Novel Blind Identification of LDPC Code Check Vector Using Soft-decision

2020 ◽  
Author(s):  
Yingke Lei ◽  
Luwei Luo
Keyword(s):  
Ldpc Code ◽  
Blind Identification ◽  
Soft Decision

scholarly journals Multiple Observations for Secret-Key Binding with SRAM PUFs

2021 ◽  
Author(s):  
Lieneke Kusters ◽  
Frans M.J. Willems
Keyword(s):  
Error Correcting Code ◽  
Ldpc Code ◽  
Soft Decision ◽  
Secret Key ◽  
Multiple Observations ◽  
Reconstruction Performance ◽  
New Strategy ◽  
The One ◽  
Helper Data ◽  
Data Scheme

We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.

scholarly journals Multiple Observations for Secret-Key Binding with SRAM PUFs

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 590
Author(s):  
Lieneke Kusters ◽  
Frans M. J. Willems
Keyword(s):  
Error Correcting Code ◽  
Ldpc Code ◽  
Soft Decision ◽  
Secret Key ◽  
Multiple Observations ◽  
Reconstruction Performance ◽  
New Strategy ◽  
The One ◽  
Helper Data ◽  
Data Scheme

We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM-PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM-PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore, it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.

Subcarrier Reliability Aware Soft-Decision LDPC Code in CO-OFDM Systems

2014 ◽  
Vol 26 (11) ◽  
pp. 1157-1160 ◽  
Author(s):  
Di Che ◽  
Hamid Khodakarami ◽  
An Li ◽  
Xi Chen ◽  
Trevor Anderson ◽  
...  
Keyword(s):  
Ldpc Code ◽  
Ofdm Systems ◽  
Soft Decision

scholarly journals Performance Analysis of Coherent Atmospheric Optical Communications with Soft-Decision Forward Error Correction

2013 ◽  
Vol 347-350 ◽  
pp. 1856-1859
Author(s):  
Jin Jing Tao ◽  
Jin Nan Zhang ◽  
Yang An Zhang ◽  
Yong Qing Huang ◽  
Xue Guang Yuan ◽  
...  
Keyword(s):  
Forward Error Correction ◽  
Ldpc Codes ◽  
Ldpc Code ◽  
Coherent Detection ◽  
Heterodyne Detection ◽  
Soft Decision ◽  
Power Requirement ◽  
Received Power ◽  
Forward Error ◽  
Atmospheric Channel

Performance of coherent atmospheric optical communication system with heterodyne detection and LDPC codes was evaluated over atmospheric channel attenuations of which are about 20-30 dB/km. To reduce bit error and enhance the system performance LDPC code was implemented in system. Combining coherent detection and LDPC codes could reduce the received power requirement ~4 dBm at the BER of 10-9.

scholarly journals An improvement and a fast DSP implementation of the bit flipping algorithms for low density parity check decoder

2021 ◽  
Vol 11 (6) ◽  
pp. 4774
Author(s):  
Mouhcine Razi ◽  
Mhammed Benhayoun ◽  
Anass Mansouri ◽  
Ali Ahaitouf
Keyword(s):  
High Speed ◽  
Ldpc Code ◽  
Low Density ◽  
Parity Check ◽  
Soft Decision ◽  
Ldpc Decoding ◽  
Low Density Parity Check ◽  
Dsp Implementation ◽  
Hard Decision ◽  
Decision Algorithms

<span lang="EN-US">For low density parity check (LDPC) decoding, hard-decision algorithms are sometimes more suitable than the soft-decision ones. Particularly in the high throughput and high speed applications. However, there exists a considerable gap in performances between these two classes of algorithms in favor of soft-decision algorithms.  In order to reduce this gap, in this work we introduce two new improved versions of the hard-decision algorithms, the adaptative gradient descent bit-flipping (AGDBF) and adaptative reliability ratio weighted GDBF (ARRWGDBF).  An adaptative weighting and correction factor is introduced in each case to improve the performances of the two algorithms allowing an important gain of bit error rate. As a second contribution of this work a real time implementation of the proposed solutions on a digital signal processors (DSP) is performed in order to optimize and improve the performance of these new approchs. The results of numerical simulations and DSP implementation reveal a faster convergence with a low processing time and a reduction in consumed memory resources when compared to soft-decision algorithms. For the irregular LDPC code, our approachs achieves gains of 0.25 and 0.15 dB respectively for the AGDBF and ARRWGDBF algorithms.</span>

scholarly journals Performance Analysis of LDPC Decoding Techniques

2021 ◽  
Vol 9 (5) ◽  
pp. 17-26
Author(s):  
Abdel Halim A. Zikry ◽  
Ashraf Y. Hassan ◽  
Wageda I. Shaban ◽  
Sahar F. Abdel-Momen
Keyword(s):  
Channel Coding ◽  
Coding Theory ◽  
Belief Propagation ◽  
Ldpc Codes ◽  
Ldpc Code ◽  
Soft Decision ◽  
Coding Gain ◽  
Ldpc Decoding ◽  
Communications Systems ◽  
Belief Propagation Algorithm

Low density parity checking codes (LDPC) are one of the most important issues in coding theory at present. LDPC-code are a type of linear-block LDPC-codes. Channel coding might be considered as the finest conversant and most potent components of cellular communications systems, that was employed for transmitting errors corrections imposed by noise, fading and interfering. LDPC-codes are advanced coding gain, i.e., new area in coding. the performances of LDPC-code are similar to the Shannon-limiting, this led to the usage of decoding in several applications in digital communications systems, like DVB-S2 and WLAN802.1..This paper aims to know what is LDPC,what its application and introduce encoding algorithms that gives rise to a linear encoding time and also show that the regular and irregular LDPC performance and also introduce different methods for decoding LDPC. I discuss in detail LDPC decoding algorithm: bit flipping algorithm, as a type from hard decision .belief propagation algorithm, sum product algorithm and minimum sum algorithm as examples from soft decision .I expect that at least some students or researchers involved in researching LDPC codes would find this paper helpful.

Blind Identification of Multichannel Systems Based on Sparse Bayesian Learning

2016 ◽  
Vol E99.B (12) ◽  
pp. 2614-2622 ◽  
Author(s):  
Kai ZHANG ◽  
Hongyi YU ◽  
Yunpeng HU ◽  
Zhixiang SHEN ◽  
Siyu TAO
Keyword(s):  
Bayesian Learning ◽  
Blind Identification ◽  
Sparse Bayesian Learning ◽  
Multichannel Systems

Performance Comparison between Turbo Code and Rate-Compatible LDPC Code for Evolved UTRA Downlink OFDM Radio Access

2009 ◽  
Vol E92-B (5) ◽  
pp. 1504-1515 ◽  
Author(s):  
Naoto OKUBO ◽  
Nobuhiko MIKI ◽  
Yoshihisa KISHIYAMA ◽  
Kenichi HIGUCHI ◽  
Mamoru SAWAHASHI
Keyword(s):  
Turbo Code ◽  
Ldpc Code ◽  
Performance Comparison ◽  
Radio Access
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