THÔNG TIN DI ĐỘNG C2

THÔNG TIN DI ĐỘNG C2

Chapter 2: Wireless Channel models

Multipath wireless propagation

reflection and diffraction

Path loss, shadowing and fading

The characteristic of (mobile) wireless channel is the variations ofthe channel strength over time and frequency

The variations can be divided into two types:

path loss of signal as a function of distance and shadowing by large objects such as buildings and hills.

interference of the multiple signal paths between transmitter and

receiver

An example of path loss, shadowing and fading

Pathloss

An example of path loss, shadowing and fading (cont.)

Path loss models

It is well known that the received signal power decays with the

square of the path length in free space

More specifically, the received envelope power is

𝑃𝑟= 𝑃𝑡𝐺𝑡𝐺𝑟(4𝜋𝑑𝜆𝑐

)2

where:

respectively

Path loss models (cont.)

The signals in land mobile radio applications, however, do not

experience free space propagation A more appropriate theoreticalmodel assumes propagation over a flat reflecting surface (the earth)

𝑃𝑟= 4𝑃𝑡

( 𝜆𝑐4𝜋𝑑

)2

where we have used the approximation sin 𝑥 ≈ 𝑥 for small 𝑥

Path loss models (cont.)

The path loss is defined by

𝐿𝑝 (𝑑𝐵) = 10 log10( 𝑃𝑡𝐺𝑡𝐺𝑟

𝑃𝑟)

{

4( 𝜆𝑐4𝜋𝑑

)2sin2( 2𝜋ℎ𝑏ℎ𝑚

Two of the useful models for 900 MHz cellular systems are:

Hata’s model based on Okumura’s prediction method and

Lee’s model

Hata’s empirical model is probably the simplest to use The

empirical data for this model was collected by Okumura in the city

of Tokyo

𝐴 + 𝐵 log10(𝑑) − 𝐶 for suburban area

𝐴 + 𝐵 log10(𝑑) − 𝐷 for open area

Okumura-Hata models (cont.)

the distance: 1 ≤ 𝑑 ≤ 20(km)

Numerical results of Okumura-Hata models

A signal transmitted through a wireless channel will typically

experience random variation due to blockage from objects in the

signal path, giving rise to random variations of the received power at

a given distance

Such variations are also caused by changes in reflecting surfaces andscattering objects

Thus, a model for the random attenuation due to these effects is

also needed Since the location, size, and dielectric properties of theblocking objects as well as the changes in reflecting surfaces and

scattering objects that cause the random attenuation are generallyunknown, statistical models must be used to characterize this

attenuation

The most common model for this additional attenuation is

log-normal shadowing

where:

received signal (where the expectation is taken over the pdf of thereceived envelope)

𝜇𝑋𝑚(dBm)= 30 + 10𝔼[log10𝑋2

𝑚]

𝜇𝑋 𝑠 (dBm)= 30 + 10𝔼[log10𝑋𝑠]

Shadowing (cont.)

Sometimes 𝑋𝑚 is called the local mean because it represents the

mean envelope level where the averaging is performed over a

distance of a few wavelengths that represents a locality

This model has been confirmed empirically to accurately model thevariation in received power in both outdoor and indoor radio

propagation environments

Fading channel model

Two Main Multipaths

Local Scattering

The complex transmitted signal can be expressed by

Over a multipath (𝐿 physical paths) propagation channel, the

received signal can be obtained by

Fading channel model (cont.)

Substituting (7) into (8) yields the following

Wireless channel modeling (cont.)

The next step in creating a useful channel model is to convert thecontinuous-time channel to a discrete-time channel

We take the usual approach of sampling theorem

Assuming that the input waveform is band-limited to 𝑊 , the

baseband equivalent can be represented by

𝑛

where 𝑥𝑛= 𝑥(𝑛/𝑊 ) and sinc(𝑡)≜ sin(𝜋𝑡)𝜋𝑡

This representation follows from the sampling theorem, which saysthat any waveform band-limited to 𝑊/2 can be expanded in terms

of the orthogonal basis functions sinc(𝑊 𝑡 − 𝑛) with coefficients bysamples (taken uniformly at integer multiples of 1/𝑊 )

Wireless channel modeling (cont.)

As a result, the baseband received signal can be determined by

𝑖

𝛼𝑖(𝑡)∑𝑛

Wireless channel modeling (cont.)

𝑖𝛼𝑖(𝑚/𝑊 )sinc (𝑙 − 𝜏𝑖(𝑚/𝑊 )𝑊 )This simple discrete-time signal model is widely used in

physical-layer transmission techniques in OFDM systems (e.g., WiFi,WiMAX, LTE)

Examples of transmitted baseband signal 𝑥𝑚

01

00 10

11

I +1 –1

–1 +1

Q b 0 b 1

0 1

I +1 –1

–1 +1

–1

–1 +1

Q b 0 b 1 b 2 b 3 +3

It is noted that multipath fading gainsℎ𝑙,𝑚 (channel impulse

response) is time-variant (depend on time index 𝑚)

Channel estimation in mobile communications

Source encoder

Channel encoder

Digital modulation

Channel

Source decoder

Channel decoder

Digital demodulation

Data S

Pilot S

Data S

Data S

Pilot S

Literature Review of Channel Estimation in Wireless

Tx signal matrix CIR vector

Rx noise vector

Noncoherent Coherent without using CSI

3-dB performance

loss

use CSI require Channel Estimation (CE)

vector matrix vector

Synch.

Imperfect Synch.

Channel Estimation (CE)

Blind Pilot Semi-blind

Joint CE and Synch.

Semi-blind

Perfect Synch.

Imperfect Synch.

Channel Estimation (CE) Pilot

Joint CE and Synch.

Semi-blind Pilot

Pilot design to minimize:

Time-variant path gain ℎ𝑙,𝑚 under mobile speed of 5 km/h

Time (in OFDM symbol duration)

f

c = 2 GHz, 128−FFT, CP length = 10,

ℎ𝑙,𝑚 under mobile speed of 50 km/h

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0.95

1 1.05

1.1

1.15

Time (in OFDM symbol duration)

l Mobile user speed = 50 km/h,

fc = 2 GHz, 128−FFT, CP length = 10,

ℎ𝑙,𝑚 under mobile speed of 300 km/h

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0.8

0.9

1 1.1

1.2

1.3

Time (in OFDM symbol duration)

l Mobile user speed = 300 km/h,