alexa MIMO Discrete Wavelet Transform for the Next Generatio
ISSN ONLINE(2278-8875) PRINT (2320-3765)

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering
Open Access

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Research Article

MIMO Discrete Wavelet Transform for the Next Generation Wireless Systems

Rameez Asif1, Tahereh S. Ghazaany2, Raed. A. Abd-Alhameed3, James M. Noras4, Steven M. R. Jones5, Jonathan Rodriguez6 and Chan H. See7
  1. Communications Research Group, School of Engineering, Design and Technology, University of Bradford, Bradford, BD7 1DP, United Kingdom
  2. Communications Research Group, School of Engineering, Design and Technology, University of Bradford, Bradford, BD7 1DP, United Kingdom
  3. Communications Research Group, School of Engineering, Design and Technology, University of Bradford, Bradford, BD7 1DP, United Kingdom
  4. Communications Research Group, School of Engineering, Design and Technology, University of Bradford, Bradford, BD7 1DP, United Kingdom
  5. Communications Research Group, School of Engineering, Design and Technology, University of Bradford, Bradford, BD7 1DP, United Kingdom
  6. Communications Research Group, School of Engineering, Design and Technology, University of Bradford, Bradford, BD7 1DP, United Kingdom
  7. Communications Research Group, School of Engineering, Design and Technology, University of Bradford, Bradford, BD7 1DP, United Kingdom
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Abstract

Study is presented into the performance of Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) and MIMO-DWT with transmit beamforming. Feedback loop has been used between the equalizer at the transmitter to the receiver which provided the channel state information which was then used to construct a steering matrix for the transmission sequence such that the received signals at the transmitter can be combined constructively in order to provide a reliable and improved system for next generation wireless systems. As convolution in time domain equals multiplication in frequency domain no such counterpart exist for the symbols in space, means linear convolution and Intersymbol Interference (ISI) generation so both zero forcing (ZF) and minimum mean squared error (MMSE) equalizations have been employed. The results show superior performance improvement and in addition allow keeping the processing, power and implementation cost at the transmitter which has less constraints and the results also show that both equalization algorithms perform alike in wavelets and the ISI is spread equally between different wavelet domains.

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