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 = - L (d). L (d). +. L (d). 0 = 0. +. Log-Likelihood Algebra. Sum of two LLRs L(d 1 ) + L(d 2 )  L ( d 1  d 2 ) = log exp[L(d 1 )] + exp [L(d 2 )] 1 + exp[L(d 1 )].exp [L(d 2 )]

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log likelihood algebra

 = - L (d)

L (d)

+

L (d)

0 = 0

+

Log-Likelihood Algebra

Sum of two LLRs

L(d1) + L(d2) L (d1 d2 )

= log exp[L(d1)] + exp [L(d2)]

1 + exp[L(d1)].exp [L(d2)]

 (-1) . sgn [L(d1)]. sgn [L(d2)] . Min ( |L(d1)| , |L(d2)| )

iterative decoding example
Iterative decoding example
  • 2D single parity code
  • di di= pij
iterative decoding example1
Iterative decoding example
  • Estimate Lc(xk)
      • = 2 xk /2
      • assuming 2= 1
iterative decoding example2

Leh( d1) = [Lc ( x 2) + L(d2) ] Lc ( x 12 ) = new L( d1 )

Leh( d2) = [Lc ( x 1) + L(d1) ] Lc ( x 12 ) = new L(d2)

Leh( d3) = [Lc ( x 4) + L(d4) ] Lc ( x 34 ) = new L(d3)

Leh( d4) = [Lc ( x 3) + L(d3) ] Lc ( x 34 ) = new L(d4)

+

+

+

+

Iterative decoding example
  • Compute
      • Le( dj ) = [Lc ( x j) + L(dj ) ] Lc ( x ij)

+

iterative decoding example3

Lev( d1) = [Lc ( x 3) + L(d3) ] Lc ( x 13 ) = new L( d1 )

Lev( d2) = [Lc ( x 4) + L(d4) ] Lc ( x 24 ) = new L(d2)

Lev( d3) = [Lc ( x 1) + L(d1) ] Lc ( x 13 ) = new L(d3)

Lev( d4) = [Lc ( x 2) + L(d2) ] Lc ( x 24 ) = new L(d4)

+

+

+

+

^

^

^

L( di ) = Lc ( x i) + Leh(di) + Lev( dj )

Iterative decoding example

After many iterations the LLR is computed for decision making

parallel concatenation codes
Parallel Concatenation Codes
  • Component codes are Convolutional codes
  • Recursive Systematic Codes
  • Should have maximum effective free distance
  • Large Eb/No maximizing minimum weight codewords
  • Small Eb/No optimizing weight distribution of the codewords
  • Interleaving to avoid low weight codewords
non systematic codes nsc

{uk}

+

L-1

L-1

uk =  g1i dk-i (mod 2) ;

i=1

vk =  g2i dk-i (mod 2) ;

i=1

{dk}

dk-1

dk-2

dk

+

{vk}

Non - Systematic Codes - NSC

G1 = [ 1 1 1 ]

G2 = [ 1 0 1 ]

recursive systematic codes rsc

{dk}

{uk}

ak-1

ak-2

ak

+

+

{vk}

L-1

ak = dk +  gi’ ak-i (mod 2) ; gi’ = g1i if uk = dk

i=1 g2i if vk = dk

Recursive Systematic Codes - RSC
trellis for nsc rsc
NSC

RSC

Trellis for NSC & RSC

00

00

a = 00

a = 00

11

11

11

11

b = 01

b = 01

00

00

10

10

c = 10

c = 10

01

01

01

01

d = 11

d = 11

10

10

concatenation of rsc codes

+

+

ak-1

ak-1

ak-2

ak-2

ak

ak

+

+

{v2k}

{v1k}

Concatenation of RSC Codes

{dk}

{uk}

Interleaver

{vk}

{ 0 0 …. 0 1 1 1 0 0 …..0 0 }

{ 0 0 …. 0 0 1 0 0 1 0 … 0 0 }

 produce low weight codewords in component coders

feedback decoder

APP  Joint Probability ki,m = P { dk = i, Sk = m / R1 N }

State at

time k

Received sequence

From time 1 to N

APP  P { dk = i / R1 N } =  ki,m ; i = 0,1 for binary

m

 k0,m

 k1,m

 k0,m

 k1,m

m

m

m

m

Likelihood Ratio  ( dk ) =

Log Likelihood Ratio  L( dk ) = Log

Feedback Decoder
feedback decoder1

Feedback Decoder
  • MAP Rule  dk =1 ; L(dk) > 0

dk =0 ; L(dk) < 0

^

^

L( dk) = Lc ( x k) + L(dk) + Le( dk )

L1( dk ) = [Lc ( x k) + Le1(dk ) ]

L2( dk ) = [ f{L1 ( dn) }n k + Le2(dk ) ]

feedback decoder2

Le2( dk )

L2( dk )

xk

L1( dn )

L1( dk )

De-Interleaver

DECODER 1

DECODER 2

De-Interleaver

Interleaver

y1k

y2k

yk

dk

Feedback Decoder

modified map vs sova
Modified MAP Vs. SOVA
  • SOVA 
    • Viterbi Algorithm acting on soft inputs over forward path of the trellis for a block of bits
    • Add BM to SM  compare  select ML path
  • Modified MAP 
    • Viterbi Algorithm acting on soft inputs over forward and reverse paths of the trellis for a block of bits
    • Multiply BM & SM  Sum in both directions  best overall statistic
map decoding example

{uk}

dk-1

dk-2

dk

{dk}

+

{vk}

MAP Decoding Example

00

a = 00

11

b = 10

00

11

01

c = 01

10

01

d = 11

10

map decoding example1

Branch Metric  ki,m = P { dk = i, Sk = m , Rk }

= P { Rk / dk = i, Sk = m }

. P {Sk = m / dk = i }

. P { dk = i }

ki,m =

P { xk / dk = i, Sk = m }

. P { yk / dk = i, Sk = m } . { ki / 2L }

P {Sk = m / dk = i } = 1 / 2 L = ¼ ;

P { dk = i } = 1 / 2 ;

MAP Decoding Example
  • d = { 1, 0, 0 }
  • u = { 1, 0, 0 }  x = { 1., 0.5, -0.6 }
  • v = { 1, 0, 1 }  y = { 0.8, 0.2, 1.2 }
  • Apriori probabilities  1 = 0 = 0.5
map decoding example2

ki,m =

ki,m =

Assuming Ak = 1 2 =1 ;

ki,m = 0.5 exp { xk . uki + yk . vki,m }

{ ki / 2L } (1/2 ) exp { - (xk – uki )2 /(2 2 ) }dxk

{ Ak ki } exp { (xk . uki )+ (yk . Vki,m )/  2 }

ki,m =

P { xk / dk = i, Sk = m }

. P { yk / dk = i, Sk = m } . { ki / 2L }

MAP Decoding Example

For AWGN channel :

. (1/2 ) exp { - (yk – vki,m )2 /(2 2 ) }dyk

subsequent steps

ki,m = 0.5 exp { xk . uki + yk . vki,m }

1

k+1m=  ki,b(j,m) kb(j,m)

J=0

Subsequent steps
  • Calculate branch metric
  • Calculate forward state metric
  • Calculate reverse state metric

1

km=  kj,m k+1f(j,m)

J=0

subsequent steps1

 km k1,m k+1f(1,m)

 km k0,m k+1f(0,m)

m

m

Subsequent steps
  • Calculate LLR for all times

Log Likelihood Ratio  L( dk ) = Log

  • Hard decision based on LLR
iterative decoding steps

{ k1}  km exp { xk . uk1 + yk . Vk1,m } k+1f(1,m)

m

{ k0} km exp { xk . uk0 + yk . Vk0,m }k+1f(0,m)

m

 km exp { yk . Vk1,m } k+1f(1,m)

m

{ k} exp { 2xk }

 km exp { yk . Vk0,m }k+1f(0,m)

m

LLR  L( dk ) = L(dk) + { 2xk } + Log [ke ]

Iterative decoding steps

Likelihood Ratio  ( dk )

=

=

=

{ k} exp { 2xk } { k e}

iterative decoding

ki,m = ke i exp { xk . uki + yk . Vki,m }

 km k1,m k+1f(1,m)

 km k0,m k+1f(0,m)

m

m

Iterative decoding
  • For the second iteration;
  • Calculate LLR for all times

Log Likelihood Ratio  L( dk ) = Log

  • Hard decision based on LLR after multiple iterations