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We know that the linear code is defined using the [[generator matrix]]. So we use the "random generator matrix" <math>G</math> as a mean to describe the randomness of the linear code. So a random generator matrix <math>G</math> of size <math>kn</math> contains <math>kn</math> elements which are chosen independently and uniformly over the field <math>\mathbb{F}_q</math>.
Recall that in a linear code, the distance = the minimum weight of the non-zero codeword. This fact is one of the [[
Denote <math>wt(y)</math> be the weight of the codeword <math>y</math>.
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Therefore <math>P = Pr_{Random G}</math> [there exists a [[vector]] <math>m \in \mathbb{F}_q^k \backslash \{ 0\}</math> such that <math>wt(mG)</math> < <math>d</math>]
By [[
<math>P \le \sum\limits_{m \in \mathbb{F}_q^k \backslash \{ 0\} } {Pr_{random\;G} } [wt(mG)</math> < <math>d</math>]
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(The later inequality comes from [http://www.cse.buffalo.edu/~atri/courses/coding-theory/lectures/lect9.pdf the upper bound of the Volume of Hamming ball])
Then
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# The Varshamov construction above is not explicit; that is, it does not specify the deterministic method to construct the linear code that satisfies the Gilbert-Varshamov bound. The naive way that we can do is to go over all the generator matrices <math>G</math> of size <math>kn</math> over the field <math>\mathbb{F}_q</math> and check if that linear code has the satisfied Hamming distance. That leads to the exponential time algorithm to implement it.
# We also have a [[
==See
# [http://en.wikipedia.org/wiki/Gilbert%E2%80%93Varshamov_bound Gilbert-Varshamov bound due to Gilbert construction for the general code]
# [[
# [http://en.wikipedia.org/wiki/Probabilistic_method Probabilistic method]
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# [http://www.cs.cmu.edu/~venkatg/teaching/codingtheory/ Coding Theory's Notes: Gilbert-Varshamov Bound. Venkatesan �Guruswami]
{{Uncategorized|date=May 2011}}
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