1,148 research outputs found
Iterative decoding for MIMO channels via modified sphere decoding
In recent years, soft iterative decoding techniques have been shown to greatly improve the bit error rate performance of various communication systems. For multiantenna systems employing space-time codes, however, it is not clear what is the best way to obtain the soft information required of the iterative scheme with low complexity. In this paper, we propose a modification of the Fincke-Pohst (sphere decoding) algorithm to estimate the maximum a posteriori probability of the received symbol sequence. The new algorithm solves a nonlinear integer least squares problem and, over a wide range of rates and signal-to-noise ratios, has polynomial-time complexity. Performance of the algorithm, combined with convolutional, turbo, and low-density parity check codes, is demonstrated on several multiantenna channels. The results for systems that employ space-time modulation schemes seem to indicate that the best performing schemes are those that support the highest mutual information between the transmitted and received signals, rather than the best diversity gain
Deriving the Normalized Min-Sum Algorithm from Cooperative Optimization
The normalized min-sum algorithm can achieve near-optimal performance at
decoding LDPC codes. However, it is a critical question to understand the
mathematical principle underlying the algorithm. Traditionally, people thought
that the normalized min-sum algorithm is a good approximation to the
sum-product algorithm, the best known algorithm for decoding LDPC codes and
Turbo codes. This paper offers an alternative approach to understand the
normalized min-sum algorithm. The algorithm is derived directly from
cooperative optimization, a newly discovered general method for
global/combinatorial optimization. This approach provides us another
theoretical basis for the algorithm and offers new insights on its power and
limitation. It also gives us a general framework for designing new decoding
algorithms.Comment: Accepted by IEEE Information Theory Workshop, Chengdu, China, 200
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