Trellis Realization Research Articles

Local Irreducibility of Tail-Biting Trellises

This paper investigates tail-biting trellis realizations for linear block codes. Intrinsic trellis properties are used to characterize irreducibility on given intervals of the time axis. It proves beneficial to always consider the trellis and its dual simultaneously. A major role is played by trellis properties that amount to observability and controllability of trellis fragments of various lengths. For fragments of length less than the minimum span length of the code it is shown that fragment observability and fragment controllability are equivalent to irreducibility. For reducible trellises, a constructive reduction procedure is presented. The considerations also lead to a characterization for when the dual of a trellis allows a product factorization into elementary (“atomic”) trellises.

Codes on Graphs: Observability, Controllability, and Local Reducibility

This paper investigates properties of realizations of linear or group codes on general graphs that lead to local reducibility. Trimness and properness are dual properties of constraint codes. A linear or group realization with a constraint code that is not both trim and proper is locally reducible. A linear or group realization on a finite cycle-free graph is minimal if and only if every local constraint code is trim and proper. A realization is called observable if there is a one-to-one correspondence between codewords and configurations, and controllable if it has independent constraints. A linear or group realization is observable if and only if its dual is controllable. A simple counting test for controllability is given. An unobservable or uncontrollable realization is locally reducible. Parity-check realizations are controllable if and only if they have independent parity checks. In an uncontrollable tail-biting trellis realization, the behavior partitions into disconnected sub-behaviors, but this property does not hold for nontrellis realizations. On a general graph, the support of an unobservable configuration is a generalized cycle.

The Treewidth of MDS and Reed–Muller Codes

The constraint complexity of a graphical realization of a linear code is the maximum dimension of the local constraint codes in the realization. The treewidth of a linear code is the least constraint complexity of any of its cycle-free graphical realizations. This notion provides a useful parameterization of the maximum-likelihood decoding complexity for linear codes. In this paper, we show the surprising fact that for maximum distance separable codes and Reed-Muller codes, treewidth equals trelliswidth, which, for a code, is defined to be the least constraint complexity (or branch complexity) of any of its trellis realizations. From this, we obtain exact expressions for the treewidth of these codes, which constitute the only known explicit expressions for the treewidth of algebraic codes.

On Minimal Tree Realizations of Linear Codes

A tree decomposition of the coordinates of a code is a mapping from the coordinate set to the set of vertices of a tree. A tree decomposition can be extended to a tree realization, i.e., a cycle-free realization of the code on the underlying tree, by specifying a state space at each edge of the tree, and a local constraint code at each vertex of the tree. The constraint complexity of a tree realization is the maximum dimension of any of its local constraint codes. A measure of the complexity of maximum-likelihood (ML) decoding for a code is its treewidth, which is the least constraint complexity of any of its tree realizations.It is known that among all tree realizations of a linear code that extends a given tree decomposition, there exists a unique minimal realization that minimizes the state-space dimension at each vertex of the underlying tree. In this paper, we give two new constructions of these minimal realizations. As a by-product of the first construction, a generalization of the state-merging procedure for trellis realizations, we obtain the fact that the minimal tree realization also minimizes the local constraint code dimension at each vertex of the underlying tree. The second construction relies on certain code decomposition techniques that we develop. We further observe that the treewidth of a code is related to a measure of graph complexity, also called treewidth. We exploit this connection to resolve a conjecture of Forney's regarding the gap between the minimum trellis constraint complexity and the treewidth of a code. We present a family of codes for which this gap can be arbitrarily large.

Constraint Complexity of Realizations of Linear Codes on Arbitrary Graphs

A graphical realization of a linear code C consists of an assignment of the coordinates of C to the vertices of a graph, along with a specification of linear state spaces and linear ``local constraint'' codes to be associated with the edges and vertices, respectively, of the graph. The $\k$-complexity of a graphical realization is defined to be the largest dimension of any of its local constraint codes. $\k$-complexity is a reasonable measure of the computational complexity of a sum-product decoding algorithm specified by a graphical realization. The main focus of this paper is on the following problem: given a linear code C and a graph G, how small can the $\k$-complexity of a realization of C on G be? As useful tools for attacking this problem, we introduce the Vertex-Cut Bound, and the notion of ``vc-treewidth'' for a graph, which is closely related to the well-known graph-theoretic notion of treewidth. Using these tools, we derive tight lower bounds on the $\k$-complexity of any realization of C on G. Our bounds enable us to conclude that good error-correcting codes can have low-complexity realizations only on graphs with large vc-treewidth. Along the way, we also prove the interesting result that the ratio of the $\k$-complexity of the best conventional trellis realization of a length-n code C to the $\k$-complexity of the best cycle-free realization of C grows at most logarithmically with codelength n. Such a logarithmic growth rate is, in fact, achievable.

Codes on graphs: constraint complexity of cycle-free realizations of linear codes

Cycle-free graphical realizations of linear codes generalize trellis realizations. Given a linear code C and a cycle-free graph topology, there exists a well-defined minimal realization for C on that graph in which each constraint is a linear code with a well-defined length and dimension. The constraint complexity of the realization is defined as maximum dimension of any constraint code. There exists a graph that minimizes constraint complexity in which all internal nodes have degree 3 and all interface nodes have degree 2, and which moreover can be put in the form of a "tree-structured trellis realization." The constraint complexity of a general cycle-free graph realization can be less than that of any conventional trellis realization, but not by very much. Such realizations can yield reductions in decoding complexity even when they do not reduce constraint complexity.

Asymptotically good codes have infinite trellis complexity

The trellis complexity s(C) of a block code C is defined as the logarithm of the maximum number of states in the minimal trellis realization of the code. The parameter s(C) governs the complexity of maximum-likelihood decoding, and is considered a fundamental descriptive characteristic of the code in a number of recent works. We derive a new lower bound on s(C) which implies that asymptotically good codes have infinite trellis complexity. More precisely, for i/spl ges/1 let C/sub i/ be a code over an alphabet of size q, of length n/sub i/, rate R/sub i/, and minimum distance d/sub i/. The infinite sequence of codes C/sub ,/ C/sub 2spl middotspl middotspl middot/ such that n/sub ispl rarrspl infin/ when i/spl rarrspl infin/ is said to be asymptotically good if both R/sub i/ and d/sub in/sub i/ are bounded away from zero as i/spl rarrspl infin/. We prove that the complexity s(C/sub i/) increases linearly with n/sub i/ in any asymptotically good sequence of codes. >