Download Introduction to Low-Density Parity

Code and Decoder Design of
LDPC Codes for Gbps Systems
Jeremy Thorpe
Presented to: Microsoft Research
2002.11.25
Talk Overview
 Introduction (13 slides)
 Wiring Complexity ( 9 slides)
 Logic Complexity (7 slides)
Reliable Communication over
Unreliable Channels
 Channel is the means by
which information is
communicated from sender
to receiver
 Sender chooses X
 Channel generates Y from
conditional probability
distribution P(Y|X)
 Receiver observes Y
X
P(Y|X)
channel
Y
Shannon’s Channel Coding
Theorem
 Using the channel n times, we can communicate k
bits of information with probability of error as
small as we like as long as
k
R C
n
 as long as n is large enough. C is a number that
characterizes any channel.
 The same is impossible if R>C.
The Coding Strategy
 Encoder chooses the mth
codeword in codebook C
and transmits it across the
channel
 Decoder observes the
channel output y and
generates m’ based on the
knowledge of the codebook
C and the channel
statistics.
x C  X n
m {0,1}k
Encoder
Channel
Decoder
m'{0,1}k
y Y n
Linear Codes
 A linear code C can be defined in terms of
either a generator matrix or parity-check
matrix.
 Generator matrix G (k×n)
C  {mG }
 Parity-check matrix H (n-k×n)
C  {c : cH '  0}
Regular LDPC Codes
 LDPC Codes linear codes defined in terms of H.
 The number of ones in each column of H is a fixed
number λ.
 The number of ones in each row of H is a fixed
number ρ.
 Typical parameters for Regular LDPC codes are
(λ,ρ)=(3,6).
Graph Representation of LDPC
Codes
v
v|c
0
...
x
Variable nodes
...
 H is represented by a
bipartite graph.
 nodes v (degree λ) on
the left represent
variables.
 Nodes c (degree ρ)on
the right represent
equations:
Check nodes
Message-Passing Decoding of
LDPC Codes
 Message Passing (or Belief Propagation)
decoding is a low-complexity algorithm
which approximately answers the question
“what is the most likely x given y?”
 MP recursively defines messages mv,c(i) and
mc,v(i) from each node variable node v to each
adjacent check node c, for iteration i=0,1,...
Two Types of Messages...
 Liklihood Ratio
x , y 
p( y | x  1)
p( y | x  0)
 Probability Difference
 x , y  p( x  1 | y )  p( x  0 | y )
 For y1,...yn independent  For x1,...xn
conditionally on x:
independent:
x , y   x , y
n
1
i
i
 x , y    x , y
i
i
i
i
...Related by the Biliniear
Transform
 Definition:
1 x
B( x) 
1 x
 Properties:
B( B( x))  x
B ( x , y )   x , y
B(  x , y )  x , y
p ( y | x  1)
)
p ( y | x  0)
p ( y | x  0)  p ( y | x  1)
p ( y | x  0)  p ( y | x  1)
p ( y | x  0)  p ( y | x  1)
2 p( y)
2 p( x  0 | y) p( y )  2 p( x  1 | y ) p( y)
2 p( y)
p( x  0 | y)  p( x  1 | y)
B ( x , y )  B (




 x, y
Variable to Check Messages
 On any iteration i, the
message from v to c is:
mv ,c
(i )
 xv , yv  B(mc ',v
( i 1)
v
c
)
c '|v  c
...
...
Check to Variable Messages
 On any iteration, the
message from c to v is:
mc ,v
(i )

 B(m
v ',c
(i )
v
c
)
v '|c  v
...
...
Decision Rule
 After sufficiently many iterations, return the
likelihood ratio:
0, if x , y  B(mc ,v (i 1) )  0
v v

c|v
xˆ  

otherwise
1,
Theorem about MP Algorithm
r
...
v
...
 If the algorithm stops after r
iterations, then the algorithm
returns the maximum a
posteriori probability estimate
of xv given y within radius r of
v.
 However, the variables within
a radius r of v must be
dependent only by the
equations within radius r of v,
...
Wiring Complexity
Physical Implementation
(VLSI)
 We have seen that the MP decoding algorithm for
LDPC codes is defined in terms of a graph
 Nodes perform local computation
 Edges carry messages from v to c, and c to v
 Instantiate this graph on a chip!


Edges →Wires
Nodes →Logic units
Complexity vs. Performance
 Longer codes provide:


More efficient use of
the channel (eg. less
power used over the
AWGN channel)
Faster throughput for
fixed technology and
decoding parameters
(number of iterations)
 Longer codes demand:


More logic resources
Way more wiring
resources
The Wiring Problem
 The number of edges in
the graph grows like
the number of nodes n.
 The length of the edges
in a random graph also
grows like n .
A random graph
Graph Construction?
 Idea: find a construction that has low wirelength and maintains good performance...
 Drawback: it is difficult to construct any
graph that has the performance of random
graph.
A Better Solution:
 Use an algorithm which generates a graph at
random, but with a preference for:


Short edge length
Quantities related to code performance
Conventional Graph Wisdom
 Short loops give rise to dependent messages
(which are assumed to be independent) after
a small number of iterations, and should be
avoided.
Simulated Annealing!
 Simulated annealing approximately
minimizes an Energy Function over a
Solution space.
 Requires a good way to traverse the solution
space.
Generating LDPC graphs with
Simulated Annealing
 Define energy function
with two components:
E  cw Ew  cl El

Wirelength
Ew   | w |
w

Loopiness
El    |l|
l
 traverse the space by
picking two edges at
random and do:
Results of Simulated Annealing
 The graph on the right
has nearly identical
performance to the one
shown previously
A graph generated by
Simulated Annealing
Logic Complexity
Complexity of Classical
Algorithm
 Original algorithm defines messages in terms
of arithmetic operations over real numbers:
mv ,c
(i )
 xv , yv  B(mc ,v
c|v  c
( i 1)
)
 However, this implies floating-point
addition, multiplication, and even division!
A modified Algorithm
( i 1)
(i )
 We define a modified
mv ,c  x , y  B(mc ,v )
c|v  c
algorithm in which all
(i )
(i )
messages are their
m'v,c  log( mv,c )
logarithms in the
( i 1)

log(

)

log(
B
(exp(
m
)))
original scheme

x ,y
c ,v
c|v  c
 The channel message
( i 1)


'


(
m
)

x ,y
c ,v
λ is similarly replaced
c|v  c
by it's logarithm.
v
v
v
v
v
v
Quantization
 Replaced a product by
a sum, but now we
have a transcendental
function φ.
 However, if we
quantize the messages,
we can pre-compute φ
for all values!
m'v,c   ' xv , yv   (mc,v
(i )
c|v  c
( i 1)
)
Quantized MP Performance
 The graph to the following page shows the
bit error rate for a regular (3,6) of length
n=10,000 code using between 2 and 4 bits of
quantization.
 (Some error floors predicted by density
evolution, some are not)
Conclusion
 There is a tradeoff between logic complexity
and performance
 Nearly optimal performance (+.1 dB = ×
1.03 power) is achievable with 4-bit
messages.
 More work is needed to avoid error-floors
due to quantization.