This article provides an overview of five physical-layer security mechanisms: Physical-Layer Authentication (PLA), Physical-Layer Confidential Communications (PLCC), Physical-Layer Key Generation (PLKG), Physical-Laye...
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This article provides an overview of five physical-layer security mechanisms: Physical-Layer Authentication (PLA), Physical-Layer Confidential Communications (PLCC), Physical-Layer Key Generation (PLKG), Physical-Layer Communications with Low probability of Detection (PLC-LPD), and Physical-Layer Steganography (PLS). We explain the relationship among five physical-layer security mechanisms. Then, we review the features of various physical-layer security mechanisms in terms of the basic idea, properties, assumptions, classification, threat model, and applications, respectively. At last, we introduce five promising plans by combining different physical-layer security mechanisms to further improve the final performance.
This paper proposes an enhanced Bayesian probability-based method to generate high-quality scenarios using a small number of collected sensory data. Diverse kinds of new scenarios can be generated inexpensively using ...
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This paper proposes an enhanced Bayesian probability-based method to generate high-quality scenarios using a small number of collected sensory data. Diverse kinds of new scenarios can be generated inexpensively using the proposed method, and these new scenarios have characteristics similar to the stochastic characteristics of manually collected datasets. The validity of this method was evaluated based on a real dataset. Experiments showed that the factor of the effect was 0.46351, indicating that the proposed method can generate scenarios that are highly consistent with real sensory big data.
In many cases, real-world experimentations are approximated by various stochastic probabilistic models when performing computer simulations. During simulation processes, random variate generations are required. Howeve...
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In many cases, real-world experimentations are approximated by various stochastic probabilistic models when performing computer simulations. During simulation processes, random variate generations are required. However, a general and exact method to satisfy such a requirement is hard to find. An approximate method would most effectively remedy this situation. In this paper, we provide an approximate algorithm method which can be used to generate arbitrary continuous non-uniform variates and to perform probability computing. The algorithm has a very good performance on convergence order, preserving monotonicity, and smoothness. In order to implement the algorithm, piecewise cubic polynomial and rational function are interpolated. Both preserve the monotonicity of the data from the distribution function or its inverse function. Spline methods are discussed. Simulation results are given.
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