A general overview of signal encoding You will learn why to use digital encoding, how signal is transmitted and received and how analog signals are converted to digital Some digital encoding methods A presentation prepared by my friend's friend. I have done no editing at all, I'm just uploading the presentation as it is.

2. Introduction
A general overview of signal encoding
You will learn why to use digital encoding, how signal
is transmitted and received and how analog signals
are converted to digital
Some digital encoding methods

3. OverviewConversion to digital signal from a analog is composed of 4
main stages
Analog signal is filtered by LPF and then sampled w.r.t time ‘T’.
LPF:
 Low Pass Filter, a filter that eliminates the high frequencies of the
input signal.
The samples are distributed over infinite set of values are
converted to final set if M values. Called quantization.
Each of these M values are converted binary representation.
PAM encoding composed of 3 stages.

4. Why PCM method?
A digital representation of an analog signal where the
magnitude of the signal is sampled regularly at
uniform intervals, then quantized to a series of
symbols in a numeric (usually binary) code.
Answer is the advantages over digitizing.
Part of them is also available in analog systems , but
cost is higher and performance is usually worse.

5. PCM
Error correction
Retransmit the damaged data again (as in TCP)
Encryption
Encrypted easily advantage in business/military purpose
Compression
Compress data take less memory
Storage
Retrieval of data using cheaper peripherals devices
Transmission
Repeater for long distance to reduce noise and regeneration
Line encoding
PCM signal is not ready to be transmitted requires line encoding
Some formal technique are used to represent data, and narrow
B/W

6. Analog => Digital
Passing the Analog signal through a LPF and sampling it.
Transferring the sampled signal through a quantizer.
Converting the quantized value to a binary representation.
Sampling and quantization of a signal (red) for 4-bit PCM

7. LPF and Sampling
Nyquest theorem, an Analog signal can be reconstructed from
a sequence of samples if the sampling rate is, at least, twice as
the highest frequency of the signal.
The LPF must come before the sampling. Filtering the
frequencies higher then the sampling rate, removing the
phenomenon called Aliasing.
Sampling rate help in calculating the time period of each
sample Ts= 1/fs.
Which defines the samples over an infinite set of values,
which is a big problem when it comes to transmission.
What to do then >>>????
We need to Quantize the data

8. Quantization
Confine the infinite set to finite set of values, defined
by letter M which is an exponential function
M = 2n
It can be easily derived from
the above table that this
quantizer has 8 levels (M=8).
The quantizer used here
is a linear quantizer.
Speech contain lower frequencies then higher
therefore we use more quantization levels then higher
X, the input voltage
[Volt]
Output voltage [Volt]
X >= 6 7
6 > X >= 4 5
4 > X >= 2 3
2 > X >= 0 1
0 > X >= -2 -1
-2 > X >= -4 -3
-4 > X >= -6 -5
-6 > X -7

9. sampling
When you sample the wave with an analog-to-digital
converter, you have control over two variables:
The sampling rate - Controls how many samples are
taken per second
The sampling precision - Controls how many
different gradations (quantization levels) are possible
when taking the sample

10. sampling
A/D Conversion
D/A Conversion
Higher rate sampling

12. Binary conversion
Last stage of PCM is the conversion of the value of
quantization to binary representation.
We used M=8 => the number of bits needed for binary
representation is n=3.
We can use any desired representation, such as octal
or hexadecimal.*
The binary representation designating each quantization
level should be also considered.

13. Gray codes
Gray code can be very useful here.
In Gray code, every two neighboring words are different
in only one bit.
Thus a error caused due to additive noise will cause only
a minor shift to neighboring frequencies
Decreasing the impact of the
error occurred significantly.
Quantizer output voltage
[Volt]
PCM output [binary
representation]
7 110
5 111
3 101
1 100
-1 000
-3 001
-5 011
-7 010

14. Problems still exist with PCM
Quantization noise
 The difference between the original samples to their quantized
values is called Quantization noise. This noise will appear at the
reconstruction of the Analog signal.
Bandwidth
 Each sample is represented by n bits, therefore the required
bandwidth is multiplied a factor of, at least, n
ISI (Inter-symbol Interference)
 Each binary representation of the samples, will be transformed at the
end to some shape, usually a pulse, called a symbol. It is very likely
that neighboring symbols will interfere each other, thus adding
difficulties to the reconstruction of the analog signal.

15. Encodings
Digital data, digital signals
How to represent bits (codes)
Analog data, digital signals
How to represent voltages (sampling)

16. Digital/Digital Encoding
Issue in comparing various techniques:
Signal spectrum
 High freq-big b/w, no dc – Better isolation
Signal synchronization capability
Signal error detecting capability
Signal interference and noise immunity
Cost and complexity

17. More A – D modulation
Pulse Amplitude Modulation (PAM)
Delta Modulation (DM)
Quantizing noise
Slope-overload noise
Differential Pulse code Modulation (DPAM)

18. NRZ-L: Non Return to Zero Level
Zero is represented as no voltage, and one by high
voltage level.
First, it has a DC component, meaning that its average voltage
is not 0 but some positive constant.
Second, it has the inability to carry synchronization
information. Again, if we have a series of ones, we won’t be
able to know how many we got.

19. Polar NRZ-L: Polar Non Return to Zero Level
Zero is represented as negative voltage level, and one
by positive voltage level.
This code is similar to the previous one. It handles the
DC component issue, meaning the average voltage level
is 0. It still has the synchronization problem.

20. NRZ-I: Non Return to Zero Inverted
Transition on one only.
Like Polar NRZ no change in voltage in the case of
zeroes sequence and no carry of synchronization
information.
This code doesn’t handle the DC component (average
is not 0).

21. Bipolar (Multilevel Binary encoding)
No voltage on zero, the first one is a positive voltage,
the second one is a negative voltage, and the voltage
values of subsequent ones alternate.
Here the problem of DC component (average not 0)
was solved by introducing negative voltage level. The
code is not sensitive for polarity but we can lose
synchronization on a long sequence of zeroes.

22. Manchester (Biphase encoding)
Zero is represented as a transition from high to low
voltage level in the middle of the bit, while one is
represented by the transition from low to high.
Good for timing as we have a transition every cycle,
fully self synchronizing.
Used on 10 Mb/s Ethernet

23. Differential Manchester (Biphase)
Always a transition in the middle of a bit, transition at the
beginning only for zero.
As in the regular Manchester code, fully self synchronizing
Another advantage here, polarity is not significant.
The drawback of this line code is the same as for the
previous one, double bandwidth.

24. Scrambling Techniques
For long distance applications, the encoding schemes
that are normally used are known as scrambling tech.
Applied in case of bipolar AMI (Alternate mark
inversion)
Solve problem of long strings of ‘0’
 B8ZS- bipolar 8 zero substitution
HDB3- high density Bipolar 3 zeros

25. 4B/5B
Insert extra bits to break up runs
4 bit vales sent as 5 bit codeword
Codeword have <2 leading 0 and <3 trailing 0; 16 of 32
used (other for ctrl)
Transmittied using NRZI
80% efficiency
Used by FDDI and 100 Mb/s ethernet

26. Complete communication system
A basic block diagram of a complete communication
system for analog signals.

27. Receiver
Modulation taking the input bits (called Baseband)
and, loading it on the transmission carrier (RF
carrier).
Detection mainly, receiving only a pre defined
frequency range.
Matched filter a filter that is match to the
transmitted signal, thus enables the best possible
reception.
Decision for every digital value received we should
decide what was the original value that was
transmitted.
D/A Digital to Analog signal convertor.