applications of statistical physics to coding theory nonlinear dynamics of iterative decoding...

Applications of Statistical Physics to Coding Theory

Nonlinear Dynamics of Iterative Decoding Systems:

Analysis and Applications

Ljupco Kocarev University of California, San Diego


Summary of the presentation

• Nonlinear Dynamics of the iterative coding systems

• Nonlinear Codes with Latin Squares (Quasi-groups) • Conclusions


When: since 2002

Who: Alex Vardy (UCSD) Gian Mario Maggio (ST, Swiss) Zarko Tasev (Kyocera, USA) Frederic Lehmann (INT, Paris, France) Pater Popovski (Tech University, Denmark) Bartolo Scanavino (Poli Torino, Italy)

Why:

1. To understand finite-length iterative decoding algorithms using tools from nonlinear systems and chaos theory; and

2. To exploit chaos theory for enhancing existing iterative coding techniques and invent new classes of error-correcting codes.

Sponsors: ARO (MURI), STMicroelectronics, University of California (DIMI)


Understanding finite-length iterative coding systems

• Codes on graphs as spin models (1989)

• Finite length scaling for iterative coding systems (2004) • Renormalization group approach for iterative coding systems (2003)

• Scale-free networks and error-correction code (2004)

• Nonlinear dynamics of iterative coding systems (2000)

As of today asymptotic behavior (as the block-length tends to infinity) of iterative coding systems is reasonably well understood

Tom Richardson: The geometry of turbo decoding dynamics (2000)


Kalman’s example

R. E. Kalman, 1956 “Nonlinear aspects of sampled-data control systems”

)(1 nn xfx

2ln7

23ln

7

3

xn+1

xn1/3 2/3

1

1/3

10

0 1

S

1

0

S

00 01 Markov process with transition probabilities:

0 11/3 1/2

2/3

1/2

Logistic map: xn+1 = a xn ( 1 – xn ) a = 3.839


Turbo codes

),,(codeword 210 sss

}1,1{ 41234 DDDDD

n=1024

Classical turbo codes: C. Berrou, A Glavieux, and P. Thitimajshima, “Near Shannon limit error-correcting coding and decoding: Turbo-Codes,”Proc. IEEE International Communications Conference, pp. 1064-70, 1993


Turbo-decoding algorithm

],),([)(

],),([)1(20

122

10211

cclXFlX

cclXFlX

),...,( 1 inii XXX

),...,( 1j

njj ccc

),...,( 1 inii FFF

BCJR algorithm, 1974

AWGN channel

X1, X2 - extrinsic information exchanged by the two SISO decoders

c0, c1, c2 - channel outputs corresponding to the input sequences


],),([)(

],),([)1(20

122

10211

cclXFlX

cclXFlX

kk

iik

iik

k Xc

g

fF n

n

20

22

1

2

11

4log

)(ln)()(ln)(1

)( 11

1

00 lplplplpn

lE ii

n

iii

The system depends smoothly on its 2n variables X1, X2 and 3n parameters c0, c1, c2

(Richardson)

E represents a posteriori average entropy

Three types of plots: E(l) versus l, E(l+1) versus E(l), and E versus SNR.


Bifurcation diagram

)(ln)()(ln)(1

)( 11

1

00 lplplplpn

lE ii

n

iii


Discrete-time Hopf bifurcation (part I)

-6.7dB -6.5dB

-5.9dB

-6.3dB -6.1dB

0.75dB 0.8dB 0.85dB


Discrete-time Hopf bifurcation (part II)

-6.7dB -6.6dB -6.5dB

-6.3dB -6.2dB -6.1dB


Example: 2D map

)1(1

1

nnn

nn

xayy

yx

•Attracting fixed point •Attracting invariant curve•Chaotic attractor

a = 1.9, 2.1, 2.16, 2.27


Tangent bifurcation (part I)

-7.65dB -7.645dB -7.6dB

0.3dB 0.35dB 0.4dB


Tangent bifurcation (part II)

-7.65dB -7.65dB

-7.64dB -7.64dB


One-dimensional approximation


Lyapunov exponents


Transient chaos

• The unequivocal fixed point becomes stable around –1.5dB. • Region 0.25dB to 1.25dB (waterfall region): transient chaos • Average chaotic transient lifetime for SNR=0.8dB is 378 iteration


Transient chaos

For each SNR, we generate 1000 different noise realizations(1000 different parameters frames) and compute the number ofdecoding trajectories that approach the fixed point in less than a given number of iterations.

At SNR of 0.6dB, there are 492 frames that converge to the unequivocal fixed point in 5 or less iterations, another 226 frames converge in 10 or less iterations, and so on, while 58 frames remain chaotic after 2000 iterations (which means that their trajectory either approaches a chaotic attractor or that the transient chaos lifetime is very large).


Stability of the fixed point


LDPC codes

Torus-breakdown route to chaos forthe regular (216,3,6) LDPC code

The values of SNR corresponding to the invariant sets, starting from the fixed point towards chaos, are: 1.19 dB, 1.23 dB, 1.27 dB, 1.33 dB, and 1.44 dB


Control of transient chaos

|}|exp{)( iii XXg

)01.09.0(

Average chaotic transient lifetime for SNR=0.8dB is 9 iterations.


Control of transient chaos: results


Conclusions

• Iterative decoding algorithms can be viewed as high-dimensional dynamical systems parameterized by a large number of parameters. However, although the iterative-decoding algorithm is a high-dimensional dynamical system, it apparently has only a few active variables.

• The waterfall region for all iterative coding systems exhibits reach dynamical behavior: chaotic attractors, transient chaos, multiple attractors and fractal basin boundaries.

• Applications:

1. We have proposed a simple adaptive technique to control transient chaos in the turbo decoding algorithm. This results in a ultra-fast convergence and a significant gain in terms of BER performance.

2. We have proposed a novel stopping criterion for turbo codes based on the average entropy of an information block. This is shown to reduce the average number of iterations and to benefit from the use of the adaptive control technique.


Open problems:

• Study how the size of the basin of attraction changes with varying SNR. • Find how the value of SNR for which the fixed point at origin becomes a stable

point related to the threshold which gives the boundary of the error-free region.

• Study whether the chaotic behavior of the iterative coding systems comes from the presence of cycles in the graph.

• Investigate how the topological structure of unstable periodic orbits embedded into the chaotic set is related to the topological structure of the factor graph.

• Use ergodic theory for dynamical systems to study the properties related to a set of trajectories, not a single trajectory.

• Find models of low-dimensional dynamical systems averaged over ensembles and/or different noise realizations.


Latin Squares (Quasigroups)

A Latin square of order N is NxN array of numbers from N-symbol alphabet (say 0, 1, 2, … N-1) in which each row and each column contains each symbol exactly once.

Latin squares are also linked to algebraic objects known as quasigroups. A quasigroup is defined in terms of a set, Q, of distinct symbols and a binary operation (called multiplication) involving only the elements of Q. A quasigroup's multiplication table turns out to be a Latin square.

D. Gligoroski, S. Markovski (since Sep 2004)


Given a quasigroup (Q,*) five new operations, so called parastrophes or adjoint operations, can be derived from the operation *.

We need only the following one defined by:

(Q,\) is also a quasigroup.


Quasigroup string transformations

e-transformation

d-transformation


Let Q+ be the set of all nonempty words (i.e. finite strings) formed by the elements of Q.


Nonlinear Codes with Latin Squares (Quasigroups)

(Q,*) Q = {0, 1, 2, … N-1}

M1, M2, …, Mr information string; each Mi is an element of Q

L1, L2, …, Lm m> r Each Li is either Mi or 0

C1, C2, …, Cm codeword


k1, k2, …, kn leaders each ki is an element of Q


(Q,\) is the left parastrophe of the quasigroup (Q,*)


Properties of the Code

• Nonlinear code

• “Almost random” code

• C1, C2, ?, C3, …, Cm L1, L2, ?, ?, …., ? (synchronous and self - synchronized stream ciphers)


Example

(Q,*) Q = {0, 1, 2, … 15} Quasigroup of order 16

Each Mi is a nibble (4-bit string)


(72,288) code with rate ¼ r = 18, m = 72


Decoding the code

BSC

Di is a nibble and D(i) is a sub-block of 4 nibbles (16 bits)


If the set Ss contains only one element we say the decoding is successful


(72,288) code with rate ¼ r = 18, m = 72

Bmax = 3


Stability of the decoding algorithm

Theorem: If the decoding is successful, then the message is recovered with probability 2-N(1-R)

Conjecture: If

then the decoding procedure converges and the cardinality of Ss is 1.

The relative Gilbert-Varshamov (GV) distance


Example

• Reed – Muller code (6,32) rate 3/16 corrects up to 7 errors

• Total number of noisy code-words the codes can correct is 4.5 x 106

N = 32 Bmax = 8 (B2 = 8)

m1 m2 02 02 02 02 02 02 m3 02 02 02 02 02 02 02

Bmax = 5 68852 ~ 4.7 107

(6,32) rate 3/16


Conclusions (open problems)

• Theoretical framework

• AWGN channel, soft decoding

• Concatenated codes: Si is intersection of two sets Si(1) and Si

(2)

applications of statistical physics to coding theory nonlinear dynamics of iterative decoding...

Documents