The Weight Hierarchy of a Family of Cyclic Codes with Arbitrary Number of Nonzeroes
Shuxing Li

TL;DR
This paper investigates the generalized Hamming weights of a specific family of cyclic codes with any number of nonzeroes, using number theory to determine their weight hierarchy.
Contribution
It extends previous work by analyzing the GHWs of cyclic codes with arbitrary nonzeroes through a novel number-theoretic approach.
Findings
Determined the weight hierarchy for the studied cyclic codes.
Provided explicit formulas for GHWs based on code parameters.
Enhanced understanding of the structure of cyclic codes with multiple nonzeroes.
Abstract
The generalized Hamming weights (GHWs) are fundamental parameters of linear codes. GHWs are of great interest in many applications since they convey detailed information of linear codes. In this paper, we continue the work of [10] to study the GHWs of a family of cyclic codes with arbitrary number of nonzeroes. The weight hierarchy is determined by employing a number-theoretic approach.
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Taxonomy
TopicsCoding theory and cryptography · graph theory and CDMA systems · Cryptographic Implementations and Security
The Weight Hierarchy of a Family of Cyclic Codes with Arbitrary Number of Nonzeroes
Shuxing Li
Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Abstract
The generalized Hamming weights (GHWs) are fundamental parameters of linear codes. GHWs are of great interest in many applications since they convey detailed information of linear codes. In this paper, we continue the work of [10] to study the GHWs of a family of cyclic codes with arbitrary number of nonzeroes. The weight hierarchy is determined by employing a number-theoretic approach.
keywords:
Cyclic codes , Generalized Hamming weights , Weight hierarchy
MSC:
11T71 , 94B05 , 94B15
††journal: Finite Fields and Their Applications
1 Introduction
An linear code over finite field is a -dimensional subspace of the linear space . For any linear subcode , the support of is defined to be
[TABLE]
For , the -th generalized Hamming weight (GHW) of is given by
[TABLE]
where denotes the cardinality of the set . By definition, is just the minimum distance of . The set is called the weight hierarchy of .
The concept of GHWs was first introduced by Helleseth, Kløve, Mykkeltveit [4, 7] and was used in the computation of weight distributions. It was rediscovered by Wei [9] to fully characterize the performance of linear codes when used in a wire-tap channel of type II or as a -resilient function. Indeed, the GHWs provide detailed structural information of linear codes, which can also be used to compute the state and branch complexity profiles of linear codes [2, 6], to determine the erasure list-decodability of linear codes [3] and so on.
In general, the determination of weight hierarchy is very difficult and there are only a few classes of linear codes whose weight hierarchies are known (see [10] for a comprehensive enumeration of related references). This paper continues the work of [10] to determine the weight hierarchy of a family of cyclic codes with arbitrary number of nonzeroes. Our result can be regarded as an extension of the results in [5, 8, 13], where the weight hierarchy of the semiprimitive codes was computed. We achieve this by generalizing a number-theoretic approach introduced in [13].
The rest of this paper is organized as follows. In Section 2, we introduce the concerned family of cyclic codes and state the main result. In Section 3, we present a number-theoretic approach to the computation of GHWs. In Section 4, we prove the main result. Section 5 concludes the paper.
2 Main Result
In this section, we introduce the concerned family of cyclic codes and describe our main result.
At first, we set up some notations which will be used throughout the rest of the paper. Let , , where is a prime, and are positive integers. Let be a primitive element of the finite field . We have the following three assumptions:
- i)
, , ;
- ii)
For , . When , for , and ;
- iii)
for and for . Here denotes the minimal polynomial of over .
Let us define
[TABLE]
When , without loss of generality, we can choose for .
We define to be the cyclic code of length over , whose parity-check polynomial is , where ’s are specified according to the three assumptions. Therefore, is an cyclic code with nonzeroes.
This family of cyclic codes was first introduced in [11], where the weight distributions were computed in several cases [11, 12]. Due to the flexibility of the parameters , , , , and , this family contains an abundance of cyclic codes and some of which are interesting cyclic codes [10]. In fact, [11] and [12] presented a unified approach to the computation of weight distributions of certain cyclic codes, which included many previous results as special cases. Moreover, these results suggest that this family of codes is highly structured and it is hopeful to obtain more detailed information such as the generalized Hamming weights. Therefore, in [10], the authors obtained the weight hierarchy in the following cases:
and ,
- 2.
, and is an arithmetic progression.
The computation relies heavily on generalizing a number-theoretic idea proposed in [13]. A key point in the computation is that, when or , the evaluation of the corresponding Gauss periods is very simple. Note that the next simplest case for the evaluation of Gauss periods is the so-called semiprimitive case. Let be a prime and be an integer with . In the semiprimitive case, there exists a positive integer , such that . In this paper, we consider the GHWs of in the semiprimitive case. More specifically, we have the following main result.
Theorem 1**.**
Let be a prime. Set and . Suppose , , and , , are positive integers satisfying and the assumptions i) and ii). Let and be positive integers specified in (1), satisfying . Let be a cyclic code of length , having parity-check polynomial . Suppose is the smallest positive integer such that . Suppose is odd and is even. For , write , where and . Suppose is the -th GHW of . Then
[TABLE]
We have the following three remarks.
Remark 2**.**
According to [11, Lemma 6], the assumption iii) always holds true if . Hence, in Theorem 1, we only need to choose integers , , and , , that satisfying the assumptions i) and ii). Meanwhile, the condition ensures that the assumption iii) also holds.
Remark 3**.**
In the semiprimitive case, by choosing , and , the resulting code is simply a semiprimitive code. The GHWs of semiprimitive codes has been studied in [5, 8, 13]. More precisely, when the code is a semiprimitive code, Theorem 1 reduces to [5, Theorem 3] and [8, Theorem 4.1] for , and to [13, Corollary 15] for general . When , i.e., the code is a reducible cyclic code, the result of Theorem 1 is new.
Remark 4**.**
The conditions being odd and being even are crucial. In fact, these two conditions are essentially used in the computation of the weight hierarchy of binary semiprimitive codes [5, 8]. Without them, the determination of the weight hierarchy, even for the simplest binary semiprimitive codes, remains a challenging problem.
To confirm the correctness of Theorem 1, we provide some numerical examples, which are obtained by using Magma.
Example 5**.**
For , , and , we have an cyclic code over with . The weight hierarchy of this code is as follows
[TABLE]
which coincides with the result of Theorem 1.
Example 6**.**
For , , and , we have a cyclic code over with . The weight hierarchy of this code is as follows
[TABLE]
which coincides with the result of Theorem 1.
3 A Number-theoretic Approach to GHWs
Let be the cyclic code defined in Section 2. In this section, we introduce a number-theoretic approach to the computation of GHWs of . Firstly, we give a brief introduction to cyclic codes, group characters and Gauss periods. Secondly, we derive two general expressions closely related to the determination of GHWs, which will be used in our computation.
3.1 Cyclic Codes
Let be an linear code over with . is called a cyclic code, if implies its cyclic shift . For a cyclic code , each codeword can be associated with a polynomial in the principal ideal ring . Under this correspondence, can be identified with an ideal of . Hence, there is a unique monic polynomial with such that and has the smallest degree among the elements in . This is called the generator polynomial of , and is called the parity-check polynomial of . When is specified, a cyclic code is uniquely determined by either the generator polynomial or the parity-check polynomial. is said to have nonzeroes if its parity-check polynomial can be factorized into a product of irreducible polynomials over . Thus, the cyclic codes defined in Section 2 may have arbitrary number of nonzeroes. A cyclic code is said to be irreducible, if it has only one nonzero. Otherwise, it is called a reducible cyclic code.
3.2 Group Characters and Gauss Periods
Let where is a prime number. The canonical additive character of is given by
[TABLE]
where is a primitive -th root of unity of , and is the trace function from to . If is a power of , by the transitivity of trace functions, we have .
Let be a primitive element of . For , denote by the multiplicative subgroup of generated by . Then for any , is called the -th cyclotomy class of . For any , we define the Gauss period as
[TABLE]
3.3 The First Expression
Now, we are going to derive the first expression related to the GHWs of .
By Delsarte’s Theorem [1], codewords of can be represented uniquely by , where runs over the set and
[TABLE]
In other words, the map
[TABLE]
is an isomorphism between two -vector spaces and , hence induces a 1-1 correspondence between -dimensional -subspaces of and -dimensional subcodes of for any . For any -vector space , denote by the set of -dimensional -subspaces of .
For any , define
[TABLE]
and for any , define
[TABLE]
Since is an isomorphism, by definition, the -th GHW of can be expressed as
[TABLE]
Define for and . According to [10, Section IV], we have the following expression
[TABLE]
where . From now on, we always consider the case where . In this case, the above expression can be further simplified as follows.
Define a linear transformation from to as
[TABLE]
where
[TABLE]
Indeed, induces an isomorphism from to
[TABLE]
and permutes all -dimensional subspaces of . Therefore, when , by (3), we have
[TABLE]
where . Note that
[TABLE]
For the sake of convenience, we rewrite as
[TABLE]
which makes no essential difference in the computation of GHWs. This is our first expression concerning .
3.4 The Second Expression
In this subsection, we derive an alternative expression of when . The main tool is the following bilinear form.
Let be a non-degenerate bilinear form given by
[TABLE]
Then for any -subspace of , we define
[TABLE]
We have the following lemma.
Lemma 7**.**
[10, Lemma 7]* Suppose . Then and*
[TABLE]
By (4) and the above lemma, we have the second expression concerning :
[TABLE]
Below, we will use this expression to determine the weight hierarchy of .
4 Proof of Theorem 1
Now we are going to prove Theorem 1. Throughout this section, we have the following assumptions.
is even, and .
- 2.
is the smallest positive integer such that and is odd.
Since and , we have .
For any and , define
[TABLE]
and
[TABLE]
By (5), we have
[TABLE]
Note that is an -vector space. Given the dimension of , as a first step, we need to consider the maximal size of the intersection . To this end, the following lemma determines the maximal size of the intersection between a cyclotomy class and an -subspace of . Given a subset , we use to denote the set .
Lemma 8**.**
Let be the smallest positive integer such that . Let be odd and be even. Let and be an -dimensional -subspace. Define a function
[TABLE]
Then, for ,
[TABLE]
where . Furthermore, the subspace that achieves the maximal value can be chosen as follows:
If , then we can choose any -dimensional subspace ;
- 2)
If , then we can choose any -dimensional subspace , which is a disjoint union of cosets of , such that .
Proof.
Suppose there exists an -dimensional subspace such that is maximal. Then, for , is an -dimensional subspace such that is also maximal. Hence, it suffices to consider the case .
Since and is odd, we have , which implies . When , by choosing a subspace , we have , which is clearly maximal.
When , for , define . Since and , we have
[TABLE]
Note that
[TABLE]
By (7), we have
[TABLE]
which implies
[TABLE]
Next, we are going to show that by choosing a proper subspace , the upper bound (8) can be achieved.
Let be an -dimensional subspace, which consists of disjoint cosets of and contains . Thus, we can write , where . For and , we claim . Suppose , where . For , since is a linear space, we have . Thus, the claim is true. Morevoer, for , we have
[TABLE]
Comparing with (7), we have for . Together with , we get
[TABLE]
Therefore, achieves the upper bound (8). ∎
For , define , where . By definition, we have
[TABLE]
which implies . By Lemma 8, it is easy to see that the maximal size of the intersection equals . Therefore, by (6), we have . To make as large as possible, we must have , which implies
[TABLE]
Moreover, for each , is chosen so that . Consequently, we have
[TABLE]
From the viewpoint of (6), (9) and (10), the subspace corresponding to the maximal can be characterized by the sequence , where . Without loss of generality, we assume that . Since , we write for some unique and .
Next, we are going to study which sequence leads to the maximal . As a preparation, we define two operations on the sequence . Suppose for some , satisfies . Then define an operation on as
[TABLE]
Suppose is of the form
[TABLE]
where . Then define an operation on as
[TABLE]
Furthermore, for , we define
[TABLE]
where is the function defined in Lemma 8. Now, we have the following lemma concerning the change of the summation when the operations and applied. Recall that , and we use to denote the unique integer such that .
Lemma 9**.**
Let satisfy . We have the following.
If , then ,
- 2)
If , then
[TABLE]
- 3)
If , then .
- 4)
If with , then .
Proof.
The proof is elementary and omitted here. ∎
For the sake of convenience, we define the operation in 1) of above lemma as . Similarly, we can define and . The above lemma indicates that when the operations , and are employed, the summation is nondecreasing. When the operation is employed, the situation is more involved. To be more precise, we define an inverse operation of as follows. Suppose for some , satisfies . Then define an operation on as
[TABLE]
Given a sequence satisfying , we have
[TABLE]
Given a sequence satisfying , we have
[TABLE]
Hence, the operation can be viewed as an inverse of . The following remark restates 2) of Lemma 9.
Remark 10**.**
Let be a sequence. If and , then
[TABLE]
If and , then
[TABLE]
Therefore, if (resp. ), the summation is nondecreasing when the operation (resp. ) applied.
We are going to show that any sequence can be transformed to one of a few sequences with special forms, by using operations , , , and . Moreover, with the help of Lemma 9 and Remark 10, we make sure that each operation used in the transformation keeps the summation nondecreasing. Hence, the sequence producing the maximal value of is among a few sequences with special forms. Next, we describe this transformation process.
Given any sequence , for the entries greater than (resp. less than) , we apply the operation (resp. ) repeatedly. Then, we can always get a sequence of the form
[TABLE]
where and . Applying the operation to repeatedly, we have the one of the following two cases:
[TABLE]
where and
[TABLE]
where .
Next, by using the operations , and , the sequence in Case A) or Case B) can be further transformed to one of the following four cases. We have Cases A1) and A2) which can be derived from Case A) and have Cases B1) and B2) which can be derived from Case B). Recall that , where and . We observe that each operation involved in the transformation keeps the summation nondecreasing.
[TABLE]
For instance, let us see how the sequences in Case A1) can be derived. In Case A), the sequence is of the form (11), where . If , we have , and . If , applying the operation repeatedly gives
[TABLE]
If , applying the operation repeatedly gives
[TABLE]
According to Lemma 9 and Remark 10, each operation involved in the transformation keeps the summation nondecreasing. Hence, the sequences in Case A1) have been obtained. Similarly, we can derive the corresponding sequences for the remaining three Cases A2), B1) and B2).
Therefore, we have shown that any sequence can be transformed to one of the above four Cases A1), A2), B1) and B2). Since each operation involved in the transformation keeps the summation nondecreasing, the sequence leading to the maximal value of must belong to one of the four cases.
If , considering Cases A2) and B1), a direct computation shows
[TABLE]
Thus, when , the sequence
[TABLE]
leads to the maximal value of . Consequently, by (10), we have
[TABLE]
If , considering Cases A1) and B2), a direct computation shows
[TABLE]
Thus, when , the sequence
[TABLE]
leads to the maximal value of . Consequently, by (10), we have
[TABLE]
Together with (2), the proof of Theorem 1 is complete.
5 Conclusion
The generalized Hamming weights are fundamental parameters of linear codes. They convey the structural information of a linear code and determine its performance in various applications. However, the computation of the GHWs of linear codes is difficult in general. This paper is a sequel of [10] and studies the GHWs of a family of cyclic codes introduced in [11], which may have arbitrary number of nonzeroes. We determine the weight hierarchy by generalizing a number-theoretic approach proposed in [13]. It is worthy to note that our main theorem can be regarded as an extension of the known results concerning the weight hierarchy of semiprimitive codes.
A very interesting question is, whether the techniques in this paper can be applied to some more complicated cases. Recall that two crucial conditions in our main theorem are being semiprimitive modulo and . We ask if the weight hierarchy can also be computed, when is semiprimitive mod and , or, when modulo belongs to the Index case, namely, generates an index subgroup of the multiplicative group of units in .
Acknowledgement
The author wishes to express his gratitude to Professor Cunsheng Ding and Professor Maosheng Xiong, for their guidance and encouragement during his stay at the Hong Kong University of Science and Technology.
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