Gilbert–Varshamov Bound
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In
coding theory Coding theory is the study of the properties of codes and their respective fitness for specific applications. Codes are used for data compression, cryptography, error detection and correction, data transmission and computer data storage, data sto ...
, the Gilbert–Varshamov bound (due to Edgar Gilbert and independently Rom Varshamov.) is a bound on the size of a (not necessarily
linear In mathematics, the term ''linear'' is used in two distinct senses for two different properties: * linearity of a '' function'' (or '' mapping''); * linearity of a '' polynomial''. An example of a linear function is the function defined by f(x) ...
)
code In communications and information processing, code is a system of rules to convert information—such as a letter, word, sound, image, or gesture—into another form, sometimes shortened or secret, for communication through a communicati ...
. It is occasionally known as the Gilbert– Shannon–Varshamov bound (or the GSV bound), but the name "Gilbert–Varshamov bound" is by far the most popular. Varshamov proved this bound by using the probabilistic method for linear codes. For more about that proof, see
Gilbert–Varshamov bound for linear codes The Gilbert–Varshamov bound for linear codes is related to the general Gilbert–Varshamov bound, which gives a lower bound on the maximal number of elements in an error-correcting code of a given block length and minimum Hamming weight over a ...
.


Statement of the bound

Recall that a code has a minimum distance d if any two elements in the code are at least a distance d apart. Let :A_q(n,d) denote the maximum possible size of a ''q''-ary code C with length ''n'' and minimum Hamming distance ''d'' (a ''q''-ary code is a code over the field \mathbb_q of ''q'' elements). Then: :A_q(n,d) \geqslant \frac.


Proof

Let C be a code of length n and minimum
Hamming distance In information theory, the Hamming distance between two String (computer science), strings or vectors of equal length is the number of positions at which the corresponding symbols are different. In other words, it measures the minimum number ...
d having maximal size: :, C, =A_q(n,d). Then for all x\in\mathbb_q^n , there exists at least one codeword c_x \in C such that the Hamming distance d(x,c_x) between x and c_x satisfies :d(x,c_x)\leqslant d-1 since otherwise we could add ''x'' to the code whilst maintaining the code's minimum Hamming distance d – a contradiction on the maximality of , C, . Hence the whole of \mathbb_q^n is contained in the union of all balls of radius d-1 having their centre at some c \in C : :\mathbb_q^n =\bigcup_ B(c,d-1). Now each ball has size : \sum_^ \binom(q-1)^j since we may allow (or choose) up to d-1 of the n components of a codeword to deviate (from the value of the corresponding component of the ball's centre) to one of (q-1) possible other values (recall: the code is q-ary: it takes values in \mathbb_q^n). Hence we deduce :q^n = \left , \mathbb_q^n \right , = \left , \bigcup_ B(c,d-1) \right , \leqslant \sum_ , B(c,d-1), = , C, \sum_^ \binom(q-1)^j That is: : A_q(n,d) = , C, \geqslant \frac.


An improvement in the prime power case

For ''q'' a prime power, one can improve the bound to A_q(n,d)\ge q^k where ''k'' is the greatest integer for which : q^k < \frac.


See also

* Elias-Bassalygo bound *
Gilbert–Varshamov bound for linear codes The Gilbert–Varshamov bound for linear codes is related to the general Gilbert–Varshamov bound, which gives a lower bound on the maximal number of elements in an error-correcting code of a given block length and minimum Hamming weight over a ...
* Griesmer bound *
Hamming bound In mathematics and computer science, in the field of coding theory, the Hamming bound is a limit on the parameters of an arbitrary block code: it is also known as the sphere-packing bound or the volume bound from an interpretation in terms of Spher ...
* Johnson bound * Plotkin bound * Singleton bound


References

{{DEFAULTSORT:Gilbert-Varshamov Bound Coding theory Articles containing proofs