Neural Computing and Applications

, Volume 31, Supplement 2, pp 1263–1273 | Cite as

Design of MC-CDMA receiver using radial basis function network to mitigate multiple access interference and nonlinear distortion

  • Ravi Kumar C.V.
  • Kala Praveen BagadiEmail author
Original Article


Multicarrier code division multiple access (MC-CDMA) is a novel wireless communication technology with high spectral efficiency and system performance. However, all multiple access techniques including MC-CDMA were most likely to have multiple access interference (MAI). So, this paper mainly aims at designing a suitable receiver for MC-CDMA system to mitigate such MAI. The classical receivers like maximal-ratio combining and minimum mean square error fail to cancel MAI when the MC-CDMA is subjected to nonlinear distortions, which may occur due to saturated power amplifiers or arbitrary channel conditions. Being highly nonlinear structures, the neural network (NN) receivers such as multilayer perceptron and radial basis function networks could be better alternative for such a case. The possibility NN receiver for a MC-CDMA system under different nonlinear conditions has been studied with respect to both performance and complexity analysis.


OFDM CDMA MAI MRC MMSE MLP RBF Maximum likelihood 


Compliance with ethical standards

Conflict of interest

We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. So we have no conflict of interest.


  1. 1.
    Viterbi AJ (1995) CDMA: principles of spread spectrum communication. Addison-Wesley, BostonzbMATHGoogle Scholar
  2. 2.
    Miller L, Lee J (1998) CDMA systems engineering handbook. Artech House, LondonGoogle Scholar
  3. 3.
    Weinstein SB, Ebert PM (1971) Data transmission by frequency-division multiplexing using the discrete Fourier transform. IEEE Trans Commun 19(5):628–634CrossRefGoogle Scholar
  4. 4.
    Prasad R (2004) OFDM for wireless communications systems. Artech House, LondonGoogle Scholar
  5. 5.
    Prasad R, Hara S (1997) Overview of multicarrier CDMA. IEEE Commun Mag 35(12):126–133CrossRefGoogle Scholar
  6. 6.
    McCormick AC, Al-Susa EA (2002) Multicarrier CDMA for future generation mobile communication. Electron Commun Eng J 14(2):52–60CrossRefGoogle Scholar
  7. 7.
    Fettweis G, Bahai AS, Anvari K (1994) On multi-carrier code division multiple access (MC-CDMA) modem design. Proc IEEE Veh Technol Conf. doi: 10.1109/VETEC.1994.345380 Google Scholar
  8. 8.
    Hanzo L, Keller T (2006) OFDM and MC-CDMA: a primer. Wiley, West SussexCrossRefGoogle Scholar
  9. 9.
    Nathan Y, Jean-Paul MGL, Gerhard F (1994) Multi-carrier CDMA in Indoor Wireless Radio Networks. IEICE Trans Commun 77(7):900–904Google Scholar
  10. 10.
    Proakis JG (1995) Digital communications. Mc-Graw Hill, New YorkzbMATHGoogle Scholar
  11. 11.
    Steele R, Hanzo L (1999) Mobile radio communications. Wiley, New YorkCrossRefGoogle Scholar
  12. 12.
    Verdu S (1998) Multiuser detection. Cambridge University Press, CambridgezbMATHGoogle Scholar
  13. 13.
    Seyman MN, Taşpınar N (2013) Symbol detection using the differential evolution algorithm in MIMO-OFDM systems. Turk J Electr Eng Comput Sci 21:373–380Google Scholar
  14. 14.
    Silva A, Teodoro S, Dinis R, Gameiro A (2014) Iterative frequency-domain detection for IA-precoded MC-CDMA system. IEEE Trans Commun 62(4):1240–1248CrossRefGoogle Scholar
  15. 15.
    Yan Y, Ma M (2015) Novel frequency-domain oversampling receiver for CP MC-CDMA systems. IEEE Commun Lett 19(4):661–664MathSciNetCrossRefGoogle Scholar
  16. 16.
    Sung WL, Chang YK, Ueng FB, Shen YS (2015) A new SAGE-based receiver for MC-CDMA communication systems. Wirel Pers Commun 85(3):1617–1634CrossRefGoogle Scholar
  17. 17.
    Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251–257MathSciNetCrossRefGoogle Scholar
  18. 18.
    Seyman MN, Taspinar N (2013) Radial basis function neural networks for channel estimation in MIMO-OFDM systems. Arab J Sci Eng 38(8):2173–2178CrossRefGoogle Scholar
  19. 19.
    Seyman MN, Taşpınar N (2013) Channel estimation based on neural network in space time block coded MIMO–OFDM system. Digit Signal Proc 23:275–280MathSciNetCrossRefGoogle Scholar
  20. 20.
    Bagadi KP, Das S (2013) Efficient complex radial basis function model for multiuser detection in a space division multiple access/multiple-input multiple-output-orthogonal frequency division multiplexing system. IET Commun 7(13):1394–1404CrossRefGoogle Scholar
  21. 21.
    Bagadi KP, Das S (2013) Neural network-based multiuser detection for SDMA–OFDM system over IEEE 802.11 n indoor wireless local area network channel models. Int J Electron 100(10):1332–1347CrossRefGoogle Scholar
  22. 22.
    Bagadi KP, Das S (2013) Neural network-based adaptive multiuser detection schemes in SDMA–OFDM system for wireless application. Neural Comput Appl 23(3):1071–1082CrossRefGoogle Scholar
  23. 23.
    Bagadi KP, Das S (2014) Multiuser detection in SDMA–OFDM wireless communication system using complex multilayer perceptron neural network. Wirel Pers Commun 77(1):21–39CrossRefGoogle Scholar
  24. 24.
    Bagadi KP, Das S (2014) Minimum symbol error rate multiuser detection using an effective invasive weed optimization for MIMO/SDMA–OFDM system. Int J Commun Syst 27(12):3837–3854CrossRefGoogle Scholar
  25. 25.
    Bagadi KP, Annepu V, Das S (2016) Recent trends in multiuser detection techniques for SDMA–OFDM communication system. Phys Commun 20:93–108CrossRefGoogle Scholar
  26. 26.
    Taspnar N, Cicek M (2013) Neural network based receiver for multiuser detection in MC-CDMA systems. Wirel Pers Commun 68(2):463–472CrossRefGoogle Scholar
  27. 27.
    Widrow B, Lehr MA (1990) 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proc IEEE 78(9):1415–1442CrossRefGoogle Scholar
  28. 28.
    Ko KB, Choi S, Kang C, Daesik H (2001) RBF multiuser detector with channel estimation capability in a synchronous MC-CDMA system. IEEE Trans Neural Netw 12(6):1536–1539CrossRefGoogle Scholar
  29. 29.
    Schumacher L, Pedersen KI, Mogensen PE (2002) From antenna spacings to theoretical capacities-guidelines for simulating MIMO systems. In: The 13th IEEE international symposium on personal, indoor and mobile radio communications, pp 587–592Google Scholar
  30. 30.
    Patra JC, Meher PK, Goutam C (2009) Nonlinear channel equalization for wireless communication systems using Legendre neural networks. Signal Process 89:2251–2262CrossRefzbMATHGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.School of Electronics (SENSE)VIT UniversityVelloreIndia

Personalised recommendations