Cooperative Multiple Input Multiple
Output Communication in Wireless
Sensor Network: An Error Correcting
Code approach using LDPC Code
Goutham Kumar Kandukuri
Introduction
•
Energy efficient data transmission is one of the key
factors for
energy
constrained wireless sensor network (WSN
).
•
Wireless Sensor Nodes are developed to enable technology advancement in
WSN.
•
Here the battery capacity of each node is limited, we try to maximize the
lifetime of the network, by following some energy consumption
constraints.
•
LDPC codes are more reliable than the block and conventional codes.
•
Cooperative communication is compared with SISO communication
considering LDPC code as an error correcting code.
Wireless Sensor Network
•
C
onsists of spatially distributed autonomous Sensor to
monitor
physical or
environmental conditions, to cooperatively pass their data through the
network to a main location.
•
Wireless
sensor networks consist of a large number of
tiny sensors
that
have only limited energy supply
.
•
Maintain long
network lifetime as well as sufficient sensing area
.
MIMO(Multiple Input Multiple Output)
•
A
signal is transmitted from one terminal to multiple users in same
bandwidth Simultaneously.
•
y =
Hx
+
n
y

Receive Vector
x

Transmit Vector
H

Channel Matrix
n

Noise
Vector
•
MIMO can be divided into 3 main categories they are
1)
Precoding
2) Spatial
Multiplexing
3) Diversity Coding
System Model
•
The system model is a centralized wireless sensor
network,
where there is a
data
gathering node
(DGN) and several clusters with several sensors
in
each
cluster.
•
Sensors in one cluster transmit the
data to
the sensors in adjacent cluster
and in step by
step the
data reach the DGN
.
•
The system considers N number of sensors in one
cluster and
the
transmitted antennas are each placed at a sensor
.
•
In this model
, a sensor with high
residual energy
is deployed as a cluster
head and it remains
the cluster
head until the network dies. The cluster
head
broadcasts
its status to the other sensors in the network
.
•
Each sensor node determines to which cluster it
wants to
belong
by
choosing
the cluster head that requires
the minimum
communication
energy.
System Model
Low Density Parity Check
Codes(LDPC)
•
LDPC codes are specified by a matrix containing mostly 0’s & relatively
few 1‘s.
•
LDPC codes are decoded by means of iterative belief propagation using the
Sum

Product (SP) algorithm.
•
The code length is designed by
n,
& Number of constraints by
m
.
•
Which gives
n
variable nodes and
m
check nodes.
•
Edges in the graph connect the variable nodes
inorder
to check nodes and
then represents the nonzero entries in H matrix.
•
The term “
low density
” conveys the fact that the fraction of nonzero entries
in H is small, in particular it is linear in block length
n
, compared to
random linear codes.(expected fraction
n^2
).
Richardson Scheme as the encoding
Technique
•
H
can
be converted to an
approximate lower
triangular
matrix
•
Considering m
x
n parity check matrix H over F,
n
–
number of variable nodes
m
–
number of check nodes
Parity check matrix H is transformed in the form of
where
A
is (
m − g
)
×
(
n − m
)
B
is (
m − g
)
×
g
,
T
is (
m
− g
)
×
(
m− g
)
C
is
g
×
(
n −m
) D
is
g
×
g
, and
E is
g
×
(
m − g
)
g is gap
T
is lower
triangular with
ones along the diagonal
Richardson Scheme as the encoding
Technique
•
This matrix is multiplied left by
And H Matrix is found as
•
The code word is broken as
x
= (
s, p
1
, p
2
)
s
–
systematic part p1,p2
–
parity part
p1 has length g
p2 has length (m

g)
Richardson Scheme as the encoding
Technique
This equation used to follow two equations.
•
Taking
as non singular, it is included that
•
And using step by step procedure, it is shown that complexity of
calculating
p1 is
p2 is
Energy Model
•
PT = PPA +
PC
PT

Total
power
consumption
PPA

Power amplifiers
PC

P
ower consumption of
all
other circuit blocks
•
PPA
= (1+
α
)
Pout
α
= (
ξ
/
η
−
1
),
η

drain
efficiency,
ξ

peak to average
ratio
•
Nf
=
Nr/N
0
When
the
channel only
experiences a
kth
power path
loss
.

average energy per
bit
Rb
is the
transmission bit
rate
Simulation Results & Discussion
Total Energy consumption over
Distance
Energy Efficiency Over Distance
Simulation Results & Discussion
Conclusion
•
Energy efficient data transmission is one of the
key factors
for energy
constraint wireless sensor network
.
•
The
energy efficiency
remains almost unchanged in different
encoding
rates.
•
Data with smaller encoding rate
shows
better BER results than larger
encoding rate for a
fixed SNR
•
The results show that the
cooperative communication
outperforms SISO
transmission at
the presence of error correction code.
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