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  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.

The binary [7,4,3] Hamming code and its single-error syndromes

Example

Over F2=Z/2Z, take the column-vector matrices

G=[1101101101111000010000100001],H=[100110101010110010111].

The code C=im⁡G=ker⁡H has parameters [7,4,3]2. The syndrome of a single-bit error is the corresponding column of H; the seven syndromes are the seven nonzero vectors of F23.

Facts & Assumptions

Given: The matrices G,H above, column-vector multiplication, and F2=Z/2Z.

[F1]

For every prime p, the two operations on Z/p make it a field: For each prime p, the residue classes Z/pZ form a field.

[F3]

Rank-nullity: dim⁡FV=nullity⁡T+rank⁡T: A linear map from a finite-dimensional domain has dim⁡V=dim⁡ker⁡T+dim⁡im⁡T.

[F5]

Row space, column space, nullspace, row rank, column rank and matrix rank: The row rank is the dimension of the row space, the nullspace is {x:Hx=0}, and matrix rank is row rank.

[L1]

A full-column-rank matrix has image dimension equal to its number of columns: Independent columns of G span a subspace of dimension equal to their number.

[L2]

Syndrome of a received word over a finite field: The syndrome map is s(r)=Hr.

[L3]

Classical distance gives error detection, unique decoding, and erasure recovery: A code of distance 3 uniquely decodes one unknown symbol error.

Verification

1.1F1F2F5L1givenalgebra

The last four rows of G form I4, so its columns are independent and [L1] gives dim⁡C=4. The first three columns of H form I3, so for every y∈F23 the input (y0,y1,y2,0,0,0,0) maps to y. Also, a linear combination of the rows of H has its first three entries equal to its three coefficients, so the rows are independent and [F5] gives rank 3. For x=(a,b,c,d), Gx=(a+b+d,a+c+d,b+c+d,a,b,c,d); applying the three rows of H gives (0,0,0). Thus HG=0 and C⊆ker⁡H.

2.1F3F4step 1.1

The map H:F27→F23 is onto by its first three columns, so [F3] gives dim⁡ker⁡H=7−3=4. The subspace C also has dimension 4 by step 1.1; [F4] now gives C=ker⁡H.

3.1F1F2L2step 2.1givenalgebra

The columns of H are 100,010,001,110,101,011,111. They are all nonzero and pairwise distinct. A weight-one vector has syndrome one of these columns; a weight-two vector has syndrome the sum of two distinct columns, which is nonzero in characteristic two. Thus C has no nonzero word of weight below 3. The word e0+e1+e3 has syndrome 100+010+110=000 and weight 3, so it lies in C and d(C)=3.

4.1L2L3step 3.1given∎

Multiplying H by the zero word and each unit vector gives s(0000000)=000, s(1000000)=100, s(0100000)=010, s(0010000)=001, s(0001000)=110, s(0000100)=101, s(0000010)=011, and s(0000001)=111; each nonzero value is the corresponding column of H. These eight syndromes are distinct, so [L3] guarantees unique correction of any one bit flip.

Remarks

The minimum weight and every syndrome are verified from the displayed matrices; no source convention is used as a proof step.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

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Sources