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

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Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
109--114
Opis fizyczny
Bibliogr. 15 poz., schem., tab., wykr.
Twórcy
  • Department of Electrical Power Techniques Engineering, AL_Ma’moon University College, Baghdad, Iraq
  • Department of Electrical Power Techniques Engineering, AL_Ma’moon University College, Baghdad, Iraq
Bibliografia
  • [1] 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. https://doi.org/10.1364/OE.15.010026
  • [2] 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. https://doi.org/10.1109/4234.905935
  • [3] 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. https://doi.org/10.3390/s18010236
  • [4] 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. https://doi.org/10.1049/el:19961141
  • [5] R. G. Gallager, “Low-Density Parity-Check Codes,” IRE Trans. Inf. Theory, vol. 8, no. 1, pp. 21–28, 1962. https://doi.org/10.1109/TIT.1962.1057683
  • [6] 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. https://doi.org/10.1109/TCSI.2005.862181
  • [7] 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. https://doi.org/10.1109/TIT.2005.844095
  • [8] 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. https://doi.org/10.1186/s13638-020-01791-5
  • [9] Ali Jasim Ghaffoori & Wameedh Riyadh Abdul-Adheem, “Control of carbon nanotube cantilever vibrator for nano-antenna applications,” Cogent Engineering., vol. 6, Issue. 1, pp. 1–12, 2019. https://doi.org/10.1080/23311916.2019.1710428
  • [10] Ali Jasim Ghaffoori, “PAPR REDUCTION IN OFDM SYSTEM USING ADAPTIVE HYBRID TECHNIQUE,” in IOP Conference Series: Materials Science and Engineering, ICSET 2019, vol. 518, Issue 5, pp. 1–7, 2019.
  • [11] 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. https://doi.org/10.1080/00207160.2020.1775820
  • [12] 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. https://doi.org/10.1109/ISSSE.2007.4294441
  • [13] J. Kennedy, J. Kennedy, and R. Eberhart, “Particle swarm optimization,” pp. 4–1942, 1995.
  • [14] 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. https://doi.org/10.3390/en10060776
  • [15] 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. https://doi.org/10.1109/SIS.2007.368035
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-14e56990-78dd-4a01-88db-56438fe6292e
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