BCH code

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In coding theory, the BCH codes form a class of cyclic error-correcting codes that are constructed using finite fields. BCH codes were invented in 1959 by French mathematician Alexis Hocquenghem, and independently in 1960 by Raj Bose and D. K. Ray-Chaudhuri.[1] The acronym BCH comprises the initials of these inventors' names.

One of the key features of BCH codes is that during code design, there is a precise control over the number of symbol errors correctable by the code. In particular, it is possible to design binary BCH codes that can correct multiple bit errors. Another advantage of BCH codes is the ease with which they can be decoded, namely, via an algebraic method known as syndrome decoding. This simplifies the design of the decoder for these codes, using small low-power electronic hardware.

BCH codes are used in applications such as satellite communications,[2] compact disc players, DVDs, disk drives, solid-state drives[3] and two-dimensional bar codes.

Definition and illustration

Primitive narrow-sense BCH codes

Given a prime power Template:Mvar and positive integers Template:Mvar and Template:Mvar with dqm − 1, a primitive narrow-sense BCH code over the finite field GF(q) with code length n = qm − 1 and distance at least Template:Mvar is constructed by the following method.

Let Template:Mvar be a primitive element of GF(qm). For any positive integer Template:Mvar, let mi(x) be the minimal polynomial of αi over GF(q). The generator polynomial of the BCH code is defined as the least common multiple g(x) = lcm(m1(x),…,md − 1(x)). It can be seen that g(x) is a polynomial with coefficients in GF(q) and divides xn − 1. Therefore, the polynomial code defined by g(x) is a cyclic code.

Example

Let q=2 and m=4 (therefore n=15). We will consider different values of Template:Mvar. There is a primitive root α in GF(16) satisfying

Template:NumBlk

its minimal polynomial over GF(2) is

The minimal polynomials of the first seven powers of α are

The BCH code with has generator polynomial

It has minimal Hamming distance at least 3 and corrects up to one error. Since the generator polynomial is of degree 4, this code has 11 data bits and 4 checksum bits.

The BCH code with has generator polynomial

It has minimal Hamming distance at least 5 and corrects up to two errors. Since the generator polynomial is of degree 8, this code has 7 data bits and 8 checksum bits.

The BCH code with and higher has generator polynomial

This code has minimal Hamming distance 15 and corrects 7 errors. It has 1 data bit and 14 checksum bits. In fact, this code has only two codewords: 000000000000000 and 111111111111111.

General BCH codes

General BCH codes differ from primitive narrow-sense BCH codes in two respects.

First, the requirement that be a primitive element of can be relaxed. By relaxing this requirement, the code length changes from to the order of the element

Second, the consecutive roots of the generator polynomial may run from instead of

Definition. Fix a finite field where is a prime power. Choose positive integers such that and is the multiplicative order of modulo

As before, let be a primitive th root of unity in and let be the minimal polynomial over of for all The generator polynomial of the BCH code is defined as the least common multiple

Note: if as in the simplified definition, then is automatically 1, and the order of modulo is automatically Therefore, the simplified definition is indeed a special case of the general one.

Special cases

The generator polynomial of a BCH code has coefficients from In general, a cyclic code over with as the generator polynomial is called a BCH code over The BCH code over with as the generator polynomial is called a Reed–Solomon code. In other words, a Reed–Solomon code is a BCH code where the decoder alphabet is the same as the channel alphabet.[4]

Properties

1. The generator polynomial of a BCH code has degree at most Moreover, if and the generator polynomial has degree at most

Proof: each minimal polynomial has degree at most

Therefore, the least common multiple of of them has degree at most Moreover, if then for all Therefore, is the least common multiple of at most minimal polynomials for odd indices each of degree at most

2. A BCH code has minimal Hamming distance at least Proof: Suppose that is a code word with fewer than non-zero terms. Then

Recall that are roots of hence of This implies that satisfy the following equations, for

In matrix form, we have

The determinant of this matrix equals

The matrix is seen to be a Vandermonde matrix, and its determinant is

which is non-zero. It therefore follows that hence

3. A BCH code is cyclic.

Proof: A polynomial code of length is cyclic if and only if its generator polynomial divides Since is the minimal polynomial with roots it suffices to check that each of is a root of This follows immediately from the fact that is, by definition, an th root of unity.

Encoding

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Decoding

There are many algorithms for decoding BCH codes. The most common ones follow this general outline:

  1. Calculate the syndromes sj for the received vector
  2. Determine the number of errors t and the error locator polynomial Λ(x) from the syndromes
  3. Calculate the roots of the error location polynomial to find the error locations Xi
  4. Calculate the error values Yi at those error locations
  5. Correct the errors

During some of these steps, the decoding algorithm may determine that the received vector has too many errors and cannot be corrected. For example, if an appropriate value of t is not found, then the correction would fail. In a truncated (not primitive) code, an error location may be out of range. If the received vector has more errors than the code can correct, the decoder may unknowingly produce an apparently valid message that is not the one that was sent.

Calculate the syndromes

The received vector is the sum of the correct codeword and an unknown error vector The syndrome values are formed by considering as a polynomial and evaluating it at Thus the syndromes are[5]

for to Since are the zeros of of which is a multiple, Examining the syndrome values thus isolates the error vector so one can begin to solve for it.

If there is no error, for all If the syndromes are all zero, then the decoding is done.

Calculate the error location polynomial

If there are nonzero syndromes, then there are errors. The decoder needs to figure out how many errors and the location of those errors.

If there is a single error, write this as where is the location of the error and is its magnitude. Then the first two syndromes are

so together they allow us to calculate and provide some information about (completely determining it in the case of Reed–Solomon codes).

If there are two or more errors,

It is not immediately obvious how to begin solving the resulting syndromes for the unknowns and First step is finding locator polynomial

compatible with computed syndromes and with minimal possible

Two popular algorithms for this task are:

  1. Peterson–Gorenstein–Zierler algorithm
  2. Berlekamp–Massey algorithm

Peterson–Gorenstein–Zierler algorithm

Peterson's algorithm is the step 2 of the generalized BCH decoding procedure. Peterson's algorithm is used to calculate the error locator polynomial coefficients of a polynomial

Now the procedure of the Peterson–Gorenstein–Zierler algorithm.[6] Expect we have at least 2t syndromes sc,...,sc+2t−1. Let v = t.

  • Form the matrix equation
       if 
       then
             declare an empty error locator polynomial
             stop Peterson procedure.
       end
       set 
       continue from the beginning of Peterson's decoding by making smaller 

Factor error locator polynomial

Now that you have the polynomial, its roots can be found in the form by brute force for example using the Chien search algorithm. The exponential powers of the primitive element will yield the positions where errors occur in the received word; hence the name 'error locator' polynomial.

The zeros of Λ(x) are αi1, ..., αiv.

Calculate error values

Once the error locations are known, the next step is to determine the error values at those locations. The error values are then used to correct the received values at those locations to recover the original codeword.

For the case of binary BCH, (with all characters readable) this is trivial; just flip the bits for the received word at these positions, and we have the corrected code word. In the more general case, the error weights can be determined by solving the linear system

Forney algorithm

However, there is a more efficient method known as the Forney algorithm.

Let

Let and

Let be the error evaluator polynomial[7]

Let where denotes here rather than multiplying in the field.

Than if syndromes could be explained by an error word, which could be nonzero only on positions , then error values are

For narrow-sense BCH codes, c = 1, so the expression simplifies to:

Explanation of Forney algorithm computation

It is based on Lagrange interpolation and techniques of generating functions.

Look at Let for simplicity for and for

Then

We could gain form of polynomial:

We want to compute unknowns and we could simplify the context by removing the terms. This leads to the error evaluator polynomial

Thanks to we have

Look at Thanks to (the Lagrange interpolation trick) the sum degenerates to only one summand

To get we just should get rid of the product. We could compute the product directly from already computed roots of but we could use simpler form.

As formal derivative we get again only one summand in

So finally

This formula is advantageous when one computes the formal derivative of form its form, gaining

where denotes here rather than multiplying in the field.

Decoding based on extended Euclidean algorithm

The process of finding both the polynomial Λ and the error values could be based on the Extended Euclidean algorithm. Correction of unreadable characters could be incorporated to the algorithm easily as well.

Let be positions of unreadable characters. One creates polynomial localising these positions Set values on unreadable positions to 0 and compute the syndromes.

As we have already defined for the Forney formula let

Let us run extended Euclidean algorithm for locating least common divisor of polynomials and The goal is not to find the least common divisor, but a polynomial of degree at most and polynomials such that Low degree of guarantees, that would satisfy extended (by ) defining conditions for

Defining and using on the place of in the Fourney formula will give us error values.

The main advantage of the algorithm is that it meanwhile computes required in the Forney formula.

Explanation of the decoding process

The goal is to find a codeword which differs from the received word minimally as possible on readable positions. When expressing the received word as a sum of nearest codeword and error word, we are trying to find error word with minimal number of non-zeros on readable positions. Syndrom restricts error word by condition We could write these conditions separately or we could create polynomial and compare coefficients near powers to

Suppose there is unreadable letter on position we could replace set of syndromes by set of syndromes defined by equation Suppose for an error word all restrictions by original set of syndromes hold, than New set of syndromes restricts error vector the same way the original set of syndromes restricted the error vector Note, that except the coordinate where an is zero, iff is zero. For the goal of locating error positions we could change the set of syndromes in the similar way to reflect all unreadable characters. This shortens the set of syndromes by

In polynomial formulation, the replacement of syndromes set by syndromes set leads to