The namely inphase (I) and quadrature (Q). These

The QAM mapper at the input converts the incoming data bits
into a QAM symbol. Each QAM symbol comprises of two componets namely inphase
(I) and quadrature (Q). These I and Q values of a data bit defines the
amplitude of the pulses given to the pulse shaping filter (S-P).The translation
of the bits  to symbol and vice versa are
represented by a symbol map diagram.

            IFFT block
returns the value of the normalized discrete, univariate, Inverse fast fourier
transform ot the values given by the pulse shaping filter in addition with the
pulse symbols. AddCP block returns the value of the added cyclic prefix length
to the symbols. It is then inputted to the Digital to Analog converter (DAC)
block. The obtained signal is then modulated to the RF range between 20 KHz to
300 GHz and transmitted over the air.

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            At the
receiver end the received RF signals are then passed through the analog to
digital converter (ADC) block to get back the pulses from the signals in Time
domain Equalization (TEQ). TEQ is now being widely used due to its simplicity
and ease of implementation. Zero forcing is one of the conventional TEQ
technique which is used to estimate the inverse channel transfer function.
Hence ZF technique is replace by Maximum Likely hood sequence (MLSD) and
Decision feedback Equalization (DFE) techniques.

             MLSD is used in low span ISI as its Complexity
increases exponentially with the channel memory, but practically speaking
wireless channel need not to operate in low span ISI hence MLSD is not used. In
contrast DFE provides better performance and robust against ISI when upper
bound is in its maximum speed. Many high speed architecture has been proposed
to overcome the high speed limitation of DFE at the cost of more hardware. The
main idea came up from reformulating the original architecture into arrays of
adders,slicers and multiplexers based on past decisions.

            Later FBF
and LUT were presented for low hardware complexity.This is acheived by
precomputing the FBF coefficients in the Lookup table with address lines and
past decisions.The filter coefficient are to be updated with the changing
channel frequently.Thus this category falls under adaptive DFE which is more
complex than non adaptive DFE due to updating time limit.To reduce this
computational complexity relaxed look-ahead technique is used.But the
architechtural complexity increases due to increased multipliers.

precomputation technique used here is Distributed Arithmetic which transforms
the Multiply and Accumulate (MAC) units to the look-up table (LUT). In tis
method the filter coefficients and inputs are stored in separate look-up tables
thus increasing the performance efficiency of the filter.In every iteration the
coefficient LUT is updated which consumes time and power.To overcome this
limitation the offset binary coding (OBC) scheme is used.On the whole the
computational complexity is reduced in the tradeoff with circuit complexity.To
overcome this limitation we proposed with low complex high efficient
architehture for both non adaptive and adaptive DFEs using the concept of pre
speed up.

            The existing
design requires the feed forward filter output for precomputation of feedback
filter which is not necessary in proposed system.


            In communication equalizers are used to mitigate the ISI and
to recover the originaal transmitted symbols. A linear equalizer is used in series
with the channel to produce an estimated channel inverse transfer function.It
consists of real and complex FIR filters to handle the real and complex data
received. Teh coefficient of these filters are updated using Least mean Square
(LMS) Algorithm.But the performance of the linear equalizer are not very good
with strong distorted cahnnel where noise is also amplified with symbols at
higher frequencies resulting in Inter symbol Interference (ISI). To over come
these limitations DFEs are used. DFEs use the feedback of the received symbol
to produce the estimated channel output. Thus DFE is fed with detected symbols
and produces an estimated output which is subtracted from the output of linear