SLANTLET TRANSFORM-BASED OFDM SCHEME
Main Article Content
Abstract
Wireless digital communication is rapidly expanding resulting in a demand for systems that
are reliable and have a high spectral efficiency. To fulfill these demands OFDM technology has drawn a lot of attention. In this paper a new technique is proposed to improve the performance of OFDM. The new technique is use the slantlet transform (SLT) instead Fast Fourier transform (FFT) in order to reduce the level of interference. This also will remove the need for Guard interval (GI) in the case of the FFT-OFDM and therefore improve the bandwidth efficiency of the OFDM. The SLT-OFDM is also better than wavelet packet (WP)-OFDM in the selective channel because the slantlet filter bank is less frequency selective than the traditional DWT filter bank, due to the shorter length of the filters and SLT algorithm is faster than WP algorithm. The main results obtained indicate that the performance of SLT-OFDM is better on average by 18dB in comparison with that of FFT-OFDM flat fading channels. For frequency selective fading channel the SLT-OFDM performs is better than the FFT-OFDM on the lower SNR region, while the situation will reverse with increase SNR values.
Article Details
Section
How to Cite
References
S. B. Weinstein and Paul M. Ebert, “Data Transmission by Frequency-Division Multiplexing
Using the Discrete Fourier Transform”, IEEE Transactions on Communication Technology,
Vol. COM-19, No. 5, October 1971, pp. 628 – 634
A.Bahai, S.Coleri, M.Ergen, A.Puri"Channel Estimation Techniques Based on Pilot
Arrangement in OFDM systems ", IEEE Trans. on Broadcasting, Vol.48, No.3, pp 223-229,
September 2002.
Edward R. Dougherty, Jaakko T. Astola, Karen O. Egiazarian "The fast parametric slantlet
transform with applications" Proceedings of SPIE -- Volume 5298, May 2004, pp. 1-12.
G. Panda, P. K. Dash, A. K. Pradhan, and S. K. Meher "Data Compression of Power Quality
Events Using the Slantlet Transform" IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 17, NO. 2, APRIL 2002.
Ivan W. Selesnick" THE SLANTLET TRANSFORM" Polytechnic University Electrical Engineering 6
Metrotech Center, Brooklyn, 0-7803-5073 1998 IEEE
I. Daubechies. Ten Lectures on Wavelets. SIAM, Philadelphia, PA, first edition, 1992.
T. Ogden and E. Parzen. Data dependent wavelet thresholding in nonparametric regression with change-point applications. Computational Statistics and Data Analysis, 2253-70, 1996