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Tytuł artykułu

An Efficient ANN Interference Cancelation for High Order Modulation over Rayleigh Fading Channel

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
High order modulation (HOM) presents a key challenge in increasing spectrum efficiency in 4G and upcoming 5G communication systems. In this paper, two non-linear adaptive equalizer techniques based on multilayer perceptron (MLP) and radial basis function (RBF) are designed and applied on HOM to optimize its performance despite its high sensitivity to noise and channel distortions. The artificial neural network’s (ANN) adaptive equalizer architectures and learning methods are simplified to avoid more complexity and to ensure greater speed in symbol decision making. They will be compared with the following popular adaptive filters: least mean square (LMS) and recursive least squares (RLS), in terms of bit error rate (BER) and minimum square error (MSE) with 16, 64, 128, 256, 512 and 1024 quadrature amplitude modulation (QAM). By that, this work will show the advantage that the MLP equalizer has, in most cases, over RBF and traditional linear equalizers.
Rocznik
Tom
Strony
75--80
Opis fizyczny
Bibliogr. 11 poz., rys., tab.
Twórcy
autor
  • LAAAS Laboratory, Department of Electronics, University of Batna 2, Batna, Algeria
autor
  • LAAAS Laboratory, Department of Electronics, University of Batna 2, Batna, Algeria
Bibliografia
  • [1] Y. Tao, L. Liu, S. Liu, and Z. Zhang, “A survey: Several technologies of non-orthogonal transmission for 5G”, China Commun., vol. 12, no. 10, pp. 1–15, 2015 (doi: 10.1109/CC.2015.7315054).
  • [2] Y. Kishiyama, A. Benjebbour, T. Nakamura, and H. Ishii, “Future steps of LTE-A: evolution toward integration of local area and wide area systems”, IEEE Wirel. Commun., vol. 20, no. 1, pp. 12–18, 2013 (doi: 10.1109/MWC.2013.6472194).
  • [3] A. Darwish and A. S. Ibrahim, “Capacity improvement via indoor small cells”, in Proc. Int. Wirel. Commun. and Mob. Comput. Conf. IWCMC 2014 , Nicosia, Cyprus, 2014, pp. 69–73 (doi: 10.1109/IWCMC.2014.6906334).
  • [4] M. Iwamoto, S. Matsuoka, H. Iwasaki, and H. Otsuka, “Transmission performance of OFDM with 1024-QAM in the presence of EVM degradation”, in Proc. 2014 IEEE Asia Pacific Conf. on Wirel. and Mob., Bali, Indonesia, 2014, pp. 12–16 (doi: 10.1109/APWiMob.2014.6920262).
  • [5] S. Ma and Y. Chen, “FPGA implementation of high-throughput complex adaptive equalizer for QAM receiver”, in Proc. 8th Int. Conf. on Wirel. Commun., Netw. and Mob. Comput., Shanghai, China, 2012, pp. 1–4 (doi: 10.1109/WiCOM.2012.6478527).
  • [6] K. Burse, R. N. Yadav, and S. C. Shrivastava, “Channel equalization using neural networks: a review”, IEEE Trans. on Syst., Man, and Cybernet., Part C (Appl. and Rev.), vol. 40, no. 3, pp. 352–357, 2010 (doi: 10.1109/TSMCC.2009.2038279).
  • [7] A. Rubaai and P. Young, “Hardware/software implementation of fuzzy-neural-network self-learning control methods for brushless DC motor drives”, IEEE Trans. on Industry Appl., vol. 52, no. 1, pp. 414–424, 2016 (doi: 10.1109/TIA.2015.2468191).
  • [8] A. Goldsmith, Wireless Communications. Cambridge University Press, 2005 (ISBN: 9780511841224, doi: 10.1017/CBO9780511841224).
  • [9] F. Bouguerra, I. Benacer, and L. Saidi, “MLP and RBF symbol tracking with 16 QAM modulation over multipath distorted channel”, in Proc. Int. Conf. on Adv. Syst. and Elec. Technol. IC ASET 2017, Hammamet, Tunisia, 2017, pp. 182–187 (doi: 10.1109/ASET.2017.7983688).
  • [10] H. Cai, Q. Zhang, Q. Li, and J. Qin, “Proactive monitoring via jamming for rate maximization over MIMO Rayleigh fading channels”, IEEE Commun. Lett., vol. 21, no. 9, pp. 2021–2024, 2017 (doi: 10.1109/LCOMM.2017.2715337).
  • [11] K. Cho and D. Yoon, “On the general BER expression of one- and two-dimensional amplitude modulations”, IEEE Trans. on Commun., vol. 50, no. 7, pp. 1074–1080, 2002 (doi: 10.1109/TCOMM.2002.800818).
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ff001084-d115-486d-8e1b-218243675604
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