
TL;DR
This paper improves the lower bounds on the maximum number of planes in a projective space that intersect in at most a point, using a new construction strategy with implications for constant dimension codes.
Contribution
It introduces a new construction method that enhances bounds on plane arrangements in projective spaces and constant dimension codes.
Findings
New lower bound for plane arrangements in PG(8,q)
Enhanced bounds for constant dimension codes A_q(9,4;3)
General construction strategy applicable to related problems
Abstract
We improve on the lower bound of the maximum number of planes in pairwise intersecting in at most a point. In terms of constant dimension codes this leads to . This result is obtained via a more general construction strategy, which also yields other improvements.
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Subspaces intersecting in at most a point
Sascha Kurz
Sascha Kurz, University of Bayreuth, 95440 Bayreuth, Germany
Abstract.
We improve on the lower bound of the maximum number of planes in pairwise intersecting in at most a point. In terms of constant dimension codes this leads to . This result is obtained via a more general construction strategy, which also yields other improvements.
Keywords: constant dimension codes, finite projective geometry, network coding
MSC: Primary 51E20; Secondary 05B25, 94B65.
1. Introduction
Let be a -dimensional vector space over the finite field with elements. We call each -dimensional linear subspace of a -space, also using the terms points, lines, and planes for -, -, and -spaces, respectively. Two -spaces , are said to trivially intersect or to be disjoint if , i.e., and do not share a common point. Sets of -spaces that are pairwise disjoint are called partial -spreads, see [10] for a recent survey on bounds for their maximum possible sizes. In finite projective geometry they are a classical topic. Here we study the rather similar objects of sets of -spaces which pairwise intersect in at most a point and have large cardinality. More generally, we can use the subspace distance to define as the maximum number of -spaces in that have minimum subspace distance , i.e., that intersect in a subspace of dimension at most . Since those sets, which are also called constant dimension codes, have applications in error correcting random network coding, see e.g. [11], bounds for have been studied intensively in the literature. For the currently best known lower and upper bounds we refer to the online tables http://subspacecodes.uni-bayreuth.de and the associated survey [7]. Due to this connection, we also call sets of -spaces codes and call their elements codewords. Due to combinatorial explosion, it is in general quite hard to obtain improvements for when the dimension of the ambient space is small, say . Our main motivation for this paper is the recently improved parametric lower bound , see [2, Theorem 3.13]. Here, we give a further improved construction for and generalize the underlying ideas to a more general combination of constant dimension codes. The latter constitutes our main Theorem, see Theorem 3, which allows to conclude also other improved parametric constructions.
2. Preliminaries
For two matrices we define the rank distance . A subset is called a rank metric code.
Theorem 1**.**
(see [4]) Let be positive integers, a prime power, and be a rank metric code with minimum rank distance . Then, .
Codes attaining this upper bound are called maximum rank distance (MRD) codes. They exist for all choices of parameters. A construction can e.g. be described using so-called linearized polynomials, see e.g. [11, Section V]. If or , then only is possible, which can be achieved by a zero matrix and may be summarized to the single upper bound . Using an identity matrix as a prefix one obtains the so-called lifted MRD codes.
Theorem 2**.**
[13, Proposition 4]** For positive integers with , , and even, the size of a lifted MRD code in with subspace distance is given by .
3. Combining subspaces
Theorem 3**.**
Let be a set of -spaces in mutually intersecting in at most a point, be a subset of such that all elements are pairwise intersecting trivially, and be a set of -spaces in mutually intersecting in at most a point, where and . If admits a -space , such that exactly elements of are contained in and all others intersect in at most a point, then
[TABLE]
- Proof. We embed in and choose a -space disjoint to the span . For each we consider the -space . If , we embed minus the codewords contained in in such that the embedding contains the -space and all codewords intersect in at most a point. If , we embed a lifted MRD code in such that the embedding contains the -space and all codewords are disjoint to . If we additionally add codewords inside , then we obtain a set of -spaces in of cardinality , since the matching lifted MRD code has cardinality . For two different we have to show that they do intersect in at most a point. By construction, there exist such that and . We have and . If , which we can assume w.l.o.g. for or , then . If , then , so that . Otherwise we have , so that also .
If we choose and such that there are two disjoint codewords, then can be chosen as a codeword, i.e., , and all codewords except itself intersect in at most a point. For brevity, we will calls sets of -spaces that are trivially intersecting and are a subset of a some set of -spaces, a clique.
Corollary 4**.**
[TABLE]
- Proof. For and we choose and as a set of planes in pairwise intersecting in at most a point [3, Theorem 2.1]. By [2, Theorem 3.12] we can choose a subset of cardinality .
We remark that this improves the very recent lover bound [2, Theorem 3.13]. As we might also have chosen the construction from [9] of the same size.111The same applies to , i.e., we can avoid to use [2, Theorem 3.12], see the subsequent Footnote 3. In our setting we always have . If we replace in Corollary 4 by the set of planes in from [8, Theorem 3], then the conditions of Theorem 3 are satisfied for and we obtain
[TABLE]
However, [12, Proposition 4.4] gives a better lower bound. For a general application of Theorem 3 the presumably hardest part is to analytically determine , i.e., a clique in . If itself is obtained via Theorem 3 and a lower bound on the clique size of the corresponding part is known, then can recursively determine suitably large cliques.
Lemma 5**.**
If is obtained from the construction of Theorem 3 and the corresponding part contains a clique whose elements are disjoint from , then admits a subset such that all elements are pairwise intersecting trivially and .
- Proof. Using the notation from Theorem 3 we construct . For each we consider and choose a clique of cardinality in and add the elements to . Using the analysis of the proof of Theorem 3 again and the fact that the elements of all are disjoint to , we conclude that the elements of are pairwise intersecting trivially.
If we choose according to [3, Theorem 2.1], we can use [2, Theorem 3.12] to conclude .
Proposition 6**.**
* for all .*
- Proof. For the induction start we choose as a set of planes in pairwise intersecting in at most a point according to [3, Theorem 2.1], which admits a clique of cardinality . For the induction step we apply Theorem 3 with , , , and . By induction, see Lemma 5, admits a clique of cardinality . The induction hypothesis for the cardinality of is
[TABLE]
and the induction step, see Theorem 3, gives as the right hand side of Equation (2), where is replaced by .
Another example of a set of planes pairwise intersecting in at most a point, where we can analytically determine a reasonably large clique, is given by [12, Proposition 4.4]: , which is the currently best known lower bound for . The essential key here is that the code contains a lifted MRD code of cardinality for rank distance . By [5, Lemma 5] the MRD code can be chosen in such a way that it contains a subcode of cardinality and rank distance .222Using linearized polynomials to described the lifted MRD code, a clique of matching size can be described as the set of monomials (including the zero polynomial). Thus we obtain a clique of cardinality and can use Theorem 3 with and to conclude
[TABLE]
which strictly improves upon [12, Proposition 4.4]. Of course we can iteratively apply the combination with the planes in to obtain an infinite parametric series as in Proposition 6. The method generalizes to cases where large constant dimension codes are obtained by using lifted MRD codes as subcodes, which frequently is the case. Also the constant dimension codes showing [9, Lemma 12, Example 4] and [8, Theorem 4] are closely related. They both arise by starting from a lifted MRD code, removing some planes, and then extending again with a larger set of planes, cf. [1]. Considering just the reduced lifted MRD code, we can deduce clique sizes of and , respectively.333Both constructions are stated in the language of linearized polynomials. For [9, Lemma 12, Example 4] the representation is used and the planes removed from the lifted MRD code correspond to for , so that the monomials for correspond to a clique of cardinality . For [8, Theorem 4] the representation , where denotes the trace-zero subspace of , is used. The planes removed from the lifted MRD code correspond to for and with , so that the monomial s for correspond to a clique of cardinality . If we choose in Theorem 3 as the mentioned code for and as the mentioned code for or the code for , see[8, Theorem 3], then we obtain
[TABLE]
and
[TABLE]
Both inequalities improve upon the (for previously best known lower bounds from [12, Proposition 4.4] and the latter improves upon Inequality (3). So, Theorem 3 can yield improved constructions, but of course not all choices of the involved parameters and codes lead to improvements. If , then , so that no strict improvement over known constructions can be obtained. For it might be necessary to use , since no example for is known. In [6] the authors have indeed shown and conjectured for all . In principle it is also possible to generalize Theorem 3 to situations where the -spaces can intersect in subspaces of dimension strictly larger than one. To this end, one may partition into subsets , , …, such that every element from intersects each different element from in dimension at most , which generalizes the partition , . If is again our special subspace and , then codewords in the code in should intersect in dimension at most , where we may also put some additional codewords into . Since we currently have no example at hand that improves upon a best known lower bound for , we refrain from giving a rigorous proof and detailed statement.
Acknowledgment
The author would like to thank Thomas Honold for his analysis of possible cliques sizes in the constant dimension codes from [9, Lemma 12, Example 4] and [8, Theorem 4], see Footnote 3. The main idea for Theorem 3 is inspired by [2].
The reference list from the paper itself. Each links out to its DOI / PubMed record.
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