Implementasi Fast Fourier Transform dan Inverse Fast Fourier Transform pada Teknologi Mobile WiMax Menggunakan FPGA

Pada umumnya teknologi telekomunikasi sekarang ini melakukan pengiriman data secara nirkabel. I/FFT merupakan metode pemecahan sinyal diskrit yang digunakan pada teknologi WIMAX (802.16e). Selama ini FFT hanya dipandang sebagai suatu sistem komputasi namun sekarang FFT sudah menjadi sesuatu yang sangat penting terutama pada komunikasi yang menggunakan BWA (Broadband Wireless Acces ).Dalam pembuatan desain fast fourier transform dan Inverse fast fourier transform di gunakan metode algoritma Cooley-Tukey, dimana spesifikasi yang digunakan mengacu pada 802.16e (1024 titik radiks-4) dengan menggunakan Xilinx Virtex-4 XCVLX25 pada board Field Programmable Gate Array (FPGA) dengan menggunakan bahasa VHSIC Hardware Description Language (VHDL). Desain dengan VHDL ini memodelkan sistem sesuai dengan kebutuhan dari sistem prosesor I/FFT 1024 titik dan mensimulasikannya dengan ModelSim sebelum perangkat lunak sintesis menerjemahkan desain dalam hardware. Kemudian hasil simulasinya dibandingkan dengan pemodelan yang telah dilakukan di MATLAB.Dari hasil pemodelan dan simulasi maka dilakukan sintesis pada tingkat hardware FPGA dengan Xilinx Shynthesize Tools. Dari hasil sintesa blok sistem prosesor I/FFT 1024 titik radiks-4 didapatkan jumlah resource yang dibutuhkan adalah jumlah slice 1%, jumlah slice flip-flop 1%, jumlah 4 LUT (Look Up Table) 1%, dan jumlah IOB 27% dengan error bit maksimum untuk FFT sebesar 5,95% sedangkan pada IFFT 0,1%. Secara keseluruhan, penelitian ini telah membuktikan bahwa I/FFT 1024 titik dengan menggunakan resource seminimal mungkin sehingga memungkinkan untuk pengembangan aplikasi dan power. Namun pada proses pengujiannya membutuhkan blok ADC/DAC. WiMAX, FPGA, VHDL, BWA, Fast Fourier Transform, Inverse Fast Fourier Transform

Fast Fourier Transform algorithm design and tradeoffs

The Fast Fourier Transform (FFT) is a mainstay of certain numerical techniques for solving fluid dynamics problems. The Connection Machine CM-2 is the target for an investigation into the design of multidimensional Single Instruction Stream/Multiple Data (SIMD) parallel FFT algorithms for high performance. Critical algorithm design issues are discussed, necessary machine performance measurements are identified and made, and the performance of the developed FFT programs are measured. Fast Fourier Transform programs are compared to the currently best Cray-2 FFT program

Hardware schemes for fast Fourier transform, part 7.4A

Real-time fast fourier transformer (FFT) processing of a MST radar data and cost-effective approaches to hardware FFT generation were studied. Previously devised hardware FFT configurations are described including the estimated number of chips used and the time required to perform a 1024-point FFT. The remaining entries in the table correspond to original designs, which presuppose the availability of a microcomputer and a modestly complicated hardware peripheral. These original designs, all of which implement a radix-4 FFT with twiddle factors, are assigned model numbers to make them easier to refer to

The Fast Fourier Transform Algorithm and Its Application in Digital Image Processing

Transforms are new image processing tools that are being applied to a wide variety of image processing problems. Fourier Transform and similar frequency transform techniques are widely used in image understanding and image enhancement techniques. Fast Fourier Transform (FFT) is the variation of Fourier transform in which the computing complexity is largely reduced. FFT is a mathematical technique for transforming a time domain digital signal into a frequency domain representation of the relative amplitude of different regions in the signal. The objective of this paper is to develop FFT based image processing algorithm. FFT can be computed faster than the Discrete Fourier Transform (DFT) on the same machine. Key words: Fast Fourier Transform, Discrete Fourier Transform, Radix-2 FFT algorithm, Decimation in Time FFT, Time complexity

The Fast Fourier Transform

This tutorial discusses the fast Fourier transform, which has numerous applications in signal and image processing. The FFT computes the frequency components of a signal that has been sampled at n points in O( n log n) time. We explain the FFT and illustrate it by examples and Pascal algorithms. We assume that you are familiar with elementary calculus