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Kenneth Zeger

Publications and source records attributed to Kenneth Zeger.

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Probability of super-regular matrices and MDS codes over finite fields

Let $C$ be an $[n,k]$ linear code chosen uniformly at random over a finite field $\mathbb{F}_q$ of size $q$. The following asymptotic probability of $C$ being maximum distance separable (MDS) as $q,n,k\to\infty$ is known: If $\frac{1}{q}\binom{n}{k} \to 0$, then $P(C\ \text{is MDS}) \to 1$. We demonstrate that this growth rate is in fact a threshold by proving: If $\frac{1}{q}\binom{n}{k} \to \infty$, then $P(C\ \text{is MDS}) \to 0$. A matrix is ($\textit{contiguous}$) $\textit{super-regular}$ if all of its (contiguous) square submatrices are nonsingular. The above results imply that for any $k \times k$ matrix $A$ chosen uniformly at random over $\mathbb{F}_q$, the following hold: If $\frac{4^k/\sqrt{k}}{q} \to 0$, then $P(A \text{ is super-regular}) \to 1$. If $\frac{4^k/\sqrt{k}}{q}\to \infty$, then $P(A \text{ is super-regular}) \to 0$. We also obtain the following asymptotic probabilities for two variations of the above questions: If $\frac{1}{q}\binom{n}{k} \to \lambda \in (0,\infty)$ and $k/n\to 0$, then $P(C\ \text{is MDS}) \to e^{-\lambda}$. If $\frac{k^3/3}{q} \to \lambda \in [0,\infty]$, then $P(A \text{ is contiguous super-regular}) \to e^{-\lambda}$. The number of super-regular $3\times 3$ matrices is known to be a polynomial in $q$. We show that the number of contiguous super-regular $3\times 3$ matrices is also a polynomial. Finally, for $4\times 4$ matrices, we show that the number of super-regular matrices is not a polynomial, nor even a quasi-polynomial of period less than $7$, whereas our experimental evidence suggests that the number of contiguous super-regular matrices is a polynomial.

cs.IT

A Characterization of Optimal Prefix Codes

A property of prefix codes called strong monotonicity is introduced, and it is proven that for a given source, a prefix code is optimal if and only if it is complete and strongly monotone.

cs.IT

Competitive Advantage of Huffman and Shannon-Fano Codes

For any finite discrete source, the competitive advantage of prefix code $C_1$ over prefix code $C_2$ is the probability $C_1$ produces a shorter codeword than $C_2$, minus the probability $C_2$ produces a shorter codeword than $C_1$. For any source, a prefix code is competitively optimal if it has a nonnegative competitive advantage over all other prefix codes. In 1991, Cover proved that Huffman codes are competitively optimal for all dyadic sources, namely sources whose symbol probabilities are negative integer powers of $2$. We prove the following asymptotic converse: As the source size grows, the probability a Huffman code for a randomly chosen non-dyadic source is competitively optimal converges to zero. We also prove: (i) For any non-dyadic source, a Huffman code has a positive competitive advantage over a Shannon-Fano code; (ii) For any source, the competitive advantage of any prefix code over a Huffman code is strictly less than $\frac{1}{3}$; (iii) For each integer $n>3$, there exists a source of size $n$ and some prefix code whose competitive advantage over a Huffman code is arbitrarily close to $\frac{1}{3}$; and (iv) For each positive integer $n$, there exists a source of size $n$ and some prefix code whose competitive advantage over a Shannon-Fano code becomes arbitrarily close to $1$ as $n\to\infty$.

cs.IT

Capacity and Achievable Rate Regions for Linear Network Coding over Ring Alphabets

The rate of a network code is the ratio of the block size of the network's messages to that of its edge codewords. We compare the linear capacities and achievable rate regions of networks using finite field alphabets to the more general cases of arbitrary ring and module alphabets. For non-commutative rings, two-sided linearity is allowed. Specifically, we prove the following for directed acyclic networks: (i) The linear rate region and the linear capacity of any network over a finite field depend only on the characteristic of the field. Furthermore, any two fields with different characteristics yield different linear capacities for at least one network. (ii) Whenever the characteristic of a given finite field divides the size of a given finite ring, each network's linear rate region over the ring is contained in its linear rate region over the field. Thus, any network's linear capacity over a field is at least its linear capacity over any other ring of the same size. An analogous result also holds for linear network codes over module alphabets. (iii) Whenever the characteristic of a given finite field does not divide the size of a given finite ring, there is some network whose linear capacity over the ring is strictly greater than its linear capacity over the field. Thus, for any finite field, there always exist rings over which some networks have higher linear capacities than over the field.

cs.IT

Linear Network Coding over Rings, Part I: Scalar Codes and Commutative Alphabets

Fixed-size commutative rings are quasi-ordered such that all scalar linearly solvable networks over any given ring are also scalar linearly solvable over any higher-ordered ring. As consequences, if a network has a scalar linear solution over some finite commutative ring, then (i) the network is also scalar linearly solvable over a maximal commutative ring of the same size, and (ii) the (unique) smallest size commutative ring over which the network has a scalar linear solution is a field. We prove that a commutative ring is maximal with respect to the quasi-order if and only if some network is scalar linearly solvable over the ring but not over any other commutative ring of the same size. Furthermore, we show that maximal commutative rings are direct products of certain fields specified by the integer partitions of the prime factor multiplicities of the maximal ring's size. Finally, we prove that there is a unique maximal commutative ring of size $m$ if and only if each prime factor of $m$ has multiplicity in $\{1,2,3,4,6\}$. In fact, whenever $p$ is prime and $k \in \{1,2,3,4,6\}$, the unique such maximal ring of size $p^k$ is the field $GF(p^k)$. However, for every field $GF(p^k)$ with $k\not\in \{1,2,3,4,6\}$, there is always some network that is not scalar linearly solvable over the field but is scalar linearly solvable over a commutative ring of the same size. These results imply that for scalar linear network coding over commutative rings, fields can always be used when the alphabet size is flexible, but alternative rings may be needed when the alphabet size is fixed.

cs.IT

Linear Network Coding over Rings, Part II: Vector Codes and Non-Commutative Alphabets

We prove the following results regarding the linear solvability of networks over various alphabets. For any network, the following are equivalent: (i) vector linear solvability over some finite field, (ii) scalar linear solvability over some ring, (iii) linear solvability over some module. Analogously, the following are equivalent: (a) scalar linear solvability over some finite field, (b) scalar linear solvability over some commutative ring, (c) linear solvability over some module whose ring is commutative. Whenever any network is linearly solvable over a module, a smallest such module arises in a vector linear solution for that network over a field. If a network is linearly solvable over some non-commutative ring but not over any commutative ring, then such a non-commutative ring must have size at least $16$, and for some networks, this bound is achieved. An infinite family of networks is demonstrated, each of which is scalar linearly solvable over some non-commutative ring but not over any commutative ring. Whenever $p$ is prime and $2 \le k \le 6$, if a network is scalar linearly solvable over some ring of size $p^k$, then it is also $k$-dimensional vector linearly solvable over the field $GF(p)$, but the converse does not necessarily hold. This result is extended to all $k\ge 2$ when the ring is commutative.

cs.IT

A Class of Non-Linearly Solvable Networks

For each integer $m \geq 2$, a network is constructed which is solvable over an alphabet of size $m$ but is not solvable over any smaller alphabets. If $m$ is composite, then the network has no vector linear solution over any $R$-module alphabet and is not asymptotically linear solvable over any finite-field alphabet. The network's capacity is shown to equal one, and when $m$ is composite, its linear capacity is shown to be bounded away from one for all finite-field alphabets.

cs.IT

Characteristic-Dependent Linear Rank Inequalities with Applications to Network Coding

Two characteristic-dependent linear rank inequalities are given for eight variables. Specifically, the first inequality holds for all finite fields whose characteristic is not three and does not in general hold over characteristic three. The second inequality holds for all finite fields whose characteristic is three and does not in general hold over characteristics other than three. Applications of these inequalities to the computation of capacity upper bounds in network coding are demonstrated.

cs.IT

Achievable Rate Regions for Network Coding

Determining the achievable rate region for networks using routing, linear coding, or non-linear coding is thought to be a difficult task in general, and few are known. We describe the achievable rate regions for four interesting networks (completely for three and partially for the fourth). In addition to the known matrix-computation method for proving outer bounds for linear coding, we present a new method which yields actual characteristic-dependent linear rank inequalities from which the desired bounds follow immediately.

cs.IT

Linear Codes, Target Function Classes, and Network Computing Capacity

We study the use of linear codes for network computing in single-receiver networks with various classes of target functions of the source messages. Such classes include reducible, injective, semi-injective, and linear target functions over finite fields. Computing capacity bounds and achievability are given with respect to these target function classes for network codes that use routing, linear coding, or nonlinear coding.

cs.IT

Non-Shannon Information Inequalities in Four Random Variables

Any unconstrained information inequality in three or fewer random variables can be written as a linear combination of instances of Shannon's inequality I(A;B|C) >= 0 . Such inequalities are sometimes referred to as "Shannon" inequalities. In 1998, Zhang and Yeung gave the first example of a "non-Shannon" information inequality in four variables. Their technique was to add two auxiliary variables with special properties and then apply Shannon inequalities to the enlarged list. Here we will show that the Zhang-Yeung inequality can actually be derived from just one auxiliary variable. Then we use their same basic technique of adding auxiliary variables to give many other non-Shannon inequalities in four variables. Our list includes the inequalities found by Xu, Wang, and Sun, but it is by no means exhaustive. Furthermore, some of the inequalities obtained may be superseded by stronger inequalities that have yet to be found. Indeed, we show that the Zhang-Yeung inequality is one of those that is superseded. We also present several infinite families of inequalities. This list includes some, but not all of the infinite families found by Matus. Then we will give a description of what additional information these inequalities tell us about entropy space. This will include a conjecture on the maximum possible failure of Ingleton's inequality. Finally, we will present an application of non-Shannon inequalities to network coding. We will demonstrate how these inequalities are useful in finding bounds on the information that can flow through a particular network called the Vamos network.

cs.IT

Linear rank inequalities on five or more variables

Ranks of subspaces of vector spaces satisfy all linear inequalities satisfied by entropies (including the standard Shannon inequalities) and an additional inequality due to Ingleton. It is known that the Shannon and Ingleton inequalities generate all such linear rank inequalities on up to four variables, but it has been an open question whether additional inequalities hold for the case of five or more variables. Here we give a list of 24 inequalities which, together with the Shannon and Ingleton inequalities, generate all linear rank inequalities on five variables. We also give a partial list of linear rank inequalities on six variables and general results which produce such inequalities on an arbitrary number of variables; we prove that there are essentially new inequalities at each number of variables beyond four (a result also proved recently by Kinser).

cs.IT