Carlos de la Isla, De la perplejidad a la utopía (original) (raw)
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In this paper de-noising techniques are investigated in connection with secure wideband chaotic communication systems. An alternate version of the recently proposed Ueda chaotic communication system based on the initial condition modulation (ICM) of the chaotic carrier by the binary message to be transmitted is proposed and evaluated in the presence of noise, demonstrating a significant improvement. It is then shown that the running average finite impulse response (FIR) filter, and the hard-threshold filtering techniques in Haar and Daubechies wavelet domain can be used to significantly improve the performance of the proposed chaotic communication system.
Application of noise reduction to chaotic communications: a case study
IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 2000
Over the past few years, several methods have been proposed for decontaminating noisy chaotic signals by exploiting the short-term predictability of chaotic signals. This work evaluates the effectiveness, for a differential chaos shift keying (DCSK) telecommunications system, of a noise reduction approach using a deterministic optimization technique. Noise reduction is governed by a cost function which consists of two terms: the first gives the distance between the noisy and enhanced orbits, while the second one checks the dynamics of the cleaned signal. These two terms are weighted by a scalar 0. The effect of this factor on the noise reduction performance is also studied. Evaluation of the noise performance of a telecommunication system by computer simulation requires a very long simulation time. We propose a computationally-efficient criterion for quantifying the performance improvement of a DCSK system. We show that the noise reduction technique improves the overall noise performance only if the energy per bit-to-noise spectral density () exceeds a certain threshold. The effect of code length on this threshold level is also evaluated. Finally, the effect of parameter mismatch, which is present in every practical system, is analyzed.
Noise Filtering in Communication with Chaos
Physical Review Letters, 1997
A method, based on fundamental properties of chaotic dynamics, is devised for filtering in-band noise of an incoming signal generated by a chaotic oscillator. Initially the 2x mod 1 map is used to illustrate the procedure and then the method is applied to recover the message encoded in a realistic chaotic signal, after the transmitted signal has been contaminated with noise.
Digital communication with chaotic and impulse wavelets
Fourier analyzer concept that generalize the waveform communications to include chaotic carriers can be used to discuss and optimize detection problems. Using this new description it is possible to develop new detector configurations with improved performance for chaotic communications that may be used in ultra-wideband radio (UWB). Radio communications via channels already occupied by traditional telecommunication systems can be achieved by using UWB radio where extremely wideband wavelets are radiated in order to reduce the power spectral density (psd) of transmitted signal. Since the recovery of these UWB carriers is not feasible, noncoherent demodulation techniques have to be used. The paper shows the Fourier analyzer concept then evaluates and compares the noise performances of the feasible noncoherent UWB modulation schemes, namely, that of the noncoherent pulse polarity modulation and the transmitted reference system.
A new nonlinear-filter-based modulation/demodulation technique for chaotic communication
2009 American Control Conference, 2009
A novel modulation/demodulation technique for digital chaotic communications using nonlinear filtering is proposed. The performance of this technique is compared in simulation with the existing nonlinear filtering based chaotic communication schemes for three different nonlinear estimators. The feasibility of the proposed technique is verified by theoretical analysis and computer simulation. The result is also compared with the theoretical bit error rate performance bound for chaotic communications.
Chaotic signals denoising using empirical mode decomposition inspired by multivariate denoising
International Journal of Electrical and Computer Engineering (IJECE), 2020
Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal component analysis to denoise multivariate signals in adaptive way. The proposed method is compared at a various signal to noise ratios (SNRs) with different techniques and different types of noise. Also, scale dependent Lyapunov exponent (SDLE) is used to test the behavior of the denoised chaotic signal comparing with clean signal. The results show that EMD-MD method has the best root mean square error (RMSE) and signal to noise ratio gain (SNRG) comparing with the conventional methods.
A new description of chaotic waveform communications: The Fourier analyzer approach
2003
Each digital receiver must have the timing information and the bandwidth of the transmitted signal to perform demodulation. In addition to these parameters, some further a priori information is also available in many cases on the elements of signal set, or equivalently, on the basis functions. This contribution shows how the chaotic basis functions can be represented by sinusoids and how the Fourier analyzer concept can be used to discuss and optimize the detection problem. Using this new description it is possible to develop new detector configurations with improved performance for chaotic communications.
Local eigenfunctions based suboptimal wavelet packet representation of contaminated chaotic signals
IMA Journal of Applied Mathematics, 1999
We report a suboptimal wavelet packet representation (SWPR) of signals emanating from a chaotic attractor contaminated by low levels of noise. Our method-geared towards choosing a suboptimal scaling function to parsimoniously represent the signal-involves extracting local eigenfunctions using artificial ensembles generated from a pseudo-probability space, and using the extracted local eigenfunctions to develop a suboptimal scaling function. The application of