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Fractional-order algorithms for tracking Rayleigh fading channels

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Abstract

This paper presents the tracking behavior of fractional-order (FO) variants of the normalized least mean square (NLMS) algorithm in a nonstationary environment modeled as time-varying Rayleigh fading sequence. The celebrated recursive least squares (RLS) or its variant extended RLS (E-RLS) algorithms fail in such situations although they exhibit faster convergence but with the undesired feature of higher computational complexity. The FO algorithms are based on the Riemann–Liouville differintegral operator which is used in the gradient calculation; such schemes provide two step sizes and an FO to control the rate of convergence. In evaluation, we consider a high-speed mobile environment with a Rayleigh channel which results in different Doppler frequency shifts depending upon the transmission frequency, relative velocity of the transmitter and receiver. The proposed algorithms are compared with the NLMS, RLS and E-RLS schemes, and numerical experiments show the superiority of the FO variants over these schemes in terms of stability and model accuracy in the steady state. A hybrid scheme is also shown where the weights of an FO variant are initially trained with RLS and then performs self-adaptation; the FO scheme is confirmed to have better performance than all traditional counterparts.

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References

  1. Mecklenbrauker, C.F., Molisch, A.F., Karedal, J., Tufvesson, F., Paier, A., Bernadó, L., Zemen, T., Klemp, O., Czink, N.: Vehicular channel characterization and its implications for wireless system design and performance. Proc. IEEE. 99(7), 1189–1212 (2011)

    Article  Google Scholar 

  2. Karedal, J., Czink, N., Paier, A., Tufvesson, F., Molisch, A.F.: Path loss modeling for vehicle-to-vehicle communications. IEEE Trans. Veh. Technol. 60(1), 323–328 (2011)

    Article  Google Scholar 

  3. Baddour, K.E., Beaulieu, N.C.: Autoregressive modeling for fading channel simulation. IEEE Trans. Wirel. Commun. 4(4), 1650–1662 (2005)

    Article  Google Scholar 

  4. Yousef, N.R., Sayed, A.H., Jalloul, L.M.A.: Robust wireless location over fading channels. IEEE Trans. Veh. Technol. 52(1), 117–126 (2003)

    Article  Google Scholar 

  5. Schwarz, S., Ikuno, J.C., Šimko, M., Taranetz, M., Wang, Q., Rupp, M.: Pushing the limits of LTE: a survey on research enhancing the standard. IEEE Access 1, 51–62 (2013)

    Article  Google Scholar 

  6. Galdino, J.F., Pinto, E.L., de Alencar, M.S.: Analytical performance of the LMS algorithm on the estimation of wide sense stationary channels. IEEE Trans. Commun. 52(6), 982–991 (2004)

    Article  Google Scholar 

  7. Haykin, S.: Adaptive Filter Theory, 4th edn. Prentice Hall, New Jersey (2002)

    MATH  Google Scholar 

  8. Eweda, E.: Comparison of RLS LMS, and sign algorithms for tracking randomly time-varying channels. IEEE Trans. Signal Process. 42(11), 2937–2944 (1994)

    Article  Google Scholar 

  9. Widrow, B., McCool, J.M., Larimore, M.G., Johnson Jr., C.R.: Stationary and nonstationary learning characteristics of the LMS adaptive filter. Proc. IEEE 64(8), 1151–1162 (1976)

    Article  MathSciNet  Google Scholar 

  10. Sternad, M., Lindbom, L., Ahlén, A.: Wiener design of adaptation algorithms with time-invariant gains. IEEE Trans. Signal Process. 50(8), 1895–1907 (2002)

    Article  MATH  Google Scholar 

  11. Filho, A.M.A., Pinto, E.L., Galdino, J.F.: Simple and robust analytically derived variable step-size least mean squares algorithm for channel estimation. IET Commun. 3(12), 1832–1842 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  12. Barhumi, I., Leus, G., Moonen, M.: Time-varying FIR equalization for doubly selective channels. IEEE Trans. Wirel. Commun. 4, 202–214 (2005)

    Article  MATH  Google Scholar 

  13. Sharma, P., Chandra, K.: Prediction of state transitions in Rayleigh fading channels. IEEE Trans. Veh. Technol. 56(2), 416–425 (2007)

    Article  Google Scholar 

  14. Kohli, A.K., Mehra, D.K.: Tracking of time-varying channels using two-step LMS-type adaptive algorithm. IEEE Trans. Signal Process. 54(7), 2606–2615 (2006)

    Article  MATH  Google Scholar 

  15. Gerzaguet, R., Ros L., Brossier, J.-M., Ghandour-Haidar, S., Belvèze, F..: Self-adaptive stochastic rayleigh flat fading channel estimation. In: Digital Signal Processing (DSP), 2013 18th International Conference on, pp. 1–6. IEEE (2013)

  16. Shu, H., Ros, L., Simon, E.P.: Simplified random-walk-model-based kalman filter for slow to moderate fading channel estimation in ofdm systems. IEEE Trans. Signal Process. 62(15), 4006–4017 (2014)

    Article  MathSciNet  Google Scholar 

  17. Ozen, A.: A novel variable step size adjustment method based on channel output autocorrelation for the LMS training algorithm. Int. J. Commun. Syst. 24(7), 938–949 (2011)

    Article  Google Scholar 

  18. Chatterjee, A., Misra, I.S.: Design and analysis of reward-punishment based variable step size LMS algorithm in Rayleigh faded channel estimation. In: 2015 IEEE Power, Communication and Information Technology Conference (PCITC), pp. 223–228. IEEE (2015)

  19. Mostafapour, E., Ghobadi, C., Nourinia, J., Amirani, M.C.: Tracking performance of incremental LMS algorithm over adaptive distributed sensor networks. J. Commun. Eng. 4(1), 1–10 (2015)

    Google Scholar 

  20. Ros, L., Hijazi, H., Simon, E.P.: Complex amplitudes tracking loop for multipath channel estimation in OFDM systems under slow to moderate fading. Signal Process. 97, 134–145 (2014)

    Article  Google Scholar 

  21. Shu, H., Simon, E.P., Ros, L.: On the use of tracking loops for low-complexity multi-path channel estimation in OFDM systems. Signal Process. 117, 174–187 (2015)

    Article  Google Scholar 

  22. Karami, E.: Performance analysis of decision directed maximum likelihood MIMO channel tracking algorithm. Int. J. Commun. Syst. 26(12), 1562–1578 (2013)

    Article  Google Scholar 

  23. Pagadarai, S., Wyglinski, A.M., Anderson, C.R.: (2013) Low-mobility channel tracking for MIMO-OFDM communication systems. EURASIP J. Adv. Signal Process. 1, 1–18 (2013)

    Google Scholar 

  24. Mah, M.C., Lim, H.S., Tan, A.W.C.: Robust joint CFO and fast time-varying channel tracking for MIMO-OFDM systems. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6494–6498. IEEE (2014)

  25. Kaddouri, S., Beaujean, P.-P.J., Bouvet, P.-J., Real, G.: Least square and trended doppler estimation in fading channel for high-frequency underwater acoustic communications. IEEE J. Ocean. Eng. 39(1), 179–188 (2014)

    Article  Google Scholar 

  26. Scarpiniti, M., Comminiello, D., Scarano, G., Parisi, R., Uncini, A.: Steady-state performance of spline adaptive filters. IEEE Trans. Signal Process. 64(4), 816–828 (2016)

    Article  MathSciNet  Google Scholar 

  27. Shu, H., Ros, L., Simon, E.P.: Third-order complex amplitudes tracking loop for slow flat fading channel online estimation. IET Commun 8(3), 360–371 (2014)

    Article  Google Scholar 

  28. Benvenistaend, A., Ruget, G.: A measure of the tracking capability of recursive stochastic algorithms with constant gains. IEEE Trans. Automat. Contr. 27, 639–649 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  29. Sayed, A.H.: Adaptive Filters. Wiley, Hoboken (2008)

    Book  Google Scholar 

  30. Diniz, S.R.: Adaptive Filtering. Algorithms and Practical Implementations. Springer, Berlin (2008)

    Book  MATH  Google Scholar 

  31. Christos, K., Christina, F., Sayed, A.H., Wesel, R.D.: Multi-input multi-output fading channel tracking and equalization using Kalman estimation. IEEE Trans. Signal Process. 50(5) (2002)

  32. Eleftherioaun, E., Falcon, D.D.: Tracking properties and steady-state performance of RLS adaptive filter algorithms. IEEE Trans. Acoust. Speech Signal Process. 34, 1097–1110 (1986)

    Article  Google Scholar 

  33. Yousef, N.R., Sayed, A.H.: A unified approach to the steady-state and tracking analyses of adaptive filters. IEEE Trans. Signal Process. 49(2), 314–324 (2001)

    Article  Google Scholar 

  34. Mathur, A., Keerthi, A.V., Shyok, J.J.: A variable step-size CM array algorithm for fast fading channels. IEEE Trans. Signal Process. 45(4), 1083–1087 (1997)

    Article  Google Scholar 

  35. El-Khamy, S.E., Abd-Elaziz, D.M., Gab-Alla, AM: The MVDR guided constant modulus adaptive array (MVDR-GCMARY) for signal separation in fading channels. In: Radio Science Conference, 2002 (NRSC 2002). Proceedings of the Nineteenth National, pp. 141–152. IEEE (2002)

  36. Pora, W., Chambers, J.A., Constantinides, A.G.: A combined Kalman filter and constant modulus algorithm beamformer for fast-fading channels. In: Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on, vol. 5, pp. 2925–2928. IEEE (1999)

  37. EL-Khamy, S.E., Gaballa, A.M.: A new variable step-size constant modulus array for signal separation in fast fading channels. In: Radio Science Conference, 2009. NRSC 2009. National, pp. 1–9. IEEE (2009)

  38. Dadashzadeh, G., Hakkak, M., Jedari, E., Kamarei, M.: LNA phase distortion effect on the constant modulus algorithm in CDMA systems. In: IEEE Antennas and Propagation Society International Symposium, vol. 1, pp. 93–96. IEEE; 1999 (2003)

  39. Badri, V., Tavazoei, M.S.: On tuning FO [PI] controllers for FOPDT processes. IET Electron. Lett. 49(21), 1326–1328 (2013)

    Article  Google Scholar 

  40. Tseng, C.C., Lee, S.L.: Design of digital Riesz fractional order differentiator. Signal Process. 102, 32–45 (2014)

    Article  Google Scholar 

  41. Ortigueira, M.D., Coito, F.J., Trujillo, J.J.: Discrete-time differential systems. Signal Process. 107, 198–217 (2015)

    Article  Google Scholar 

  42. Shah, S.M., Samar, R., Khan, N.M., Raja, M.A.Z.: Fractional order adaptive signal processing strategies for active noise control systems. Nonlinear Dyn. 85, 1363–1376 (2016)

    Article  MathSciNet  Google Scholar 

  43. Shah, S.M., Samar, R., Raja, M.A.Z., Chambers, J.A.: Fractional normalised filtered-error least mean squares algorithm for application in active noise control systems. Electron. Lett. 50(14), 973–975 (2014)

    Article  Google Scholar 

  44. Raja, M.A.Z., Chaudhary, N.I.: Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems. Signal Process. 107, 327–339 (2015)

    Article  Google Scholar 

  45. Shah, S.M., Samar, R., Naqvi, S.M., Chambers, J.A.: Fractional order constant modulus blind algorithms with application to channel equalisation. Electron. Lett. 50(23), 1702–1704 (2014)

    Article  Google Scholar 

  46. Tan, Y., He, Z., Tian, B.: Generalization of modified LMS algorithm to fractional order. IEEE Signal Process. Lett. 22(9), 1244–1248 (2015)

    Article  Google Scholar 

  47. Yin, C., Chen, Y.Q., Zhong, S.M.: Fractional-order sliding mode based extremum seeking control of a class of nonlinear systems. Automatica 50(12), 3173–3181 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  48. Hsue W.L., Chang W.C. (2015) Real discrete fractional fourier hartley, generalized fourier and Generalized hartley transforms with many parameters. IEEE Trans. Circuits Syst. I Regul. Pap. 62(10):2594–2605

  49. Zhou, R., Zhang, R.F., Chen, D.Y.: Fractional-order \(\text{ L }_{\beta }\text{ C }_{\alpha }\) low pass filter circuit. J. Electr. Eng. Technol. 10(4), 1597–1609 (2015)

    Article  Google Scholar 

  50. Wei, Y.H., Du, B., Cheng, S.S., Wang, Y.: Fractional order systems time-optimal control and its application. J. Optim. Theory Appl. https://doi.org/10.1007/s10957-015-0851-4

  51. Wei, Y.H., Peter, W.T., Du, B., Wang, Y.: An innovative fixed-pole numerical approximation for fractional order systems. ISA Trans. 62, 94–102 (2016)

    Article  Google Scholar 

  52. Podlubny, I.: Fractional Differential Equations: An Introduction to Fractional Derivatives, Fractional Differential Equations, to Methods of Their Solution and Some of Their Applications. Academic Press, London (1998)

    MATH  Google Scholar 

  53. Oldham, K.B., Spanier, J.: The Fractional Calculus: Theory and Applications of Differentiation and Integration to Arbitrary Order. Academic Press, London (1974)

    MATH  Google Scholar 

  54. Sheng, H., Chen, Y., Qiu, T.: Fractional Processes and Fractional-Order Signal Processing: Techniques and Applications. Springer, Berlin (2011)

    MATH  Google Scholar 

  55. Pu, Y.F., Zhou, J.L., Zhang, Y., Zhang, N., Huang, G., Siarry, P.: Fractional extreme value adaptive training method: fractional steepest descent approach. IEEE Trans. Neural Netw. Learn. Syst. 26(4), 653–662 (2015)

    Article  MathSciNet  Google Scholar 

  56. Principe, J.C., Rao, Y.N., Erdogmus, D.: Error whitening wiener filters: theory and algorithms chapter-10. In: Haykin, S., Widrow, B. (eds.) Least-Mean-Square Adaptive Filters. Wiley, New York (2003)

    Google Scholar 

  57. Rao, Y.N., Erdogmus, D., Principe, J.C.: Error whitening criterion for adaptive filtering: theory and algorithms. IEEE Trans. Signal Process. 53(3), 1057–1069 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  58. Douglas, S.C.: Adaptive filters employing partial updates. IEEE Trans. Circuits Syst. II: Analog Digit. Signal Process. 44(3), 209–216 (1997)

    Google Scholar 

  59. Shah, S.M.: Riemann–Liouville operator-based fractional normalised least mean square algorithm with application to decision feedback equalisation of multipath channels. IET Signal Process. 10(6), 575–582 (2016)

    Article  Google Scholar 

  60. Shah, S.M., Samar, R., Khan, N.M., Raja, M.A.Z.: Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization. Nonlinear Dyn. 88(2), 839–858 (2017)

    Article  MATH  Google Scholar 

  61. Yang, X.-J., Machado, J.A., Tenreiro, C., Carlo, Gao F.: On a fractal LC-electric circuit modeled by local fractional calculus. Commun. Nonlinear Sci. Numer. Simul. 47, 200–206 (2017)

    Article  Google Scholar 

  62. Yang, X.-J., Feng, G., Machado, J.A., Baleanu. D.: A new fractional derivative involving the normalized sinc function without singular kernel. ArXiv preprint arXiv:1701.05590 SPSURLTILT (2017)

  63. Zhou, Y., Feckan, M., Liu, F., Machado, J.A.T.: Advances in fractional differential equations (IV): Time-fractional PDEs. Comput. Math. Appl. 6(73), 873 (2017)

    Article  MathSciNet  Google Scholar 

  64. Moghaddam, B.P., Yaghoobi, S., Machado, J.A.T.: An extended predictor-corrector algorithm for variable-order fractional delay differential equations. J. Comput. Nonlinear Dyn. 11(6), 061001 (2016)

    Article  Google Scholar 

  65. Machado, J.A.T.: Fractional dynamics in the Rayleigh’s piston. Commun. Nonlinear Sci. Numer. Simul. 31(1), 76–82 (2016)

    Article  Google Scholar 

  66. Yang, X.-J., Machado, J.A.T., Srivastava, H.M.: A new numerical technique for solving the local fractional diffusion equation: two-dimensional extended differential transform approach. Appl. Math. Comput. 274, 143–151 (2016)

    MathSciNet  Google Scholar 

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Shah, S.M., Samar, R. & Raja, M.A.Z. Fractional-order algorithms for tracking Rayleigh fading channels. Nonlinear Dyn 92, 1243–1259 (2018). https://doi.org/10.1007/s11071-018-4122-4

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