Sub-Nyquist spectrum sensing and learning challenge
Yue GAO1(), Zihang SONG1, Han ZHANG1, Sean FULLER2, Andrew LAMBERT3, Zhinong YING4, Petri MÄHÖNEN5, Yonina ELDAR6, Shuguang CUI7, Mark D. PLUMBLEY1, Clive PARINI8, Arumugam NALLANATHAN8
1. School of Computer Science and Electronic Engineering, University of Surrey, Guildford, Surrey GU2 7XH, UK 2. National Instruments Corporation (UK) Ltd, Newbury, Berkshire RG14 2PZ, UK 3. Electronic Media Services Ltd, Bordon, Hampshire GU35 0FJ, UK 4. Sony Research Center, Sony Corporation, Lund 221 88, Sweden 5. Institute for Networked Systems, RWTH Aachen University, Kackertstrasse 9, Aachen 52072, Germany 6. Faculty of Math & CS, Weizmann institute of Science, Rehovot 7610001, Israel 7. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China 8. School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Y C Eldar. Sampling Theory: Beyond Bandlimited Systems. Cambridge University Press, 2015
2
J A Tropp, J N Laska, M F Duarte, J K Romberg, R G Baraniuk. Beyond Nyquist: efficient sampling of sparse bandlimited signals. IEEE Transactions on Information Theory, 2009, 56(1): 520–544 https://doi.org/10.1109/TIT.2009.2034811
3
M Wakin, S Becker, E Nakamura, M Grant, E Sovero, D Ching, J Yoo, J Romberg, A Emami-Neyestanak, E Candes. A nonuniform sampler for wideband spectrally-sparse environments. IEEE Journal on Emerging & Selected Topics in Circuits & Systems, 2012, 2(3): 516–529 https://doi.org/10.1109/JETCAS.2012.2214635
4
M Mishali, Y C Eldar. 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 https://doi.org/10.1109/JSTSP.2010.2042414
5
D Cohen, S Tsiper, Y C Eldar. Analog-to-digital cognitive radio: sampling, detection, and hardware. IEEE Signal Processing Magazine, 2018, 35(1): 137–166 https://doi.org/10.1109/MSP.2017.2740966
6
M Mishali, Y C Eldar, O Dounaevsky, E Shoshan. Xampling: analog to digital at sub-Nyquist rates. Circuits Devices & Systems Iet, 2009, 5(1): 8–20 https://doi.org/10.1049/iet-cds.2010.0147
7
E Israeli, S Tsiper, D Cohen, E Shoshan, R Hilgendorf, A Reysenson, Y C Eldar. Hardware calibration of the modulated wideband converter. In: Proceedings of 2014 IEEE Global Communications Conference. 2014, 948–953 https://doi.org/10.1109/GLOCOM.2014.7036931
8
J Yoo, S Becker, M Loh, M Monge, A Emami-Neyestanak. A 100MHz–2GHz 12.5x sub-Nyquist rate receiver in 90nm CMOS. In: Proceedings of Radio Frequency Integrated Circuits Symposium. 2012 https://doi.org/10.1109/RFIC.2012.6242225
9
Z Song, H Qi, Y Gao. 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 https://doi.org/10.1145/3349624.3356767
10
E J Candes, J K Romberg, T Tao. 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 https://doi.org/10.1002/cpa.20124
11
D P Palomar, Y C Eldar. Convex Optimization in Signal Processing and Communications. Singapo Cambridge University Press, 2010 https://doi.org/10.1017/CBO9780511804458
12
X Zhang, Y Ma, Y Gao, W Zhang. Autonomous compressive-sensingaugmented spectrum sensing. IEEE Transactions on Vehicular Technology, 2018, 67(8): 6970–6980 https://doi.org/10.1109/TVT.2018.2822776
13
J A Tropp, A C Gilbert. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 2007, 53(12): 4655–4666 https://doi.org/10.1109/TIT.2007.909108
14
J A Tropp, A C Gilbert, M J Strauss. Simultaneous sparse approximation via greedy pursuit. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005, 721–724
15
D Needell, J A Tropp. Cosamp: iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis, 2009, 26(3): 301–321 https://doi.org/10.1016/j.acha.2008.07.002
16
H Qi, X Zhang, Y Gao. Low-complexity subspace-aided compressive spectrum sensing over wideband whitespace. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11762–11777 https://doi.org/10.1109/TVT.2019.2937649
17
Y C Eldar, P Kuppinger, H Bolcskei. Block-sparse signals: uncertainty relations and efficient recovery. IEEE Transactions on Signal Processing, 2010, 58(6): 3042–3054 https://doi.org/10.1109/TSP.2010.2044837
18
W Chen, I J Wassell. A decentralized bayesian algorithm for distributed compressive sensing in networked sensing systems. IEEE Transactions on Wireless Communications, 2015, 15(2): 1282–1292 https://doi.org/10.1109/TWC.2015.2487989
K Kim, Y Xin, S Rangarajan. Energy detection based spectrum sensing for cognitive radio: an experimental study. In: Proceedings of 2010 IEEE Global Telecommunications Conference GLOBECOM 2010. 2011 https://doi.org/10.1109/GLOCOM.2010.5683560
22
M Oner, F Jondral. 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
D Cohen, Y C Eldar. Sub-Nyquist cyclostationary detection for cognitive radio. IEEE Transactions on Signal Processing, 2017, 65(11): 3004–3019 https://doi.org/10.1109/TSP.2017.2684743
24
Z Qin, X Zhou, L Zhang, Y Gao, Y C Liang, G Y Li. 20 years of evolution from cognitive to intelligent communications. IEEE Transactions on Cognitive Communications and Networking, 2019, 6(1): 6–20 https://doi.org/10.1109/TCCN.2019.2949279
25
A Toma, A Krayani, M Farrukh, H Qi, L Marcenaro, Y Gao, C S Regazzoni. 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 https://doi.org/10.1109/TCCN.2020.2970693
26
K M Thilina, K W Choi, N Saquib, E Hossain. Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 2013, 31(11): 2209–2221 https://doi.org/10.1109/JSAC.2013.131120
27
H Hu, Y Wang, J Song. 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 https://doi.org/10.1109/AINA.2008.27
28
O Naparstek, K Cohen. Deep multi-user reinforcement learning for distributed dynamic spectrum access. IEEE Transactions on Wireless Communications, 2018, 18(1): 310–323 https://doi.org/10.1109/TWC.2018.2879433
29
Y Zhang, P Wan, S Zhang, Y Wang, N Li. A spectrum sensing method based on signal feature and clustering algorithm in cognitive wireless multimedia sensor networks. Advances in Multimedia, 2017 https://doi.org/10.1155/2017/2895680
30
D Malafaia, J Vieira, A Tomé. Adaptive threshold spectrum sensing based on expectation maximization algorithm. Physical Communication, 2016, 21: 60–69 https://doi.org/10.1016/j.phycom.2016.10.004
31
P Boufounos, M F Duarte, R G Baraniuk. Sparse signal reconstruction from noisy compressive measurements using cross validation. In : Proceedings of the 14th IEEE/SPWorkshop on Statistical Signal Processing. 2007, 299–303 https://doi.org/10.1109/SSP.2007.4301267
32
Y Wang, Z Tian, C Feng. Sparsity order estimation and its application in compressive spectrum sensing for cognitive radios. IEEE Transactions on Wireless Communications, 2012, 11(6): 2116–2125 https://doi.org/10.1109/TWC.2012.050112.110505