Decoding methods

${\displaystyle C\subset \mathbb {F} _{2}^{n}}$ is considered a binary code with the length ${\displaystyle n}$; ${\displaystyle x,y}$ shall be elements of ${\displaystyle \mathbb {F} _{2}^{n}}$; and ${\displaystyle d(x,y)}$ is the distance between those elements.

One may be given the message ${\displaystyle x\in \mathbb {F} _{2}^{n}}$, then ideal observer decoding generates the codeword ${\displaystyle y\in C}$. The process results in this solution:

${\displaystyle \mathbb {P} (y{\mbox{ sent}}\mid x{\mbox{ received}})}$

For example, a person can choose the codeword ${\displaystyle y}$ that is most likely to be received as the message ${\displaystyle x}$ after transmission.

Given a received vector ${\displaystyle x\in \mathbb {F} _{2}^{n}}$ maximum likelihood decoding picks a codeword ${\displaystyle y\in C}$ that maximizes

${\displaystyle \mathbb {P} (x{\mbox{ received}}\mid y{\mbox{ sent}})}$,

that is, the codeword ${\displaystyle y}$ that maximizes the probability that ${\displaystyle x}$ was received, given that ${\displaystyle y}$ was sent. If all codewords are equally likely to be sent then this scheme is equivalent to ideal observer decoding. In fact, by Bayes Theorem,

${\displaystyle {\begin{aligned}\mathbb {P} (x{\mbox{ received}}\mid y{\mbox{ sent}})&{}={\frac {\mathbb {P} (x{\mbox{ received}},y{\mbox{ sent}})}{\mathbb {P} (y{\mbox{ sent}})}}\\&{}=\mathbb {P} (y{\mbox{ sent}}\mid x{\mbox{ received}})\cdot {\frac {\mathbb {P} (x{\mbox{ received}})}{\mathbb {P} (y{\mbox{ sent}})}}.\end{aligned}}}$

Upon fixing ${\displaystyle \mathbb {P} (x{\mbox{ received}})}$, ${\displaystyle x}$ is restructured and ${\displaystyle \mathbb {P} (y{\mbox{ sent}})}$ is constant as all codewords are equally likely to be sent. Therefore, ${\displaystyle \mathbb {P} (x{\mbox{ received}}\mid y{\mbox{ sent}})}$ is maximised as a function of the variable ${\displaystyle y}$ precisely when ${\displaystyle \mathbb {P} (y{\mbox{ sent}}\mid x{\mbox{ received}})}$ is maximised, and the claim follows.

As with ideal observer decoding, a convention must be agreed to for non-unique decoding.

The maximum likelihood decoding problem can also be modeled as an integer programming problem.[1]

The maximum likelihood decoding algorithm is an instance of the "marginalize a product function" problem which is solved by applying the generalized distributive law.[2]

Given a received codeword ${\displaystyle x\in \mathbb {F} _{2}^{n}}$, minimum distance decoding picks a codeword ${\displaystyle y\in C}$ to minimise the Hamming distance:

${\displaystyle d(x,y)=\#\{i:x_{i}\not =y_{i}\}}$

i.e. choose the codeword ${\displaystyle y}$ that is as close as possible to ${\displaystyle x}$.

Note that if the probability of error on a discrete memoryless channel ${\displaystyle p}$ is strictly less than one half, then minimum distance decoding is equivalent to maximum likelihood decoding, since if

${\displaystyle d(x,y)=d,\,}$

then:

${\displaystyle {\begin{aligned}\mathbb {P} (y{\mbox{ received}}\mid x{\mbox{ sent}})&{}=(1-p)^{n-d}\cdot p^{d}\\&{}=(1-p)^{n}\cdot \left({\frac {p}{1-p}}\right)^{d}\\\end{aligned}}}$

which (since p is less than one half) is maximised by minimising d.

Minimum distance decoding is also known as nearest neighbour decoding. It can be assisted or automated by using a standard array. Minimum distance decoding is a reasonable decoding method when the following conditions are met:

1. The probability ${\displaystyle p}$ that an error occurs is independent of the position of the symbol.
2. Errors are independent events – an error at one position in the message does not affect other positions.

These assumptions may be reasonable for transmissions over a binary symmetric channel. They may be unreasonable for other media, such as a DVD, where a single scratch on the disk can cause an error in many neighbouring symbols or codewords.

As with other decoding methods, a convention must be agreed to for non-unique decoding.

Syndrome decoding is a highly efficient method of decoding a linear code over a noisy channel, i.e. one on which errors are made. In essence, syndrome decoding is minimum distance decoding using a reduced lookup table. This is allowed by the linearity of the code.[3]

Suppose that ${\displaystyle C\subset \mathbb {F} _{2}^{n}}$ is a linear code of length ${\displaystyle n}$ and minimum distance ${\displaystyle d}$ with parity-check matrix ${\displaystyle H}$. Then clearly ${\displaystyle C}$ is capable of correcting up to

${\displaystyle t=\left\lfloor {\frac {d-1}{2}}\right\rfloor }$

errors made by the channel (since if no more than ${\displaystyle t}$ errors are made then minimum distance decoding will still correctly decode the incorrectly transmitted codeword).

Now suppose that a codeword ${\displaystyle x\in \mathbb {F} _{2}^{n}}$ is sent over the channel and the error pattern ${\displaystyle e\in \mathbb {F} _{2}^{n}}$ occurs. Then ${\displaystyle z=x+e}$ is received. Ordinary minimum distance decoding would lookup the vector ${\displaystyle z}$ in a table of size ${\displaystyle |C|}$ for the nearest match - i.e. an element (not necessarily unique) ${\displaystyle c\in C}$ with

${\displaystyle d(c,z)\leq d(y,z)}$

for all ${\displaystyle y\in C}$. Syndrome decoding takes advantage of the property of the parity matrix that:

${\displaystyle Hx=0}$

for all ${\displaystyle x\in C}$. The syndrome of the received ${\displaystyle z=x+e}$ is defined to be:

${\displaystyle Hz=H(x+e)=Hx+He=0+He=He}$

To perform ML decoding in a binary symmetric channel, one has to look-up a precomputed table of size ${\displaystyle 2^{n-k}}$, mapping ${\displaystyle He}$ to ${\displaystyle e}$.

Note that this is already of significantly less complexity than that of a standard array decoding.

However, under the assumption that no more than ${\displaystyle t}$ errors were made during transmission, the receiver can look up the value ${\displaystyle He}$ in a further reduced table of size

${\displaystyle {\begin{matrix}\sum _{i=0}^{t}{\binom {n}{i}}\\\end{matrix}}}$

This is a family of Las Vegas-probabilistic methods all based on the observation that it is easier to guess enough error-free positions, than it is to guess all the error-positions.

The simplest form is due to Prange: Let ${\displaystyle G}$ be the ${\displaystyle k\times n}$ generator matrix of ${\displaystyle C}$ used for encoding. Select ${\displaystyle k}$ columns of ${\displaystyle G}$ at random, and denote by ${\displaystyle G'}$ the corresponding submatrix of ${\displaystyle G}$. With reasonable probability ${\displaystyle G'}$ will have full rank, which means that if we let ${\displaystyle c'}$ be the sub-vector for the corresponding positions of any codeword ${\displaystyle c=mG}$ of ${\displaystyle C}$ for a message ${\displaystyle m}$, we can recover ${\displaystyle m}$ as ${\displaystyle m=c'G'^{-1}}$. Hence, if we were lucky that these ${\displaystyle k}$ positions of the received word ${\displaystyle y}$ contained no errors, and hence equalled the positions of the sent codeword, then we may decode.

If ${\displaystyle t}$ errors occurred, the probability of such a fortunate selection of columns is given by ${\displaystyle \textstyle {\binom {n-t}{k}}/{\binom {n}{k}}}$.

This method has been improved in various ways, e.g. by Stern[4] and Canteaut and Sendrier.[5]

Partial response maximum likelihood (PRML) is a method for converting the weak analog signal from the head of a magnetic disk or tape drive into a digital signal.

A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using forward error correction based on a convolutional code. The Hamming distance is used as a metric for hard decision Viterbi decoders. The squared Euclidean distance is used as a metric for soft decision decoders.

Optimal decision decoding algorithm (ODDA) for an asymmetric TWRC system.[6]

• Don't care alarm
• Error detection and correction
• Forbidden input

• Hill, Raymond (1986). A first course in coding theory. Oxford Applied Mathematics and Computing Science Series. Oxford University Press. ISBN 978-0-19-853803-5.
• Pless, Vera (1982). Introduction to the theory of error-correcting codes. Wiley-Interscience Series in Discrete Mathematics. John Wiley & Sons. ISBN 978-0-471-08684-0.
• van Lint, Jacobus H. (1992). Introduction to Coding Theory. Graduate Texts in Mathematics (GTM). Vol. 86 (2 ed.). Springer-Verlag. ISBN 978-3-540-54894-2.