An Empirical Model for Nonstationary Ricean Fading

Ricean fading is common in dense urban cellular networks and, as a mobile moves through that environment, the K-factor of the Ricean fading will change. This paper presents a statistical model for dense urban vehicular nonstationary Ricean fading, where the K-factor gradually changes due to movement through changing surroundings. This model is empirical and is based on K-factor fluctuations that are observed in dense urban cellular radio channel measurements. The K -factor is modeled using a random process with a distribution that is fit to the measured K-factor values. An autoregressive (AR) model is also used to ensure that the autocorrelation of the simulated K-factor process matches the empirical data. The nonstationary Ricean fading envelope that is generated using this model is verified by comparing it with the fading envelope that is observed in the measurements.

[1]  Norman C. Beaulieu,et al.  Estimation of Ricean K parameter and local average SNR from noisy correlated channel samples , 2007, IEEE Transactions on Wireless Communications.

[2]  M. Patzold,et al.  On the statistical properties of deterministic simulation models for mobile fading channels , 1998 .

[3]  Victor R. Prybutok,et al.  HOW THE MOBILE COMMUNICATION , 2005 .

[4]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[5]  Ali Abdi,et al.  The Ricean K factor: estimation and performance analysis , 2003, IEEE Trans. Wirel. Commun..

[6]  Simon Haykin,et al.  Adaptive filter theory (2nd ed.) , 1991 .

[7]  Robert W. Heath,et al.  Adaptive modulation and MIMO coding for broadband wireless data networks , 2002, IEEE Commun. Mag..

[8]  Matthias Patzold,et al.  A study of a land mobile satellite channel model with asymmetrical Doppler power spectrum and lognormally distributed line-of-sight component , 1998 .

[9]  Sailes K. Sengijpta Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[10]  John G. Proakis,et al.  Digital Communications , 1983 .

[11]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[12]  V. Erceg,et al.  Multiple-input multiple-output fixed wireless radio channel measurements and modeling using dual-polarized antennas at 2.5 GHz , 2004, IEEE Transactions on Wireless Communications.

[13]  A. Pollok,et al.  Analysis of correlation between Ricean K-factor and vegetation density surrounding a CDMA mobile terminal , 2004, 1st International Symposium onWireless Communication Systems, 2004..

[14]  Barry G. Evans,et al.  Rice factor estimation algorithm , 2001 .

[15]  Ken-Huang Lin,et al.  Ricean K-factor Estimation in Cellular Communications Using Kolmogorov-Smirnov Statistic , 2006, 2006 Asia-Pacific Conference on Communications.

[16]  Mohamed-Slim Alouini,et al.  Estimation of Nakagami-m fading channel parameters with application to optimized transmitter diversity systems , 2003, IEEE Trans. Wirel. Commun..

[17]  Aarnout Brombacher,et al.  Probability... , 2009, Qual. Reliab. Eng. Int..

[18]  Boualem Boashash,et al.  Ricean K-factor estimation in mobile communication systems , 2004, IEEE Communications Letters.

[19]  Mohamed-Slim Alouini,et al.  Digital Communication over Fading Channels: Simon/Digital Communications 2e , 2004 .

[20]  Anthony E.-L. Liou,et al.  Issues in the Estimation of Ricean K-Factor from Correlated Samples , 2006, IEEE Vehicular Technology Conference.

[21]  Larry J. Greenstein,et al.  Moment-method estimation of the Ricean K-factor , 1999, IEEE Communications Letters.

[22]  Grigorios Kalivas,et al.  Rician K Factor Estimation for Wireless Communication Systems , 2006, 2006 International Conference on Wireless and Mobile Communications (ICWMC'06).

[23]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .