Optimizing the Bit-flipping Method for Decoding Low-density Parity-check Codes in Wireless Networks by Using the Artificial Spider Algorithm

Authors

  • Wameedh Riyadh Abdul-Adheem Department of Electrical Power Techniques Engineering Al-Ma'moun University College, Baghdad
  • Ali Jasim Ghaffoori Department of Electrical Power Techniques Engineering Al-Ma'moun University College, Baghdad

Abstract

In this paper, the performance of Low-Density Parity-Check (LDPC) codes is improved, which leads to reduce the complexity of hard-decision Bit-Flipping (BF) decoding by utilizing the Artificial Spider Algorithm (ASA). The ASA is used to solve the optimization problem of decoding thresholds. Two decoding thresholds are used to flip multiple bits in each round of iteration to reduce the probability of errors and accelerate decoding convergence speed while improving decoding performance. These errors occur every time the bits are flipped. Then, the BF algorithm with a low-complexity optimizer only requires real number operations before iteration and logical operations in each iteration. The ASA is better than the optimized decoding scheme that uses the Particle Swarm Optimization (PSO) algorithm. The proposed scheme can improve the performance of wireless network applications with good proficiency and results. Simulation results show that the ASA-based algorithm for solving highly nonlinear unconstrained problems exhibits fast decoding convergence speed and excellent decoding performance. Thus, it is suitable for applications in broadband wireless networks.

References

I. B. Djordjevic, “LDPC-coded MIMO optical communication over the atmospheric turbulence channel using Q-ary pulse-position modulation,” Opt. Express, vol. 15, no. 16, p. 10026, 2007.

S. Y. Chung, G. David Forney, T. J. Richardson, and R. Urbanke, “On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit,” IEEE Commun. Lett., vol. 5, no. 2, pp. 58–60, Feb. 2001.

J. Meng, D. Zhao, H. Tian, and L. Zhang, “Sum of the magnitude for hard decision decoding algorithm based on loop update detection,” Sensors (Switzerland), vol. 18, no. 1, Jan. 2018.

D. J. C. MacKay and R. M. Neal, “Near Shannon limit performance of low density parity check codes,” Electron. Lett., vol. 32, no. 18, p. 1645, 1996.

R. G. Gallager, “Low-Density Parity-Check Codes,” IRE Trans. Inf. Theory, vol. 8, no. 1, pp. 21–28, 1962.

S. H. Kang and I. C. Park, “Loosely coupled memory-based edcoding architecture for low density parity check codes,” IEEE Trans. Circuits Syst. I Regul. Pap., vol. 53, no. 5, pp. 1045–1056, May 2006.

N. Miladinovic and M. P. C. Fossorier, “Improved bit-flipping decoding of low-density parity-check codes,” IEEE Trans. Inf. Theory, vol. 51, no. 4, pp. 1594–1606, Apr. 2005.

S. Haddadi, M. Farhang, and M. Derakhtian, “Low-complexity decoding of LDPC codes using reduced-set WBF-based algorithms,” Eurasip J. Wirel. Commun. Netw., vol. 2020, no. 1, p. 180, Dec. 2020.

I. Develi and Y. Kabalci, “A comparative simulation study on the performance of LDPC coded communication systems over Weibull fading channels,” J. Appl. Res. Technol., vol. 14, no. 2, pp. 101–107, Apr. 2016.

M. Qiu, Z. Zhang, and Y. Huang, “An Improved Bit Flipping Min Sum Algorithm with Difference to Sum Ratio Factor Based on Unreliable Received Messages,” in International Conference on Communication Technology Proceedings, ICCT, 2020, vol. 2020-October, pp. 1582–1586.

B. Attaran, A. Ghanbarzadeh, and S. Moradi, “A novel evolutionary optimization algorithm inspired in the intelligent behaviour of the hunter spider,” Int. J. Comput. Math., 2020.

Z. He, S. Roy, and P. Fortier, “Powerful LDPC codes for broadband wireless networks: High-performance code construction and high-speed encoder/decoder design,” in Conference Proceedings of the International Symposium on Signals, Systems and Electronics, 2007, pp. 173–176.

J. Kennedy, J. Kennedy, and R. Eberhart, “Particle swarm optimization,” pp. 4--1942, 1995.

A. Othman and H. Gabbar, “Enhanced Microgrid Dynamic Performance Using a Modulated Power Filter Based on Enhanced Bacterial Foraging Optimization,” Energies, vol. 10, no. 6, p. 776, Jun. 2017.

D. Bratton and J. Kennedy, “Defining a standard for particle swarm optimization,” in Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007, 2007, pp. 120–127.

Downloads

Published

2024-04-19

Issue

Section

Signals, Circuits, Systems