Entropy,
Journal Year:
2024,
Volume and Issue:
26(2), P. 102 - 102
Published: Jan. 24, 2024
In
recent
years,
semantic
communication
has
received
significant
attention
from
both
academia
and
industry,
driven
by
the
growing
demands
for
ultra-low
latency
high-throughput
capabilities
in
emerging
intelligent
services.
Nonetheless,
a
comprehensive
effective
theoretical
framework
yet
to
be
established.
particular,
finding
fundamental
limits
of
communication,
exploring
semantic-aware
networks,
or
utilizing
guidance
deep
learning
are
very
important
still
unresolved
issues.
general,
mathematical
theory
representation
semantics
referred
as
information
theory.
this
paper,
we
introduce
pertinent
advancements
Grounded
foundational
work
Claude
Shannon,
present
latest
developments
entropy,
rate-distortion,
channel
capacity.
Additionally,
analyze
some
open
problems
measurement
coding,
providing
basis
design
system.
Furthermore,
carefully
review
several
theories
tools
evaluate
their
applicability
context
communication.
Finally,
shed
light
on
challenges
encountered
IEEE Communications Magazine,
Journal Year:
2024,
Volume and Issue:
62(9), P. 122 - 127
Published: Sept. 1, 2024
To
support
the
development
of
Internet
Things
(IoT)
applications,
an
enormous
population
low-power
devices
are
expected
to
be
incorporated
in
wireless
networks
performing
sensing
and
communication
tasks.
As
a
key
technology
for
improving
data
collection
efficiency,
integrated
sensing,
(ISAC)
enables
simultaneous
transmission
radar
by
reusing
same
radio
signals.
In
addition
information
carriers,
signals
can
also
serve
as
energy
delivery,
which
power
transfer
(SWIPT).
improve
spectrum
advantages
ISAC
SWIPT
exploited,
leading
emerging
integrating
communication,
(ISCPT).
this
article,
timely
overview
ISCPT
is
provided
with
description
fundamentals,
characterization
theoretical
boundary,
discussion
on
technologies,
demonstration
implementation
platform.
IEEE Transactions on Wireless Communications,
Journal Year:
2023,
Volume and Issue:
23(2), P. 1327 - 1342
Published: June 27, 2023
Vertical
federated
learning
(FL)
is
a
collaborative
machine
framework
that
enables
devices
to
learn
global
model
from
the
feature-partition
datasets
without
sharing
local
raw
data.
However,
as
number
of
intermediate
outputs
proportional
training
samples,
it
critical
develop
communication-efficient
techniques
for
wireless
vertical
FL
support
high-dimensional
aggregation
with
full
device
participation.
In
this
paper,
we
propose
novel
cloud
radio
access
network
(Cloud-RAN)
based
system
enable
fast
and
accurate
by
leveraging
over-the-air
computation
(AirComp)
alleviating
communication
straggler
issue
cooperative
among
geographically
distributed
edge
servers.
error
caused
AirComp
quantization
errors
limited
fronthaul
capacity
degrade
performance
FL.
To
address
these
issues,
characterize
convergence
behavior
algorithm
considering
both
uplink
downlink
transmissions.
improve
performance,
establish
optimization
joint
transceiver
design,
which
successive
convex
approximation
alternate
search
algorithms
are
developed.
We
conduct
extensive
simulations
demonstrate
effectiveness
proposed
architecture
IEEE Transactions on Wireless Communications,
Journal Year:
2024,
Volume and Issue:
23(8), P. 8232 - 8247
Published: Jan. 4, 2024
Federated
learning
(FL)
enables
edge
devices
to
collaboratively
train
machine
models,
with
model
communication
replacing
direct
data
uploading.
While
over-the-air
aggregation
improves
efficiency,
uploading
models
an
server
over
wireless
networks
can
pose
privacy
risks.
Differential
(DP)
is
a
widely
used
quantitative
technique
measure
statistical
in
FL.
Previous
research
has
focused
on
FL
single-antenna
server,
leveraging
noise
enhance
user-level
DP.
This
approach
achieves
the
so-called
"free
DP"
by
controlling
transmit
power
rather
than
introducing
additional
DP-preserving
mechanisms
at
devices,
such
as
adding
artificial
noise.
In
this
paper,
we
study
differentially
private
multiple-input
multiple-output
(MIMO)
fading
channel.
We
show
that
multiple-antenna
amplifies
leakage
when
employs
separate
receive
combining
for
and
information
inference.
Consequently,
relying
solely
noise,
done
single-output
system,
cannot
meet
high
requirements,
device-side
privacy-preserving
mechanism
necessary
optimal
DP
design.
analyze
convergence
loss
of
studied
system
propose
transceiver
design
algorithm
based
alternating
optimization.
Numerical
results
demonstrate
proposed
method
better
privacy-learning
trade-off
compared
prior
work.
Entropy,
Journal Year:
2024,
Volume and Issue:
26(2), P. 102 - 102
Published: Jan. 24, 2024
In
recent
years,
semantic
communication
has
received
significant
attention
from
both
academia
and
industry,
driven
by
the
growing
demands
for
ultra-low
latency
high-throughput
capabilities
in
emerging
intelligent
services.
Nonetheless,
a
comprehensive
effective
theoretical
framework
yet
to
be
established.
particular,
finding
fundamental
limits
of
communication,
exploring
semantic-aware
networks,
or
utilizing
guidance
deep
learning
are
very
important
still
unresolved
issues.
general,
mathematical
theory
representation
semantics
referred
as
information
theory.
this
paper,
we
introduce
pertinent
advancements
Grounded
foundational
work
Claude
Shannon,
present
latest
developments
entropy,
rate-distortion,
channel
capacity.
Additionally,
analyze
some
open
problems
measurement
coding,
providing
basis
design
system.
Furthermore,
carefully
review
several
theories
tools
evaluate
their
applicability
context
communication.
Finally,
shed
light
on
challenges
encountered
