Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 4

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: cross-entropy

Sortuj według:

Ogranicz wyniki do:

Stop criteria for retransmission termination in soft-combining algorithms

100%

Zepernick H.-J. , Caldera M.

Journal of Telecommunications and Information Technology

2001

tom nr 3

61-65

Soft-combining algorithms use retransmissions of the same codeword to improve the reliability of communication over very noisy channels. In this paper, soft-outputs from a maximum a posteriori (MAP) decoder are used as a priori information for decoding of retransmitted codewords. As all received words may not need the same number of retransmissions to achieve satisfactory reliability, a stop criterion to terminate retransmissions needs to be identified. As a first and very simple stop criterion, we propose an algorithm which uses the sign of the soft-output at the MAP decoder. The performance obtained with this stop criterion is compared with the one assuming a genius observer, which identifies otherwise undetectable errors. Since this technique needs always a particular number of initial retransmissions, we exploit cross-entropy between subsequent retransmissions as a more advanced but still simple stop criterion. Simulation results show that significant performance improvement can be gained with soft-combining techniques compared to simple hard or soft decision decoding. It also shows that the examined stop criteria perform very close to the optimistic case of a genius observer.

Uniform Cross-entropy Clustering

100%

Brzeski M. , Spurek P.

tom Vol. 25

117--126

Robust mixture models approaches, which use non-normal distributions have recently been upgraded to accommodate data with fixed bounds. In this article we propose a new method based on uniform distributions and Cross- Entropy Clustering (CEC). We combine a simple density model with a clustering method which allows to treat groups separately and estimate parameters in each cluster individually. Consequently, we introduce an effective clustering algorithm which deals with non-normal data.

Sum-α Stopping Criterion for Turbo Decoding

88%

Ouardi A.

International Journal of Electronics and Telecommunications

2021

tom Vol. 67, No. 3

477--482

In this article, we propose a new stopping criterion for turbo codes. This criterion is based on the behaviour of the probabilistic values alpha 'α' calculated in the forward recursion during turbo decoding. We called this criterion Sum-α. The simulation results show that the Bit Error Rates BER are very close to those of the Cross-Entropy CE criterion with the same average number of iterations.

Sum-α Stopping Criterion for Turbo Decoding

44%

Ouardi A. , Ouardi A.

nr 3