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A Comprehensive Survey on Machine Learning for Signal Processing: Evolution, Applications and Research Opportunities

Padma Charan Sahu, Ratnakar Dash

Abstract


Machine Learning has been getting a charge out of a remarkable many applications that take care of issues and empower computerization in different areas. Essentially, this is because of the blast in the accessibility of information, huge enhancements in ML methods, and headway in registering abilities. Without a doubt, ML has been applied to different unremarkable and complex issues emerging in signal processing activity and the executives. There are different overviews on ML for explicit zones in signal processing or for explicit advances. This overview is unique, since it together presents the use of assorted ML procedures in different key territories of signal handling across various system advances. Right now, will profit by a thorough conversation on the distinctive learning ideal models and ML methods applied to crucial issues in signal processing, including estimation of Bit Error rate, Signal to Noise apportion just as productivity. Besides, this review depicts the confinements, give bits of knowledge, and examine difficulties and future chances to propel ML in signal processing. In this way, this is an opportune commitment of the ramifications of ML for signal processing, that is pushing the boundaries of various system and activity.

 

Keywords: Machine learning, signal processing, bit error rate, SNR


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References


Amaral P., Dinis J., Pinto P., et al. Machine learning in software defined networks: Data collection and traffic classification. In: Network Protocols (ICNP), 2016 IEEE 24th International Conference on, IEEE. 2016, 1–5p.

Bkassiny M., Li Y., Jayaweera S.K. A survey on machine-learning techniques in cognitive radios. IEEE Communications Surveys & Tutorials. 2013, 15(3), 1136–1159p.

Blenk A., Kalmbach P., Smagt V.P., Kellerer W. Boost online virtual network embedding: Using neural networks for admission control. In: Network and Service Management (CNSM), 2016 12th International Conference on, IEEE. 2016, 10–18p.

Brownlee J. Practical machine learning problems. 2013. machinelearningmastery.com /practical-machine-learning-problems/. Accessed 01 Apr 2018

Buczak A.L., Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials. 2016, 18(2), 1153–1176p.

Erickson B.J., Korfiatis P., Akkus Z., Kline T.L. Machine learning for medical imaging. Radio Graphics. 2017, 37(2), 505–515p.

IMPACT Cyber Trust. Information Marketplace for Policy and Analysis of Cyber-risk and Trust. https://www.impactcybertrust.org, Accessed 01 Aug 2017.

Klaine P.V., Imran M.A., Onireti O., Souza R.D. A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Communications Surveys & Tutorials. 2017, 1(1), 99p.

Li Y., Ma R., Jiao R. A hybrid malicious code detection method based on deep learning. Methods. 2015, 9(5)

Li Y., Liu H., Yang W., et al. Inter-datacenter network traffic prediction with elephant flows. In: Proceedings of IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE. 2016, 206–213p.

Mijumbi R., Gorricho J.L., Serrat J., et al. Design and evaluation of learning algorithms for dynamic resource management in virtual networks. In: Network Operations and Management Symposium (NOMS), IEEE. 2014, 1–9p.


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