Talk abstract:
Codes and Systems on Graphs: Generalized
State Realizations
G. David Forney, Jr.
Laboratory for Information and Decision Systems
Massachusetts Institute of Technology
forney@lids.mit.edu
Dave_Forney-LUSE27@email.mot.com
In coding theory or behavioral system theory, a code or system
is simply a set of possible output sequences. In a state realization
of a code/system (e.g., a trellis), a set of state variables
is defined as well. Conventionally, state variables are defined
on a sequential time axis corresponding to a subset of the integers.
In a generalized state realization of a code/system, state
variables may be connected according to an arbitrary graph topology;
i.e., the time axis is represented by a general graph. If the
graph has cycles, then a substantial reduction in state complexity
may be obtained. For example, the state complexity of a single-cycle
graph realization (tail-biting trellis) can be as little as
the square root of the state complexity of a conventional state
realization (trellis). For another example, Reed-Muller codes
have very simple generalized state realizations, in general
with cycles. In general, the question of minimal generalized
state realizations is wide open.
The sum-product algorithm may be applied to decode any generalized
state realization. If the associated graph is finite and cycle-free,
then sum-product decoding completes in a finite time and is
exact. If the graph has cycles, then sum-product decoding becomes
an approximate iterative decoding algorithm.
A linear or group generalized state realization generates
a linear or group code/system. The dual of such a realization,
appropriately defined, generates the dual code/system. The dual
realization has the same symbol and state variables in the same
graph topology as the primal realization, but replaces primal
local constraints by their duals. A group code may be decoded
using the dual graph, with appropriate Fourier transforms of
the inputs and outputs; this can simplify decoding of high-rate
codes.
Material used during the talk
Back to Codes, Systems and Graphical Models
1998-1999
Mathematics in Biology