Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions
作者： Manuel Eugenio Morocho-Cayamcela , Haeyoung Lee , Wansu Lim
DOI: 10.1109/ACCESS.2019.2942390
关键词:
摘要: … of mobile and wireless communication technologies also … problems in mobile and wireless communications are non-linear or … But like any form of technology, ML is not entirely perfect. …
sci-hub.se 参考文章(178)
Nedim Šrndić, Battista Biggio, Giorgio Giacinto, Igino Corona, Fabio Roli, Davide Maiorca, Blaine Nelson, Pavel Laskov, Evasion attacks against machine learning at test time european conference on machine learning. ,vol. 8190, pp. 387- 402 ,(2013) , 10.1007/978-3-642-40994-3_25
P. J. G. Lisboa, Interpretability in Machine Learning – Principles and Practice international workshop on fuzzy logic and applications. pp. 15- 21 ,(2013) , 10.1007/978-3-319-03200-9_2
Martijn van Otterlo, Marco Wiering, Reinforcement Learning and Markov Decision Processes Reinforcement Learning. pp. 3- 42 ,(2012) , 10.1007/978-3-642-27645-3_1
Terrence J. Sejnowski, Geoffrey E. Hinton, Unsupervised learning : foundations of neural computation Published in <b>1999</b> in Cambridge Mass) by MIT press. ,(1999)
Matthias Wellens, Petri Heikki Mähönen, Empirical modelling of spectrum use and evaluation of adaptive spectrum sensing in dynamic spectrum access networks Publikationsserver der RWTH Aachen University. ,(2010)
I. Bratko, Machine Learning: Between Accuracy and Interpretability Springer, Vienna. pp. 163- 177 ,(1997) , 10.1007/978-3-7091-2668-4_10
Martin Sauter, From GSM to LTE-Advanced: An Introduction to Mobile Networks and Mobile Broadband ,(2011)
Mihail Mihaylov, Yann-Aël Le Borgne, Karl Tuyls, Ann Nowé, Reinforcement Learning for Self-organizing Wake-Up Scheduling in Wireless Sensor Networks international conference on agents and artificial intelligence. pp. 382- 396 ,(2011) , 10.1007/978-3-642-29966-7_25
Filip M. Mulier, Vladimir Cherkassky, Learning from Data: Concepts, Theory, and Methods John Wiley & Sons, Inc.. ,(1998)
Christopher J.C.H. Watkins, Peter Dayan, Technical Note Q-Learning Machine Learning. ,vol. 8, pp. 279- 292 ,(1992) , 10.1023/A:1022676722315