Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Cyclostationarity-based joint sensing and equalisation of fast convolution-based DWPT block-filtered orthogonal frequency division multiplexing for fifth-generation wireless systems

© The Institution of Engineering and Technology
Received 01/02/2018, Accepted 03/08/2018, Revised 31/05/2018, Published 31/08/2018

Traditional cyclic prefix-orthogonal frequency division multiplexing is a popular multicarrier modulation (MCM) scheme, which provides advantages of good bandwidth utilisation and simplicity in transceiver design. However, the waveform suffers from poor spectral utilisation due to the utilisation of rectangular window and lack of flexibility. Hence, an alternative MCM scheme based on wavelet filter-bank architecture is considered in this study for fifth-generation wireless access systems. Cyclostationarity-based joint sensing and channel equalisation is applied at the receiver side, which demonstrates improved signal detection performance. Adaptive line enhancement adaptive line enhancement (ALE)-based noise cancellation is explored to improve the sensing performance. The combined noise cancellation and sensing scheme improve the cyclostationarity-based equalisation and demodulation performance.

Inspec keywords: interference suppression; equalisers; filtering theory; signal detection; OFDM modulation; channel bank filters

Other keywords: channel equalisation; good bandwidth utilisation; cyclostationarity-based equalisation; fast convolution-based DWPT block-filtered orthogonal frequency division; fifth-generation wireless access systems; fifth-generation wireless systems; wavelet filter-bank architecture; demodulation performance; poor spectral utilisation; rectangular window; adaptive line enhancement-based noise cancellation; alternative MCM scheme; popular multicarrier modulation scheme; traditional cyclic prefix-orthogonal frequency division multiplexing; sensing performance

Subjects: Other topics in statistics; Modulation and coding methods; Filtering methods in signal processing; Electromagnetic compatibility and interference; Integral transforms; Radio links and equipment

References

    1. 1)
      • 30. Shlezinger, N., Dabora, R.: ‘Frequency-shift filtering for OFDM signal recovery in narrowband power line communications’, IEEE Trans. Commun., 2014, 62, (4), pp. 1283–1295.
    2. 2)
      • 19. Loulou, A., Yli-Kaakinen, J., Renfors, M.: ‘Efficient fast-convolution based implementation of 5G waveform processing using circular convolution decomposition’. Proc. Int. Conf. IEEE ICC Signal Processing Communications, Paris, France, May 2017, pp. 1–7.
    3. 3)
      • 15. Yli-Kaakinen, J., Renfors, M.: ‘Optimized reconfigurable fast convolution based transmultiplexers for flexible radio access’, IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process., 2018, 65, (1), pp. 130–134.
    4. 4)
      • 22. Roberts, R.S., Brown, W.A., Loomis, H.H.: ‘Computationally efficient algorithms for cyclic spectral analysis’, IEEE Signal Process. Mag., 1991, 8, (2), pp. 38–49.
    5. 5)
      • 27. Shlezinger, N., Todros, K., Dabora, R.: ‘Adaptive filtering based on time-averaged MSE for cyclostationary signals’, IEEE Trans. Commun., 2017, 65, (4), pp. 1746–1761.
    6. 6)
      • 21. Gardner, W.A., Brown, W.A., Chen, C.-K.: ‘Spectral correlation of modulated signals: part II-digital modulation’, IEEE Trans. Commun., 1987, 35, (6), pp. 595–601.
    7. 7)
      • 9. Hosseini, H., Yusof, S.K.B.S., Fisal, N., et al: ‘Wavelet packet-based transceiver for cognitive UWBÉ metteur-récepteurà base de paquet d'ondelette pour les applications cognitives UWB’, Can. J. Electr. Comput. Eng., 2014, 37, (2), pp. 59–64.
    8. 8)
      • 25. Ciblat, P., Loubaton, P., Serpedin, E.: ‘Asymptotic analysis of blind cyclic correlation based symbol rate estimation’, IEEE Trans. Inf. Theory, 2002, 48, (7), pp. 1922–1934.
    9. 9)
      • 11. Bopardikar, A.S., Raghuveer, M.R., Adiga, B.S.: ‘Perfect reconstruction circular convolution filter banks and their application to the implementation of band limited discrete wavelet transforms’. Proc. Int. Conf. IEEE Acoustics Speech Signal Processing, Munich, Germany, April 1997, pp. 3665–3668.
    10. 10)
      • 3. Nikookar, H.: ‘Wavelet radio: adaptive and reconfigurable wireless systems based on wavelets’ (Cambridge University Press, Cambridge UK, 2013).
    11. 11)
      • 16. Yli-Kaakinen, J., Renfors, M.: ‘Optimization of flexible filter banks based on fast convolution’, J. Signal Process. Syst., 2016, 85, pp. 101–111.
    12. 12)
      • 10. Alimosaymer, M., Mohseni, R.: ‘Systematic approach in designing wavelet packet modulation-orthogonal frequency division multiplexing radar signal by applying the criterion of least-squares’, IET Signal Process., 2014, 8, (5), pp. 475–482.
    13. 13)
      • 13. Taheri, S., Ghoraishi, M., Xiao, P., et al: ‘Efficient implementation of filter bank multicarrier systems using circular fast convolution’, IEEE. Access, 2017, 5, pp. 2855–2869.
    14. 14)
      • 24. Bourgeois, T., Sanada, Y.: ‘Cyclostationarity-based detector for OFDM signals with correlated pilot subcarriers’. Proc. Int. Conf. Wireless Personal Multimedia Communications (WPMC), Brest, France, October 2011, pp. 1–5.
    15. 15)
      • 14. Yli-Kaakinen, J., Levanen, T., Valkonen, S., et al: ‘Efficient fast-convolution based waveform processing for 5G physical layer’, IEEE Sel. Areas Commun., 2017, 35, (6), pp. 1309–1326.
    16. 16)
      • 5. Chafii, M., Palicot, J., Gribonval, R., et al: ‘Adaptive wavelet packet modulation’, IEEE Trans. Commun., 2018, 66, (7), pp. 2947–2957.
    17. 17)
      • 4. Zakaria, J., Salleh, M.F.M.: ‘PAPR reduction scheme: wavelet packet-based PTS with embedded side information data scheme’, IET Commun., 2017, 11, (1), pp. 127–135.
    18. 18)
      • 28. Carrick, M., Reed, J.H., Harris, F.: ‘An optimal filter for signals with time-varying cyclostationary statistics’. Int. Conf. Digital Signal Processing, London, UK, August 2017, pp. 1–5.
    19. 19)
      • 18. Yli-Kaakinen, J., Renfors, M.: ‘Flexible fast-convolution implementation of single-carrier waveform processing for 5G’. Proc. Int. Conf. IEEE ICCW, London, UK, June 2015, pp. 1269–1274.
    20. 20)
      • 7. Hosseini, H., Anpalagan, A., Raahemifar, K., et al: ‘Joint wavelet-based spectrum sensing and FBMC modulation for cognitive mmWave small cell networks’, IET Commun., 2016, 10, (14), pp. 1803–1809.
    21. 21)
      • 2. Gerzaguet, R., Demmer, D., Dore, J.-B., et al: ‘Block-filtered OFDM: a new promising waveform for multi-service scenarios’. Proc. IEEE Int. Conf. Communications, Paris, France, May 2017, pp. 1–6.
    22. 22)
      • 23. Dikmese, S., Renfors, M., Guvenc, I.: ‘Flexible filter bank based spectrum sensing and waveform processing for mission critical communications’. IEEE MilCom, Baltimore, MD, USA, October 2017, pp. 174–179.
    23. 23)
      • 6. Lakshmanan, M.K., Nikookar, H.: ‘Construction of optimum wavelet packets for multi-carrier based spectrum pooling systems’, Wirel. Pers. Commun., 2010, 54, pp. 95–121.
    24. 24)
      • 12. Wu, B., Lv, X., Zhou, X.: ‘Fast implementation of long sequence wavelet transform based on partition’. Proc. SPIE Int. Conf. Space Information Technology, Wuhan, China, November 2005, pp. 477–481.
    25. 25)
      • 8. Hosseini, H., Anpalagan, A., Raahemifar, K., et al: ‘Wavelet-based cognitive SCMA system for mmWave 5G communication networks’, IET Commun., 2017, 11, (6), pp. 831–836.
    26. 26)
      • 26. Martins, C.R.: ‘Sub-band structures for adaptive line enhancement’. Int. Conf. Circuits Systems MWSCAS, Dayton, OH, USA, August 2001, pp. 138–141.
    27. 27)
      • 20. Renfors, M., Yli-Kaakinen, J., Levanen, T., et al: ‘Efficient fast-convolution implementation of filtered CP-OFDM waveform processing for 5G’. Proc. Int. Conf. GC Wkshps, San Diego, CA, USA, December 2015, pp. 1–7.
    28. 28)
      • 1. Demmer, D., Gerzaguet, R., Dore, J.-B., et al: ‘Block-filtered OFDM: a novel waveform for future wireless technologies’. Proc. IEEE Int. Conf. Communications, Paris, France, May 2017, pp. 1–6.
    29. 29)
      • 17. Yli-Kaakinen, J., Renfors, M.: ‘Channel equalization in fast-convolution filter bank based receivers for professional mobile radio’. Proc. Int. Conf. European Wireless, Barcelona, Spain, May 2014.
    30. 30)
      • 29. Carrick, M., Reed, J.H.: ‘Improved GFDM equalization in severe frequency selective fading’. Int. Conf. 38th Sarnoff, Newark, NJ, USA, September 2017, pp. 1–6.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2018.5455
Loading

Related content

content/journals/10.1049/iet-com.2018.5455
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address