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Spread spectrum based on chaotic sequences is an alternative to spread spectrum based on pseudo-noise sequences. In this letter we present the application of two multi-user detection methods (parallel and successive interference cancellation schemes) to this type of code division multiple access. We demonstrate that, also for chaotic sequence-based communications, multi-user receivers significantly improve the BER with respect to single-user receivers, and that their use is practically essential with a high number of users.

In many applications, sequences generated by chaotic maps have been considered as pseudo-random sequences. This paper deals with the correlation between chaotic sequences generated by a given piecewise linear map; we have based the measure of the correlation on the statistics of the Kendall tau, which is usually used in the field of statistics. We considered three piecewise linear maps to generate chaotic sequences and computed the statistics of the Kendall tau of couples of sequences obtained from randomly chosen couples of initial conditions. We essentially found that the results depend on the considered chaotic map and that it is possible to approach the uncorrelated case.

How to choose a chaotic sequence to construct a chaotic S-box is a key factor to determine the performance of the S-box. In this paper, a construction method for chaotic S-box based on fireworks algorithm is proposed, in which the fireworks algorithm is used to search for the optimal solution in the solution space of the Lorenz chaotic system. The performances of S-box are tested through the nonlinearity, differential uniformity, strict avalanche criterion, output bits independence criterion and bijectivity, and compared with the existing classical S-boxes based on the Lorenz chaotic system. The results indicate that the S-box in this paper has more excellent cryptographic characteristics, which will provide a new idea for the efficient generation of chaotic S-box.

Based on the dispersion entropy model, combined with multiscale analysis method and fractional order information entropy theory, this paper proposes new models — the generalized fractional order multiscale dispersion entropy (GMDE) and the generalized fractional order refined composite multiscale dispersion entropy (GRCMDE). The new models take the amplitude value information of the sequence itself into consideration, which can make better use of some key information in the sequence and have a higher stability and accuracy. In addition, extending the algorithm to generalized fractional order can make the model better capture the small evolution of the signal data, which is more advantageous for studying the dynamic characteristics of complex systems. This paper verifies the effectiveness of the new models by combining theoretical analysis with empirical research, and further studies the complexity of the financial system and the nature of its multiple time scales. The results show that the proposed GMDE, GRCMDE can better detect the intrinsic nature of financial time series and can distinguish the financial market complexity of different countries.