Fast Fourier Transform (FFT) is used in Orthogonal Frequency Division Multiplexing (OFDM) to efficiently convert data from the time domain to the frequency domain. This allows for the simultaneous transmission of multiple signals over different subcarriers, maximizing bandwidth efficiency and minimizing interference. FFT reduces the computational complexity of the required discrete Fourier transform, making it feasible for real-time applications. Overall, FFT is crucial for achieving the high data rates and robustness that characterize OFDM systems.
because they have a high speed compared to fft
Orthogonal frequency-division multiplexing
Orthogonal frequency-division multiplexing (OFDM) is a method of encoding digital. Pilot signals and training symbols (preambles) may also be used for time.
hi.... for DIT fft algorithm, refer to this link, it has c-code for that. http://cnx.org/content/m12016/latest/
2.4GHz is used & OFDM
SISO-OFDM is an OFDM system with one transmit and one receive antenna.
because they have a high speed compared to fft
FFT reduces the computation since no. of complex multiplications required in FFT are N/2(log2N). FFT is used to compute discrete Fourier transform.
plot(abs(fft(vectorname)))the FFT function returns a complex vector thus when you plot it, you get a complex graph. If you plot the absolute value of the FFT array, you will get the magnitude of the FFT.
In MATLAB, the radix-4 Fast Fourier Transform (FFT) can be implemented using the fft function, which computes the FFT efficiently for power-of-two input sizes. For radix-4 specifically, you can manually implement the algorithm by recursively breaking down the FFT into smaller FFTs of size N/4. This involves reordering the input data and performing the necessary butterfly operations. However, it's often more efficient to simply use MATLAB's built-in fft function, which is optimized for various FFT lengths, including radix-4.
FT is needed for spectrum analysis, FFT is fast FT meaning it is used to obtain spectrum of a signal quickly, the FFT algorithm inherently is fast algorithm than the conventional FT algorithm
OFDM uses 48 subchannels for data and 4 are used as Pilot Carriers.
There's no need for it.
In OFDM, sub-carrier spacing is maintained in such a way that the maximum of one sub-carrier occurs at the minimum of the successive sub-carrier, a loss of orthogonality results if this pattern is not achieved in the sub-carriers of OFDM transmission. Loss of orthogonality is due to ISI, ICS, Frequency offset amongst the sub-carriers of OFDM.
Inter symbol interference (ISI) in OFDM systems can be minimized by using a cyclic prefix. This involves adding a copy of the end of each OFDM symbol to the beginning before transmission. The cyclic prefix helps to mitigate the effects of multipath fading and reduces ISI by allowing the receiver to separate the OFDM symbols with a guard interval.
Orthogonal frequency-division multiplexing
FFT is faster than DFT because no. of complex multiplication in DFT is N^2 while in FFT no. of complex multiplications are N/2(log2N). for example if N=8 no. of complex multiplications required in DFT are 64. while no. of complex multiplications required in FFT are 12 thus reduces computation time.