Estimating the list size for BEAST-APP decoding

The BEAST-APP decoding algorithm is a low-complexity bidirectional algorithm that searches code trees to find the list of the most likely codewords, which are used to compute approximate a posteriori probabilities (APPs) of the transmitted symbols. It can be applied to APP-decoding of any linear block code, as well as in iterative structures for decoding concatenated block codes. Previous work has shown that the list size sufficient to achieve the performance of true-APP decoding is very small. This paper aims at providing a theoretical justification for this result. The sufficient list size is estimated first via the minimum list distance - a parameter that is defined and analyzed as a key factor that governs the performance of list-based algorithms. Additionally, statistical properties of the codeword likelihoods are investigated and the typical list structure is presented. Preliminary simulation results for iterative BEAST decoding confirm the list-size estimates obtained from both approaches

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