IF Akyildiz, W-Y Lee, MC Vuran, S Mohanty, NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Comput. Netw. **50**(13), 2127–2159 (2006). https://doi.org/10.1016/j.comnet.2006.05.001.

Article
MATH
Google Scholar

C Baylis, M Fellows, L Cohen, RJM II, Solving the spectrum crisis: intelligent, reconfigurable microwave transmitter amplifiers for cognitive radar. IEEE Microw. Mag. **15**(5), 94–107 (2014). https://doi.org/10.1109/mmm.2014.2321253.

Article
Google Scholar

YC Liang, KC Chen, GY Li, P Mahonen, Cognitive radio networking and communications: an overview. IEEE Trans. Veh. Technol. **60**(7), 3386–3407 (2011). https://doi.org/10.1109/TVT.2011.2158673.

Article
Google Scholar

IF Akyildiz, W-Y Lee, MC Vuran, S Mohanty, A survey on spectrum management in cognitive radio networks. IEEE Commun. Mag. **46**(4), 40–48 (2008). https://doi.org/10.1109/mcom.2008.4481339.

Article
Google Scholar

X Xing, T Jing, W Cheng, Y Huo, X Cheng, Spectrum prediction in cognitive radio networks. IEEE Wirel. Commun.**20**(2), 90–96 (2013). https://doi.org/10.1109/mwc.2013.6507399.

Article
Google Scholar

Y Saleem, MH Rehmani, Primary radio user activity models for cognitive radio networks: a survey. J. Netw. Comput. Appl. **43:**, 1–16 (2014). https://doi.org/10.1016/j.jnca.2014.04.001.

Article
Google Scholar

Y Chen, H-S Oh, A survey of measurement-based spectrum occupancy modeling for cognitive radios. IEEE Commun. Surv. Tutorials. **18**(1), 848–859 (2016). https://doi.org/10.1109/comst.2014.2364316.

Article
Google Scholar

A Al-Hourani, V Trajkovic, S Chandrasekharan, S Kandeepan, Spectrum occupancy measurements for different urban environments. 2015 Eur. Conf. Netw. Commun. (EuCNC) (2015). https://doi.org/10.1109/eucnc.2015.7194048.

SJ Kim, GB Giannakis, in *2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)*. Dynamic learning for cognitive radio sensing (IEEE, Piscataway, 2013), pp. 388–391. https://doi.org/10.1109/CAMSAP.2013.6714089.

Chapter
Google Scholar

Z Chen, N Guo, Z Hu, RC Qiu, Experimental validation of channel state prediction considering delays in practical cognitive radio. IEEE Trans. Veh. Technol. **60**(4), 1314–1325 (2011). https://doi.org/10.1109/TVT.2011.2116051.

Article
Google Scholar

L Yin, S Yin, W Hong, S Li, in *Military Communications Conference (MILCOM)*. Spectrum behaviour learning in cognitive radio based on artificial neural network (IEEE, Piscataway, 2011), pp. 25–30. https://doi.org/10.1109/MILCOM.2011.6127671.

Google Scholar

J Lee, HK Park, Channel prediction-based channel aladdress scheme for multichannel cognitive radio networks. J. Commun. Netw. **16**(2), 209–216 (2014). https://doi.org/10.1109/jcn.2014.000032.

Article
Google Scholar

W Pu, IF Akyildiz, Asymptotic queuing analysis for dynamic spectrum access networks in the presence of heavy tails. IEEE J. Sel. Areas Commun.**31**(3), 514–522 (2013). https://doi.org/10.1109/JSAC.2013.130316.

Article
Google Scholar

X Li, SA Zekavat, Cognitive radio based spectrum sharing: evaluating channel availability via traffic pattern prediction. J. Commun. Netw. **11**(2), 104–114 (2009). https://doi.org/10.1109/JCN.2009.6391385.

Article
Google Scholar

VK Tumuluru, P Wang, D Niyato, Channel status prediction for cognitive radio networks. Wirel. Commun. Mob. Comput. **12**(10), 862–874 (2012). https://doi.org/10.1002/wcm.1017.

Article
Google Scholar

S Chen, L Tong, Maximum throughput region of multiuser cognitive access of continuous time Markovian channels. IEEE J. Sel. Areas Commun. **29**(10), 1959–1969 (2011). https://doi.org/10.1109/JSAC.2011.111206.

Article
Google Scholar

M Bkassiny, Y Li, SK Jayaweera, A survey on machine-learning techniques in cognitive radios. IEEE Commun. Surv. Tutorials. **15**(3), 1136–1159 (2013). https://doi.org/10.1109/surv.2012.100412.00017.

Article
Google Scholar

A He, KK Bae, TR Newman, J Gaeddert, K Kim, R Menon, L Morales-Tirado, JJ Neel, Y Zhao, JH Reed, WH Tranter, A survey of artificial intelligence for cognitive radios. IEEE Trans. Veh. Technol.**59**(4), 1578–1592 (2010). https://doi.org/10.1109/tvt.2010.2043968.

Article
Google Scholar

N Cesa-Bianchi, G Lugosi, *Prediction, Learning, and Games* (Cambridge University Press, New York, 2006). https://doi.org/10.1017/cbo9780511546921.

Book
MATH
Google Scholar

N Merhav, M Feder, Universal prediction. IEEE Trans. Inf. Theory. **44**(6), 2124–2147 (1998). https://doi.org/10.1109/18.720534.

Article
MathSciNet
MATH
Google Scholar

H Bolfarine, S Zacks, *Prediction theory for finite populations, Springer Series in Statistics* (Springer, New York, 1992). https://doi.org/10.1007/978-1-4612-2904-9.

Book
MATH
Google Scholar

CE Shannon, Prediction and entropy of printed english. Bell Syst. Tech. J. **30**(1), 50–64 (1951). https://doi.org/10.1002/j.1538-7305.1951.tb01366.x.

Article
MATH
Google Scholar

J Rissanen, Universal coding, information, prediction, and estimation. IEEE Trans. Inf. Theory.**30**(4), 629–636 (1984). https://doi.org/10.1109/tit.1984.1056936.

Article
MathSciNet
MATH
Google Scholar

H Kobayashi, BL Mark, W Turin, *Probability, random processes, and statistical analysis: applications to communications, signal processing, queueing theory and mathematical finance* (Cambridge University Press, New York, 2011).

Book
Google Scholar

J Ziv, A Lempel, A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory.**23**(3), 337–343 (1977). https://doi.org/10.1109/tit.1977.1055714.

Article
MathSciNet
MATH
Google Scholar

JL KELLY, A new interpretation of information rate. IRE Trans. Inf. Theory.**2**(3), 25–34 (2011).

Google Scholar

Kotł, W,owski, Gru, P̈,nwald, in *IEEE Information Theory Workshop (ITW), 2012*. Sequential normalized maximum likelihood in log-loss prediction (IEEE, Piscataway, 2012), pp. 547–551. https://doi.org/10.1109/ITW.2012.6404734.

Chapter
Google Scholar

M Hutter, Convergence and loss bounds for bayesian sequence prediction. IEEE Trans. Inf. Theory. **49**(8), 2061–2067 (2003). https://doi.org/10.1109/tit.2003.814488.

Article
MathSciNet
MATH
Google Scholar

G Shafer, V Vovk, *Probability and finance: it’s only a game! Wiley Series in Probability and Statistics* (Wiley, New York, 2005). https://doi.org/10.1002/0471249696.

MATH
Google Scholar

PD Grnwald, IJ Myung, MA Pitt, *Advances in minimum description length: theory and applications (Neural Information Processing)* (The MIT Press, Cambridge, 2005).

Google Scholar

N Merhav, M Feder, A strong version of the redundancy-capacity theorem of universal coding. IEEE Trans. Inf. Theory. **41**(3), 714–722 (1995). https://doi.org/10.1109/18.382017.

Article
MATH
Google Scholar

NN Cencov, *Statistical decision rules and optimal inference (translations of mathematical monographs), vol. 53* (American Mathematical Society, Providence, 2000).

Google Scholar

PP Vaidyanathan, The theory of linear prediction. Synth. Lect. Signal Process.**2**(1), 1–184 (2007). https://doi.org/10.2200/s00086ed1v01y200712spr003.

Article
Google Scholar

PH Algoet, The strong law of large numbers for sequential decisions under uncertainty. IEEE Trans. Inf. Theory. **40**(3), 609–633 (1994). https://doi.org/10.1109/18.335876.

Article
MathSciNet
MATH
Google Scholar

EA Wan, RVD Merwe, in *Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium*. The unscented kalman filter for nonlinear estimation (IEEE, Piscataway, 2000), pp. 153–158. https://doi.org/10.1109/asspcc.2000.882463.

Google Scholar

B Ristic, S Arulampalam, NJ Gordon, *Beyond the Kalman filter: particle filters for tracking applications* (Artech house, London, 2004).

MATH
Google Scholar

JGD Gooijer, RJ Hyndman, 25 years of time series forecasting. Int. J. Forecast.**22**(3), 443–473 (2006). https://doi.org/10.1016/j.ijforecast.2006.01.001.

Article
Google Scholar

TM Cover, JA Thomas, *Elements of information theory* (Wiley, New York, 2006).

MATH
Google Scholar

A Goldsmith, P Varaiya, Capacity, mutual information, and coding for finite-state Markov channels. IEEE Trans. Inf. Theory. **42**(3), 868–886 (1996). https://doi.org/10.1109/isit.1994.394696.

Article
MATH
Google Scholar

RM Neal, Markov chain sampling methods for Dirichlet process mixture models. J. Comput. Graph. Stat. **9**(2), 249–265 (2000). https://doi.org/10.1080/10618600.2000.10474879.

MathSciNet
Google Scholar

M Dudí, SJ Phillips, RE Schapire, in *Learning Theory*. Performance guarantees for regularized maximum entropy density estimation (Springer, Berlin, Heidelberg, 2004), pp. 472–486.

Chapter
Google Scholar

YW Teh, *Dirichlet Process*. (C Sammut, GI Webb, eds.) (Springer, Boston, 2010). https://doi.org/10.1007/978-0-387-30164-8.

Google Scholar

M Wellens, P Mähönen, Lessons learned from an extensive spectrum occupancy measurement campaign and a stochastic duty cycle model. Mob. Netw. Appl. **15**(3), 461–474 (2010). https://doi.org/10.1007/s11036-009-0199-9.

Article
Google Scholar

MH Islam, CL Koh, SW Oh, X Qing, YY Lai, C Wang, Y-C Liang, BE Toh, F Chin, GL Tan, W Toh, in *2008 3*
^{rd} *International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008)*. Spectrum survey in singapore: occupancy measurements and analyses (IEEE, Piscataway, 2008), pp. 1–7. https://doi.org/10.1109/crowncom.2008.4562457.

W Tang, J Zhou, H Yu, S Li, A fair scheduling scheme based on collision statistics for cognitive radio networks. Concurr. Comput. Pract. Experience.**25**(9), 1091–1100 (2012). https://doi.org/10.1002/cpe.2879.

Article
Google Scholar

C Xianfu, Z Honggang, AB Mackenzie, M Matinmikko, Predicting spectrum occupancies using a non-stationary hidden Markov model. IEEE Wirel. Commun. Lett.**3**(4), 333–336 (2014). https://doi.org/10.1109/LWC.2014.2315040.

Article
Google Scholar

C Xu, H Jianwei, Evolutionarily stable spectrum access. IEEE Trans. Mob. Comput. **12**(7), 1281–1293 (2013). https://doi.org/10.1109/TMC.2012.94.

Article
Google Scholar

P De, Y-C Liang, Blind spectrum sensing algorithms for cognitive radio networks. IEEE Trans. Veh. Technol. **57**(5), 2834–2842 (2008). https://doi.org/10.1109/tvt.2008.915520.

Article
Google Scholar

P Huang, C-J Liu, L Xiao, J Chen, Wireless spectrum occupancy prediction based on partial periodic pattern mining. 2012 IEEE 20th Int. Symp. Model. Anal. Simul. Comput. Telecommun. Syst.**25**(7), 1925–1934 (2012). https://doi.org/10.1109/mascots.2012.16.

Google Scholar

S Arunthavanathan, S Kandeepan, RJ Evans, in *2013 IEEE Globecom Workshops (GC)*. Reinforcement learning based secondary user transmissions in cognitive radio networks (IEEE, Piscataway, 2013), pp. 374–379. https://doi.org/10.1109/glocomw.2013.6825016.

Chapter
Google Scholar

J Yang, H Zhao, X Chen, in *IEEE 2nd International Conference on Computer and Communications (ICCC)*. Genetic algorithm optimized training for neural network spectrum prediction (IEEE, Piscataway, 2016), pp. 2949–2954. https://doi.org/10.1109/compcomm.2016.7925237.

Google Scholar

S Ni, X Bai, Z Wang, B Guo, in *IEEE International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)*. A new method of cognitive radio spectrum prediction research (IEEE, Piscataway, 2016), pp. 982–986. https://doi.org/10.1109/cisp-bmei.2016.7852855.

Google Scholar

A Agarwal, S Dubey, MA Khan, R Gangopadhyay, S Debnath, in *2016 International Conference on Signal Processing and Communications (SPCOM)*. Learning based primary user activity prediction in cognitive radio networks for efficient dynamic spectrum access (IEEE, Piscataway, 2016), pp. 1–5. https://doi.org/10.1109/SPCOM.2016.7746632.

Google Scholar

C Clancy, J Hecker, E Stuntebeck, T O’Shea, Applications of machine learning to cognitive radio networks. IEEE Wirel. Commun. **14**(4), 47–52 (2007). https://doi.org/10.1109/MWC.2007.4300983.

Article
Google Scholar

L Gavrilovska, V Atanasovski, I Macaluso, LA DaSilva, Learning and reasoning in cognitive radio networks. IEEE Commun. Surv. Tutorials. **15**(4), 1761–1777 (2013). https://doi.org/10.1109/surv.2013.030713.00113.

Article
Google Scholar

DC Karia, BK Lande, RD Daruwala, Performance analysis of HMM- and ANN-based spectrum vacancy predictor behaviour for cognitive radios. Int. J. Ad Hoc Ubiquit. Comput.**11**(4), 206–213 (2012). https://doi.org/10.1504/ijahuc.2012.050439.

Article
Google Scholar

S-S Gu, S-N Yu, A chaotic neural network-based algorithm for relational structure matching. IEEE 2004 Int. Conf. Mach. Learn. Cybern. **6:**, 3328–3333 (2004). https://doi.org/10.1109/icmlc.2004.1380353.

Article
Google Scholar

MH Rehmani, AC Viana, H Khalife, S Fdida, SURF: A distributed channel selection strategy for data dissemination in multi-hop cognitive radio networks. Comput. Commun.**36**(10), 1172–1185 (2013). https://doi.org/10.1016/j.comcom.2013.03.005.

Article
Google Scholar

S Bayhan, F Alagöz, Distributed channel selection in CRAHNs: A non-selfish scheme for mitigating spectrum fragmentation. Ad Hoc Netw.**10**(5), 774–788 (2012). https://doi.org/10.1016/j.adhoc.2011.04.010. Special Issue on Cognitive Radio Ad Hoc Networks.

Article
Google Scholar

DP Bertsekas, JN Tsitsiklis, *Introduction to probability, Athena Scientific books* (Athena Scientific, Belmont, 2002). https://doi.org/10.1017/cbo9780511996504.005.

Google Scholar

A Banaei, CN Georghiades, in *2009 IEEE International Conference on Communications*. Throughput analysis of a randomized sensing scheme in cell-based ad-hoc cognitive networks (IEEE, Piscataway, 2009), pp. 1–6. https://doi.org/10.1109/icc.2009.5199524.

Google Scholar

J Gambini, O Simeone, U Spagnolini, Y Bar-Ness, Y Kim, in *2008 IEEE International Conference on Communications*. Cognitive radio with secondary packet-by-packet vertical handover (IEEE, Piscataway, 2008), pp. 1050–1054. https://doi.org/10.1109/icc.2008.205.

Chapter
Google Scholar

M Derakhshani, T Le-Ngoc, Learning-based opportunistic spectrum access with adaptive hopping transmission strategy. IEEE Trans. Wirel. Commun. **11**(11), 3957–3967 (2012). https://doi.org/10.1109/twc.2012.091812.111873.

Article
Google Scholar

P Thakur, A Kumar, S Pandit, G Singh, SN Satashia, in *IEEE Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC)*. Performance improvement of cognitive radio network using spectrum prediction and monitoring techniques for spectrum mobility (IEEE, Piscataway, 2016), pp. 679–684. https://doi.org/10.1109/pdgc.2016.7913208.

Google Scholar

M Khabazian, S Aissa, N Tadayon, Performance modeling of a two-tier primary-secondary network operated with IEEE 802.11 DCF mechanism. IEEE Trans. Wirel. Commun. **11**(9), 3047–3057 (2012). http://doi.org/10.1109/twc.2012.071612.110010.

Article
Google Scholar

Z Wang, S Salous, Spectrum occupancy statistics and time series models for cognitive radio. J. Signal Process. Syst. **62**(2), 145–155 (2011). https://doi.org/10.1007/s11265-009-0352-5.

Article
Google Scholar

J Zhang, G Ding, Y Xu, F Song, in *IEEE 8th International Conference on Wireless Communications & Signal Processing (WCSP)*. On the usefulness of spectrum prediction for dynamic spectrum access (IEEE, Piscataway, 2016), pp. 1–4. https://doi.org/10.1109/wcsp.2016.7752555.

Google Scholar

S Joshi, P Pawelczak, D Cabric, J Villasenor, When channel bonding is beneficial for opportunistic spectrum access networks. IEEE Trans. Wirel. Commun. **11**(11), 3942–3956 (2012). http://doi.org/10.1109/twc.2012.092512.111730.

Article
Google Scholar

W Wang, T Lv, T Wang, X Yu, in *2010 IEEE 72nd Vehicular Technology Conference - Fall*. Primary user activity based channel allocation in cognitive radio network (IEEE, Ottawa, 2010), pp. 1–5. https://doi.org/10.1109/vetecf.2010.5594260, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5594260&isnumber=5594061.

Google Scholar

J Yang, H Zhao, Enhanced throughput of cognitive radio networks by imperfect spectrum prediction. IEEE Commun. Lett. **19**(10), 1738–1741 (2015). https://doi.org/10.1109/lcomm.2015.2442571.

Article
Google Scholar

RD Smallwood, EJ Sondik, The optimal control of partially observable Markov processes over a finite horizon. Oper. Res.**21**(5), 1071–1088 (1973). https://doi.org/10.1287/opre.21.5.1071.

Article
MATH
Google Scholar

D Blackwell, in *Transactions of the First Prague Conference on Information Theory, Statistical Decision Functions, Random Processes Held at Liblice Near Prague from November*. The entropy of functions of finite-state Markov chains, vol. 28 (Czechoslovak Academy of sciences, Czech Republic, 1957), pp. 13–20.

Google Scholar

T Kaijser, A limit theorem for partially observed Markov chains. Ann. Probab.**3**(4), 677–696 (1975). https://doi.org/10.1214/aop/1176996308.

Article
MathSciNet
MATH
Google Scholar

J Marroquin, S Mitter, T Poggio, Probabilistic solution of ill-posed problems in computational vision. J. Am. Stat. Assoc. **82**(397), 76–89 (1987). https://doi.org/10.2307/2289127.

Article
MATH
Google Scholar

LR Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE. **77**(2), 257–286 (1989). https://doi.org/10.1109/5.18626, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=18626&isnumber=698.

Article
Google Scholar

MS Arulampalam, S Maskell, N Gordon, T Clapp, A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Signal Proc.**50**(2), 174–188 (2002). https://doi.org/10.1109/78.978374.

Article
Google Scholar

D Haussler, A Barron, SCCRL University of California, *How well do Bayes methods work for on-line prediction of [[+ or - ]1] values?, Technical reports* (University of California, Santa Cruz, Computer Research Laboratory, California, 1992).

Google Scholar

D Barber, *Bayesian time series models* (Cambridge University Press, New York, 2011).

Book
MATH
Google Scholar

L Csurgai-Horváth, J Bito, in *Proceedings of the 2011 11*
^{th} *International Conference on Telecommunications (ConTEL)*. Primary and secondary user activity models for cognitive wireless network (IEEE, Piscataway, pp. 301–306.

S Bayhan, F Alagöz, A Markovian approach for best-fit channel selection in cognitive radio networks. Ad Hoc Netw. **12:**, 165–177 (2014). https://doi.org/10.1016/j.adhoc.2011.08.007.

Article
Google Scholar

AW Min, KG Shin, Exploiting multi-channel diversity in spectrum-agile networks. IEEE Conf. Comput. Commun (2008). https://doi.org/10.1109/infocom.2007.256.

Q Zhao, L Tong, A Swami, Y Chen, Decentralized cognitive mac for opportunistic spectrum access in ad-hoc networks: A pomdp framework. IEEE J. Sel. Areas Commun. **25**(3), 589–600 (2007). https://doi.org/10.1109/jsac.2007.070409.

Article
Google Scholar

H Eltom, S Kandeepan, B Moran, RJ Evans, in *2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS)*. Spectrum occupancy prediction using a hidden Markov modelIEEEPiscataway, 2015), pp. 1–8. https://doi.org/10.1109/icspcs.2015.7391772.

Google Scholar

Y Li, Y-N Dong, H Zhang, H-T Zhao, H-X Shi, X-X Zhao, in *IEEE 10th International Conference on Computer and Information Technology (CIT)*. Spectrum usage prediction based on high-order Markov model for cognitive radio networks (IEEE, Piscataway, 2010), pp. 2784–2788. https://doi.org/10.1109/cit.2010.464.

Google Scholar

J Riihijärvi, J Nasreddine, P Mähönen, in *European Wireless Conference (EW)*. Impact of primary user activity patterns on spatial spectrum reuse opportunities (IEEE, Piscataway, 2010), pp. 962–968. https://doi.org/10.1109/ew.2010.5483445.

Chapter
Google Scholar

M Wellens, J Riihijarvi, P Mahonen, in *IEEE Annual Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks Workshops*. Modelling primary system activity in dynamic spectrum access networks by aggregated on/off-processes (IEEE, Piscataway, 2009), pp. 1–6. https://doi.org/10.1109/SAHCNW.2009.5172946.

Google Scholar

S Wang, J Zhang, L Tong, A characterization of delay performance of cognitive medium access. IEEE Trans. Wirel. Commun.**11**(2), 800–809 (2012). https://doi.org/10.1109/twc.2012.010312.110765.

Article
Google Scholar

L Jiao, E Song, V Pla, FY Li, Capacity upper bound of channel assembling in cognitive radio networks with quasistationary primary user activities. IEEE Trans. Veh. Technol.**62**(4), 1849–1855 (2013). https://doi.org/10.1109/tvt.2012.2236115.

Article
Google Scholar

SD Barnes, BT Maharaj, Prediction based channel allocation performance for cognitive radio. AEU - Int. J. Electron. Commun.**68**(4), 336–345 (2014). https://doi.org/10.1016/j.aeue.2013.09.009.

Article
Google Scholar

L Meliá Gutiérrez, S Zazo, JL Blanco-Murillo, I Pérez-Álvarez, A García-Rodríguez, B Pérez-Díaz, HF spectrum activity prediction model based on HMM for cognitive radio applications. Phys. Commun.**9:**, 199–211 (2013). https://doi.org/10.1016/j.phycom.2012.09.004.

Article
Google Scholar

T Nguyen, BL Mark, Y Ephraim, Spectrum sensing using a hidden bivariate Markov model. IEEE Trans. Wirel. Commun. **12**(9), 4582–4591 (2013). https://doi.org/10.1109/twc.2013.072513.121864.

Article
Google Scholar

A Saad, B Staehle, R Knorr, in *IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)*. Spectrum prediction using hidden Markov models for industrial cognitive radio (IEEE, Piscataway, 2016), pp. 1–7. https://doi.org/10.1109/wimob.2016.7763231.

Google Scholar

H Eltom, S Kandeepan, YC Liang, B Moran, RJ Evans, in *2016 IEEE International Conference on Communications Workshops (ICC)*. HMM based cooperative spectrum occupancy prediction using hard fusion (IEEE, Piscataway, 2016), pp. 669–675. https://doi.org/10.1109/iccw.2016.7503864.

Chapter
Google Scholar

C-H Liu, D Cabric, Prediction of Erlang-2 distributed primary user traffic for dynamic spectrum access. IEEE Wirel. Commun. Lett.**4**(5), 481–484 (2015). https://doi.org/10.1109/lwc.2015.2442249.

Article
Google Scholar

SH Sohn, HMM-based adaptive frequency-hopping cognitive radio system to reduce interference time and to improve throughput. KSII Trans. Internet Inf. Syst. **4**(4), 475–490 (2010). https://doi.org/10.3837/tiis.2010.08.002.

Google Scholar

Y Zhao, Z Hong, G Wang, J Huang, in *IEEE 25th International Conference on Computer Communication and Networks (ICCCN)*. High-order hidden bivariate Markov model: A novel approach on spectrum prediction (IEEE, Piscataway, 2016), pp. 1–7. https://doi.org/10.1109/icccn.2016.7568528.

Google Scholar

SS Dias, MGS Bruno, Cooperative target tracking using decentralized particle filtering and RSS sensors. IEEE Trans. Signal Proc. **61**(14), 3632–3646 (2013). https://doi.org/10.1109/tsp.2013.2262276.

Article
MathSciNet
Google Scholar

X Xing, T Jing, W Cheng, Y Huo, X Cheng, T Znati, Cooperative spectrum prediction in multi-PU multi-SU cognitive radio networks. Mob. Netw. Appl.**19**(4), 502–511 (2014). https://doi.org/10.1007/s11036-014-0507-x.

Article
Google Scholar

D Dash, A Sabharwal, Paranoid secondary: waterfilling in a cognitive interference channel with partial knowledge. IEEE Trans. Wirel. Commun.**11**(3), 1045–1055 (2012). https://doi.org/10.1109/TWC.2012.012412.110348.

Article
Google Scholar

B Canberk, IF Akyildiz, S Oktug, Primary user activity modeling using first-difference filter clustering and correlation in cognitive radio networks. IEEE/ACM Trans. Netw.**19**(1), 170–183 (2011). https://doi.org/10.1109/tnet.2010.2065031.

Article
Google Scholar

Z Wen, T Luo, W Xiang, S Majhi, Y Ma, in *ICC Workshops - 2008 IEEE International Conference on Communications Workshops*. Autoregressive spectrum hole prediction model for cognitive radio systems (IEEE, Beijing, 2008), pp. 154–157. https://doi.org/10.1109/ICCW.2008.34, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4531882&isnumber=4531848.

Chapter
Google Scholar

D Willkomm, S Machiraju, J Bolot, A Wolisz, Primary user behavior in cellular networks and implications for dynamic spectrum access. IEEE Commun. Mag.**47**(3), 88–95 (2009). https://doi.org/10.1109/mcom.2009.4804392.

Article
Google Scholar

A Eltholth, in *2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS)*. Forward Backward autoregressive spectrum prediction scheme in cognitive radio systems (IEEE, Piscataway, 2015), pp. 1–5. https://doi.org/10.1109/ICSPCS.2015.7391770.

Google Scholar

K Sithamparanathan, A Giorgetti, *Cognitive radio techniques: spectrum sensing, interference mitigation, and localizatio. Artech House mobile communications library* (Artech House, Boston, 2012).

Google Scholar

W Saad, Z Han, HV Poor, T Basar, JB Song, A cooperative bayesian nonparametric framework for primary user activity monitoring in cognitive radio networks. IEEE J. Sel. Areas Commun.**30**(9), 1815–1822 (2012). https://doi.org/10.1109/JSAC.2012.121027.

Article
Google Scholar

M Lopez-Benitez, F Casadevall, Time-dimension models of spectrum usage for the analysis, design, and simulation of cognitive radio networks. IEEE Trans. Veh. Technol.**62**(5), 2091–2104 (2013). https://doi.org/10.1109/tvt.2013.2238960.

Article
Google Scholar

VA Epanechnikov, Non-parametric estimation of a multivariate probability density. Theory Probab. Appl.**14**(1), 153–158 (1969). https://doi.org/10.1137/1114019.

Article
MathSciNet
MATH
Google Scholar

I Macaluso, D Finn, B Ozgul, LA DaSilva, Complexity of spectrum activity and benefits of reinforcement learning for dynamic channel selection. IEEE J. Sel. Areas Commun.**31**(11), 2237–2248 (2013). https://doi.org/10.1109/JSAC.2013.131115.

Article
Google Scholar

J Rissanen, Strong optimality of the normalized ML models as universal codes and information in data. IEEE Trans. Inf. Theory.**47**(5), 1712–1717 (2001). https://doi.org/10.1109/18.93091.

Article
MathSciNet
MATH
Google Scholar

F Hou, X Chen, H Huang, X Jing, in *2016 16*
^{th} *International Symposium on Communications and Information Technologies (ISCIT)*. Throughput performance improvement in cognitive radio networks based on spectrum prediction, (2016), pp. 655–658. https://doi.org/10.1109/iscit.2016.7751715.

H Li, RC Qiu, in *IEEE Global Telecommunications Conference (GLOBECOM)*. A graphical framework for spectrum modeling and decision making in cognitive radio networks (IEEE, Piscataway, 2010), pp. 1–6. https://doi.org/10.1109/GLOCOM.2010.5683361.

Google Scholar

IA Akbar, WH Tranter, in *Proceedings of 2007 IEEE SoutheastCon*. Dynamic spectrum aladdress in cognitive radio using hidden Markov models: Poisson distributed case (IEEE, Piscataway, 2007), pp. 196–201. https://doi.org/10.1109/SECON.2007.342884.

Chapter
Google Scholar