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International Conference on Networking

Weighted IS Method of Estimating FER of LDPC Codes in High SNR Region

Guangwen Li, Guangzeng Feng

Published Apr 1, 2007• 3 citations
Publisher

Abstract

In this paper, one improved frame error rate (FER) estimation approach in high Signal Noise Ratio (SNR) region for low-density parity-check (LDPC) codes is reported. Firstly dominant trapping sets (TSs), accounting for most of FER in high SNR region, are picked out as decoding result, with input of decoder consisting of a group of biased bits. And how those biased bits are chosen is reduced to finding solution to an binary integer program. Then decoder termination conditions are slightly modified so that history of the tentative decoding could facilitate action of searching for dominant TSs effectively. Without no prior knowledge of shape of error regions for specific LDPC code, we implement weighted mean translation (MT) of importance sampling (IS) method to compute FER in high SNR region. Simulation results demonstrate estimated FER curve conforms well with that of standard monte carlo (MC) simulation in overlapped SNR region.

DOI
10.1109/icn.2007.105

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Elementary trapping sets (ETSs) have been found to be the main cause of error floor in the decoding of low-density parity-check (LDPC) codes. Moreover, irregular LDPC codes that avoid harmful [w; u; e] ETSs have been constructed and have shown to possess superior error performance compared with other LDPC codes. In this paper, we attempt to evaluate irregular LDPC code performance under an additive white Gaussian noise channel within the high SNR region using the importance sampling (IS) approach in conjunction with the identification of [w; u; e] ETSs. For any given irregular LDPC code, we will first apply a three-step method that aims to search as many ETSs within the code as possible. Then, we will classify these ETSs into different groups based on their labels, i.e., [w; u; e]'s. Further, by dividing the error region into various sub-regions centered by ETSs, we apply the IS simulator to evaluate the error rate of each of the sub-regions. Based on the error rates of all the sub-regions, we can estimate the overall error rate of the LDPC code. Results have indicated that our proposed IS scheme can produce speed-up gains up to 3.9×109 times compared to Monte Carlo simulations.

IET Communications•Jul 22, 2011•7 citations
In this paper, one improved frame error rate (FER) estimation approach in high Signal Noise Ratio (SNR) region for low-density parity-check (LDPC) codes is reported. Firstly dominant trapping sets (TSs), accounting for most of FER in high SNR region, are picked out as decoding result, with input of decoder consisting of a group of biased bits. And how those biased bits are chosen is reduced to finding solution to an binary integer program. Then decoder termination conditions are slightly modified so that history of the tentative decoding could facilitate action of searching for dominant TSs effectively. Without no prior knowledge of shape of error regions for specific LDPC code, we implement weighted mean translation (MT) of importance sampling (IS) method to compute FER in high SNR region. Simulation results demonstrate estimated FER curve conforms well with that of standard monte carlo (MC) simulation in overlapped SNR region.
This study shows a new design method of a simulation probability density function (PDF) for a fast frame error rate (FER) evaluation of low-density parity-check (LDPC) codes. It is difficult to evaluate a very low FER of LDPC codes by Monte Carlo simulation methods since it has excellent performance under iterative decoding. Cole et al. proposed a three-step estimation method of the low FER of LDPC codes of moderate block length. By applying the importance sampling method, which is one of the fast simulation methods, the simulation time of the error performance of LDPC codes can be reduced. Another simulation PDF, which can treat more dominant trapping sets (TS) than Cole et al.'s method, is proposed. The selection of the dominant TS as proportional to its probability reduces the number of simulation runs without deteriorating the accuracy of the estimator. Unbiased estimator is given as well as that that of the Cole et al.'s method. Some numerical examples are shown to demonstrate the effectiveness of the proposed method. The simulation time for an LDPC code of length 504 is reduced to 1/10 when we evaluate FER of around 10-14.
In this paper we propose a novel importance sampling (IS) scheme to estimate the low error-rate performance of low-density parity-check (LDPC) codes over Rayleigh fading channels. The proposed scheme exploits the structural weakness of LDPC codes due to trapping sets (TSs). The Rayleigh fading distribution on the bits belonging to a TS is biased by parameter scaling (PS), while the noise distribution on them is biased via mean translation (MT) according to their fading coefficients. The biases in PS and MT are determined so that the variance of the proposed IS estimator is minimized. The proposed IS scheme is compared with the Monte Carlo (MC) simulator and other IS schemes modified from the conventional IS scheme employed for performance estimation of LDPC codes over an AWGN channel. Numerical results show that it provides much more accurate performance than other IS schemes. Furthermore, the proposed IS estimator is even more efficient than the MC estimator and other IS estimators from the viewpoint of the number of required simulation runs.
We present an importance sampling method for the evaluation of the low frame error rate (FER) performance of LDPC codes under iterative decoding. It relies on a combinatorial characterization of absorbing sets, which are the dominant cause of decoder failure in the low FER region. The biased density in the importance sampling scheme is a mean-shifted version of the original Gaussian density, which is suitably centered between a codeword and a dominant absorbing set. This choice of biased density yields an unbiased estimator for the FER with a variance lower by several orders of magnitude than the standard Monte Carlo estimator. Using this importance sampling scheme in software, we obtain good agreement with the experimental results obtained from a fast hardware emulator of the decoder.
The error-correcting performance of low-density parity check (LDPC) codes, when decoded using practical iterative decoding algorithms, is known to be close to Shannon limits for codes with suitably large blocklengths. A substantial limitation to the use of finite-length LDPC codes is the presence of an error floor in the low frame error rate (FER) region. This paper develops a deterministic method of predicting error floors, based on high signal-to-noise ratio (SNR) asymptotics, applied to absorbing sets within structured LDPC codes. The approach is illustrated using a class of array-based LDPC codes, taken as exemplars of high-performance structured LDPC codes. The results are in very good agreement with a stochastic method based on importance sampling which, in turn, matches the hardware-based experimental results. The importance sampling scheme uses a mean-shifted version of the original Gaussian density, appropriately centered between a codeword and a dominant absorbing set, to produce an unbiased estimator of the FER with substantial computational savings over a standard Monte Carlo estimator. Our deterministic estimates are guaranteed to be a lower bound to the error probability in the high SNR regime, and extend the prediction of the error probability to as low as 10-30. By adopting a channel-independent viewpoint, the usefulness of these results is demonstrated for both the standard Gaussian channel and a channel with mixture noise.
In this paper, we propose an estimation method of a very low bit error rate (HER) of low-density parity-check (LDPC) codes. No analytical tool is available to evaluate performance of LDPC codes, and traditional Monte Carlo simulation methods can not estimate low HER of LDPC codes due to the limitation of time. Cole et al, show a fast frame error rate simulation method of LDPC codes by applying importance sampling (IS). At first, we extend Cole et aI.'s method to the HER estimation of LDPC codes. Since the simulation time of the HER evaluation version of the Cole et aI.'s method is proportional to the number of total dominant trapping sets used in the simulation, we should appropriately choose the dominant trapping sets to reduce the simulation time. To avoid the difficulty, we propose another simulation method, which selects a dominant trapping set as proportional to its upper bound. Additionally, we show some numerical examples to demonstrate the effectiveness of the proposed method. When we evaluate HER of 10−16, the simulation time of the proposed method is reduced to 1/6.6 of that of the HER evaluation version of the Cole et al.'s method under the condition of the same accuracy of the estimator.
Elementary trapping sets (ETSs) have been found to be the main cause of error floor in the decoding of low-density parity-check (LDPC) codes. Moreover, irregular LDPC codes that avoid harmful [w; u; e] ETSs have been constructed and have shown to possess superior error performance compared with other LDPC codes. In this paper, we attempt to evaluate irregular LDPC code performance under an additive white Gaussian noise channel within the high SNR region using the importance sampling (IS) approach in conjunction with the identification of [w; u; e] ETSs. For any given irregular LDPC code, we will first apply a three-step method that aims to search as many ETSs within the code as possible. Then, we will classify these ETSs into different groups based on their labels, i.e., [w; u; e]'s. Further, by dividing the error region into various sub-regions centered by ETSs, we apply the IS simulator to evaluate the error rate of each of the sub-regions. Based on the error rates of all the sub-regions, we can estimate the overall error rate of the LDPC code. Results have indicated that our proposed IS scheme can produce speed-up gains up to 3.9×109 times compared to Monte Carlo simulations.