Function
LDPC codes are defined by a sparse parity-check matrix. This sparse matrix is often randomly generated, subject to the sparsity constraints. These codes were first designed by Gallager in 1962.
Below is a graph fragment of an example LDPC code using Forney's factor graph notation. In this graph, n variable nodes in the top of the graph are connected to (n−k) constraint nodes in the bottom of the graph. This is a popular way of graphically representing an (n, k) LDPC code. The bits of a valid message, when placed on the T's at the top of the graph, satisfy the graphical constraints. Specifically, all lines connecting to a variable node (box with an '=' sign) have the same value, and all values connecting to a factor node (box with a '+' sign) must sum, modulo two, to zero (in other words, they must sum to an even number).
Ignoring any lines going out of the picture, there are 8 possible 6-bit strings corresponding to valid codewords: (i.e., 000000, 011001, 110010, 101011, 111100, 100101, 001110, 010111). This LDPC code fragment represents a 3-bit message encoded as six bits. Redundancy is used, here, to increase the chance of recovering from channel errors. This is a (6, 3) linear code, with n = 6 and k = 3.
Once again ignoring lines going out of the picture, the parity-check matrix representing this graph fragment is
In this matrix, each row represents one of the three parity-check constraints, while each column represents one of the six bits in the received codeword.
In this example, the eight codewords can be obtained by putting the parity-check matrix H into this form through basic row operations:
From this, the generator matrix G can be obtained as (noting that in the special case of this being a binary code ), or specifically:
Finally, by multiplying all eight possible 3-bit strings by G, all eight valid codewords are obtained. For example, the codeword for the bit-string '101' is obtained by:
Read more about this topic: Low-density Parity-check Code
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