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Johnson bound

In applied mathematics, the Johnson bound (named after Selmer Martin Johnson) is a limit on the size of error-correcting codes, as used in coding theory for data transmission or communications.

01Definition

Let C be a q-ary code of length n, i.e. a subset of \mathbb {F} _{q}^{n}. Let d be the minimum distance of C, i.e.

d=\min _{x,y\in C,x\neq y}d(x,y),

where d(x,y) is the Hamming distance between x and y.

Let C_{q}(n,d) be the set of all q-ary codes with length n and minimum distance d and let C_{q}(n,d,w) denote the set of codes in C_{q}(n,d) such that every element has exactly w nonzero entries.

Denote by |C| the number of elements in C. Then, we define A_{q}(n,d) to be the largest size of a code with length n and minimum distance d:

A_{q}(n,d)=\max _{C\in C_{q}(n,d)}|C|.

Similarly, we define A_{q}(n,d,w) to be the largest size of a code in C_{q}(n,d,w):

A_{q}(n,d,w)=\max _{C\in C_{q}(n,d,w)}|C|.

Theorem 1 (Johnson bound for A_{q}(n,d)):

If d=2t+1,

A_{q}(n,d)\leq {\frac {q^{n}}{\sum _{i=0}^{t}{n \choose i}(q-1)^{i}+{\frac {{n \choose t+1}(q-1)^{t+1}-{d \choose t}A_{q}(n,d,d)}{A_{q}(n,d,t+1)}}}}.

If d=2t+2,

A_{q}(n,d)\leq {\frac {q^{n}}{\sum _{i=0}^{t}{n \choose i}(q-1)^{i}+{\frac {{n \choose t+1}(q-1)^{t+1}}{A_{q}(n,d,t+1)}}}}.

Theorem 2 (Johnson bound for A_{q}(n,d,w)):

(i) If d>2w,

A_{q}(n,d,w)=1.

(ii) If d\leq 2w, then define the variable e as follows. If d is even, then define e through the relation d=2e; if d is odd, define e through the relation d=2e-1. Let q^{*}=q-1. Then,

A_{q}(n,d,w)\leq \left\lfloor {\frac {nq^{*}}{w}}\left\lfloor {\frac {(n-1)q^{*}}{w-1}}\left\lfloor \cdots \left\lfloor {\frac {(n-w+e)q^{*}}{e}}\right\rfloor \cdots \right\rfloor \right\rfloor \right\rfloor

where \lfloor ~~\rfloor is the floor function.

Remark: Plugging the bound of Theorem 2 into the bound of Theorem 1 produces a numerical upper bound on A_{q}(n,d).

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