1. www.huawei.com
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HUAWEI TECHNOLOGIES CO., LTD.
Cycle’s topological optimizations and
the iterative decoding problem
on general graphs
Author/ Email: Usatyuk Vasiliy, Usatyuk.Vasily@huawei.com
Coding Competence Center, Moscow Research Center

2. Statistical inference problem:
Computing Marginal Probabilities
• Decoding error-correcting codes;
• Inference in Bayesian networks;
• Machine Learning;
• Statistical physics of magnets…

SXX
SS XpXp
)()(
Fundamental for
General approach have exponential complexity
because of the huge number of terms in the sum.

3. Probabilistic graphical model under
Factor Graphs without cycles:







101
011
H
321 XXX
2f
1f
1X
2X
3X
1f
2f
),(),(),,( 312211321 XXfXXfXXXf 
 



2 3
2 31
),(),(
),(),(),,()(
312211
312211
32111
X X
X XXX
XXfXXf
XXfXXfXXXfXp

5. Soft decoding algorithms
Wireless channels
Optical channels
Consider MAP under codeword (Block-wise) – Maximum likelihood regularization using AP
data(LLRs) to estimate probability of frame error rate. Bit error rate require linear model and
we consider at second part of presentation.
LLRs }1,0{}1,0{}1,0{

6. General graph for linear codes
Parity-check matrix of size :
Codes:  TT
cHFcC 0|2 











11100
11010
00111
H
Bipartite graph (Tanner graph):
54321 XXXXX
Direct Problem - Statistical Inference: find the "most likely” transmitted word
using soft a posterior probability log-likelihood rations
 
 ii
ii
i
yc
yc
where
|1Pr
|0Pr
ln



 ,,...,, 110  n
ML problem is NP-hard!
nk 
4)3(5)2(4 XfXfX xorxor Cycle 4:
)3(xorf
)2(xorf
)1(xorf
c
Variable
nodes
Check
nodes

7. We can approximate using Message-passing iterative decoding
algorithm(Belief Propagation, Sum Product algorithm)
All operations become local related
to exchange messages between columns
(variable nodes) and check nodes
without any loops in algorithm.
iX
)(iXORf
ML problem became O(N)-time!
If the factor graph is finite and cycle-free,
then the algorithm is finite (finish after finite
number of iterations) and exact.
If the graph has cycles, then the algorithm
becomes iterative and approximate.
:)(iXORi fX 
:)( iiXOR Xf 

8. Asymptotical properties of graph girth(shortest cycles) and
hamming distance grown from graph cardinally
To have enough error correcting capability (code distance) necessary to have a
lot of short cycles in Tanner graph

9. After decoding iterations Belief Propagation algorithm under the tanner graph with girth
produce wrong decoding result due cycles:
.1
4
 m
g
m
Computation tree of Belief propagation (BP) under graph with cycles :
m g
Tanner QC-LDPC code [155,64,20] computation(Wiberg) tree
after 4 iterations(4 generations) of BP decoder
Circle is variable nodes, black square – check nodes, gray variable nodes show “weak nodes”
which produce uncorrected error under BP which can corrected according code distance
Example of Subgraph
for which "" MAPBP 

10. After decoding iterations Belief Propagation algorithm under the tanner graph with girth
produce wrong decoding result due Trapping sets:
.1
4
 m
g
m
Trapping sets - subgraphs formed by cycles or it’s union:
m g
),( baTrapping set is a sub-graph with a variable nodes and b odd degree checks.
For example, TS(5,3) produced by three 8-cycles;
TS(a,0) is most harmfulness pseudocodeword of weight a formed by cycles 2*a.

11. But what TS sets broke mean from performance point of
view, especially under AWGN with L=7 (iterations)?
Page 11
Joint probability of all VNs in TS in error as a measure of harmfulness of TS
Deka K., Rajesh A., Bora P.K. Comparison of the Detrimental Effects of Trapping Sets in LDPC
Codes

12. To illustrate harmfulness just add TS(8,0)
Page 12
On low SNR region all TS influence on performance much worse than codeword of weigh 8, but
from EB/No 3.1 performance mostly depend from codeword's of weigh 8

13. Linear size TS (‘big cycles’) knowledge can make
prediction of waterfall performance
    *
),( nQnPBLOCK
.
,
*
ensemblecodeofsticscharacteriscalea
errorofyprobabilitchannel
evolutiondensityfromtreshold
parameterscalealengthcoden






Equation work only under expurgated
ensemble, i.e., we are looking at the
subset of graph of the ensemble that
do not contain TS of sizes smaller than
some value.
When n becomes small, probability ->0
(distribution of minimal TS follows
a Poisson distribution)

14. BER and FER performance of the original and modified DVB-S2 QC-
LDPC codes of information length K=43200 and rate 2/3.
TS(9,0)
TS(10,1)
Without TS(9,0).
Without TS(9,0)
And TS(10,1).
Inverse problem – Learning (construct graph) from samples
with restrictions
Sublinear size TS from number of nodes
(‘small cycles’) make error-floor problem

15. Problem related to cycles eliminate:
Based on current cycle optimizations algorithm we can construct structured QC-LDPC codes :
Could it be better cycle broke algorithm?
*Y. Wang, S. C. Draper and J. S. Yedidia, "Hierarchical and High-Girth QC LDPC Codes," in IEEE Transactions on Information Theory, vol. 59, no. 7,
pp. 4553-4583, July 2013.
**M. Diouf, D. Declercq, M. Fossorier, S. Ouya and B. Vasić, "Improved PEG construction of large girth QC-LDPC codes," 2016 9th International
Symposium on Turbo Codes and Iterative Information Processing (ISTC), Brest, 2016, pp. 146-150.
***M.E. O'Sullivan. "Algebraic construction of sparse matrices with large girth". IEEE Trans. In! Theory, vo1.S2, no.2, pp.718-727, Feb. 2006.
Column
number,
L
Our
approach
Hill
Climbin
g*
Improved
QC-PEG**
4 37 39 37
5 61 63 61
6 91 103 91
7 155 160 155
8 215 233 227
9 304 329 323
10 412 439 429
11 545 577 571
12 709 758 -
Minimal value of circulant L for regular mother
matrix with row number m=3 and column
number n with girth 10
Column
number,
L
Our
approach
Improv
ed QC-
PEG**
TableV***
4 73 73 97
5 160 163 239
6 320 369 479
7 614 679 881
8 1060 1291 1493
9 1745 1963 2087
10 2734 - -
11 4083 - -
12 5964 - -
Minimal value of circulant L for regular mother
matrix with row number m=3 and column
number n with girth 12
Does better Poly-time complexity general algorithm for cycles eliminating exist?

16. Problem related to Trapping sets search:
For Trapping sets search we use “nauty” graph algorithms library.
Enumerating of all cycles and it union is NP-hard problem (for example, Hamiltonian Cycle).
Using Cole method* with noise injection we can search TS with a <20-30 variable (under
structured codes, quasi-cyclic):
1. Could we search/eliminate Trapping sets in more efficient way (heuristics, probability
algorithms)?
For example, consider factor graph with cycles as Electrical grid and search bottleneck
using solving system of linear equations.
2. For some structured parity-check matrix (Quasi-Cyclic) with fixed column weight
distribution we can proof some simple equation to eliminate harmful trapping sets.
How it can be generalized for any (practically) regular and irregular codes on the graph?
*Chad A. Cole and etc A General Method for Finding Low Error Rates of LDPC Codes https://arxiv.org/pdf/cs/0605051
timedO dv
c )( variablesofnumberdfactorsofnumberd vc  ,
Zacc ll mod121

hfactorgrapincyclesconsideredofsizea
valuesmautomorphihfactorgrapZ
cyclegeneratewhichshiftcirculantofvaluec il



2

17. To reach theoretical bound>
increase «dense» of graph (make more
correlations between nodes, improve weight
spectrum properties)
and solve trouble of TS:
Screening of TS
by precoding and
puncturing
(ME-LDPC
approach)
Turbo code use it and
It core idea of ME-LDPC
Scheduler (how exchange message)
(Polar code)
This is why Polar code doesn’t effect
by trapping sets
Divide & Conquer
Decode part of
graph by ML(BCJR):
Turbo code nature,
Non Binary-LDPC
and Generalized LDPC,
Codes Over Abelian
Groups
cut-set conditioning
junction tree
algorithm

18. Problem related to Trapping sets bypass using BP message scheduler:
We can bypass Trapping sets using sequentially BP decoder scheduler under general graph:
In Polar code using this method sequential BP decoder (Successive cancellation) bypass
short cycles. As result, harmfulness TS weight equal to code distance.
Girth in Polar code graph for N=8
2X







11
11
H
1X
)1(xorf
)2(xorf
1X
2X
)1(xorf
)2(xorf
and construct graph in such way that some variable more reliable
(have some a prior information about graph structures).
Base on this knowledge we can sequentially decode graph by BP:
Make new unobservable variable node and
corresponding check node
212,1 XXX 
.02,121)2,1(  XXXfxor











111
100
100
H
1X 2X 2,1
X
)1(xorf
)2(xorf
)2,1(xorf
1X
2X
)1(xorf
)2(xorf
2,1
X
)2,1(xorf
Cycle 4 bypass by cost of sequential decoding step.
1. How this sequentially BP decoder scheduler can applied to any arbitrary graph?
2. How to make trade-off between number of sequential step and size of bypassed TS?

19. Make ‘a prior screening’ using 0 LLRs value in
dense nodes and special design of subgraphs
(ME-LDPC):
cut-set conditioning
in 16 node

20. Consider of junction tree approach to trapping sets
elimination
Definition. Let T(a, b) be an elementary trapping set. Let Ck ={c1,c2,…,ck} be a set of
check nodes of degree 2 in T. A set S Ck. Is called critical
if by converting the single parity checks in S to the super checks (super factor), the
trapping set is not harmful anymore.
Definition. Let T(a, b) be an elementary trapping set. The minimum size of a critical set
in T is denoted by s(a,b) (T).
Page 20
s(5,3) (T)=2 s(4,4) (T)=1
GLDPC super check nodes
TS became
TS(6,4) TS(5,5)
Girth 10Girth 8 girth became

21. Can there exist a new method to reach
theoretical threshold-> increase «dense» of graph
(make more correlations between nodes,
improve weight spectrum properties)
and solve trouble of TS?
Screening of TS
by precoding and
Puncturing
(ME-LDPC)
Scheduler
of message
Polar code
and
polar subcodes
Divide & Conquer
optimal processing
of subgraph by ML
Non Binary codes
?
junction tree
algorithm
cut-set
conditioning

22. Graph model problems:
1. What properties of graph flows allow for variational representation?
2. When do they have unique and efficiently computable solution?
3. Can we create systematic methods to solve Mixed-Integer-Non-Linear-Programming
problems over graph flow by using Linear Programming-Belief Propagation hierarchy?
4. Is it possible to exploit the natural separation of variables that exists in graph expansion
planning problems to accelerate classical Mixed Integer Programming algorithms such as
Bender's Decomposition(divide-and-conquer)?
5. Can non-linear programming techniques be incorporated as heuristics to accelerate
mixed integer program solution of graph’s model flow optimization problems with
physical constraints?
6. What properties of graph flows enable simplification of robust optimization problems?
7. How can algorithms for computation of probabilities over graphs be efficiently
incorporated within optimization problems?

23. Everything is a graph(model)…
Thank you!

24. Soft decoding algorithms
Wireless channels
Optical channels
Consider case of Symbol-wise MAP
LLRs }1,0{}1,0{}1,0{

25. Absorbing sets model
(Symbol-wise MAP, way to estimate BER):
Because TS cardinality high to create some practical model of dynamic analysis of LLR. Use more special
set – absorbing sets:
Absorbing sets is TS each of these bit nodes is connected to more
even-degree checks than odd-degree checks, and all remaining check nodes
outside of the induced subgraph have even degree with respect to the bipartite
Tanner graph corresponding to the parity-check matrix.
Schlegel C., Zhang S. On the Dynamics of the Error Floor Behavior in (Regular) LDPC Codes ITIT
2010 a VN and b odd degree CN
),( ba a

26. Linear model of absorbing sets:
1. Variable perform simple addition
and check nodes choose the minimum
of the incoming signals.
2. absorption set converges slower
than the remaining nodes in the code
absorption set converges slower than
the remaining nodes in the code
3. each (satisfied) check node is
connected exactly to two absorption
set variables, the minimum absolute-
value signal into the participating check
nodes will come from one of the
absorption set variables.
-> check nodes simply exchange the
signals on the connections to the
absorption set variable nodes.

27. Linear model of absorbing sets:
VNof
edgeeveryfrommessageoutgoingisxi
VNof
edgeeveryfrommessageisyi incoming
vectorLLRsx  40,0 
exchangesmartrixnpermutatioCCxy  ,00
1 2 3 8
ext
1
ext
8
ext
VCx )1(1  
setABintoinjectedsignalsextrinsiciteration ian
ext
i
iI
i
iI
i
i VCVCx )(
10
)()(   

maxmaxmax
0
)()( vvVC Ti
i
iI
i




Using Perron-Frobenius spectral theorem, we can simplify the expression:

28. Linear model of absorbing sets:
1 2 3 8
ext
1
ext
8
 
































I
j
j
j
I
j
j
I
j
j
j
A
ext
ext
m
m
mm
m
QP
1
2
max
)(2
1 max
1 max
)(
1
1
22
0Pr







.5, max
a
b

max
Absorbing set which topology look like regular graph we can use approximate equation
Spectral parameter show how faster external LLR’s grown compare to internal Absorbing set LLRs
In the case of the (8,8) absorbing set:
0
8
1 1 max








 
   i
I
j
j
i
ext
ji
i



.
We can’t exactly know , since these
value depend on the received signals.
we may substitute by average values.
which we can calculate using
Gaussian density evolution
calculation:
ext
ji
    ,111
1)1(1)( 

c
extext
di
v
i
mdmm 

The bit error probability of absorbing set in this
case equal:where 2
2

bE
m  is mean of i und
)(i
extm
 is the check node mean transfer function
is the mean of extrinsic signal

29. Use of absorbing sets
to lowering error-floor (ignore sublinear size TS):
max
Scaling 2 times LLR from all unsatisfied check nodes in Absorbing set (8,2) during first 4 iteration
(after it without scaling). Using this simple modification lowering error floor by nearly an order
of magnitude at 6.5 dB.
Figure. Dominant Absorbing sets(8,2) of IEEE 802.3an [2048,1723,14] regular (6,32) LDPC

30. Response of absorbing sets depend from
LLRs saturation value, bits width, etc:
Table . Error statistics of RS-LDPC(2048, 1723)
Figure . InfoBER/FER curves of RS-LDPC(2048, 1723). SPA left, MS –ASPA and SPA right figure

31. )2(GF












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
























0
0
0
11100
11010
00111
5
4
3
2
1
c
c
c
c
c
Hc
0,1,2,0,0]1,[)( cW
]01110[
]10110[
]11000[
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3
2
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

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T
T
T
T
c
c
c
c
3)(
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2)(
0)(
1
33
1
22
1
11
1
00


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
TT
TT
TT
TT
ccw
ccw
ccw
ccw
vectorzero
vectorweight 2 vectorsweight 3
32
21)( zzzf 

32. Graph construction problem
Bipartite graph
4)3(5)2(4 XfXfX xorxor Cycle 4:











11100
11010
00111
H
54321 XXXXX
)3(xorf
)2(xorf
)1(xorf
1.How to enumerate all cycles (Hamilton too) with or complexity;
2. How to found with reasonable complexity “bad connected” subgraph?
“bad connected” – cycles or union of cycles which connected with minimal number of
nodes
)(NO NNO log)(

33. Simple examples of linear programming model:.
For parity-check matrix:











1111
1100
1010
H
0
0
0
4321
43
42



xxxx
xx
xx
We construct constrain systems using inequality which is equivalent
10,1
/
  
i
Vhi
i
Vi
i xoddVwhereVxx
i
Using it on the first row of parity-check matrix:
}{
}{
11
11
1010
1010
,
}{
}{
11
11
11
11
4
2
4
3
2
1
4
2
4
2
xV
xV
x
x
x
x
xV
xV
x
x































































For second row:
}{
}{
11
11
11
11
4
3
4
3
xV
xV
x
x


























}{
}{
11
11
1100
1100
4
3
4
3
2
1
xV
xV
x
x
x
x





































,
.
For third row:
},,,{
},,{
},,{
},,{
}{
}{
}{
}{
13
13
13
13
11
11
11
11
1111
1111
1111
1111
1111
1111
1111
1111
432
431
421
321
4
3
2
1
4
3
2
1
xxxV
xxxV
xxxV
xxxV
xV
xV
xV
xV
x
x
x
x



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
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


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
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
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































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
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



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34. Graph construction problem
.





  )()( 2
0 pQcyclesc 
e all-one vector in Euclidean space of LP, p – pseucodeword (weight of cycles and it union).
The goal of LP is to find maximum likelihood codeword
 .2mod0|
min
2
TTn
T
cHFcc
c


.
.
2
)(
p
pe
p
T


 

x
t
dtexQ 2
2
2
1
)(

We can weigh of cycle and it union under some defined statistical model.)(p
 
 ii
ii
i
yc
yc
where
|1Pr
|0Pr
ln


 ,,...,, 110  n
For example, binary-input additive white Gaussian noise: