The Complete Hierarchical Locality of the Punctured Simplex Code
Matthias Grezet, Camilla Hollanti

TL;DR
This paper introduces a new bound for codes with hierarchical locality, derives all possible localities for a class of punctured Simplex codes, and demonstrates their optimality in hierarchical locality.
Contribution
It provides the first alphabet-dependent bound for hierarchical locality and characterizes the localities of punctured Simplex codes, proving their optimality.
Findings
New alphabet-dependent bound for hierarchical locality
Complete list of localities for punctured Simplex codes
Punctured Simplex codes are optimal with hierarchical locality
Abstract
This paper presents a new alphabet-dependent bound for codes with hierarchical locality. Then, the complete list of possible localities is derived for a class of codes obtained by deleting specific columns from a Simplex code. This list is used to show that these codes are optimal codes with hierarchical locality.
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11institutetext: Matthias Grezet 22institutetext: Camilla Hollanti 33institutetext: Department of Mathematics and Systems Analysis, Aalto University, Finland
33email: [email protected], [email protected]
The complete hierarchical locality of the punctured simplex code
††thanks: Part of the results were submitted without any proofs to IEEE International Symposium Information Theory (ISIT) 2019.
Matthias Grezet
Camilla Hollanti
(Received: date / Accepted: date)
Abstract
This paper presents a new alphabet-dependent bound for codes with hierarchical locality. Then, the complete list of possible localities is derived for a class of codes obtained by deleting specific columns from a Simplex code. This list is used to show that these codes are optimal codes with hierarchical locality.
Keywords:
Hierarchical locality Alphabet-dependent bound Simplex code Matroid theory
MSC:
94B65 94B60 05B35
††journal: Designs, Codes and Cryptography
1 Introduction
In modern distributed storage systems (DSSs) failures happen frequently, whence decreasing the number of connections required for node repair is crucial. Locally repairable codes (LRCs) are a subclass of erasure-correcting codes, which allow a small number of failed nodes to be repaired by accessing only a few other nodes. LRCs were introduced in gopalan12 , papailiopoulos12 where the codes can locally repair one failure. They were later extended in prakash12 , kamath13 to be able to locally repair up to failures.
An linear code of length , dimension , and minimum hamming distance , has all-symbol locality if for all code symbols , there exists a set containing such that and the minimum distance of the restriction of to is at least . We refer to as an -LRC and to the sets as repair sets or local sets. Related Singleton-type bounds have been derived for various cases in gopalan12 ; papailiopoulos12 ; prakash12 and the first bound with a fixed code alphabet was obtained in cadambe15 for . Constructions achieving the Singleton-type bounds and the bound in cadambe15 for were provided in papailiopoulos12 ; prakash12 ; kamath13 ; rawat16 ; kamath13b ; rawat14b ; tamo14 ; westerback15 ; silberstein18 ; zeh15 .
The authors of agarwal18 proposed the first alphabet-dependent bound on LRCs over the alphabet using an upper bound on the cardinality of a code of length and minimum distance . The global bound is as follows:
[TABLE]
Recently, grezet18LRC provided a different alphabet-dependent bound for LRCs of the same type as the bound in cadambe15 using the Griesmer bound . The bound has the following form : For any linear -LRC with the upper bound on the local dimension of the repair sets,
[TABLE]
where such that and .
In sasidharan15 , the authors introduced the notion of codes with hierarchical locality (H-LRCs), which optimizes futher the number of nodes contacted for repair according to the number of failures. A 2-level H-LRC is a code where the restrictions to the repair sets are themselves LRCs, thus providing an extra layer of locality. If an H-LRC has locality , then the number of nodes contacted to repair up to failures is at most and the number of nodes contacted for repair is at most if the number of failures is and . This concept can be easily generalized to an arbitrary level of hierarchy. A Singleton-type bound for codes with hierarchical locality was derived in sasidharan15 and constructions attaining the bound were provided in sasidharan15 ; ballentine18 .
In Section 3, we show how we can adapt the construction algorithms provided in sasidharan15 to obtain an alphabet-dependent bound for H-LRCs of the same type as in cadambe15 . By construction, this bound is at least as good as the Singleton-type bound derived in sasidharan15 .
In Section 4, we study the locality of one particular construction of LRCs presented in silberstein18 . The general idea of this construction is to remove a Simplex code from another Simplex code of higher dimension. It was shown in silberstein18 that these codes achieve the Griesmer bound and are LRCs with . The goal in this section is to prove the locality for every dimension and higher and show that this construction leads to optimal LRCs for every locality by the bound (2) and to optimal H-LRCs by the new bound derived in Section 3.
As a first step, we describe the restrictions of dimension and prove the locality for this dimension using combinatorial techniques. These results allow us to derive the weight enumerator of the constructed codes. As a second step, we use a recursive argument to get all the restrictions of these codes to closed sets. Our main contribution is the complete list of possible localities for these codes. In particular, this shows that the constructed codes are alphabet-optimal H-LRCs. Finally, since a special case of this construction leads to the Reed–Muller codes RM, we obtain as a corollary to our result that the Reed–Muller codes RM are H-LRCs and we derive their locality parameters.
2 Preliminaries
We denote the set by and the set of all subsets of by . The set of all positive integers including [math] is denoted by .
The Gaussian coefficient, which counts the number of subspaces of dimension in the vector space , is denoted by
[TABLE]
For a length- vector and a set , the vector denotes the restriction of the vector to the coordinates in the set . A generator matrix of a linear code is where is a column vector for . The shortening of a code to the set of coordinates is defined by and the restriction of a code to is defined by . For convenience, we call the codes obtained by a restriction restricted codes. Two linear codes and are called isomorphic if can be obtained by a permutation on the coordinates of the codewords of .
The support of a codeword is and its weight is . The weight enumerator of is defined as
[TABLE]
The Simplex code , or sometimes , is a linear code over obtained via the generator matrix consisting of all pairwise linearly independent vectors in . The parameters of are therefore .
Since most of the work in this paper is done on subsets of coordinates and restricted codes, it is preferable to use the notion of an entropy function on the subsets that corresponds to the dimension of the restriction to . We only state here the definition for linear codes but it can be generalize to bigger class of codes (see westerback18 ).
Definition 1
Let be a linear code of length and . The entropy associated to is the function with .
For ease of notation, if the underlying code of is clear, we drop the specification to . For a subset , is equivalent to the rank of the submatrix of the generator matrix formed by the columns with or the rank function of in the associated matroid of . As such, it has the following standard properties.
Proposition 1
Let be a linear code of length and the entropy function associated to . For , we have
, 2. 2.
If then , 3. 3.
.
The entropy function also behaves nicely for restricted codes. Let and the restriction of to the set . Then for , we have .
Finally, we define a closure operation on the subsets of for linear codes.
Definition 2
Let be a linear code of length and . The closure operator is . A set is a closed set if .
One can think of the closure operator via the generator matrix of where is the set of all columns in contained in the linear span of the columns indexed by .
2.1 Preliminaries on matroids
Since we work on Simplex codes which have a lot of combinatorial properties, we use some tools coming from matroid theory. Matroids have many equivalent definitions in the literature. Here, we choose to present matroids via their rank functions. Much of the contents in this part can be found in more detail in freij18 .
Definition 3
A (finite) matroid is a finite set together with a rank function such that for all subsets ,
[TABLE]
Any matrix over a field generates a matroid , where is the set of columns of , and is the rank of the submatrix of formed by the columns indexed by . As elementary row operations preserve the row space of for all , it follows that row-equivalent matrices generate the same matroid.
Thus, there is a straightforward connection between linear codes and matroids. Let be a linear code over a field . Then any two different generator matrices of will have the same row space by definition, so they will generate the same matroid. Therefore, without any inconsistency, we can denote the matroid associated to these generator matrices by where is the rank of the submatrix of formed by the columns indexed by .
Two matroids and are isomorphic if there exists a bijection such that for all subsets . We denote two isomorphic matroids by . This implies in particular that if and are two linear codes and is isomorphic to , then is isomorphic to .
One way of defining a new matroid from an existing one is obtained by restricting the matroid to one of its subsets. For a given set , we define the restriction of to to be the matroid by for all subsets . The restriction of to is called the deletion of and is denoted by . The two previous operations correspond to the restriction and puncturing of a linear code .
Let be a matroid. The closure operator is defined by . A subset is a flat if . The collection of flats of is denoted by and forms a lattice under the inclusion. For , the meet is and join is . We denote by the covering relation, i.e., if and there is no with . A hyperplane is a flat of with and the collection of all hyperplanes is denoted by . One interesting property of is that every flat can be expressed as an intersection of hyperplanes, i.e., . Finally, for , we can express the flats of via the flats of by the relation .
3 Bound for H-LRCs
Codes with hierarchical locality were introduced in sasidharan15 to optimize further the number of nodes contacted for repair. In this section, we first focus on H-LRCs with -level hierarchy and derive an alphabet-dependent bound for these codes based on cadambe15 . Then, we extend this bound to H-LRCs with -level hierarchy.
Definition 4
Let and . An linear code is a code with hierarchical locality having locality parameters if for all code symbols , there exists a set such that
, 2. 2.
, 3. 3.
The minimum distance of is at least , 4. 4.
is an LRC with -locality.
The codes are called middle codes and their restrictions of dimension and minimum distance are called local codes. Similarly, the middle sets and local sets are the sets and the sets such that the restrictions to them give the local codes. Notice that, contrary to the standard definition of LRCs, the authors of sasidharan15 bound the dimension of the restricted codes instead of the size. It was proven in (sasidharan15, , Theorem 2.1) that any linear code with hierarchical locality satisfies the following Singleton-type bound
[TABLE]
To obtain the alphabet-dependent bound in cadambe15 , the authors proved two results: the construction of a restricted code with small dimension and large size and a lemma about shortened codes. The lemma is the following.
Lemma 1 (cadambe15 , Lemma 2)
Let be an linear code over and such that . Then the shortened code has parameters .
Therefore, to get an alphabet-dependent bound for H-LRCs, we need to construct a set with an upper bound on its entropy and such that its size uses the hierarchical locality property to be as large as possible. To achieve this requirement, we modify the construction algorithm used in the proof of the Singleton-type bound in sasidharan15 .
Lemma 2
Let be an H-LRC with locality and with . Then, there exists a set such that
- •
,
- •
.
Proof
We use the construction given in Algorithm 1 to build the set . In the algorithm, denotes the incremental entropy and is a lower bound on the incremental size. Indeed if and is a local set such that and , then . Similarly, if and is a middle set such that and , then .
Denote by the set obtained at the end of Algorithm 1 and by and the last indices. The total number of local sets visited in this algorithm is since the union of arbitrary local sets has an entropy less than . By the same argument, we have .
Let and such that and which is always possible to construct. Finally, let . We prove now that has the desired properties.
By construction, we have . For the size, we have
[TABLE]
Hence, has the desired entropy and size. ∎
Thus, we obtain the following bound for 2-level H-LRCs.
Theorem 3.1
Let be an H-LRC over with locality . Then we have
[TABLE]
where and is the largest possible dimension of a code of length , for a given alphabet size and minimum distance .
Proof
The theorem follows from Lemma 1 and 2 in the same manner as in cadambe15 by using the estimation of the size coming from Lemma 2. ∎
Lemma 2 can be seen as a proof of concept that we can modify the algorithms in sasidharan15 to obtain an alphabet-dependent bound. Indeed, the algorithm presented here and the one presented in sasidharan15 are equivalent in the sense that if and is the set obtained by the algorithm in the proof of Lemma 2, we obtain again the Singleton-type bound (3) via the relation . This implies that the bound (4) is at least as good as the Singleton-type bound (3).
3.1 Extension of the bound to arbitrary levels of hierarchy
In this part, we extend the alphabet-dependent bound to H-LRCs with -level hierarchical locality. The definition and notation of these codes is the following.
Definition 5
An linear code is a code with -level hierarchical locality having locality parameters if for all code symbols , there exists a collection of sets such that for every , we have
, 2. 2.
3. 3.
The minimum distance of is at least , 4. 4.
is a code with -level hierarchical locality having locality
parameters .
A set for is referred to as a level- set. It was proven in (sasidharan15, , Theorem 3.1) that any H-LRC with hierarchical locality satisfies the following Singleton-type bound
[TABLE]
Obtaining an alphabet-dependent bound for these codes is done in the same manner as in the case of .
Lemma 3
Let be an H-LRC with locality and with . Then, there exists a set such that
- •
,
- •
.
Proof
We use the construction given in Algorithm 2 to build the set . The basic idea is the same as in the proof of Lemma 2. First, the algorithm identifies the smallest such that a level- set has . This is important when or during the process of the algorithm when becomes small. After this set is found, the algorithm visits recursively the sets with such that each of them is contained in the previous one and identifies a level- set . Then, it starts adding to the level- sets contained in . When every symbol of has been added to , it steps back one level, finds a new that contains new symbols not in , and adds them in the same manner using level- sets. At some point, the algorithm adds to a level- set containing the last remaining symbols of . The second while loop is not satisfied anymore and because the last added set was a level- set, the algorithm adds one to the count of the visited sets per level between and . Next, it pursues by identifying another level- set satisfying or, if all level- sets exceed , the algorithm searches for valid level- sets.
As in the proof of Lemma 2, denotes the incremental entropy and is a lower bound on the incremental size corrected accordingly to the level of the added set. The counters for are the number of level- sets visited by the algorithm.
When the algorithm terminates, the counters are lower-bounded by
[TABLE]
since the union of arbitrary level- sets has an entropy less than .
Denote by the set obtained at the end of the algorithm and by the last values of the counters. Let and such that and . Finally, let . We prove now that has the desired properties. By construction, we have . For the size, we have
[TABLE]
Hence has the desired entropy and size.
Thus, we obtain the following extension of the bound (4) for -level H-LRCs.
Theorem 3.2
Let be an H-LRC over with locality . Then, we have
[TABLE]
where and is the largest possible dimension of a code of length , for a given alphabet size and minimum distance .
As in the case , for and the set obtained by Algorithm 2, we obtain again the Singleton-type bound (5) via the relation , which shows that the bound (6) is at least as good as the Singleton-type bound (5). Moreover, the bound (6) yields that H-LRCs achieving any bound on the parameters only are directly alphabet-optimal H-LRCs by setting .
4 Hierarchical locality of
In silberstein18 , the authors presented four different constructions of linear LRCs with small locality and high availability. The constructions are based on a method developed in farrell70 where the generator matrix of a code is obtained by deleting specific columns from the generator matrix of a Simplex code. In this section, we are interested in the locality of one particular construction in silberstein18 where the deleted columns form again a Simplex code. This construction is highly combinatorial and yields optimal codes achieving the Griesmer bound. The objective is to describe the locality parameters for and all dimensions. We show that these codes are locally repairable codes for every dimension implying a complete optimization of the number of nodes contacted for repair according to the number of failures. Moreover, using combinatorial techniques, we establish the complete list of possible locality and show how the local sets can be arranged to form a hierarchical locality. Finally, we prove that these codes are optimal LRCs for all localities and alphabet-optimal H-LRCs by the new bound (6).
We start by formally defining the construction of these linear LRCs.
Construction 1 (silberstein18 Construction IV)
Let be an generator matrix of the Simplex code and an generator matrix of the Simplex code with . Let be the generator matrix obtained by prepending zeros to every column of . Let be the matrix obtained by deleting the columns of from . Then generates a linear code over denoted by .
It was proven in (silberstein18, , Theorem 14) that the code with and is a linear LRC over with locality if or if and , and with locality when and . Moreover, achieves the Griesmer bound by (silberstein18, , Lemma 16). Notice that the code is isomorphic to the Reed–Muller code RM.
The following example illustrates Construction 1.
Example 1
Let and be the generator matrices of the binary Simplex codes and respectively. Then is a binary code generated by the matrix
{1}$${0}$${0}$${0}$${1}$${1}$${1}$${0}$${0}$${0}$${1}$${1}$${1}$${0}$${1}$${0}$${1}$${0}$${0}$${1}$${0}$${0}$${1}$${1}$${0}$${1}$${1}$${0}$${1}$${1}$${0}$${0}$${1}$${0}$${0}$${1}$${0}$${1}$${0}$${1}$${1}$${0}$${1}$${1}$${1}$${0}$${0}$${0}$${1}$${0}$${0}$${1}$${0}$${1}$${1}$${0}$${1}$${1}$${1}$${1}$$\left(\vbox{\hrule height=25.65344pt,depth=25.65344pt,width=0.0pt}\right.$$\left.\vbox{\hrule height=25.65344pt,depth=25.65344pt,width=0.0pt}\right)
where the shadowed columns , and are deleted.
4.1 Locality of with dimension
The goal of this subsection is to obtain the locality of with a dimension of . For this, we make a detour to matroid theory by studying the relation between the hyperplanes of the matroid associated to the Simplex code and the hyperplanes of the matroid associated to . Indeed, the Simplex code has intrinsically a lot of useful combinatorial structures and Construction 1 corresponds to a deletion in matroid theory. Therefore, matroid theory is used here as a tool to understand the closed sets of of dimension and to construct the local set for every code symbol. We start by presenting a lemma that gives the relation between a flat and the hyperplanes of the matroid associated to the Simplex code .
Lemma 4
Let be the matroid associated to the Simplex code and a flat with . Then, for all hyperplanes either or .
Proof
Suppose . Then . Now is isomorphic to the lattice of linear spaces of . Therefore is a modular lattice and the intervals and are two isomorphic sublattices. Since , then and . ∎
Because the extended in Construction 1 is a closed set, we use the previous lemma applied to the extended to understand the hyperplanes of via the hyperplanes of and the deletion corresponding to removing the columns in the generator matrix of .
Proposition 2
Let be the matroid associated to the Simplex code and the matroid associated to the code . Let also be such that . Then, the map
[TABLE]
is a bijection.
Proof
By construction, the image of is a subset of . Firstly, we prove that is contained in the image of . Secondly, we prove that is well-defined which implies with the first part that is a surjection. Finally, we prove that is an injection.
The flats of can be obtained by the flats of via the relation
[TABLE]
and .
Therefore, we have that . Moreover, since , all hyperplanes of have a rank equal to . Combining this with the fact that implies that if , then there exists such that . Thus is contained in the image of .
Let and let a set. To show that is well-defined, it is enough to prove that , since . For this, we use an argument from coding theory. Since the restricted code is isomorphic to the Simplex code , its minimum distance is equal to . By construction of and , the size of is . We distinguish two cases.
Assume that . Let . By restriction and deletion definitions, we have
[TABLE]
Let be the minimum distance of . We have
[TABLE]
Since , we have . Thus, .
Assume now that . Then, there is a unique hyperplane of that contains which is itself. Let with . By Lemma 4, we have . Since the restricted code is isomorphic to the Simplex code , we have . By using the same argument on the minimum distance of , we have
[TABLE]
Hence, is well-defined and since the image of contains , is a surjection.
To prove the injection, suppose that for with . Then, . Now we look at these sets in . Since we showed that , we have that because is a hyperplane containing . Similarly, we have . Hence and is an injection. ∎
The map in Proposition 2 gives us the relation between the hyperplanes of and the hyperplanes of the matroid . We can now completely describe the restrictions of to hyperplanes and see that these restrictions are in fact isomorphic to certain codes obtained by Construction 1.
Proposition 3
Let be the code , the matroid associated to , and . Then is either isomorphic to the code or to .
Proof
Let be the matroid associated with and such that . By Proposition 2, there exists such that . We prove the isomorphism by distinguishing two cases depending on Lemma 4.
Suppose that . By restriction properties, we have
[TABLE]
By construction of the Simplex code, the matroid is isomorphic to , the matroid associated to the Simplex code . Since , we have that is isomorphic to .
Let and be the ground sets of and respectively. Then, there exists a bijection that preserves the rank. Let be a matrix associated to , the set of columns of and . Then is isomorphic to . Since the submatrix formed by the columns indexed in has a rank equal to , we can perform some suitable row operations on to transform the columns with such that they are of the form . Let be the matrix obtained after the row operations. Then the matrix is exactly the generator matrix of the code . By the previous isomorphism, we have . Hence we have indeed that is isomorphic to .
For the other case, assume now that . By Lemma 4, we have that with . Now this case follows the previous case in a similar manner by replacing by and by since is isomorphic to . Therefore the same type of isomorphisms yields that is isomorphic to . ∎
It remains to show the existence of such closed sets for every code symbol in order to prove that the code is an LRC with locality obtained by restrictions to closed sets of dimension .
Theorem 4.1
Let be the linear code of length with and . Then, for all we have the following.
- •
If , there is a set containing such that is isomorphic to .
- •
If , there is a set containing such that is isomorphic to .
- •
If , there exist two sets containing with such that is isomorphic to and is isomorphic to .
Proof
Let be the matroid associated with the Simplex code and such that . Let . The general idea of the proof is the following. First, we construct a specific hyperplane in containing . Secondly, we use Proposition 2 to get a hyperplane of . Finally, we apply Proposition 3 to this hyperplane to obtain the isomorphism.
If , then the code is the Simplex code and there is therefore a hyperplane containing such that is isomorphic to .
If , then and is the only hyperplane containing . We know that there exists at least one hyperplane in that contains . Let be such a hyperplane. By Proposition 2, is a hyperplane of containing . Applying Proposition 3 yields that is isomorphic to since is not contained in .
Suppose now that . Since , there is a hyperplane in that contains . Therefore, Proposition 2 and Proposition 3 yield that is a hyperplane of containing and is isomorphic to .
Let such that and let . By the hyperplanes property, we have . Thus, there exists such that and . Applying Proposition 2 and Proposition 3 yields that is a hyperplane of containing and is isomorphic to . ∎
Theorem 4.1 can be seen as showing the existence of certain hyperplanes while Proposition 3 is of the form of a uniqueness statement on the parameters size, dimension and minimum distance of the hyperplanes. Therefore, the two combined with Proposition 2 yield the complete characterization of all the hyperplanes of the matroid associated to and thus the characterization of all restrictions of to closed sets of dimension .
4.2 Weight enumerator of
Before we continue deriving the localities with dimension less than , the results developed so far allow us to compute the weight enumerator of the codes obtained by Construction 1. For this, we use a theorem from oxley92 that links the hyperplanes and the codeword supports.
Theorem 4.2 (oxley92 Theorem 9.2.4)
For each linear code , the hyperplanes of are precisely the complement of the minimal non-empty codeword supports of .
In order to compute the weight enumerator of , the idea is to associate the codewords of with the codewords of and the hyperplanes of . Then, we can use Lemma 4 to understand the effect of the puncturing of on the hyperplanes of . We start by a lemma that links the codewords of and the hyperplanes of .
Lemma 5
Let be the Simplex code of dimension over and the associated matroid. Define as the equivalence relation on the non-zero codewords of given by if with . Then the map
[TABLE]
is a bijection.
Proof
It is clear that is indeed an equivalence relation. Furthermore, the map is well-defined since all multiples of a codewords share the same support. Then, Theorem 4.2 implies that is a surjection. Now, there are equivalence classes of codewords and hyperplanes in since they are exactly the linear spaces of dimension in . Hence is a bijection. ∎
We can now state the formula for the weight enumerator of .
Theorem 4.3
The weight enumerator of the code over is
[TABLE]
Proof
Let be a non-zero codeword of and such that , where is the length of . Since both codes and have the same dimension, there is a bijection given by . Let be such that . We have
[TABLE]
Let be the hyperplane obtained by in Lemma 5. Then, and , since is the set of coordinates where for . Hence, if denotes the length of , we obtain
[TABLE]
This shows that the weight of can be computed from the hyperplanes of and their relation with . By Lemma 4, the hyperplanes of split into two disjoint sets depending on whether they contain . We consider these two cases separately.
Suppose first that . Then, we have
[TABLE]
Now, the number of different hyperplane containing in is . By Lemma 5, each hyperplanes yields a different equivalence class of codewords. Since there are codewords in each class, the number of codewords of weight is equal to .
Suppose now that . By Lemma 4 and the flats of the Simplex code, we know that . Then, we have
[TABLE]
The number of hyperplanes not containing is . By the same previous argument, the number of codewords of weight is then equal to and this concludes the proof. ∎
4.3 The complete locality of
In this subsection, we describe the restrictions of of dimension less than to obtain the rest of the possible localities. The main observation is that Theorem 4.1 was carefully written as a recursive statement on the same type of construction already considered, i.e., a Simplex code removed from another Simplex code. Therefore, we can apply Theorem 4.1 again on the restriction to obtain, up to isomorphism, the restricted codes of dimension of and thus of as well. Moreover, since every code symbol is contained in a restriction of of dimension , applying Theorem 4.1 again on the restricted codes implies that every code symbol is also contained in a restriction of of dimension . This is crucial when considering the locality of . We start with an example.
Example 2
Let be the binary linear code of Example 1. Since , Theorem 4.1 implies that for all code symbols , there exist two sets containing such that is isomorphic to and is isomorphic to . In other words, there are two restrictions containing with parameters and respectively. Therefore, is an LRC with locality and also an LRC with locality . Notice that for the first locality even if the dimension of the restricted codes is equal to . This is due to the fact that must satisfy as opposed to the definition of H-LRCs where .
If we apply Theorem 4.1 to , we obtain by isomorphism that there exist containing such that is isomorphic to and is isomorphic to . is a code and thus does not provide an extra locality. On the other hand, is a code which implies that is also an LRC with locality . Furthermore, by construction of the local sets, is an H-LRC with locality .
Example 2 illustrates how Theorem 4.1 can be used to obtain the locality for different dimensions. Moreover, because of the recursive form of Theorem 4.1, the local sets can be arranged in such a way that we obtain a hierarchical locality. We break down what happens to the restrictions when we iterate Theorem 4.1 by considering the restriction types, i.e., the different isomorphic restrictions. Suppose for simplicity that is sufficiently large and is close to half of . As illustrated in Figure 1, applying Theorem 4.1 on the two restriction types of dimension gives three new restriction types of dimension , as two of them lead to the same isomorphic code.
Suppose now that, after some iterations of Theorem 4.1, we obtain the restriction types of dimension . Let such that all restriction types are of the form with . Now, the restriction types of dimension can be obtained by applying Theorem 4.1 on the restriction types of dimension . Two extremal cases need to be taken into account. If , the only restriction type that we get from is . If , the only restriction type is . This is illustrated in Figure 2 where the two dashed boxes represent the conditional new restriction types that exist only if or . This sketches the high level idea of the proof of the next theorem describing precisely the different restriction types for a given dimension .
Theorem 4.4
Let with and . Let and be an integer such that . Then for all code symbols , there is a set containing such that is isomorphic to .
Remark 1
Notice that . Indeed we have since and . Since and , then and . Thus, and we have that . Therefore, the claim of Theorem 4.4 is that there always exist such restrictions for all dimensions .
Proof
We denote by the set . Let also be an arbitrary code symbol.
The case is exactly Theorem 4.1 with the set being equal to when , if , and if .
We now prove the theorem by doing a reverse induction on as long as . Assume that the claim is true for , i.e., for all and for all code symbol , there exists a set containing such that is isomorphic to . The goal is to construct the restrictions of dimension .
Let . By the induction hypothesis, there exists a set containing such that is isomorphic to .
If then applying Theorem 4.1 on yields that for all symbols , with being the length of the code , there is a set containing such that is isomorphic to . Therefore, there exists a set containing such that is isomorphic to since we have that . Since , then we have that .
If , then the third case of Theorem 4.1 applied to and the corresponding isomorphisms yields that there exists a set such that is isomorphic to . Moreover, since , then .
Let . Notice that this interval might be empty for example if . If the interval is not empty, let the set containing by the induction hypothesis such that is isomorphic to . Since by definition of the interval, we have that , we can apply Theorem 4.1 to obtain a set containing such that is isomorphic to .
Finally, if , then let and containing such that is isomorphic to . Since , we can use the third case of Theorem 4.1 applied to to obtain a set such that is isomorphic to . Moreover, we have .
Hence, we have constructed a set for isomorphic to for all in the set
[TABLE]
Since was an arbitrary symbol, this concludes the proof. ∎
As a corollary of Theorem 4.4, we have that is an LRC as long as the restriction types have a minimum distance greater than or equal to . We choose to give here the length, dimension and minimum distance of the local codes to avoid confusion on the parameter and in the two definitions of LRCs and H-LRCs.
Corollary 1
Let with and . Let and be an integer such that . Then is an LRC with local code parameters
[TABLE]
Furthermore, for , we have the following local parameters:
- •
If , then is an LRC with local parameters
- •
If and then is an LRC with local parameters .
Proof
When , Theorem 4.4 guaranties that for every code symbol , there is a restriction containing isomorphic to . Therefore is an LRC code with the parameters of the local sets given by the parameters of with a minimum distance greater or equal than .
When , we need to distinguish some cases as the minimum distance might be smaller than . Let be the range of the parameter when . If , then and by Theorem 4.4, there is a restriction containing isomorphic to . The parameters of the restriction is then and the minimum distance is thus greater or equal than .
If , then so and . By Theorem 4.4, every code symbol is contained in a restriction isomorphic to and in another restriction isomorphic to . Since the parameters of are , then the minimum distance is greater or equal than if and only if .
Finally, if , then and . Thus, is an LRC code with locality parameters if . ∎
Notice that when , we obtain again the locality proven in silberstein18 . We explain next why the list of localities of Corollary 1 is the complete list of possible localities with closed local sets. In Corollary 1, the list of different localities is established by removing the restrictions leading to codes with minimum distance from the list of Theorem 4.4. But the list of Theorem 4.4 relies on consecutive iterations of Theorem 4.1, where Proposition 3 can be applied at each iteration guaranteeing the uniqueness of the restriction types. Therefore, Theorem 4.4 contains the complete list of restriction types and Corollary 1 contains the complete list of possible localities with closed local sets.
We will now look at two special cases of Theorem 4.4 and Corollary 1. In the first example, we study the case when .
Example 3
Let with . Let be the range of the parameter in Theorem 4.4. Since , we have that . Then Theorem 4.4 and Corollary 1 imply that is an LRC code with local parameters for all . This result was already proven in grezet18LRC .
For the second example, we look at the case when and corresponds to the Reed–Muller code RM.
Example 4
Let with corresponding to the Reed–Muller code RM. Let the set . Since , we have . Hence Theorem 4.4 and Corollary 1 imply that RM is an LRC with local parameters for all and the local codes are isomorphic to the Reed–Muller code RM. Furthermore, if , then RM is also an LRC with local parameters . Moreover, since every locality is obtained by restrictions on the previous local sets, we get that the Reed–Muller codes RM are H-LRCs with -level hierarchy over the binary field and with -level hierarchy over with .
We conclude this section by showing the optimality of with respect to the bounds (2) and (6) and present a table containing binary codes obtained by Construction 1 and their hierarchical localities.
Theorem 4.5
Let with and . Then is an optimal LRC for every locality described in Corollary 1. Moreover, is an optimal H-LRC with
[TABLE]
Proof
Since already achieves the Griesmer bound on the parameters , it will achieves the bound (2) for and thus be an optimal LRC code for each locality described in Corollary 1.
We exhibit now a particular chain of subsets that yields the hierarchical locality. Vaguely speaking, the chain is obtained by taking the left foremost diagonal restriction types in Figures 1 and 2.
Formally, let be an arbitrary code symbol. By Theorem 4.4, for all , there exists a set containing such that is isomorphic to . Define . Each was obtained in the proof of Theorem 4.4 by applying Theorem 4.1 to . Therefore, we have . Moreover, because is isomorphic to , for every , there exists such a chain with and is isomorphic to for .
Now if is the minimum distance of , then the minimum distance of is
[TABLE]
Notice that is less than if and only if , , and by Corollary 1.
Hence, if or and , then is an H-LRC with -level hierarchical locality having locality parameters
[TABLE]
When and , is an H-LRC with -level hierarchical locality having locality parameters
[TABLE]
Finally, achieves the new alphabet-dependent bound (6) for H-LRC codes when and is taken to be the Griesmer bound. ∎
The table in Figure 3 represents the binary linear codes obtained by the construction . The columns are sorted by and the rows are sorted by . Moreover, the lines describes the locality of dimension obtained by Theorem 4.1. Therefore, if and are two codes in the table such that there exists a path from to , then has locality , i.e., each symbol of is contained in a restriction isomorphic to . Figure 3 gives also the hierarchical locality of a binary code via the paths to smaller codes. Finally, the codes in blue are the binary Reed–Muller codes RM.
5 Conclusion
In this paper, we presented a new alphabet-dependent bound for codes with hierarchical locality. Then, we worked on a class of codes obtained by deleting a Simplex code from another Simplex code of higher dimension. We derived the weight enumerator of these codes and the complete list of possible localities with closed repair sets. Finally, we used this list to show that these codes are optimal LRCs and optimal H-LRCs by the new bound.
Acknowledgements.
This work was supported in part by the Academy of Finland, under grants #276031, #282938, and #303819, and by the Technical University of Munich – Institute for Advanced Study, funded by the German Excellence Initiative and the EU 7th Framework Programme under grant agreement #291763, via a Hans Fischer Fellowship.
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