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Turbo decoding as iterative constrained maximum-likelihood sequence detection
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/1525
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| Title: | Turbo decoding as iterative constrained maximum-likelihood sequence detection |
| Authors: | Walsh, John MacLaren
Regalia, Phillip A.
Johnson Jr., C. Richard |
| Keywords: | Constrained optimization
maximum-likelihood decoding
turbo decoder convergence analysis |
| Issue Date: | Dec-2006 |
| Publisher: | IEEE Institute of Electrical and Electronics Engineers |
| Citation: | IEEE Transactions on Information Theory, 52(12): pp. 5426-5437. |
| Abstract: | The turbo decoder was not originally introduced as a
solution to an optimization problem, which has impeded attempts
to explain its excellent performance. Here it is shown, that the
turbo decoder is an iterative method seeking a solution to an intuitively
pleasing constrained optimization problem. In particular,
the turbo decoder seeks the maximum-likelihood sequence (MLS)
under the false assumption that the input to the encoders are
chosen independently of each other in the parallel case, or that the
output of the outer encoder is chosen independently of the input
to the inner encoder in the serial case. To control the error introduced
by the false assumption, the optimizations are performed
subject to a constraint on the probability that the independent
messages happen to coincide. When the constraining probability
equals one, the global maximum of the constrained optimization
problem is the maximum-likelihood sequence detection (MLSD),
allowing for a theoretical connection between turbo decoding and
MLSD. It is then shown that the turbo decoder is a nonlinear
block Gauss–Seidel iteration that aims to solve the optimization
problem by zeroing the gradient of the Lagrangian with a Lagrange
multiplier of 1. Some conditions for the convergence
for the turbo decoder are then given by adapting the existing
literature for Gauss–Seidel iterations. |
| URI: | http://dx.doi.org/10.1109/TIT.2006.885535
http://hdl.handle.net/1860/1525 |
| Appears in Collections: | Faculty Research and Publications (ECE)
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