Stochastic-calculus-based numerical evaluation and performance analysis of chaotic communication systems

Performance evaluation of a self-synchronizing Lorenz chaotic system is formulated as a stochastic differential equation problem. Based on stochastic calculus, we provide a rigorous formulation of the numerical evaluation and analysis of the self-synchronization capability and error probabilities of two chaotic Lorenz communication systems with additive white Gaussian noise disturbance. By using the Ito theorem, we are able to analyze the first two moments behavior of the self-synchronization error of a drive-response Lorenz chaotic system. The moment stability condition of the synchronization error dynamic is explicitly derived. These results provide further understanding on the robust self-synchronization ability of the Lorenz system to noise. Various time-scaling factors affecting the speed of system evolution are also discussed. Moreover, an approximate model of the variance of the sufficient statistic of the chaotic communication is derived, which permits a comparison of the chaotic communication system performance to the conventional binary pulse amplitude modulation communication system. Due to synchronization difficulties of chaotic systems, known synchronization-based chaotic communication system performance is quite poor. Thus, alternative synchronization-free chaotic communication systems are needed in the future, The use of a stochastic calculus approach as considered here, however, is still applicable if the considered chaotic communication system is governed by nonlinear stochastic differential equations.

[1]  Carroll,et al.  Synchronization in chaotic systems. , 1990, Physical review letters.

[2]  Mona E. Zaghloul,et al.  Improved masking algorithm for chaotic communications systems , 1996 .

[3]  C. Desoer,et al.  Linear System Theory , 1963 .

[4]  N. Jeremy Kasdin,et al.  Runge-Kutta Algorithm for the Numerical Integration of Stochastic Differential Equations , 1995 .

[5]  Richard E. Mortensen,et al.  Mathematical problems of modeling stochastic nonlinear dynamic systems , 1969 .

[6]  Benjamin C. Kuo,et al.  AUTOMATIC CONTROL SYSTEMS , 1962, Universum:Technical sciences.

[7]  L. Arnold Stochastic Differential Equations: Theory and Applications , 1992 .

[8]  R. L. Stratonovich A New Representation for Stochastic Integrals and Equations , 1966 .

[9]  Michael Peter Kennedy,et al.  Chaos shift keying : modulation and demodulation of a chaotic carrier using self-sychronizing chua"s circuits , 1993 .

[10]  Eugene Wong,et al.  Stochastic processes in information and dynamical systems , 1979 .

[11]  Alan V. Oppenheim,et al.  Synchronization of Lorenz-based chaotic circuits with applications to communications , 1993 .

[12]  Leon O. Chua,et al.  Secure Communications via Chaotic Synchronization II: Noise Reduction by Cascading Two Identical Receivers , 1993 .

[13]  Mannella,et al.  Fast and precise algorithm for computer simulation of stochastic differential equations. , 1989, Physical review. A, General physics.

[14]  Leon O. Chua,et al.  Experimental Demonstration of Secure Communications via Chaotic Synchronization , 1992, Chua's Circuit.