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We present a new model for LT codes which simplifies the analysis of the error probability of decoding by belief propagation. For any given degree distribution, we provide the first rigorous expression for the limiting error probability as…

Information Theory · Computer Science 2007-07-13 Elitza N. Maneva , Amin Shokrollahi

The probability density function (PDF) and cumulative distribution function of the sum of L independent but not necessarily identically distributed squared \eta-\mu variates, applicable to the output statistics of maximal ratio combining…

Information Theory · Computer Science 2012-10-02 Imran Shafique Ansari , Ferkan Yilmaz , Mohamed-Slim Alouini

Products codes (PCs) are conventionally decoded with efficient iterative bounded-distance decoding (iBDD) based on hard-decision channel outputs which entails a performance loss compared to a soft-decision decoder. Recently, several hybrid…

Information Theory · Computer Science 2022-11-22 Sisi Miao , Lukas Rapp , Laurent Schmalen

Reed-Muller (RM) codes are known for their good maximum likelihood (ML) performance in the short block-length regime. Despite being one of the oldest classes of channel codes, finding a low complexity soft-input decoding scheme is still an…

Information Theory · Computer Science 2021-07-19 Marvin Geiselhart , Ahmed Elkelesh , Moustafa Ebada , Sebastian Cammerer , Stephan ten Brink

The probability density function (PDF) associated with a given set of samples is approximated by a piecewise-linear polynomial constructed with respect to a binning of the sample space. The kernel functions are a compactly supported basis…

Numerical Analysis · Mathematics 2020-08-04 Giacomo Capodaglio , Max Gunzburger

The bit-error threshold of the standard ensemble of Low Density Parity Check (LDPC) codes is known to be close to capacity, if there is a non-zero fraction of degree-two bit nodes. However, the degree-two bit nodes preclude the possibility…

Information Theory · Computer Science 2013-05-22 Asit Kumar Pradhan , Arunkumar Subramanian , Andrew Thangaraj

It is shown that for any binary-input discrete memoryless channel $W$ with symmetric capacity $I(W)$ and any rate $R <I(W)$, the probability of block decoding error for polar coding under successive cancellation decoding satisfies $P_e \le…

Information Theory · Computer Science 2009-04-06 Erdal Arikan , Emre Telatar

We derive a new upper bound on the reliability function for channel coding over discrete memoryless channels. Our bounding technique relies on two main elements: (i) adding an auxiliary genie-receiver that reveals to the original receiver a…

Information Theory · Computer Science 2022-09-05 Anelia Somekh-Baruch

Minimum Bayes Risk (MBR) decoding optimizes output selection by maximizing the expected utility value of an underlying human distribution. While prior work has shown the effectiveness of MBR decoding through empirical evaluation, few…

Computation and Language · Computer Science 2025-06-23 Yuki Ichihara , Yuu Jinnai , Kaito Ariu , Tetsuro Morimura , Eiji Uchibe

We introduce a novel universal soft-decision decoding algorithm for binary block codes called ordered reliability direct error pattern testing (ORDEPT). Our results, obtained for a variety of popular short high-rate codes, demonstrate that…

Information Theory · Computer Science 2023-10-19 Reza Hadavian , Xiaoting Huang , Dmitri Truhachev , Kamal El-Sankary , Hamid Ebrahimzad , Hossein Najafi

Assuming iterative decoding for binary erasure channels (BECs), a novel tree-based technique for upper bounding the bit error rates (BERs) of arbitrary, finite low-density parity-check (LDPC) codes is provided and the resulting bound can be…

Information Theory · Computer Science 2007-07-13 Chih-Chun Wang , Sanjeev R. Kulkarni , H. Vincent Poor

This paper develops an algorithmic approach for obtaining approximate, numerical estimates of the sizes of subcodes of Reed-Muller (RM) codes, all of the codewords in which satisfy a given constraint. Our algorithm is based on a statistical…

Information Theory · Computer Science 2023-09-20 V. Arvind Rameshwar , Shreyas Jain , Navin Kashyap

In this paper, we study binary constrained codes that are resilient to bit-flip errors and erasures. In our first approach, we compute the sizes of constrained subcodes of linear codes. Since there exist well-known linear codes that achieve…

Information Theory · Computer Science 2023-04-20 V. Arvind Rameshwar , Navin Kashyap

We discuss the performance of Low-Density-Parity-Check (LDPC) codes decoded by means of Linear Programming (LP) at moderate and large Signal-to-Noise-Ratios (SNR). Utilizing a combination of the previously introduced pseudo-codeword-search…

Information Theory · Computer Science 2007-07-13 Michael Chertkov , Mikhail Stepanov

Reverberation negatively impacts the performance of automatic speech recognition (ASR). Prior work on quantifying the effect of reverberation has shown that clarity (C50), a parameter that can be estimated from the acoustic impulse…

Audio and Speech Processing · Electrical Eng. & Systems 2020-02-17 Hannes Gamper , Dimitra Emmanouilidou , Sebastian Braun , Ivan J. Tashev

This paper studies random-coding error exponents of randomised list decoding, in which the decoder randomly selects $L$ messages with probabilities proportional to the decoding metric of the codewords. The exponents (or bounds) are given…

Information Theory · Computer Science 2026-01-15 Henrique K. Miyamoto , Sheng Yang

Non-binary linear block codes (NB-LBCs) are an important class of error-correcting codes that are especially competent in correcting burst errors. They have broad applications in modern communications and storage systems. However, efficient…

Information Theory · Computer Science 2026-01-21 Jingyu Lin , Li Chen , Xiaoqian Ye

Polarization-adjusted convolutional (PAC) codes can approach the theoretical bound for block error rate (BLER) performance at short-to-medium codeword length. PAC codes have excellent BLER performance using Monte Carlo (MC) rate-profiles…

Information Theory · Computer Science 2023-04-25 Shirong Jiang , Jiahao Wang , Chunfang Xia , Xiang Li

Bayesian optimization (BO) is a sample-efficient method and has been widely used for optimizing expensive black-box functions. Recently, there has been a considerable interest in BO literature in optimizing functions that are affected by…

Machine Learning · Computer Science 2023-12-22 Xiaobin Huang , Lei Song , Ke Xue , Chao Qian

We consider probabilistic shaping to maximize the achievable information rate of coded modulation (CM) with hard decision decoding. The proposed scheme using binary staircase codes outperforms its uniform CM counterpart by more than 1.3 dB…

Information Theory · Computer Science 2017-05-08 Alireza Sheikh , Alexandre Graell i Amat , Gianluigi Liva