Abstract
This paper explores the learning capability of hidden Markov model (HMM) in capturing the temporal correlation and predicting primary user (PU) activity pattern of real spectrum data of GSM-900 band through an USRP-LabVIEW platform for cognitive radio (CR) systems. The inability of the widely used stationary Markov model in estimating the occupancy pattern of primary channels for a long duration of time has been verified. We proposed an alternative duty cycle (DC)–based two-state discrete-time Markov chain (DTMC-DC) model. Analysis of empirical data indicates that DC required for a non-stationary DTMC-DC model can be well approximated by a trapezoidal shape and the PU spectrum usage pattern estimated using DTMC-DC is capable of learning the statistical behavior (length of idle and busy interval periods) of a real channel accurately with a reduced complexity.
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References
Monteiro JME (2012) Cognitive radio: survey on communication protocols, spectrum decision issues, and future research directions. Wirel Netw 18(2):147. https://doi.org/10.1007/s11276-011-0392-1
Wang B, Liu KJR (2011) Advances in cognitive radio networks: a survey. IEEE J Sel Top Signal Process 5(1):5. https://doi.org/10.1109/JSTSP.2010.2093210
Pandit G, Singh S (2017) An overview of spectrum sharing techniques in cognitive radio communication system. Wirel Netw 23(2):497. https://doi.org/10.1007/s11276-015-1171-1
Liang YC, Zeng Y, Peh EC, Hoang AT (2008) Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans Wirel Commun 7(4):1326
Khalid L, Anpalagan A (2016) Adaptive assignment of heterogeneous users for group-based cooperative spectrum sensing. IEEE Trans Wirel Commun 15(1):232
Chen CY, Chou YH, Chao HC, Lo CH (2012) Secure centralized spectrum sensing for cognitive radio networks. Wirel Netw 18(6):667. https://doi.org/10.1007/s11276-012-0426-3
Thakur P, Kumar A, Pandit S, Singh SN, Satashia G (2017) Performance analysis of high-traffic cognitive radio communication system using hybrid spectrum access, prediction and monitoring techniques. Wirel Netw: 1–11. https://doi.org/10.1007/s11276-016-1440-7
Sung KW, Kim SL, Zander J (2010) Temporal spectrum sharing based on primary user activity prediction. IEEE Trans Wirel Commun 9(12):3848
He A, Bae KK, Newman TR, Gaeddert J, Kim K, Menon R, Morales-Tirado L, Zhao Y, Reed JH, Tranter WH et al (2010) A survey of artificial intelligence for cognitive radios. IEEE Trans Veh Technol 59(4):1578
Saad W, Han Z, Poor HV, Basar T, JuBin S (2012) A cooperative bayesian nonparametric framework for primary user activity monitoring in cognitive radio networks. IEEE J Sel Areas Commun 30(9):1815
Grimmer J (2011) An introduction to bayesian inference via variational approximations. Polit Anal 19(1):32
Schrodt PA (2006) In: Programming for peace. Springer, pp 161–184
Tumuluru VK, Wang P, Niyato D (2012) Channel status prediction for cognitive radio networks. Wireless Communications and Mobile Computing 12:862–874. https://doi.org/10.1002/wcm.1017
Ahmadi H, Chew YH, Tang PK, Nijsure YA (2011) In: 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, pp 401–405
Cacciapuoti AS, Caleffi M, Marino F, Paura L (2016) On the impact of primary traffic correlation in tv white space. IEEE Access 4:7199
Melián-Gutiérrez L, Zazo S, Blanco-Murillo JL, Pérez-Álvarez I, García-rodríguez A, Pérez-díaz B (2013) Hf spectrum activity prediction model based on hmm for cognitive radio applications. Phys Commun 9:199
Chen Z, Guo N, Hu Z, Qiu RC (2011) Experimental validation of channel state prediction considering delays in practical cognitive radio. IEEE Trans Veh Technol 60(4):1314
Nguyen T, Mark BL, Ephraim Y (2013) Spectrum sensing using a hidden bivariate markov model. IEEE Trans Wirel Commun 12(9):4582
Akbar IA, Tranter WH (2007) In: SoutheastCon, 2007. Proceedings. IEEE. IEEE, pp 196–201
Zhao Q, Swami A (2007) A decision-theoretic framework for opportunistic spectrum access. IEEE Wireless Communications 14(4):14–20. https://doi.org/10.1109/MWC.2007.4300978
Rondeau TW, Rieser CJ, Gallagher TM, Bostian CW (2004) In: Microwave Symposium Digest, 2004 IEEE MTT-S International. IEEE, vol 2, pp 739–742
Ghosh C, Cordeiro C, Agrawal DP, Rao MB (2009) In: IEEE International Conference on Pervasive Computing and Communications. IEEE, pp 1–6
Carniani LVR, Giupponi A (2010) https://doi.org/10.1109/ew.2010.5483438
Abdou A, Najajri O, Jamoos A (2017) In: 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp 1–5. https://doi.org/10.1109/AEECT.2017.8257748
Luis RDRBL, Oliveira M (2017) Rf-spectrum opportunities for cognitive radio networks operating over gsm channels. IEEE Transactions on Cognitive Communications and Networking, pp 3. https://doi.org/10.1109/TCCN.2017.2771558
Xing X, Jing T, Huo Y, Li H, Cheng X (2013) In: INFOCOM, 2013 Proceedings IEEE. IEEE, pp 1465–1473
Chen X, Zhang H, MacKenzie AB, Matinmikko M (2014) Predicting spectrum occupancies using a non-stationary hidden markov model. IEEE Wirel Commun Lett 3(4):333
Bepari D, Kumar P, Choudhary SK (2018) In: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp 1–5. https://doi.org/10.1109/ICCCNT.2018.8493839
Macaluso I, Ahmadi H, DaSilva LA (2015) Fungible orthogonal channel sets for multi-user exploitation of spectrum. IEEE Trans Wirel Commun 14(4):2281
López-Benítez M, Casadevall F (2011) In: 2011 IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN). IEEE, pp 90–99
Lopez-Benitez M, Casadevall F (2011) Empirical time-dimension model of spectrum use based on a discrete-time markov chain with deterministic and stochastic duty cycle models. IEEE Trans Veh Technol 60(6):2519
Koley S, Mirza V, Islam S, Mitra D (2015) Gradient-based real-time spectrum sensing at low snr. IEEE Commun Lett 19(3): 391
Yin S, Chen D, Zhang Q, Liu M, Li S (2012) Mining spectrum usage data: a large-scale spectrum measurement study. IEEE Trans Mob Comput 11(6):1033
Tranter WH, Rappaport TS, Kosbar KL, Shanmugan KS (2004) Principles of communication systems simulation with wireless applications, vol 1. Prentice Hall, New Jersey
Candy JV (2009) Bayesian signal processing: classical, modern, and particle filtering methods, vol 1. Wiley-Interscience
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Bepari, D., Koley, S. & Mitra, D. Empirical validation and performance of duty cycle–based DTMC model in channel estimation. Ann. Telecommun. 75, 229–240 (2020). https://doi.org/10.1007/s12243-019-00747-1
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DOI: https://doi.org/10.1007/s12243-019-00747-1