 Open Access
 Total Downloads : 680
 Authors : Ramesh Kumar.B, K.Satyanarayana Raju
 Paper ID : IJERTV2IS80622
 Volume & Issue : Volume 02, Issue 08 (August 2013)
 Published (First Online): 30082013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Design and Simulation of 64Point FFT Using Radix4 Algorithm
Ramesh Kumar.B1, K.Satyanarayana Raju2
1,2Department of ECE, MVGR College of Engineering, Vizianagaram, Andhrapradesh
Abstract: A parallel and pipelined Fast Fourier Transform (FFT) processor for use in the Orthogonal Frequency division Multiplexer (OFDM) . Unlike being stored in the traditional ROM, the twiddle factors in our pipelined FFT processor can be accessed directly. A novel simple address mapping scheme and the modified radix 4 FFT also proposed. Finally, the pipelined 64point FFT processor can be completely implemented.
Keywords : OFDM, radix 4,DITFFT,DIFFFT

INTRODUCTION
Active radar systems are commonly used in military and commercial applications for the detection and tracking of objects through a medium such as air. Most active radar systems work by transmitting a signal pulse through the medium and receiving the reflected energy wave field as it scatters off objects in the medium. By processing the received wave field, an active radar system can determine an object's distance, velocity, and other features. A passive radar system can consist solely of an array of receiving antennas as opposed to an active radar system with a colocated transmitter and receiver.
Without a transmitter the passive radar system relies on other sources of electromagnetic waves, such as AM or FM radio waves, TV broadcast, nearby radar, cell towers, or a wideband waveform to illuminate objects in the medium. Various signal processing algorithms can be applied to the data from the antenna array. One algorithm of interest is the Direction of Arrival (DOA) of the incoming scattered waveform and therefore the object. A computationally efficient version of the DFT is known as the Fast Fourier Transform (FFT) and is commonly used to reduce the computational burden.
In the communication world of today more and more OFDM systems are brought online with an ever increasing number of standards, applications, and services, all with different requirements on the physical layer of the transceiver. The physical layer is most often, due to speed and power constraints, implemented as an ASIC and thus locked to one specific case. Hence, the number of required implementations will grow fast. Due
to this fact, there will be an interest from the industrys point of view to search for a flexible architecture that can be configured to function with several standards, applications, or services. A flexible architecture will lead to reusage of design and implementation and thus reduced cost. This project addresses both OFDM in theory and the implementation aspects of a flexible hardware solution for digital baseband OFDM.
In this project a flexible FFT processor and a
flexible
OFDM transmitter are presented. The FFT processor is a central part of an OFDM transceiver, and has been fabricated both as a stand alone chip and as part of an OFDM transmitter chip. The transmitter chip includes in addition to the FFT, a signal mapper and a cyclic prefix inserter. It is shown that high flexibility is obtained in the transmitter with a reasonable amount of extra hardware. Comprehensive measurements on power consumption are presented for both chips.
A scheme to reduce hardware and delay in an OFDM transceiver is proposed in this project. The scheme shows that the data buffers in an OFDM transceiver, due to a cyclic prefix and a bit reversing pipelined FFT processor, can be significantly reduced with a cyclic suffix and a bidirectional FFT processor.

BASIC THEORY OF OFDM
Figure 2.1: The digital baseband parts of an OFDM transceiver.
The digital baseband parts of an OFDM transceiver [12], is shown in Figure 1. The basic idea of OFDM is to divide the available spectrum into N orthogonal subchannels. In the mapper, data is converted to signals located in the frequency domain where each
subchannel is assigned one signal. The signals are then transformed to the time domain with the inverse fast Fourier transform (IFFT). The FFT is an efficient method to implement the DFT algorithm, based on a divide and conquer approach [13]. The implementation of an FFT is discussed in Chapter 3 and from now on, only the FFT notation will be used. The last digital part of the transmitter inserts a cyclic extension to remove the effects of intersymbol interference (ISI) and interchannel interference (ICI).
The FFT and the IFFT of length N, is defined by the equations
Where is called twiddle factor, i is the sample index, and
k is the subcarrier index.
In the receiver the cyclic extension is discarded and the transmitted signals are transformed back to the frequency domain with an FFT. The N received signals points Y, can be written as
Y = FFT(IFFT(X)g + )
where is a circular convolution, X the N transmitted signal points, g the channel impulse response, and is the channel noise. Due to the fact that the cyclic extension removes both ISI and ICI, and that the signals are located in the frequency domain, the effect of the channel impulse is easily removed in the equalizer, compared to single tone systems. An estimate of the transmitted signals is written as
where G is the FFT of g. To estimate g known data are transmitted over the channel, these data are called pilots. In the demapper the estimated signals X are converted back to data.

IFFT AND FFT
The Inverse Fast Fourier Transform (IFFT) transforms signals from frequency domain to time domain and the FFT performs the reverse operation. To keep signals in the frequency domain simplifies some signal processing operations, e.g. convolution in the time
domain becomes multiplication in the frequency domain. The transformation into the time domain is done in order to reduce the number of backend RFoscillators and demodulators [5].
The available bandwidth (B) in frequency is split into N sub channels, one for each subcarrier, where each has a power spectrum shape of a squared sine pulse. The individual power spectra, after the IFFT in the transmitter, of a number of subcarriers. Since the IFFT is a linear operation the subcarriers can be separated again with the FFT, even though there spectrum overlap. This is also true after the signals have passed a multipath channel, due to the cyclic extension. The property that each subcarrier is unaffected by the other subcarriers is called orthogonality, hence the name OFDM.
FFT
The first designed chip is an FFT processor. The FFT processor has a central position both in the OFDM transmitter and receiver. The FFT is a computationally demanding operation that requires an ASIC implementation to reach high performance, i.e. high throughput combined with low energy consumption.
FFT Architecture
The FFT and IFFT Equation 2.1 and 2.2 has the property that, if
FFT(Re(xi)+ jIm(xi)) = Re(Xi)+ jIm(Xi)
and
IFFT(Re(Xi)+ jIm(Xi)) = Re(xi)+ jIm(xi),
where xi and Xi are N words long sequences of complex valued, samples and subcarriers respectively, then
1/N * FFT(Im(Xi)+ jRe(Xi)) = Im(xi)+ jRe(xi).
Thus, it is only necessary to discuss and implement the FFT equation. To calculate the inverse transform, the real and imaginary part of the input and output are swapped. Since N is a power of two, scaling with 1/N is the same as right shift the binary word Log2(N) bits. Even simpler, is to just remember that the binary point has moved log2(N) bits to the left. Not performing the bit shift until, if ever, it is necessary, which depends on how the output frm the IFFT will be used.
Figure 3.1: (a)A radix2 DIF butterfly and (b) a radix2 DIT butterfly
where W is the twiddle factor.
The FFT algorithm can be realized with a butterfly operation as the basic building block [23]. There are two types of butterfly operations, decimation in time (DIT) and decimation in frequency (DIF), both are shown in Figure 3.1. The difference between DIT and DIF lies in the position of the twiddle factor multiplication, which is either performed before or after the subtraction and addition. Since the FFT is based on divide and conquer, it will be most efficient if the input sequence is of length N = rp, where N is called point, r is called radix, and p is a positive integer. To compute an Npoint FFT, p stages of butterflies are connected. An example of a hardware mapped N = 16point radix2 DIF FFT is shown in Figure 3.2. Observe that the input data, x(n), occurs in consecutive order, whereas the output data, X(n), is rearranged. The order of the rearranged data is called bit reversed order and is explained in Section 3. It is possible to reconfigure the FFT in Figure 3.2 so that the input occurs in bit reversed order and the output occurs in natural order. The reconfiguration can be thought of as vertically moving the horizontal data paths in Figure 3.2, while the cross connections remain fixed to respective path, until the output is in natural order. Also notice that, if all arrows in Figure 3.2 is turned around a DIT FFT is performed instead of a DIF FFT.
Figure 3.2: A hardware mapped N = 16point radix2 DITFFT Algorithm.
Figure:3.3 RADIX4 64 points FFT architecture
Figure 3.4: A Simple Radix 4 DIF FFT algorithm.
When N is a power of 4, i.e. N =4p, a radix4 FFT can be used instead of a radix2 FFT. With a radix4 the computational complexity is reduced, i.e. the number of complex multiplications is reduced compared to a radix2 FFT. The drawback with a radix4 is that the butterfly structure is more complicated. Another, better type of radix4 is the radix22, there the butterflies still look like radix2 butterflies but the number of complex multipliers are reduced as if it were a radix4 [24]. Figure 3.2 shows an N = 16point radix22 DIF FFT. Each stage in a radix22 FFT consists of two butterflies, one trivial multiplier, i.e. multiplication with j, and one complex multiplier that multiplies data with the twiddle factor.

SIMULATIONRESULTS
Figure: 4.1
Figure: 4.2
Figure: 4.3
Figure: 4.1, 4.2 & 4.3 simulation results of the 64point FFT

CONCLUSION
In this project it is shown that a baseband ASIC can be fast and at the same time flexible with a low power consumption. The term fast do not refer to extreme clock frequencies but to the fact that no part of the designs needs more than one clock cycle to process a sample once the pipe is filled. Hence, the design does not need to be clocked any faster than the requested bandwidth and compared to modern CMOS technology this is a low number, in the order of 5100 MHz. There are several advantages with a low clock frequency, firsit is possible use a low power/low speed cell library with low static leakage current and secondly, it is easier to create a clock tree. In addition, there will be more margins to reduce supply voltage if the required clock frequency is low. To have a low leakage power is important in these designs, since flexibility is achieved with independent modules, where the operation mode decides if a module should be used or not. The unused modules are not clocked and hence only consume static leakage power.
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