research-article

Machine learning for cooperative spectrum sensing and sharing: : A survey

Authors:

Sandeep KumarAuthors Info & Claims

Transactions on Emerging Telecommunications Technologies, Volume 33, Issue 1

https://doi.org/10.1002/ett.4352

Published: 09 January 2022 Publication History

Abstract

With the rapid development of next‐generation wireless communication technologies and the increasing demand of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in cognitive radio networks (CRN). In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing (SS) and spectrum sharing. The application of machine learning (ML) techniques has added new aspects to SS and spectrum sharing. This paper offers a survey on various ML‐based algorithms in the cooperative spectrum sensing (CSS) and dynamic spectrum sharing (DSS) domain, with its emphasis on types of features extracted from primary user signal, types of ML algorithm, and performance metrics utilized for evaluation of ML algorithms. Starting with the basic principles and challenges of SS, this paper also justifies the applicability of supervised, unsupervised, and reinforcement ML algorithms in the CSS domain. The application of ML algorithms, to solve the DSS problem has also been reviewed. Finally, the survey paper is concluded with some suggested open research challenges and future directions for ML application in next‐generation communication technologies.

Graphical Abstract

With the rapid development of next‐generation wireless communication technologies and increasing the requirement of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in the sensing and sharing of spectrum in cognitive radio networks. The application of ML techniques has added new aspects to these fundamental problems.

References

[1]

Cisco . Cisco annual Internet report (2018–2023) white paper; 2020.

[2]

Rasethuntsa TR, Kumar S. An integrated performance evaluation of ED‐based spectrum sensing over α−κ−μ and α−κ−μ‐extreme fading channels. Trans Emerg Telecommun Technol. 2019;30(5):e3569.

Digital Library

[3]

Yadav P, Kumar S, Kumar R. A comprehensive survey of physical layer security over fading channels: classifications, applications, and challenges. Trans Emerg Telecommun Technol. 2021;e4270. https://doi.org/10.1002/ett.4270

[4]

Cabric D, Mishra SM, Brodersen RW. Implementation issues in spectrum sensing for cognitive radios. Conference Record of the Thirty‐Eighth Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA; IEEE: 2004.

[5]

Kaur M, Yadav RK. Performance analysis of Beaulieu‐Xie fading channel with MRC diversity reception. Trans Emerg Telecommun Technol. 2020;31(7):e3949.

Digital Library

[6]

Sahai A, Tandra R, Mishra SM, Hoven N. Fundamental design tradeoffs in cognitive radio systems. Paper presented at: Proc. of TAPAS'06; 2006; Boston, MA.

[7]

Kumar S, Chauhan PS, Raghuwanshi P, Kaur M. ED performance over α‐η‐μ/IG and α‐κ‐μ/IG generalized fading channels with diversity reception and cooperative sensing: a unified approach. AEU Int J Electron Commun. 2018;97:273‐279.

[8]

Kumar S, Kaur M, Singh NK, Singh K, Chauhan PS. Energy detection based spectrum sensing for gamma shadowed α–η–μ and α–κ–μ fading channels. AEU Int J Electron Commun. 2018;93:26‐31.

[9]

Zhou X, Sun M, Li G, Juang B‐H. Machine learning and cognitive technology for intelligent wireless networks; 2017. ArXiv, vol. abs/1710.11240.

[10]

Alshawaqfeh M, Wang X, Ekti AR, Shakir MZ, Qaraqe K, Serpedin E. A survey of machine learning algorithms and their applications in cognitive radio. Cognitive Radio Oriented Wireless Networks. Doha, Qatar: CrownCom; 2015.

[11]

Bkassiny M, Li Y, Jayaweera SK. A survey on machine‐learning techniques in cognitive radios. IEEE Commun Surv Tutor. 2013;15(3):1136‐1159.

[12]

Zhou X, Sun M, Li GY, Juang BF. Intelligent wireless communications enabled by cognitive radio and machine learning. China Commun. 2018;15(12):16‐48.

[13]

Arjoune Y, Kaabouch N. A comprehensive survey on spectrum sensing in cognitive radio networks: recent advances new challenges and future research directions. Sensors. 2019;19(1):126.

[14]

Thilina KM, Choi KW, Saquib N, Hossain E. Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE J Selected Areas Commun. 2013;31(11):2209‐2221.

[15]

A. Ranjan, and B. Singh, "Design and analysis of spectrum sensing in cognitive radio based on energy detection," in International Conference on Signal and Information Processing (IConSIP), Vishnupuri, India, IEEE: 2016.

[16]

Alom MZ, Godder TK, Morshed MN, Maali A. Enhanced spectrum sensing based on energy detection in cognitive radio network using adaptive threshold. International Conference on Networking, Systems and Security. Dhaka, Bangladesh; IEEE: 2017.

[17]

Kumar S. Energy detection in Hoyt/gamma fading channel with micro‐diversity reception. Wirel Pers Commun. 2018;101(2):723‐734.

Digital Library

[18]

Kumar S, Soni SK, Jain P. Performance of MRC receiver over Hoyt‐lognormal composite fading channel. Int J Electron. 2018;105(9):1433‐1450.

[19]

Kumar S. Performance of ED based spectrum sensing over α–η–μ fading channel. Wirel Pers Commun. 2018;100(4):1845‐1857.

Digital Library

[20]

Mahapatra R, Krusheel M. Cyclostationary detection for cognitive radio with multiple receivers. Proceedings of the 2008 IEEE International Symposium on Wireless Communication Systems, ISWCS'08. Reykjavik, Iceland; IEEE: 2008.

[21]

Ilyas I, Paul S, Rahman A, Kundu RK. Comparative evaluation of cyclostationary detection based cognitive spectrum sensing. In Proceedings of the Ubiquitous Computing, Electronics, and Mobile Communication Conference. New York, NY; IEEE: 2016.

[22]

Salahdine F, Ghazi HE, Kaabouch N, Fihri WF. Matched filter detection with dynamic threshold for cognitive radio networks. International Conference on Wireless Networks and Mobile Communications (WINCOM). Marrakech, Moracco; IEEE: 2015.

[23]

Zhang X, Chai R, Gao F. Matched filter based spectrum sensing and power level detection for cognitive radio network. IEEE Global Conference on Signal and Information Processing (GlobalSIP). Atlanta, GA; IEEE: 2014.

[24]

Tang H. Some physical layer issues of wide‐band cognitive radio systems. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Baltimore, MD; IEEE: 2005.

[25]

Salahdine F, Kaabouch N, Ghazi HE. A survey on compressive sensing techniques for cognitive radio networks. Phys Commun. 2016;20:61‐73.

Digital Library

[26]

An T, Song I, Lee S, Min H. Detection of signals with observations in multiple subbands: a scheme of wideband Spectrum sensing for cognitive radio with multiple antennas. IEEE Trans Wirel Commun. 2014;13(12):6968‐6981.

[27]

Sun H, Chiu W, Nallanathan A. Adaptive compressive spectrum sensing for wideband cognitive radios. IEEE Commun Lett. 2012;16(11):1812‐1815.

[28]

Jin M, Guo Q, Li Y. On covariance matrix based Spectrum sensing over frequency‐selective channels. IEEE Access. 2018;6:29532‐29540.

[29]

Zeng Y, Liang Y. Spectrum‐sensing algorithms for cognitive radio based on statistical covariances. IEEE Trans Veh Technol. 2009;58(4):1804‐1815.

[30]

Zeng Y, Liang Y. Maximum‐minimum eigenvalue detection for cognitive radio. International Symposium on Personal, Indoor and Mobile Radio Communications. Athens, Greece; IEEE: 2007.

[31]

Zeng Y, Liang Y. Covariance based signal detections for cognitive radio. 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Dublin; IEEE: 2007.

[32]

Alsheikh MA, Lin S, Niyato D, Tan H. Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun Surv Tutor. 2014;16(4):1996‐2018.

[33]

Jiang C, Zhang H, Ren Y, Han Z, Chen K, Hanzo L. Machine learning paradigms for next‐generation wireless networks. IEEE Wirel Commun. 2017;24(2):98‐105.

Digital Library

[34]

Bishop CM. Pattern Recognition and Machine Learning. Germany: Springer; 2006.

Digital Library

[35]

Mikaeil AM, Guo B, Wang Z. Machine learning to data fusion approach for cooperative Spectrum sensing. International Conference on Cyber‐Enabled Distributed Computing and Knowledge Discovery. Shanghai, China; IEEE: 2014.

[36]

Lu Y, Zhu P, Wang D, Fattouche M. Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks. 2016 IEEE Wireless Communications and Networking Conference. Doha, Qatar; IEEE: 2016.

[37]

Azmat F, Chen Y, Stocks N. Analysis of Spectrum occupancy using machine learning algorithms. IEEE Trans Veh Technol. 2016;65(9):6853‐6860.

[38]

Ghazizadeh E, Abbasi D, Nezamabadi‐pour H. An enhanced two‐phase SVM algorithm for cooperative spectrum sensing in cognitive radio networks. Int J Commun Syst. 2018;32(2):e3856.

[39]

O. P. Awe, Z. Zhu, S. Lambotharan, "Eigenvalue and support vector machine techniques for spectrum sensing in cognitive radio networks," in Conference on Technologies and Applications of Artificial Intelligence, Taipei, Taiwan, IEEE: 2013.

[40]

Coluccia A, Fascista A, Ricci G. Spectrum sensing by higher‐order SVM‐based detection. 27th European Signal Processing Conference (EUSIPCO). A Coruna, Spain; IEEE: 2019.

[41]

Huang Y‐D, Liang Y‐C, Yang G. A fuzzy support vector machine algorithm for cooperative Spectrum sensing with noise uncertainty. Global Communications Conference (GLOBECOM). Washington, DC; IEEE: 2016.

[42]

Awe OP, Lambotharan S. Cooperative spectrum sensing in cognitive radio networks using multi‐class support vector machine algorithms. 9th International Conference on Signal Processing and Communication Systems (ICSPCS). Cairns, QLD; IEEE: 2015.

[43]

Awe OP, Deligiannis A, Lambotharan S. Spatio‐temporal Spectrum sensing in cognitive radio networks using Beamformer‐aided SVM algorithms. IEEE Access. 2018;6:25377‐25388.

[44]

Jan SU, Vu V‐H, Koo I. Throughput maximization using an SVM for multi‐class hypothesis‐based spectrum sensing in cognitive radio. Appl Sci. 2018;8(3):421.

[45]

Tian J, Cheng P, Chen Z, et al. A machine learning‐enabled Spectrum sensing method for OFDM systems. IEEE Trans Veh Technol. 2019;68(11):11374‐11378.

[46]

Awe OP, Naqvi SM, Lambotharan S. Variational Bayesian learning technique for spectrum sensing in cognitive radio networks. IEEE Global Conference on Signal and Information Processing (GlobalSIP). Atlanta, GA; IEEE: 2014.

[47]

Xu Y, Cheng P, Chen Z, Li Y, Vucetic B. Mobile collaborative spectrum sensing for heterogeneous networks: a Bayesian machine learning approach. IEEE Trans Signal Process. 2018;66(21):5634‐5647.

Digital Library

[48]

Basumatary N, Sarma N, Nath B. Applying classification methods for spectrum sensing in cognitive radio networks: An empirical study. Advances in Electronics, Communication and Computing. Singapore: Springer; 2017:85‐92.

[49]

Li Z, Wu W, Liu X, Qi P. Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks. IET Commun. 2018;12(19):2485‐2492.

[50]

Vyas MR, Patel DK, Lopez‐Benitez M. Artificial neural network based hybrid spectrum sensing scheme for cognitive radio. IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). Montreal, QC: IEEE: 2017.

[51]

Kumar V, Kandpal DC, Jain M, Gangopadhyay R, Debnath S. K‐mean clustering based cooperative spectrum sensing in generalized к‐μ fading channels. 2016 Twenty Second National Conference on Communication (NCC). Guwahati, India; IEEE: 2016.

[52]

Chen X, Hou F, Huang H, Jing X. Principle component analysis based cooperative spectrum sensing in cognitive radio. 16th International Symposium on Communications and Information Technologies (ISCIT). Qingdao, China; IEEE: 2016.

[53]

Sobabe GC, Song Y, Bai X, Guo B. A cooperative spectrum sensing algorithm based on unsupervised learning. 10th International Congress on Image and Signal Processing. Shanghai, China: BioMedical Engineering and Informatics (CISP‐BMEI); 2017.

[54]

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. Adv Multimedia. 2017;2017:1–10.

[55]

Wang Y, Zhang Y, Zhang S, Li X, Wan P. A cooperative spectrum sensing method based on a feature and clustering algorithm. Chinese Automation Congress. Xi'an, China; IEEE: 2018.

[56]

Wang Y, Zhang Y, Wan P, Zhang S, Yang J. A spectrum sensing method based on empirical mode decomposition and K‐means clustering algorithm. Wirel Commun Mobile Comput. 2018;2018(1):1‐10.

[57]

Zhang Y, Wang Y, Wan P, Zhang S, Li N. A spectrum sensing method based on null space pursuit algorithm and FCM clustering algorithm. 4th International Conference. Haikou, China: ICCCS; 2018.

[58]

Wang Y, Zhang S, Zhang Y, Wan P, Li J. A cooperative spectrum sensing method based on empirical mode decomposition and information geometry in complex electromagnetic environment. Complexity. 2019;2019:1‐13.

[59]

Zhang S, Wang Y, Li J, Wan P, Zhang Y, Li N. A cooperative spectrum sensing method based on information geometry and fuzzy c‐means clustering algorithm. EURASIP J Wirel Commun Netw. 2019;2019(1):17‐29.

[60]

Zhuang J, Wang Y, Zhang S, Wan P, Sun C. A multi‐antenna spectrum sensing scheme based on main information extraction and genetic algorithm clustering. IEEE Access. 2019;7:119620‐119630.

[61]

Zhang S, Wang Y, Yuan H, Wan P, Zhang Y, “Multiple‐antenna cooperative spectrum sensing based on the wavelet transform and Gaussian mixture model,” Sensors, 2019;19(18):3863–3881.

[62]

Bhatti DMS, Zaidi SB, Rehman SU. Channel error detection based cluster formation for cooperative Spectrum sensing. International Conference on Information and Communication Technology Convergence (ICTC). Jeju, South Korea; IEEE: 2018.

[63]

Maity S, Chatterjee S, Tamaghna A. On optimal fuzzy c‐means clustering for energy efficient cooperative spectrum sensing in cognitive radio networks. Digit Signal Process. 2016;49:104‐115.

Digital Library

[64]

Paul A, Maity SP. Kernel fuzzy c‐means clustering on energy detection based cooperative spectrum sensing. Digit Commun Netw. 2016;2(4):196‐205.

[65]

Bhatti DMS, Shaikh B, Zaidi SIH. Fuzzy C‐means and spatial correlation based clustering for cooperative spectrum sensing. International Conference on Information and Communication Technology Convergence (ICTC). Jeju, South Korea; IEEE: 2017.

[66]

Bhatti DMS, Ahmed S, Chan AS, Saleem K. Clustering formation in cognitive radio networks using machine learning. Int J Electron Commun. 2020;114:152–994.

[67]

Lo BF, Akyildiz IF. Reinforcement learning‐based cooperative sensing in cognitive radio ad hoc networks. 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. Instanbul, Turkey; IEEE: 2010.

[68]

Lundén J, Koivunen V, Kulkarni SR, Poor HV. Reinforcement learning based distributed multiagent sensing policy for cognitive radio networks. IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). Aachen, Germany: IEEE: 2011.

[69]

Lundén J, Kulkarni SR, Koivunen V, Poor HV. Multiagent reinforcement learning based Spectrum sensing policies for cognitive radio networks. IEEE J Selected Topics Signal Process. 2013;7(5):858‐868.

[70]

Oksanen J, Lundén J, Koivunen V. Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks. Neurocomputing. 2012;80:102‐110.

[71]

Zhang M, Wang L, Feng Y. Distributed cooperative spectrum sensing based on reinforcement learning in cognitive radio networks. AEU‐Int J Electron C. 2018;94:359‐366.

[72]

Menéndez ML, Morales D, Pardo L, Salicrú M. Statistical tests based on geodesic distances. Appl Math Lett. 1995;8(1):65‐69.

[73]

Arnaudon M, Barbaresco F, Yang L. Riemannian medians and means with applications to radar signal processing. IEEE J Selected Topics Signal Process. 2013;7(4):595‐604.

[74]

Moakher M. A differential geometric approach to the geometric mean of symmetric positive‐definite matrices. SIAM J Matrix Anal Appl. 2008;26(3):735‐747.

[75]

Liu X, Jia M, Zhang X, Lu W. A novel multichannel internet of things based on dynamic spectrum sharing in 5G communication. IEEE Internet Things J. 2019;6(4):5962‐5970.

[76]

Yang C, Li J, Guizani M, Anpalagan A, El M. Advanced spectrum sharing in 5G cognitive heterogeneous networks. IEEE Wirel Commun. 2016;23(2):94‐101.

Digital Library

[77]

Wang H, Nguyen DN, Dutkiewicz E, Fang G. Negotiable auction based on mixed graph: a novel Spectrum sharing framework. IEEE Trans Cognit Commun Netw. 2017;3(3):390‐403.

[78]

Bany Salameh HA, Krunz M, Younis O. Cooperative adaptive spectrum sharing in cognitive radio networks. IEEE/ACM Trans Netw. 2010;18(4):1181‐1194.

[79]

Sharma SK, Bogale TE, Le LB, Chatzinot S. Dynamic spectrum sharing in 5G wireless networks with full‐duplex technology: recent advances and research challenges. IEEE Commun Surv Tutor. 2018;20(1):674‐707.

[80]

Bhardwaj P, Panwar A, Ozdemir MEO. Enhanced dynamic spectrum access in multiband cognitive radio networks via optimized resource allocation. IEEE Trans Wirel Commun. 2016;15(12):8093‐8106.

Digital Library

[81]

Kaur A, Kumar K. Energy‐efficient resource allocation in cognitive radio networks under cooperative multi‐agent model‐free reinforcement learning schemes. IEEE Trans Netw Serv Manag. 2020;17(3):1337‐1348.

[82]

Morozs N, Clarke T, Grace D. Heuristically accelerated reinforcement learning for dynamic secondary spectrum sharing. IEEE Access. 2015;3:2771‐2783.

[83]

Li X, Fang J, Cheng W, Duan H, Chen Z, Li H. Intelligent power control for spectrum sharing in cognitive radios: a deep reinforcement learning approach. IEEE Access. 2018;6:25463‐25473.

[84]

Zhang H, Yang N, Huangfu W, Long K, Leung VCM. Power control based on deep reinforcement learning for spectrum sharing. IEEE Trans Wirel Commun. 2020;19(6):4209‐4219.

[85]

Zaky AB, Huang JZ, Wu K, ElHalawany BM. Generative neural network based spectrum sharing using linear sum assignment problems. China Commun. 2020;17(2):14–29.

[86]

Jacob S, Menon VG, Joseph S, et al. A novel spectrum sharing scheme using dynamic long short‐term memory with CP‐OFDMA in 5G networks. IEEE Trans Cognit Commun Netw. 2020;6(3):926‐934.

[87]

Doshi A, Yerramalli S, Ferrari L, Yoo T, Andrews JG. A deep reinforcement learning framework for contention‐based Spectrum sharing. IEEE J Selected Areas Commun. 2021;39(8):2526–2540.

[88]

Ahmed R, Chen Y, Hassan B, Du L. CR‐IoTNet: machine learning based joint spectrum sensing and allocation for cognitive radio enabled IoT cellular networks. Ad Hoc Netw. 2021;112:1570‐8705.

[89]

Kaur A, Kumar K. A reinforcement learning based evolutionary multi‐objective optimization algorithm for spectrum allocation in cognitive radio networks. Phys Commun. 2020;43:1874‐4907.

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Index Terms

Machine learning for cooperative spectrum sensing and sharing: A survey
1. Computing methodologies
  1. Machine learning
2. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

Index terms have been assigned to the content through auto-classification.

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cover image Transactions on Emerging Telecommunications Technologies

Transactions on Emerging Telecommunications Technologies Volume 33, Issue 1

January 2022

479 pages

ISSN:2161-3915

EISSN:2161-3915

DOI:10.1002/ett.v33.1

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© 2021 John Wiley & Sons, Ltd.

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John Wiley & Sons, Inc.

United States

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Published: 09 January 2022

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Cited By

Newaliya NSingh Y(2024)Multivariate hierarchical DBSCAN model for enhanced maritime data analyticsData & Knowledge Engineering10.1016/j.datak.2024.102282150:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.datak.2024.102282
Liang HWu JWang CSu MBao JGan JCao W(2024)Energy efficiency analysis of cooperative spectrum sensingTransactions on Emerging Telecommunications Technologies10.1002/ett.492735:1Online publication date: 15-Jan-2024
https://dl.acm.org/doi/10.1002/ett.4927
Odwa LChen Y(2023)Enhancing Spectral and Energy Efficiencies in a Cognitive Radio CRAN with PUEAsWireless Communications & Mobile Computing10.1155/2023/29539862023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/2953986
Radhi AAbdullah HAkkar H(2022)Denoised Jarque-Bera features-based K-Means algorithm for intelligent cooperative spectrum sensingDigital Signal Processing10.1016/j.dsp.2022.103659129:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.dsp.2022.103659
Kumar SChauhan PBansal RKaur MYadav R(2022)Performance Analysis of CSS Over α–η–μ and α–κ–μ Fading Channel Using Clustering-Based TechniqueWireless Personal Communications: An International Journal10.1007/s11277-022-09880-y126:4(3595-3610)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11277-022-09880-y

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