A Novel FRFT Beamformer for Rayleigh Faded Channels
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A method of optimal beamforming for flat Rayleigh faded channels using the Fractional Fourier Transform (FRFT) is considered in this paper. It has been demonstrated through simulations that optimal beamforming with FRFT allows smaller mean-square errors in restoring signals degraded with linear time-or frequency variant distortions and Additive White Gaussian Noise. This is made possible by the additional flexibility that comes with free parameter ‘a’ of the fractional Fourier transform as oppose to the classical Fourier transform (FT). The method is especially useful in moving source problems, where Doppler Effect produces frequency shift when the source is moving, as in mobile and wireless communication where user produces the frequency shift while moving. In this paper it is shown through simulations that beamforming in fractional domain reduces BER as compared to time or frequency domain.
KeywordsBeamforming Fractional fourier transform Time frequency varying channels
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- 1.Wireless World Research Forum (WWRF). (2001). The book of visions 2001—visions of the wireless world, version 1.0, December 2001. Available at: http://www.wirelessworld-research.org.
- 2.Khana R., Saxena R., Singh K. (2005) Optimal beamforming for Rayleigh faded time-frequency varying channels using fraction fourier transform. Journal of the Indian Institute of Science 85: 27–38Google Scholar
- 3.Van Veen B.D., Buckley K.M. (1998) Beam forming: A versatile approach to spatial filtering. IEEE Acoustics, Speech, Signal Processing Magazine 5: 4–24Google Scholar
- 4.Haykins S. (2002) Adaptive filter theory. Pearson Education, SingaporeGoogle Scholar
- 5.Massimiliano (Max) Martone. (2001). A multicarrier system based on the fractional fourier transform for time–frequency-selective channels. IEEE Transactions on Communications, 49(6), 1011–1020.Google Scholar
- 7.Ozaktas H.M., Kutay M.A., Zalevsky Z. (2000) The fractional fourier transform with applications in optics and signal processing. Wiley, New YorkGoogle Scholar
- 14.Yetik, I. S., Kutay, M. A., Ozaktas, H., & Ozaktas, H. M. (2000). Continuous and Discrete Fractional Fourier Domain Decomposition. In Proceedings IEEE International Conference Acoustics Speech Signal Processing (pp. 96–96).Google Scholar
- 15.Proakis J.G. (2001) Digital communications. McGraw Hill, New YorkGoogle Scholar