1. DIGITAL COMMUNICATIONS Linear Block Codes
EEM 467
DIGITAL COMMUNICATIONS Linear Block Codes
Assist.Prof.Dr. Nuray At

2. Codes can either correct or merely detect errors
Error Control Coding
Designing codes for the reliable transmission of digital information over a noisy channel.
Codes can either correct or merely detect errors
Codes that can detect errors are called error-detecting codes
Codes that can correct errors are called error-correcting codes
Error correction is more complex than error detection!
Error control codes are classified into
Block Codes
Convolutional Codes
The channel is discrete when the alphabets of X and Y are both finite
The channel is memoryless when the current output depends only on the current input and not on any of the previous inputs
is a channel transition probability
Entropy can be changed from one base to another

3. Channel Coding
The channel encoder introduces systematic redundancy into the data stream
The combined objective of the channel encoder and decoder is to minimize the effect of channel noise
Channel Coding Theorem:
Given a DMS X with entropy H(X) and a DMC with capacity C, if , there exists a coding scheme for which the source output can be transmitted over the channel with an arbitrary small probability of error.
Entropy can be changed from one base to another

4. Block Codes
Data sequence is divided into sequential blocks each k bits long
Each k-bit block is converted into an n-bit block, where n > k
The resultant block code is called (n,k) block code and the ratio k/n is called
code rate.
Entropy can be changed from one base to another

5. Linear Block Codes
Binary Field: The set K = {0, 1} is a binary field. The binary field has two operations, addition and multiplication
Addition
Multiplication
Entropy can be changed from one base to another

6. Let and be two codewords in C.
Linear Codes:
Let and be two codewords in C.
A code C is called linear if the sum of two codewords is also a codeword in C.
A linear code C must contain the zero codeword
Hamming Weight and Distance:
Let a, b, and c be codewords of length n.
The Hamming weight of c, denoted by w(c), is the number of 1's in c.
The Hamming distance between a and b, denoted by d(a, b), is the number of positions in which a and b differ.
Entropy can be changed from one base to another

7. Thus, the Hamming weight of a codeword c is the Hamming distance between c and 0, that is Similarly, the Hamming distance can be written in terms of Hamming weight as Minimum Distance: The minimum distance dmin of a linear code C is defined as the smallest Hamming distance between any pair of codewords in C. Theorem: The minimum distance dmin of a linear code C is the smallest Hamming weight of the nonzero codeword in the C.
Entropy can be changed from one base to another

8. Error Detection and Correction Capabilities:
The minimum distance dmin of a linear code C determines the error detection and correction capabilities of C.
A linear code C of minimum distance dmin can detect up to t errors iff
A linear code C of minimum distance dmin can correct up to t errors iff
Entropy can be changed from one base to another

9. Generator Matrix: In an (n,k) linear block code C, If the data bits appear in specified location of c, the code C is called systematic. That is, Here we assume that the first k bits of c are the data bits.
Entropy can be changed from one base to another

10. In a matrix form Hence, and The k x n matrix G is called the generator matrix.
Entropy can be changed from one base to another

11. Parity-Check Matrix: Let H denote an m x n matrix defined by where
Parity-Check Matrix: Let H denote an m x n matrix defined by where . The matrix H is called the parity-check matrix of C. We have Thus,
Entropy can be changed from one base to another

12. Syndrome Decoding
Let r denote the received word of length n when codeword c of length n was sent over a noisy channel.
where e is called the error pattern. Consider first the case of a single error in the ith position. Then,
Evaluate as
where s is called syndrome of r.
Using s and noting that is the ith row of HT, we can identify the error position by comparing s to the rows of HT.
Note that the zero syndrome indicates that r is a codeword and is presumably correct.
Entropy can be changed from one base to another

13. Determine the generator matrix G.
Example: Consider a linear block code with the following parity-check matrix
Determine the generator matrix G.
Suppose that the received word is r = [ ]. Decode this received word, i.e., find c and d.
Entropy can be changed from one base to another

14. The Hamming Codes Code length: Number of parity symbols: n – k = m
Error correcting capability: t = 1
The parity-check matrices for binary Hamming codes are quite easy to construct. For a Hamming code of length construct a matrix whose columns consist of all nonzero m-tuples. For example, a parity-check matrix for a (15,11) Hamming code
The ordering of columns is arbitrary; another arrangement would still define a (15,11) Hamming code.
Entropy can be changed from one base to another