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RM0020

Function API

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Description:

Computes the N-point radix-4 complex to complex frac16/frac32 in-place fast Fourier

transform (FFT). Input 2-bit reversed 16-bit fractional data are stored in second half of

inout_buffer, output 32-bit fractional data are written over the input data into

inout_buffer. There is used radix-4 FFT algorithm.

The output scaling is configurable by constant definition in the function source file:

.set

SCALING

#/* 0 - off, 1 - on */

By default it is turned on. The scaling is performed by dividing by 4 before each radix-

4 stage.

Algorithm:

Equation 3

for each n from 0 to N-1, scaled down by N if scaling is turned on

Equation 4

where i is the imaginary unit with the property that:

Note: There must be at least 32 bytes of readable memory behind inout_buffer.

Performance: See Section 7 and Table 35.

Example 4. fft_radix4_frac32

#include "libdsp2.h"

int inout_buffer[2*256];

void main() {

/* bit reverse permutation and calculation of 256-point FFT */

bitrev_table_16bit(16, (short *)(inout_buffer+256),

seed_radix4_16bit_256);

fft_radix4_frac32(256, inout_buffer,

w_table_radix4_frac32_256);

}

twiddle_factor_table in

pointer to the vector of twiddle factors of length 3*N/2-4, vector

must be double word aligned

memory layout:

w_Re(0) w_Im(0) w_Re(1) w_Im(1) ... w_Re(3*N/4-3)

w_Im(3*N/4-3)

Use predefined w_table_radix4_frac32_N arrays.

The w_table_radix4_frac32_N is computed according to this

formula:

for k = 0, 1, ... 3*N/4-3

Table 4.

fft_radix4_frac32 arguments (continued)

w_Re k

 iw_Im k







 w



–



i

--------

–

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