1. DIGITAL COMMUNICATION Coding
EEE436
DIGITAL COMMUNICATION
Coding
En. Mohd Nazri Mahmud
MPhil (Cambridge, UK)
BEng (Essex, UK)
Room 2.14
EE436 Lecture Notes

2. Error Detection and Correction Syndrome Decoding
Decoding involves parity-check information derived from the code’s coefficient matrix, P.
Associated with any systematic linear (n,k) block code is a (n-k)-by-n matrix, H called the parity-check matrix.
H is defined as
H = [In-k PT]
Where PT is the transpose of the coefficient matrix, P and is an
(n-k)-by-k matrix.
In-k is the (n-k)-by-(n-k) identity matrix.
For error detection purposes, the parity check matrix, H has the following property
c.HT = (0 0 ….. 0) (ie Null matrix)
EE436 Lecture Notes

3. c.HT = (0 0 ….. 0) (ie Null matrix) Since c=m.G, therefore
Syndrome Decoding
c.HT = (0 0 ….. 0) (ie Null matrix)
Since c=m.G, therefore
m.G.HT = (0 0 …. 0)
This property is satisfied only when c is correctly received.
Errors are indicated by the presence of non-zero elements in the matrix.
Let r denotes the 1-by-n received vector that results from sending the code vector c over a noisy channel.
When there is an error, the decoding operation will give a syndrome vector, s whose elements contain at least 1 non-zero element.
EE436 Lecture Notes

4. 1 1 Syndrome Decoding – Example for the (7,4) Hamming Code
A (7,4) Hamming code with the following parameters
n=7; k=4, m=7-4=3
The k-by-(n-k) (4-by-3) coefficient matrix, P =
The generator matrix, G is, G = 1
P = 1
G =
EE436 Lecture Notes

5. Syndrome Decoding –Example for (7,4) Hamming Code
Associated with the (7,4) Hamming Code is a 3-by-7 matrix, H called the parity-check matrix.
H is defined as
H = [In-k PT]
When a codeword is correctly received, the c.HT will result in a null matrix, otherwise it will result in a syndrome vector, s. 1
EE436 Lecture Notes

6. Syndrome Decoding –Example for (7,4) Hamming Code
Example: The received code vector is [ ], check whether this is a correct codeword
c.HT = [ ] 1
EE436 Lecture Notes

7. Syndrome Decoding –Example for (7,4) Hamming Code
Example: The received code vector is [ ], check whether this is a correct codeword
c.HT = [ ] 1
= [0 0 1] – this is called
the error syndrome
EE436 Lecture Notes

8. Error pattern
Error pattern is an error vector E whose nonzero element mark the position of the transmission errors in the received codeword
We can work out all syndromes and find the corresponding error patterns and store them in a look up table for decoding purposes
For example the (7,4) Hamming code
EE436 Lecture Notes

9. Error detection & correction
The error pattern, E is essentially the modulo-2 sum of the correct code vector and the erroneous received code vector. For example , c = and r= (ie error in the 3rd bit)
c + r =E =
This error pattern corresponds to a syndrome vector in the look up table, 001
Recall that the syndrome vector, s = rHT
s = (c + E)HT
= cHT + EHT
= EHT
EE436 Lecture Notes

10. Error detection and correction
Therefore, the decoding procedure involves working out the syndrome for the received code vector and look up for the corresponding error pattern.
Then, modulo-2 sum the error pattern, E and the received vector, r , so that c = r + E, and the correct codeword can be recovered.
EE436 Lecture Notes

11. Error detection and correction Example
For message word 0010, the correctly encoded codeword is c = Due to channel noise, the received code vector is r = [ ]. Show how the decoder recover the correct codeword.
The decoder uses r and the HT to find the error syndrome, s
S=r.HT = 001
2) Using the resulting syndrome, refer the look up table for the corresponding assumed error vector, E.
S=001 corresponds to assumed error vector, E =
3) Then ex-OR E and r to recover the correct codeword
E+r = =
EE436 Lecture Notes

12. Error detection and correction Exercise
For message word 0110, the correctly encoded codeword is c = Due to channel noise, the received code vector is r = [ ]. Show how the decoder recover the correct codeword.
For message word 0110, the correctly encoded codeword is c = Due to channel noise, the received code vector is r = [ ]. Show how the decoder performs its decoding operation. What is your observation and explain it.
EE436 Lecture Notes

13. BCH Codes
A class of cyclic codes discovered in 1959 by Hocquenghem and in 1960 by Bose and Ray-Chaudhuri.
Include both binary and multilevel codes
Identified in the form of (n,k) BCH code for example (15,7) BCH code
A t-error Binary BCH codes consist of binary sequences of length n= 2m – 1 ; m indicates the corresponding Galois Field
Specified by its generator polynomial, g
The generator polynomial is specified in terms of its roots from the Galois Field, GF(2m)
EE436 Lecture Notes

14. BCH Codes
To work out the corresponding generator polynomial for example (15,7) BCH code
First, we need to find m; since n=2m -1, therefore, for n=15; m = 4
Then we need to find the primitive polynomial for m=4 from a specified reference table
Then, based on the primitive polynomial, construct the elements of GF(24)
Then find the minimal polynomials of the elements of GF(24) from a specified reference table
Then based on these minimal polynomials , we can work out the generator polynomial.
EE436 Lecture Notes

15. Binary BCH Codes
For our example, (15,7) BCH code consider a Galois Field with m=4 GF(24)
A polynomial p(X) over GF(24) of degree 4 that is primitive is taken from the following Table of primitive polynomials
EE436 Lecture Notes

16. Binary BCH Codes
Then, based on the primitive polynomial, 1 + X + X4 construct the elements of GF(24)
Let alpha (ά) be a primitive element in GF(2m)
Set p(ά)=1+ ά+ ά4 = 0 , then ά4 = 1+ ά
Using this relation, we can construct GF(24) elements as below
EE436 Lecture Notes

17. Binary BCH Codes
Then find the minimal polynomials of the elements of GF(24) from a specified reference table
EE436 Lecture Notes

18. Binary BCH Codes
Then the generator polynomial of a t-error correcting BCH code of length 2m – 1 is given by
g(x) = LCM { ǿ1(X), ǿ2(X) , ….., ǿ2t (X) }
Since every even power of ά in the elements sequence has the same minimal polynomial as some preceding odd power of ά in the elements sequence
g(x) = LCM { ǿ1(X), ǿ3(X) , ….., ǿ2t-1 (X) }
EE436 Lecture Notes

19. Binary BCH Codes generator polynomial – Example
A(15,7) BCH code (ie n=15)
m=2m-1; m=4
Therefore , refer to Galois Field with m=4 GF(24)
A polynomial p(X) over GF(24) of degree 4 that is primitive is taken the table
p(X)= 1 + X + X4
Then, based on the primitive polynomial, 1 + X + X4 construct the elements of GF(24)
Then find the minimal polynomials of the elements of GF(24) from the table
Then the generator polynomial of a t-error correcting BCH code of length
2m – 1 is given by g(x) = LCM { ǿ1(X), ǿ3(X) , ….., ǿ2t-1 (X) }
EE436 Lecture Notes

20. Binary BCH Codes generator polynomial – Example
Then the generator polynomial of a t-error correcting BCH code of length
2m – 1 is given by g(x) = LCM { ǿ1(X), ǿ3(X) , ….., ǿ2t-1 (X) }
For 2-error correcting; t=2
Therefore, g(x) = LCM { ǿ1(X), ǿ3(X)}
ǿ1(X) = 1 + X + X4 and ǿ3(X)= 1 + X + X2 + X3 + X4
g(x) = ǿ1(X). ǿ3(X)
= 1 + X4 + X6 + X7 + X8
Exercise : Try out for 3-error correcting BCH code of the same length
EE436 Lecture Notes

21. Binary BCH Codes generator polynomials – Example
EE436 Lecture Notes

22. Non-Binary or M-ary BCH Codes
Unlike binary these codes are multilevel codes
Operates on multiple bits rather than individual bits
The general (n,k) encoder encodes k m-bit symbols into blocks consisting of n=2m-1 symbols of total m(2m-1) bits
Thus the encoding expands a block of k symbols to n symbols by adding n-k redundant symbols
An example of non-binary BCH code is the Reed-Solomon Code
EE436 Lecture Notes

23. RS Codes A t-error-correcting RS code has the following parameters
Block length n= 2m-1
Message size k symbols
Parity-check size n-k=2t symbols
Minimum distance = 2t + 1
Example RS(7,4) with m=3 bits
EE436 Lecture Notes