X1X1 X2X2 Encoding : Bits are transmitting over 2 different independent channels. Rn bits...
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![Page 1: X1X1 X2X2 Encoding : Bits are transmitting over 2 different independent channels. Rn bits Correlation channel (1-R)n bits Wireless channel Code Design:](https://reader037.fdocuments.in/reader037/viewer/2022110405/56649ed25503460f94be1837/html5/thumbnails/1.jpg)
X1 X2
Encoding:
Bits are transmitting over 2 different independent channels. Rn bits Correlation channel (1-R)n bits Wireless channel
Code Design:
Possible Design Methodologies:
1)Design an LDPC code for the equivalent channel
2)Design a non-uniform LDPC code
Use ensemble of bipartite graphs, where, is the variable node degree distribution of each set and is the check node degree distribution.
),( g )}(),({ 21 xx )(xi )(x
Simulation:
0.
H(X2|X1)
ber
P=0.11 RX2=H(p)=0.5
LDPC rate=2/3, n=1000
}0993.01422.07585.0,{)( 8432 xxxxx }5.0,5.0{)( 1110 xxx
Extensions of Distributed Source coding of correlated sources
Mina Sartipi, Nazanin Rahnavard, Faramarz Fekri
Abstract
Energy-Efficient Data Gathering and Broadcasting in Sensor Networks using Channel Codes
Goal: Energy-efficient and reliable communication in wireless sensor networks
Communication involves: Data Gathering (Sensors to sink)
Multicasting / Broadcasting (Sink to sensors)
Data Gathering: Correlated Data
Distributed Source Coding
Multicasting / Broadcasting Redundant Transmission Correlated Data
Rateless Code
RX1
RX2
A
B
C
H(X2|X1)
H(X2)
H(X1|X2) H(X1)
+
Corner Point:RX1 = H(X1)
RX2 = H(X2 | X1)
Encode X2 as follows:
X2 is fed into a rate R systematic LDPC encoder.
PX2 , the corresponding parity bits, is sent through the wireless channel.
RX2=1/R-1 bit per input bit.
RX1 H(X1|X2)
RX2 H(X2|X1)
RX1 +RX2 H(X1,X2)
Correlation Model:
Distributed Source Coding on Corner Points:
X1, X2 : I.I.D binary sequence; Prob [ Xi =0] = Prob [ Xi=1]=1/2.
Prob [ X1 X2 | X1 ]=p
BSCp
Slepian-Wolf rate region for two sources:
Distributed Source coding of correlated sources using LDPC Codes
Motivation:
Distributed Source Coding:
Many sensors have highly correlated data that is slowly varying.
How do we exploit correlation structure with low-power algorithms?
X2 22 XX ˆEncoder Decoder
X1
Goal: Compressing X2
With the knowledge that X1 is present at the decoder
Without communicating with X1
c1
(X2 ,PX2 )
k
(1-R)n
Decoder P'X2
PX2
X2
Channel
X1
Encoder
X2
CorrelationChannel
Wireless
n
Systematic
Channel Rate R
Rn
c2
X2
Non-uniform Channels
Modeling Distributed Source Coding with Parallel Channels:
method 2 outperforms method 1
Scaling to more than two correlated sources DSC at arbitrary rate on Slepian-Wolf rate region DSC with unknwon correlation parameter
Future activity: Energy-efficient broadcasting
Motivation An easy, energy-efficient, and scalable broadcasting scheme Providing reliability with little penalty Low complexity Require no optimization and no topology information
Proposed Approach Use an efficient erasure coding (rateless coding) to recover for losses
Channel parameters are different and unknown A source can generate potentially infinite supply of encoding packets from the original data Any receiver collects as many packets as it needs to complete the decoding Receivers are at one hop distance from the sender Extra cares needed for multi-hop wireless networks!
BEC (2)
Rec 1
Rec 2
Rec i
Rateless coding
0
0
1
1
BEC (1)
BEC (i)0
Future workFuture work
Rateless (Fountain) CodesRateless (Fountain) Codes
Distributed source coding Implement the algorithm on testebed to evaluate the real energy saving benefits (considering the power usage for encoding/decoding) Study the extensions of DSC
Multicasting / Broadcasting: Propose an energy-efficient method for broadcasting / multicasting Apply distributed source coding to eliminate redundancy Need route optimization while having load balancing