15,795 research outputs found
Enhanced Recursive Reed-Muller Erasure Decoding
Recent work have shown that Reed-Muller (RM) codes achieve the erasure
channel capacity. However, this performance is obtained with maximum-likelihood
decoding which can be costly for practical applications. In this paper, we
propose an encoding/decoding scheme for Reed-Muller codes on the packet erasure
channel based on Plotkin construction. We present several improvements over the
generic decoding. They allow, for a light cost, to compete with
maximum-likelihood decoding performance, especially on high-rate codes, while
significantly outperforming it in terms of speed
Efficient Decoding Algorithms for the Compute-and-Forward Strategy
We address in this paper decoding aspects of the Compute-and-Forward (CF)
physical-layer network coding strategy. It is known that the original decoder
for the CF is asymptotically optimal. However, its performance gap to optimal
decoders in practical settings are still not known. In this work, we develop
and assess the performance of novel decoding algorithms for the CF operating in
the multiple access channel. For the fading channel, we analyze the ML decoder
and develop a novel diophantine approximation-based decoding algorithm showed
numerically to outperform the original CF decoder. For the Gaussian channel, we
investigate the maximum a posteriori (MAP) decoder. We derive a novel MAP
decoding metric and develop practical decoding algorithms proved numerically to
outperform the original one
Hardness of decoding quantum stabilizer codes
In this article we address the computational hardness of optimally decoding a
quantum stabilizer code. Much like classical linear codes, errors are detected
by measuring certain check operators which yield an error syndrome, and the
decoding problem consists of determining the most likely recovery given the
syndrome. The corresponding classical problem is known to be NP-complete, and a
similar decoding problem for quantum codes is also known to be NP-complete.
However, this decoding strategy is not optimal in the quantum setting as it
does not take into account error degeneracy, which causes distinct errors to
have the same effect on the code. Here, we show that optimal decoding of
stabilizer codes is computationally much harder than optimal decoding of
classical linear codes, it is #P
Relaxation Bounds on the Minimum Pseudo-Weight of Linear Block Codes
Just as the Hamming weight spectrum of a linear block code sheds light on the
performance of a maximum likelihood decoder, the pseudo-weight spectrum
provides insight into the performance of a linear programming decoder. Using
properties of polyhedral cones, we find the pseudo-weight spectrum of some
short codes. We also present two general lower bounds on the minimum
pseudo-weight. The first bound is based on the column weight of the
parity-check matrix. The second bound is computed by solving an optimization
problem. In some cases, this bound is more tractable to compute than previously
known bounds and thus can be applied to longer codes.Comment: To appear in the proceedings of the 2005 IEEE International Symposium
on Information Theory, Adelaide, Australia, September 4-9, 200
Iterative Algebraic Soft-Decision List Decoding of Reed-Solomon Codes
In this paper, we present an iterative soft-decision decoding algorithm for
Reed-Solomon codes offering both complexity and performance advantages over
previously known decoding algorithms. Our algorithm is a list decoding
algorithm which combines two powerful soft decision decoding techniques which
were previously regarded in the literature as competitive, namely, the
Koetter-Vardy algebraic soft-decision decoding algorithm and belief-propagation
based on adaptive parity check matrices, recently proposed by Jiang and
Narayanan. Building on the Jiang-Narayanan algorithm, we present a
belief-propagation based algorithm with a significant reduction in
computational complexity. We introduce the concept of using a
belief-propagation based decoder to enhance the soft-input information prior to
decoding with an algebraic soft-decision decoder. Our algorithm can also be
viewed as an interpolation multiplicity assignment scheme for algebraic
soft-decision decoding of Reed-Solomon codes.Comment: Submitted to IEEE for publication in Jan 200
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