Sub-Nyquist spectrum sensing and learning challenge

Yue GAO , Zihang SONG , Han ZHANG , Sean FULLER , Andrew LAMBERT , Zhinong YING , Petri MÄHÖNEN , Yonina ELDAR , Shuguang CUI , Mark D. PLUMBLEY , Clive PARINI , Arumugam NALLANATHAN

Front. Comput. Sci. ›› 2021, Vol. 15 ›› Issue (4) : 154504

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Front. Comput. Sci. ›› 2021, Vol. 15 ›› Issue (4) : 154504 DOI: 10.1007/s11704-021-1275-y
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Sub-Nyquist spectrum sensing and learning challenge

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Yue GAO, Zihang SONG, Han ZHANG, Sean FULLER, Andrew LAMBERT, Zhinong YING, Petri MÄHÖNEN, Yonina ELDAR, Shuguang CUI, Mark D. PLUMBLEY, Clive PARINI, Arumugam NALLANATHAN. Sub-Nyquist spectrum sensing and learning challenge. Front. Comput. Sci., 2021, 15(4): 154504 DOI:10.1007/s11704-021-1275-y

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References

[1]

Eldar Y C. Sampling Theory: Beyond Bandlimited Systems. Cambridge University Press, 2015

[2]

Tropp J A, Laska J N, Duarte M F, Romberg J K, Baraniuk R G. Beyond Nyquist: efficient sampling of sparse bandlimited signals. IEEE Transactions on Information Theory, 2009, 56(1): 520–544

[3]

Wakin M, Becker S, Nakamura E, Grant M, Sovero E, Ching D, Yoo J, Romberg J, Emami-Neyestanak A, Candes E. A nonuniform sampler for wideband spectrally-sparse environments. IEEE Journal on Emerging & Selected Topics in Circuits & Systems, 2012, 2(3): 516–529

[4]

Mishali M, Eldar Y C. From theory to practice: sub-Nyquist sampling of sparse wideband analog signals. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 375–391

[5]

Cohen D, Tsiper S, Eldar Y C. Analog-to-digital cognitive radio: sampling, detection, and hardware. IEEE Signal Processing Magazine, 2018, 35(1): 137–166

[6]

Mishali M, Eldar Y C, Dounaevsky O, Shoshan E. Xampling: analog to digital at sub-Nyquist rates. Circuits Devices & Systems Iet, 2009, 5(1): 8–20

[7]

Israeli E, Tsiper S, Cohen D, Shoshan E, Hilgendorf R, Reysenson A, Eldar Y C. Hardware calibration of the modulated wideband converter. In: Proceedings of 2014 IEEE Global Communications Conference. 2014, 948–953

[8]

Yoo J, Becker S, Loh M, Monge M, Emami-Neyestanak A. A 100MHz–2GHz 12.5x sub-Nyquist rate receiver in 90nm CMOS. In: Proceedings of Radio Frequency Integrated Circuits Symposium. 2012

[9]

Song Z, Qi H, Gao Y. Real-time multi-gigahertz sub-Nyquist spectrum sensing system for mmwave. In: Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems. 2019, 33–38

[10]

Candes E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics: a Journal Issued by the Courant Institute of Mathematical Sciences, 2006, 59(8): 1207–1223

[11]

Palomar D P, Eldar Y C. Convex Optimization in Signal Processing and Communications. Singapo Cambridge University Press, 2010

[12]

Zhang X, Ma Y, Gao Y, Zhang W. Autonomous compressive-sensingaugmented spectrum sensing. IEEE Transactions on Vehicular Technology, 2018, 67(8): 6970–6980

[13]

Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 2007, 53(12): 4655–4666

[14]

Tropp J A, Gilbert A C, Strauss M J. Simultaneous sparse approximation via greedy pursuit. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005, 721–724

[15]

Needell D, Tropp J A. Cosamp: iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis, 2009, 26(3): 301–321

[16]

Qi H, Zhang X, Gao Y. Low-complexity subspace-aided compressive spectrum sensing over wideband whitespace. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11762–11777

[17]

Eldar Y C, Kuppinger P, Bolcskei H. Block-sparse signals: uncertainty relations and efficient recovery. IEEE Transactions on Signal Processing, 2010, 58(6): 3042–3054

[18]

Chen W, Wassell I J. A decentralized bayesian algorithm for distributed compressive sensing in networked sensing systems. IEEE Transactions on Wireless Communications, 2015, 15(2): 1282–1292

[19]

Urkowitz H. Energy detection of unknown deterministic signals. Proceedings of the IEEE, 1967, 55(4): 523–531

[20]

Tandra R, Sahai A. SNR walls for signal detection. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 4–17

[21]

Kim K, Xin Y, Rangarajan S. Energy detection based spectrum sensing for cognitive radio: an experimental study. In: Proceedings of 2010 IEEE Global Telecommunications Conference GLOBECOM 2010. 2011

[22]

Oner M, Jondral F. Cyclostationarity-based methods for the extraction of the channel allocation information in a spectrum pooling system. In: Proceedings of 2004 IEEE Radio and Wireless Conference. 2004, 279–282

[23]

Cohen D, Eldar Y C. Sub-Nyquist cyclostationary detection for cognitive radio. IEEE Transactions on Signal Processing, 2017, 65(11): 3004–3019

[24]

Qin Z, Zhou X, Zhang L, Gao Y, Liang Y C, Li G Y. 20 years of evolution from cognitive to intelligent communications. IEEE Transactions on Cognitive Communications and Networking, 2019, 6(1): 6–20

[25]

Toma A, Krayani A, Farrukh M, Qi H, Marcenaro L, Gao Y, Regazzoni C S. AI-based abnormality detection at the PHY-layer of cognitive radio by learning generative models. IEEE Transactions on Cognitive Communications and Networking, 2020, 6(1): 21–34

[26]

Thilina K M, Choi K W, Saquib N, Hossain E. Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 2013, 31(11): 2209–2221

[27]

Hu H, Wang Y, Song J. Signal classification based on spectral correlation analysis and SVM in cognitive radio. In: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications. 2008, 883–887

[28]

Naparstek O, Cohen K. Deep multi-user reinforcement learning for distributed dynamic spectrum access. IEEE Transactions on Wireless Communications, 2018, 18(1): 310–323

[29]

Zhang Y, Wan P, Zhang S, Wang Y, Li N. A spectrum sensing method based on signal feature and clustering algorithm in cognitive wireless multimedia sensor networks. Advances in Multimedia, 2017

[30]

Malafaia D, Vieira J, Tomé A. Adaptive threshold spectrum sensing based on expectation maximization algorithm. Physical Communication, 2016, 21: 60–69

[31]

Boufounos P, Duarte M F, Baraniuk R G. Sparse signal reconstruction from noisy compressive measurements using cross validation. In : Proceedings of the 14th IEEE/SPWorkshop on Statistical Signal Processing. 2007, 299–303

[32]

Wang Y, Tian Z, Feng C. Sparsity order estimation and its application in compressive spectrum sensing for cognitive radios. IEEE Transactions on Wireless Communications, 2012, 11(6): 2116–2125

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