ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Novel Approach for Modeling Wireless Fading Channels Using a Finite State Markov Chain

  • Salam, Ahmed Abdul (Faculty of Engineering and Informatics, University of Bradford) ;
  • Sheriff, Ray (Faculty of Engineering and Informatics, University of Bradford) ;
  • Al-Araji, Saleh (College of Engineering, Khalifa University of Science and Technology) ;
  • Mezher, Kahtan (College of Engineering, Khalifa University of Science and Technology) ;
  • Nasir, Qassim (College of Engineering, University of Sharjah)
  • Received : 2017.04.03
  • Accepted : 2017.07.03
  • Published : 2017.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal-to-noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first-order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.

Keywords

References

  1. P. Sadeghi et al., "Finite-State Markov Modeling of Fading Channels: A Survey of Principles and Applications," IEEE Signal Process. mag., vol. 25, no. 5, Sept. 2008, pp. 57-80. https://doi.org/10.1109/MSP.2008.926683
  2. H.S. Wang and N. Moayeri, "Finite-State Markov Channel-A Useful Model for Radio Communication Channels," IEEE Trans. Veh. Technol., vol. 44, no. 1, Feb. 1995, pp. 163-171. https://doi.org/10.1109/25.350282
  3. Q. Zhang and S.A. Kassam, "Finite-State Markov Model for Rayleigh Fading Channels," IEEE Trans. Commun., vol. 47, no. 11, Nov. 1999, pp. 1688-1692. https://doi.org/10.1109/26.803503
  4. F. Babich, O.E. Kelly, and G. Lombardi, "Generalised FSMC Model for Radio Channels with Correlated Fading," IEEE Proc. Commun., vol. 48, no. 4, Apr. 2000, pp. 547-551. https://doi.org/10.1109/26.843121
  5. C.C. Tan and N.C. Beaulieu, "On First-OrderMarkovModeling for the Rayleigh Fading Channel," IEEE Trans. Commun., vol. 48, no. 12, Dec. 2000, pp. 2032-2040. https://doi.org/10.1109/26.891214
  6. J.M. Park and G.U. Hwang, "Mathematical Modeling of Rayleigh Fading Channels Based on Finite State Markov Chains," IEEE Commun. Lett., vol. 13, no. 10, Oct. 2009, pp. 764-766. https://doi.org/10.1109/LCOMM.2009.090635
  7. D. Dalalah, L. Cheng, and G. Tonkay, "Modeling End-to-End Wireless Lossy Channels: A Finite-State Markov Approach," IEEE Trans. Wireless Commun., vol. 7, no. 4, Apr. 2008, pp. 1236-1243. https://doi.org/10.1109/TWC.2008.060807
  8. S. Fazeli-Dehkordy et al., "Markovian-Based Framework for Cooperative Channel Selection in Cognitive Radio Networks," IET Commun., vol. 8, no. 14, 2014, pp. 2458-2468. https://doi.org/10.1049/iet-com.2013.1158
  9. R. Zhang et al., "Channel Measurement and Packet-Level Modeling for V2I Spatial Multiplexing Uplink using Massive MIMO," IEEE Trans. Veh. Technol., vol. 65, no. 10, Oct. 2016, pp. 7831-7843. https://doi.org/10.1109/TVT.2016.2536627
  10. S. Lin et al., "Finite-State Markov Modeling for High-Speed Railway Fading Channels," IEEE Anten. Wireless Propag. Lett., vol. 14, 2015, pp. 954-957. https://doi.org/10.1109/LAWP.2015.2388701
  11. R. Zhang and L. Cai, "A Packet-Level Model for UWB Channel with People Shadowing Process Based on Angular Spectrum Analysis," IEEE Trans. Wireless Commun., vol. 8, no. 8, Aug. 2009, pp. 4048-4055. https://doi.org/10.1109/TWC.2009.080087
  12. Recommendation ITU-R P.1407-5, Multipath Propagation and Parameterization of its Characteristics, 09/2013.
  13. A. Goldsmith, Wireless Communications, Cambridge, UK: Cambridge University Press, 2005.
  14. K.E. Baddour and N.C. Beaulieu, "Autoregressive Modeling for Fading Channel Simulation," IEEE Trans. Wireless Commun., vol. 4, no. 4, July 2005, pp. 1650-1662. https://doi.org/10.1109/TWC.2005.850327
  15. H. Mehrpouyan and S.D. Blostein, "ARMA Synthesis of Fading Channels," IEEE Trans. Wirel. Commun., vol. 7, no. 8, Aug. 2008, pp. 2846-2850. https://doi.org/10.1109/TWC.2008.060737
  16. G. Tauchen, "Finite State Markov-Chain Approximations to Univariate and Vector Autoregressions," Econ. Lett., vol. 20, no. 2, 1986, pp. 177-181. https://doi.org/10.1016/0165-1765(86)90168-0
  17. K.A. Kopecky and R.M.H. Suen, "Finite State Markov-Chain Approximations to Highly Persistent Processes," Rev. Econ. Dynamics, vol. 13, no. 3, July 2010, pp. 701-714. https://doi.org/10.1016/j.red.2010.02.002
  18. L.R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. IEEE, vol. 77, no. 2, Feb. 1989, pp. 257-286. https://doi.org/10.1109/5.18626
  19. W. Khreich et al., "On the Memory Complexity of the Forward-Backward Algorithm," Pattern Recogn. Lett., vol. 31, no. 2, Jan. 2010, pp. 91-99. https://doi.org/10.1016/j.patrec.2009.09.023
  20. F.W.J. Olver et al., NIST Handbook of Mathematical Functions, Cambridge, UK; New York, USA: Cambridge University Press; NIST, 2010.

Cited by

  1. The Arbitrarily Varying Relay Channel vol.21, pp.5, 2019, https://doi.org/10.3390/e21050516