NN-based IDF demodulator in band-limited communication system

NN-based IDF demodulator in band-limited communication system

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

To meet the spectrum requirement in the future wireless communication system, the m-ary phase position shift keying as a type of the spectrally efficient modulation has been considered in this study. However, the inter-symbol interference (ISI), the waveform distortion and the non-white noise are introduced by the band-pass infinite impulse response filters, which is adopted to limit the signal bandwidth and eliminate the out-band interference. The traditional demodulation methods based on impacting filter or matched filter cannot work well in these scenarios. Therefore, a neural network (NN)-based demodulator is proposed to solve the problem of waveform distortion. Additionally, a novel NN-based iterative decision feedback (IDF) demodulator is also proposed to further reduce the ISI iteratively by using the previous estimated symbol information. Simulation results show that both the NN-based demodulator and the NN-based IDF demodulator can greatly outperform the traditional demodulation methods in the band-limited communication system, and the NN-based IDF demodulator achieves better performance than the normal NN-based demodulator.


    1. 1)
      • 1. Wu, L.: ‘UNB modulation in high speed space communications’, Proc. SPIE, 2007, 6795, (679510), pp. 16.
    2. 2)
      • 2. Feng, M., Wu, L.: ‘Special non-linear filter and extension to Shannon's channel capacity’, Digit. Signal. Process., 2009, 19, (5), pp. 861873.
    3. 3)
      • 3. Feng, M., Wu, L., Gao, P.: ‘From special analogous crystal filters to digital impacting filters’, Academic Press Inc., 2012, 22, (4), pp. 690696.
    4. 4)
      • 4. Lu, C., Wu, L., Chen, P., et al: ‘M-ary phase position shift keying with orthogonal signalling’, IET Commun., 2015, 9, (13), pp. 16271634.
    5. 5)
      • 5. Wu, L., Feng, M.: ‘On BER performance of EBPSK-MODEM in AWGN channel’, Adam Hilger Ser. Sens., 2010, 10, (4), pp. 38243834.
    6. 6)
      • 6. Vaidyanathan, P., Regalia, P., Mitra, S.: ‘Design of doubly-complementary IIR digital filters, using a single complex allpass filter’. IEEE Int. Conf. ICASSP, 1986, pp. 25472550.
    7. 7)
      • 7. Qi, C., Wu, L.: ‘Pll demodulation technique for m-ray position phase shift keying’, J. Electron. (China), 2009, 26, (3), pp. 289295.
    8. 8)
      • 8. Wu, L., Feng, H.: ‘Extended binary phase shift keying modulation and demodulation method for frequency spectrum compression’. Chinese Patent CN101582868B, October 2009.
    9. 9)
      • 9. Chen, Z., Wu, L., Chen, P.: ‘Efficient modulation and demodulation methods for multi-carrier communication’, IET Commun., 2016, 10, (5), pp. 567576.
    10. 10)
      • 10. Proakism, J., Salehi, M.: ‘Digital communications’ (McGraw-Hill, New York, 1995).
    11. 11)
      • 11. Salz, J.: ‘Optimum mean-square decision feedback equalization’, Bell Syst. Tech. J., 1973, 52, (8), pp. 13411373.
    12. 12)
      • 12. Keller, P.: ‘Artificial neural networks: an introduction’, vol. TT68 (SPIE Publications, 2005).
    13. 13)
      • 13. Qiu, J., Gao, H., Ding, S.X.: ‘Recent advances on fuzzy-model-based nonlinear networked control systems: a survey’, IEEE Trans. Ind. Electron., 2016, 63, (2), pp. 12071217.
    14. 14)
      • 14. Zhang, H., Luo, Y., Liu, D.: ‘Neural-network-based near-optimal control for a class of discrete-time affine nonlinear systems with control constraints’, IEEE Trans. Neural Netw., 2009, 20, (9), pp. 14901503.
    15. 15)
      • 15. LeCun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, (7553), pp. 436444.
    16. 16)
      • 16. Lerkvaranyu, S., Dejhan, K., Miyanaga, Y.: ‘M-QAM demodulation in an OFDM system with RBF neural network’, Circuits Syst., 2004, 2, (2), pp. 581584.
    17. 17)
      • 17. Onder, M., Akan, A.: ‘Pilot tone investigation for joint channel estimation, equalization, and demodulation based on neural networks’. Int. Conf. Electrical and Electronics Engineering, 2015, pp. 749752.
    18. 18)
      • 18. Hong, X., Chen, S., Harris, C.J., et al: ‘Single-carrier frequency domain equalization for hammerstein communication systems using complex-valued neural networks’, IEEE Trans. Signal Process., 2014, 62, (17), pp. 44674478.
    19. 19)
      • 19. Zhao, H.Q., Zeng, X.P., He, Z.Y., et al: ‘Complex-valued pipelined decision feedback recurrent neural network for non-linear channel equalisation’, IEt Commun., 2012, 6, (9), pp. 10821096.
    20. 20)
      • 20. Oppenheim, A., Schafer, R.: ‘Discrete-time signal processing’ (Prentice-Hall Inc., Upper Saddle River, NJ, USA, 1989).
    21. 21)
      • 21. Ying, P., Wu, L.: ‘New scheme of MPPSK modem’, J. Southeast Univ., 2012, 42, (2), pp. 564568.
    22. 22)
      • 22. Glorot, X., Bordes, A., Bengio, Y.: ‘Deep sparse rectifier neural networks’. Proc. Fourteenth Int. Conf. Artificial Intelligence and Statistics, PMLR 15, 2011, pp. 315323.
    23. 23)
      • 23. Abramson, N., Braverman, D., Sebestyen, G.: ‘Pattern recognition and machine learning’, IEEE Trans. Inf. Theory, 2003, 9, (4), pp. 257261.
    24. 24)
      • 24. Yin, P., Zhang, S., Qi, Y., et alQuantization and training of low bit-width convolutional neural networks for object detection’, arXiv preprint arXiv:1612.06052, 2016.
    25. 25)
      • 25. Zhou, A., Yao, A., Guo, Y., et alIncremental network quantization: towards lossless CNNs with low-precision weights’, arXiv preprint arXiv:1702.03044, 2017.

Related content

This is a required field
Please enter a valid email address