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Encoding Matrices and Linear Codes

July 4th, 2009

In the previous post here,  the message 1101 is encoded into 11011101. By writing 1101 as the row vector , we can write the encoded message as

So the idea here is that we can use a matrix to encode messages efficiently. From now on we will refer the messages as words and the encoded messages as codewords. Suppose we have a word of length and a matrix G, turn this word into a codeword of lengh . Then it is clear that is of the size . We also want that each codeword correspond to a unique word. In order to do so we need to have a full rank, i.e., we need the rank of equals .

We can view the codewords as the set of all vectors of the form where . Note that if are the rows of , then

.

So the codewords are the set of all linear combination of the vector rows . Hence the set of codewords is a -sub vector space of . Since , then is a basis of this vector space. From this observation one can see that to study the encoding mechanism it is enough to study subspaces of .

We are ready for the definition of liner codes.

Definition


An linear codes is a -subspace of of dimension .

It is natural to ask the other way around, given an   linear code , is there a matrix such that ?

Since is of dimension , has a basis . Let define a matrix where it’s rows are . Previously we seen that is the set of linear combination of rows of , i.e., linear combination of . But is a basis of . Therefore .

Any matrix that satisfies is called an encoding matrix or a generator matrix for

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