Performance improvement of chaos-based communications by using neural filtering

In recent years chaos in communication systems have achieved quite outstanding outcome. Chaotic communication signals are spread spectrum signals, which utilize large bandwidth and have low power spectrum density. In traditional communication systems, the analog sinusoid waveforms are linear. In contrast in chaotic communication systems the waveforms are nonlinear. Due to nonlinear, unsteady, nonperiodic and deterministic characteristic of chaos it has numerous opportunities to develop communication research. In this paper, we have described a chaotic communication schemes which contains three important parts named as chaos generation, masking of signal and filtering of recovered signal. Lorenz attractor is used for chaos generation whereas an artificial neural network based on back propagation is used as filter named as neural filter to reduce the noise from the recovered signal. From the simulated result it has been cleared that neural filtering provides good peak signal to noise ratio (PSNR) in both chaotic and conventional communication system.