coexistence between radar and LTE-U systems: Survey on the 5 GHz band , 2016, 2016 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM).