The authors construct the piecewise vibrational resonance (VR) method by introducing the piecewise idea and combine the re‐scaled and twice sampling methods for the linear frequency modulated (LFM) signal. Importantly, they put forward a new spectral amplification factor to quantify the VR. The new spectral amplification factor has some advantages as compared with the cross‐correlated coefficient, which can also be used to quantify the aperiodic VR. The top value of the spectrum amplification factor is the necessary and sufficient condition for the occurrence of the optimal VR phenomenon. Here, they discuss the effects of different parameters on VR by using some numerical examples. The method described in this study provides an effective way to improve the LFM signal, and even other kinds of frequency modulated signals.

Recent work has shown that chaos-based communication systems can yield performances as good as their non-chaotic counterparts in additive white Gaussian noise (AWGN), and behave even better in flat-fading channels. However, much of this work relies on computer simulations and there is still a need to study in depth the implementation issues of such systems. The authors address for the first time a fixed-point arithmetic implementation of the iterative decoding algorithm for a recently proposed and successful class of parallel concatenated chaos-based coded modulations. The novel digital signal processor (DSP) results demonstrate that it is possible to implement in standard hardware competitive chaos-based communication systems.

In this paper, we explain how to build a turbo-like structure with binary inputs and chaotic outputs for efficient coding and decoding in additive white Gaussian noise (AWGN). We analyze the convergence of the decoding algorithm, the performance in the error floor region and explain minimum distance properties of the resulting codes.

Most of congestion control schemes require users to behave in a cooperative way, so that they respect some "social responsible" rules. However, without forcing end users to adopt a centralized mandated algorithm controlling their behavior (which is not advisable), it is not possible to guarantee that they will not act in a selfish manner. Consequently, a fundamental issue is to evaluate the impact of having users that act in such a manner. In such a scenario, having a Nash equilibrium guarantees that no selfish user has incentive to unilaterally deviate from its current state (i.e., it guarantees that we are in a stable state in the presence of selfish users). However, here we formally prove that an efficient Nash equilibrium can not be reached in practice for any oblivious control policy.