**Modeling Radar and Communication Channels
in a Highly Scintillated Environment**

Pierre-Richard Cornely, Ph.D.

The Raytheon Company

238 Presidential Avenue

Woburn, MA 01854

The Haitian Scientific Society

University of Massachusetts

100 Morrissey Boulevard

Boston, MA 02125

**Abstract:**

An accurate and systematic channel simulation technique is critical for performance evaluation and verification in radar and communication applications especially in severe ionospheric scintillation environments. Ionospheric scintillation is the rapid fluctuation in signal amplitude and phase due to the signals interaction with free electrons in the ionosphere. The ionosphere is this region of the earth’s atmosphere from 100 to 1000 kilometers above the earth’s surface. The signal fluctuations due to ionospheric scintillations can produce multipath effects resulting in signal fading, and focusing which severely impact the performance of radar and communication systems. The Nakagami-m distribution has gained widespread application in modeling amplitude fading due multipath effects in radar and communication channels. The primary justification for the use of the Nakagami-m amplitude model is its good fit to empirical fading data [1]. However, despite the abundant results on Nakagami-m channel simulation available in the literature, the accurate simulation of Nakagami-m fading channels satisfying a prescribed temporal autocorrelation property is still an open problem [2-7]. Furthermore, the generation of Nakagami-m sequences satisfying the proper Nakagami-m phase distribution has yet to be fully studied. The current work was inspired by past and recent developments in [8-10]. In this presentation, we summarize the findings of the current work. We will discuss a new cumulative distribution function (CDF) mapping method for the reconstruction of complex, correlated Nakagami-m fading sequences. We also provide a method for these sequences to satisfy an arbitrary pre-specified auto-correlation function with the proper Nakagami-m phase property. In our method, we map the real and imaginary parts of Gausssian sequences independently and use them to form complex Nakagami-m sequences. Simulation results verify that our approach can accurately reconstruct an arbitrary pre-specified auto-correlation property for the Nakagami-m channel with the proper phase property.

**References:**

1. M. Nakagami, “The m-distribution - A general formula of intensity distribution of rapid fading,” in Statistical Methods in Radio Wave Propagation, W. G. Hoffman, Ed. Oxford: Pergamon Press, 1960, pp. 3–36.

2. D. J. Young and N. C. Beaulieu, “The generation of correlated Rayleigh random variates by inverse discrete Fourier transform,” IEEE Trans. Commun., vol. 48, pp. 1114–1227, Jul. 2000.

3. K. E. Baddour and N. C. Beaulieu, “Accurate simulation of multiple cross-correlated Rician fading channels,” IEEE Trans. on Communications, vol. 52, pp. 1980–1987, Nov. 2004.

4. Q. T. Zhang, “A decomposition technique for efficient generation of correlated Nakagami fading channels,” IEEE J. Select. Areas Commun., vol. 18, pp. 2385–2392, Nov. 2000.

5. K. Zhang, Z. Song, and Y. L. Guan, “Simulation of Nakagami fading channels with arbitrary cross-correlation and fading parameters,” IEEE Trans. Wireless Commun., vol. 3, pp. 1463–1468, Sept. 2004.

6. M. D. Yacoub, J. E. V. Bautista, and L. G. de Rezende Guedes, “On higher order statistics of the Nakagami-m distribution,” IEEE Trans. Veh. Technol., vol. 48, pp. 790–794, May. 1999.

7. K. W. Yip and T. S. Ng, “A simulation model for Nakagami-m fading channels, m < 1,” IEEE Trans. Commun., vol. 48, pp. 214–221, Feb. 2000.

8. N. C. Beaulieu and C. Cheng, “Efficient Nakagami-m fading channel simulation,” IEEE Trans. Veh. Technol., vol. 54, pp. 413–424, Mar. 2005.

9. G. F. M. D. Yacoub and J. S. Filho, “Nakagami-m phase-envelope joint distribution,” IEE Electronics Letters, vol. 41, pp. 259–261, Mar. 2005.

10. David J. Young, and Norman C. Beaulieu, “The generation of correlated random variates by inverse Discrete Fourier Transform”, IEEE Trans. On Comm. Vol. 48, No. 7, July 2000.

**Biographical Sketch:**

Pierre-Richard Jean Cornely received the B.S/M.Sc degree from Northeastern University in 1989, and the M.Sc and Ph.D. degrees from the University of Massachusetts, in 1993 and 1999, all in Electrical Engineering. Upon graduating in 1999, Dr. Cornely joined a startup company, 374’s Research Corporation, D.B.A. 374’s Electric Power Corporation. As President and CEO of the corporation, Dr. Cornely managed the day-to-day affairs of the corporation and designed the first family of surf hydroelectric power conversion systems currently in existence. At the beginning of 2004, Dr. Cornely returned to investigating various research subjects from his core areas of expertise: atmospheric modeling and its effects on signal propagation; signal processing and its applications. Later that year, Dr. Cornely joined the systems engineering technical staff of the Raytheon Company in Sudbury, Massachusetts, where he has been designing state of the art radar systems and algorithms. In addition, in collaboration with the Mathematics Department of the University of Massachusetts at Lowell, Dr. Cornely has developed several iterative algorithms based on projection onto convex sets. These algorithms play an important role in the formulation and solution of several important problems in signal processing. Within the last four years, Dr. Cornely has developed a keen interest in the inner workings of the human mind. As a result, he founded the Institute For Advanced Mind Studies (IFAMS). The IFAMS is a unique institution that has proposed a new philosophy of human life based on the recognition that an individual can accomplish anything he really desires, if he can correctly and efficiently practice the philosophy of the mind. Dr. Cornely is currently the President of The Haitian Scientific Society, the Technical Director of Bayasssociates Power Systems, and the Director of the Institute For Advanced Mind Studies. His current research interests include: Electrical Conversion Systems; Physical Geography as it relates to Climatology & Atmospheric Weather Prediction; Emission, Absorption & Ionospheric Tomography; Ionospheric Physics & Modeling; Signal & Image Processing; Evolutionary Computation & Machine Learning and Mastering the basic functions of the Human Mind.

**Location:**

University of Massachusetts Boston

Science Hall Second Floor Room 65

100 William T. Morrissey Boulevard

Boston, MA 02125

Directions and parking information can be found at: http://www.umb.edu/parking_transport/directions.html

**Date and time: **

January 30 , 2010

12:45 AM—1:45 PM