IntroductionA general overview of signal encodingYou 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

OverviewConversion to digital signal from a analog is composed of 4

main stagesAnalog 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.

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.

PCMError correction

Retransmit the damaged data again (as in TCP)Encryption

Encrypted easily advantage in business/military purposeCompression

Compress data take less memoryStorage

Retrieval of data using cheaper peripherals devicesTransmission

Repeater for long distance to reduce noise and regenerationLine encoding

PCM signal is not ready to be transmitted requires line encoding

Some formal technique are used to represent data, and narrow B/W

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

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

QuantizationConfine the infinite set to finite set of values, defined

by letter M which is an exponential functionM = 2n

It can be easily derived fromthe above table that thisquantizer 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 76 > X >= 4 54 > X >= 2 32 > X >= 0 10 > X >= -2 -1-2 > X >= -4 -3-4 > X >= -6 -5-6 > X -7

samplingWhen 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

sampling

A/D Conversion

D/A Conversion

Higher rate sampling

Binary conversionLast 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.

Gray codesGray 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 frequenciesDecreasing the impact of the

error occurred significantly.Quantizer output voltage [Volt]

PCM output [binary representation]

7 1105 1113 1011 100-1 000-3 001-5 011-7 010

Problems still exist with PCMQuantization 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, nISI (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.

EncodingsDigital data, digital signals

How to represent bits (codes)Analog data, digital signals

How to represent voltages (sampling)

Digital/Digital EncodingIssue in comparing various techniques:

Signal spectrum High freq-big b/w, no dc – Better isolation

Signal synchronization capabilitySignal error detecting capabilitySignal interference and noise immunityCost and complexity

More A – D modulationPulse Amplitude Modulation (PAM)Delta Modulation (DM)

Quantizing noiseSlope-overload noise

Differential Pulse code Modulation (DPAM)

NRZ-L: Non Return to Zero LevelZero 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.

Polar NRZ-L: Polar Non Return to Zero LevelZero 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. 

NRZ-I: Non Return to Zero InvertedTransition 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).

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.

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

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

synchronizingAnother advantage here, polarity is not

significant.The drawback of this line code is the same as for

the previous one, double bandwidth.

Scrambling TechniquesFor 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 substitutionHDB3- high density Bipolar 3 zeros

4B/5BInsert extra bits to break up runs4 bit vales sent as 5 bit codewordCodeword have <2 leading 0 and <3 trailing

0; 16 of 32 used (other for ctrl)Transmittied using NRZI80% efficiencyUsed by FDDI and 100 Mb/s ethernet

Complete communication systemA basic block diagram of a complete

communication system for analog signals.

ReceiverModulation 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.