A Modified Design Of Acf Operation For Reducing Papr Of Ofdm Signal

International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
DOI : 10.5121/ijngn.2014.6103 31
A Modified Design Of Acf Operation For
Reducing Papr Of Ofdm Signal
Md. Munjure Mowla1
, Md. Yeakub Ali2
and S.M. Mahmud Hasan3
1,2,3
Department of Electronics & Telecommunication Engineering
Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
ABSTRACT
Next generation wireless communication technology long term evolution (LTE) has implemented
orthogonal frequency division multiplexing (OFDM) technique as a strong candidate for radio access
systems. It has several attributes such as providing robustness to multipath fading & impulse noise,
eliminating intersymbol interference (ISI), inter carrier interference (ICI) & the need for equalizers. The
major challenging issue of OFDM technique is the high peak to average power ratio (PAPR) which is
defined as the ratio of the peak power to the average power of the OFDM signal. A trade-off is
necessary for reducing PAPR with increasing bit error rate (BER), computational complexity or data rate
loss etc. In this paper, a moderately modified design of amplitude clipping & filtering operation (ACF) is
proposed and implemented which shows the significant improvement in case of PAPR reduction for both
quadrature phase shift keying (QPSK) & quadrature amplitude modulation (QAM) while increasing slight
BER match up to to an existing method.
KEYWORDS
Bit Error rate (BER), Complementary Cumulative Distribution Function (CCDF), Long Term Evolution
(LTE), Orthogonal Frequency Division Multiplexing (OFDM) and Peak to Average Power Ratio (PAPR).
1. INTRODUCTION
Third generation partnership project (3GPP) adopted next generation wireless communication
technology Long Term Evolution (LTE) is designed to increase the capacity and speed of existing
mobile telephone & data networks. LTE has adopted a multicarrier transmission technique known
as orthogonal frequency division multiplexing (OFDM). OFDM meets the LTE requirement for
spectrum flexibility and enables cost-efficient solutions for very wide carriers [1]. The additional
increasing demand on high data rates in wireless communications systems has arisen in order to
carry broadband services. OFDM offers high spectral efficiency, immune to the multipath fading,
low inter-symbol interference (ISI), immunity to frequency selective fading and high power
efficiency. Today, OFDM is used in many emerging fields like wired Asymmetric Digital
Subscriber Line (ADSL), wireless Digital Audio Broadcast (DAB), wireless Digital Video
Broadcast - Terrestrial (DVB - T), IEEE 802.11 Wireless Local Area Network (WLAN), IEEE
802.16 Broadband Wireless Access (BWA), Wireless Metropolitan Area Networks (IEEE
802.16d), European ETSI Hiperlan/2 etc [2].
One of the key problems of OFDM is high peak to average power ratio (PAPR) of the transmit
signal. If the peak transmit power is limited by application constraints, the effect is to reduce the
average power allowed under multicarrier transmission compare to that under constant power
modulation techniques. This lessens the range of multicarrier transmission. Furthermore, the
transmit power amplifier must be operated in its linear region (i.e., with a large input back-off),
where the power conversion is inefficient to avoid spectral growth of the multicarrier signal in the

32
form of intermodulation among subcarriers and out-of-band radiation. It may cause a detrimental
effect on battery lifetime in some mobile applications. As handy devices have a finite battery life,
it is considerable to find ways of reducing the PAPR allowing for a smaller more efficient high
power amplifier (HPA), which in turn will mean a more lasting battery life. In many low-cost
applications, the problem of high PAPR may outweigh all the potential benefits of multicarrier
transmission systems [3].
A number of promising approaches or processes have been proposed & implemented to reduce
PAPR with the expense of increase transmit signal power, bit error rate (BER) & computational
complexity and loss of data rate, etc. So, a system trade-off is required. These techniques include
Amplitude Clipping and Filtering, Peak Windowing, Peak Cancellation, Peak Reduction Carrier,
Envelope Scaling, Decision-Aided Reconstruction (DAR), Coding, Partial Transmit Sequence
(PTS), Selective Mapping (SLM), Interleaving, Tone Reservation (TR), Tone Injection (TI),
Active Constellation Extension (ACE), Clustered OFDM, Pilot Symbol Assisted Modulation,
Nonlinear Companding Transforms etc [4].
2. CONCEPTUAL MODEL OF OFDM SYSTEM
OFDM is a special form of multicarrier modulation (MCM) with densely spaced subcarriers with
overlapping spectra, thus allowing multiple-access.MCM works on the principle of transmitting
data by dividing the stream into several bit streams, each of which has a much lower bit rate and
by using these sub-streams to modulate several carriers[5].
In multicarrier transmission, bandwidth divided in many non-overlapping subcarriers but not
essential that all subcarriers are orthogonal to each other as shown in figure 1 (a) [2]. In OFDM
the sub-channels overlap each other to a certain extent as can be seen in figure 1 (b), which
directs to a resourceful use of the total bandwidth. The information sequence is mapped into
symbols, which are distributed and sent over the N sub-channels, one symbol per channel. To
allow dense packing and still ensure that a minimum of interference between the sub-channels is
met, the carrier frequencies must be chosen carefully. By using orthogonal carriers, frequency
domain can be viewed so as the frequency space between two sub-carriers is given by the distance
to the first spectral null [2].
2.1. Mathematical Explanation of OFDM Signals
In OFDM systems, a defined number of consecutive input data samples are modulated first (e.g,
QPSK or QAM), and then jointly correlated together using inverse fast Fourier transform (IFFT)
at the transmitter side [5]. IFFT is used to produce orthogonal data subcarriers. Let, data block of
length N is represented by a vector, X=[X0, X1……. XN-1]T
. Duration of any symbol XK in the set X
is T and represents one of the sub-carriers set. As the N sub-carriers chosen to transmit the signal
are orthogonal, so we can have, fn = n∆f, where n∆f = 1/NT and NT is the duration of the OFDM
data block X. The complex data block for the OFDM signal to be transmitted is given by [3],
∑
−
=
∆
=
1
0
2
1
)
(
N
n
ft
n
j
ne
X
N
t
x 
NT
t ≤
≤
0 (1)
Figure 1. Spectra of (a) An OFDM Sub-channel and (b) An OFDM Signal [2]

33
Where,
1
−
=
j , ∆f is the subcarrier spacing and NT denotes the useful data block period. In the
OFDM technique, the subcarriers are definitely chosen as orthogonal characteristics (i.e., ∆f =
1/NT). On the other hand, OFDM output symbols typically have large dynamic envelope range
due to the superposition procedure performed at the IFFT stage in the transmitter stage.
3. BASICS OF PAPR
Due to the incidence of large number of independently modulated sub-carriers in an OFDM
system, the peak value of the system may be very high as compared to the average of the total
system. The coherent summation of N signals of same phase produces a peak which is N times
the average signal [3]. PAPR is an vital factor in the design of both high power amplifier (HPA)
and digital-to-analog (DAC) converter, for generating almost error-free (minimum errors)
transmitted OFDM symbols. So, the ratio of peak power to average power is known as PAPR.
The PAPR of the transmitted signal is defined as [6],
2 2
0 0
2
0
max | ( ) | max | ( ) |
[ ( )]
1
| ( ) |
t NT t NT
NT
av
x t x t
PAPR x t
P
x t dt
NT
≤ ≤ ≤ ≤
= =
∫
(2)
Where, Pav is the average power of and it can be computed in the frequency domain because
Inverse Fast Fourier Transform (IFFT) is a (scaled) unitary transformation.
For superior estimated the PAPR of continuous time OFDM signals, the OFDM signals samples
are obtained by L times oversampling [3]. L times oversampled time domain samples are LN
point IFFT of the data block with (L-1)N zero-padding. Therefore, the oversampled IFFT output
can be expressed as,
∑
−
=
=
1
0
/
2
1
]
[
N
k
LN
nk
j
k e
X
N
n
x 
1
0 −
≤
≤ NL
n (3)
3.1. Objectives behind PAPR reduction
The difficulties associated with OFDM are inherited by OFDMA (Orthogonal frequency division
multiple access) technique. OFDMA also suffers from high peak to average power ratio (PAPR)
because it is inherently made up of so many subcarriers [7]. The subcarriers are summed up
constructively to form large peaks. Hence, high peak power requires High Power Amplifiers
(HPA), ADC and DAC converters. Most of the wireless systems employ the HPA in the
transmitter to obtain enough transmission power. For the proposed of achieving the maximum
output power efficiency, the HPA is usually operated at or near the saturation region [6].
The efficiency of power is pivotal in wireless communication as it provides sufficient area
coverage, saves power consumption and allows small size terminals etc. It is important to target at
a power efficient operation of the non-linear HPA with low back-off values and try to give
possible solutions to the interference problem. In addition, the nonlinear characteristic of the
HPA is very approachable to the variation in signal amplitudes. The variation of OFDM signal
Power
Average
Power
Peak
PAPR
_
_
=

34
amplitudes is very wide with high PAPR. So, HPA will introduce inter-modulation between the
different subcarriers and create extra interference into the systems due to high PAPR of OFDM
signals. This additional interference forwards to BER increment. To reduce the signal distortion
and keep a minimum BER, it requires a linear work in its linear amplifier region with a large
dynamic range. Actually, this linear amplifier has poor efficiency and is so expensive. Therefore,
a better solution is to prevent the occurrence of such interference by reducing the PAPR of the
transmitted signal with some manipulations of the OFDM signal itself [6].
Large PAPR also demands the DAC with large dynamic range to accommodate the large peaks of
the OFDM signals. Although, a high precision DAC supports high PAPR with a reasonable
amount of quantization noise, but it might be very expensive for a given sampling rate of the
system. Moreover, OFDM signals show Gaussian distribution for large number of subcarriers,
which means the peak signal quite rarely occur and uniform quantization by the ADC is not
desirable. If the peak signal is clipped, it will introduce in-band distortion (BER increment) and
out-of-band radiation (adjacent channel interference) into the channel. It is therefore the perfect
solution to reduce the PAPR before OFDM signals are transmitted into nonlinear HPA and DAC.
4. CONVENTIONAL CLIPPING AND FILTERING
Amplitude Clipping and Filtering is one of the easiest techniques which may be under taken for
PAPR reduction for an OFDM system. A threshold value of the amplitude is fixed in this case to
limit the peak envelope of the input signal [8].
The clipping ratio (CR) is defined as,

A
CR = (4)
Where, A is the amplitude and is the root mean squared value of the unclipped OFDM signal.
The clipping function is performed in digital time domain, before the D/A conversion and the
process is described by the following expression,



= )
( k
x
j
k
c
k
Ae
x
x 
A
x
A
x
k
k
>
≤
|
|
|
|
1
0 −
≤
≤ N
k (5)
Where , is the clipped signal, is the transmitted signal, A is the amplitude and is the
phase of the transmitted signal
Figure 2. Clipping Function

35
In the figure 3, the conventional block diagram of clipping & filtering method is shown which
have some limitations described in below.
4.1. Limitations of Clipping and Filtering
 Clipping causes in-band signal distortion, therefore BER performance is degraded.
 Another problem of clipping is out-of-band radiation (channel interference), which
imposes out-of-band interference signals to adjacent channels. Though the out-of-band
radiation caused by clipping can be reduced by filtering, it may affect high-frequency
components of in-band signal (aliasing) when the clipping is performed with the
Nyquist sampling rate in the discrete-time domain [9].
 On the other hand, if clipping is performed for the sufficiently-oversampled OFDM
signals (e.g., L ≥4) in the discrete-time domain before a low-pass filter (LPF) and the
signal passes through a band-pass filter (BPF), the BER performance will be less
degraded [9].
 Filtering the clipped signal can reduce out-of-band radiation at the cost of peak regrowth.
The signal after filtering operation may exceed the clipping level specified for the
clipping operation [3].
5. PROPOSED CLIPPING AND FILTERING SCHEME
As our main focus is to reduce PAPR, so, in this simulation, we have trade-off between PAPR
reduction with BER increment. Very less amount of BER increment is desirable. Pointing out the
third limitation in section 4.1, (Yong Soo et.al) [9] shows that if clipped signal passes through a
Composed filter (FIR based BPF) before passing a LPF to reduce out-of-band radiation, then it
causes less BER degradation with medium amount of PAPR reduction.
Considering this concept, we design a scheme for clipping & filtering method where clipped
signal passes through a Composed filter (IIR based BPF) before passing a LPF, then it causes a
little bit more BER degradation but more amount of PAPR reduction than existing method[9].
This proposed scheme is shown in the figure 4. It shows a block diagram of a PAPR reduction
scheme using clipping and ﬁltering, where L is the oversampling factor and N is the number of
subcarriers. The input of the IFFT block is the interpolated signal introducing N(L −1) zeros
(also, known as zero padding) in the middle of the original signal is expressed as,
Figure 3. Block Diagram of PAPR reduction using Conventional Clipping & Filtering [9]
Zero
Padding
L.N Point IFFT
and Oversampling
Peak
Clipping
L.N Point
FFT
Band Pass
Filter
L.N Point
IFFT
Low Pass
Filter

36
]
Proposed Filter
(6)
In this system, the L-times oversampled discrete-time signal is generated as,
N
L
fk
m
j
N
L
k
e
k
X
N
L
m
x .
2
1
.
0
.
]
[
.
1
]
[
∆
−
=
∑ ′
=
′

, m = 0,1,…NL – 1 (7)
and is then modulated with carrier frequency fc to yield a passband signal .
Let denote the clipped version of which is expressed as,
(8)
Or,
(9)
Where, A is the pre-specified clipping level. After clipping, the signals are passed through the
Composed filter (Proposed Filter). This composed filter itself consists on a set of FFT-IFFT
operations where filtering takes place in frequency domain after the FFT function. The FFT
function transforms the clipped signal to frequency domain yielding The
information components of are passed to a band pass filter (BPF) producing This
filtered signal is passed to the unchanged condition of IFFT block and the out-of-band radiation
that fell in the zeros is set back to zero. The IFFT block of the filter transforms the signal to time
domain and thus obtain .
6. DESIGN AND SIMULATION PARAMETERS
In this simulation, an IIR digital filter (Chebyshev Type I) is used in the composed filtering.
Chebyshev Type I filter is equiripple in the passband and monotonic in the stopband.
Type I filter rolls off faster than type II filters. Chebyshev filter has the property that it
Figure 4. Block Diagram of Proposed Clipping & Filtering Scheme.
L.N
Point
IFFT
fc
Digital
up
Conver
sion
Clipping
BPF LPF
L.N
Point
FFT
L.N
Point
IFFT

37
minimizes the error between the idealized and the actual filter characteristic over the
range of the filter. Because of the passband equiripple behaviour inherent in Chebyshev
Type I filter, it has a smoother response. Using the special type of bandpass filter in the
composed filter, significant improvement is observed in the case of PAPR reduction. Table 1
shows the values of parameters used in the QPSK & QAM system for analyzing the performance
of clipping and ﬁltering technique [9]. We have simulated the both methods (Existing and
Proposed) with the same parameters at first. But, simulation results show the significant
improvement occurs in PAPR reduction for proposed method.
Table 1. Parameters Used for Simulation of Clipping and Filtering.
Parameters Value
Bandwidth ( BW) 1 MHz
Over sampling factor (L) 8
Sampling frequency, fs = BW*L 8 MHz
Carrier frequency, fc 2 MHz
FFT Size / No. of Subscribers (N) 128
CP / GI size 32
Modulation Format QPSK and QAM
Clipping Ratio (CR) 0.8, 1.0, 1.2, 1.4, 1.6
6.1. Simulation Results for PAPR Reduction
At first, we simulate the PAPR distribution for CR values =0.8, 1.0, 1.2, 1.4, 1.6 with QPSK
modulation and N=128. Then, we simulate with QAM modulation and N=128 and compare
different situations.
6.1.1 Simulation Results: (QPSK and N=128)
In the existing method, PAPR distribution for different CR value is shown in figure 5 (a). At
CCDF =10-1
, the unclipped signal value is 13.46 dB and others values for different CR are
tabulated in the table 2.
Figure 5. PAPR Distribution for CR=0.8,1.0,1.2,1.4,1.6 [QPSK and N=128]

38
In the proposed method, simulation shows the significant reduction of PAPR is shown for
different CR values in figure 5(b). At CCDF =10-1
, the unclipped signal value is 13.52 dB and
others values for different CR are tabulated in the table 2. From table 2, it is clearly observed that
the proposed method reduces PAPR significantly with respect to existing Yong Soo [9] analysis.
Proposed method of filter design is done with the same parameters that used in [9].
Table 2. Comparison of Existing with Proposed Method for PAPR value [QPSK and N=128]
6.1.2 Simulation Results: (QAM and N=128)
The simulation results are now shown for QAM modulation and no. of subscribers, N=128. In the
existing method, PAPR distribution for different CR value is shown in figure 6 (a). At CCDF
=10-1
, the unclipped signal value is 13.42 dB and others values for different CR are tabulated in
the table 3. In the Proposed method, simulation shows the significant reduction of PAPR is
shown for different CR values in figure 6(b). At CCDF =10-1
, the unclipped signal value is 13.65
dB and others values for different CR are tabulated in the Table 3.
Figure 6. PAPR Distribution for CR=0.8,1.0,1.2,1.4,1.6 [QAM and N=128]
The existing method and proposed method PAPR distribution values for different CR values are
tabulated and differences are shown in table 3. From table 3, it is clearly observed that the
proposed method reduces PAPR significantly with respect to existing Yong Soo [9] analysis for
QAM and N=128 also. So, proposed method works on efficiently for both QPSK & QAM.
CR
value
PAPR value
(dB)
(Existing)
PAPR value
(dB)
(Proposed)
Improvement in PAPR value
(dB)
0.8 8.10 4.21 3.89
1.0 8.35 4.67 3.68
1.2 8.72 5.21 3.51
1.4 8.81 5.72 3.09
1.6 9.22 6.29 2.93

39
Table 3. Comparison of Existing with Proposed Method for PAPR value [QAM and N=128]
CR value PAPR value
(dB)
(Existing)
PAPR value
(dB)
(Proposed)
Improvement in PAPR value
(dB)
0.8 8.23 4.21 4.02
1.0 8.32 4.65 3.67
1.2 8.65 5.11 3.54
1.4 8.92 5.71 3.21
1.6 9.15 6.27 2.88
Now, if we compare the values for different CR values in case of QPSK & QAM to show the
effect of modulation on proposed filter design, it is observed that for the same number of
subscribers (N=128) & low CR=0.8, there is no differences between using QAM & QPSK. But,
with the increasing value of CR, QAM provides less PAPR than QPSK. So, for high CR, QAM is
more suitable than QPSK in case of proposed filter.
6.2. Simulation Results for BER Performance
The clipped & filtered signal is then passed through the AWGN channel and BER are measured
for both existing & proposed methods. We have also simulated the analytical BER curve that is
shown in the curve. Figure 7 and figure 8 show the BER performance for QPSK and QAM with
N=128. It is seen from these figures that the BER increases as the CR decreases.
6.2.1 Simulation Results: (QPSK and N=128)
Now, for QPSK & N=128 with all other same data mentioned in table 1, both existing and
proposed methods are executed and resulted graphs are shown in figure 7(a) & figure 7(b)
respectively. It is observed from these two figures that BER increases slightly in proposed method
with respect to existing method for same value of CR.
Figure 7. BER Performance [QPSK and N=128]
The measured BER value at 6 dB point is tabulated in table 4.

40
Table 4. Comparison of BER value for Existing & Proposed Method [QPSK and N=128]
CR value BER value
(Existing)
BER Value
(Proposed)
Difference in BER value
0.8 0.07413 0.10631 -0.03218
1.0 0.06984 0.09012 -0.02028
1.2 0.05982 0.07846 -0.01864
1.4 0.04891 0.06358 -0.01467
1.6 0.04449 0.05748 -0.01299
BER performance is measured and compared in both the table 4 (QPSK) & table 5(QAM) which
shows different CR values for both existing & proposed method in case of same parameter value.
From table 4, it is observed that, for CR values (0.8,1.0,1.2,1.4 & 1.6) , the difference magnitude
between existing & proposed method are 0.3218,0.02028,0.01864,0.01467 & 0.01299
respectively in QPSK. These BER degradations are acceptable as these are very low values.
6.1.2 Simulation Results: (QAM and N=128)
Again, for QAM & N=128 with all other same data mentioned in table 1, both existing and
proposed methods are executed and resulted graphs are shown in figure 8(a) & figure 8(b)
respectively. It is observed from these two figures that BER increases slightly in proposed method
with respect to existing method for same value of CR.
Figure 8. BER Performance [QAM and N=128]
The measured BER value at 6 dB point is tabulated in table 5.

41
Table 5. Comparison of BER value for Existing & Proposed Method [QAM and N=128]
CR value BER value
(Existing)
BER Value
(Proposed)
Difference in BER value
0.8 0.05563 0.07535 -0.01972
1.0 0.04614 0.06098 -0.01484
1.2 0.04016 0.05433 -0.01417
1.4 0.03268 0.04631 -0.01363
1.6 0.03055 0.04211 -0.01156
From table 5, it is observed that, for CR values (0.8,1.0,1.2,1.4 & 1.6) , the difference magnitude
between existing & proposed method are 0.01972,0.01484,0.01417,0.01363 & 0.01156
respectively in QAM. These BER degradations are acceptable as these are very low values.
Now, if we compare the values for different CR values in case of QPSK & QAM to show the
effect of modulation on proposed filter design, it is observed that for the same simulation
parameters, QAM provides less BER degradation than QPSK in all cases. With gradual
increasing of CR values, the differences of BER values between QPSK & QAM also becomes
decreasing. So, QAM is more suitable for proposed method.
6. CONCLUSION
In this paper, a comparative scheme of amplitude clipping & filtering based PAPR reduction
technique has been analyzed where PAPR reduces significantly compare to an existing method
with slightly increase of BER. At first phase, simulation has been executed for existing method
with QPSK modulation and number of subscriber (N=128) and then executed for the proposed
method for same parameter and observed that PAPR reduces significantly. Next, the simulation
has been performed for QAM modulation & N=128 and result shows the considerable
improvement in case of QAM also. Then, the proposed method results for both QAM & QPSK
modulation with N=128 has been compared. It is observed from that for the same number of
subscribers (N=128) & low CR=0.8, there is no differences between using QAM & QPSK. But,
with the increasing value of CR, QAM provides less PAPR than QPSK. So, for high CR, QAM is
more suitable than QPSK in case of proposed filter. In case of BER, with gradual increasing of
CR values, the differences of BER values between QPSK & QAM also becomes decreasing. So,
QAM is more suitable for proposed method. In the present simulation study, ideal channel
characteristics have been assumed. In order to estimate the OFDM system performance in real
world, Multipath Rayleigh fading would be a major consideration in next time. The increase
number of subscribers (N) & other modulation parameters could be another assumption.
REFERENCES
[1] Eric Dahlman, Stefan Parkvall, and Johan Skold, 4G LTE / LTE-Advanced for Mobile Broadband,
United Kingdom : ELSEVIER Academic Press ,2011.
[2] R. Prasad, OFDM for Wireless Communications Systems. London, Boston: Artech House, Inc, 2004.
[3] S. H. Han and J. H. Lee, “An overview of peak-to-average power ratio reduction techniques for
multicarrier transmission”, IEEE Wireless Comm, vol. 12, no.2, pp.56-65, Apr. 2005.
[4] M.M.Mowla, S.M.A Razzak and M.O.Goni, Improvement of PAPR Reduction for OFDM Signal
in LTE System, Germany: LAP Lambert Academic Publishing, 2013.

42
[5] M.M.Mowla and S.M.M Hasan, “Performance Improvement of PAPR Reduction for OFDM Signal
In LTE System”, International Journal of Wireless & Mobile Networks (IJWMN), Vol. 5, No. 4,
pp.35-47, doi:10.5121/ijwmn.2013.5403, August 2013
[6] T. Jiang and Y. Wu, “An Overview: peak-to-average power ratio reduction techniques for OFDM
signals”, IEEE Trans. Broadcast., vol. 54, no. 2, pp. 257-268, June 2008.
[7] G.Sikri and Rajni, “A Comparison of Different PAPR Reduction Techniques in OFDM Using
Various Modulations”, International Journal of Mobile Network Communications & Telematics
(IJMNCT) vol.2, no.4, pp. 53-60, 2012.
[8] Natalia Revuelto, “PAPR reduction in OFDM systems”, M.Sc Dissertation, Universitat Politecnica de
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[9] Y.S. Cho, J. Kim, W.Y.Yang and C.G. Kang, MIMO OFDM Wireless Communications with
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Authors
Md. Munjure Mowla is now working as an Assistant Professor in Electronics &
Telecommunication Engineering department of Rajshahi University Engineering
& Technology (RUET) since November 2010. He has completed M.Sc
Engineering degree in Electrical & Electronic Engineering (EEE) from RUET in
May 2013. He has four years telecom job experience in the operators, vendors,
ICX etc of Bangladesh telecom market. Mr. Mowla has published several
international journals as well as conference papers and three books. He is a
member of communication society COMSOC of IEEE, Institutions of Engineers, Bangladesh (IEB) and
Bangladesh Electronics Society (BES). His research interest includes advanced wireless communication
including LTE, LTE-Advanced, green communication, smart grid communication.
Md. Yeakub Ali has completed B.Sc Engineering degree in Electronics &
Telecommunication Engineering (ETE) from Rajshahi University Engineering &
Technology (RUET) in January 2014. Now, he is working as an Engineer in a
telecommunication company. His research area includes Wireless & Mobile
Communication, Satellite Communication & Radar.
S. M. Mahmud Hasan was born in the Rajshahi, the northern city of Bangladesh
on 30 January 1991. He has completed B.Sc Engineering degree in Electronics &
Telecommunication Engineering (ETE) from Rajshahi University Engineering &
Technology (RUET) in September 2012. Now, he is working in the Bangladesh
Government Power Development Board (BPDB) as an Assistant Engineer. His research interest includes
advanced wireless communication (LTE, LTE-A) etc.

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