Decoding methods

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In coding theory, decoding is the process of translating received messages into codewords of a given code. There have been many common methods of mapping messages to codewords. These are often used to recover messages sent over a noisy channel, such as a binary symmetric channel.


Henceforth, C \subset \mathbb{F}_2^n could have been considered a code with the length n; x,y shall be elements of \mathbb{F}_2^n; and d(x,y) would be

Ideal observer decoding[edit]

One may be given the message x \in \mathbb{F}_2^n, then ideal observer decoding generates the codeword y \in C. The process results in this solution:

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

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

Decoding conventions[edit]

Each codeword does not have an expected possibility: there may be more than one codeword with an equal likelihood of mutating into the received message. In such a case, the sender and receiver(s) must agree ahead of time on a decoding convention. Popular conventions include:

  1. Request that the codeword be resent -- automatic repeat-request
  2. Choose any random codeword from the set of most likely codewords which is nearer to that.

Maximum likelihood decoding[edit]

Further information: Maximum likelihood

Given a received codeword x \in \mathbb{F}_2^n maximum likelihood decoding picks a codeword y \in C to maximize:

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

i.e. choose the codeword y that maximizes the probability that x was received, given that y was sent. Note that if all codewords are equally likely to be sent then this scheme is equivalent to ideal observer decoding. In fact, by Bayes Theorem we have

\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})}.

Upon fixing \mathbb{P}(x \mbox{ received}), x is restructured and \mathbb{P}(y \mbox{ sent}) is constant as all codewords are equally likely to be sent. Therefore 
\mathbb{P}(x \mbox{ received} \mid y \mbox{ sent}) 
is maximised as a function of the variable y precisely when 
\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 ML decoding problem can also be modeled as an integer programming problem.[1]

The ML decoding algorithm has been found to be an instance of the MPF problem which is solved by applying the generalized distributive law. [2]

Minimum distance decoding[edit]

Given a received codeword x \in \mathbb{F}_2^n, minimum distance decoding picks a codeword y \in C to minimise the Hamming distance :

d(x,y) = \# \{i : x_i \not = y_i \}

i.e. choose the codeword y that is as close as possible to x.

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

d(x,y) = d,\,


\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 \\

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 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[edit]

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. It is the linearity of the code which allows

Suppose that C\subset \mathbb{F}_2^n is a linear code of length n and minimum distance d with parity-check matrix H. Then clearly C is capable of correcting up to

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

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

Now suppose that a codeword x \in \mathbb{F}_2^n is sent over the channel and the error pattern e \in \mathbb{F}_2^n occurs. Then z=x+e is received. Ordinary minimum distance decoding would lookup the vector z in a table of size |C| for the nearest match - i.e. an element (not necessarily unique) c \in C with

d(c,z) \leq d(y,z)

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

Hx = 0

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

Hz = H(x+e) =Hx + He = 0 + He = He

Under the assumption that no more than t errors were made during transmission, the receiver looks up the value He in a table of size

\sum_{i=0}^t \binom{n}{i} < |C| \\

(for a binary code) against pre-computed values of He for all possible error patterns e \in \mathbb{F}_2^n. Knowing what e is, it is then trivial to decode x as:

x = z - e

Partial response maximum likelihood[edit]

Main article: PRML

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.

Viterbi decoder[edit]

Main article: Viterbi decoder

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.

See also[edit]



  1. ^ "Using linear programming to Decode Binary linear codes," J.Feldman, M.J.Wainwright and D.R.Karger, IEEE Transactions on Information Theory, 51:954-972, March 2005.
  2. ^ Aji, S.M.; McEliece, R.J. (Mar 2000). "The generalized distributive law". Information Theory, IEEE Transactions on 46 (2): 325–343. doi:10.1109/18.825794.