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8 Johnson Bound

8.1 Overview

This chapter proves the binary Johnson bound: for a binary code \(C\) of length \(n\) with minimum distance \(d\), if every codeword has weight at most \(w \le J_2(n,d)\) where

\[ J_2(n,d) = \frac{n}{2} - \sqrt{\frac{n(n-d)}{4}}, \]

then \(|C| \le 2n\).

The proof reduces to a Rankin bound from geometry: a set of unit vectors in \(\mathbb {R}^n\) with pairwise non-positive inner products has size at most \(2n\). Codewords are embedded via a shifted \(\{ \pm 1\} \) map so that the distance and weight constraints force the inner-product condition.

8.2 Basic definitions

Definition 8.1 Binary codeword
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A binary codeword of length \(n\) is a function \(\mathrm{Fin}\, n \to \mathrm{Bool}\).

Definition 8.2 Hamming weight and distance
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The Hamming weight \(\mathrm{wt}(x) = |\{ i : x_i = 1\} |\); the Hamming distance \(\mathrm{hdist}(x,y) = \mathrm{wt}(x \oplus y)\).

Definition 8.3 Admissible code
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A code \(C \subseteq \{ 0,1\} ^n\) is \((n,d,w)\)-admissible if every pair of distinct codewords has Hamming distance \(\ge d\) and every codeword has weight \(\le w\).

Definition 8.4 Johnson radius and shift parameter
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\[ J_2(n,d) = \frac{n}{2} - \sqrt{\frac{n(n-d)}{4}}, \qquad \alpha (n,d) = \frac{1}{\sqrt{n(n-d)^{-1}} + 1} - \frac{1}{\sqrt{n/d}+1}. \]

8.3 Embedding and inner-product bounds

Definition 8.5 Shifted \(\pm 1\) embedding
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Map \(x \in \{ 0,1\} ^n\) to the vector \(\hat{x}^\alpha _i = (-1)^{x_i} - \alpha \) (with \(\alpha \ge 0\)), viewed in \(\mathbb {R}^n\).

Lemma 8.6 Inner product formula for \(\pm 1\) vectors
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\(\langle \hat{x},\hat{y}\rangle = n - 2\, \mathrm{hdist}(x,y)\).

Lemma 8.7 Inner product bound for shifted vectors
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For \(\alpha \ge 0\), \(\mathrm{hdist}(x,y) \ge d\), and \(\mathrm{wt}(x),\mathrm{wt}(y) \le w\),

\[ \bigl\langle \hat{x}^\alpha ,\hat{y}^\alpha \bigr\rangle \; \le \; (n - 2d) + \alpha ^2 n + 2\alpha (2w - n). \]
Lemma 8.8 Johnson arithmetic
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If \(n \gt 0\), \(1 \le d\), \(2d \le n\), and \(w \le J_2(n,d)\), then \((n - 2d) + \alpha ^2 n + 2\alpha (2w - n) \le 0\) for the choice \(\alpha = \alpha (n,d)\).

8.4 Rankin bound

Lemma 8.9 Rankin reduction
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In a finite inner-product space, if a set \(S\) of unit vectors has pairwise non-positive inner products, then one can project away one vector and reduce to a smaller set in the orthogonal complement with the same property.

Theorem 8.10 Rankin bound
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A set of unit vectors in an \(n\)-dimensional real inner product space with pairwise non-positive inner products has size at most \(2n\).

Corollary 8.11 Rankin bound for finite sets in \(\mathbb {R}^n\)
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A finite set of unit vectors in \(\mathbb {R}^n\) with pairwise non-positive inner products has cardinality at most \(2n\).

8.5 Johnson bound

Theorem 8.12 Johnson bound, parametric form

Let \(C \subseteq \{ 0,1\} ^n\) be a code with minimum distance \(\ge d\) and all weights \(\le w\). For any \(\alpha \ge 0\) with \((n-2d) + \alpha ^2 n + 2\alpha (2w-n) \le 0\),

\[ |C| \; \le \; 2n. \]
Theorem 8.13 Johnson bound

If \(n \gt 0\), \(1 \le d\), \(2d \le n\), and all codewords of \(C\) have weight \(\le w \le J_2(n,d)\) and minimum distance \(\ge d\), then \(|C| \le 2n\).

Theorem 8.14 Johnson bound for admissible codes

Every \((n,d,w)\)-admissible code \(C\) with \(w \le J_2(n,d)\) satisfies \(|C| \le 2n\).

Definition 8.15 Maximum admissible code size
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\(A_0(n,d,w)\) is the maximum size of an \((n,d,w)\)-admissible code.

Theorem 8.16 Johnson bound, radius form
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If \(n \gt 0\), \(1 \le d\), \(2d \le n\), and \(w \le J_2(n,d)\), then

\[ A_0(n,d,w) \; \le \; 2n. \]