Linear Block Codessite.iugaza.edu.ps/.../files/2010/02/Linear-Block-Codes.pdfTitle Microsoft...

Linear Block Codes

Outline

• Definition of Linear and systemic codes.

• Generator Matrix.

• Parity check matrix / encoding.

•• Dual code.

• Standard Arrays decoding.

• Syndrome decoding.

• Error detecting and correcting capabilities.

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Definition of Linear Codes

• Let u be a k-bit information sequence;

v be the corresponding n-bit codeword.

• A total of 2k n-bit codewords constitute (n,k) code.• A total of 2 n-bit codewords constitute (n,k) code.

• Linear code: The sum of any two codewords is a

codeword.

• Observation: The all-zero sequence is a codeword

in every linear block code.

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Systematic codes

• Any linear block code can be put in systematic

form

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n-k

check bits

k

information bits

Generator Matrix

• All 2k codewords can be generated

from a set of k linearly independent

codewords.

• Let g0, g

1, …, g

k-1be a set of k

= 1

0

g

g

G

• Let g0, g

1, …, g

k-1be a set of k

independent codewords.

• v = u·G

=

−1

1

kg

g

MG

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Generator Matrix (cont’d)

• The simplest choice of this set is the kcodewords corresponding to the information sequences that have a single nonzero element.

• Illustration: The generating set for the (7,4) • Illustration: The generating set for the (7,4) code:

1000 ===> 1101000

0100 ===> 0110100

0010 ===> 1110010

0001 ===> 1010001

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Linear Code Example

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Generator Matrix (cont’d)

• Every codeword is a linear combination of these 4 codewords.

That is:

v = u∙ G

1 1 0

0 1 1

1 0 0 0

0 1 0 0

• Storage requirement reduced from 2k(n+k) to k(n-k).

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[ ]G P | Ik

=

=

× − ×

0 1 1

1 1 1

1 0 1

0 1 0 0

0 0 1 0

0 0 0 1

k n k k k( )

124 34 1 24 34

Generator matrix example (3.2)

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Parity-Check Matrix

For G = [ P | Ik], define the matrix H = [I

n-k| PT]

(The size of H is (n-k)xn).

Check that GHT = 0.

Since v = uG, then vHT = uGHT = 0.

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H =

1 0 0 1 0 1 1

0 1 0 1 1 1 0

0 0 1 0 1 1 1

=

1000101

0100111

0010110

0001011

G

Encoding Using H Matrix

(Parity Check Equations)

[ ]6543210

011

100

010

001

vvvvvvv =

0

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[ ]

3212

2101

3200

6542

5431

6530

6542

5431

6530

6543210

uuu=v

uuu=v

uuu=v

vvv=v

vvv=v

vvv=v

0vvv+v

0vvv+v

0vvv+v

101

111

110

++

++

++

⇒

++

++

++

⇒

=++

=++

=++

information

Encoding Circuit

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The Dual Code

• The H matrix, formed from G, has (n-k) rows

of length n that are linearly independent.

• The H matrix can therefore be used to generate

an (n,n-k) linear code C .an (n,n-k) linear code Cd.

• Cd

is called the dual code of the code C

generated by G.

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More on the Minimum Distance of Linear Codes

• DF: The weight of a codeword v , denoted by w(v), is the number of the nonzero elements in the codeword.

• DF: Hamming distance between v and w, denoted by d(v,w), is the number of locations where they differ.Note that d(v,w) = w(v+w)

• DF: The minimum weight of a code, w , is the smallest • DF: The minimum weight of a code, wmin

, is the smallest

weight of the nonzero codewords in the code.wmin

= min {w(v): for every v in C; v ≠0}.

• DF: The minimum distance of the code d

min= min {d(v,w): v,w in C, v ≠ w}

• TH3.1: In any linear code, dmin

= wmin

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Minimum Distance, Cont.

• TH3.2 For each codeword of Hamming weight l there exists l

columns of H such that the vector sum of these columns is

zero. Conversely, if there exist l columns of H whose vector

sum is zero, there exists a codeword of weight l.

• COL 3.2.2• COL 3.2.2

dmin

of C is equal to the minimum numbers of columns in H

that sum to zero.

• Demo:

H matrix of (7,4)

Hamming code.

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=

1110100

0111010

1101001

H

Standard Array (�)

k

k

22322212

2321

vev+ev+ev+e

vev+ev+ev+e

vvv0v

+

+

=

L

L

L

correctab

le error p

atterns

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kknknknkn

k

22322212

23332313

veveveve

vev+ev+ev+e

----++++

+

MMMM

L

correctab

le error p

atterns

TH. No two n-tuples in the same row of a standard array are identical. Every

n-tuple appears in one and only one row.

Standard Array (�)

1. List the 2k codewords in a row, starting with the all-zero

codeword c0.

2. Select an error pattern e1

and place it below c0. This error

pattern will be a correctable error pattern, therefore it should

be selected such that:be selected such that:

(i) it has the smallest weight possible (most probable error)

(ii) it has not appeared before in the array.

3. Repeat step 2 until all the possible error patterns have been

accounted for. There will always be 2 n-k rows in the array.

Each row is called a coset. The leading error pattern is the

coset leader.

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Standard Array Decoding (�)• For an (n,k) linear code, standard array decoding is able to

correct exactly 2n-k error patterns, including the all-zero error

pattern.

• Illustration 1: The (7,4) Hamming code

# of correctable error patterns = 23 = 8# of correctable error patterns = 23 = 8

# of single-error patterns = 7

Therefore, all single-error patterns, and only single-error

patterns can be corrected.

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Standard Array Decoding (�)

Illustration 2: The (6,3) code defined by the H matrix:

H =

1 0 0 0 1 1

0 1 0 1 0 1

Codewords

000000

110001

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0 0 1 1 1 0

c = c c

c = c c

c = c c

5

4

4

1 6

2 6

3 5

+

+

+

101010

011011

011100

101101

110110

000111

3dmin=

Standard Array Decoding (�)

• Can correct all single errors and one double error pattern

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The Syndrome (�)

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The Syndrome (�)

• Ex (3.5), For the (7,4) code, let v=(1001011) and r=(1001001).

• s = rHT=111.

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The Syndrome (�)

Error Pattern Syndrome

0000000 0001000000 100

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1000000 1000100000 0100010000 0010001000 1100000100 0110000010 1110000001 101

Syndrome Decoding (table-

lookup)Decoding Procedure:

1. For the received vector r, compute the syndrome s = rHT.

2. Using the table, identify the error pattern e.

3. Add e to r to recover the transmitted codeword c.3. Add e to r to recover the transmitted codeword c.

Example:

r = 1110101 ==> s = 001 ==> e = 0010000

Then, c = 1100101

• Syndrome decoding reduces storage memory and the

searching time considerably.

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Hardware Implementation

• Let r = r0

r1

r2

r3

r4

r5

r6

and s = s0

s1

s2

• From the H matrix for the (7,4) code:

s0

= r0

+ r3

+ r5

+ r6

s1

= r1

+ r3

+ r4

+ r5

s = r + r + r + rs2

= r2

+ r4

+ r5

+ r6

• From the table of syndromes and their corresponding

correctable error patterns, a truth table can be constructed. A

combinational logic circuit with s0

, s1

, s2

as input and

e0

, e1

, e2

, e3

, e4

, e5

, e6

as outputs can be designed.

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Decoding Circuit for the (7,4) HC

Error Pattern Syndrome

0000000 0001000000 100

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1000000 1000100000 0100010000 0010001000 1100000100 0110000010 1110000001 101

Error detecting capability

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Error Correcting Capability

• # of errors that can be corrected = t =

• A codeword with dmin

can detect all error patterns of weight

dmin

– 1 or less. It can detect many higher error patterns as

well, but not all. Therefore,

−

2

1min

d

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∑+=

−−

≤

n

ti

ini

upp

i

nP

1

)1(

Probability of undetected error (�)

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Probability of undetected error (�)

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Probability of undetected error

(�)

31

Simple Upper Bound on Pu

• Knowing weight distribution

∑=

−

−=

n

di

ini

iuppAP

min

)1(

• The weight distribution is only known to few codes.

• As an upper bound,

)(2

2

12 kn

n

k

uP

−−

≅−

≤

=di min

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Next lecture

• We will cover several classes of important

linear block codes.

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