Symbol Level Decoding for Duo-Binary Turbo Codes

Beeharry, Yogesh; Fowdur, Tulsi Pawan; Soyjaudah, K. M. S.

doi:10.31436/iiumej.v18i1.661

Cited by 4 publications

(9 citation statements)

References 8 publications

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“…The complete decoding mechanism for DBTCs is shown in Figure C The parameters shown in Figure C are defined as follows:…”

Section: System Modelmentioning

confidence: 99%

“…The backward recursive variable for the first decoder is computed as follows :

β_{t}^{1} ((), l) = \max ((), β_{t + 1}^{1} + γ_{t + 1}^{1, i} ((),, l^{'}, l)), for 0 \leq l^{'} \leq M_{S}^{1} - 1 .

…”

Section: System Modelmentioning

confidence: 99%

“…The equation for the LLR is as follows :

Λ_{1, i} ((), t) = \max ((), α_{t - 1}^{1} + γ_{t}^{1, i} ((),, l^{'}, l) + β_{t}^{1} ((), l)) - \max ((), α_{t - 1}^{1} + γ_{t}^{1, 0} ((),, l^{'}, l) + β_{t}^{1} ((), l)), for 0 \leq l^{'} \leq M_{S}^{1} - 1,

where Λ 1, i ( t ) is the LLR of the symbol i , where i Є {1, 2 and 3} for DBTCs.…”

Section: System Modelmentioning

confidence: 99%

“…The a posteriori LLR comprises three LLRs, namely, the a priori LLR, the intrinsic LLR, and the extrinsic LLR, which are related as follows :

normalA posteriori normalLLR = normalA priori normalLLR + Intrinsic normalLLR + Extrinsic normalLLR,

Λ_{1, i} ((), t) = {\overset{true‾}{Λ}}_{2, e}^{i} ((), t) + Λ_{1, in}^{i} ((), t) + Λ_{1, normale}^{i} ((), t),

where

Λ_{1, in}^{i} ((), t)

is the intrinsic LLR for decoder 1. The extrinsic LLR for the decoder 1 is thus calculated as follows:

Λ_{1, normale}^{i} ((), t) = Λ_{1, i} ((), t) - Λ_{1, in}^{i} ((), t) - {\overset{true‾}{Λ}}_{2, normale}^{i} ((), t) .

…”

Section: System Modelmentioning

confidence: 99%

“…The intrinsic LLRs are computed as follows:

Λ_{1, in}^{i} = \frac{L_{normalc}}{2} ((), ([], ((), {italicrA}_{t}) . ((), {italicxA}_{t}^{((), i)})) + ([], ((), {italicrB}_{t}) . ((), {italicxB}_{t}^{((), i)}))) .

…”

Section: System Modelmentioning

confidence: 99%

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Performance of duo‐binary turbo codes with optimized hierarchical modulation

Beeharry

Fowdur

Soyjaudah

2018

Internet Technology Letters

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Non‐binary turbo codes (TCs) have become an important class of TCs following the adoption of duo‐binary TCs (DBTCs) in the digital video broadcasting with return channel via satellite (DVB‐RCS) standard. Several hierarchical modulation (HM) schemes for binary TCs have been proposed with a view to enhancing their performance. However, the existing techniques make use of fixed parameters throughout the Eb/N0 range and have not been applied to non‐binary TCs. In this paper, optimum parameters have been derived from exhaustive simulations for performing HM with 8‐PSK integrated with DBTCs. To validate the improved error performances obtained with the optimized parameters for HM, EXIT charts have been generated. Moreover, a model that could predict the optimized parameters at each Eb/N0 using K‐nearest neighbor (K‐NN)‐based binary classification has been derived from the results of exhaustive simulations. Significant improvement in error performances have been observed with the proposed scheme. For example, with a couple length of 48, code rate of 1/3 or 1/2, and 8‐PSK modulation, a significant average gain of 0.5 dB is obtained over conventional DBTCs without HM.

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“…The complete decoding mechanism for DBTCs is shown in Figure C The parameters shown in Figure C are defined as follows:…”

Section: System Modelmentioning

confidence: 99%

“…The backward recursive variable for the first decoder is computed as follows :

β_{t}^{1} ((), l) = \max ((), β_{t + 1}^{1} + γ_{t + 1}^{1, i} ((),, l^{'}, l)), for 0 \leq l^{'} \leq M_{S}^{1} - 1 .

…”

Section: System Modelmentioning

confidence: 99%

“…The equation for the LLR is as follows :

Λ_{1, i} ((), t) = \max ((), α_{t - 1}^{1} + γ_{t}^{1, i} ((),, l^{'}, l) + β_{t}^{1} ((), l)) - \max ((), α_{t - 1}^{1} + γ_{t}^{1, 0} ((),, l^{'}, l) + β_{t}^{1} ((), l)), for 0 \leq l^{'} \leq M_{S}^{1} - 1,

where Λ 1, i ( t ) is the LLR of the symbol i , where i Є {1, 2 and 3} for DBTCs.…”

Section: System Modelmentioning

confidence: 99%

“…The a posteriori LLR comprises three LLRs, namely, the a priori LLR, the intrinsic LLR, and the extrinsic LLR, which are related as follows :

normalA posteriori normalLLR = normalA priori normalLLR + Intrinsic normalLLR + Extrinsic normalLLR,

Λ_{1, i} ((), t) = {\overset{true‾}{Λ}}_{2, e}^{i} ((), t) + Λ_{1, in}^{i} ((), t) + Λ_{1, normale}^{i} ((), t),

where

Λ_{1, in}^{i} ((), t)

is the intrinsic LLR for decoder 1. The extrinsic LLR for the decoder 1 is thus calculated as follows:

Λ_{1, normale}^{i} ((), t) = Λ_{1, i} ((), t) - Λ_{1, in}^{i} ((), t) - {\overset{true‾}{Λ}}_{2, normale}^{i} ((), t) .

…”

Section: System Modelmentioning

confidence: 99%

“…The intrinsic LLRs are computed as follows:

Λ_{1, in}^{i} = \frac{L_{normalc}}{2} ((), ([], ((), {italicrA}_{t}) . ((), {italicxA}_{t}^{((), i)})) + ([], ((), {italicrB}_{t}) . ((), {italicxB}_{t}^{((), i)}))) .

…”

Section: System Modelmentioning

confidence: 99%

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Performance of duo‐binary turbo codes with optimized hierarchical modulation

Beeharry

Fowdur

Soyjaudah

2018

Internet Technology Letters

Self Cite

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Optimizing the Performance of Triple-Binary Turbo Codes with Hierarchical QAM and K-NN Based Classification

Beeharry

Fowdur

2019

Smart and Sustainable Engineering for Next Generation Applications

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Performance of Extrinsic Information Scaling Mechanisms with Duo-Binary Turbo Codes

Beeharry¹,

Pawan²,

S.³

2017

JESTR

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Non-binary Turbo codes have gained significant research interest following the adoption of duo-binary Turbo codes in the Digital Video Broadcasting with Return Channel via Satellite (DVB-RCS) standard. In order to meet the Quality of Service (QoS) requirements of applications in communication systems, error control codes with enhanced error performance and low decoding complexity are highly desired. In this paper, the performance analysis of duo-binary Turbo codes using Sign Difference Ratio (SDR) and Regression analysis based extrinsic information scaling mechanisms has been performed. Different techniques for computing the scaling coefficients using the symbol and bit level Log-Likelihood Ratios (LLRs) have been presented and analyzed. Results show that the best gain in error performance is obtained using the mean of scaling coefficients computed from bit-level LLRs with both SDR and Regression based algorithms. The schemes give an average gain of 0.1 dB as compared to the conventional scheme.

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Symbol Level Decoding for Duo-Binary Turbo Codes

Cited by 4 publications

References 8 publications

Performance of duo‐binary turbo codes with optimized hierarchical modulation

Performance of duo‐binary turbo codes with optimized hierarchical modulation

Optimizing the Performance of Triple-Binary Turbo Codes with Hierarchical QAM and K-NN Based Classification

Performance of Extrinsic Information Scaling Mechanisms with Duo-Binary Turbo Codes

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