scholarly journals List Viterbi Decoding of PAC Codes

2020 ◽  
Author(s):  
Mohammad Rowshan ◽  
Emanuele Viterbo
Keyword(s):  
Error Correction ◽  
Viterbi Algorithm ◽  
Hamming Distance ◽  
Poor Performance ◽  
Superior Performance ◽  
List Decoding ◽  
Viterbi Decoding ◽  
Vector Channel ◽  
Constraint Length ◽  
List Viterbi Algorithm

<div>Polarization-adjusted convolutional (PAC) codes are special concatenated codes in which we employ a one-to-one convolutional transform as a pre-coding step before the polar transform. In this scheme, the polar transform (as a mapper) and the successive cancellation process (as a demapper) present a synthetic vector channel to the convolutional transformation. The numerical results show that this concatenation improves the Hamming distance properties of polar codes. </div><div>In this work, we implement the parallel list Viterbi algorithm (LVA) and show how the error correction performance moves from the poor performance of the Viterbi algorithm (VA) to the superior performance of list decoding by changing the constraint length, list size, and the sorting strategy (local sorting and global sorting) in the LVA. Also, we analyze the latency of the local sorting of the paths in LVA relative to the global sorting in the list decoding and the trade-off between the sorting latency and the error correction performance.</div>

scholarly journals List Viterbi Decoding of PAC Codes

2020 ◽  
Author(s):  
Mohammad Rowshan ◽  
Emanuele Viterbo
Keyword(s):  
Error Correction ◽  
Viterbi Algorithm ◽  
Hamming Distance ◽  
Poor Performance ◽  
Superior Performance ◽  
List Decoding ◽  
Viterbi Decoding ◽  
Vector Channel ◽  
Constraint Length ◽  
List Viterbi Algorithm

<div>Polarization-adjusted convolutional (PAC) codes are special concatenated codes in which we employ a one-to-one convolutional transform as a pre-coding step before the polar transform. In this scheme, the polar transform (as a mapper) and the successive cancellation process (as a demapper) present a synthetic vector channel to the convolutional transformation. The numerical results show that this concatenation improves the Hamming distance properties of polar codes. </div><div>In this work, we implement the parallel list Viterbi algorithm (LVA) and show how the error correction performance moves from the poor performance of the Viterbi algorithm (VA) to the superior performance of list decoding by changing the constraint length, list size, and the sorting strategy (local sorting and global sorting) in the LVA. Also, we analyze the latency of the local sorting of the paths in LVA relative to the global sorting in the list decoding and the trade-off between the sorting latency and the error correction performance.</div>

scholarly journals List Viterbi Decoding of PAC Codes

2021 ◽  
Author(s):  
Mohammad Rowshan ◽  
Emanuele Viterbo
Keyword(s):  
Error Correction ◽  
Viterbi Algorithm ◽  
Hamming Distance ◽  
Poor Performance ◽  
Superior Performance ◽  
List Decoding ◽  
Viterbi Decoding ◽  
Vector Channel ◽  
Constraint Length ◽  
List Viterbi Algorithm

<div>Polarization-adjusted convolutional (PAC) codes are special concatenated codes in which we employ a one-to-one convolutional transform as a pre-coding step before the polar transform. In this scheme, the polar transform (as a mapper) and the successive cancellation process (as a demapper) present a synthetic vector channel to the convolutional transformation. The numerical results show that this concatenation improves the Hamming distance properties of polar codes. </div><div>In this work, we implement the parallel list Viterbi algorithm (LVA) and show how the error correction performance moves from the poor performance of the Viterbi algorithm (VA) to the superior performance of list decoding by changing the constraint length, list size, and the sorting strategy (local sorting and global sorting) in the LVA. Also, we analyze the latency of the local sorting of the paths in LVA relative to the global sorting in the list decoding and the trade-off between the sorting latency and the error correction performance.</div>

scholarly journals A Form of List Viterbi Algorithm for Decoding Convolutional Codes

2018 ◽  
Vol 4 (2) ◽  
pp. 42-48
Author(s):  
Shamsuddeen Hassan Muhammad ◽  
Abdulrasheed Mustapha
Keyword(s):  
Communication Systems ◽  
Viterbi Algorithm ◽  
Convolutional Code ◽  
Convolutional Codes ◽  
Wireless Communication Systems ◽  
Voice Over Internet Protocol ◽  
Viterbi Decoding ◽  
Maximum Likelihood Decoding ◽  
Error Performance ◽  
List Viterbi Algorithm

Viterbi algorithm is a maximum likelihood decoding algorithm. It is used to decode convolutional code in several wireless communication systems, including Wi-Fi. The standard Viterbi algorithm gives just one decoded output, which may be correct or incorrect. Incorrect packets are normally discarded thereby necessitating retransmission and hence resulting in considerable energy loss and delay. Some real-time applications such as Voice over Internet Protocol (VoIP) telephony do not tolerate excessive delay. This makes the conventional Viterbi decoding strategy sub-optimal. In this regard, a modified approach, which involves a form of List Viterbi for decoding the convolutional code is investigated. The technique employed combines the bit-error correction capabilities of both the Viterbi algorithm and the Cyclic Redundancy Check (CRC) procedures. It first uses a form of ‘List Viterbi Algorithm’ (LVA), which generates a list of possible decoded output candidates after the trellis search. The CRC check is then used to determine the presence of correct outcome. Results of experiments conducted using simulation shows considerable improvement in bit-error performance when compared to classical approach.

scholarly journals Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarv Priya ◽  
Tanya Aggarwal ◽  
Caitlin Ward ◽  
Girish Bathla ◽  
Mathews Jacob ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pulmonary Hypertension ◽  
Feature Selection ◽  
Subgroup Analysis ◽  
Cardiac Mri ◽  
Intraclass Correlation ◽  
Poor Performance ◽  
Superior Performance ◽  
Feature Filtering ◽  
The Impact

AbstractSide experiments are performed on radiomics models to improve their reproducibility. We measure the impact of myocardial masks, radiomic side experiments and data augmentation for information transfer (DAFIT) approach to differentiate patients with and without pulmonary hypertension (PH) using cardiac MRI (CMRI) derived radiomics. Feature extraction was performed from the left ventricle (LV) and right ventricle (RV) myocardial masks using CMRI in 82 patients (42 PH and 40 controls). Various side study experiments were evaluated: Original data without and with intraclass correlation (ICC) feature-filtering and DAFIT approach (without and with ICC feature-filtering). Multiple machine learning and feature selection strategies were evaluated. Primary analysis included all PH patients with subgroup analysis including PH patients with preserved LVEF (≥ 50%). For both primary and subgroup analysis, DAFIT approach without feature-filtering was the highest performer (AUC 0.957–0.958). ICC approaches showed poor performance compared to DAFIT approach. The performance of combined LV and RV masks was superior to individual masks alone. There was variation in top performing models across all approaches (AUC 0.862–0.958). DAFIT approach with features from combined LV and RV masks provide superior performance with poor performance of feature filtering approaches. Model performance varies based upon the feature selection and model combination.

scholarly journals Hamming Codes: Error Reducing Techniques

2021 ◽  
Vol 9 (11) ◽  
pp. 1972-1974
Author(s):  
Rohitkumar R Upadhyay
Keyword(s):  
Lower Bound ◽  
Error Correction ◽  
Coding Theory ◽  
Hamming Distance ◽  
Error Reduction ◽  
Error Correcting Codes ◽  
Significant Other ◽  
Hamming Codes ◽  
Decoding Algorithms ◽  
Reducing Properties

Abstract: Hamming codes for all intents and purposes are the first nontrivial family of error-correcting codes that can actually correct one error in a block of binary symbols, which literally is fairly significant. In this paper we definitely extend the notion of error correction to error-reduction and particularly present particularly several decoding methods with the particularly goal of improving the error-reducing capabilities of Hamming codes, which is quite significant. First, the error-reducing properties of Hamming codes with pretty standard decoding definitely are demonstrated and explored. We show a sort of lower bound on the definitely average number of errors present in a decoded message when two errors for the most part are introduced by the channel for for all intents and purposes general Hamming codes, which actually is quite significant. Other decoding algorithms are investigated experimentally, and it generally is definitely found that these algorithms for the most part improve the error reduction capabilities of Hamming codes beyond the aforementioned lower bound of for all intents and purposes standard decoding. Keywords: coding theory, hamming codes, hamming distance

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