On the Undetected Error Probability of m-out-of-n Codes on the Binary Symmetric Channel

2000 ◽  
pp. 102-110 ◽  
Author(s):  
Fang-Wei Fu ◽  
Torleiv Kløve ◽  
Shu-Tao Xia
Keyword(s):  
Error Probability ◽  
Binary Symmetric Channel ◽  
Symmetric Channel ◽  
Undetected Error Probability

Enhancement of safety communication model

2022 ◽  
Vol 70 (1) ◽  
pp. 38-52
Author(s):  
Frank Schiller ◽  
Dan Judd ◽  
Peerasan Supavatanakul ◽  
Tina Hardt ◽  
Felix Wieczorek
Keyword(s):  
Error Probability ◽  
Channel Model ◽  
Residual Error ◽  
Communication Model ◽  
Safety Communication ◽  
Binary Symmetric Channel ◽  
Data Errors ◽  
Symmetric Channel ◽  
Data Error ◽  
Error Types

Abstract A fundamental measure of safety communication is the residual error probability, i. e., the probability of undetected errors. For the detection of data errors, typically a Cyclic Redundancy Check (CRC) is applied, and the resulting residual error probability is determined based on the Binary Symmetric Channel (BSC) model. The use of this model had been questioned since several error types cannot be sufficiently described. Especially the increasing introduction of security algorithms into underlying communication layers requires a more adequate channel model. This paper introduces an enhanced model that extends the list of considered data error types by combining the BSC model with a Uniformly Distributed Segments (UDS) model. Although models beyond BSC are applied, the hitherto method of the calculation of the residual error probability can be maintained.

scholarly journals Digital Link—A

2021 ◽  
pp. 115-142
Author(s):  
Jean Walrand
Keyword(s):  
Decision Making ◽  
Decision Problem ◽  
False Negative ◽  
Main Tool ◽  
Maximum Likelihood Estimates ◽  
Modulation Scheme ◽  
Bayes Rule ◽  
Binary Symmetric Channel ◽  
Symmetric Channel ◽  
Complex Modulation

AbstractIn a digital link, a transmitter converts bits into signals and a receiver converts the signals it receives into bits. The receiver faces a decision problem that we study in Sect. 7.1. The main tool is Bayes’ Rule. The key notions are maximum a posteriori and maximum likelihood estimates. Transmission systems use codes to reduce the number of bits they need to transmit. Section 7.2 explains the Huffman codes that minimize the expected number of bits needed to transmit symbols; the idea is to use fewer bits for more likely symbols. Section 7.3 explores a commonly used model of a communication channel: the binary symmetric channel. It explains how to calculate the probability of errors. Section 7.4 studies a more complex modulation scheme employed by most smartphones and computers: QAM. Section 7.5 is devoted to a central problem in decision making: how to infer which situation is in force from observations. Does a test reveal the presence of a disease; how to balance the probability of false positive and that of false negative? The main result of that section is the Neyman–Pearson Theorem that the section illustrates with many examples.

scholarly journals Transmission of Hidden Cipher Text over a Binary Symmetric Channel

2012 ◽  
Vol 53 (15) ◽  
pp. 40-43
Author(s):  
Arun Rana ◽  
Nitin Sharma ◽  
Parveen Malik
Keyword(s):  
Binary Symmetric Channel ◽  
Cipher Text ◽  
Symmetric Channel
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