Data Link Layer- Error Detection and Control_2.pptx

Data Link Layer
Objectives
The data link layer transforms the physical layer, a raw
transmission facility, to a link responsible for node-to-node (hop-
to-hop) communication.
Specific responsibilities of the data link layer include framing,
addressing, flow control, error control, and media access
control.
The data link layer divides the stream of bits received from the
network layer into manageable data units called frames.
The data link layer adds a header to the frame to define the
addresses of the sender and receiver of the frame.

Data Link Layer
Objectives
If the rate at which the data are absorbed by the receiver is less
than the rate at which data are produced in the sender, the data link
layer imposes a flow control mechanism to avoid overwhelming
the receiver.
The data link layer also adds reliability to the physical layer by
adding mechanisms to detect and retransmit damaged,
duplicate, or lost frames.
When two or more devices are connected to the same link, data
link layer protocols are necessary to determine which device
has control over the link at any given time.

Error Detection
and
Correction

Data can be corrupted
during transmission.
Some applications require that
errors be detected and corrected.

10.7
Networks must be able to transfer data from one device to another
with acceptable accuracy.
For most applications, a system must guarantee that the data
received are identical to the data transmitted.
Any time data are transmitted from one node to the next, they can
become corrupted in passage.
Many factors can alter one or more bits of a message. Some
applications require a mechanism for detecting and correcting errors.
Some applications can tolerate a small level of error.
For example, random errors in audio or video transmissions may be
tolerable, but when we transfer text, we expect a very high level of
accuracy.
Error Detection and Correction

10.8
Error Detection and Correction
 Types of Errors
 Redundancy
 Détection Versus Correction
 Forward Error Correction Versus Retransmission
 Coding
 Modular Arithmetic

10.9
Error Detection and Correction-Types of Errors
 Whenever bits flow from one point to another, they are
subject to unpredictable changes because of interference.
 This interference can change the shape of the signal.
 In a single-bit error, a 0 is changed to a 1 or a 1 to a 0.
 In a burst error, multiple bits are changed.

10.10
In a single-bit error, only 1 bit in the data
unit has changed.

10.11
Figure: Single-bit error

10.12
A burst error means that 2 or more bits
in the data unit have changed.

10.13
Figure 10.2 Burst error of length 8
Note that a burst error does not necessarily mean that the errors
occur in consecutive bits.
The length of the burst is measured from the first corrupted bit to
the last corrupted bit.
Some bits in between may not have been corrupted.

Burst Error
A burst error is more likely to occur than a single-bit error.
The duration of noise is normally longer than the duration of 1 bit,
which means that when noise affects data, it affects a set of bits.
The number of bits affected depends on the data rate and duration
of noise.
For example, if we are sending data at 1 kbps, a noise of 11100s can
affect 10 bits; if we are sending data at 1 Mbps, the same noise can
affect 10,000 bits.

Redundancy
The central concept in detecting or correcting errors is redundancy.
To be able to detect or correct errors, we need to send some extra
bits with our data.
These redundant bits are added by the sender and removed by the
receiver.
Their presence allows the receiver to detect or correct corrupted
bits.

10.16
To detect or correct errors, we need to
send extra (redundant) bits with data.

10.17
Figure 10.3 The structure of encoder and decoder

10.18
In modulo-N arithmetic, we use only the
integers in the range 0 to N −1,
inclusive.
Note

10.19
Figure 10.4 XORing of two single bits or two words

10.20
10-2 BLOCK CODING
In block coding, we divide our message into blocks,
each of k bits, called datawords. We add r redundant
bits to each block to make the length n = k + r. The
resulting n-bit blocks are called codewords.
Error Detection
Error Correction
Hamming Distance
Minimum Hamming Distance
Topics discussed in this section:

10.21
Figure 10.5 Datawords and codewords in block coding

10.22
In 4B/5B block coding coding scheme,
k = 4 and n = 5. As we saw, we have 2k = 16 datawords
and 2n = 32 codewords. We saw that 16 out of 32
codewords are used for message transfer and the rest are
either used for other purposes or unused.
Example 10.1

10.23
Error Detection
 Enough redundancy is added to detect an error.
 The receiver knows an error occurred but does not
know which bit(s) is(are) in error.
 Has less overhead than error correction.

10.24
Figure 10.6 Process of error detection in block coding

10.25
Let us assume that k = 2 and n = 3. Table 10.1 shows the
list of datawords and codewords. Later, we will see how
to derive a codeword from a dataword.
Assume the sender encodes the dataword 01 as 011 and
sends it to the receiver. Consider the following cases:
1. The receiver receives 011. It is a valid codeword. The
receiver extracts the dataword 01 from it.
Example 10.2

10.26
2. The codeword is corrupted during transmission, and
111 is received. This is not a valid codeword and is
discarded.
3. The codeword is corrupted during transmission, and
000 is received. This is a valid codeword. The receiver
incorrectly extracts the dataword 00. Two corrupted
bits have made the error undetectable.
Example 10.2 (continued)

10.27
Table 10.1 A code for error detection (Example 10.2)

10.28
An error-detecting code can detect
only the types of errors for which it is
designed; other types of errors may
remain undetected.

10.29
Error Correction
 As we said before, error correction is much more difficult
than error detection.
 In error detection, the receiver needs to know only that the
received codeword is invalid;
 In error correction the receiver needs to find (or guess) the
original codeword sent.
 We can say that we need more redundant bits for error
correction than for error detection.
 Figure 10.7 shows the role of block coding in error correction.

10.30
Figure 10.7 Structure of encoder and decoder in error correction

10.31
Let us add more redundant bits to Example 10.2 to see if the receiver can correct
an error without knowing what was actually sent.
We add 3 redundant bits to the 2-bit dataword to make 5-bit codewords.
Table 10.2 shows the datawords and codewords. Assume the dataword is 01.
The sender creates the codeword 01011. The codeword is corrupted during
transmission, and 01001 is received.
First, the receiver finds that the received codeword is not in the table. This means
an error has occurred.
The receiver, assuming that there is only 1 bit corrupted, uses the following
strategy to guess the correct dataword.
Example 10.3

10.32
1. Comparing the received codeword with the first codeword in the table (01001
versus 00000), the receiver decides that the first codeword is not the one that
was sent because there are two different bits.
2. By the same reasoning, the original codeword cannot be the third or fourth one
in the table.
3. The original codeword must be the second one in the table because this is the
only one that differs from the received codeword by 1 bit. The receiver replaces
01001 with 01011 and consults the table to find the dataword 01.

10.33
Table 10.2 A code for error correction (Example 10.3)

10.
Hamming Distance
One of the central concepts in coding for error control is the idea of
the Hamming distance.
The Hamming distance between two words (of the same size) is the
number of differences between the corresponding bits.
We show the Hamming distance between two words x and y as d(x,
y).
The Hamming distance can easily be found if we apply the XOR
operation (Ꚛ) on the two words and count the number of 1s in the
result.
Note that the Hamming distance is a value >0.

10.
The Hamming distance between two
words is the number of differences
between corresponding bits.
Note

10.
Let us find the Hamming distance between two
pairs of words.
1. The Hamming distance d(000, 011) is 2 because
Example 10.4
2. The Hamming distance d(10101, 11110) is 3
because

10.
The minimum Hamming distance is the
smallest Hamming distance between
all possible pairs in a set of words.
Note

10.
Find the minimum Hamming distance of the
coding scheme in Table 10.1.
Solution
We first find all Hamming distances.
Example 10.5
The dmin in this case is 2.

10.
Find the minimum Hamming distance of the
coding scheme in Table 10.2.
Solution
We first find all the Hamming distances.
The dmin in this case is 3.
Example 10.6

10.
Before we continue with our discussion, we need to mention
that any coding scheme needs to have at least three
parameters: the codeword size n, the dataword size k, and
the minimum Hamming distance dmin.
A coding scheme C is written as C(n, k) with a separate
expression for dmin -
For example, we can call our first coding scheme C(3, 2) with
dmin =2 and our second coding scheme C(5, 2) with dmin = 3.
Three Parameters

10.
When a codeword is corrupted during transmission, the
Hamming distance between the sent and received codewords
is the number of bits affected by the error.
In other words, the Hamming distance between the received
codeword and the sent codeword is the number of bits that
are corrupted during transmission.
For example, if the codeword 00000 is sent and 01101 is
received, 3 bits are in error and the Hamming distance between
the two is d(00000, 01101) =3.
Hamming Distance and Error

10.
To guarantee the detection of up to s
errors in all cases, the minimum
Hamming distance in a block
code must be dmin = s + 1.
Note

10.
The minimum Hamming distance for our first code
scheme (Table 10.1) is 2. This code guarantees
detection of only a single error. For example, if the
third codeword (101) is sent and one error occurs,
the received codeword does not match any valid
codeword. If two errors occur, however, the
received codeword may match a valid codeword
and the errors are not detected.
Example 10.7

10.
Our second block code scheme (Table 10.2) has
dmin = 3. This code can detect up to two errors.
Again, we see that when any of the valid
codewords is sent, two errors create a codeword
which is not in the table of valid codewords. The
receiver cannot be fooled.
However, some combinations of three errors
change a valid codeword to another valid
codeword. The receiver accepts the received
codeword and the errors are undetected.
Example 10.8

10.
Figure 10.8 Geometric concept for finding dmin in error detection

10.
Figure 10.9 Geometric concept for finding dmin in error correction

10.
To guarantee correction of up to t errors
in all cases, the minimum Hamming
distance in a block code
must be dmin = 2t + 1.
Note

10.
A code scheme has a Hamming distance dmin = 4.
What is the error detection and correction
capability of this scheme?
Solution
This code guarantees the detection of up to three errors
(s = 3), but it can correct up to one error. In other words,
if this code is used for error correction, part of its capability
is wasted. Error correction codes need to have an odd
minimum distance (3, 5, 7, . . . ).
Example 10.9

10.
LINEAR BLOCK CODES
Almost all block codes used today belong to a subset called
linear block codes.
The use of nonlinear block codes for error detection and
correction is not as widespread because their structure
makes theoretical analysis and implementation difficult.
We therefore concentrate on linear block codes.
A linear block code is a code in which the exclusive OR
(addition modulo-2) of two valid codewords creates
another valid codeword.
 Minimum Distance for Linear Block Codes
 Some Linear Block Codes

10.
In a linear block code, the exclusive OR
(XOR) of any two valid codewords
creates another valid codeword.
Note

10.
Let us see if the two codes we defined in Table 10.1 and Table
10.2 belong to the class of linear block codes.
1. The scheme in Table 10.1 is a linear block code
because the result of XORing any codeword with any
other codeword is a valid codeword.
For example, the XORing of the second and third codewords
creates the fourth one.
2. The scheme in Table 10.2 is also a linear block code.
We can create all four codewords by XORing two
other codewords.
Example 10.10

10.
In our first code (Table 10.1), the numbers of 1s in the
nonzero codewords are 2, 2, and 2. So the minimum
Hamming distance is dmin = 2.
In our second code (Table 10.2), the numbers of 1s in the
nonzero codewords are 3, 3, and 4. So in this code we have
dmin = 3.
Example 10.11

10.
Some Linear Block Codes
Let us now show some linear block codes-
Simple Parity-Check Code
Perhaps the most familiar error-detecting code is the simple
parity-check code.
In this code, a k-bit dataword (100) is changed to an n-bit
codeword (1001) where n = k + 1.
The extra bit, called the parity bit, is selected to make the total
number of 1s in the codeword even.
Although some implementations specify an odd number of
1s, we discuss the even case.
The minimum Hamming distance for this category is dmin =2,
which means that the code is a single-bit error-detecting code;
it cannot correct any error.

10.
A simple parity-check code is a
single-bit error-detecting
code in which
n = k + 1 with dmin = 2.
Even parity (ensures that a codeword has an
even number of 1’s) and odd parity (ensures
that there are an odd number of 1’s in the
codeword)
Note

10.
Our first code (Table 10.1) is a parity-check code with k = 2
and n =3.
The code in Table 10.3 is also a parity-check code with k =4 and
n =5.
Figure 10.10 shows a possible structure of an encoder (at the
sender) and a decoder (at the receiver).
The encoder uses a generator that takes a copy of a 4-bit
dataword (a0, a1, a2, and a3) and generates a parity bit r0.
The dataword bits and the parity bit create the 5-bit codeword
(a0, a1, a2, a3, r0) .
The parity bit that is added makes the number of 1s in the
codeword even.

10.
This is normally done by adding the 4 bits of the dataword
(modulo-2); the result is the parity bit. In other words-
If the number of 1’s is even, the result is 0; if the number of 1’s
is odd, the result is 1.
In both cases, the total number of 1s in the codeword is even.
The sender sends the codeword which may be corrupted during
transmission. The receiver receives a 5-bit word.
The checker at the receiver does the same thing as the
generator in the sender with one exception: The addition is
done over all 5 bits.
The result, which is called the syndrome, is just 1 bit. The
syndrome is 0 when the number of 1s in the received codeword
is even; otherwise, it is 1.

10.
The result, which is called the syndrome, is just 1 bit. The
syndrome is 0 when the number of 1s in the received codeword
is even; otherwise, it is 1.
The syndrome is passed to the decision logic analyzer.
If the syndrome is 0, there is no error in the received codeword;
the data portion of the received codeword is accepted as the
dataword;
if the syndrome is 1, the data portion of the received codeword
is discarded. The dataword is not created.

10.
Table 10.3 Simple parity-check code C(5, 4)

10.
Figure 10.10 Encoder and decoder for simple parity-check code

10.
Let us look at some transmission scenarios. Assume the
sender sends the dataword 1011. The codeword created
from this dataword is 10111, which is sent to the receiver.
We examine five cases:
1. No error occurs; the received codeword is 10111. The
syndrome is 0. The dataword 1011 is created.
2. One single-bit error changes a1 . The received
codeword is 10011. The syndrome is 1. No dataword
is created.
3. One single-bit error changes r0 . The received codeword
is 10110. The syndrome is 1. No dataword is created.
Example 10.12

10.
4. An error changes r0 and a second error changes a3 .The
received codeword is 00110. The syndrome is 0. The
dataword 0011 is created at the receiver. Note that here the
dataword is wrongly created due to the syndrome value.
5. Three bits—a3, a2, and a1—are changed by errors. The
received codeword is 01011. The syndrome is 1. The
dataword is not created. This shows that the simple parity
check, guaranteed to detect one single error, can also find
any odd number of errors.

10.
A simple parity-check code can detect an
odd number of errors.
Note

10.
All Hamming codes discussed in this
book have dmin = 3 (2 bit error detection
and single bit error correction).
A codeword consists of n bits of which k
are data bits and r are check bits.
Let m = r, then we have: n = 2m -1
and k = n-m
Note

10.
Figure 10.11 Two-dimensional parity-check code

10.
Table 10.4 Hamming code C(7, 4) - n=7, k = 4

10.
Modulo 2 arithmetic:
r0 = a2 + a1 + a0
r1 = a3 + a2 + a1
r2 = a1 + a0 + a3
Calculating the parity bits at the transmitter
:
Calculating the syndrome at the receiver:
s0 = b2 + b1 + b0 +q0
s1 = b3 + b2 + b1+q1
s2 = b1 + b0 + b3+q2

10.
Figure 10.12
The structure of the encoder and decoder for a Hamming code

10.
Table 10.5 Logical decision made by the correction logic analyzer

10.
Let us trace the path of three datawords from the sender to
the destination:
1. The dataword 0100 becomes the codeword 0100011.
The codeword 0100011 (b3b2b1b0q2q1q0) is received.
The syndrome is
000, the final dataword is 0100.
The received codeword is: 0011001. The syndrome is
011. After flipping b2 (changing the 1 to 0), the final
dataword is 0111.
The syndrome is 101. After flipping b0, we get 0000,
the wrong dataword. This shows that our code cannot
correct two errors.
Example 10.13

10.
We need a dataword of at least 7 bits. Calculate
values of k and n that satisfy this requirement.
Solution
We need to make k = n − m greater than or equal to 7, or
2m − 1 − m ≥ 7.
1. If we set m = 3, the result is n = 23 − 1=7 and k = 7 − 3,
or 4, which is < 7.
2. If we set m = 4, then n = 24 − 1 = 15 and k = 15 − 4 =
11, which satisfies the condition k>7. So the code is
Example 10.14
C(15, 11)

Burst Errors
 A Hamming code can only correct a single error or detect a
double error.
 However, there is a way to make it detect a burst error, as
shown in Figure 10.13.
 The key is to split a burst error between several codewords, one
error for each codeword.
 In data communications, we normally send a packet or a frame
of data.
 To make the Hamming code respond to a burst error of size N,
we need to make N codewords out of our frame.
 Then, instead of sending one codeword at a time, we arrange
the codewords in a table and send the bits in the table a column
at a time.

Burst Errors
 In Figure 10.13, the bits are sent colunm by column (from the
left).
 In each column, the bits are sent from the bottom to the top.
 In this way, a frame is made out of the four codewords and
sent to the receiver.
 Figure 10.13 shows that when a burst error of size 4 corrupts
the frame, only 1 bit from each codeword is corrupted.
 The corrupted bit in each codeword can then easily be corrected
at the receiver.

Burst Errors
 Burst errors are very common, in particular in wireless
environments where a fade will affect a group of bits in
transit. The length of the burst is dependent on the
duration of the fade.
 One way to counter burst errors, is to break up a
transmission into shorter words and create a block (one
word per row), then have a parity check per word.
 The words are then sent column by column. When a burst
error occurs, it will affect 1 bit in several words as the
transmission is read back into the block format and each
word is checked individually.

10.
Figure 10.13 Burst error correction using Hamming code

10.
10-4 CYCLIC CODES
Cyclic codes are special linear block codes with one extra
property. In a cyclic code, if a codeword is cyclically shifted
(rotated), the result is another codeword.
For example,
if 1011000 is a codeword and we cyclically left-shift, then 0110001
is also a codeword.
Cyclic Redundancy Check
Hardware Implementation
Polynomials
Cyclic Code Analysis
Advantages of Cyclic Codes
Other Cyclic Codes

10.
Table 10.6 A CRC code with C(7, 4)

10.
Figure 10.14 CRC encoder and decoder

10.
Figure 10.15 Division in CRC encoder

10.
Figure 10.16 Division in the CRC decoder for two cases

10.
Hardwired design of the divisor in CRC
The following is the step-by-step process that can be used to
simulate the division process in hardware (or even in software)-
1. We assume that the remainder is originally all 0s (000 in our
example).
2. At each time click (arrival of 1 bit from an augmented dataword),
we repeat the following two actions:
a. We use the leftmost bit to make a decision about the
divisor (011 or 000).
b. The other 2 bits of the remainder and the next bit from the
augmented dataword (total of 3 bits) are XORed with the 3-
bit divisor to create the next remainder.
Figure 10.18 shows this simulator, but note that this is not the final
design; there will be more improvements.

10.
Figure 10.17 Hardwired design of the divisor in CRC

10.
Figure 10.18 Simulation of division in CRC encoder

10.
Figure 10.19 The CRC encoder design using shift registers

10.
Figure 10.20
General design of encoder and decoder of a CRC code

Using Polynomials for cyclic codes
 A better way to understand cyclic codes and how they
can be analyzed is to represent them as polynomials.
 A pattern of 0s and 1s (binary word) can be represented as a
polynomial with coefficients of 0 and 1.
 The power of each term shows the position of the bit; the
coefficient shows the value of the bit.
 The rightmost bit represents the “0” power term. The bit
next to it the “1” power term, etc.
 If the bit is of value zero, the power term is deleted from
the expression.

10.
Figure 10.21 A polynomial to represent a binary word
Figure 10.21 shows one immediate benefit; a 7-bit pattern can be replaced by
3 terms.
The benefit is even more conspicuous when we have a polynomial such as x24 +X3
+ 1. Here the bit pattern is 25 bits in length (three 1s and twenty-one 0s) while the
polynomial is just 3 terms.

10.
CRC division using polynomials
Figure 10.22 is the polynomial version of Figure 10.15. We can see that the
process is shorter.
The dataword 1001 is represented as x3 + 1. The divisor 1011 is represented as
x3 + x + 1.
To find the augmented dataword (1001000), we have left-shifted the dataword 3
bits (multiplying by x3 . The result is x6 + x3.
Division is straightforward.
We divide the first term of the dividend, x6, by the first term of the divisor, x3.
The first term of the quotient is then x6/x3, or x3.
Then we multiply x3 by the divisor and subtract (according to our previous
definition of subtraction) the result from the dividend.

10.
Figure 10.22 CRC division using polynomials

10.
The divisor in a cyclic code is normally
called the generator polynomial
or simply the generator.
Note

10.
In a cyclic code,
If s(x) ≠ 0, one or more bits is corrupted.
If s(x) = 0, either
a. No bit is corrupted.
or
b. Some bits are corrupted, but the
decoder failed to detect them.
Note

10.
In a cyclic code, those e(x) errors that are divisible by
g(x) are not caught.
Received codeword (c(x) + e(x))/g(x) =
c(x)/g(x) + e(x)/gx
The first part is by definition divisible the second part
will determine the error. If “0” conclusion -> no error
occurred. Note: that could mean that an error went
undetected.
Note

10.
If the generator has more than one term
and the coefficient of x0 is 1,
all single errors can be caught.
Note

10.
Which of the following g(x) values guarantees that
a single-bit error is caught? For each case, what is
the error that cannot be caught?
a. x + 1 b. x3 c. 1
Solution
a. No xi can be divisible by x + 1. Any single-bit error can
be caught.
b. If i is equal to or greater than 3, xi is divisible by g(x).
All single-bit errors in positions 1 to 3 are caught.
c. All values of i make xi divisible by g(x). No single-bit
error can be caught. This g(x) is useless.
Example 10.15

10.
Figure 10.23
Representation of two isolated single-bit errors using polynomials

10.
Find the suitability of the following generators in
relation to burst errors of different lengths.
a. x6 + 1 b. x18 + x7 + x + 1 c. x32 + x23 + x7 +
1
Solution
a. This generator can detect all burst errors with a length
less than or equal to 6 bits; 3 out of 100 burst errors
with length 7 will slip by; 16 out of 1000 burst errors of
length 8 or more will slip by.
Example 10.17

10.
b. This generator can detect all burst errors with a length
less than or equal to 18 bits; 8 out of 1 million burst
errors with length 19 will slip by; 4 out of 1 million
burst errors of length 20 or more will slip by.
c. This generator can detect all burst errors with a length
less than or equal to 32 bits; 5 out of 10 billion burst
errors with length 33 will slip by; 3 out of 10 billion
burst errors of length 34 or more will slip by.

10.
A good polynomial generator needs to
have the following characteristics:
1. It should have at least two terms.
2. The coefficient of the term x0 should
be 1.
3. It should not divide xt + 1, for t
between 2 and n − 1.
4. It should have the factor x + 1.
Note

10.
Table 10.7 Standard polynomials

10.
10-5 CHECKSUM
The checksum is used in the Internet by several
protocols although not at the data link layer.
However, we briefly discuss it here to complete our
discussion on error checking
Idea
One’s Complement
Internet Checksum

10.
Suppose our data is a list of five 4-bit numbers that we want to
send to a destination. In addition to sending these numbers, we
send the sum of the numbers. For example, if the set of
numbers is (7, 11, 12, 0, 6), we send (7, 11, 12, 0, 6, 36), where
36 is the sum of the original numbers.
The receiver adds the five numbers and compares the result
with the sum. If the two are the same, the receiver assumes no
error, accepts the five numbers, and discards the sum.
Otherwise, there is an error somewhere and the data are not
accepted.
Example 10.18

10.
We can make the job of the receiver easier if we
send the negative (complement) of the sum, called
the checksum. In this case, we send (7, 11, 12, 0, 6,
−36). The receiver can add all the numbers
received (including the checksum). If the result is
0, it assumes no error; otherwise, there is an error.
Example 10.19

10.
How can we represent the number 21 in one’s
complement arithmetic using only four bits?
Solution
The number 21 in binary is 10101 (it needs five bits). We
can wrap the leftmost bit and add it to the four rightmost
bits. We have (0101 + 1) = 0110 or 6.
Example 10.20

10.
How can we represent the number −6 in one’s complement arithmetic
using only four bits?
Solution
In one’s complement arithmetic, the negative or complement of a
number is found by inverting all bits.
Positive 6 is 0110; negative 6 is 1001. If we consider only unsigned
numbers, this is 9.
1001+1=1010 (2’s Complement)
In other words, the complement of 6 is 9. Another way to find the
complement of a number in one’s complement arithmetic is to
subtract the number from 2n − 1 (16 − 1 in this case).
15-6=9
Example 10.21

10.
Let us redo Exercise 10.19 using one’s complement arithmetic. Figure
10.24 shows the process at the sender and at the receiver.
The sender initializes the checksum to 0 and adds all data items and the
checksum (the checksum is considered as one data item and is shown in
color).
The result is 36. However, 36 cannot be expressed in 4 bits. The extra two
bits are wrapped and added with the sum to create the wrapped sum value
6.
In the figure, we have shown the details in binary. The sum is then
complemented, resulting in the checksum value 9 (15 − 6 = 9). The sender
now sends six data items to the receiver including the checksum 9.
Example 10.22

10.
The receiver follows the same procedure as the sender. It adds all data
items (including the checksum); the result is 45. The sum is wrapped and
becomes 15.
The wrapped sum is complemented and becomes 0. Since the value of the
checksum is 0, this means that the data is not corrupted.
The receiver drops the checksum and keeps the other data items. If the
checksum is not zero, the entire packet is dropped.

10.
Figure 10.24 Example 10.22
1

10.
Sender site:
1. The message is divided into 16-bit words.
2. The value of the checksum word is set to 0.
3. All words including the checksum are
added using one’s complement addition.
4. The sum is complemented and becomes the
checksum.
5. The checksum is sent with the data.
Note

10.
Receiver site:
1. The message (including checksum) is
divided into 16-bit words.
2. All words are added using one’s
complement addition.
3. The sum is complemented and becomes the
new checksum.
4. If the value of checksum is 0, the message
is accepted; otherwise, it is rejected.
Note

10.
Let us calculate the checksum for a text of 8 characters
(“Forouzan”). The text needs to be divided into 2-byte (16-
bit) words. We use ASCII to change each byte to a 2-digit
hexadecimal number. For example, F is represented as
0x46 and o is represented as 0x6F. Figure 10.25 shows how
the checksum is calculated at the sender and receiver
sites. In part a of the figure, the value of partial sum for the
first column is 0x36. We keep the rightmost digit (6) and
insert the leftmost digit (3) as the carry in the second
column. The process is repeated for each column. Note
that if there is any corruption, the checksum recalculated
by the receiver is not all 0s. We leave this an exercise.
Example 10.23

10.
10-3 LINEAR BLOCK CODES
Almost all block codes used today belong to a
subset called linear block codes. A linear block
code is a code in which the exclusive OR
(addition modulo-2) of two valid codewords
creates another valid codeword.
Minimum Distance for Linear
Block Codes

10.
Let us see if the two codes we defined in Table 10.1 and
Table 10.2 belong to the class of linear block codes.
1. The scheme in Table 10.1 is a linear block code
because the result of XORing any codeword with any
other codeword is a valid codeword. For example, the
XORing of the second and third codewords creates the
fourth one.
2. The scheme in Table 10.2 is also a linear block code.
We can create all four codewords by XORing two
other codewords.
Example 10.10

10.
In our first code (Table 10.1), the numbers of 1s in
the nonzero codewords are 2, 2, and 2. So the
minimum Hamming distance is dmin = 2. In our
second code (Table 10.2), the numbers of 1s in the
nonzero codewords are 3, 3, and 4. So in this code
we have dmin = 3.
Example 10.11

10.
A simple parity-check code is a
single-bit error-detecting
code in which
n = k + 1 with dmin = 2.
Even parity (ensures that a codeword
has an even number of 1’s) and odd
parity (ensures that there are an odd
number of 1’s in the codeword)
Note

10.
4. An error changes r0 and a second error changes a3 .
The received codeword is 00110. The syndrome is 0.
The dataword 0011 is created at the receiver. Note that
here the dataword is wrongly created due to the
syndrome value.
5. Three bits—a3, a2, and a1—are changed by errors.
The received codeword is 01011. The syndrome is 1.
The dataword is not created. This shows that the simple
parity check, guaranteed to detect one single error, can
also find any odd number of errors.

10.
Modulo 2 arithmetic:
r0 = a2 + a1 + a0
r1 = a3 + a2 + a1
r2 = a1 + a0 + a3
Calculating the parity bits at the transmitter:
Calculating the syndrome at the receiver:
s0 = b2 + b1 + b0
s1 = b3 + b2 + b1
s2 = b1 + b0 + b3

10.
Table 10.5
Logical decision made by the correction logic analyzer
Note that the generator is not concerned with the four cases shaded in
Table 10.5 because there is either no error or an error in the parity bit.
In the other four cases, 1 of the bits must be flipped (changed from 0 to 1
or 1 to 0) to find the correct dataword.
There are two points we need to emphasize here. First, if two errors
occur during transmission, the created dataword might not be the right
one.
Second, if we want to use the above code for error detection, we need
a different design.

10.
Let us trace the path of three datawords from the sender to the
destination:
The codeword 0100011 is received. The syndrome is
000, the final dataword is 0100.
The received codeword is: 0011001. The syndrome is 011. After
flipping b2 (changing the 1 to 0), the final dataword is 0111.
Received codeword: 0001000. The syndrome is 101. After flipping b0,
we get 0000, the wrong dataword. This shows that our code cannot
correct two errors.
Example 10.13

Burst Errors
 Burst errors are very common, in particular in
wireless environments where a fade will affect a
group of bits in transit. The length of the burst is
dependent on the duration of the fade.
 One way to counter burst errors, is to break up a
transmission into shorter words and create a block
(one word per row), then have a parity check per
word.
 The words are then sent column by column. When a
burst error occurs, it will affect 1 bit in several words
as the transmission is read back into the block format
and each word is checked individually.

Burst Errors
 A Hamming code can only correct a single error or detect
a double error.
 However, there is a way to make it detect a burst error, as
shown in Figure 10.13.
 The key is to split a burst error between several
codewords, one error for each codeword.
 In data communications, we normally send a packet or a
frame of data.
 To make the Hamming code respond to a burst error of size N,
we need to make N codewords out of our frame.
 Then, instead of sending one codeword at a time, we
arrange the codewords in a table and send the bits in the table
a column at a time.

10.
10-4 CYCLIC CODES
Cyclic codes are special linear block codes with one
extra property. In a cyclic code, if a codeword is
cyclically shifted (rotated), the result is another
codeword.
Hardware Implementation
Polynomials
Cyclic Code Analysis
Advantages of Cyclic Codes
Other Cyclic Codes

10.
10-4 CYCLIC CODES
Cyclic codes
For example, if 1011000 is a codeword and we cyclically
left-shift, then 0110001 is also a codeword.
In this case, if we call the bits in the first word a0 to a6
and the bits in the second word b0 to b6, we can shift
the bits by using the following:
In the rightmost equation, the last bit of the first word is
wrapped around and becomes the first bit of the second
word.

10.
10-4 CYCLIC CODES
We can create cyclic codes to correct errors. However, the theoretical
background required is beyond the scope of our syllabus.
In this section, we simply discuss a category of cyclic codes called the
cyclic redundancy check (CRC) that is used in networks such as
LANs and WANs.
Table 10.6 shows an example of a CRC code. We can see both the
linear and cyclic properties of this code.

10.
10-4 CYCLIC CODES
In the encoder, the dataword has k bits (4 here); the codeword has n
bits (7 here).
The size of the dataword is augmented by adding n - k (3 here) 0s to
the right-hand side of the word.
The n-bit result is fed into the generator. The generator uses a divisor
of size n - k + 1 (4 here), predefined and agreed upon.
The generator divides the augmented dataword by the divisor
(modulo-2 division).
The quotient of the division is discarded; the remainder (r2 rl r0) is
appended to the dataword to create the codeword.

10.
10-4 CYCLIC CODES
The decoder receives the possibly corrupted codeword.
A copy of all n bits is fed to the checker which is a replica of the
generator.
The remainder produced by the checker is a syndrome of n - k (3
here) bits, which is fed to the decision logic analyzer.
The analyzer has a simple function.
If the syndrome bits are all as, the 4 leftmost bits of the codeword are
accepted as the dataword (interpreted as no error); otherwise, the 4 bits
are discarded (error).

10.
Figure 10.20 General design of encoder and decoder of a CRC co

Using Polynomials
 We can use a polynomial to represent a
binary word.
 Each bit from right to left is mapped onto a
power term.
 The rightmost bit represents the “0” power
term. The bit next to it the “1” power term,
etc.
 If the bit is of value zero, the power term is
deleted from the expression.

10.
Figure 10.21 A polynomial to represent a binary word

10.
In a cyclic code,
If s(x) ≠ 0, one or more bits is corrupted.
If s(x) = 0, either
a. No bit is corrupted. or
b. Some bits are corrupted, but the
decoder failed to detect them.
Note

10.
In a cyclic code, those e(x) errors that
are divisible by g(x) are not caught.
Received codeword (c(x) + e(x))/g(x) =
c(x)/g(x) + e(x)/gx
The first part is by definition divisible the
second part will determine the error. If
“0” conclusion -> no error occurred.
Note: that could mean that an error went
undetected.
Note

10.
If a generator cannot divide xt + 1
(t between 0 and n – 1),
then all isolated double errors
can be detected.
Note

10.
Find the status of the following generators related
to two isolated, single-bit errors.
a. x + 1 b. x4 + 1 c. x7 + x6 + 1 d. x15 + x14 +
1
Solution
a. This is a very poor choice for a generator. Any two
errors next to each other cannot be detected.
b. This generator cannot detect two errors that are four
positions apart.
c. This is a good choice for this purpose.
d. This polynomial cannot divide xt + 1 if t is less than
32,768. A codeword with two isolated errors up to
32,768 bits apart can be detected by this generator.
Example 10.16

10.
A generator that contains a factor of
x + 1 can detect all odd-numbered errors.
Note

10.
❏ All burst errors with L ≤ r will be
detected.
❏ All burst errors with L = r + 1 will be
detected with probability 1 – (1/2)r–1.
❏ All burst errors with L > r + 1 will be
detected with probability 1 – (1/2)r.
Note

10.
10-5 CHECKSUM
The last error detection method we discuss
here is called the checksum. The checksum is
used in the Internet by several protocols
although not at the data link layer. However, we
briefly discuss it here to complete our
discussion on error checking
Idea
One’s Complement
Internet Checksum

10.
Suppose our data is a list of five 4-bit numbers
that we want to send to a destination. In addition
to sending these numbers, we send the sum of the
numbers. For example, if the set of numbers is (7,
11, 12, 0, 6), we send (7, 11, 12, 0, 6, 36), where 36
is the sum of the original numbers. The receiver
adds the five numbers and compares the result
with the sum. If the two are the same, the receiver
assumes no error, accepts the five numbers, and
discards the sum. Otherwise, there is an error
somewhere and the data are not accepted.
Example 10.18

10.
How can we represent the number −6 in one’s
complement arithmetic using only four bits?
Solution
In one’s complement arithmetic, the negative or
complement of a number is found by inverting all bits.
Positive 6 is 0110; negative 6 is 1001. If we consider only
unsigned numbers, this is 9. In other words, the
complement of 6 is 9. Another way to find the complement
of a number in one’s complement arithmetic is to subtract
the number from 2n − 1 (16 − 1 in this case).
Example 10.21

10.
Let us redo Exercise 10.19 using one’s
complement arithmetic. Figure 10.24 shows the
process at the sender and at the receiver. The
sender initializes the checksum to 0 and adds all
data items and the checksum (the checksum is
considered as one data item and is shown in
color). The result is 36. However, 36 cannot be
expressed in 4 bits. The extra two bits are
wrapped and added with the sum to create the
wrapped sum value 6. In the figure, we have
shown the details in binary. The sum is then
complemented, resulting in the checksum value 9
(15 − 6 = 9). The sender now sends six data items
Example 10.22

10.
The receiver follows the same procedure as the
sender. It adds all data items (including the
checksum); the result is 45. The sum is wrapped
and becomes 15. The wrapped sum is
complemented and becomes 0. Since the value of
the checksum is 0, this means that the data is not
corrupted. The receiver drops the checksum and
keeps the other data items. If the checksum is not
zero, the entire packet is dropped.

10.
Figure 10.24 Example 10.22
1 1 1 1
0 0 0 0
1 0 0 1

10.
Let us calculate the checksum for a text of 8 characters
(“Forouzan”). The text needs to be divided into 2-byte (16-
bit) words. We use ASCII (see Appendix A) to change each
byte to a 2-digit hexadecimal number. For example, F is
represented as 0x46 and o is represented as 0x6F. Figure
10.25 shows how the checksum is calculated at the sender
and receiver sites. In part a of the figure, the value of
partial sum for the first column is 0x36. We keep the
rightmost digit (6) and insert the leftmost digit (3) as the
carry in the second column. The process is repeated for
each column. Note that if there is any corruption, the
checksum recalculated by the receiver is not all 0s. We
leave this an exercise.
Example 10.23

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