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2019 | OriginalPaper | Buchkapitel

33. Adaptive Learning in Cognitive Radio

verfasst von : Husheng Li

Erschienen in: Handbook of Cognitive Radio

Verlag: Springer Singapore

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Abstract

Machine learning is a powerful tool for cognitive radio users to learn its sensing and transmission strategy from the experience. This chapter provides a brief introduction to a variety of machine-learning techniques. The basic setup of machine learning, as well as the dichotomy, is explained. Then, the supervised, unsupervised, semi-supervised, and reinforcement learning techniques are briefly discussed. The single-agent learning is then extended to the case of multiagent learning. Then, the machine-learning techniques are applied in various cases of machine learning, such as channel selection and routing.

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Literatur
1.
Zurück zum Zitat Aggarwal CC (2016) Recommender systems: the textbook. Springer, New York Aggarwal CC (2016) Recommender systems: the textbook. Springer, New York
2.
Zurück zum Zitat Anthony M, Bartlett P (1999) Neural network learning: theoretical foundations. Cambridge University Press, CambridgeCrossRef Anthony M, Bartlett P (1999) Neural network learning: theoretical foundations. Cambridge University Press, CambridgeCrossRef
3.
Zurück zum Zitat Azar Y, Fiat A, Karlin A, McSherry F, Saia J (2001) Spectral analysis of data. In: Proceedings of the 33rd ACM symposium on theory of computing (STOC), 6 July 2001, pp 619–626 Azar Y, Fiat A, Karlin A, McSherry F, Saia J (2001) Spectral analysis of data. In: Proceedings of the 33rd ACM symposium on theory of computing (STOC), 6 July 2001, pp 619–626
4.
Zurück zum Zitat Berkvosky S, Kuflik T, Ricci F (2007) Distributed collaborative filtering with domain specialization. In: Proceedings of ACM conference recommender systems (RecSys), 19 Oct 2007, pp 33–40 Berkvosky S, Kuflik T, Ricci F (2007) Distributed collaborative filtering with domain specialization. In: Proceedings of ACM conference recommender systems (RecSys), 19 Oct 2007, pp 33–40
5.
Zurück zum Zitat Bertsekas DP (1987) Dynamic programming: deterministic and stochastic models. Prentice Hall, Englewood CliffsMATH Bertsekas DP (1987) Dynamic programming: deterministic and stochastic models. Prentice Hall, Englewood CliffsMATH
6.
Zurück zum Zitat Busoniu L, Babuska R, Schutter BD (2008) A comprehensive survey of multiagent reinforcement learning. IEEE Trans Syst Man Cybern Part C Appl Rev 38(2):156–172CrossRef Busoniu L, Babuska R, Schutter BD (2008) A comprehensive survey of multiagent reinforcement learning. IEEE Trans Syst Man Cybern Part C Appl Rev 38(2):156–172CrossRef
7.
Zurück zum Zitat Chapelle O, Scholkopf B, Zien A (2006) Semi-supervised learning. The MIT Press, CambridgeCrossRef Chapelle O, Scholkopf B, Zien A (2006) Semi-supervised learning. The MIT Press, CambridgeCrossRef
8.
Zurück zum Zitat Devroye L, Lugosi G (2001) Combinatorial methods in density estimation. Springer, New YorkCrossRef Devroye L, Lugosi G (2001) Combinatorial methods in density estimation. Springer, New YorkCrossRef
9.
Zurück zum Zitat Drineas P, Kerenidis I, Raghavan P (2002) Competitive recommendation systems. In: Proceedings of the 34th ACM symposium on theory of computing (STOC), 19 May 2002, pp 82–90 Drineas P, Kerenidis I, Raghavan P (2002) Competitive recommendation systems. In: Proceedings of the 34th ACM symposium on theory of computing (STOC), 19 May 2002, pp 82–90
10.
Zurück zum Zitat Fudenberg D, Tirole J (1991) Game theory. The MIT Press, CambridgeMATH Fudenberg D, Tirole J (1991) Game theory. The MIT Press, CambridgeMATH
11.
Zurück zum Zitat Gabor Z, Kalmar Z, Szepesvari C (1998) Multi-criteria reinforcement learning. In: Proceedings of the 15th International conference on machine learning (ICML), 24 July 1998, vol 98, pp 197–205 Gabor Z, Kalmar Z, Szepesvari C (1998) Multi-criteria reinforcement learning. In: Proceedings of the 15th International conference on machine learning (ICML), 24 July 1998, vol 98, pp 197–205
12.
Zurück zum Zitat Gittins JC (1979) Bandit processes and dynamic allocation indices. J R Stat Soc Ser B (Stat Methodol) 41(2):148–177MathSciNetMATH Gittins JC (1979) Bandit processes and dynamic allocation indices. J R Stat Soc Ser B (Stat Methodol) 41(2):148–177MathSciNetMATH
13.
Zurück zum Zitat Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70CrossRef Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70CrossRef
14.
Zurück zum Zitat Goodfellow I, Bengio Y (2016) Deep learning. The MIT Press, CambridgeMATH Goodfellow I, Bengio Y (2016) Deep learning. The MIT Press, CambridgeMATH
15.
Zurück zum Zitat Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New YorkCrossRef Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New YorkCrossRef
16.
Zurück zum Zitat Kushner HJ, Yin GG (2003) Stochastic approximation and recursive algorithms and applications. Springer, New YorkMATH Kushner HJ, Yin GG (2003) Stochastic approximation and recursive algorithms and applications. Springer, New YorkMATH
17.
Zurück zum Zitat Lai L, Gamal HE, Jiang H, Poor HV (2011) Cognitive medium access: exploration, exploitation, and competition. IEEE Trans Mob Comput 10(2):239–253CrossRef Lai L, Gamal HE, Jiang H, Poor HV (2011) Cognitive medium access: exploration, exploitation, and competition. IEEE Trans Mob Comput 10(2):239–253CrossRef
18.
Zurück zum Zitat Li H (2009) Multi-agent Q-learning of channel selection in multi-user cognitive radio systems: a two by two case. In: Proceedings of IEEE International conference on systems, man and cybernetics, 11 Oct 2009, pp 1893–1898 Li H (2009) Multi-agent Q-learning of channel selection in multi-user cognitive radio systems: a two by two case. In: Proceedings of IEEE International conference on systems, man and cybernetics, 11 Oct 2009, pp 1893–1898
19.
Zurück zum Zitat Li H (2009) Learning the spectrum via collaborative filtering in cognitive radio networks. In: Proceedings of IEEE symposium on new frontiers in dynamic spectrum (DySPAN), 6 Apr 2010, pp 1–12 Li H (2009) Learning the spectrum via collaborative filtering in cognitive radio networks. In: Proceedings of IEEE symposium on new frontiers in dynamic spectrum (DySPAN), 6 Apr 2010, pp 1–12
20.
Zurück zum Zitat Littman ML (2001) Value-function reinforcement learning in Markov games. J Cogn Syst Res 2(1):55–66CrossRef Littman ML (2001) Value-function reinforcement learning in Markov games. J Cogn Syst Res 2(1):55–66CrossRef
21.
Zurück zum Zitat Metrick A, Polak B (1994) Fictitious play in 2 × 2 games: a geometric proof of convergence. Econ Theory 4(6):923–933MathSciNetCrossRef Metrick A, Polak B (1994) Fictitious play in 2 × 2 games: a geometric proof of convergence. Econ Theory 4(6):923–933MathSciNetCrossRef
22.
Zurück zum Zitat Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of machine learning. The MIT Press, CambridgeMATH Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of machine learning. The MIT Press, CambridgeMATH
23.
Zurück zum Zitat O’Shea TJ, Corgan J, Clancy TC (2016) Convolutional radio modulation recognition networks. In: Proceedings of International conference on engineering applications of neural networks, 2 Sept 2016, pp 213–226 O’Shea TJ, Corgan J, Clancy TC (2016) Convolutional radio modulation recognition networks. In: Proceedings of International conference on engineering applications of neural networks, 2 Sept 2016, pp 213–226
24.
Zurück zum Zitat O’Shea TJ, Corgan J, Clancy TC (2016) Unsupervised representation learning of structured radio communication signals. In: Proceedings of 1st International workshop on sensing, processing and learning for intelligent machines (SPLINE), 6 July 2016, pp 1–5 O’Shea TJ, Corgan J, Clancy TC (2016) Unsupervised representation learning of structured radio communication signals. In: Proceedings of 1st International workshop on sensing, processing and learning for intelligent machines (SPLINE), 6 July 2016, pp 1–5
25.
27.
Zurück zum Zitat Schapire RE, Freud Y (2014) Boosting: foundations and algorithms. The MIT Press, Cambridge Schapire RE, Freud Y (2014) Boosting: foundations and algorithms. The MIT Press, Cambridge
28.
Zurück zum Zitat Schlkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization and beyond. The MIT Press, Cambridge Schlkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization and beyond. The MIT Press, Cambridge
29.
Zurück zum Zitat Sutton RS (1998) Reinforcement learning: an introduction. The MIT Press, Cambridge Sutton RS (1998) Reinforcement learning: an introduction. The MIT Press, Cambridge
30.
Zurück zum Zitat Thilina KM, Choi KW, Saquib N, Hossain E (2013) Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. J Sel Areas Commun 31(11): 2209–2221CrossRef Thilina KM, Choi KW, Saquib N, Hossain E (2013) Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. J Sel Areas Commun 31(11): 2209–2221CrossRef
31.
Zurück zum Zitat Watkins CJCH (1989) Learning from delayed rewards. Ph.D. thesis, Cambridge University, Cambridge Watkins CJCH (1989) Learning from delayed rewards. Ph.D. thesis, Cambridge University, Cambridge
32.
Zurück zum Zitat Wiering M, Otterio M (2012) Reinforcement learning: state-of-the-art. Springer, Berlin/HeidelbergCrossRef Wiering M, Otterio M (2012) Reinforcement learning: state-of-the-art. Springer, Berlin/HeidelbergCrossRef
33.
Zurück zum Zitat Zheng K, Li H (2011) Multi-objective reinforcement learning based routing in cognitive radio networks: walking in a random maze. In: Proceedings of IEEE International conferences on computing, networking and communications (ICNC), 30 Jan 2011, pp 359–363 Zheng K, Li H (2011) Multi-objective reinforcement learning based routing in cognitive radio networks: walking in a random maze. In: Proceedings of IEEE International conferences on computing, networking and communications (ICNC), 30 Jan 2011, pp 359–363
Metadaten
Titel
Adaptive Learning in Cognitive Radio
verfasst von
Husheng Li
Copyright-Jahr
2019
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-1394-2_41

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