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distance d is the minimum weight among all the nonzero vectors in C, i.e., d= min

u∈Cu̸=0

w(u)

A linear code can be concisely specified with the help of agenerator matrix.

Definition 2.18. A generator matrix G of an [n, k, d] linear code C over Fq is any k×n matrix whose rows form a basis for C.

Note that ifG is a generator matrix for C then, C ={vG:v∈Fkq}.

We can also describe C with the help of an (n−k)×n matrix H, called parity check matrix, such that C is the null space of H, i.e.,

v∈C ⇐⇒ HvT = 0.

If the generator matrix G of a linear code C has the form G = [Ik|A], then we say that Gis instandard form. The following result shows the relationship between the generator matrix in standard form and the parity check matrix of a linear code C.

Theorem 2.26. If G= [Ik |A]is a generator matrix of a linear code C, then H = [−AT |In−k] is a parity check matrix for C.

Example 2.27. [22] The following matrices are respectively generator and parity check matrices for a [7,4,3] binary code.

G=

1 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0 1 1 0 0 0 0 1 1 1 1

H=

0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 0 1 0 0 1

 .

The next result indicates the relationship between a linear code and a set of independent vectors, which we make use of in Section 6.4 to construct sets of frequency rectangles. First, we give here the definition of t–independent vectors.

Definition 2.19. A set S of vectors in (Fq)n is said to be t–independent if each of its subset of size t is linearly independent.

Theorem 2.28. [10] A linear code C with a parity check matrix H has min- imum distance d if and only if the matrix H contains a set of d dependent columns but each set of d−1 columns is independent.

In the theory of codes, for fixed parameters n and M, a code with the highest possibledis desirable. Similarly, for fixednandd, a code of maximum size is preferred. Therefore a significant effort has been put into finding the bounds in terms of these parameters. Here we include some of these results.

Theorem 2.29 (Singleton bound). [3] If C is an (n, M, d)q-ary code, then M ≤qn−d+1. In the case of linear [n, k, d] code, d≤n−k+ 1.

Theorem 2.30 (Sphere-packing bound). [3]If C is an(n, M, d)q-ary code of packing radius ρ=⌊(d−1)/2⌋, then

M

1 + (q−1)n+ (q−1)2 n

2

+· · ·+ (q−1)ρ n

ρ

≤qn,

and in the linear case:

ρ

X

i=0

(q−1)i n

i

≤qn−k.

The next result gives a lower bound on n for a linear [n, k, d] code, given its minimum weight and dimension.

Theorem 2.31 (Griesmer Bound). [22]Let C be an[n, k, d]q-ary code with k ≥1. Then

n≥

k−1

X

i=0

d qi

.

We refer the reader to [22] for a detailed overview of research in this field.

A useful online repository for the bounds on codes over finite fields of size less than or equal to 9 is available at [16].

References

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Applications to Experimental

Designs