Berlekamp–Massey algorithm
![](http://upload.wikimedia.org/wikipedia/commons/thumb/c/c1/Berlekamp%E2%80%93Massey_algorithm.png/220px-Berlekamp%E2%80%93Massey_algorithm.png)
The Berlekamp–Massey algorithm is an algorithm that will find the shortest linear-feedback shift register (LFSR) for a given binary output sequence. The algorithm will also find the minimal polynomial of a linearly recurrent sequence in an arbitrary field. The field requirement means that the Berlekamp–Massey algorithm requires all non-zero elements to have a multiplicative inverse.[1] Reeds and Sloane offer an extension to handle a ring.[2]
Elwyn Berlekamp invented an algorithm for decoding Bose–Chaudhuri–Hocquenghem (BCH) codes.[3][4] James Massey recognized its application to linear feedback shift registers and simplified the algorithm.[5][6] Massey termed the algorithm the LFSR Synthesis Algorithm (Berlekamp Iterative Algorithm),[7] but it is now known as the Berlekamp–Massey algorithm.
Description of algorithm
[edit]The Berlekamp–Massey algorithm is an alternative to the Reed–Solomon Peterson decoder for solving the set of linear equations. It can be summarized as finding the coefficients Λj of a polynomial Λ(x) so that for all positions i in an input stream S:
In the code examples below, C(x) is a potential instance of Λ(x). The error locator polynomial C(x) for L errors is defined as:
or reversed:
The goal of the algorithm is to determine the minimal degree L and C(x) which results in all syndromes
being equal to 0:
Algorithm: C(x) is initialized to 1, L is the current number of assumed errors, and initialized to zero. N is the total number of syndromes. n is used as the main iterator and to index the syndromes from 0 to N−1. B(x) is a copy of the last C(x) since L was updated and initialized to 1. b is a copy of the last discrepancy d (explained below) since L was updated and initialized to 1. m is the number of iterations since L, B(x), and b were updated and initialized to 1.
Each iteration of the algorithm calculates a discrepancy d. At iteration k this would be:
If d is zero, the algorithm assumes that C(x) and L are correct for the moment, increments m, and continues.
If d is not zero, the algorithm adjusts C(x) so that a recalculation of d would be zero:
The xm term shifts B(x) so it follows the syndromes corresponding to b. If the previous update of L occurred on iteration j, then m = k − j, and a recalculated discrepancy would be:
This would change a recalculated discrepancy to:
The algorithm also needs to increase L (number of errors) as needed. If L equals the actual number of errors, then during the iteration process, the discrepancies will become zero before n becomes greater than or equal to 2L. Otherwise L is updated and algorithm will update B(x), b, increase L, and reset m = 1. The formula L = (n + 1 − L) limits L to the number of available syndromes used to calculate discrepancies, and also handles the case where L increases by more than 1.
Pseudocode
[edit]The algorithm from Massey (1969, p. 124) for an arbitrary field:
polynomial(field K) s(x) = ... /* coeffs are s_j; output sequence as N-1 degree polynomial) */
/* connection polynomial */
polynomial(field K) C(x) = 1; /* coeffs are c_j */
polynomial(field K) B(x) = 1;
int L = 0;
int m = 1;
field K b = 1;
int n;
/* steps 2. and 6. */
for (n = 0; n < N; n++) {
/* step 2. calculate discrepancy */
field K d = s_n + ;
if (d == 0) {
/* step 3. discrepancy is zero; annihilation continues */
m = m + 1;
} else if (2 * L <= n) {
/* step 5. */
/* temporary copy of C(x) */
polynomial(field K) T(x) = C(x);
C(x) = C(x) - d B(x);
L = n + 1 - L;
B(x) = T(x);
b = d;
m = 1;
} else {
/* step 4. */
C(x) = C(x) - d B(x);
m = m + 1;
}
}
return L;
In the case of binary GF(2) BCH code, the discrepancy d will be zero on all odd steps, so a check can be added to avoid calculating it.
/* ... */
for (n = 0; n < N; n++) {
/* if odd step number, discrepancy == 0, no need to calculate it */
if ((n&1) != 0) {
m = m + 1;
continue;
}
/* ... */
See also
[edit]- Reed–Solomon error correction
- Reeds–Sloane algorithm, an extension for sequences over integers mod n
- Nonlinear-feedback shift register (NLFSR)
References
[edit]- ^ Reeds & Sloane 1985, p. 2
- ^ Reeds, J. A.; Sloane, N. J. A. (1985), "Shift-Register Synthesis (Modulo n)" (PDF), SIAM Journal on Computing, 14 (3): 505–513, CiteSeerX 10.1.1.48.4652, doi:10.1137/0214038
- ^ Berlekamp, Elwyn R. (1967), Nonbinary BCH decoding, International Symposium on Information Theory, San Remo, Italy
{{citation}}
: CS1 maint: location missing publisher (link) - ^ Berlekamp, Elwyn R. (1984) [1968], Algebraic Coding Theory (Revised ed.), Laguna Hills, CA: Aegean Park Press, ISBN 978-0-89412-063-3. Previous publisher McGraw-Hill, New York, NY.
- ^ Massey, J. L. (January 1969), "Shift-register synthesis and BCH decoding" (PDF), IEEE Transactions on Information Theory, IT-15 (1): 122–127, doi:10.1109/TIT.1969.1054260, S2CID 9003708
- ^ Ben Atti, Nadia; Diaz-Toca, Gema M.; Lombardi, Henri (April 2006), "The Berlekamp–Massey Algorithm revisited", Applicable Algebra in Engineering, Communication and Computing, 17 (1): 75–82, arXiv:2211.11721, CiteSeerX 10.1.1.96.2743, doi:10.1007/s00200-005-0190-z, S2CID 14944277
- ^ Massey 1969, p. 124
External links
[edit]- "Berlekamp-Massey algorithm", Encyclopedia of Mathematics, EMS Press, 2001 [1994]
- Berlekamp–Massey algorithm at PlanetMath.
- Weisstein, Eric W. "Berlekamp–Massey Algorithm". MathWorld.
- GF(2) implementation in Mathematica
- (in German) Applet Berlekamp–Massey algorithm
- Online GF(2) Berlekamp-Massey calculator