A Construction of Linear Codes and Their Complete Weight Enumerators
Shudi Yang, Xiangli Kong, Chunming Tang

TL;DR
This paper constructs a new class of linear codes using defining sets based on trace functions over finite fields, explicitly determines their weight enumerators, and identifies several optimal codes with potential applications in cryptography and combinatorics.
Contribution
It introduces a novel construction of linear codes from specific trace-based defining sets and explicitly computes their complete weight enumerators using cyclotomic numbers and Gauss sums.
Findings
Explicit formulas for weight enumerators of the constructed codes
Identification of several optimal codes with few weights
Codes have higher rates suitable for cryptographic applications
Abstract
Recently, linear codes constructed from defining sets have been studied extensively. They may have nice parameters if the defining set is chosen properly. Let be a positive integer. For an odd prime , let and be the absolute trace function from onto . In this paper, we give a construction of linear codes by defining the code where Its complete weight enumerator and weight enumerator are determined explicitly by employing cyclotomic numbers and Gauss sums. In addition, we obtain several optimal linear codes with a few weights. They have higher rate compared with other codes, which enables them to have essential applications in areas such as association schemes and secret sharing…
| Weight | Frequency |
|---|---|
| 0 | 1 |
| Weight | Frequency |
|---|---|
| 0 | 1 |
| Weight | Frequency |
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| 0 | 1 |
| Weight | Frequency |
|---|---|
| 0 | 1 |
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Taxonomy
TopicsCoding theory and cryptography · graph theory and CDMA systems · Cooperative Communication and Network Coding
A Construction of Linear Codes and Their Complete Weight Enumerators
Shudi Yang, Xiangli Kong, Chunming Tang S. Yang is with the School of Mathematical Sciences, Qufu Normal University, Shandong 273165, P.R.China.
X. Kong is with the School of Mathematical Sciences, Qufu Normal University, Shandong 273165, P.R.China.
C. Tang is with School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, P.R.China.
E-mail: [email protected], [email protected], [email protected] received *********; revised ********.
Abstract
Recently, linear codes constructed from defining sets have been studied extensively. They may have nice parameters if the defining set is chosen properly. Let be a positive integer. For an odd prime , let and Tr be the absolute trace function from onto . In this paper, we give a construction of linear codes by defining the code
[TABLE]
where Its complete weight enumerator and weight enumerator are determined explicitly by employing cyclotomic numbers and Gauss sums. In addition, we obtain several optimal linear codes with a few weights. They have higher rate compared with other codes, which enables them to have essential applications in areas such as association schemes and secret sharing schemes.
Index Terms:
Linear code, complete weight enumerator, Gauss sum, cyclotomic number.
I Introduction
Throughout this paper, let be an odd prime, and let for a positive integer . Denote by a finite field with elements. The absolute trace function is denoted by . An linear code over is a -dimensional subspace of with minimum distance . The fraction is called the rate, or information rate, and gives a measure of the number of information coordinates relative to the total number of coordinates. The higher the rate, the higher the proportion of coordinates in a codeword actually contain information rather than redundancy (see [1]). The complete weight enumerator of a code over , will enumerate the codewords according to the number of symbols of each kind contained in each codeword (see [2]). Denote elements of the field by , where . For a vector , the composition of , denoted by , is defined as
[TABLE]
where is the number of components of that equal to . It is easy to see that . Let be the number of codewords with . Then the complete weight enumerator of the code is the polynomial
[TABLE]
where . One sees that the key to determining of a code is determining those and such that .
The complete weight enumerators of linear codes have been of fundamental importance to theories and practices since they not only give the weight enumerators but also demonstrate the frequency of each symbol appearing in each codeword. Blake and Kith investigated the complete weight enumerator of Reed-Solomon codes and showed that they could be helpful in soft decision decoding [3, 4]. Kuzmin and Nechaev studied the generalized Kerdock code and related linear codes over Galois rings and estimated their complete weight enumerators in [5] and [6]. Nebe [7] described the complete weight enumerators of generalized doubly-even self-dual codes. In [8], the study of the monomial and quadratic bent functions was related to the complete weight enumerators of linear codes. Recently, a lot of progress has been made on this subject. Ding [9, 10] showed that complete weight enumerators can be applied to the calculation of the deception probabilities of certain authentication codes. In [11, 12, 13], the authors studied the complete weight enumerators of some constant composition codes and presented some families of optimal constant composition codes.
We introduce the the generic construction of linear codes developed by Ding in [14, 15, 16]. Set , where . A linear code associated with is defined by
[TABLE]
Then is called the defining set of this code . In [16], the authors constructed the code with two or three weights whose defining set is , and its complete weight enumerator was established in [17, 18]. Along this inspired idea, many new results are dedicated to computing the complete weight enumerators and weight enumerators of specific codes, see [17, 19, 20, 21, 22, 23, 24, 25, 26, 27]. All of these researches are concerning the defining set with only one trace function. If we restrict the defining set with two or more trace functions, then it is possible to obtain linear codes with higher rate compared with others.
In this paper, we define the defining set
[TABLE]
and investigate the corresponding code of (1). To be precise, we present explicitly its complete weight enumerator and weight enumerator. Besides, we obtain several optimal linear codes with respect to the Griesmer bound. We show that they have higher rate compared with other codes so that they have many applications in association schemes [28] and secret sharing schemes [16].
The organization of this paper is as follows. Section 2 briefly recalls some definitions and results on cyclotomic numbers and Gauss sums over finite fields. Section 3 is devoted to the complete weight enumerator and weight enumerator of . We provide some examples to illustrate our main results. Finally, Section 4 concludes this paper and makes some remarks on this topic.
II Mathematical foundations
We begin with some preliminaries by introducing the concept of cyclotomic numbers and Gauss sums over finite fields. Recall that . Let be a primitive element of and for two positive integers , . The cyclotomic classes of order in are the cosets for , where denotes the subgroup of generated by . We know that .
For fixed and , we define the cyclotomic number to be the number of solutions of the equation
[TABLE]
where is the multiplicative unit of . That is, is the number of ordered pairs such that
[TABLE]
If is a multiplicative character of , then we can define the Gauss sum over as
[TABLE]
Let denote the quadratic character of by defining . The quadratic Gauss sum over is denoted by . When , we briefly write as , where is the quadratic character over .
Next, let us review some results on cyclotomic numbers and Gauss sums.
Lemma 1**.**
[29*]** When , the cyclotomic numbers are given by
even: , .
odd: , .*
Lemma 2**.**
[30]** Let be the quadratic character of , where , . Then
[TABLE]
In particular, and .
Lemma 3**.**
[30]** Let and with . Then
[TABLE]
where is the quadratic character of .
The following is the well-known Griesmer bound (see [31]) for linear codes over finite fields.
Lemma 4**.**
[31]** (Griesmer Bound) Let be an linear code over with and is a power of . Then
[TABLE]
where the symbol denotes the smallest integer not less than .
III Main results
In this section, we will focus our attention on the complete weight enumerator of defined by (1), where
[TABLE]
Now we contribute to determine the parameters of . It is obvious that the length is equal to the cardinality , which is given in the following Lemma. For later use, we write for simplicity.
Lemma 5**.**
[26]** For , define
[TABLE]
*The following assertions hold.
If , then we have*
[TABLE]
* If , then we have*
[TABLE]
It follows immediately from the previous lemma that the length of is .
Let and . For a codeword of , we denote to be the number of components of that are equal to . Then
[TABLE]
where
[TABLE]
We are going to determine the values of and in Lemmas 6, 7 and 8. For convenience, we denote and .
Lemma 6**.**
For and , we have
[TABLE]
Proof.
It follows from the definition that
[TABLE]
Note that the equation has solutions if and only if for . Then if . Hence, if , we have by the orthogonal property of additive characters that
[TABLE]
The desired conclusion then follows. ∎
Lemma 7**.**
For , we have
[TABLE]
Proof.
It follows from Lemma 3 that
[TABLE]
The desired conclusion then follows. ∎
Lemma 8**.**
*For and , we have the following assertions.
If and , then*
[TABLE]
* If and , then
if , we have*
[TABLE]
* if , we have*
[TABLE]
* If and , then*
[TABLE]
* If and , then
if , we have*
[TABLE]
* if , we have*
[TABLE]
Proof.
It follows from Lemma 3 that
[TABLE]
Thus there are four distinct cases to consider:
- (1)
and , 2. (2)
and , 3. (3)
and , 4. (4)
and .
Case (1): Suppose that and . We obtain by (III) that
[TABLE]
If , then
[TABLE]
If and , then we have from Lemma 3 that
[TABLE]
If , again from Lemma 3, we have
[TABLE]
Case (2): Suppose that and . We obtain by (III) that
[TABLE]
If and , then
[TABLE]
If and , then
[TABLE]
Let . If , then
[TABLE]
Therefore, if and , then
[TABLE]
Suppose that and . Then
[TABLE]
where we denote . Note that the equation over has two distinct solutions if and only if . Therefore, when we obtain
[TABLE]
Since as , we have
[TABLE]
Case (3): Suppose that and . We obtain by (III) that
[TABLE]
If , then
[TABLE]
If and , then we have from Lemma 3 that
[TABLE]
If , again from Lemma 3, we have
[TABLE]
Case (4): Suppose that and . We obtain by (III) that
[TABLE]
If and , then
[TABLE]
If and , then
[TABLE]
Set . If , then
[TABLE]
since .
Therefore, if and , then and
[TABLE]
Suppose that and . We denote as before. Then
[TABLE]
This finishes the proof. ∎
The following lemmas will be required when calculating the frequency of each component in .
Lemma 9**.**
For and , define
[TABLE]
*Denote . Then the following assertions hold.
If and , then*
[TABLE]
* If and , then*
[TABLE]
* If and , then*
[TABLE]
* If and , then*
[TABLE]
Proof.
By definition, we see that
[TABLE]
where
[TABLE]
Note that . It is easily verified that
[TABLE]
To determine , we observe that
[TABLE]
Case (1): Suppose that and . Then
[TABLE]
Case (2): Suppose that and . Then
[TABLE]
Case (3): Suppose that and . Then
[TABLE]
Case (4): Suppose that and . Then
[TABLE]
Combining the above with (III) gives us the desired conclusion, which completes the whole proof. ∎
Lemma 10**.**
Suppose that , and . For , let denote the number of the pairs such that . Then we have
[TABLE]
Proof.
We first consider that . So and the number of the pairs satisfying is .
Now suppose that . Note that yields that
[TABLE]
Set . We count the number of the pairs for a fixed such that (resp. ). It follows from Lemma 1 that this number is equal to
[TABLE]
So the number of the pairs such that (resp. ) is (resp. ). We conclude that (resp. ), and hence the result follows. ∎
Lemma 11**.**
Suppose that , and . For , let denote the number of the pairs such that . Then we have
[TABLE]
and
[TABLE]
Proof.
The proof is similar to that of Lemma 10 and so it is omitted here. ∎
With the above preparations, we are ready to determine the complete weight enumerator of , which is stated in the next theorem and then illustrated with some examples.
Theorem 12**.**
*Let be the linear code defined by (1), where the defining set . Define and . Assume that .
If and , then has parameters . Its complete weight enumerator is given as follows.*
. This value occurs only once.
. This value occurs times.
. This value occurs times.
N_{\rho}=\left\{\begin{array}[]{ll}p^{m-2}&\text{ if }\rho=\rho_{0}\\ 0&\text{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs only once.*
N_{\rho}=\left\{\begin{array}[]{ll}p^{m-3}-(p-1)(-1)^{\frac{(p-1)m}{4}}p^{\frac{m-4}{2}}&\text{ if }\rho=\rho_{0}\\ p^{m-3}+(-1)^{\frac{(p-1)m}{4}}p^{\frac{m-4}{2}}&\text{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs times.
If and , then has parameters , where*
[TABLE]
Its complete weight enumerator is given as follows.
. This value occurs only once,
. This value occurs times.
. This value occurs \frac{p-1}{2}{\big{(}{p^{m-1}+(-1)^{\frac{(p-1)m}{4}}p^{\frac{m}{2}}}\big{)}} times.
N_{\rho}=\left\{\begin{array}[]{ll}n&\text{ if }\rho=\rho_{0}\\ 0&\text{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs only once.*
N_{\rho}=\left\{\begin{array}[]{ll}p^{m-3}-(-1)^{\frac{(p-1)m}{4}}p^{\frac{m-2}{2}}&\text{ if }\rho=\rho_{0}\\ p^{m-3}&\text{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs times.*
N_{\rho}=\left\{\begin{array}[]{ll}p^{m-3}-(p-1)(-1)^{\frac{(p-1)m}{4}}p^{\frac{m-4}{2}}&\text{ if }\rho=\rho_{0}\\ p^{m-3}+(-1)^{\frac{(p-1)m}{4}}p^{\frac{m-4}{2}}&\text{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs times.*
N_{\rho}=\left\{\begin{array}[]{ll}p^{m-3}-(p-1)(-1)^{\frac{(p-1)m}{4}}p^{\frac{m-4}{2}}&\text{ if }\rho=\rho_{0},\rho_{1}\\ p^{m-3}+(-1)^{\frac{(p-1)m}{4}}p^{\frac{m-4}{2}}&\text{ if }\rho\neq\rho_{0},\rho_{1}\end{array}\right.* as two distinct elements run through . Each value occurs times.
If and , then has parameters . Its complete weight enumerator is given as follows.*
. This value occurs only once.
. This value occurs times.
. This value occurs times.
. This value occurs times.
N_{\rho}=\left\{\begin{array}[]{ll}p^{m-2}&\text{ if }\rho=\rho_{0}\\ 0&\text{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs only once.*
N_{\rho}=\left\{\begin{array}[]{ll}p^{m-3}&\text{ if }\rho=\rho_{0}\\ p^{m-3}+\eta(\rho-\rho_{0})(-1)^{\frac{(p-1)(m+1)}{4}}p^{\frac{m-3}{2}}&\text{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs times.*
N_{\rho}=\left\{\begin{array}[]{ll}p^{m-3}&\text{ if }\rho=\rho_{0}\\ p^{m-3}-\eta(\rho-\rho_{0})(-1)^{\frac{(p-1)(m+1)}{4}}p^{\frac{m-3}{2}}&\text{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs times.
If and , then has parameters , where*
[TABLE]
Its complete weight enumerator is given as follows.
. This value occurs only once.
. This value occurs times.
N_{\rho}=\left\{\begin{array}[]{ll}n&\text{ if }\rho=\rho_{0}\\ 0&\text{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs only once.*
N_{\rho}=\left\{\begin{array}[]{lr}p^{m-3}&\textup{ if }\rho=\rho_{0}\\ p^{m-3}+\eta(-m_{p})\eta(\rho^{2}-\rho\rho_{0})(-1)^{\frac{(p-1)(m+1)}{4}}p^{\frac{m-3}{2}}&\textup{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs times.*
N_{\rho}=\left\{\begin{array}[]{lr}p^{m-3}-\eta(-m_{p})(-1)^{\frac{(p-1)(m+1)}{4}}p^{\frac{m-3}{2}}&\textup{ if }\rho=\rho_{0}\\ p^{m-3}&\textup{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through . Each value occurs times.*
N_{\rho}=\left\{\begin{array}[]{lr}p^{m-3}&\textup{ if }\rho=\rho_{0},\rho_{1}\\ p^{m-3}+\eta{\big{(}{-m_{p}(\rho-\rho_{0})(\rho-\rho_{1})}\big{)}}(-1)^{\frac{(p-1)(m+1)}{4}}p^{\frac{m-3}{2}}&\textup{ if }\rho\neq\rho_{0},\rho_{1}\end{array}\right.* as two distinct elements run through . Each value occurs times.*
N_{\rho}=p^{m-3}+\eta(-m_{p})\eta{\big{(}{m_{p}^{2}\rho^{2}-\Delta}\big{)}}(-1)^{\frac{(p-1)(m+1)}{4}}p^{\frac{m-3}{2}}, as runs through such that . Each value occurs times.
N_{\rho}=\left\{\begin{array}[]{lr}p^{m-3}-\eta(m_{p})(-1)^{\frac{(p-1)(m+1)}{4}}p^{\frac{m-3}{2}}&\textup{ if }\rho=\rho_{0}\\ p^{m-3}+\eta(-m_{p})\eta{\big{(}{m_{p}^{2}(\rho-\rho_{0})^{2}-\Delta}\big{)}}(-1)^{\frac{(p-1)(m+1)}{4}}p^{\frac{m-3}{2}}&\textup{ if }\rho\neq\rho_{0}\end{array}\right.* as runs through and runs through such that . Each value occurs times.*
Proof.
From the definition, this code has length which follows from Lemma 5 and dimension . As before , for . Recall that by (III) for . We will divide the proof into four parts and employ Lemmas 6, 7 and 8 to compute .
We first consider the case that is even and . In this case the length is . If , then , , consequently
[TABLE]
Each value occurs only once.
Suppose that . Under this assumption we have . If , then
[TABLE]
By Lemma 5, the frequency is as .
If , , then
[TABLE]
By Lemma 9, the frequency is (p-1){\big{(}{p^{m-2}-p^{-1}G_{m}}\big{)}} for all .
Hence we conclude that occurs times.
If , , then
[TABLE]
It follows from Lemma 5 that this value occurs for all .
If , then
[TABLE]
where we denote . By Lemma 9, the frequency is for all .
We now consider the case that is even and . In this case the length is . If , then , , consequently and
[TABLE]
Each value occurs only once.
Suppose that . Under this assumption we have . If , then
[TABLE]
By Lemma 5, the frequency is as .
If , , then
[TABLE]
where we denote . By Lemma 5, the frequency is .
If and , then
[TABLE]
where we denote since and imply that . By Lemma 9, the frequency is as .
If and , then
[TABLE]
where we denote as two distinct roots of the equation since . By Lemma 9, the frequency is .
If and , then
[TABLE]
By Lemmas 9 and 10, the frequency is \frac{1}{2}(p-1){\big{(}{p^{m-1}+G_{m}}\big{)}}.
Suppose that is odd and . In this case the length is . If , then , , consequently
[TABLE]
Each value occurs only once.
Suppose that . Under this assumption we have . If , then and
[TABLE]
By Lemma 5, the frequency is as .
If , , then
[TABLE]
By Lemma 9, the frequency is for all .
Hence we conclude that occurs times.
If , , then
[TABLE]
This indicates that or . According to Lemma 5, the frequency of each value is for all .
If , then
[TABLE]
where we denote . This induces that
[TABLE]
or
[TABLE]
According to Lemma 9, each value occurs for all .
Assume that is odd and . In this case the length is . If , then , , consequently , . Hence
[TABLE]
Each value occurs only once.
Suppose that . Under this assumption we have . If , then
[TABLE]
By Lemma 5, the frequency is as .
If , , then
[TABLE]
where we denote . By Lemma 5, the frequency is .
If and , then , consequently
[TABLE]
where we denote since and imply that . By Lemma 9, the frequency is as .
If and , then
[TABLE]
where . If , then the equation must have two distinct roots, which are denoted by and . Thus can be represented as . Therefore, if and , we have
[TABLE]
By Lemmas 9, the frequency is . Moreover there are such values by Lemma 10.
If and , then
[TABLE]
More precisely, by writing f(\rho)=-m_{p}{\big{(}{\rho-B/m_{p}}\big{)}}^{2}+\Delta/m_{p}, we deduce that
[TABLE]
where we denote and . The number of such values is . On the other hand, if , then
[TABLE]
The number of such values is . Again from Lemma 9, each value occurs times.
This completes the whole proof. ∎
Remark 1**.**
For a fixed element in , if we define the set
[TABLE]
then we get the code of the form (1). It is interesting to see that any code has the same codewords. Actually, there exists a mapping such that
[TABLE]
This implies that the code is equal to . Thus Theorem 12 actually demonstrates the complete weight enumerator of for all .
The next result describes the weight distribution of .
Corollary 13**.**
*Let be the linear code defined by (1), where the defining set . The following assertions hold.
If and , then the weight distribution of is given in Table I.
If and , then the weight distribution of is given in Table II, where .
If and , then the weight distribution of is given in Table III.
If and , then the weight distribution of is given in Table IV, where ,*
[TABLE]
and
[TABLE]
Proof.
To calculate the weight distribution of , we will consider four distinct cases:
- (1)
and , 2. (2)
and , 3. (3)
and , 4. (4)
and .
The results for cases , and will come from the corresponding complete weight enumerator as shown in Theorem 12, by observing that
[TABLE]
where . For the last case, we will present a direct calculation by considering the number of components of that are equal to [math], which is denoted by , where . That is,
[TABLE]
Then the weight of codeword is given by
[TABLE]
where is the length of . Substituting in (III) yields that
[TABLE]
where
[TABLE]
Note that and . In the same manner as in Lemmas 6, 7 and 8, we can show
[TABLE]
The desired conclusion then follows from (5), (6), Lemmas 5, 9 and 11. ∎
Corollary 14**.**
Let be the linear code defined by (1), where the defining set . Then it is optimal with respect to the Griesmer bound only if . Furthermore, when , it is MDS and it has parameters if , or if , it has parameters if and if .
Proof.
We only show the case of odd since other case can be similarly verified. If , then it follows from Corollary 13 that has parameters . Taking , we can deduce that
[TABLE]
So the equation gives that , which means that . As , we must have . Therefore if , the code is MDS with parameters .
Suppose that and . From Corollary 13, the code has parameters . Then
[TABLE]
So the equation gives that , consequently . Thus is MDS with parameters .
Suppose that and . In the same manner we obtain that is MDS with parameters when and .
Hence we conclude that is an optimal code achieving the Griesmer bound by Lemma 4 provided that . ∎
In the following, we present some examples to illustrate our results.
Example 15**.**
Let . This corresponds to the case that and . By Theorem 12, the code is an linear code. Its complete weight enumerator is
[TABLE]
and its weight enumerator is
[TABLE]
These results coincide with numerical computation by Magma.
Let us define the code of (1) where
[TABLE]
Magma works out that has parameters . It is clear that the rate of is higher than that of .
Example 16**.**
Let . This corresponds to the case that and . By Theorem 12, the code is a linear code. Its complete weight enumerator is
[TABLE]
and its weight enumerator is
[TABLE]
These results coincide with numerical computation by Magma. This code is optimal according to Markus Grassl’s table (see http://www.codetables.de/).
Let us define the code of (1) with the defining set
[TABLE]
The code has been studied in [16], which has parameters . It is clear that the rate of is higher than that of .
Example 17**.**
Let . This corresponds to the case that and . We have . By Corollary 14, the code is MDS with parameters . Its complete weight enumerator is
[TABLE]
and its weight enumerator is
[TABLE]
These results coincide with numerical computation by Magma.
IV Concluding remarks
We have constructed a class of linear codes with a few weights by giving two restrictions in the defining set. In particular, we obtain MDS codes. Moreover, the codes defined in this paper may have shorter length and higher information rate. So they can be employed to construct authentication codes using the framework of [9] and [10] and the complete weight distributions of the codes allow the determination of the success probability with respect to certain attacks. More codes can be constructed in this way and we leave this for future work.
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- 5[5] A. Kuzmin, A. Nechaev, Complete weight enumerators of generalized Kerdock code and linear recursive codes over Galois ring, in: Workshop on Coding and Cryptography, 1999, pp. 332–336.
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