Coding Theory. Ruud Pellikaan MasterMath 2MMC30. Lecture 11.1 May

Transcription

1 Coding Theory Ruud Pellikaan MasterMath 2MMC30 /k Lecture 11.1 May

2 Content lecture 11 2/31 In Lecture 8.2 we introduced the Key equation Now we introduce two algorithms which solve the key equation and thus decode cyclic codes efficiently Interpolating with a twovariate polynomial gives list decoding 1. Decoding by key equation Algorithm of Euclid-Sugiyama Algorithm of Berlekamp-Massey 2. List decoding Johnson bound Algorithm of Sudan

3 Key equation - 1 3/31 In Lecture 8.2 we have seen that the decoding of BCH code with designed minimum distance δ is reduced to the problem of finding a pair of polynomials (σ (Z), ω(z)) satisfying the following key equation for a given syndrome polynomial S(Z) = δ 1 i=1 S i Z i 1 σ (Z)S(Z) ω(z) (mod Z δ 1 ) such that deg(σ (Z)) t = (δ 1)/2 and deg(ω(z)) < deg(σ (Z)) σ (Z) = t i=0 σ i Z i 1 is the error-locator polynomial and ω(z) = t 1 i=0 ω i Z i 1 is the error-evaluator polynomial Note that σ 0 = 1 by definition

4 Key equation - 2 4/31 Given the key equation the Euclid-Sugiyama algorithm or Sugiyama algorithm finds the error-locator and error-evaluator polynomials by an iterative procedure This algorithm is based on the well-known Euclidean algorithm We briefly review the Euclidean algorithm first For a pair of univariate polynomials r 1 (Z) and r 0 (Z) the Euclidean algorithm finds their greatest common divisor denoted by gcd(r 1 (Z), r 0 (Z))

5 Algorithm of Euclid - 1 5/31 The Euclidean algorithm proceeds as follows r 1 (Z) = q 1 (Z)r 0 (Z) + r 1 (Z), deg(r 1 (Z)) < deg(r 0 (Z)) r 0 (Z) = q 2 (Z)r 1 (Z) + r 2 (Z), deg(r 2 (Z)) < deg(r 1 (Z))... r s 2 (Z) = q s (Z)r s 1 (Z) + r s (Z), deg(r s (Z)) < deg(r s 1 (Z)) r s 1 (Z) = q s+1 (Z)r s (Z).

6 Algorithm of Euclid - 2 6/31 In each iteration of the algorithm, the operation of r j 2 (Z) = q j (Z)r j 1 (Z) + r j (Z) with deg(r j (Z)) < deg(r j 1 (Z)) is implemented by division of polynomials that is dividing r j 2 (Z) by r j 1 (Z) with quotient q j (Z) and remainder r j (Z) The algorithm stops after it completes the s-iteration where s is the smallest j such that r j+1 (Z) = 0 It is easy to prove that /k r s (Z) = gcd(r 1 (Z), r 0 (Z))

7 Algorithm of Euclid-Sugiyama - 1 7/31 Input: r 1 (Z) = Z δ 1, r 0 (Z) = S(Z), U 1 (Z) = 0 and U 0 (Z) = 1 Proceed with the Euclidean algorithm for r 1 (Z) and r 0 (Z) until an r s (Z) is reached such that deg(r s 1 (Z)) 1 2 (δ 1) and deg(r s(z)) 1 (δ 3) 2 Update U j (Z) = q j (Z)U j 1 (Z) + U j 2 (Z) Output: The following pair of polynomials: σ (Z) = ɛu s (Z) /k ω(z) = ( 1) s ɛr s (Z) where ɛ is chosen such that σ 0 = σ (0) = 1

8 Algorithm of Euclid-Sugiyama - 2 8/31 The error-locator and evaluator polynomials are given as σ (Z) = ɛu s (Z) and ω(z) = ( 1) s ɛr s (Z) Note that the Euclid-Sugiyama algorithm does not have to run the Euclidean algorithm completely it has a different stopping parameter s

9 EXAMPLE - 1 9/31 Consider the code C as given in Examples before It is a narrow sense BCH code of length 15 over F 16 of designed minimum distance δ = 5 Let r be the received word r = (α 5, α 8, α 11, α 10, α 10, α 7, α 12, α 11, 1, α, α 12, α 14, α 12, α 2, 0) Then S 1 = α 12, S 2 = α 7, S 3 = 0 and S 4 = α 2 So S(Z) = α 12 + α 7 Z + α 2 Z 3

10 EXAMPLE /31 j r j 1 (Z) r j (Z) U j 1 (Z) U j (Z) 0 Z 4 α 2 Z 3 + α 7 Z + α α 2 Z 3 + α 7 Z + α 12 α 5 Z 2 + α 10 Z 1 α 13 Z 2 α 5 Z 2 + α 10 Z α 2 Z + α 12 α 13 Z α 10 Z 2 + Z + 1 Thus the error-locator polynomial is σ (Z) = U 2 (Z) = 1 + Z + α 10 Z 2 the error-evaluator polynomial is ω(z) = r 2 (Z) = α 12 + α 2 Z

11 Key equation 11/31 Suppose is given Consider the key equation δ 1 S(Z) = S i Z i 1 i=1 σ (Z)S(Z) ω(z) (mod Z δ 1 ) such that and /k deg(σ (Z)) t = (δ 1)/2 deg(ω(z)) deg(σ (Z)) 1

12 System of linear euqations 12/31 It is easy to show that the problem of solving the key equation is equivalent to the problem of solving the following matrix equation with unknown (σ 1,..., σ t ) T S t S t 1... S 1 S t+1 S t... S 2... S 2t 1 S 2t 2... S t σ 1 σ 2. σ t = S t+1 S t+2. S 2t

13 Algorithm of Berlekamp-Massey /31 The Berlekamp-Massey algorithm can solve this matrix equation by finding σ 1,..., σ t for the following recursion t S i = σ j S i j, j=1 i = t + 1,..., 2t

14 Algorithm of Berlekamp-Massey /31 We should point out that the Berlekamp-Massey algorithm actually solves a more general problem that is, for a given sequence of length N: /k E 0, E 1, E 2,..., E N 1 which we denote by E it finds the recursion E i = L j E i j, i = L,..., N 1 j=1 for which L is smallest If the matrix equation has no solution the Berlekamp-Massey algorithm then finds a recursion with L > t

15 Algorithm of Berlekamp-Massey /31 To make it more convenient to present the Berlekamp-Massey algorithm and to prove the correctness, we denote (Z) = L i Z i i=0 with 0 = 1 The above recursion is denoted by is called the length of the recursion ( (Z), L) and L = deg( (Z))

16 Algorithm of Berlekamp-Massey /31 The Berlekamp-Massey algorithm is an iterative procedure for finding the shortest recursion for producing successive terms of the sequence E The r-th iteration of the algorithm finds the shortest recursion ( (r) (Z), L r ) where L r = deg( (r) (X)) for producing the first r terms of the sequence E, that is /k L r E i = (r) j E i j, i = L r,..., r 1 j=1 or equivalently with (r) 0 = 0 L r j=0 (r) j E i j = 0, i = L r,..., r 1

17 Algorithm of Berlekamp-Massey /31 (Initialization) r = 0, (Z) = B(Z) = 1, L = 0, λ = 1, and b = 1. (1) If r = N, stop. Otherwise, compute = L j=0 j E r j (2) If = 0, then λ λ + 1, and go to (5) (3) If = 0 and 2L > r, then (Z) (Z) b 1 Z λ B(Z) λ λ + 1 and go to (5) (4) If = 0 and 2L r, then T(Z) (Z) (temporary storage of (Z)) (Z) (Z) b 1 Z λ B(Z) L r + 1 L B(Z) T(Z) b λ 1 (5) r r + 1 and return to (1) For the the proof of correctness we refer to the literature /k

18 EXAMPLE /31 Consider again the code C of the previous Example Let r be the received word Then r = (α 5, α 8, α 11, α 10, α 10, α 7, α 12, α 11, 1, α, α 12, α 14, α 12, α 2, 0) /k S 1 = α 12, S 2 = α 7, S 3 = 0, S 4 = α 2 Now let us compute the error-locator polynomial σ (Z) by using the Berlekamp-Massey algorithm Letting E i = S i+1, for i = 0, 1, 2, 3, we have a sequence as the input of the algorithm E = {E 0, E 1, E 2, E 3 } = {α 12, α 7, 0, α 2 }

19 EXAMPLE /31 The intermediate and final results of the algorithm are given in the following table /k r B(Z) (Z) L 0 α α 12 Z 1 2 α α 10 Z 1 3 α 1 + α 10 Z 1 + α 10 Z + α 5 Z α 10 Z 1 + Z + α 10 Z 2 2

20 EXAMPLE /31 The result of the last iteration the Berlekamp-Massey algorithm, (Z), is the error-locator polynomial That is σ (Z) = σ 1 + σ 2 Z + σ 2 Z 2 = (Z) = 0 + λ 1 Z + 2 Z 2 = 1 + Z + α 10 Z 2 Substituting this into the key equation, we then get ω(z) = α 12 + α 2 Z

21 List-decoding /31 A decoding algorithm is efficient if the complexity is bounded above by a polynomial in the code length Brute-force decoding is not efficient because for a received word it may need to compare q k codewords for the closest codewords The idea behind list decoding is that instead of returning a unique codeword the decoder returns a small list of codewords A list-decoding algorithm is efficient if both the complexity and the size of the output list of the algorithm are bounded above by polynomials in the code length List decoding was first introduced by Elias and Wozencraft in 1950 s /k

22 List-decoding /31 Suppose C is a q-ary [n, k, d] code Let t n is a positive integer For any received word r = (r 1,, r n ) F n q we refer to any codeword c in C satisfying d(c, r) t as a t-consistent codeword Let l be a positive integer less than or equal to q k The code C is called (t, l)-decodable, if for any word r F n q the number of t-consistent codewords is at most l If for any received word, a list decoder can find all the t-consistent codewords and the output list has at most l codewords then the decoder is called a (t, l)-list decoder /k

23 List-decoding /31 In 1997 for the first time Sudan proposed an efficient list-decoding algorithm for Reed-Solomon codes Later Sudan s list-decoding algorithm was generalized to decoding algebraic-geometric codes and Reed-Muller codes

24 Error-correcting capacity 24/31 Suppose a decoding algorithm can find all the t-consistent codewords for any received word We call t the error-correcting capacity or decoding radius of the decoding algorithm As we have seen for any [n, k, d] code, if t d 1 2 then there is only one t-consistent codeword for any received word In other words, any [n, k, d] code is ( ) d 1 2, 1 -decodable The decoding algorithms in the previous sections return a unique codeword for any received word and they achieve an error-correcting capability less than or equal to d 1 2 The list decoding achieves a decoding radius greater than d 1 2 and the size of the output list must be bounded by a polynomial in n /k

25 Johnson bound 25/31 It is natural to ask the following question for a class of codes C For an [n, k, d] F q -linear code C in C what is the maximal value t, such that C is (t, l)-decodable for a l which is bounded above by a polynomial in n? In the following, we give a lower bound on the maximum t such that C is (t, l)-decodable This is called the Johnson bound

26 PROPOSITION Johnson bound 26/31 Let 0 < β < 1 and 0 < γ < 1 Let C F n q be a linear code of minimum distance Letd = (1 1/q)(1 β)n Let t = (1 1/q)(1 γ )n Then for any word r F n q { { min n(q 1), (1 β)/(γ B t (r) C 2 β) }, when γ > β 2n(q 1) 1, when γ = β where /k B t (r) = {x F n q d(x, r) t} is the Hamming ball of radius t around r Proof is referred to the literature

27 THEOREM Johnson bound 27/31 For any linear code C F n q of relative minimum distance δ = d/n, it is (t, l(n))-decodable with l(n) bounded above by a linear function in n, provided that ( t n 1 1 ) ( 1 1 q ) q q 1 δ

28 PROOF Johnson bound 28/31 For any received word r F n q, the set of t-consistent codewords {c C d(c, r) t} = B t (r) C Let β be a positive real number and β < 1 Denote d = (1 1/q) (1 β)n Let t = (1 1/q) (1 γ )n for some 0 < γ < 1 Suppose Then, γ /k ( t n 1 1 ) ( 1 1 q ) q q 1 δ 1 q q 1 d n = β By the previous Proposition, the number of t-consistent codewords, l(n), which is B t (r) C, is bounded above by a polynomial in n, here q is viewed as a constant

29 REMARK /31 The classical error-correcting capability is t = d 1 2 For a linear [n, k] code of minimum distance d, we have d n k + 1 Note that for Reed-Solomon codes, d = n k + 1 Thus, the normalized capability τ = t/n τ = t n 1 n k n = R where R is the code rate

30 REMARK /31 Let us compare this with the Johnson bound From the Theorem and d n k + 1, the Johnson bound is ( ) ( ) q q q 1 ( ) ( δ ) q q q 1 (1 k n + 1 n ) 1 R for large n and large q

31 Figure of Johnson bound 31/31