A Novelty in Blahut-Arimoto T ype Algorithms: Optimal Control over Noisy Commu nication Channels

2020 ◽  
Author(s):  
Makan Zamanipour
Keyword(s):  
Optimal Control ◽  
Channel Coding ◽  
Joint Source Channel Coding ◽  
Mathematical Expressions ◽  
Type Algorithm ◽  
Theoretic Problem ◽  
Probability Mass ◽  
Information Constraints ◽  
First Time ◽  
Mass Functions

A probability-theoretic problem under information constraints for the concept of optimal control over a noisy-memoryless channel is considered. For our \textit{Observer-Controller} block, i.e., the lossy joint-source-channel-coding (JSCC) scheme, after providing the relative mathematical expressions, we propose a \textit{Blahut-Arimoto}-type algorithm $-$ which is, to the best of our knowledge, for the first time. The algorithm efficiently finds the probability-mass-functions (PMFs) required for .......................................

scholarly journals A Novelty in Blahut-Arimoto T ype Algorithms: Optimal Control over Noisy Commu nication Channels

2020 ◽  
Author(s):  
Makan Zamanipour
Keyword(s):  
Optimal Control ◽  
Channel Coding ◽  
Joint Source Channel Coding ◽  
Mathematical Expressions ◽  
Type Algorithm ◽  
Theoretic Problem ◽  
Probability Mass ◽  
Information Constraints ◽  
First Time ◽  
Mass Functions

A probability-theoretic problem under information constraints for the concept of optimal control over a noisy-memoryless channel is considered. For our \textit{Observer-Controller} block, i.e., the lossy joint-source-channel-coding (JSCC) scheme, after providing the relative mathematical expressions, we propose a \textit{Blahut-Arimoto}-type algorithm $-$ which is, to the best of our knowledge, for the first time. The algorithm efficiently finds the probability-mass-functions (PMFs) required for .......................................

scholarly journals Two Useful Discrete Distributions to Model Overdispersed Count Data

2020 ◽  
Vol 43 (1) ◽  
pp. 21-48
Author(s):  
Josmar Mazucheli ◽  
Wesley Bertoli ◽  
Ricardo Puziol Oliveira
Keyword(s):  
Infinite Series ◽  
Count Data ◽  
Survival Function ◽  
Continuous Distribution ◽  
Discrete Distributions ◽  
Mathematical Expressions ◽  
Probability Mass ◽  
The Difference ◽  
Discrete Analogues ◽  
Mass Functions

The methods to obtain discrete analogues of continuous distributions have been widely considered in recent years. In general, the discretization process provides probability mass functions that can be competitive with the traditional model used in the analysis of count data, the Poisson distribution. The discretization procedure also avoids the use of continuous distribution in the analysis of strictly discrete data. In this paper, we seek to introduce two discrete analogues for the Shanker distribution using the method of the infinite series and the method based on the survival function as alternatives to model overdispersed datasets. Despite the difference between discretization methods, the resulting distributions are interchangeable. However, the distribution generated by the method of infinite series method has simpler mathematical expressions for the shape, the generating functions and the central moments. The maximum likelihood theory is considered for estimation and asymptotic inference concerns. A simulation study is carried out in order to evaluate some frequentist properties of the developed methodology. The usefulness of the proposed models is evaluated using real datasets provided by the literature.

scholarly journals Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks

2021 ◽  
Vol 28 ◽  
pp. 728-732
Author(s):  
Johannes Dommel ◽  
Zoran Utkovski ◽  
Osvaldo Simeone ◽  
Slawomir Stanczak
Keyword(s):  
Channel Coding ◽  
Joint Source Channel Coding ◽  
Radio Access ◽  
Source Channel Coding

scholarly journals Stochastic methods defeat regular RSA exponentiation algorithms with combined blinding methods

2021 ◽  
Vol 15 (1) ◽  
pp. 408-433
Author(s):  
Margaux Dugardin ◽  
Werner Schindler ◽  
Sylvain Guilley
Keyword(s):  
State Of The Art ◽  
Stochastic Methods ◽  
Rsa Algorithm ◽  
Montgomery Ladder ◽  
Channel Information ◽  
Probability Mass ◽  
The Cost ◽  
Noisy Measurements ◽  
Mass Functions ◽  
Larger Sample

Abstract Extra-reductions occurring in Montgomery multiplications disclose side-channel information which can be exploited even in stringent contexts. In this article, we derive stochastic attacks to defeat Rivest-Shamir-Adleman (RSA) with Montgomery ladder regular exponentiation coupled with base blinding. Namely, we leverage on precharacterized multivariate probability mass functions of extra-reductions between pairs of (multiplication, square) in one iteration of the RSA algorithm and that of the next one(s) to build a maximum likelihood distinguisher. The efficiency of our attack (in terms of required traces) is more than double compared to the state-of-the-art. In addition to this result, we also apply our method to the case of regular exponentiation, base blinding, and modulus blinding. Quite surprisingly, modulus blinding does not make our attack impossible, and so even for large sizes of the modulus randomizing element. At the cost of larger sample sizes our attacks tolerate noisy measurements. Fortunately, effective countermeasures exist.

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