begin correction
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@@ -14,8 +14,11 @@
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\usepackage{amsmath}
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\PassOptionsToPackage{hyphens}{url}
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\usepackage{hyperref} % allows urls to follow line breaks of text
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\usepackage{glossaries}
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\usepackage[style=ieee, backend=biber, maxnames=1, minnames=1]{biblatex}
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\addbibresource{entropy.bib}
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\addbibresource{correction.bib}
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\newacronym{snr}{SNR}{signal-to-noise ratio}
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@@ -25,7 +28,34 @@
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\begin{document}
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\maketitle
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(theorems: Hamming condition, Varsham-Gilbert)
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\section{Introduction}
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When messages are transmitted over real media, errors are inevitably introduced.
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Though the source of errors are manifold, they are ubiquitous and need to be accounted for if we want any
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real chance of reading a correct message at the end.
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On a basic level, we can understand that a channel with a lower \textit{\acrfull{snr}},
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i.e. more errors per intended information will require more effort to retain the message information.
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\[ \mathrm{SNR} = \frac{P_{signal}}{P_{noise}} \]
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In an analog system, one might attempt to simply increase transmission power $P_{signal}$
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as is done for example in professional audio equipment.
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This would however require the modification of the transmission channel itself, which is not possible for e.g. wireless transmissions.
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In a digital system, we could understand \acrshort{snr} as the probability of a bit being flipped in transit.
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For example, if a binary message is sent electronically, with a 1 being represented by a voltage of one volt
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and a 0 being represented by zero volts,
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the receiver is likely to observe some intermediate value such as .82 volts.
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Using a nearest-neighbor criterion, the receiver might consider anything over .5 volts to be the binary signal 1
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and anything less than .5 volts to be the binary signal 0.
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If the noise level is small on average, most transmissions will be interpreted correctly.
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However, there generally remains a chance that an intended 1 will be received as .47 volts and hence interpreted incorrectly as a 0.
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\cite{enwiki:signal-to-noise}
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In this digital system, we can improve communication reliability by using a coding scheme that is tolerant of errors.
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\section{Hamming Condition}
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theorems: Hamming condition, Varsham-Gilbert
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Shannon-Hartley
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Convolutional Code
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Reed-Solomon
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CRC
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\printbibliography
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\end{document}
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