Reed–Muller

Reed–Muller

Reed-Muller codes are a family of linear error-correcting codes used in communications. They are named after their discoverers, Irving S. Reed and D. E. Muller. Muller discovered the codes, and Reed proposed the majority logic decoding for the first time. A first order Reed-Muller code is equivalent to a Hadamard code. Reed-Muller codes are listed as RM(d,r), where d is the order of the code, and r is parameter related to the length of code, $n = 2 r$ . RM codes are related to binary functions on field $G F (2 r)$ over the elements [0,1].

RM(0,r) codes are repetition codes of length $n = 2 r$ , rate ${R=\tfrac{1}{n}}$ and minimum distance $d_{min} = \tfrac{n}{2}$ .

RM(1,r) codes are parity check codes of length $n = 2 r$ , rate $R=\tfrac{r+1}{n}$ and minimum distance $d_{min} = \tfrac{n}{2}$ .

RM(r-1,r) codes are parity check codes of length $n = 2 r$ .

RM(r-2,r) codes are the family of extended Hamming codes of length $n = 2 r$ with minimum distance $d m i n = 4$ .^[1]

1 Construction
2 Example 1
3 Example 2
4 Properties
- 4.1 Proof
5 Decoding RM codes
6 See also
7 References

Construction

A generating matrix for a Reed–Muller code of length n = 2^d can be constructed like this. Let us write:

$X = \mathbb{F}_2^d = \{ x_1, \ldots, x_{2^d} \}.$

We define in n-dimensional space $\mathbb{F}_2^n$ the indicator vectors

$\mathbb{I}_A \in \mathbb{F}_2^n$

on subsets $A \subset X$ by:

$\left( \mathbb{I}_A \right)_i = \begin{cases} 1 & \mbox{ if } x_i \in A \\ 0 & \mbox{ otherwise} \\ \end{cases}$

together with, also in $\mathbb{F}_2^n$ , the binary operation

$w \wedge z = (w_1 \times z_1, \ldots , w_n \times z_n ),$

referred to as the wedge product.

$\mathbb{F}_2^d$ is a d-dimensional vector space over the field $\mathbb{F}_2$ , so it is possible to write

$(\mathbb{F}_2)^d = \{ (y_d, \ldots , y_1) | y_i \in \mathbb{F}_2 \}$

We define in n-dimensional space $\mathbb{F}_2^n$ the following vectors with length n: v₀ = (1, 1, 1, 1, 1, 1, 1, 1) and

$v_i = \mathbb{I}_{ H_i }$

where the H_i are hyperplanes in $(\mathbb{F}_2)^d$ (with dimension d −1):

$H_i = \{ y \in ( \mathbb{F}_2 ) ^d \mid y_i = 0 \}$

The Reed–Muller RM(d, r) code of order r and length n = 2^d is the code generated by v₀ and the wedge products of up to r of the v_i (where by convention a wedge product of fewer than one vector is the identity for the operation).

Example 1

Let d = 3. Then n = 8, and

$X = \mathbb{F}_2^3 = \{ (0,0,0), (0,0,1), \ldots, (1,1,1) \},$

and

$\begin{matrix} v_0 & = & (1,1,1,1,1,1,1,1) \\[2pt] v_1 & = & (1,0,1,0,1,0,1,0) \\[2pt] v_2 & = & (1,1,0,0,1,1,0,0) \\[2pt] v_3 & = & (1,1,1,1,0,0,0,0). \\ \end{matrix}$

The RM(3,1) code is generated by the set

$\{ v_0, v_1, v_2, v_3 \},\,$

or more explicitly by the rows of the matrix

$\begin{pmatrix} 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\ 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 \\ 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 \\ \end{pmatrix}$

Example 2

The RM(3,2) code is generated by the set:

$\{ v_0, v_1, v_2, v_3, v_1 \wedge v_2, v_1 \wedge v_3, v_2 \wedge v_3 \}$

or more explicitly by the rows of the matrix:

$\begin{pmatrix} 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 \\ 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 \\ 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 \\ 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ \end{pmatrix}$

Properties

The following properties hold:

1 The set of all possible wedge products of up to d of the v_i form a basis for $\mathbb{F}_2^n$ .

2 The RM (d, r) code has rank

$\sum_{s=0}^r {d \choose s}.$

3 RM (d, r) = RM (d − 1, r) | RM (d − 1, r − 1) where '|' denotes the bar product of two codes.

4 RM (d, r) has minimum Hamming weight 2^{d − r}.

Proof

There are

$\sum_{s=0}^d { d \choose s } = 2^d = n$

such vectors and $\mathbb{F}_2^n$ has dimension n so it is sufficient to check that the n vectors span; equivalently it is sufficient to check that RM(d, d) = $\mathbb{F}_2^n$ .

Let x be an element of X and define

$y_i = \begin{cases} v_i & \mbox{ if } x_i = 0 \\ 1+v_i & \mbox{ if } x_i = 1 \\ \end{cases}$

Then $\mathbb{I}_{ \{ x \} } = y_i \wedge \ldots \wedge y_d$

Expansion via the distributivity of the wedge product gives $\mathbb{I}_{ \{ x \} } \in \mbox{ RM(d,d)}$ . Then since the vectors $\{ \mathbb{I}_{ \{ x \} } \mid x \in X \}$ span $\mathbb{F}_2^n$ we have RM(d, d) = $\mathbb{F}_2^n$ .

By 1, all such wedge products must be linearly independent, so the rank of RM(d, r) must simply be the number of such vectors.

Omitted.

By induction.

The RM(d,0) code is the repetition code of length n=2^d and weight n = 2^d-0 = 2^d-0. By 1 RM(d, d) = $\mathbb{F}_2^n$ and has weight 1 = 2⁰ = 2^d-d.

The article bar product (coding theory) gives a proof that the weight of the bar product of two codes C₁ , C₂ is given by

min{2 w (C 1), w (C 2)}

If 0 < r < d and if

i) RM(d-1,r) has weight 2^d-1-r

ii) RM(d-1,r-1) has weight 2^d-1-(r-1) = 2^d-r

then the bar product has weight

$\min \{ 2 \times d^{d-1-r}, 2^{d-r} \} = 2^{d-r} .$

Decoding RM codes

RM(r,m) codes can be decoded using the majority logic decoding. The basic idea of majority logic decoding is to build several checksums for each received code word element. Since each of the different checksums must all have the same value (i.e the value of the message word element weight), we can use a majority logic decoding to decipher the value of the message word element. Once each order of the polynomial is decoded, the received word is modified accordingly by removing the corresponding codewords weighted by the decoded message contributions, up to the present stage. So for a $r$ -th order RM code, we have to decode iteratively r+1, times before we arrive at the final received code-word. Also, the values of the message bits are calculated through this scheme; finally we can calculate the codeword by multiplying the message word (just decoded) with the generator matrix.

One clue if the decoding succeeded, is to have an all-zero modified received word, at the end of r+1-stage decoding through the majority logic decoding. This technique was proposed by Irving. S. Reed, and is more general when applied to other finite geometry codes.

References

^ Trellis and Turbo Coding, C. Schlegel & L. Perez, Wiley Interscience, 2004, p149.

Shu Lin; Daniel Costello (2005). Error Control Coding, 2nd ed, Pearson. ISBN 0130179736. Chapter 4.
J.H. van Lint (1992). Introduction to Coding Theory, 2nd ed, GTM 86, Springer-Verlag. ISBN 3-540-54894-7. Chapter 4.5.

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