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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2014.10
- 수록면
- 585 - 592 (8page)
이용수
초록· 키워드
오류제보하기
It is well known that serial belief propagation (BP) decoding for low-density parity-check (LDPC) codes achieves faster convergence without any increase of decoding complexity per iteration and bit error rate (BER) performance loss than standard parallel BP (PBP) decoding. Serial BP (SBP) decoding, such as horizontal SBP (H-SBP) decoding or vertical SBP (V-SBP) decoding, updates check nodes or variable nodes faster than standard PBP decoding within a single iteration.
In this paper, we propose combined horizontal-vertical SBP (CHV-SBP) decoding. By the same reasoning, CHV-SBP decoding updates check nodes or variable nodes faster than SBP decoding within a serialized step in an iteration. CHV-SBP decoding achieves faster convergence than H-SBP or V-SBP decoding. We compare these decoding schemes in details. We also show in simulations that the convergence rate, in iterations, for CHV-SBP decoding is about 1/6 of that for standard PBP decoding, while the convergence rate for SBP decoding is about 1/2 of that for standard PBP decoding. In simulations, we use recently proposed generalized LDPC (GLDPC) codes with binary cyclic codes (BCC).
In this paper, we propose combined horizontal-vertical SBP (CHV-SBP) decoding. By the same reasoning, CHV-SBP decoding updates check nodes or variable nodes faster than SBP decoding within a serialized step in an iteration. CHV-SBP decoding achieves faster convergence than H-SBP or V-SBP decoding. We compare these decoding schemes in details. We also show in simulations that the convergence rate, in iterations, for CHV-SBP decoding is about 1/6 of that for standard PBP decoding, while the convergence rate for SBP decoding is about 1/2 of that for standard PBP decoding. In simulations, we use recently proposed generalized LDPC (GLDPC) codes with binary cyclic codes (BCC).
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Reviews of PBP, H-SBP, and V-SBP Decoding Schemes
Ⅲ. Derivation of CHV-SBP Decoding Schemes
Ⅳ. Simulation Results and Discussions
Ⅴ. Conclusion
References
참고문헌 (17)
참고문헌 신청
1. [학술지(정기간행물)] - R. G. Gallager / 1962 / Low-density parity-check codes / IRE Trans. Inf. Theory IT-8 : 21 ~ 28
2. [학술지(정기간행물)] - D. J. C. MacKay / 1999 / Good error correcting codes based on very sparse matrices / IEEE Trans. Inf. Theory 45 (2) : 399 ~ 431
3. [학술지(정기간행물)] - T. Richardson / 2001 / Design of capacity-approaching low-density parity-check codes / IEEE Trans. Inf. Theory 47 (2) : 619 ~ 637
4. [학술지(정기간행물)] - T. J. Richardson / 2001 / The capacity of low-density parity-check codes under message-passing decoding / IEEE Trans. Inf. Theory 47 (2) : 599 ~ 618
5. [단행본] - J. Pearl / 1988 / Probabilistic reasoning in intelligent systems: networks of plausible inference / Morgan Kaufmann
2. [학술지(정기간행물)] - D. J. C. MacKay / 1999 / Good error correcting codes based on very sparse matrices / IEEE Trans. Inf. Theory 45 (2) : 399 ~ 431
3. [학술지(정기간행물)] - T. Richardson / 2001 / Design of capacity-approaching low-density parity-check codes / IEEE Trans. Inf. Theory 47 (2) : 619 ~ 637
4. [학술지(정기간행물)] - T. J. Richardson / 2001 / The capacity of low-density parity-check codes under message-passing decoding / IEEE Trans. Inf. Theory 47 (2) : 599 ~ 618
5. [단행본] - J. Pearl / 1988 / Probabilistic reasoning in intelligent systems: networks of plausible inference / Morgan Kaufmann
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UCI(KEPA) : I410-ECN-0101-2015-560-002671892