This document summarizes a research paper on designing a near perfect reconstruction cosine modulated filter-bank transmultiplexer using Kaiser window functions. It discusses using Kaiser windows with high side-lobe fall off rates to design prototype filters for analysis and synthesis sections. It also compares using an optimization algorithm versus no optimization to reduce inter-symbol interference and inter-carrier interference. The simulation results show the optimization approach provides less error compared to no optimization.

1. INTERNATIONAL JOURNAL OF ELECTRONICS AND
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 4, Issue 2, March – April, 2013, pp. 225-228
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COSINE MODULATED FILTER-BANK TRANSMULTIPLEXER USING
KAISER WINDOW
Saurabh Khandelwal, Narendra Singh, Hemdutt Joshi, Sandeep Kumar arya
Electronics and communication, Jaypee University of engineering & technology
Guna - 473226 (M.P), INDIA.
ABSTRACT
This paper presents the design of near perfect reconstruction (NPR) cosine modulated
filter-bank (CMFB) transmultiplexer using Kaiser Window approach. Cosine modulation is
used to design the synthesis an analysis sections of the transmultiplexer. The prototype filter
is designed by using high side-lobe fall off rate (SLFOR) Kaiser window functions. A
bisection optimization algorithm has been used, and without optimization algorithm used.
The use of optimization algorithm is reduce the effect of ISI (inter symbol interference) and
ICI (inter carrier interference).
Keywords -OFDM, ICI, ISI, Kaiser Window SLFOR
I. INTRODUCTION
Orthogonal frequency division multiplexing (OFDM) and discrete multitone
transmission (DMT) are the widely use technologies in multicarrier communication. Both use
IFFT or DFT for the modulation and demodulation of signals. Due to multipath fading over
wireless communication, the consecutive OFDM symbols overlap at the receiver and gives
rise to ISI and ICI .To minimize the ISI in OFDM system the guard band is used, which may
loss of spectral efficiency. The DFT based modulation filter has side lobes of -13 dB .it
provides better stop band performance with less complexity. The main contribution of this
paper can summarize as below;
1 The fixed Kaiser Window functions have certain limitations in selecting input parameters.
Therefore, popular variable window functions with high side lobe falloff rate (SLFOR) are
used for the design of prototype filters.
2. A iteration method and without optimization is discussed.
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2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
II. CONVOLUTION CODES
Convolution codes are specified on the basis of three parameters (n, k, L), where n, k, L
are length of the codeword, number of input bits and constraint length respectively. The
constraint length L, defines the past number of input bits in the memory register that affect
the output code word. The rate of Convolution code is defined as the ratio of number of
output bits to the input bits and is denoted by ‘r’. Therefore code rate r = k/n. [2]
III. DFT MULTICARRIER TRANSMISSION TECHNIQUE
To generate OFDM successfully the orthogonality principle between the carriers must
be maintained. For this reason, OFDM is generated by first choosing the spectrum required,
based on the input data, and modulation scheme used. Each carrier to be produced is assigned
some data to transmit. The required amplitude and phase of the carrier is then calculated
based on the modulation scheme (typically differential BPSK, QPSK, or QAM). The required
spectrum is then converted back to its time domain signal using an Inverse Fourier
Transform. In most applications, an Inverse Fast Fourier Transform (IFFT) is used. The IFFT
performs the transformation very efficiently, and provides a simple way of ensuring the
carrier signals produced are orthogonal.
The Fast Fourier Transform (FFT) transforms a cyclic time domain signal into its
equivalent frequency spectrum. The IFFT performs the reverse process, transforming a
spectrum (amplitude and phase of each component) into a time domain signal. An IFFT
converts a number of complex data points, into the time domain signal of the same number of
points. Each data point in frequency spectrum used for an FFT or IFFT is called a bin. The
orthogonal carriers required for the OFDM signal can be easily generated by setting the
amplitude and phase of each frequency bin, then performing the IFFT. Since each bin of an
IFFT corresponds to the amplitude and phase of a set of orthogonal sinusoids, the reverse
process guarantees that the carriers generated are orthogonal. Figure 1 below shows the bock
diagram of basic OFDM transmitter and receiver. The signal generated is at base-band and so
to generate an RF signal the signal must be filtered and mixed to the desired transmission
frequency.
Fig.1 Block Diagram of OFDM Transmitter and Receiver
IV. TRANSMULTIPLEXER SYSTEM:
The M-channel maximally decimated transmultiplexer is shown in Fig.2. Basically it is a
TDM-FDM-TDM converter. It consists of synthesis block at transmitter end and precedes the
analysis block at receiver end. At the transmitter end, M-input signals are first interpolated by
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3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
the factor of M and synthesized into one composite signal using synthesis filter bank Fk (z)
bank,
for k = 0, 1, . . ., M − 1. Conversely, at the receiver end, the composite signal is split out into
composite
M-output signals with the help of the analysis filter bank Hk (z) and then decimated by a
output )
factor of M. the equation of the transmultiplexer is given as;
.
n −1
ˆ
X k (Z ) = ∑ S km ( Z ) X (Z ) (1)
m
m =0
ˆ (2)
X ( z ) = s( z ) x( z )
If, s (z) is diagonal then it is free from cross talk.
z)
ˆ
X k ( z ) = S kk ( z ) xk ( z ) (3)
Fig: 2 M-channel transmultiplexer
V. SIMULATION RESULT
0 0
-50
-50
-100
B
B
d
d
-150
-100
-200
-250 -150
0 0.5 1 0 0.5 1
Normalized Frequency Normalized Frequency
1 -61
-61.5
M g itu e
M n itu e
0.9
an d
ag d
-62
-62.5
0.8
-63
0.7 -63.5
0 0.5 1 0 0.5 1
Normalized Frequency Normalized Frequency
Fig.3: With optimization algorithm using Kaiser Window approach
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4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
Without optimization the cosine modulated filter bank results are:
For M=8:
For M=16:
VI. CONCLUSION
In this paper, the performance of TMUX-OFDM systems and cosine modulated filter
bank systems is discussed. The cosine modulated transmultiplexer using Kaiser Window
approach is give better result as Blackman window. The optimization algorithm gives less
error as compared to without optimization.
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