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Serially concatenated belief propagation decoder for low‐density parity‐check codes

Published: 27 September 2017 Publication History

Abstract

A new decoding strategy of belief propagation (BP) for low‐density parity‐check codes is presented by serially concatenating the traditional simultaneous decoding (also known as flooding) and the informed dynamic decoding (IDD) (e.g. node‐wise residual belief propagation (NW RBP) or informed variable‐to‐check (IVC) RBP). The frame error rates (FER) and bit error rates (BER) of the concatenated BP decoder outperform that of the non‐concatenated single decoder. In addition, as the signal‐to‐noise ratio (SNR) increases, the average number of decoding iterations required for the concatenated BP decoder decreases, and tends to merge with that of the first sub‐decoder in the high SNR region. Moreover, since the IDD strategies concerned in this paper require extra effort for the residual computation, and the sequential updating of IDD is much more time consuming than the simultaneous updating of flooding. Therefore, compared with using a single IDD, serially concatenating flooding and IDD reduces both the approximate decoding complexity and the decoding latency because the concatenated decoder is still dominated by the sub‐decoder flooding.

References

[1]
Gallager R.: ‘Low‐density parity‐check codes’, IET Trans. Inf. Theory, 1962, 8, (1), pp. 21–28
[2]
IEEE Std 802.16e‐2005 and IEEE Std 802.16TM‐2004/Cor1‐2005 : ‘Amendment 2 and Corrigendum 1 to IEEE Std 802.16‐2004’, 2006
[3]
IEEE Std 802.11nTM‐2009: ‘Amendment 5: Enhancements for Higher Throughput’, 2009
[4]
DVB T2: ‘ETSI EN 302 307 V1.1.1’, 2009
[5]
R1‐163961 : ‘3GPP TSG RAN WG1 Meeting #85’, 2016
[6]
Kschischang F., Frey B.J.R., Loeliger H.A.: ‘Factor graphs and the sum‐product algorithm’, IEEE Trans. Inf. Theory, 2001, 47, (2), pp. 498–519
[7]
McEliece R.J., MacKay D.J.C., Cheng J.F.: ‘Turbo decoding as an instance of Pearl's ‘belief propagation’ algorithm’, IEEE J. Sel. Areas Commun., 1998, 16, (2), pp. 140–152
[8]
Kschischang F.R., Frey B.J.: ‘Iterative decoding of compound codes by probability propagation in graphical models’, IEEE J. Sel. Areas Commun., 1998, 16, (2), pp. 219–230
[9]
Hocevar D.: ‘A reduced complexity decoder architecture via layered decoding of LDPC codes’. Proc. IEEE Workshop Signal Processing Systems, Austin, USA, October 2004, pp. 107–112
[10]
Elidan G., McGraw I., Koller D.: ‘Residual belief propagation: informed scheduling for asynchronous message passing’. Proc. 22nd Conf. on Uncertainty in Artificial Intelligence, Cambridge, USA, July 2006, pp. 1–9
[11]
Casado A.I.V., Griot M., Wesel R.D.: ‘LDPC decoders with informed dynamic scheduling’, IEEE Trans. Commun., 2010, 58, (12), pp. 3470–3479
[12]
Han G.J., Liu X.C.: ‘An efficient dynamic schedule for layered belief‐propagation decoding of LDPC codes’, IEEE Commun. Lett., 2009, 13, (12), pp. 950–952
[13]
Kim J.‐H., Nam M.‐Y., Song H.‐Y.: ‘Variable‐to‐check residual belief propagation for LDPC codes’, IET Electron. Lett., 2009, 45, (2), pp. 117–118
[14]
Gong Y., Liu X.C., Ye W.C., et al.: ‘Effective informed dynamic scheduling for belief propagation decoding of LDPC codes’, IEEE Trans. Commun., 2011, 59, (10), pp. 2683–2691
[15]
Liu X., Zhang Y., Cui R.: ‘Variable‐node‐based dynamic scheduling strategy for belief‐propagation decoding of LDPC codes’, IEEE Commun. Lett., 2015, 19, (2), pp. 147–150
[16]
Liu X., Zhou Z., Cui R., et al.: ‘Informed decoding algorithms of LDPC codes based on Dynamic selection strategy’, IEEE Trans. Commun., 2016, 64, (4), pp. 1357–1366
[17]
Roberts M.K., Jayabalan R.: ‘An improved low‐complexity sum‐product decoding algorithm for low‐density parity‐check codes’, Front. Inf. Technol. Electron. Eng., 2015, 16, (6), pp. 511–518
[18]
Zhou H., Li P., Feng J., et al.: ‘Flooding and node‐wise RBP sequentially concatenated decoder for LDPC codes’. Proc. Symp. Communication and Control Theory', Shenzhen, China, November 2015, pp. 1–4
[19]
Tanner R.M., Sridhara D., Sridharan A., et al.: ‘LDPC block and convolutional codes based on circulant matrices’, IEEE Trans. Inf. Theory, 2004, 50, (12), pp. 2966–2984
[20]
IEEE C802.16e‐05/0066r3: ‘LDPC coding for OFDMA PHY’, 2005

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