10-04-2017, 09:21 PM
Prepared by:
Khaizuran Abdullah
Zahir M. Hussain
Abstract
Comparative studies on DWT-OFDM and FFTOFDM systems are presented. The model for DWT-OFDM includes zero-padding and vector transpose for transmitting the OFDM signal. MATLAB simulation commands are also described. The discrete wavelet transform-OFDM (DWT-OFDM) has to satisfy the orthonormal bases and the perfect reconstruction properties to be considered as wavelet transform. Different wavelet families have been used and compared with the conventional FFT-OFDM system. It is found that the DWTOFDM platform has less mean amplitude for transmitting the signal as compared to conventional FFT-OFDM system. Results also show that DWT-OFDM is superior as compared to FFTOFDM with regards to the bit error rate (BER) performance, especially when it uses bior5.5 or rbior3.3 wavelet family.
INTRODUCTION
An Orthogonal Frequency Division Multiplexing (OFDM) system is a multi-carrier system which utilises a parallel processing technique allowing the simultaneous transmission of data on many closely spaced, orthogonal sub-carriers. Inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) in a conventional OFDM system are used to multiplex the signals together and decode the signal at the receiver respectively. The system adds cyclic prefixes (CP) before transmitting the signal. The purpose of this is to increase the delay spread of the channel so that it becomes longer than the channel impulse response. The purpose of this is to minimize inter-symbol interference (ISI). However, the CP has the disadvantage of reducing the spectral containment of the channels.
Wavelet transforms have been considered as alternative platforms for replacing IFFT and FFT. By using the transform, the spectral containment of the channels is better since it does not use CP. One type of wavelet transform is namely as Discrete Wavelet Transform OFDM (DWT-OFDM). It employs Low Pass Filter (LPF) and High Pass Filter(HPF) operating as Quadrature Mirror Filters satisfying perfect reconstruction and orthonormal bases properties. The transform uses filter coefficients as approximate and detail in LPF and HPF respectively. The approximated coefficients is sometimes referred to as scaling coefficients, whereas, the detailed is referred to wavelet coefficients [3]. Sometimes these two filters can be called subband coding since the signals are divided into sub-signals of low and high frequencies respectively.
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http://iccpproceedings/2009/Papers/ICCP09-081.pdf