Performance of Rate ½ Convolutional Encoder with Adaptive Feedback-Controlled on Hyperchaotic-Chaotic States

In this paper we have studied the performance of rate-1/2 convolutional encoders with adaptive states developed in chaotic and hyperchaotic regions. These states are generated by varying the control parameters in a feedback-controlled system. Several sets of closed-loop simulations are performed to demonstrate the benefit of information-based chaos system. In particular, it is demonstrated that two varieties of an information-based systems provide improved performance over all the encoder choices when hyperchaos states are utilized. Special attention was paid to the algorithmic complexity of the systems for an entire class of rate-1/2 encoders. The decoder is able to recover the encrypted data and is able to reasonably estimate the bit error rate for different signal strengths under a noisy AWGN channel. This indicates that the encoder can update the information map in real time to compensate for changing data for both chaotic and hyperchaotic states. This is the evidence that occasional changes in the data stream can be handled by the decoder in a real time application. Numerical evidence indicates algorithmic complexity associated with the hyperchaotic-encrypted and convolutionally-encoded data, provide better security along with the increase in the error correcting capacity of the decoder.