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In this paper, we focus on the Generalized Belief Propagation (GBP) algorithm to solve trapping sets in Low-Density Parity-Check (LDPC) codes. Trapping sets are topological structures in Tanner graphs of LDPC codes that are not correctly decoded by Belief Propagation (BP), leading to exhibit an error-floor in the Bit-Error Rate (BER). Stemming from statistical physics of spin glasses, GBP consists...
We introduce the use of fast flat histogram (FFH) method employing Wang Landau algorithm in an adaptive noise sampling framework using random walk to find out the pseudo-codewords and consequently the pseudo-weights for the belief propagation (BP) decoding of LDPC codes over an additive white Gaussian noise (AWGN) channel. The FFH method enables us to tease out pseudo-codewords at very high signal-to-noise...
Standard Monte Carlo (SMC) simulation is employed to evaluate the performance of forward error correcting (FEC) codes. This performance is in terms of the probability of error during the transmission of information through digital communication systems. The time taken by SMC simulation to estimate the FER increases exponentially with the increase in signal-to-noise ratio (SNR). We hereby present an...
The quality of transmission in digital communication systems is usually measured by frame error rate (FER). The time taken by standard Monte Carlo (MC) simulation to estimate the FER increases exponentially with the increase in signal-to-noise ratio (SNR). In this correspondence, we present an Adaptive Importance Sampling (AIS) technique inspired by statistical physics called fast flat histogram (FFH)...
An efficient, flat histogram Monte Carlo algorithm is proposed that simulates long-range spin models in the multicanonical ensemble with very low dynamic exponents and drastically reduced computational effort. The method combines a random-walk in energy space with cluster updates, where bond weights depend continuously on the lattice energy. Application to q-state Potts chains with power-law decaying...
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