Wireless Communications Systems

Dr. Jose A. Santos

Course Objectives and Learning Outcomes

The course aim is to “Introduce the theory and practice of analogue and digital wireless communications systems and to enable a clear understanding of the “state of the art” of wireless technology.

Learning Outcomes

Upon the successful completion of this module a successful student will:

1. Be equipped with a sound knowledge of modern wireless communications technologies

2. Comprehend the techniques of spread spectrum and be able to explain its pervasiveness in wireless technologies.

Learning Outcomes

3. Understand, the design of a modern wireless communication system and be capable of analysing such systems satellite communications, cellular wireless, cordless systems and wireless local loop.

Learning Outcomes

4. Understand different Wireless LANs technologies and Identify key elements that characterize the protocol architecture of such technologies.

5. Have gained practical experience in the implementation of wireless technologies and systems in MATLAB and be capable of performing experimental tests on these systems, analysing the results

Course Contents

Basic Mathematical Communication Concepts – The Decibel (Week 1)

Time Characterization of Signals (Week 1)

Frequency Characterization of Signals (Week 1)

Subject Area 1: Transmission Fundamentals & Principles

Course Contents

The Fourier Transform and Its Properties (Week 1)

Analogue and Digital Data Transmission (Week 2)

Channel Capacity, Data Rate, Bandwidth and Transmission Media (Week 2)

Subject Area 1: Transmission Fundamentals & Principles

Course Contents

Antennas and Propagation (Week 3) Signal Encoding Techniques (Week 4) Spread Spectrum Techniques (Week 5) Coding and Error Control in Wireless

Transmissions (Week 6)

Subject Area 2: Wireless Communications Technologies

Course Content

Satellite Communications (Week 7) Cellular Wireless Networks (Week 8 & 9) Cordless Systems (Week 10) Wireless Local Loop (Week 10) Mobile IP and Wireless Access Protocol

(Week 11)

Subject Area 3: Wireless Networking

Course Content

Wireless LAN Technologies (Week 12)

IEEE 802.11 Wireless LAN Standards (Mandatory Reading)

Bluetooth (Mandatory Reading)

Subject Area 4: Wireless LANs

Course Content

Introduction to MATLAB, Fourier Analysis & Power Spectrum Generation (Week 3-4)

Functions in MATLAB, Modulation Techniques (AM, FM, ASK, FSK) (Week 5-7)

Introduction to Simulink, Basic Communications Models, Error Control, Modulation Systems. (Week 8)

Laboratory Exam (Week 12)

Subject Area 5: MATLAB Practicals

Teaching Schedule & Evaluation

End of year Examination (75%) Coursework (25%)

Literature Review Paper (50%) Week 5 Essay(25%) Week 9 Laboratory Exam (25%) Week 12

For Details on CW and Exam consult the CW Handout Document on the module website.

http://www.scis.ulster.ac.uk/~jose/COM586/index.html

Course Reading List

Essential: Stallings, W. “Wireless Communications and

Networks,” Prentice Hall. 2002 and 2nd Ed. 2005. Additional Resources:

Mark, J and Zhuang W. “Wireless Communications and Networking” Prentice Hall. 2003

Shankar, P.M. “Introduction to Wireless Systems”, John Wiley & Sons Inc. 2002.

Proakis, J. “Communication Systems Engineering” 2nd Ed. Prentice Hall, 2002.

Haykin, S. and Moher, M. “Modern Wireless Communications,” International Ed. Prentice Hall, 2005.

Module Delivery

Class Structures: Theory Class 2 Hours (Every week) Tutorials 1 Hours (Selected weeks) Labs (3 Practicals in 11 Weeks)

Availability and ContactDr. Jose A. SantosRoom MG121E

ja.santos@ulster.ac.ukhttp://www.scis.ulster.ac.uk/~jose

Introduction to Wireless Systems

Class 1 Contents - Introduction

Introduction & Review of Mathematical Concepts Wireless and the OSI Model The Decibel Concept dB & dBm – Application to logarithmic

formulas Signal Concepts

Time Domain Signals Frequency Domain Concepts

Class Contents

Introduction to Fourier Analysis of SignalsThe Fourier Transform TheoremThe Fourier Series Representation

Wireless & Open System Interconnection Model Wireless is only one component of the

complex systems that allows seamless communications world wide.

Wireless is concerned with 3 of the 7 layers of the OSI reference model:

Wireless & Open System Interconnection Model Physical Layer: Physical Mechanisms for

transmission of binary digits. (Modulation, Demodulation & Transmission Medium Issues).

Data-Link Layer: Error correction and detection, retransmission of packets, sharing of the medium.

Wireless & Open System Interconnection Model Network Layer: Determination of the

routing of the information, determination of the QoS and flow control.

Wireless Systems with mobile nodes place greater demands on the network layer.

The Decibel

In telecommunications, we are often concerned with the comparison of one power level to another.

The unit of measurement used to compare two power levels is the decibel (dB).

A decibel is not an absolute measurement. It is a relative measurement that indicates the relationship of one power level to another.

The Decibel

1

2log10dBP

P

It is usual in telecommunications to express absolute dB quantities:

i.e. An antenna gain, the free space loss, etc.

All those quantities are being compared with the basic power unit:

W

P

1log10dB 2

Exercise 1

An antenna is said to have an output of 25 dB, calculate the actual power of the antenna in Watts.

The dBm Another important quantity used in

communications is the dBm.

It is, like the dB, a measure of power comparison but with respect to 1mW

1mW

Plog10dBm1

Exercise 2 & 3

An antenna is said to have an output of 25 dBm, calculate the actual power of the antenna in Watts.

Calculate the Output of the Antenna in dB

Usefulness of dB and dBm

It is useful to know that using decibels and logarithms, most of the problems in communications can be simplified.

Example – Formula Simplification:

CBAC

BAloglogloglog

Signals

A signal is an electromagnetic wave that is used to represent and/or transmit information.

An electromagnetic signal is a function that varies with time, but also can be represented as a function of frequency.

Time Domain Properties

As a function of time, a signal can be:

Analogue: Intensity varies smoothly over time.

Digital: Maintains a constant intensity over a period of time and then changes to another intensities.

Analogue Signal

Intensity

Digital Signal

Discrete Levels

Periodic & Aperiodic Signals

Signals can be further classified in:Periodic SignalsAperiodic Signals

Periodic signals are those that repeat themselves over time:

Periodic and Aperiodic Signals

T is called the PERIOD of the signal and is the smallest value that satisfies the equation.

Aperiodic Signals do not comply with the periodicity condition

ttsTts )()(

The Sine Wave

It is the fundamental analogue signal

It is represented by 3 parameters: Amplitude, Frequency - Period Phase

Amplitude: Is the peak value of the intensity (A). Period: Is the duration in time of 1 cycle (T) Frequency: Is the rate in cycles/sec [Hz] at

which the signal repeats

Phase: Is the measure of the relative position in time with respect of a single period of the signal.

Parameter Definitions

T

1f

T

tAtx

2sin)(

Frequency Domain Concepts

In practice an EM signal will be made of many frequency components.

A frequency representation of a signal can also be obtained.

The characterization of the signal if made from another point of view: frequency

Frequency Domain

s(t)

-1.5

-1

-0.5

0

0.5

1

1.5

0 0.5 1 1.5 2

time (s)

Am

pli

tud

e

tftfts

32sin

3

12sin

4)(

Frequency Domain

The signal is composed of sinusoidal signals at frequencies f and 3f

If enough sinusoidal signals are added and weighed together, any signal can be represented.

THIS IS THE PRINCIPLE OF THE FOURIER ANALYSIS

Frequency Domain

The second frequency of the signal is an integer multiple of the first frequency ( f ).

When all frequency components are integer multiple of one frequency, the latter is referred to as the FUNDAMENTAL FREQUENCY (fo)

Frequency Domain

All the other frequency components, are known as the HARMONICS of the signal.

The fundamental frequency is represented by:

The period of the signal is equal to the corresponding period of the fundamental frequency.

oo T

f1

Summary Any electromagnetic signal can be shown to

consist of a collection of periodic analogue signals (sine waves) at different amplitudes, frequencies and phases.

Bandwidth of the Signal

The Spectrum of the signal is the range of frequencies that it contains.

The width of the spectrum is known as the ABSOLUTE BANDWIDTH.

Bandwidth of the Signal

Many signals have infinite absolute bandwidth, but with most of the energy contained in a relatively narrow band of frequencies.

This band of frequencies is referred to as the EFFECTIVE BANDWIDTH or simply: “BANDWIDTH”

Bandwidth Calculation For a signal made of the fundamental

and two harmonics (odd multiples only), the frequency graph will be the spectrum.

The bandwidth will be the highest frequency minus the fundamental:

BW=5. fo – fo = 4. fo Hz The bandwidth of a signal is expressed

in Hertz.

The Wavelength

Another important quantity that goes hand in hand with the frequency is the WAVELENGTH.

It is a measure of the distance (in length units) of the period of the signal. i.e. it is the period of the signal expressed in length units.

The Wavelength The wavelength together with the frequency

define the speed at which an electromagnetic signal is travelling through the medium

m/s10x3 8

c

cf

Frequency Domain Visualization

Fourier Transform Principle of Operation

Conditions: Dirichlet Conditions:

1. x(t) is integrable on the real line (time line)

2. The number of maxima and minima of x(t) in any finite interval on the real line is finite.

3. The number of discontinuities of x(t) in any finite interval on the real line is finite.

dttx )(

The Fourier Transform Theorem

The Fourier Transform Theorem

The Fourier Transform X(f) of x(t) is given by:

The original signal can be obtained back from the Fourier Transform using:

dtetxfX tfj 2)()(

dfefXtx tfj 2)()(

The Fourier Transform

X(f) is in general a complex function.

Its magnitude and phase, represent the amplitude and phase of various frequency components in x(t).

The function X(f) is sometimes referred to as the SPECTRUM of the signal x(t). (Voltage Spectrum).

Fourier Transform Notation

)]([)(

)()(

)]([)(

1 fXFtx

fXtxFormShort

txFfX

Fourier Transform Properties

a) Linearity: The Fourier Transform operation is linear.

b) Duality:

)()()()(

)()()()(

2121

2211

fXfXtxtx

then

fXtxandfXtxif

)]([)(

)]([)(

)]([)(

tXFfx

andtXFfx

then

txFfXif

Fourier Transform Properties

c) Shift Property: A shift of to in the time domain, causes a phase shift of -2 .p f.t0 in the frequency domain:

)]([)]([ 2 txFettxF otfjo

FT: Example 1

The Unit Impulse or delta function is a special function that is defined as:

dtet tfj 2)(

FT: Example 1

The unit impulse is the base for the shifting of functions in time:

The Fourier Transform of the unit impulse yields (using the shift property):

)()()( oo txtttx

1)]([

propertyshiftingtheusing)()]([

02

2

fj

tfj

etF

dtettF

FT: Example 2

The unit impulse spectrum is:

Using the Duality Property of the Fourier Transform:

)(]1[ fF

Example 3: The Square Pulse

Notation:

t

The Square Pulse

)(sinc][

)sin(][

2

1][

2

1][ 2/22/2

2/

2/

2

ftF

ff

tF

j

ee

ftF

eefj

dtetF

fjfj

fjfjtfj

τbydividingandgmultiplyin

The sinc function

If the previous result is plotted with a value of t=1 we obtain the following.

Periodic Signals: Fourier Series Representation The Fourier series is based in the fact that any

function can be represented as a sum of sinusoids, this is known as the Fourier Series

A periodic signal x(t) with a fundamental period T0, that meets the dirichlet conditions, can be represented in terms of its Fourier Series as Follows

Fourier Series Expansion of x(t)

where

Fourier Series – Sine-Cosine Representation

T

n

T

n

T

dttfntxT

B

dttfntxT

AdttxT

A

ComponentDCA

HarmoniclFundamentaorFrequencylFundamentaf

0

0

0

0

0

0

0

0

)2sin()(2

)2cos()(2

)(2

1

000 )2sin()2cos(

2)(

nnn tfnBtfnA

Atx

Fourier Series – Amplitude-Phase Representation Representation:

This Relates to the sine-cosine as follows

1

00 )2cos(

2)(

nnn tfnC

Ctx

n

nn

nnn

A

B

BAC

AC

1

22

00

tan

Examples

Triangular Wave (Period T, Amplitude A)

Sawtooth Wave (Period T, Amplitude A)

oddn

tfnn

Atx

102

)2cos()(

8)(

oddn

n tfnn

Atx

10 )2sin(

2)1()(

Fourier Series: Exponential Representation

n

tfnjn

oextx 2)(

Where, for some arbitrary a:

o

o

Ttfnj

on dtetx

Tx

2)(1

tatusdiscontinoistxiftxtx

tatcontinousistxiftxtx

)(2

)()()()(

)(

and

Observations

The coefficients, xn, are called the Fourier series coefficients of x(t).

These coefficients are complex numbers.

The parameter a is arbitrary, It is chosen to simplify the calculation.

Useful Parameters Calculated from the Fourier Series Expansion Given the following Fourier expansion

(Amplitude = 2, T=10ms), Calculate:

Amplitude of the Fundamental ComponentAmplitude of the 3rd HarmonicBandwidth of the Signal for if 5 harmonics are

used

oddn

tfnjen

Atx

,1

2

2

20

)(

2)(

Solution:

Next Week

Solve Tutorial 1 Transmission Fundamentals and

Principles