Reporter ：林煜星 Advisor ： Prof. Y.M. Huang - PowerPoint PPT Presentation

Iterative Joint Source-Channel Soft-Decision Sequential Decoding Algorithms for Parallel Concatenated Variable Length Code and Convolutional Code

Reporter：林煜星

Advisor：Prof. Y.M. Huang

• Introduction
• Related Research
• Transmission Model for BCJR
• Simulation for BCJR Algorithm
• Proposed Methodology
• Transmission Model for Sequential
• Simulation for Soft-Decision Sequential Algorithm
• Conclusion

Discrete

source

Source

Encoder

Channel

Encoder

Modulator

錯誤更正碼

資料壓縮

User

Source

Decoder

Joint Decoder

Channel

Decoder

Demodulator

Channel

• [1]L. Guivarch, J.C. Carlach and P. Siohan
• [2]M. Jeanne, J.C. Carlach, P. Siohan and L.Guivarch
• [3]M. Jeanne, J.C. Carlach, Pierre Siohan

Huffman Coding

Turbo decoding

Utilization

of the SUBMAP

Independent

Source

or first order

Markov Source

Turbo Coding

parallel

concatenation

P symbols

K bits

a priori

Additive White

Gaussian Noise

Channel

Huffman

Decoding

K bits

P symbols

Huffman Coding

Turbo decoding

Utilization

of the SUBMAP

Independent

Source

or first order

Markov Source

Turbo Coding

parallel

concatenation

P symbols

K bits

a priori

Additive White

Gaussian Noise

Channel

Huffman

Decoding

K bits

P symbols

Example:

Huffman Coding

Turbo decoding

Utilization

of the SUBMAP

Independent

Source

or first order

Markov Source

Turbo Coding

parallel

concatenation

P symbols

K bits

a priori

Additive White

Gaussian Noise

Channel

Huffman

Decoding

K bits

P symbols

Huffman Coding

Turbo decoding

Utilization

of the SUBMAP

Independent

Source

or first order

Markov Source

Turbo Coding

parallel

concatenation

P symbols

K bits

a priori

Additive White

Gaussian Noise

Channel

Huffman

Decoding

K bits

P symbols

u

d = (11101)

v

Non Systematic

Convolution code

(0,3)

(1,3)

(2,3)

(3,3)

(4,3)

(5,3)

1

0

10

01

(0,1)

(1,1)

(2,1)

(3,1)

(4,1)

(5,1)

01

(0,2)

(1,2)

(2,2)

(3,2)

(4,2)

(5,2)

00

(0,0)

(1,0)

(2,0)

(3,0)

(5,0)

(4,0)

11

d = (11101)

Recursive Systematic Convolution(RSC)

Rate=1/2

Rate=1/4

Interleaver

Turbo Code rate1/3

Rate=1/4

Turbo Code rate=1/2

Huffman Coding

Turbo decoding

Utilization

of the SUBMAP

Independent

Source

or first order

Markov Source

Turbo Coding

parallel

concatenation

P symbols

K bits

a priori

Additive White

Gaussian Noise

Channel

Huffman

Decoding

K bits

P symbols

Huffman Coding

Turbo decoding

Utilization

of the SUBMAP

Independent

Source

or first order

Markov Source

Turbo Coding

parallel

concatenation

P symbols

K bits

a priori

Additive White

Gaussian Noise

Channel

Huffman

Decoding

K bits

P symbols

priori

BCJR2

BCJR1

MAP Decoder

Define

Logarithm of Likelihood Ratio(LLR)

Recall

10

10

(4,3)

(5,3)

1

0

(4,1)

(5,1)

01

01

(5,2)

00

00

11

11

(2,3)

(3,3)

(0,0)

(5,0)

priori

BCJR2

BCJR1

• The end of the transmission occurs when either the maximum bit error number fixed to 1000, or the maximum transmitted bits equal to 10 000 000 is reached.
• Input date into blocks of 4096 bits

1NP：1次iteration

independent source

No Use a priori probability

1NP：1次iteration

independent source

Use a priori probability

1NP：

1次iteration

Markov Source

No use a proiri probability

1MP：

1次iteration

Markov Source

Use Markvo a priori

probability

12D：

1次iteration

Independent Source

Use a priori probability

Bit time(level)、Convolution

state

13D：

1次iteration

Independent Source

Use a priori probability

Bit time(level)、tree state

Convolution state

• [4]Catherine Lamy, Lisa Perros-Meilhac

priori

BCJR2

Sequential

1 : if

Code word bits

0 : Otherwise

Example:

r=(-1, 3,2,1,-2,-1,-3,-1,1,2)

4

y=(1,0,0,0,1,1,1,1,0,0)

(4,3)

3

4

1

2

01

(1,1)

(2,1)

(3,1)

(3,1)

(5,1)

10

2

2

(5,1)

0

00

(4,2)

(4,2)

(0,0)

3

(4,2)

(1,1)

(4,2)

1

1

11

4

5

4

(3,1)

(1,1)

(4,3)

(3,1)

11

11

11

(1,0)

(0,0)

(1,0)

(2,0)

(2,0)

(3,0)

(5,0)

00

00

00

4

(2,0)

(3,0)

(3,0)

(4,3)

(2,0)

y=(00)

y=(00)

y=(10)

y=(11)

y=(11)

4

(1,1)

(1,0)

(1,0)

(3,0)

(2,1)

|r|=(12)

|r|=(21)

|r|=(13)

|r|=(31)

|r|=(21)

5

(2,1)

(1,1)

(2,1)

(5,0)

(0,0)

Origin node

(0,0)

Open

Close

2D1：

1次iteration

Independent Source

Use a priori probability

Bit time(level)、Convolution

state

3D1：

1次iteration

Independent Source

Use a priori probability

Bit time(level)、Convolution

state、tree state

• Heuristic方法求Sequential Decoder Soft-Output value運用在Iterative解碼架構，雖然使錯誤降低，節省運算時間，但解碼效果無法接近Tubro Decoder的解碼效果，為來將繼續研究更佳的方法求Sequential Decoder Soft-Output value使解碼效果更逼近Turbo Decoder的解碼效果