This paper focuses on the hybrid power line/wireless single-relay channel (HSRC) model, which is constituted by parallel power line and wireless single-relay channel (SRC) models, for data communication purposes. Closed-form expressions for ergodic achievable data rates and outage probabilities are presented. These expressions are based on the fact that both power line and wireless are frequency-selective block-fading channels corrupted by additive random noise with a definite power spectral density. Moreover, comparisons among HSRC, power line SRC, and wireless SRC under the sum power constraint with optimal and uniform power allocations, the use of amplify-and-forward and decode-and-forward cooperative protocols, and different relay positions are discussed. Based on asymptotic, analytical, and numerical results, we show that HSRC outperforms both power line and wireless SRCs in all considered scenarios. Furthermore, a performance comparison between HSRC and the so-called two wireless SRC shows that the former surpasses the latter when the sum power constraint with optimal power allocation is adopted.

This paper presents a statistical characterization of power lines used by the flight test instrumentation of a corporate jet as a medium for data communication purposes. We quantify the main parameters that drive the design of power line communication technologies at the physical layer. We show that the limit of -83.01 dBm/Hz on the power spectral density (1.7-86 MHz), imposed by the standard RTCA/DO160, results in data rates as high as 90 Mbps

In recent years, many chaos-based encryption algorithms have been proposed. Many of these are based on established designs and populate their S-boxes with values derived from chaotic maps, following conventional implementation strategies to enable comparison with their original non-chaotic counterparts. In contrast, this work proposes a novel approach: a Chaos-Based Substitution Box (CB-SBox) implementation, in which conventional ROM-based S-boxes are replaced by a digital circuit that directly executes a selected chaotic map. This method enables the construction of S-boxes with long word lengths through an FPGA-based programmable circuit that allows for variable S-box lengths, facilitating the analysis of S-boxes of varying sizes, and ultimately enhancing security, particularly for larger S-boxes, as demonstrated by increased resistance to linear and differential cryptanalysis. Furthermore, the proposed CB-SBox achieves reductions in both area and power consumption compared to size-comparable ROM-based S-boxes. A 19-bit chaos-based S-box consumes just 0.0238% of the area and 0.0241% of the power required by an equivalent ROM-implemented S-box while providing the same level of security. The inherent unpredictability of non-linear chaotic behavior causes the proposed chaos-based S-boxes to exhibit non-bijective characteristics, making them well suited for application in non-invertible cryptographic primitives, such as hash functions and Feistel networks. The proposed CB-SBox is implemented in a Feistel network as described in the literature, and the results are provided.

Transmission medium is always perturbed by noise with a random nature which can be characterized by taking a sequence of noise samples and, after analyzing the sequence, attributing a probabilistic model to represent the randomness of the noise. If thermal noise (receiver generated) is the only noise impairing the transmission (our only focus is digital transmission) the memoryless stationary discrete-time Gaussian process is the best model to probabilistically represent the noise. The mathematical representation of the transmission medium in such a situation yields the well known Gaussian Channel. As Information Theory points out, for a fixed noise power, the Gaussian channel is the worst channel to send information through. If thermal noise is not the only noise impairing the transmission (as in sonar communication and power line communication) finding the probabilistic model other than the single-parameter Gaussian process, which best match the noise can much improve the communication system design. The Bernoulli-Gaussian process, a three parameters model, is a common considered option. Finding the three parameters of the Bernoulli-Gaussian model (from known noise samples) is a formidable task that can be made simpler by considering the (original) results presented in the current paper. The Bernoulli-Gaussian model can be characterized, analytically, by using the noise power and two additional quantities: the expectation of the absolute value of the noise process plus the expected value of the third power of the absolute value. In practice the parameters would be calculated using estimates of the mentioned expected values. The communication system design can be much improved if a well fit Bernoulli-Gaussian stochastic process is selected to model the noise. This is an alternative to model the communication using power lines which is often modeled as Middleton Class-A. The rate harvested when modeling the medium as a Bernoulli-Gaussian channel, it is shown, is increased when compared to modeling the medium with the easily obtained Gaussian channel.