Wireless Personal Communications

, Volume 104, Issue 3, pp 1121–1131 | Cite as

Performance Evaluation of SFBC MIMO–OFDM Using FrFT Under TWDP Fading Channel

  • Tanvi ChawlaEmail author
  • Ankush Kansal


Multiple-input–multiple-output orthogonal frequency division multiplexing (MIMO–OFDM) is a promising 4G technology to increase data rate and capacity of a system. Further, the space–frequency-block-coding (SFBC) technique in MIMO–OFDM system has increased reliability of system in high mobility channels. Fourier transform in conventional OFDM system can obtain either in time or frequency domain, hence it can be acceptable to reduce inter-carrier-interference while it fails for doubly dispersive channels where both the domain are changing simultaneously. Another mathematical tool named as fractional Fourier transform (FrFT) cancels the effect of rapidly varying time–frequency-distortions by considering new rotating axis \(\left( {u,v} \right)\) at an angle \(\alpha\) from conventional time–frequency plane. In this paper, the performance of SFBC encoded MIMO OFDM system using FrFT in place of FFT has been analyzed under two-wave-diffused-power (TWDP) frequency selective fading channel. In this paper, the novel closed form expression for average bit-error-rate (BER) for coded and un-coded FrFT based MIMO–OFDM system under TWDP has also been presented in this paper. The results carried out using simulation shows that the proposed model achieves BER of 10−2 at 5 dB SNR whereas conventional achieves the same at 14 dB hence, concluded that the novel system performance improved by nearly 9 dB.





  1. 1.
    Bauch G. (2003). Space–time block codes versus space–frequency block codes. In IEEE (pp. 567–571).Google Scholar
  2. 2.
    Gupta, B., & Saini, D. S. (2013). Space–time/space–frequency/space–time–frequency block coded MIMO–OFDM system with equalizers in quasi static mobile radio channels using higher tap order. Wireless Personal Communications, 69(4), 1947–1968.CrossRefGoogle Scholar
  3. 3.
    Almeida, L. B. (1994). The fractional Fourier transform and time-frequency representations. IEEE Transaction of Signal Processing, 42(11), 3084–3091.CrossRefGoogle Scholar
  4. 4.
    Kumari, S. (2013). Exact BER analysis of FRFT-OFDM system over frequency selective Rayleigh fading channel with CFO. Electronics Letters, 49(20), 1299–1301.CrossRefGoogle Scholar
  5. 5.
    Chen, E., Tao, R., & Meng, X. (2006). The OFDM system based on the fractional Fourier transform. In Innovative computing, information and control, ICICIC’06, first international conference on IEEE (vol. 3, pp. 14–17.Google Scholar
  6. 6.
    Wang, H., & Ma, H. (2010). MIMO OFDM systems based on the optimal fractional Fourier transform. Wireless Personal Communications, 55(2), 265–272.CrossRefGoogle Scholar
  7. 7.
    Torabi, M., Aissa, S., & Soleymani, M. R. (2007). On the BER performance of space–frequency block coded OFDM systems in fading MIMO channels. IEEE Transactions on Wireless Communications, 6(4), 1366–1373.CrossRefGoogle Scholar
  8. 8.
    Durgin, G. D., Rappaport, T. S., & De Wolf, D. A. (2002). New analytical models and probability density functions for fading in wireless communications. IEEE Transactions on Communications, 50(6), 1005–1015.CrossRefGoogle Scholar
  9. 9.
    Bauch, G. (2003). Space–time block codes versus space–frequency block codes. Vehicular Technology Conference, 1, 567–571.Google Scholar
  10. 10.
    Alamouti, S. M. (1998). A simple transmit diversity technique for wireless communications. IEEE Journal on Select Areas in Communications, 16(8), 1451–1458.CrossRefGoogle Scholar
  11. 11.
    Shi, L., Zhang, W., & Xia, X. G. (2013). Space–frequency codes for MIMO–OFDM systems with partial interference cancellation group decoding. IEEE Transactions on Communications, 61(8), 3270–3280.CrossRefGoogle Scholar
  12. 12.
    Martone, M. (2001). A multicarrier system based on the fractional Fourier transform for time–frequency-selective channels. IEEE Transactions on Communications, 49(6), 1011–1020.CrossRefzbMATHGoogle Scholar
  13. 13.
    Singh, K., Saxena, R., & Kumar, S. (2013). Caputo-based fractional derivative in fractional Fourier transform domain. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 3(3), 330–337.CrossRefGoogle Scholar
  14. 14.
    Zheng, J., & Wang, Z. (2010). ICI analysis for FRFT–OFDM systems to frequency offset in time–frequency selective fading channels. IEEE Communications Letters, 14(10), 888–890.CrossRefGoogle Scholar
  15. 15.
    Papantoniou, S. J. (1992). A multipath channel model for mobile-radio communications. In Personal, indoor and mobile radio communications (pp. 92–97).Google Scholar
  16. 16.
    Saberali, S. A., & Beaulieu, N. C. (2013). New expressions for TWDP fading statistics. IEEE Wireless Communications Letters, 2(6), 643–646.CrossRefGoogle Scholar
  17. 17.
    Singh, A. K., & Saxena, R. (2010). Development of convolution theorem in FRFT domain. In Signal processing and communications (SPCOM), international conference on IEEE (pp. 1–3).Google Scholar
  18. 18.
    Zayed, A. I. (1998). A convolution and product theorem for the fractional Fourier transform. IEEE Signal Processing Letters, 5(4), 101–103.CrossRefGoogle Scholar
  19. 19.
    Singh, D., & Joshi, H. D. (2016). BER performance of SFBC OFDM system over TWDP fading channel. IEEE Communications Letters, 20(12), 2426–2429.CrossRefGoogle Scholar
  20. 20.
    Gradshteyn, I. S., & Ryzhik, I. M. (2014). Table of integrals, series, and products. Cambridge: Academic Press.zbMATHGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Thapar UniversityPatialaIndia

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