Cooperative Multiple Input Multiple

foamyflumpMobile - Wireless

Nov 21, 2013 (3 years and 4 months ago)

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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.

ANY QUESTION