wireless com. report 20111115 - english

8/3/2019 Wireless Com. Report 20111115 - English

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Wireless communication

Wireline/Wireless transmission

over block-fading channel

Editor: 10DTLT - Group 3B

Email: [email protected]

mailto:[email protected]






Contents

IntroductionWireless channel models

Digital modulation

Channel estimation

Simulation

Conclusion

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Wireless Communication 2



Requirements

Simulate Wireline & Wireless transmission over block-fadingchannel

Using M-ary QAM (expanded M=2048)

Estimating imperfect channel

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Wireless Communication



Wireless channel models

Characterized by Variations of the channel strength over timeand frequency

Large-scale fading

Small-scale fading

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Wireless channel models

Path loss is caused by dissipation of the power radiated by thetransmiiter as well as effects of the propagation channel.

Shadowing is caused by obstacles between the transmitter and

receiver that absorb power.

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Path loss models

The path loss is defined by

Several useful empirical models for macrocellular systems:

Hata’s model

Lee’s model

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)4(2

sin4

4log10log10 2

2

1010

d

hh

d P

GGP L

c

mbc

r

r t t dB p



Multipath fading channels

The discrete-time channel model can be given by

This is also the received signal at MS over multipath channels.

where

is multipath fading gains (channel impulse response) which is

time-variant (depend on time index m) whi

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)10( / 1

0

. W mwh x y b

L

l

mllmm

i

iml W mW lcW mh 1, sin



Advantages over analog modulation

Cheaper, faster and more power-efficient than analogtransceiver.

More importantly, digital modulation offers:

Higher data rates,

Powerful error correction techniques,

Resistance to channel imparments,

More efficient multiple access stratergies, and

Better security and privacy.

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Signal and system model

Every T seconds, the system sends bits of information through the channel for a data rate of

bps.

There are possible sequences of K bits and each bit

sequence of length K comprises a message ,where is the set of all such messages

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M K 2logT K R

K M 2

M bbm K i ,,1

M mm M ,,1



Signal space representation

By representing signals as projection onto a set of basisfunctions, we obtain a one-to-one correspondence between the

set of transmiteed signals and their vector representations.

Thus, we can analyze signals in finite-dimensional vector

space instead of infinite-dimensional function space, usingclassical notions of distance for vector spaces.

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Wireless Communication 10Figure 3: An example of two-demensional signal space



Signal space representation (cont.)

In order to analyze signals via a signal space representation,we need to use some definitions for the vector characterization

in vector spae . In particular, the length of a vector in is

defined as

The distance between two signal constellation points si and sk

is thus

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)22(1

2

,

N

j

jii ss

)23()()(0

2

1

2

,,

T

k i

N

j

jk jik i dt t st sssss



Maximum likelihood detection

Once the signal space has been partitioned by decision regions,for a received vector , the optimal receiver outputs the

message estimate . The receiver processing consistsof

Computing the received vector r from r(t)

Finding which decision region Zi contain r

And outputting the corresponding message mi.

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i Z r

immˆ



Error probability analysis

With , the error probability of the MLreceiver:

Pe is the probability of a symbol (message) error where mi

corresponds to a message with bits. However,

typically the bit error probability, also called the bit error rate(BER), is interesting than in the symbol error probability, since

bit errors drive the performace of higher layer networking

protocols and end-to-end performace.

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M sent m p i / 1

M 2log

)42(log 2 M

PP eb



Quarature amplitude modulation MQAM

the modulation signal can be represent as

in terms of its in-phase and quarature componets as

The energy in is

The distance between any pair of symbols in the signal

constellation is

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)(t si

)43()(2cos)()(2cos)()( 00 t t f t t t f t t s C C

)44(2sin)(2cos)(

2sin)(sin)(2cos)(cos)()(

t f t st f t s

t f t t t f t t t s

cQc I

cc

)46()( 2

0

2

i

T

iS Adt t s E i

)47(2

2,2,

2

1,1,, k ik ik ik i ssssssd



MQAM (cont.)

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4QAM and 16QAM constellations and Decision regions for

MQAM with M=16



MQAM (cont.)

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Table 1 – BPSK encoding table

Input bit (b0) I-out Q-out

0 -1 01 1 0

Table 2 – QPSK encoding table

Input bit

(b0)

I-out Input bit

(b1)

Q-out

0 -1 0 -1

1 1 1 1



Channel estimation in block-fading channel

To reduce the complexity of channel estimation with discrete-time domain (Without OFDM), the number of resolvable pathL is equal to 1 (L=1) on block fading channel.

In once iteration, we utilize h0 which is assumed the knownCIR of header. So the known received signal is

where yh and sh are symbols in header of each frame which istransmitted. And wh is coressponding noise samples.

Using the known received signal, one could compute channel

estimation of all symbols in this frame

where H is the length of header.

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hhh wsh y 0

,1

ˆ

0

0

1

0h

wh

s

y

H h h

H

i i

i



Simulation model

The Matlab script performs the following

Setting system

Compute power average of noise and signal power

Generating symbol sequences to transmit

Digital modulation

Channel estimation h0

Adding AWGN noise

Demodulation

Count the number of errors to compute BER

Repeating of multiple values of number of bursts

Repeating values of SNR.

Plot the simulation results

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Simulation

Parameters:M = 4

Number of frames = 100

Number of MQAM complex symbols = 1000

Header = 1% frame

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M=4

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0 5 10 15 20 25 3010

-4

10-3

10-2

10-1

100

SNR (dB)

B i t e r r o r r a t e

( B E R )

Wireless transmission without CSI

Wireless transmission with perfect channelWireless transmission with imperfect channel

Wireline transmission, i.e., AWGN channels



M=2048

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0 5 10 15 20 25 3010

-2

10-1

100

SNR (dB)

B i t e r r o r r a t e ( B

E R )

Wireless transmission without CSI

Wireless transmission with perfect channel

Wireless transmission with imperfect channel

Wireline transmission, i.e., AWGN channels



Conclusion

From these results, it can be seen that the BER in imperfectchannel which is always higher than BER in perfect channel. If

we increase the length of header, the two curves will tend

nearerly. The result also shown performance which dues to

number of constellation points M.

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wireless com. report 20111115 - english

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