Enumerator polynomial

In mathematics, the weight enumerator polynomial of a binary linear code specifies the number of words of each possible Hamming weight.

Let $C\subset \mathbb {F} _{2}^{n}$ be a binary linear code length $n$ . The weight distribution is the sequence of numbers

A_{t}=\#\{c\in C\mid w(c)=t\}

giving the number of codewords c in C having weight t as t ranges from 0 to n. The weight enumerator is the bivariate polynomial

W(C;x,y)=\sum _{w=0}^{n}A_{w}x^{w}y^{n-w}.

Basic properties

$W(C;0,1)=A_{0}=1$
$W(C;1,1)=\sum _{w=0}^{n}A_{w}=|C|$
$W(C;1,0)=A_{n}=1{\mbox{ iff }}(1,\ldots ,1)\in C\ {\mbox{ and }}0{\mbox{ otherwise.}}$
$W(C;1,-1)=\sum _{w=0}^{n}A_{w}(-1)^{n-w}=A_{n}+(-1)^{1}A_{n-1}+\ldots +(-1)^{n-1}A_{1}+(-1)^{n}A_{0}$

Denote the dual code of $C\subset \mathbb {F} _{2}^{n}$ by

C^{\perp }=\{x\in \mathbb {F} _{2}^{n}\,\mid \,\langle x,c\rangle =0{\mbox{  }}\forall c\in C\}

(where $<,>$ denotes the vector dot product and which is taken over $\mathbb {F} _{2}$ ).

The MacWilliams identity states that

W(C^{\perp };x,y)={\frac {1}{\mid C\mid }}W(C;y-x,y+x).

The identity is named after Jessie MacWilliams.

The distance distribution or inner distribution of a code C of size M and length n is the sequence of numbers

A_{i}={\frac {1}{M}}\#\left\lbrace (c_{1},c_{2})\in C\times C\mid d(c_{1},c_{2})=i\right\rbrace

where i ranges from 0 to n. The distance enumerator polynomial is

A(C;x,y)=\sum _{i=0}^{n}A_{i}x^{i}y^{n-i}

and when C is linear this is equal to the weight enumerator.

Hill, Raymond (1986). A first course in coding theory. Oxford Applied Mathematics and Computing Science Series. Oxford University Press. pp. 165–173. ISBN 0-19-853803-0.
Pless, Vera (1982). Introduction to the theory of error-correcting codes. Wiley-Interscience Series in Discrete Mathematics. John Wiley & Sons. pp. 103–119. ISBN 0-471-08684-3.
J.H. van Lint (1992). Introduction to Coding Theory. GTM. Vol. 86 (2nd ed ed.). Springer-Verlag. ISBN 3-540-54894-7. {{cite book}}: |edition= has extra text (help) Chapters 3.5 and 4.3.