JSIP  Vol.7 No.2 , May 2016
Second-Order Kalman Filtering Application to Fading Channels Supported by Real Data
Abstract: The lack of effective techniques for estimation of shadow power in fading mobile wireless communication channels motivated the use of Kalman Filtering as an effective alternative. In this paper, linear second-order state space Kalman Filtering is further investigated and tested for applicability. This is important to optimize estimates of received power signals to improve control of handoffs. Simulation models were used extensively in the initial stage of this research to validate the proposed theory. Recently, we managed to further confirm validation of the concept through experiments supported by data from real scenarios. Our results have shown that the linear second-order state space Kalman Filter (KF) can be more accurate in predicting local shadow power profiles than the first-order Kalman Filter, even in channels with imposed non-Gaussian measurement noise.
Cite this paper: Kapetanovic, A. , Mawari, R. and Zohdy, M. (2016) Second-Order Kalman Filtering Application to Fading Channels Supported by Real Data. Journal of Signal and Information Processing, 7, 61-74. doi: 10.4236/jsip.2016.72008.

[1]   Jiang, T., Sidiropoulos, N.D. and Giannakis G.B. (2003) Kalman Filtering for Power Estimation in Mobile Communications. IEEE Transactions on Wireless Communications, 2,151-161.

[2]   Pahlavan, K. and Krishnamurthy, P. (2002) Characteristics of the Wireless Networks. Prentice Hall PTR, Upper Saddle River.

[3]   Patwari, N. (2011) Wireless Communication Systems Course.

[4]   Rappaport, T.S. (2010) Wireless Communications Principles and Practice. 2nd Edition, Persons Education, New York City.

[5]   (2016) CDMA-Fading.

[6]   (2016) Handoff.

[7]   Kalman, R.E. (1960) A New Approach to Linear Filtering and Prediction Problems. Research Institute for Advanced Study, Baltimore.

[8]   Simon, D. (2006) Optimal State Estimation: Kalman, H ∞ and Nonlinear Approaches. 1st Edition. John Wiley & Sons Inc., Hoboken.

[9]   Yarhmatter, E. and Kapetanovic, A. (2012) Power Estimation in Mobile Communications: Comparison of the First Order AR Model to Second Order AR Model. Oakland University, Rochester, Unpublished.

[10]   Brown, R.G and Hwang, P.Y.C. (2012) Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises. 4th Edition, John Wiley & Sons Inc., Hoboken.

[11]   Grewal, M.S. and Andrews, A.P. (2014) Kalman Filtering Theory and Practice Using MATLAB. 4th Edition, John Wiley & Sons Inc., New York.

[12]   Welch, G. and Bishop, G. (2016) An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill, Department of Computer Science, Chapel Hill.

[13]   Zeng, Q.A. and Agrawal, D.P. (2010) Handoff in Wireless Mobile Networks.

[14]   Frenkiel, H. (1979) Cellular Radiotelephone System Structured for Flexible Use of Different Cell Sizes. US Patent No. 4,144,411.

[15]   Zetterbeg, P. (2014) Interference Alignment (IA) and Coordinated Multi-Point (CoMP) Overheads and RF Impairments: Test Bed Results. IEEE 80th Vehicle Technology Conference, Vancouver, 14-17 September 2014, 1-7.

[16]   Dey, I., Messier, G.G. and Magierowski, S. (2014) Joint Fading and Shadowing Model for Large Office Indoor WLAN Environments. IEEE Transactions on Antennas and Propagation, 62, 2209-2222.

[17]   Mawari, R. and Anderson, A. (2015) Two-Dimensional Small-Scale Fading Modeling and Measurements. Georgia Tech University, Atlanta, Unpublished.