TY - GEN

T1 - Non-repetitive encoding with increased degree-1 encoding symbols for LT codes

AU - Yen, Kuo Kuang

AU - Liao, Yen Chin

AU - Chen, Chih Lung

AU - Chang, Hsie-Chia

PY - 2012/12/1

Y1 - 2012/12/1

N2 - For LT codes with robust Soliton distribution, the ripple size is relatively small in the beginning of BP decoding process. Therefore, most of decoding termination occurs due to lack of ripple at early stage. In this study, we aim at reducing early decoding termination for low symbol loss probability. First, given k input symbols, the degree-1 proportion is increased to enlarge the average ripple size within the range 0 ≤ n ≤ k=2, where n is the number of decoded input symbols. Second, we propose Non-Repetitive (NR) encoding scheme to avoid generating repeated degree-1 encoding symbols. An NR encoder forces the first k degree-1 encoding symbols to connect to different input symbols. Simulation results show that NR encoding outperforms LT encoding in terms of symbol loss probability. Besides, less encoding symbols is needed to achieve high successful decoding probability when our scheme is applied. With k = 2000, NR encoding reaches a successful decoding probability of 99.6% when overhead is 0.2, while LT encoding requires an overhead of 0.32 to reach the same probability.

AB - For LT codes with robust Soliton distribution, the ripple size is relatively small in the beginning of BP decoding process. Therefore, most of decoding termination occurs due to lack of ripple at early stage. In this study, we aim at reducing early decoding termination for low symbol loss probability. First, given k input symbols, the degree-1 proportion is increased to enlarge the average ripple size within the range 0 ≤ n ≤ k=2, where n is the number of decoded input symbols. Second, we propose Non-Repetitive (NR) encoding scheme to avoid generating repeated degree-1 encoding symbols. An NR encoder forces the first k degree-1 encoding symbols to connect to different input symbols. Simulation results show that NR encoding outperforms LT encoding in terms of symbol loss probability. Besides, less encoding symbols is needed to achieve high successful decoding probability when our scheme is applied. With k = 2000, NR encoding reaches a successful decoding probability of 99.6% when overhead is 0.2, while LT encoding requires an overhead of 0.32 to reach the same probability.

KW - BP decoding

KW - degree

KW - LT code

KW - Non-Repetitive encoding

UR - http://www.scopus.com/inward/record.url?scp=84874146078&partnerID=8YFLogxK

U2 - 10.1109/APCCAS.2012.6419120

DO - 10.1109/APCCAS.2012.6419120

M3 - Conference contribution

AN - SCOPUS:84874146078

SN - 9781457717291

T3 - IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS

SP - 655

EP - 658

BT - 2012 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2012

Y2 - 2 December 2012 through 5 December 2012

ER -