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

PDF(342 KB)
PDF(342 KB)
Front. Comput. Sci. ›› 2021, Vol. 15 ›› Issue (4) : 154504. DOI: 10.1007/s11704-021-1275-y
BRIEF COMMUNICATION

Sub-Nyquist spectrum sensing and learning challenge

Author information +
History +

Cite this article

Download citation ▾
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 https://doi.org/10.1007/s11704-021-1275-y

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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[11]
Palomar D P, Eldar Y C. Convex Optimization in Signal Processing and Communications. Singapo Cambridge University Press, 2010
CrossRef Google scholar
[12]
Zhang X, Ma Y, Gao Y, Zhang W. Autonomous compressive-sensingaugmented spectrum sensing. IEEE Transactions on Vehicular Technology, 2018, 67(8): 6970–6980
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[19]
Urkowitz H. Energy detection of unknown deterministic signals. Proceedings of the IEEE, 1967, 55(4): 523–531
CrossRef Google scholar
[20]
Tandra R, Sahai A. SNR walls for signal detection. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 4–17
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[30]
Malafaia D, Vieira J, Tomé A. Adaptive threshold spectrum sensing based on expectation maximization algorithm. Physical Communication, 2016, 21: 60–69
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar

RIGHTS & PERMISSIONS

2021 The Author(s)
AI Summary AI Mindmap
PDF(342 KB)

Accesses

Citations

Detail

Sections
Recommended

/