Higher Weights and Generalized MDS codes Steven T. Dougherty Department of Mathematics University of Scranton Scranton, PA 18510 USA [email protected] and Sunghyu Han Department of Mathematics University of Louisville Louisville, KY 40292 USA [email protected] June 22, 2011 Abstract We study codes meeting a generalized version of the Singleton bound for higher weights. We show that some of the higher weight enumerators of these codes are uniquely determined. We give the higher weight enumerators for MDS codes, the Simplex codes, the Hamming codes, the first order Reed-Muller codes and their dual codes. For the putative [72, 36, 16] code we find the i-th higher weight enumerators for i = 12 to 36. Additionally, we give a version of the generalized Singleton bound for non-linear codes.

Key Words: Higher weight enumerators, higher weights, MDS codes. MSC: 94B65

1

1

Introduction

Maximum Distance Separable (MDS) codes are an important class of codes. They are significant since they are optimal codes for their length and dimension but also because of their relation to various combinatorial structures, see [8] for a complete description. In this paper, we study a generalization of MDS codes, namely those codes meeting a generalized Singleton bound for higher weights. We shall define higher weights and then describe some basic results of generalized MDS codes which we will generalize. We also give the higher weight enumerators for the Simplex codes, the Hamming codes, the first order Reed-Muller codes and their dual codes. We give a version of the generalized Singleton bound for non-linear codes. We use these results to find a significant number of the higher weight enumerators for the [48, 24, 12] Type II code and the putative [72, 36, 16] Type II code. Previously only the i-th higher weight enumerators for i = 1, 2, 35, and 36 were known. We find the i-th higher weight enumerators for i = 12 to 34 as well using the techniques of the paper. We also use the techniques of the paper to determine some previously unknown higher weight enumerators of the codes associated with the projective plane of order 5. We begin with some basic definitions from coding theory. For any undefined terms from coding theory see [8] or [14]. A code C of length n over Fq is a subset of Fnq , and if it is a vector space then we say it is a linear code. We attach the standard inner product to the ambient space Fnq , that is P [v, w] = vi wi , and define C ⊥ = {v | [v, w] = 0 for all w ∈ C}. If C = C ⊥ then C is said to be a self-dual code. The Hamming weight of a vector is the number of non-zero coordinates of the vector.

1.1

Higher Weights

We describe higher weights which are generalizations of Hamming weights. These were introduced by Wei in [15]. Throughout we use the notation in [13]. Let C be a code and let D ⊆ Fnq be a linear subcode of C. We define ||D|| = |Supp(D)|, where Supp(D) = {i | ∃v ∈ D, vi 6= 0}. We define the r-minimum weight for a linear code C as dr = dr (C) = min{||D|| | D ⊆ C, dim(D) = r}. Then d1 (C) is the minimum Hamming weight. The weight spectrum is generalized to the higher weight spectrum as follows: Ari = Ari (C) = |{D ⊆ C | dim(D) = r, ||D|| = i}|. 2

Weight enumerators are generalized to the following: X W r (C; y) = W r (C) = Ari y i . For each r ≤ dim(C) there is a weight enumerator. The weight enumerator W 1 (C; y) P is not the Hamming weight enumerator HC (y) = αi y i where there are αi vectors of 1 (HC (y) − 1) since each multiple of Hamming weight i in C. Rather we have W 1 (C; y) = q−1 a vector has the same support and the zero vector does not generate a subcode dimension 1. As noted in [3], simply because two codes have identical Hamming weight enumerators, this does not imply that the codes have identical W r (C; y) weight enumerators for all r. For 2 example, consider two [16, 8, 4] binary extremal self-dual codes d+ 16 and e8 . Both have the same Hamming weight enumerator (1 + 14y 4 + y 8 )2 , but from [3, Table 3], d3 of d+ 16 is 8 and 2 d3 of e8 is 7, so their higher weight enumerators are not the same. As a homogeneous polynomial we can write the higher weight enumerators as follows: X W r (C; x, y) := xn−kDk y kDk D⊆C

=

dim D=r n X Ari (C)xn−i y i . i=0

MacWilliams identities exist for these weights (see [9] or [13]). They are: s X

r



[s]r W (C ; y) = q

−sk

s X (1 + (q − 1)y) [s]r W r (C; s

n

r=0

r=0

1−y ), 1 + (q s − 1)y

(1)

Q s j s ⊥ where the code has dimension k in Fnq , and [s]r = r−1 j=0 (q − q ). To find W (C ; y) one must use W r (C; y) for all r, with 0 ≤ r ≤ s. We shall drop the y from the notation when it is not necessary.

1.2

MDS codes

There are various proofs of the Singleton bound. It can be proven both combinatorially and algebraically. We describe the algebraic proof whose techniques we shall use in the paper. This proof comes from Proposition 5.4 in [14]. Namely, take a generator matrix G = (I A) for C, where I is the k × k identity matrix. The minimum weight d is less than or equal to the weight of first row of G which is less than or equal to n − k + 1. This gives the well known bound which we state as a theorem. Theorem 1.1. (Singleton Bound) If C is a linear [n, k, d] code over Fq , then d ≤ n − k + 1. 3

(2)

This leads naturally to the following definition. Definition 1. A linear [n, k, d] code over Fq with d = n−k+1 is called a Maximum Distance Separable(MDS) code. The combinatorial proof of the Singleton bound in Equation 2 holds for codes over any alphabet, namely d ≤ n − logq (|C|) + 1 where q is the size of the alphabet. The algebraic version has been generalized to a variety of rings, for example codes meeting the generalized algebraic singleton bound for linear codes are called MDR codes, see [5] for a description of these codes over Zk . The most general version is given in [7] and is proven for codes over quasi-Frobenius rings. A well known theorem relates MDS codes and their duals. It can be found in Theorem 9.2 in [14]. The usual proof is to let d⊥ be the minimum distance of C ⊥ and to let H be a parity check matrix for C. Then we note that every n − k columns of H are linearly independent. Consider a codeword aH in C ⊥ and suppose the weight of aH is less than k + 1. We can eliminate k columns of H to obtain H, an (n − k) × (n − k) submatrix of H, such that aH = 0. Since H is nonsingular, a = 0 and aH = 0. This fact proves that d⊥ ≥ k + 1. By the Singleton bound we have d⊥ ≤ k + 1, giving d⊥ = k + 1. This gives the following. Theorem 1.2. If a linear [n, k, d] code C is MDS, then so is its dual C ⊥ . Later we shall give a proof of this theorem which will follow from results about higher weights. The following is given in Theorem 7.4.1 in [8]. Let C be an [n, k, d] MDS code over Fq . The weight distribution of C is given by A0 = 1, Ai = 0 for 1 ≤ i < d, and  X   i−d n j i Ai = (−1) (q i+1−d−j − 1) i j=0 j

(3)

for d ≤ i ≤ n, where d = n − k + 1. As a corollary we then have that if C is an [n, k, d] MDS code over Fq , then   n Ad = (q − 1) . (4) d See [8] for a complete description.

1.3

r-MDS codes

In this section we shall define codes meeting a generalized Singleton bound for higher weights. We shall describe some results that we will use throughout the paper. We begin with a result

4

that will be used to give a generalized Singleton bound. The following result can be found as Theorem 7.10.1 in [8]. If C is a linear [n, k, d] code over Fq , then d = d1 (C) < d2 (C) · · · < dk (C) ≤ n.

(5)

This leads to the following result known as the Generalized Singleton Bound. Namely, if C is a linear [n, k, d] code over Fq , then dr ≤ n − k + r

(6)

for 1 ≤ r ≤ k. The proof can be found in Theorem 7.10.6 in [8]. As before this bound leads naturally to the following definition [13], [15]. Definition 2. A linear [n, k, d] code over Fq with dr (C) = n − k + r is called an r-th maximum distance separable (r-MDS) code. By this definition an MDS code is a 1-MDS code. The following result is Theorem 7.10.7 in [8] and the proof follows from the proof of Equation 6. If C is an MDS code over Fq , then C is an r-MDS code for all 1 ≤ r ≤ k. Additionally, we have the following result which is Corollary 3.2 in [13]. It follows from the proof of Equation 6. Namely, if C is an r-MDS code over Fq , then C is an r1 -MDS code for all r ≤ r1 ≤ k. This leads to the following definition. Definition 3. If C is not an (r − 1)-MDS code but it is an r-MDS code over Fq , then C is called a proper r-MDS (Pr -MDS) code. By this definition an MDS code is a P1 -MDS code. Example 1. Let C be the [7, 4, 3] binary Hamming code. Then C ⊥ is the [7, 3, 4] binary simplex code. We can easily calculate the following: d1 (C) = 3, d2 (C) = 5, d3 (C) = 6, d4 (C) = 7; d1 (C ⊥ ) = 4, d2 (C ⊥ ) = 6, d3 (C ⊥ ) = 7. This gives that both C and C ⊥ are P2 -MDS codes. The following can be found in [15] and appears as Theorem 7.10.4 in [8]. Namely, let C be an [n, k] code over Fq . Then {dr (C)|1 ≤ r ≤ k} = {1, 2, . . . , n}\{n + 1 − dr (C ⊥ )|1 ≤ r ≤ n − k}.

5

(7)

2

r-MDS codes

We shall now investigate the properties of Pr -MDS codes. Using Equation 7, we have the following. (Note: Similar statements to Theorem 2.1 are in Corollary 4.1 in [13].) Theorem 2.1. Let C be an [n, k, d] code and let C ⊥ be an [n, n − k, d⊥ ] code. 1. If d⊥ = 1, then C is not a Pr -MDS code for any 1 ≤ r ≤ k. 2. If d⊥ > 1, then 1 ≤ k − d⊥ + 2 ≤ k and C is a Pk−d⊥ +2 -MDS code. Proof. Suppose d⊥ = 1. Since 1 ∈ {dr (C ⊥ )|1 ≤ r ≤ n − k}, we have n 6∈ {dr (C)|1 ≤ r ≤ k}. Therefore C is not a Pk -MDS code and C is not a Pr -MDS code for any 1 ≤ r ≤ k. Suppose d⊥ > 1. By the Singleton bound, d⊥ ≤ k + 1. Then we have that 1 ≤ k − d⊥ + 2 ≤ k. Since 1, 2, . . . , d⊥ − 1 6∈ {dr (C ⊥ )|1 ≤ r ≤ n − k}, we have n − d⊥ + 2, n − d⊥ + 3, . . . , n ∈ {dr (C)|1 ≤ r ≤ k}. Then since d⊥ ∈ {dr (C ⊥ )|1 ≤ r ≤ n − k}, we have n − d⊥ + 1 6∈ {dr (C)|1 ≤ r ≤ k}. Therefore, we have dk−d⊥ +1 < n − d⊥ + 1 and dk−d⊥ +2 = n − d⊥ + 2. We conclude that C is a Pk−d⊥ +2 -MDS code. Corollary 2.2. If C is an [n, n2 , d] self-dual code over Fq , then C is a P n2 −d+2 -MDS code. Proof. It follows from Theorem 2.1 noting that d = d⊥ and k = n − k. While non-trivial binary MDS codes do not exist, by the previous corollary it is easy to see that non-trivial binary r-MDS codes are easily found. Using Theorem 2.1, we can reprove Theorem 1.2. Corollary 2.3. If a linear [n, k, d] code C is MDS, then so is its dual C ⊥ . Proof. Since C is a P1 -MDS code, k − d⊥ + 2 = 1, and it follows that d⊥ = k + 1. Example 2. Let Hq,r be the [(q r − 1)/(q − 1), (q r − 1)/(q − 1) − r, 3] Hamming code over Fq . Then its dual Sq,r is the [(q r − 1)/(q − 1), r, q r−1 ] simplex code. By Theorem 2.1, Hq,r is a P(qr −1)/(q−1)−r−qr−1 +2 -MDS code and Sq,r is a Pr−1 -MDS code. Theorem 2.4. There exist Pr -MDS codes for all r ≥ 1. Proof. The theorem follows from the fact Sq,r is a Pr−1 -MDS code. We shall give a generalization of the result in Equation 3 for Pr -MDS codes. First we need the following notation and the following result. Define " # (q k − 1)(q k − q) . . . (q k − q r−1 ) k = r , (q − 1)(q r − q) . . . (q r − q r−1 ) r which is the number of subspaces of dimension r in a k dimensional space over Fq . 6

The following result is Theorem 2 in [12]. Let C be an [n, k, d] code and let C ⊥ be an [n, n − k, d⊥ ] code. If k + 1 − d⊥ < r ≤ k, then " #    k+i−r−n X n i k − n + i − j Ari (C) = (−1)j . (8) n−i j k − r − n + i − j j=0 Furthermore, if we know W 1 (C ⊥ ) then for k + 2 − d2 (C ⊥ ) < r ≤ k + 1 − d⊥ , we have that k−1−r k−1−r Ari (C) = Un−i (II) + Un−i (I),

(9)

k−1−r k−1−r where Un−i (II) and Un−i (I) are given in [12]. k−1−r k−1−r Remark 1. In Equation 9, Un−i (II) and Un−i (I) are complex formulas. In [12], it is r remarked that it will be possible to compute Ai (C) for

k + 3 − min{d3 (C ⊥ ), 2d⊥ } < r ≤ k − d2 (C ⊥ ) + 2 provided W 2 (C ⊥ ) is known. Now we give a generalization of the result in Equation 3 for Pr -MDS codes. Theorem 2.5. Let C be an [n, k] Pr -MDS code over Fq . Then, for r ≤ i ≤ k, the higher weight spectrum of W i (C; y) is determined by the parameters n, k, and q. In other words, it is given by Aij (C) = 0 for 0 ≤ j < di , and #  "   j−d Xi j n j + i − d − t i (−1)t Aij (C) = t j t=0 i for di ≤ j ≤ n, where di = di (C) = n − k + i. Proof. By Theorem 2.1, r = k − d⊥ + 2. Now the theorem is an easy consequence of Equation 8. The following corollary is a generalization of Equation 4. Corollary 2.6. If C is an [n, k] Pr -MDS code over Fq with di = n − k + i (r ≤ i ≤ k), then  Aidi = dni . Proof. It follows from Theorem 2.5. Example 3. Using Theorem 2.5, we can calculate all higher weight spectrums for MDS codes. For example, consider a [6, 4, 3] Reed-Solomon code C over F7 . Then the weight spectrums are given by the following: W 0 = 1, W 1 = 20y 3 + 60y 4 + 162y 5 + 158y 6 , W 2 = 15y 4 + 312y 5 + 2523y 6 , W 3 = 6y 5 + 394y 6 , W 4 = y6. 7

Example 4. Using Theorem 2.5, we can calculate some higher weight spectrums for selfdual codes. For the [24, 12, 8] Golay Type II code, we can determine W i (C; y) for 6 ≤ i ≤ 12. For the unique [48, 24, 12] Type II code, we can determine W i (C; y) for 14 ≤ i ≤ 24. For the putative [72, 36, 16] Type II code, we can determine W i (C; y) for 22 ≤ i ≤ 36. Later we shall find even more of these weight enumerators. The higher weight spectrums of the Simplex codes Sq,r were stated in [9]. In the following we provide another proof. Theorem 2.7. Recall that Sq,r is the [(q r − 1)/(q − 1), r, q r−1 ] simplex code. Then the higher weight spectrums of Sq,r are given by the following. For 1 ≤ i ≤ r, A"ij (Sq,r #) r . i

j i

−1 q r−i qq−1

otherwise

0

Proof. The following simple property of ||D|| is known [13]. If dim D = r, we have ||D|| =

X 1 ||v||. q r − q r−1 v∈D

Note that all nonzero codewords have Hamming weight q r−1 . Let dim D = i and D ⊆ Sq,r . Then X 1 ||D|| = i ||v|| q − q i−1 v∈D 1 · (q i − 1)q r−1 − q i−1 qi − 1 = q r−i . q−1 =

qi

From Equation 1, if we know W r (C; y), (0 ≤ r ≤ s), then we can calculate W s (C ⊥ ; y). But there is also an explicit expression for W s (C ⊥ ; y) in [9, Theorem 2], namely the following. Theorem 2.8. For all s ≥ 0 we have s



W (C ; y) =

s X i X

q ((s−i)(s−i−1)/2)−i(s−i)−r(i−r)−ik [s − i]s−i [i − r]i−r i=0 r=0   1−y ×(1 + (q i − 1)y)n W r C; . 1 + (q i − 1)y (−1)s−i

8

Using Theorem 2.7 and Theorem 2.8, we calculated the higher weight spectrums of S2,j and H2,j for j = 2, 3, 4, and S3,j and H3,j for j = 2, 3 and present them in Table 1 to Table 12. In the following, we give the higher weight spectrums of the first order Reed-Muller codes and their dual codes. We start with the following notations. For u, v ∈ Fnq , |u ∨ v| = Supp(hu, vi), |u ∧ v| = |Supp(u) ∩ Supp(v)|. Theorem 2.9. Let R(1, r) be the [2r , r + 1, 2r−1 ] first order Reed-Muller code. Then the higher weight spectrums of R(1, r) are given by the following. If 1 ≤ i ≤ r, then j " 2r − 2r−i

Aij (R(1, # "r))

r+1 i "

2r otherwise

r − i−1 # r i−1 0

# .

r

If i = r + 1, then W i (R(1, r); y) = y 2 . Proof. If i = 1 or i = r + 1, then the theorem is true. Assume that 2 ≤ i ≤ r. Let G2 be the matrix " # 0 1 1 G2 = . 1 0 1 For r ≥ 3, define Gr inductively by    Gr =   

0···0 1 1···1 0 . Gr−1 .. Gr−1 0

Then Gr is a generator matrix for the binary Simplex G(1, r) to be the following (r + 1) × 2r matrix:  1 1···1   0 G(1, r) =   .. Gr  . 0

   .  

code S2,r , (r ≥ 2) [8, p. 30]. Define    .  

Then G(1, r) is a generator matrix for R(1, r) [8, p. 36]. We can think of an i dimensional subspace for R(1, r) in three ways. 9

1. The i dimensional subspace is generated by the rows of G(1, r) except the first row. Then every vector in the subspace is of the form (0, a), where a is a vector in i dimensional subspace for S2,r . Therefore the support of the i dimensional subspace is 2r − 2r−i . 2. First we make an i − 1 dimensional subspace which is generated by the rows of G(1, r) except the first row. Then by adding the first row, we make an i dimensional subspace. Therefore, in this case, the weight spectrum is given by the following: " # r r y2 . i−1 3. The i dimensional subspace is generated by the following vectors: (δ1 , a1 + δ1 1), (δ2 , a2 + δ2 1), . . . , (δi , ai + δi 1), where aj is a codeword in S2,r and δj = 0, 1 for 1 ≤ j ≤ i (at least one δj is 1) and 1 is the all 1 vector of length 2r − 1. Without loss of generality, we may assume that the generating vectors can be changed to the following vectors. (0, a1 ), (0, a2 ), . . . , (0, ai−1 ), (1, ai + 1). Then the support of the i dimensional subspace is 1 + |a1 ∨ a2 ∨ · · · ∨ ai−1 ∨ (ai + 1)|. Note that |a1 ∨ a2 ∨ · · · ∨ ai−1 ∨ (ai + 1)| = |a1 ∨ a2 ∨ · · · ∨ ai−1 | + |(ai + 1)| − |(a1 ∨ a2 ∨ · · · ∨ ai−1 ) ∧ (ai + 1)|. Note that a1 , a2 , . . . , ai−1 are linearly independent. If ai is a linear combination of a1 , a2 , . . . , ai−1 , then we can reduce this case to above case. We can then assume that a1 , a2 , . . . , ai are linearly independent. Since |(a1 ∨ a2 ∨ · · · ∨ ai−1 ) ∧ ai | = |a1 ∨ a2 ∨ · · · ∨ ai−1 | + |ai | − |a1 ∨ a2 ∨ · · · ∨ ai | = (2r − 2r−i+1 ) + 2r−1 − (2r − 2r−i ) = 2r−1 − 2r−i , we have |(a1 ∨ a2 ∨ · · · ∨ ai−1 ) ∧ (ai + 1)| = |(a1 ∨ a2 ∨ · · · ∨ ai−1 )| − |(a1 ∨ a2 ∨ · · · ∨ ai−1 ) ∧ ai | = (2r − 2r−i+1 ) − (2r−1 − 2r−i ) = 2r−1 − 2r−i . 10

From the above values, the support of the i dimensional subspace is 1 + (2r − 2r−i+1 ) + (2r − 1 − 2r−1 ) − (2r−1 − 2r−i ) = 2r − 2r−i . From the above arguments, we complete the proof. In the Reed-Muller codes, there are the following dual relations [8, Theorem 1.10.1]: R(r, r) = {0}⊥ , R(r − 1, r) = R(0, r)⊥ , R(r − 2, r) = R(1, r)⊥ . r

Since we already know W 0 ({0}; y) = 1 and W 1 (R(0, r); y) = y 2 , using Theorem 2.9 and Theorem 2.8, we can easily calculate the higher weight spectrums of R(r − 2, r), R(r − 1, r) r and R(r, r). (Note that R(r, r) is the entire space F22 ). We give the computational results for R(1, r) and R(r − 2, r) for r = 2, 3, 4 in Table 13 to Table 18. (Note that R(1, 3) is the [8, 4, 4] extended Hamming self-dual code).

2.1

Higher weight distributions of codes from projective planes

We can examine some of the higher weight enumerators which were not found for the projective plane of order 5 in [4]. Let Π be the projective plane of order n and let Cp (Π) be the code from the projective plane of order n. The Hull of the plane Π is defined as Hullp (Π) = Cp (Π) ∩ Cp (Π)⊥ and is a self-orthogonal code. We know from [4] that Hullp (Π) is of codimension 1 in Cp (Π) and Cp (Π) = hHullp (Π), 1i. If p sharply divides n, then Hullp (Π) = Cp (Π)⊥ . Additionally we know that for 1 ≤ k ≤ dim(Cp (Π)), we have dk (Cp (Π)) ≥ dk−1 (Hullp (Π)). For more details, refer to [4]. We shall calculate some of the higher weight distributions of the code and the Hull of the projective plane of order 5. We know that C5 (Π) is a [31, 16] code over F5 and that Hull5 (Π) is a [31, 15] code over F5 . We use the known weight enumerator W 1 (C5 (Π)) [11] to obtain by Equation 1 the weight enumerator W 1 (Hull5 (Π)). Then we have that d1 (C5 (Π)) = 6 and d1 (Hull5 (Π)) = 10. Let W i (C5 (Π)) = ai+8 y i+8 + ai+9 y i+9 + · · · + a31 y 31 , W i (Hull5 (Π)) = bi+9 y i+9 + bi+10 y i+10 + · · · + b31 y 31 , where (2 ≤ i ≤ 16). We apply the MacWilliams relations to W 0 (C5 (Π)), W 1 (C5 (Π)), . . . , W i (C5 (Π)) 11

and W 0 (Hull5 (Π)), W 1 (Hull5 (Π)), . . . , W i (Hull5 (Π)) for (2 ≤ i ≤ 16), sequentially. By a Maple calculation, we determined W i (C5 (Π)) for 8 ≤ i ≤ 16 and W i (Hull5 (Π)) for 8 ≤ i ≤ 15. But the others remain undetermined. The weight enumerators can be found in [1]. Remark 2. Note that C5 (Π) is a P8 -MDS code and that Hull5 (Π) is a P11 -MDS code. Compared to what follows from Theorem 2.5, we have obtained additional results for W 8 (Hull5 (Π)), W 9 (Hull5 (Π)), and W 10 (Hull5 (Π)) by the above calculation. Remark 3. If we use the fact that d2 (C5 (Π)) ≥ 10, then the weight enumerators W i (Hull5 (Π)) (i ≥ 8) can be calculated using Equation 9.

3

A non-linear version of the generalized Singleton bound

The Singleton bound applies to either linear or non-linear codes. For non-linear codes it applies to the distance between vectors rather than the weight of vectors, where the distance between two vectors is the number of coordinates in which they differ. Specifically, if d is the minimum distance of a length n code C (linear or not) over an alphabet of size q with |C| = M then M ≤ q n−d+1 . This immediately gives the usual form of the Singleton bound. logq (M ) ≤ n − d + 1. We shall generalize this to higher weights. We begin with a definition. Let C be a length n code over an alphabet of size q. Let D ⊆ C. Note that we are not assuming that either C or D is linear. Define ||D|| = |{i | there exists v, w ∈ D with vi 6= wi }|. (c.f. This definition is the same as the definition of the support of a non-linear code given in [13].) That is, it is the number of coordinates where there are at least two vectors that differ there. If D is linear then ||D|| is the size of the support of D since the zero vector is present. Therefore, this definition coincides with our previous definition for linear codes. Define dr = dr (C) = min{||D|| | D ⊆ C, q r−1 < |D| ≤ q r }. (10) Again, if C and D are linear then this coincides with the usual definition. 12

Lemma 3.1. Let C be a length n code over an alphabet of size q with |C| = M and q k−1 < M ≤ q k for some k. Then 1 ≤ d1 (C) < d2 (C) < d3 (C) < · · · < dk (C) ≤ n. Proof. Let D be a code with ||D|| = dr . Pick a coordinate i where there exist v, w ∈ D with vi 6= wi . Let α be the element that appears the most in that coordinate. Let D0 = {v | vi = α}. The pigeon hole principle gives that |D0 | ≥ 1q |D|, with equality when dealing with linear codes. Remove elements of D0 to form D00 so that q r−2 < |D00 | ≤ q r−1 . Then ||D00 || < dr , since the number of coordinates in D00 where there are vectors that differ is strictly less than that of D. This gives that dr > dr−1 . Theorem 3.2. Let C be a length n code over an alphabet of size q with |C| = M and q k−1 < M ≤ q k for some k. Then for 1 ≤ r ≤ k, logq (M ) ≤ n − dr + r. Proof. We shall prove the result by induction. Let r = k. Using M ≤ q k , we have logq (M ) ≤ k. Then we have that n ≤ n−logq (M )+k and clearly dk ≤ n. Therefore dk ≤ n−logq (M )+k and the theorem holds for r = k. Assume dr ≤ n−logq (M )+r. By Lemma 3.1, dr−1 ≤ dr −1, which gives that dr−1 ≤ n − logq (M ) + (r − 1). Then we have the result. If M = q k this gives the usual k ≤ n − dr + r. Rearranging we have dr ≤ n − k + r. Theorem 3.3. Let C be an [n, k] linear code over Fq and E = a + C for some a ∈ Fnq . Then the value dr (C) as a linear code is equal to the value dr (E) as a non-linear code. Proof. Let D0 ⊆ C with dim(D0 ) = r and ||D0 || = dr (C). Let D00 = a + D0 . Note that ||D00 || = ||D0 || and ||D00 || ∈ {||D|| | D ⊆ E, q r−1 < |D| ≤ q r }. This gives that ||D0 || ≥ dr (E) and dr (C) ≥ dr (E). For the other inequality, let D0 ⊆ E with q r−1 < |D0 | ≤ q r and ||D0 || = dr (E). Fix an element v ∈ D0 . Define D00 = h{v−w|w ∈ D0 }i. Note that ||D00 || = ||D0 || and dim(D00 ) ≥ r. So, ||D00 || ≥ dr (C) and dr (C) ≤ dr (E). Example 5. Let C be a Type I binary self-dual code and S be the shadow code of C. From Theorem 3.3, the value dr (S) as a non-linear code is equal to the value dr (C) as a linear code. Note that this does not imply that the minimum weight of the shadow and the code is the same but rather that their minimum distances are the same. 13

Theorem 3.4. Let C be the (16, 256, 6) Nordstrom-Robinson code. Then {dr (C)|1 ≤ r ≤ 8} = {6, 9, 10, 12, 13, 14, 15, 16}. Proof. By definition, d1 = 6. Note that we can rewrite Equation 10 as the following: dr = dr (C) = min{||D|| | D ⊆ C, |D| = 2r−1 + 1}. To show d2 = 9, let D = {v1 , v2 , v3 } ⊆ C with ||D|| = d2 . By [10, Fig. 2.18], (a1 |v1 ), (a2 |v2 ), (a3 |v3 ) are codewords of the Golay code of length 24, where a1 , a2 , a3 ∈ {(0, 0, 0, 0, 0, 0, 0, 0), (1, 0, 0, 0, 0, 0, 0, 1), (0, 1, 0, 0, 0, 0, 0, 1), (0, 0, 1, 0, 0, 0, 0, 1), . . . , (0, 0, 0, 0, 0, 0, 1, 1)}. We know that for the Golay code d2 is 12. Then we have 12 ≤ ||ha1 |v1 − a2 |v2 , a1 |v1 − a3 |v3 i|| = ||ha1 − a2 , a1 − a3 i|| + ||hv1 − v2 , v1 − v3 i|| ≤ 3 + d2 . This gives that 9 ≤ d2 . By [8, Example 12.2.5], C is the Gray image of the O8 Z4 -linear code, the octacode, with generator matrix:   1 0 0 0 3 1 2 1  0 1 0 0 1 2 3 1    G= .  0 0 1 0 3 3 3 2  0 0 0 1 2 3 1 1 The set of two Gray images of the first and second rows of G and the zero codeword has support size 9. This gives that d2 = 9. By Magma [2] computation, we confirmed that d3 = 10 and d4 = 12. The remaining parts of the theorem follow from Lemma 3.1. Remark 4. By Magma computation, we calculated the following values for the NordstromRobinson code. Number of codewords 2 3 4 5 6 7 8 9 Minimum support size 6 9 10 10 10 11 11 12

4

Calculation of higher weight distributions for some Type II self-dual codes

One of the most interesting and difficult open problems in coding theory is the existence of extremal [24k, 12k, 4k + 4] codes. For k = 1 and k = 2 there are unique codes. For k = 3 14

the question remains open. We shall compute many of the higher weight enumerators for these codes when 1 ≤ k ≤ 3. In Example 4, we showed that we can calculate some higher weight distributions of the unique [48, 24, 12] Type II code and the putative [72, 36, 16] Type II codes using Theorem 2.5. The weight enumerators for the [24, 12, 8] code were computed in [3]. The techniques of this paper were also used to verify those results. In this section, we show that we can calculate these higher weight distributions with MacWilliams identities. In fact, we can calculate much more than Example 4 without Theorem 2.5. We will need the following well known lemma which can be found in Exercise 373 in [8]. Lemma 4.1. Let M be a q k × n matrix whose rows are the codewords of an [n, k] code C over Fq . Then a column of M either consists of zeros or contains every element of Fq an equal number of times.

4.1

The [48, 24, 12] Type II code

Let C be the [48, 24, 12] Type II code. We already know W 0 and W 1 from Gleason’s Theorem. Lemma 4.2. Let C be the [48, 24, 12] Type II code. The following results hold for the weight hierarchy of C: d1 = 12, d2 = 18, 21 ≤ d3 ≤ 23, 23 ≤ d4 ≤ 26, 24 ≤ d5 ≤ 27, 26 ≤ d6 ≤ 28, d7 = 29, d8 = 30, d9+i = 32 + i(0 ≤ i ≤ 4), d14+i = 38 + i(0 ≤ i ≤ 10). Proof. By definition d1 = 12 and d2 = 18 by [3]. Using Lemma 4.1, we have 21 ≤ d3 and 23 ≤ d4 . By the Table in [6], we have 26 ≤ d6 . Using the MacWilliams identities and Maple software, we can show that there is no feasible solution for 24 ≤ d3 . The remaining parts are similar to the proof of Lemma 4.3, we shall only show the details there. Let W 2 = a18 y 18 + a20 y 20 + a22 y 22 + · · · + a48 y 48 . Apply MacWillams identities to W 0 , W 1 , and W 2 . Then we can determine all coefficients of W 2 . Let W 3 = a21 y 21 + a22 y 22 + · · · + a48 y 48 . Then apply the MacWilliams identities to W 0 , W 1 , W 2 , and W 3 . Then we can not determine all coefficients of W 3 . In a similar way we can not determine W i , i = 4, 5, 6. Up to this point, we have several undetermined variables. However, we can deduce that 26 < d6 ≤ 28, in other words, by a Maple calculation we checked that d6 6= 26. We continue this process for W 7 , . . . , W 24 sequentially and we can determine all the weight distributions for W i , 7 ≤ i ≤ 24 15

sequentially. In conclusion, we know all W i except W i , i = 3, 4, 5, 6. (Note: We applied W 0 , W 1 , W 2 , and W 3 with 4 undetermined variables to the Gleason-type Theorem given in [3, Theorem 5.4]. But we did not obtain any new information.) The higher weight enumerators for this code can be found at [1]. Remark 5. The second higher weight distribution of Type II [48, 24, 12] code was computed in [3] using a biweight enumerator and its invariant theory. Remark 6. Equation 9 and Remark 1 indicate that W i (7 ≤ i) can be calculated since d2 = 18, d3 ≥ 21, and we know W 2 for the Type II [48, 24, 12] code.

4.2

The [72, 36, 16] Type II code

Let C be the [72, 36, 16] Type II code. We know W 0 , W 1 , and W 2 [3]. Lemma 4.3. Let C be the [72, 36, 16] Type II code. Then for the weight hierarchy of C, the following hold: d1 = 16, d2 = 24, 28 ≤ d3 ≤ 33, 30 ≤ d4 ≤ 38, 31 ≤ d5 ≤ 39, 32 ≤ d6 ≤ 40, 35 ≤ d7 ≤ 41, 36 ≤ d8 ≤ 42, 38 ≤ d9 ≤ 43, 40 ≤ d10 ≤ 44, 41 ≤ d11 ≤ 45, d12 = 46, d13 = 47, d14 = 48, d15+i = 50 + i(0 ≤ i ≤ 6), d22+i = 58 + i(0 ≤ i ≤ 14). Proof. By definition d1 = 16 and we have d2 = 24 by [3]. Using Lemma 4.1, we can prove that 28 ≤ d3 by the following argument. Suppose that D is a 3-dimensional subcode with support size 27. Let M be a 8 × 72 matrix whose rows are the codewords of D. By Lemma 4.1, 1 appears 4 · 27 = 108 times in M ; but M has 7 rows of weight at least 16, implying that 1 appears at least 112 times in M , a contradiction. We can apply this argument to prove 30 ≤ d4 . Then we have 28 ≤ d3 , 30 ≤ d4 . Using Equation 7, we have the following result. Since 1, 2, · · · , 15 6∈ {dr (C)|1 ≤ r ≤ 36}, 58, 59, · · · , 72 ∈ {dr (C ⊥ )|1 ≤ r ≤ 36}. Since 16 ∈ {dr (C)|1 ≤ r ≤ 36}, 57 6∈ {dr (C ⊥ )|1 ≤ r ≤ 36}. Since 17, 18, · · · , 23 6∈ {dr (C)|1 ≤ r ≤ 36}, 50, 51, · · · , 56 ∈ {dr (C ⊥ )|1 ≤ r ≤ 36}. Since 24 ∈ {dr (C)|1 ≤ r ≤ 36}, 49 6∈ {dr (C ⊥ )|1 ≤ r ≤ 36}. Since 25, 26, 27 6∈ {dr (C)|1 ≤ r ≤ 36}, 46, 47, 48 ∈ {dr (C ⊥ )|1 ≤ r ≤ 36}. In conclusion, we have the following. {16, 24, 46, 47, 48, 50, . . . , 56, 58, . . . , 72} ⊂ {dr |1 ≤ r ≤ 36},

(11)

{dr |1 ≤ r ≤ 36} ⊂ ({16, 24, 46, 47, 48, 50, . . . , 56, 58, . . . , 72} ∪ {28, . . . , 45}). (12) By Equations 11, 12, and 5, the following hold: d22+i = 58 + i(0 ≤ i ≤ 14), d15+i = 50 + i(0 ≤ i ≤ 6), d14 = 48, d13 = 47, d12 = 46, 16

and {dr |3 ≤ r ≤ 11} ⊂ {28, . . . , 45}. By the table in [6], the highest minimum distance of a [34, 7] binary linear code is 15. Therefore 35 ≤ d7 . We can apply similar argument to d9 and d10 . Then we have 35 ≤ d7 , 38 ≤ d9 , 40 ≤ d10 . Using the MacWilliams identities, we can check that d3 ≤ 33 as follows. We know W 1 P i and W 2 [3]. Let W 3 = 72 i=28 ai y . First we have relations among ai s by applying Equation 1 to W 0 , W 1 , W 2 and W 3 . Then we check whether there is a possible solution for all nonnegative ai s. In our Maple calculation, there is a feasible solution for 28 ≤ d3 ≤ 33. But there is no feasible solution for 34 ≤ d3 . This gives that d3 ≤ 33. This completes the proof of Lemma 4.3. Let W 3 = a28 y 28 + a29 y 29 + · · · + a72 y 72 . Apply the MacWilliams relations to W 0 , W 1 , W 2 , W 3 . We can not determine all coefficients of W 3 . In a similar way we can not determine W i for 4 ≤ i ≤ 11. There remain many undetermined variables in these cases. But we do obtain the following: 39 ≤ d9 ≤ 43 and 44 ≤ d11 ≤ 45. In other words, by a Maple calculation we checked that d9 6= 38 and d11 6= 41, 42, 43. We continue this process for W 12 , . . . , W 36 sequentially and we can determine all the weight distributions for W i , 12 ≤ i ≤ 36 sequentially. In conclusion, we know all W i except W i for 3 ≤ i ≤ 11. The higher weight enumerators for this code can be found at [1]. Remark 7. Equation 9 and Remark 1 indicate that W i , (12 ≤ i), can be calculated since d2 = 24, d3 ≥ 28, and we know W 2 for the Type II [72, 36, 16] code. Open Problem: Determine the remaining higher weight enumerators for the extremal Type II codes. That is, for the [48, 24, 12] code find W i for 3 ≤ i ≤ 6 and for the [72, 36, 16] code find W i for 3 ≤ i ≤ 11.

References [1] http://math.yonsei.ac.kr/sunghyu/HW-P5-48-72.pdf [2] Cannon J., Playoust C.: An Introduction to Magma. University of Sydney, Sydney, Australia (1994). 17

[3] Dougherty S.T., Gulliver T.A., Oura M.: Higher weights and graded rings for binary self-dual codes. Discrete Applied Mathematics 128, 121–143 (2003). [4] Dougherty S.T., Ramadurai R.: Higher weight distributions of codes from projective planes. Math. J. Okayama Univ. 49, 149–161 (2007). [5] Dougherty S.T., Shiromoto K.: MDR Codes over Zk . IEEE Trans. Inform. Theory 46, 265–269 (2000). [6] Grassl M.: Bounds on the minimum distance of linear codes. online available at http://www.codetables.de. Accessed on 2008-03-09. [7] Horimoto H., Shiromoto K.: A Singleton bound for linear codes over quasi-Frobenius rings. Proceedings of the 13th International Symposium on Applied Algebra, Algebraic Algorithms, and Error-Correcting Codes, Hawaii (USA), 51–52 (1999). [8] Huffman W.C., Pless V.S.: Fundamentals of Error-correcting Codes. Cambridge: Cambridge University Press (2003). [9] Kløve T.: Support weight distribution of linear codes. Discrete Math. 106/107, 311– 316 (1992). [10] MacWilliams F.J., Sloane N.J.A.: The Theory of Error-Correcting Codes. Amsterdam, The Netherlands: North-Holland (1977). [11] McGuire G., Ward H.N.: The weight enumerator of the code of the projective plane of order 5. Geometriae Dedicata 73(1), 63–77 (1998). [12] Schaathun H.G.: Duality and Support Weight Distributions. IEEE Trans. Inform. Theory 50, 862–867 (2004). [13] Tsfasman M.A., Vl˘adut¸ S.G.: Geometric approach to higher weights. IEEE Trans. Inform. Theory 41, 1564–1588 (1995). [14] Vermani L.R.: Elements of Algebraic Coding Theory. UK: Chapman & Hall; 1st ed. (1996). [15] Wei V.K.: Generalized Hamming weights for linear codes. IEEE Trans. Inform. Theory 37, 1412–1418 (1991).

18

Table 1: Higher weight enumerators for the [3, 2, 2] binary Simplex S2,2 code W1 3

W2 1

weight 2 3

Table 2: Higher weight enumerators for the [3, 1, 3] binary Hamming H2,2 code W1 1

weight 3

Table 3: Higher weight enumerators for the [7, 3, 4] binary Simplex S2,3 code W1 7

W2

W3

7 1

weight 4 6 7

Table 4: Higher weight enumerators for the [7, 4, 3] binary Hamming H2,3 code W1 7 7

1

W2

W3

W4

21 7 7

7 8

1

weight 3 4 5 6 7

Table 5: Higher weight enumerators for the [15, 4, 8] binary Simplex S2,4 code W1 15

W2

W3

W4

35 15 1

19

weight 8 12 14 15

Table 6: Higher weight enumerators for the [15, 11, 3] binary Hamming H2,4 code W1 35 105 168 280 435 435 280 168 105 35

1

W2

W3

W4

W5

315 2485 8925 26565 65030 114618 158130 156310 109935 46305 9409

105 5700 47145 277340 1202670 3886260 9108890 14759220 14771625 6897016

15 6315 110530 1158318 8450400 43540840 152824350 329587890 330572849

5005 159159 2769585 31435495 234932775 1062248565 2217286235

weight 3 4 5 6 7 8 9 10 11 12 13 14 15

Table 7: Higher weight enumerators for the [15, 11, 3] binary Hamming H2,4 code W6 3003 158340 4248335 69018600 654729225 2820679316

W7

W8

W9

W 10

W 11

1365 110565 4220790 86436990 775481797

455 52290 2507820 48395406

105 15135 682787

15 2032

1

weight 10 11 12 13 14 15

Table 8: Higher weight enumerators for the [4, 2, 3] ternary Simplex S3,2 code W1 4

W2 1

weight 3 4

Table 9: Higher weight enumerators for the [4, 2, 3] ternary Hamming H3,2 code W1 4

W2 1

weight 3 4

Table 10: Higher weight enumerators for the [13, 3, 9] ternary Simplex S3,3 code W1 13

W2

W3

13 1

20

weight 9 12 13

Table 11: Higher weight enumerators for the [13, 10, 3] ternary Hamming H3,3 code W1 52 234 702 2028 4212 5967 6721 5616 2808 1040 144

W2

W3

W4

W5

13 1170 12480 101868 616824 2740387 8775858 19146816 25530141 15710864

1716 56628 1181466 17407390 182133666 1293886386 5610032220 11222028288

1716 145431 7119970 234801996 5172625458 69183987171 426179154300

1287 253825 27418248 1873641276 76432302375 1428138550917

weight 3 4 5 6 7 8 9 10 11 12 13

Table 12: Higher weight enumerators for the [13, 10, 3] ternary Hamming H3,3 code W6

W7

W8

W9

715 309738 68974776 8681213034 492027337779

286 254982 104394238 18222078254

78 127777 72508566

13 29511

W 10

1

weight 9 10 11 12 13

Table 13: Higher weight enumerators for the [4, 3, 2] Reed-Muller R(1, 2) code W1 6

W2

W3

4 3

1

1

weight 2 3 4

Table 14: Higher weight enumerators for the [4, 1, 4] Reed-Muller R(0, 2) code W1 1

weight 4

Table 15: Higher weight enumerators for the [8, 4, 4] Reed-Muller R(1, 3) code W1 14

W2

W3

W4

8 7

1

28 1

7

21

weight 4 6 7 8

Table 16: Higher weight enumerators for the [16, 5, 8] Reed-Muller R(1, 4) code W1 30

W2

W3

W4

W5

16 15

1

weight 8 12 14 15 16

140 120 1

15

35

Table 17: Higher weight enumerators for the [16, 11, 4] Reed-Muller R(2, 4) code W1 140 448 870 448 140

1

W2

W3

W4

W5

840 4480 12810 35840 78960 123648 159320 143360 94920 36736 7113

240 11130 73920 392000 1535520 4541880 9813440 14764800 13787760 6035281

30 11200 169008 1592640 10603600 50326080 163872240 329710784 309965925

8008 227136 3598140 37582720 260441160 1098339648 2148640007

weight 4 6 7 8 9 10 11 12 13 14 15 16

Table 18: Higher weight enumerators for the [16, 11, 4] Reed-Muller R(2, 4) code W6 4368 209300 5164320 77771760 688008272 2777678799

W7

W8

W9

W 10

W 11

1820 135520 4794720 91560160 769759287

560 59640 2667056 48228715

120 16128 681779

16 2031

1

22

weight 11 12 13 14 15 16

Higher Weights and Generalized MDS codes

Jun 22, 2011 - The following is given in Theorem 7.4.1 in [8]. Let C be an [n, k, d] MDS code over Fq. The weight distribution of C is given by A0 = 1, Ai = 0 for 1 ...

260KB Sizes 2 Downloads 244 Views

Recommend Documents

Higher Weights and Graded Rings for Binary Self-Dual Codes
5.1 A Gleason-type Theorem. In this subsection, we shall use the previous results to produce a Gleason-type theorem for binary self-dual codes. Let C be a self-dual Type I code and Wr(C;x, y) its symmetric higher weight enumerator. Consider the polyn

Higher Weights and Binary Self-Dual Codes
[email protected] and. T. Aaron Gulliver. Department of Electrical and Computer Engineering. University of Victoria. P.O. Box 3055, STN CSC. Victoria, BC, Canada V8W 3P6 [email protected]. June 22, 2011. Abstract. The theory of higher wei

Higher Weights of Codes from Projective Planes and ...
Jun 22, 2011 - where wt(c) is the number of the non-zero coordinates in c. Often we set x = 1 and simply write HC(y). Let D ⊆ Fn p be a linear subspace, then ||D|| = |Supp(D)|, where Supp(D) = {i | ∃v ∈. D, vi = 0}. For a linear code C we defin

On Generalized Weights for Codes over Zk
Jun 22, 2011 - introduced the GHWR of a linear code C over a finite chain ring and studied some properties of the GHWR. For any g, 1 ≤ g ≤ rank(C), we define the higher weight spectrum as. A g i = |{D : D is a Zk-submodule of C with rank(D) = g a

Higher Weights for Codes over Rings
Jun 22, 2011 - number of basis elements for a code over this class of rings is just the rank of this code. 1.3 Higher Weights. Let R be a finite ...... (c1,ททท ,cg)∈C1ืทททืCg. ∏ a∈Rg j. Xna(c1,ททท ,cg) a. , where cl = (cl1

Generalized and Doubly Generalized LDPC Codes ...
The developed analytical tool is then exploited to design capacity ... error floor than capacity approaching LDPC and GLDPC codes, at the cost of increased.

Generalized Additive Codes, Self-Dual Codes and ...
x = (x0,x1,··· ,xn−1) ∈ Zn. 4 is an arbitrary vector, and suppose ϕ(xj)=(xj,0,xj,1) for all j = 0,1,··· ,n − 1, then. ϕ(x0,x1,··· ,xn−1)=(x0,0,x1,0,··· ,xn−1,0,x0,1,x1,1,··· ,xn−1,1). (2.3). In this paper, we shall consid

NEAR MDS POSET CODES AND DISTRIBUTIONS 1 ...
q is called a Maximum Distance Separable (MDS) code if the Hamming distance .... We write (t, n, r, q) OOA for an orthogonal array of strength t in Fr,n q .

Generalized Silver Codes
gineering, Indian Institute of Science, Bangalore 560012, India (e-mail: [email protected]; [email protected]). Communicated by E. Viterbo, Associate ...... 2006, he was with Robert Bosch India limited, Bangalore. Currently, he is a wor

Near MDS poset codes and distributions
and uniform distributions of points in the unit cube Un. In our study of NMDS codes in the following sections, we analyze the properties of the code simultaneously as a linear code and as a linear orthogonal array. Define the I-neighborhood of a pose

Higher Weights for Ternary and Quaternary Self-Dual ...
Jun 22, 2011 - C (1,0,0,0,0, y,0,0,0,0, y,0,0,0,0,y). − J. (2). C (1,0,0,0,0,0, y,0,0,0,0, y,0, y,0,0). − J. (2). C (1,0,0,0,0,0,0, y,0, y,0,0,0,0, y,0) + 4). 3 Type III and Type IV Codes. In this section, we consider the higher weights of ternar

MDS Codes over Finite Principal Ideal Rings
A finite family (ai)n i=1 of ideals of R, such that the canonical homomorphism of R to. ∏n i=1. (R/ai) is an isomorphism is called a direct decomposition of R.

Asymptotic Interference Alignment for Optimal Repair of MDS Codes in ...
Viveck R. Cadambe, Member, IEEE, Syed Ali Jafar, Senior Member, IEEE, Hamed Maleki, ... distance separable (MDS) codes, interference alignment, network.

Ranks and Kernels of Codes from Generalized ...
Jun 29, 2015 - Hence, bjk + Bi = Bs for i ∈ {1, 2,...,q}, where ... to any other Bs for s = i, we can conclude that when ek ⊕ bjk ...... in Computer Science, vol.

Generalized Shadows of Codes over Rings
Jun 22, 2011 - Let R be finite commutative ring. A code over R is a subset of Rn and a linear code is a submodule of this space. To the ambient space Rn ...

Generalized Union Bound for Space-Time Codes - Semantic Scholar
Department of Electronic Engineering, King's College London (email: [email protected]) ... Index Terms⎯Gallager bounds, optimization, space-time coding, union ...

Generalized quasi-cyclic low-density parity-check codes ... - IEEE Xplore
Email: {sd07501,hmatsui,smita}@toyota-ti.ac.jp. Abstract—In this study, we proved that several promising classes of codes based on finite geometries cannot be ...

Generalized Union Bound for Space-Time Codes - Semantic Scholar
Cambridge, MA: MIT Press, 1963. [16] R. M. Fano ... Cambridge, UK: Cambridge University Press, 1993. ... Wireless Commun., accepted for publication. [30] J. J. ...

2017 weights and scan data.pdf
39 08D RRL HXC BIG IRON 0024X 1/19/2016 4.33 1200 37.9 3.80 14.2 0.15 weighed 2/19/17. 40 D40 JHJ LMAN KING ROB 8621 1/15/2016 4.20 1140 35.8 ...

GENERALIZED HIGHER ORDER SPT-FUNCTIONS 1 ...
[5] A. O. L. Atkin and F. Garvan, Relations between the ranks and cranks of partitions, Ramanujan J., 7. (2003), 343–366. [6] F.J. Dyson, Some guesses in the theory of partitions, Eureka (Cambridge) 8 (1944), 10–15. [7] F. J. Dyson, Mappings and

Generalized Higher-Order Dependency Parsing ... - Research at Google
to related work in Section 6. Our chart-based .... plus potentially any additional interactions of these roles. ..... features versus exact decoding trade-off in depen-.

MDS Co Bds 10
Oct 15, 2009 - BARBOUR COUNTY BOARD OF EDUCATION. PUBLIC SCHOOL SUPPORT PROGRAM. MONTHLY DISTRIBUTION SCHEDULE. FOR THE ...

Hiking Gear Weights and Costs - Spreadsheet Instructions.pdf ...
Hiking Gear Weights and Costs - Spreadsheet Instructions.pdf. Hiking Gear Weights and Costs - Spreadsheet Instructions.pdf. Open. Extract. Open with. Sign In.