International Journal of Antennas and Propagation,
Journal Year:
2024,
Volume and Issue:
2024(1)
Published: Jan. 1, 2024
Reconfigurable
antennas
(RAs)
are
the
key
component
in
modern
wireless
communication
applications
as
these
have
ability
of
multifunctional
and
altered
radiating
properties
form
radiation
patterns
polarizations.
The
need
for
multiple
a
single
device
is
becoming
more
prominent
days
due
to
increasing
user
demands
daily
life.
Consequently,
cover
maximum
space
devices;
therefore,
mitigate
this
problem,
RAs
best
solution.
This
article
discusses
various
types
RA
along
with
reconfigurable
methods
detail.
These
include
frequency,
pattern,
polarization,
compound
RAs,
electrical
(with
switches),
mechanical,
optical,
material
change.
Based
on
requirement
application,
proper
been
chosen
by
authors
achieve
reconfigurability
antennas.
find
sectors
including
terrestrial
satellite
communications,
vehicular
its
movement
rural
urban
areas,
cognitive
radio.
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.
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.
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
