(modeling, simulation, and mitigation) - · pdf file(modeling, simulation, and mitigation)...

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“Wireless Channel Modeling”(Modeling, Simulation, and Mitigation)

Dr. Syed Junaid NawazAssistant Professor

Department of Electrical EngineeringCOMSATS Institute of Information Technology

Islamabad, Pakistan.

Courtesy of, Dr. Noor M. Khan (MA Jinnah University, Pakistan)Dr. M. N. Patwary(Staffordshire University, UK)Dr Muhammad Ali Imran (University of Surrey, UK)

Dr. Syed Junaid Nawaz

Current:

Assistant Professor: COMSATS Institute of IT, Islamabad.

Previous Institutes:

Research Fellow: Aristotle University of Thessaloniki, Greece,

Assistant Professor/HoD: Federal Urdu University AST, Islamabad

Research Fellow: Staffordshire University, UK

Research Fellow: Mohammad Ali Jinnah University, Islamabad.

Assistant Professor: Federal Urdu University of AST, Islamabad

Research Associate/ Lecturer: COMSATS Institute of IT, Abbottabad

Education:

Ph.D., Electronics Engineering, MA Jinnah University, Islamabad.

BS and MS, Computer Engineering, CIIT, Abbottabad.

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Dr. Syed Junaid Nawaz

Undergraduate Courses:

Analog Communications

Digital Communications

Digital Logic Design

Digital Systems

Computer Organization

Object Oriented Programming

Graduate Courses:

Advanced Topics in Wireless Communications

Adaptive Signal Processing

Wireless Channel Modeling

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Dr. Syed Junaid NawazResearch Contributions in the area of Channel Moeling:

Syed Junaid Nawaz, B. H. Qureshi, N. M. Khan, “A Generalized 3-D Scattering Model for Macrocell Environment with Directional Antenna at BS,” IEEE Transactions on Veh. Technol. vol. 59, no. 7, pp. 3193-3204, May. 2010. [IF: 1.92].

Syed Junaid Nawaz, Noor M. Khan, Mohammad N. Patwary, and Mansour Moniri, “Effect of Directional Antenna on the Doppler Spectrum in 3-D Mobile Radio Propagation Environment," IEEE Transactions on Veh. Technol., vol. 60, no. 7, pp. 2895-2903, Jul. 2011. [Impact Factor: 1.92].

Syed Junaid Nawaz, M. Khawza, M.N. Patwary, and Noor M. Khan, “Superimposed Training Based Compressed Sensing of Sparse Multipath Channels”, IET Communications, Dec. 2012. [Impact Factor: 0.83].

M. Riaz, Syed Junaid Nawaz, and Noor M. Khan, “3D Ellipsoidal Model for Mobile-to-Mobile Radio Propagation Environments”, Wireless Personal Communication, Springer US, DOI: 10.1007/s11277-013-1158-0, Apr. 2013. [IF: 0.46]

Syed Junaid Nawaz, M. N. Patwary, Noor M. Khan, and Hongnian Yu, “3-D Gaussian Scatter Density Propagation Model Employing a Directional Antenna at BS,” in Proc. of IEEE, 5th Adv. Satellite Multimedia Systems Conf., vol. 1, Sep. 2010, pp. 395-400.

Syed Junaid Nawaz, Bilal H. Qureshi, Noor M. Khan, and M. Abdel-Maguid,” Effect of Directional Antenna on the Spatial Characteristics of 3-D Macrocell Environment”, in Proc. of IEEE, Int. Conf. on future comp. and commun., vol. 1, May 2010, pp. 552-556.

Syed Junaid Nawaz, Bilal H. Qureshi, and Noor M. Khan, ”Angle of Arrival Statistics for 3-D Macrocell Environment using Directional Antenna at BS”, in Proc. of IEEE, 13th Int. Multitopic Conf., vol. 1, Dec. 2009, pp. 1-5.

Syed Junaid Nawaz, S. Mohsin, and Ataul-aziz, ”Neural Network based MIMO-OFDM Channel equalizer using Comb-Type pilot arrangement”, in Proc. of IEEE, Int. Conf. on future comp. and comm., vol. 1, Apr. 2009, pp. 36 - 41.

Saif-Ur-Rehman, Syed Junaid Nawaz, M. N. Patwary, and M. Abdul Muguid, “Impact of Terrain Variance and Velocity on the Handover Performance of LTE Systems,” in Proc. of IEEE, Int. Conf. on Wireless Commun. and Signal Processing, vol. 1, Nov. 2010, pp.1-5.

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Introduction to the course

Deeper understanding of the “wireless communication channels”

Main focus Identifying notable models to represent physical phenomena for

theoretical studies

Developing an understanding of how “models” can be used to gain “insights” to a more complicated “reality”

Concepts vs. fine details

Interactive sessions Questions highly appreciated

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Pre-requisites

Your feedback required to steer the pace and direction of the course

Ideally, it is expected that the attendees have at least taken the following, or related, courses or some exposure: Probability and Stochastic Processes

Linear Algebra and its applications

Signals and Systems

Communication Theory

Information theory will be an extra plus.

Attempt will be to keep the presentation based on very basic principles but provide concepts and insights of the most recent advances/understandings

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Grades Evaluation Policy

Assignments + Quizzes: 15 Marks

Research Project: 15 Marks

Mid –Term: 30 Marks

Final Exam: 40 Marks

Individual Research Project:

(Reproduction of the results presented in identified IEEE Transactions articles on wireless channel modeling)

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Course Outline 1/4Communication Model

• Fundamentals of digital communication (through wired and wireless Channels)

Introduction to Wireless Propagation

• Wireless Propagation Basics

• Shadowing and multipath

Channel Modeling Parameters

• Multipath spread parameters Delay spread, angular spread and Doppler shift.

Coherence time, coherence bandwidth

Introduction to Multipath

• Direct Signal, Angle of Arrival, Time of Arrival, and Doppler Shift

• Direct and Reflected Signals

• Two scatterers and multiple scatterers model

• Link between moving terminals

• Path loss

• Large vs Small-Scale Fading

• Frequency flat vs frequency selective fading

• Slow vs fast fading

• Narrowband and wideband channels

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Course Outline 2/4Wireless Propagation Environments

• Cellular communications

• Street Canyon

• Vehicle to vehicle communications

• Underwater communications

• Aeronautical communications

• Satellite communications

Statistical, Empirical, Stochastic Geometry based Channel Models

• Path loss model

• Okumura Model

• Hatta Model

• The Clark Model

• Rayleigh distribution model

• Rician Distribution Model

• Land Mobile Satellite Channels

• Other notable models.

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• Stochastic geometry based notable 2D and 3D models for angular, temporal, and Doppler spectrum characteristics of outdoor and indoor radio cellular propagation environments.

• Shape factors (angular spread, angular constriction, and direction of maximum fading)

• Second order statistics (level crossing rate, average fade duration, spatial correlation and coherence distance)

Course Outline 3/4

Directional Channels

• MIMO Channel modeling Statistical Modeling of the MIMO Channel

MIMO Channel Modeling Using the Multiple Point Scatterers

Generating Clustered Point-Scatterer Propagation Scenarios

• Massive MIMO channels

Mitigation Techniques

• Mitigation of Path loss and fading

• Diversity Techniques for Fading Multipath Channels

• Mitigation of ISI

• Estimation

• Equalization

• Mitigation of time variations

• Processing Techniques: LS, zero forcing, MMSE, and LMS

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Course Outline 4/4

Simulation Projects• Two-ray wall reflection model.

• Two-ray ground reflection model.

• Street Canyon Propagation

• Wideband Indoor Propagation

• Tapped Delay-Line Models – COST 207

• Rayleigh model.

• Rician model.

Recent research topics 5G Communications

Millimeter wave range.

Massive MIMO.

Small sized cells.

Measurement campaigns for F2M and M2M channels.

Aeronautical communication channels. 11/30

Introduction of Students

• Any student with non-engineering Background?

• Any students without qualifying Pre-requisites?

• Any Student with NO knowledge of wireless communications?

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Lecture 1

Introduction to the Course

Challenges of Wireless Communications

Brushing up the required tools Complex numbers – Not so complex

Phasors – A convenient notation

The scales of channel variation Large scale variations

Small scale variation

Understanding Small scale variations Multipath fading

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Communication Problem

Message

Source

Channel

Destination

Channel

Source Destination

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Our first model

Message at the Source = x Channel

Multiplicative changes = h Additive changes = n

Message at the Destination = y

Channel

Source Destination

y=hx+n

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Our first model

Channel

Source Destination

y=hx+n

• Wired Communication Channels• Dedicated• No Mobility

• Wireless Communication Channels• Fixed to Fixed (F2F)• Fixed to Mobile (F2M)• Mobile to Mobile (M2M)

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Wireless Channels

Mechanisms creating multipaths, • Scattering• Reflection• Refraction• Diffraction

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Wireless Channels, F2F

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Wireless Channels, F2M

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Wireless Channels, M2M

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Using Model to Gain Insights 1/4

y=hx+n

Assume x has two possible values (equal probability) 1

-1

Probability density function of x

Assume a nice channel with h=1 y=x+n To understand distribution of y, we need to model n

Probability that value is x

x1

1/2

-1

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Additive noise is usually modelled as “Gaussian”

What does “Gaussian” imply?

Probability density function of n

-10 -5 0 5 100

0.2

0.4

0.6

0.8

1

values of n

pro

bai

lity

that

th

e va

lue

is n

var 2var 1var 0.5var 00

5.0

21

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Probability density function of y

Recall y=x+n for the nice channel and x is +1 or -1 with equal probability

-6 -4 -2 0 2 4 60

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

values of n

pro

bai

lity

that

th

e va

lue

is n

var 2var 1var 0.5

+1-1

5.0

21

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We use a decision boundary to guess what was transmitted

-6 -4 -2 0 2 4 60

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

values of n

pro

bai

lity

that

th

e va

lue

is n

var 2var 1var 0.5

+1-1

If received y lies in this range, we assume x = +1 was transmitted

If received y lies in this range, we assume x = -1 was transmitted

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Decision Boundary

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Errors in our “guess” may occur when noise pushes the input over the other side of the boundary

Cross-over probability is the probability of this error

-6 -4 -2 0 2 4 60

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

values of n

pro

bai

lity

that

th

e va

lue

is n

var 2var 1var 0.5

+1-1

>>

Cross-over Probability

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21

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We saw, larger variance implies higher probability of cross-over

To keep the cross-over probability low, larger variance of noise requires “larger input values” (e.g. +2,-2 or +3,-3)

-6 -4 -2 0 2 4 60

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

values of n

pro

bai

lity

that

th

e va

lue

is n

2 1 0.5

Equal Corss-over Probability

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21

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Wireless Channel

Why wireless is different from wired link/network: No privacy – no dedicated “wire” channel

High interference due to the lack of privacy

Variations in the channel strength (why? will see shortly!)

Recall original model: y=hx+n

The variations in channel strength are modelled by h A multiplicative factor with the input, models the

attenuation/amplification caused by the channel

We have to revert to our original model:

y=hx+n

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Variations in the channel strength

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Scales of variation

Increasing separation between the receiver and transmitter

An illustration from Goldsmith: Not realistic dataFast

Medium

Slow

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Path Loss (very large scale)

Physical phenomena,

longer distance implies the radiation is spread over larger surface area

z

y

x

Source

Close

Far

Power attenuated proportional to inverse of a power of distance (2 in free space) => Power-Law Path Loss

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Shadowing (Large Scale)

Power attenuated due to absorption by several obstructions (just like light is obstructed by opaque objects, causing shadows)

Several independent random multiplicative factors: modelled by log-normal distribution – will see details later

1 Object

Source

3 Objects

2 Objects

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Multipath Fading

Each wireless link is composed of several reflected paths and (optionally) the line-of-sight (specular) path

How does this change the channel strength?

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ISI: Inter symbol Interference

ACME Centre for Research in Wireless Communications

It is necessary to have good understanding of mobile radio channel.

Travelling wave

ft2cos

)(2cos ttf

c

dt

Source Destinationc

dt

ft2cos )(2cos ttf

Signal at Source

Signal at destination

Time to reach

destination

Over single path

Oversimplification of EM Wave:

“Modelling”

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Travelling wave over “Multipath”

c

dt 11

Source Destination

ft2cos )(2cos 1ttf

Path 1

Over multiple

path

Path 2

Path 3 c

dt 2

2

c

dt 3

3

12

3

)(2cos 2ttf )(2cos 3ttf

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Phasors to visualize “Multipath”

How to visualize the sum of “shifted cosines”

)(2cos 1ttf )(2cos 2ttf )(2cos 3ttf

We need some “convenient” representation of cosines and its shifted versions

We will use the “complex numbers” and the “phasors” brief review follows!

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Complex numbers

A convenient representation of two dimensional space

One example is Cartesian plane

Another example is representation of a general sinusoid (any phase, any amplitude).

Since each general sinusoid can be expressed as sum of two components: One pure sine wave (zero phase delay) and another pure cosine wave (90 degree phase delay from pure sine).

If we use a vector (called phasor) to represent the “amplitude” and “phase delay angle” of a generalised sine wave, all general sinusoids span the two dimensional plane and we can

use complex numbers to express them as their components

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Complex numbers

cos sinje j

arctan2 2cos sin

yj

j xZ x j y Z e Z j x y e

2 2Re ImZ Z Z

Imarctan

Re

ZZ

Z

Z x j y

22 2Z Z x j y x j y x y Z

Z x j y Rex Z Imy ZComplex No

Real

Imaginary

Magnitude

Euler’s formula

Convenient Notation

Conjugate

Thanks, Neikirk UT Austin

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x, Re(Z)

y, Im(Z)

-1 1

1

-1

2-2

-2

2

0

If Z is a complex number: Z=x+j.y, It can be plotted on the complex plane

Z y=1

x=1

Z=1 +j.1

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• Any linear combination of cosine waves of the same period but different phase shifts is also a cosine wave with the same period, but (a third) different phase shift• Linear combination of a sine and cosine wave (which is just a sine wave with a phase shift of π/2)

• seen as “Euler’s formula” before• This is similar to the complex number notation and hence provides motivation to use complex plane to visualize the generalised cosines

a

bba

ba

arctancos

sincos

22

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x, Re(Z)

y, Im(Z)

-1 1

1

-1

2-2

-2

2

0

Z y

x

•Each cosine with an arbitrary phase shift is a “phasor” (location vector of a complex number on the complex plane)

sinZy

cosZx

22 yxZ

x

yZ arctan

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Travelling wave – moving source

Moving Source

Destinationc

dt

cos

2cos

ft

Z

tfft

ttfZ

),cos(

)22cos(

)(2cos

Recall

x, Re(Z)

y, Im(Z)

-1 1

1

-1

2-2

-2

2

0

Z

If the source moves, received cosine Z is a “randomly”

rotating phasor and rotates at a rate at which the received

phase changes

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Constructive-Destructive multipath sum

Each path changes independently over time: three random vectors sum up at the destination

Source Destination

12

3

x, Re(Z)

y, Im(Z)

-1 1

1

-1

2-2

-2

2

0Zr

x, Re(Z)

y, Im(Z)

-1 1

1

-1

2-2

-2

2

0

Zr

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Thank You

Questions and Discussions

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