Machine Learning based Compact MIMO Antenna Array for 38 GHz Millimeter Wave Application with Robust Isolation and High Efficiency Performance
Results in Engineering,
Journal Year:
2025,
Volume and Issue:
unknown, P. 104006 - 104006
Published: Jan. 1, 2025
Language: Английский
Machine learning-based technique for gain prediction of mm-wave miniaturized 5G MIMO slotted antenna array with high isolation characteristics
Md Ashraful Haque,
No information about this author
Jamal Hossain Nirob,
No information about this author
Kamal Hossain Nahin
No information about this author
et al.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Jan. 2, 2025
This
study
presents
the
design
and
analysis
of
a
compact
28
GHz
MIMO
antenna
for
5G
wireless
networks,
incorporating
simulations,
measurements,
machine
learning
(ML)
techniques
to
optimize
its
performance.
With
dimensions
3.19
λ₀
×
λ₀,
offers
bandwidth
5.1
GHz,
peak
gain
9.43
dBi,
high
isolation
31.37
dB,
an
efficiency
99.6%.
Simulations
conducted
in
CST
Studio
were
validated
through
prototype
showing
strong
agreement
between
measured
simulated
results.
To
further
validate
design,
equivalent
RLC
circuit
model
was
developed
analyzed
using
ADS,
with
reflection
coefficient
results
closely
matching
those
from
CST.
Additionally,
supervised
ML
employed
predict
antenna's
gain,
evaluating
nine
models
metrics
such
as
R-squared,
variance
score,
mean
absolute
error,
root
squared
error.
Among
models,
Random
Forest
Regression
achieved
highest
accuracy,
delivering
approximately
99%
reliability
prediction.
integration
underscores
potential
performance
enhance
efficiency.
size,
isolation,
exceptional
efficiency,
proposed
is
promising
candidate
applications,
offering
innovative
solutions
next-generation
communication.
Language: Английский
Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications
Md Ashraful Haque,
No information about this author
Kamal Hossain Nahin,
No information about this author
Jamal Hossain Nirob
No information about this author
et al.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: March 5, 2025
Language: Английский
Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches
Kamal Hossain Nahin,
No information about this author
Jamal Hossain Nirob,
No information about this author
Akil Ahmad Taki
No information about this author
et al.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 4, 2025
This
paper
introduces
the
design
and
exploration
of
a
compact,
high-performance
multiple-input
multiple-output
(MIMO)
antenna
for
6G
applications
operating
in
terahertz
(THz)
frequency
range.
Leveraging
meta
learner-based
stacked
generalization
ensemble
strategy,
this
study
integrates
classical
machine
learning
techniques
with
an
optimized
multi-feature
to
predict
properties
greater
accuracy.
Specifically,
neural
network
is
applied
as
base
learner
predicting
parameters,
resulting
increased
predictive
performance,
achieving
R²,
EVS,
MSE,
RMSE,
MAE
values
0.96,
0.998,
0.00842,
0.00453,
0.00999,
respectively.
Utilizing
regression-based
learning,
parameters
are
attain
dual-band
resonance
bandwidths
3.34
THz
1
across
two
bands,
ensuring
robust
data
throughput
communication
stability.
The
antenna,
designed
dimensions
70
×
280
μm²,
demonstrates
maximum
gain
15.82
dB,
excellent
isolation
exceeding
−
32.9
remarkable
efficiency
99.8%,
underscoring
its
suitability
high-density,
high-speed
environments.
methodology
CST
simulations
RLC
equivalent
circuit
model,
substantiated
by
ADS
simulations,
comparable
reflection
coefficients
validating
accuracy
models.
With
compact
footprint,
broad
bandwidth,
efficiency,
proposed
MIMO
positioned
ideal
candidate
future
applications.
Language: Английский
Performance Improvement of THz MIMO Antenna with Graphene and Prediction Bandwidth Through Machine Learning Analysis for 6G Application.
Md Ashraful Haque,
No information about this author
Redwan A. Ananta,
No information about this author
Jamal Hossain Nirob
No information about this author
et al.
Results in Engineering,
Journal Year:
2024,
Volume and Issue:
unknown, P. 103216 - 103216
Published: Oct. 1, 2024
Language: Английский
Regression Supervised Model Techniques THz MIMO Antenna for 6G Wireless Communication and IoT Application with Isolation prediction
Md Ashraful Haque,
No information about this author
Jamal Hossain Nirob,
No information about this author
Kamal Hossain Nahin
No information about this author
et al.
Results in Engineering,
Journal Year:
2024,
Volume and Issue:
unknown, P. 103507 - 103507
Published: Nov. 1, 2024
Language: Английский
Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications
Md Ashraful Haque,
No information about this author
Kamal Hossain Nahin,
No information about this author
Jamal Hossain Nirob
No information about this author
et al.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Dec. 31, 2024
This
study
discusses
the
results
of
using
a
regression
machine
learning
technique
to
improve
performance
6G
applications
that
use
multiple-input
multiple-output
(MIMO)
antennas
operating
at
terahertz
(THz)
frequency
band.
research
evaluates
an
antenna's
various
methodologies,
such
as
simulation
and
RLC
equivalent
circuit
models.
The
suggested
design
has
broad
bandwidth
2.5
THz
spans
from
6.2
8.7
GHz,
maximum
gain
14.59
dB,
small
dimensions
(100
×
300)
µm
Language: Английский
Meta Learner-Based Optimization for Antenna Efficiency Prediction and High-Performance Thz Mimo Antenna Applications
Md Ashraful Haque,
No information about this author
Md. Kawsar Ahmed,
No information about this author
Kamal Hossain Nahin
No information about this author
et al.
Published: Jan. 1, 2025
A
number
of
classical
machine
learning
approaches
have
been
used
to
predict
antenna
efficiency.
However,
needs
be
enhanced
more
accurately.
The
stacked
generalization
approach
has
shown
capable
from
features
and
meta
features.
In
this
paper,
we
propose
a
learner-based
ensemble
strategy
that
passes
output
an
optimized
multi-feature
ensemble.
For
the
optimizer,
grid
search
is
employed.
Applying
ANN
model
with
ML
as
base
learner
for
predicting
efficiency
leads
increased
performance
in
terms
R2,
EVS,
MAE,
RMSE
0.9998,
0.0001,
respectively,
MSE
tending
zero.
This
improved
accuracy
significantly
aids
designing
our
THz
MIMO
antenna,
which
resonates
at
frequencies
5.093
8.229
extensive
bandwidth
5.2
THz.
configuration,
utilizing
graphene
patch
copper
ground,
achieves
impressive
isolation
-29.6
dB,
gain
12.64
91.5%.
exhibits
exceptional
diversity
performance,
Envelope
Correlation
Coefficient
(ECC)
low
0.0007
Diversity
Gain
(DG)
9.994.
Furthermore,
RLC
circuit
precisely
replicates
reflection
coefficients
affirming
reliability
predictive
models.
These
results
suggest
prediction
high-performance
design
are
well-suited
advancing
next-generation
Language: Английский
Meander Line Loaded 2- Port Stepped Impedance Feed Slotted MIMO Antenna For C and Lower X-Band Communication
Physica Scripta,
Journal Year:
2024,
Volume and Issue:
99(12), P. 125540 - 125540
Published: Nov. 8, 2024
Abstract
In
this
communication,
a
wideband
2-Port
Multiple-Input-Multiple-Output
(MIMO)
antenna
of
the
dimension
0.13
λ
0
×
0.68
0.02
mm
3
(λ
,
measured
at
lower
frequency
is
4.43
GHz)
investigated
for
C-band
and
Partial
X-Band
Applications.
The
concept
defective
ground
introduced
to
enhance
impedance
bandwidth
by
engraving
rectangular
slots.
proposed
configuration
mender
line
incorporated
as
decoupling
network
between
elements
isolation
(>18
dB).
quarter
wavelength
long
stepped
matched
transmission
used
excite
configurations.
(4.43–9.85
GHz)/
83%.
To
explore
diversity
MIMO
system
ECC
<
0.04,
DG
>
9.8
dB,
CCL<
0.3
bps
Hz
−1
are
reported.
Excellent
agreement
simulation
results
obtained,
justifying
antenna’s
applicability
wireless
applications.
Language: Английский