Parameter estimation of block and helical scan interleavers in the presence of bit errors

Swaminathan, R; Madhukumar, A. S.; Ng, Wee Teck; See, Chong Meng Samson

doi:10.1016/j.dsp.2016.08.006

Cited by 18 publications

(22 citation statements)

References 17 publications

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“…We show the detection probability of the proposed method in Fig. 3 by varying k from min(w, L) -1 to min(w, L) -4, when the interleaving period is 60, (15,11) BCH code is used, γ is 0.75, and M is 2520 (60×60×0.7) bits. From Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…We depict the detection probability and the number of false alarms of the proposed method for various values of γ in Figs. 4 and 5, respectively, when the interleaving period is 60, (15,11) BCH code is used, k is min(w, L) -1, and M is 2520 (60×60×0.7) bits. We can see from Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…We show the detection probabilities when the interleaving period is 49 and (7,4) Hamming code is used in Fig. 6, and when the interleaving period is 60 and (15,11) BCH code is used in Fig. 7.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…7. We also depict the number of false alarms when the interleaving period is 60 and (15,11) BCH code is used in Fig. 8.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Reference [14] estimated helical scan interleaver for block channel coded data. Regarding convolutional channel coded data, [15] estimated block and helical scan interleavers, and [16] estimated convolutional and helical interleavers. Further, [17] jointly recognized the type of channel codes and interleaver parameters, and [18] jointly estimated Reed-Solomon code and block interleaver parameters.…”

Section: Introductionmentioning

confidence: 99%

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Blind Interleaver Parameter Estimation From Scant Data

Jang

Kim

et al. 2020

IEEE Access

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A method for blind estimation of interleaver parameter was recently reported which made additional data from a limited amount of received data. However, the process of making additional data creates undesirable linearity which degrades estimation performance. Promise for improved estimation therefore lies in enhancing blind estimation of interleaver parameter without making additional data. In this paper, we propose an improved method to blindly estimate interleaver parameter under the condition of scant data. We first generate a matrix by using the received data. From this matrix we then make square submatrices and obtain their rank deficiency distribution. Finally, we estimate the interleaver parameter by comparing the rank deficiency distribution of the square submatrices and that of random binary matrices. Through computer simulations, we validate the proposed method in terms of detection probability and the number of false alarms. Simulation results show that the proposed method works better than the conventional method given scarce received data.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%