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COMMUNICATION SYSTEM

IV SEMESTER

ECC-206

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Communication system- Simon Haykins

Modern Analog and Digital system– B.P. Lathi

Communication System- Schaum Series

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Communication System

Communication is the process of transformation of information from source to destination or from transmitter to receiver.

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TRANSMITTER

RECEIVER

INFORMATION

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Subject Introduction

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A typical communication system starts with a source of information of interest that we want to send somewhere. Normally, it is required to be converted to electrical form using a transducer. After its electrical conversion, we use an equipment called transmitter, the work of which is to prepare the signal for the physical medium which it is to be sent over. In essence, the job of the transmitter is to match the properties of he information signal to the properties of the physical medium over which it is to be sent so that efficient communication is possible. An abstraction of all kinds of physical mediums through which transmission can take place is termed as a channel. At the other end or destination, there is a receiver the job of which is to interpret the received information in a way that is usable to the user. Hence, in a communication system, there are five major blocks connected as shown.

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Block Diagram of Communication System( Wired comm. System)

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Transmitter

Receiver

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Information Sources

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A typical spectrum of human speech signals can be drawn as shown here. As can be seen in the graph, there might be some peaks, but the maximum amount of the intelligent data used in the speech/ voice communication can be seen to lie in the frequency range of 300Hz to 3300Hz. Including some guard band, we may infer that the bandwidth (maximum possible relevant frequency in the spectrum) of a speech signal is 4KHz. It is noteworthy though, that the actual spectrum of the voice signal may go up to 7 – 8 KHZ. Thus, for studio quality voice processing, the bandwidth must be kept of the same order. In telephonic communication, however, the above mentioned 4KHz bandwidth is considered adequate.

Frequency Spectrum of typical Speech signal

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Note

Voice………. 300 Hz to 3.5Khz [ variation of acoustic pressure with time]
Audio……….20 Hz to 20KHz
Video………0 to 4.5 MHz [ variation of light intensity with time]

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Information Source

Information source is the source of information.

Source Transducer:

Source transducer converts a physical signal to electrical equivalent.

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Channel:

Channel is the medium through which signal is transmitted from one place to another.

Note:

1. Wired comm. System is preferred for short distance communication where the channel will be
Co-axial cable
Parallel wire
Twisted Cable

2. For long distance comm. Wireless comm is preferred where the channel will be free space.

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Receiving Transducer:

It converts electrical signal to physical signal.

E.g. Loudspeaker

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Block Diagram of Wireless communication System:

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For transmission of signal to very much long distance through free space, modulation has to be used.

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Modulation

This process of imposing an input signal onto a carrier wave is called modulation.
It is the process in which one of the parameter ( Amplitude, frequency, or phase) of the signal will be varied linearly in accordance with message signal amplitude variation.

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Need of Modulation

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Note

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2. Multiplexing

It is the process of transmission of multiple no. of signal through a single channel at the same time.
Generally, without modulation, Multiplexing is not possible.
The lowest possible frequency contained by a signal must be taken into reference to decide Antenna height.

Note

1. Modulation is used in wired communication system for multiplexing.

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Next topic is Fourier transform.

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Fourier Transform

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Example:

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Note:

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Note:

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4. For band Limiting a signal, all the significant frequencies should be retained, and insignificant frequencies has to be eliminated.

5. For Band Limiting, generally the signal will be passed through proper LPF.

Note:

To use the channel B.W. efficiently, we generally transmit significant frequencies only.

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Properties of Fourier transform

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Frequency Shifting property:

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Concept of Modulation:

if a signal contains all the significant all the significant low frequency, then such signals are called “Baseband Signal”.

These baseband signals have significant low frequencies hence they require huge Antenna heights, which is impossible to construct.

Hence the process of modulation is introduced such that the frequency is increased to reduce antenna heights.

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If a signal contains only significant high frequencies, then such signals are called BAND PASS SIGNAL.

NOTE:

A Base Band Signal can’t be transmitted faithfully as it requires huge antenna, but a Band Pass Signal can be transmitted faithfully.

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Concept of Demodulation

Process of receiving back the message signal from the modulated signal .

Demodulation will be done at receiver end.

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Single tone modulation Multitone modul.

( single frequency message (Multi freq. message

Signal modulation) signal modulation)

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Classification of modulation

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Channel

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A channel is basically a physical medium. Some examples are cables, optical fibre, free space, etc. Another way of looking at a channel is as an abstraction. This means that channel is to be modelled in an abstract fashion. Thus, all the effects of the transmitter, receiver and physical medium that are undesirable or unexpected are modelled as a block called channel. These undesired effects can be simply called noise. Hence, to say that noise exists in a communication system simply means that noise exists in the channel.

Noise in Communication Systems

Broadly, noise sources can be classified into two categories: Internal noise and External Noise. External noises are attributed to sources outside the circuit. Natural sources may include lightening, atmospheric noises, cosmic EM waves, etc. Man-made sources may include EM distortions generated by electrical lines, commutator switches in motors, ignition noises in automobiles, etc. Along with this, other Radio frequency interferences have become more prominent with the advent of multiuser communication over similar frequency spectrums. Multipath fading is also a common issue in wireless communication. An in-depth discussion of different forms of noises will be done later in this course.

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Mathematical Models of Communication Channels

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In the design of communication systems for transmitting information through physical channels, we find it convenient to construct mathematical models that reflect the most important characteristics of the transmission medium. Then, the mathematical model for the channel is used in the design of the channel encoder and modulator at the transmitter and the demodulator and channel decoder at the receiver. Mentioned below is a brief description of the channel models that are frequently used to characterize many of the physical channels that we encounter in practice.

Additional Noise Channel

The simplest mathematical model for a communication channel is the additive noise channel. In this model the transmitted signal s(t) is corrupted by an additive random noise process n(t). Physically, the additive noise process may arise from electronic components and amplifiers at the receiver of the communication system or from interference encountered in transmission as in the case of radio signal transmission.

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Mathematical Models of Communication Channels

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Mathematical Models of Communication Channels

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Propagation in a Communication Channel

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The various methods of propagation of signals in a communication channel depends upon the type of channel itself. Based on this criterion, the classification can be done as follows:

EM wave propagation channel – Free space channel

Guided EM wave propagation channel

Optical Channels

As such, pure free space communication cannot take place in the earth’s atmosphere. But still wireless communication can be considered free space communication under specific assumptions.

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Propagation in a Communication Channel

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As is known, a medium bounded on one side by a conductor gives rise to guided waves. In the case shown here, transmitter antenna is sending an EM wave to a nearby receiver. In such a case, the Earth’s surface would act as a conductor and the curvature of Earth guides the wave. This is called the ground wave propagation and classified as a guided transmission (type b). The limitation of such a system is that it can only be used for low frequency transmission, since for higher frequencies, the attenuation due to Earth’s surface increases significantly. Therefore, ground wave propagation is well suited for lower frequencies.

Consider now, that the communication needs to take place between a transmitter at the Earth’s surface and an Aeroplane. Such communication can only take place in Line of Sight (LoS). Since this involved transmission outwards from the Earth’s surface, for all practical purposes it can be termed as free space communication, although a more suited name is Sky Wave Communication. This transmission can work for higher frequencies as well.

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Propagation in a Communication Channel

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Propagation in a Communication Channel

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LoS communication is also possible between stations on the Earth’s surface. This is, though, limited by the distance between the stations. Due to the curvature of the Earth, there will be a point beyond which the signal will be obstructed by the surface itself.

The way out of this problem is to transmit the wave skywards and somehow have it reflected to the destination. One way of doing so is making use of a natural phenomenon. In the Earth’s atmosphere, there is a layer, called Ionosphere, which is made up of charged ions and acts as a passive reflector. The limitation of this is that the ionosphere is capable of reflecting frequencies up to 30MHz. Higher frequencies will escape the ionosphere and move into free space.

For higher frequencies (>>30MHz), we need to have a reflecting satellite in an orbit around the planet. If that satellite only reflects the wave, it is called a passive satellite. In case it received and retransmits the wave, it is called an active satellite.

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Propagation in a Communication Channel

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Propagation in a Communication Channel

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Among the guided wave channels, some more examples that can be quoted are:

a) Cables and Wire pairs: Depending upon the lower frequencies or higher frequencies, a pair of wires may be modelled as a lumped circuit or transmission lines respectively. In each case, they are used as guided media to transmit EM waves.

b) Waveguides: They are essentially pipes (cylindrical or rectangular) used to transfer EM waves from transmitter circuit to the antenna.

Optical Channels

Optical communication is possible using Fibre Optics as well as Free Space Optics. Fibre optics is a cylindrical waveguide used for transmission of optical signals, which in turn are essentially EM waves. Free Space Optics is typically like a Free Space EM communication. The main difference is that the information is transmitted in the form of highly focussed beam of light to enable transmission from point A to B. Mainly, Free Space Optics is used in sky wave communication, but it is also finding many terrestrial applications as well.

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Random Variables

In an experiment, a measurement is usually

denoted by a variable such as X.

In a random experiment, a variable whose

measured value can change (from one replicate of

the experiment to another) is referred to as a

random variable.

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Random Variables

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Probability

Used to quantify likelihood or chance

Used to represent risk or uncertainty in engineering

applications

Can be interpreted as our degree of belief or

relative frequency

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Probability

Probability statements describe the likelihood that

particular values occur.

The likelihood is quantified by assigning a number

from the interval [0, 1] to the set of values (or a

percentage from 0 to 100%).

Higher numbers indicate that the set of values is

more likely.

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Probability

A probability is usually expressed in terms of a

random variable.

For the part length example, X denotes the part

length and the probability statement can be written

in either of the following forms

Both equations state that the probability that the

random variable X assumes a value in [10.8, 11.2] is

0.25.

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Probability

Complement of an Event

Given a set E, the complement of E is the set of

elements that are not in E. The complement is

denoted as E’.

Mutually Exclusive Events

The sets E₁ , E₂ ,...,E_k are mutually exclusive if the

intersection of any pair is empty. That is, each

element is in one and only one of the sets E₁ , E₂

,...,E_k .

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Probability

Probability Properties

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Probability

Events

A measured value is not always obtained from an

experiment. Sometimes, the result is only classified

(into one of several possible categories).

These categories are often referred to as events.

Illustrations

The current measurement might only be

recorded as low, medium, or high; a manufactured

electronic component might be classified only as

defective or not; and either a message is sent through a network or not.

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Continuous Random Variables

3-4.1 Probability Density Function

The probability distribution or simply distribution of a random variable X is a description of the set of the probabilities associated with the possible values for X.

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Continuous Random Variables

3-4.1 Probability Density Function

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Continuous Random Variables

3-4.1 Probability Density Function

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Continuous Random Variables

Probability Density Function

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Continuous Random Variables

Probability Density Function

Undoubtedly, the most widely used model for the distribution of a random variable is a normal distribution.

Central limit theorem
Gaussian distribution

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Important Continuous Distributions

Normal Distribution

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Important Continuous Distributions

Normal Distribution

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Important Continuous Distributions

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Important Continuous Distributions

Normal Distribution

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Important Continuous Distributions

Normal Distribution

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Important Continuous Distributions

Normal Distribution

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Important Continuous Distributions

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Important Continuous Distributions

Normal Distribution

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Important Continuous Distributions

Normal Distribution

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Binomial Distribution

A trial with only two possible outcomes is used so

frequently as a building block of a random experiment

that it is called a Bernoulli trial.

It is usually assumed that the trials that constitute the

random experiment are independent. This implies that

the outcome from one trial has no effect on the

outcome to be obtained from any other trial.

Furthermore, it is often reasonable to assume that the

probability of a success on each trial is constant.

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Binomial Distribution

Consider the following random experiments and random variables.

Flip a coin 10 times. Let X = the number of heads obtained.
Of all bits transmitted through a digital transmission channel, 10% are received in error. Let X = the number of bits in error in the next 4 bits transmitted.

Do they meet the following criteria:

Does the experiment consist of Bernoulli trials?
Are the trials that constitute the random experiment are independent?
Is probability of a success on each trial is constant?

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Binomial Distribution

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Binomial Distribution

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Binomial Distribution

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Poisson Process

Exponential Distribution

The discussion of the Poisson distribution defined a random variable to be the number of flaws along a length of copper wire. The distance between flaws is another random variable that is often of interest.
Let the random variable X denote the length from any starting point on the wire until a flaw is detected.
As you might expect, the distribution of X can be obtained from knowledge of the distribution of the number of flaws. The key to the relationship is the following concept:

The distance to the first flaw exceeds 3 millimeters if and only if there are no flaws within a length of 3 millimeters—simple, but sufficient for an analysis of the distribution of X.

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Functions of Random Variables

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Functions of Random Variables

Linear Combinations of Independent

Random Variables

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Functions of Random Variables

Linear Combinations of Independent

Random Variables

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Functions of Random Variables

Linear Combinations of Independent

Random Variables

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Functions of Random Variables

What If the Random Variables Are Not Independent?

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Functions of Random Variables

What If the Random Variables Are Not Independent?

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Random Samples, Statistics, and

The Central Limit Theorem

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Random Samples, Statistics, and

The Central Limit Theorem

Central Limit Theorem

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Random Samples, Statistics, and

The Central Limit Theorem

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Random Samples, Statistics, and

The Central Limit Theorem

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Random Samples, Statistics, and

The Central Limit Theorem