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. 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.
- 1 Definition and illustration
- 2 Properties
- 3 Encoding
- 4 Decoding
- 4.1 Calculate the syndromes
- 4.2 Calculate the error location polynomial
- 4.3 Factor error locator polynomial
- 4.4 Calculate error values
- 4.5 Decoding based on extended Euclidean algorithm
- 4.6 Correct the errors
- 4.7 Decoding examples
- 5 Citations
- 6 References
Definition and illustration
Primitive narrow-sense BCH codes
Given a prime q and positive integers m and d with d ≤ qm - 1, a primitive narrow-sense BCH code over the finite field GF(q) with code length n = qm - 1 and distance at least d is constructed by the following method.
Let α be a primitive element of GF(qm). For any positive integer i, let mi(x) be the minimal polynomial of αi. 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.
Let q=2 and m=4 (therefore n=15). We will consider different values of d. There is a primitive root α in GF(16) satisfying
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
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.
- A BCH code with is called a narrow-sense BCH code.
- A BCH code with is called primitive.
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.
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.
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There are many algorithms for decoding BCH codes. The most common ones follow this general outline:
- Calculate the syndromes sj for the received vector
- Determine the number of errors t and the error locator polynomial Λ(x) from the syndromes
- Calculate the roots of the error location polynomial to find the error locations Xi
- Calculate the error values Yi at those error locations
- 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
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 syndroms and with minimal possible
Two popular algorithms for this task are:
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. Expect we have at least 2t syndromes sc,...,sc+2t-1. Let v=t.
- Start by generating the matrix with elements that are syndrome values
- Generate a vector with elements
- Let denote the unknown polynomial coefficients, which are given by
- Form the matrix equation
- If the determinant of matrix is nonzero, then we can actually find an inverse of this matrix and solve for the values of unknown values.
- If then follow
if then declare an empty error locator polynomial stop Peterson procedure. end set continue from the beginning of Peterson's decoding by making smaller
- After you have values of , you have with you the error locator polynomial.
- Stop Peterson procedure.
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 α-i1, ..., α-iv.
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
- . . .
However, there is a more efficient method known as the Forney algorithm.
Let be the error evaluator polynomial
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
Look at Let for simplicity for and for
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
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 syndroms by set of syndroms defined by equation Suppose for an error word all restrictions by original set of syndroms hold, than New set of syndroms restricts error vector the same way the original set of syndroms 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 syndroms in the similar way to reflect all unreadable characters. This shortens the set of syndroms by
In polynomial formulation, the replacement of syndroms set by syndroms set leads to Therefore
After replacement of by , one would require equation for coefficients near powers
One could consider looking for error positions from the point of view of eliminating influence of given positions similarly as for unreadable characters. If we found positions such that eliminating their influence leads to obtaining set of syndroms consisting of all zeros, than there exists error vector with errors only on these coordinates. If denotes the polynomial eliminating the influence of these coordinates, we obtain
In Euclidean algorithm, we try to correct at most errors (on readable positions), because with bigger error count there could be more codewords in the same distance from the received word. Therefore, for we are looking for, the equation must hold for coefficients near powers starting from
In Forney formula, could be multiplied by a scalar giving the same result.
It could happen that the Euclidean algorithm finds of degree higher than having number of different roots equal to its degree, where the Fourney formula would be able to correct errors in all its roots, anyways correcting such many errors could be risky (especially with no other restrictions on received word). Usually after getting of higher degree, we decide not to correct the errors. Correction could fail in the case has roots with higher multiplicity or the number of roots is smaller than its degree. Fail could be detected as well by Forney formula returning error outside the transmitted alphabet.
Correct the errors
Using the error values and error location, correct the errors and form a corrected code vector by subtracting error values at error locations.
Decoding of binary code without unreadable characters
Consider a BCH code in GF(24) with and . (This is used in QR codes.) Let the message to be transmitted be [1 1 0 1 1], or in polynomial notation, The "checksum" symbols are calculated by dividing by and taking the remainder, resulting in or [ 1 0 0 0 0 1 0 1 0 0 ]. These are appended to the message, so the transmitted codeword is [ 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 ].
Now, imagine that there are two bit-errors in the transmission, so the received codeword is [ 1 0 0 1 1 1 0 0 0 1 1 0 1 0 0 ]. In polynomial notation:
In order to correct the errors, first calculate the syndromes. Taking we have and Next, apply the Peterson procedure by row-reducing the following augmented matrix.
Due to the zero row, S3×3 is singular, which is no surprise since only two errors were introduced into the codeword. However, the upper-left corner of the matrix is identical to [S2×2 | C2×1], which gives rise to the solution The resulting error locator polynomial is which has zeros at and The exponents of correspond to the error locations. There is no need to calculate the error values in this example, as the only possible value is 1.
Decoding with unreadable characters
Suppose the same scenario, but the received word has two unreadable characters [ 1 0 0 ? 1 1 ? 0 0 1 1 0 1 0 0 ]. We replace the unreadable characters by zeros while creating the polynom reflecting their positions We compute the syndroms and (Using log notation which is independent on GF(24) isomorphisms. For computation checking we can use the same representation for addition as was used in previous example. Hexadecimal description of the powers of are consecutively 1,2,4,8,3,6,C,B,5,A,7,E,F,D,9 with the addition based on bitwise xor.)
Let us make syndrome polynomial compute
Run Extended Euclidean algorithm:
We have reached polynomial of degree at most 3, and as we get
Let Don't worry that Find by brute force a root of The roots are and (after finding for example we can divide by corresponding monom and the root of resulting monom could be found easily).
Let and let Let us look for error values using formula where are roots of We get Fact, that should not be surprising.
Corrected code is therefore [ 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0].
Decoding with unreadable characters with a small number of errors
Let us show the algorithm behaviour for the case with small number of errors. Let the received word is [ 1 0 0 ? 1 1 ? 0 0 0 1 0 1 0 0 ].
Again, replace the unreadable characters by zeros while creating the polynom reflecting their positions Compute the syndroms and Create syndrom polynommial and Let us run Extended Euclidean algorithm:
We have reached polynomial of degree at most 3, and as we get
Let Don't worry that The root of is
Let and Let us look for error values using formula where are roots of polynomial We get Fact, that should not be surprising.
Corrected code is therefore [ 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0].
- Hocquenghem, A. (September 1959), "Codes correcteurs d'erreurs", Chiffres (in French) (Paris) 2: 147–156
- Bose, R. C.; Ray-Chaudhuri, D. K. (March 1960), "On A Class of Error Correcting Binary Group Codes", Information and Control 3 (1): 68–79, ISSN 0890-5401
- Gilbert, W. J.; Nicholson, W. K. (2004), Modern Algebra with Applications (2nd ed.), John Wiley
- Gill, John (unknown), EE387 Notes #7, Handout #28, Stanford University, pp. 42–45, retrieved April 21, 2010 [dead link] Course notes are apparently being redone for 2012: http://www.stanford.edu/class/ee387/
- Gorenstein, Daniel; Peterson, W. Wesley; Zierler, Neal (1960), "Two-Error Correcting Bose-Chaudhuri Codes are Quasi-Perfect", Information and Control 3 (3): 291–294
- Lidl, Rudolf; Pilz, Günter (1999), Applied Abstract Algebra (2nd ed.), John Wiley
- Lin, S.; Costello, D. (2004), Error Control Coding: Fundamentals and Applications, Englewood Cliffs, NJ: Prentice-Hall
- MacWilliams, F. J.; Sloane, N. J. A. (1977), The Theory of Error-Correcting Codes, New York, NY: North-Holland Publishing Company
- Reed, Irving S.; Chen, Xuemin (1999), Error-Control Coding for Data Networks, Boston, MA: Kluwer Academic Publishers, ISBN 0-7923-8528-4
- Rudra, Atri, CSE 545, Error Correcting Codes: Combinatorics, Algorithms and Applications, University at Buffalo, retrieved April 21, 2010