Optimal sensing interval in cognitive radio networks with imperfect spectrum sensing
Authors: 
Boyang Liu, Zan Li, Jiangbo Si, Fuhui ZhouAuthors Info & Claims
Abstract
Spectrum sensing is performed at the beginning of each time slot in traditional cognitive radio networks, which is unreasonable and needless since the presence or the absence of a primary user (PU) always lasts several time slots. A hidden Markov model is used to describe the imperfect spectrum sensing process over Rayleigh fading channels. On the basis of the sensing results, a hybrid interweave/underlay mode is exploited by the secondary user (SU) to achieve a higher throughput. To solve the tradeoff problem among the average energy consumption for spectrum sensing, the average throughput of SU and the average interference to the PU, an optimisation problem is proposed. The optimal sensing interval to determine when the next spectrum sensing should be performed is obtained by solving the optimisation problem. Numerical results are given to verify the authors’ analysis.
References
[1]
Dohler M.Heath R.Lozano A. et al.: ‘Is the PHY layer dead?’, IEEE Commun. Mag., 2011, 49, (4), pp. 159–165
[2]
FCC : ‘Spectrum policy task force report’, ET Docker 02‐155, November 2002
[3]
Mitola J.Maguire G.Q.: ‘Cognitive radio: making software radios more personal’, IEEE Pers. Commun., 1999, 6, (4), pp. 13–18
[4]
Haykin S.: ‘Cognitive radio: Brain‐empowered wireless communications’, IEEE J. Sel. Areas Commun., 2005, 23, (2), pp. 201–220
[5]
Yucek T.Arslan H.: ‘A survey of spectrum sensing algorithms for cognitive radio applications’, IEEE Commun. Surv. Tutor., 2009, 11, (1), pp. 116–130
[6]
Liu J.Li Z.: ‘Lowering the signal‐to‐noise ratio wall for energy detection using parameter‐induced stochastic resonator’, IET Commun., 2015, 9, (1), pp. 101–107
[7]
Li B.Li X.F.Nallanathan A. et al.: ‘Energy detection based spectrum sensing for cognitive radios over time‐frequency doubly selective fading channels’, IEEE Trans. Signal Process., 2014, 63, (2), pp. 402–417
[8]
López‐Benítez M.Casadevall F.: ‘Improved energy detection spectrum sensing for cognitive radio’, IET Commun., 2012, 6, (8), pp. 785–796
[9]
Gardner W.A.: ‘Exploitation of spectral redundancy in cyclostationary signals’, IEEE Signal Process. Mag., 1991, 8, (2), pp. 14–36
[10]
Zeng Y.Liang Y.C.: ‘Eigenvalue‐based spectrum sensing algorithms for cognitive radio’, IEEE Trans. Commun., 2009, 57, (6), pp. 1784–1793
[11]
Mishra S.M.Sahai A.Brodersen R.W.: ‘Cooperative sensing among cognitive radios’. Proc. IEEE ICC, Istanbul, Turkey, June 2006, pp. 1658–1663
[12]
Quan Z.Cui S.Poor H.V. et al.: ‘Collaborative wideband sensing for cognitive radios’, IEEE Signal Process. Mag., 2008, 25, (6), pp. 60–73
[13]
Chaudhari S.Lunden J.Koivunen V. et al.: ‘Cooperative sensing with imperfect reporting channels: hard decisions or soft decisions?’, IEEE Trans. Signal Process., 2012, 60, (1), pp. 18–28
[14]
Srinivasa S.Jafar S.: ‘Cognitive radios for dynamic spectrum access the throughput potential of cognitive radio: a theoretical perspective’, IEEE Commun. Mag., 2007, 45, (5), pp. 73–79
[15]
Goldsmith A.Jafar S.A.Maric I. et al.: ‘Breaking spectrum gridlock with cognitive radios: an information theoretic perspective’, Proc. IEEE, 2009, 97, (5), pp. 894–914
[16]
Willkomm D.Machiraju S.Bolot J. et al.: ‘Primary user behavior in cellular networks and implications for dynamic spectrum access’, IEEE Commun. Mag., 2009, 47, (3), pp. 88–95
[17]
Xing X.S.Jing T.Li H. et al.: ‘Optimal spectrum sensing interval in cognitive radio networks’, IEEE Trans. Parallel Distrib. Syst., 2013, 25, (9), pp. 2408–2417
[18]
Ghosh C.Cordeiro C.Agrawal D. et al.: ‘Markov chain existence and hidden Markov models in spectrum sensing’, IEEE Pervasive Comput. Commun., 2009, pp. 1–6
[19]
Rabiner L.R.: ‘A tutorial on hidden Markov models and selected applications in speech recognition’, Proc. IEEE, 1989, 77, (2), pp. 257–286
[20]
Treeumnuk D.Popescu D.C.: ‘Using hidden Markov models to evaluate performance of cooperative spectrum sensing’, IET Commun., 2013, 7, (17), pp. 1969–1973
[21]
Nguyen T.Mark B.L.Ephraim Y.: ‘Spectrum sensing using a hidden bivariate Markov model’, IEEE Trans. Wirel. Commun., 2013, 12, (9), pp. 4582–4591
[22]
Chen Z.Hu Z.Qiu R.: ‘Quickest spectrum detection using hidden Markov model for cognitive radio’. Proc. IEEE Military Communications Conf. (MILCOM), Boston, USA, October 2009, pp. 1–7
[23]
Stamp M.: ‘A Revealing Introduction to Hidden Markov Models’, 2012, Available at http://www.cs.sjsu.edu/%7Estamp/RUA/HMM.pdf
[24]
Digham F.F.Alouini M.‐S.Simon M.K.: ‘On the energy detection of unknown signals over fading channels’. Proc. IEEE ICC, Anchorage, AK, USA, May 2003, pp. 3575–3579
[25]
Gradshteyn I.S.Ryzhik I.M.: ‘Table of integrals, series, and products’ (Academic Press, San Diego, CA, 2000, 6th edn.)
[26]
Harsini J.S.Zorzi M.: ‘Transmission strategy design in cognitive radio systems with primary ARQ control and QoS provisioning’, IEEE Trans. Commun., 2014, 62, (6), pp. 1790–1802
[27]
Kwan R.Leung C.: ‘Gamma variate ratio distribution with application to CDMA performance analysis’. Proc. IEEE Symp. Advanced Wired/Wireless Communication, Princeton, NJ, USA, April 2005, pp. 188–191
[28]
Simon M.K.Alouini M.S.: ‘Digital Communication over Fading Channels’ (Wiley‐IEEE Press, 2004, 2nd edn.)
[29]
Grimmett G.R.Stirzaker D.R.: ‘Probability and Random Processes’ (Oxford University Press, 2001)
[30]
Peh E.Liang Y.C.Guan Y.L. et al.: ‘Energy‐efficient cooperative spectrum sensing in cognitive radio networks’. Proc. IEEE GLOBECOM, Houston, TX, USA, 2011, pp. 1–5
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© 2021 The Institution of Engineering and Technology.
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John Wiley & Sons, Inc.
United States
Publication History
Published: 01 January 2016
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