Channel Estimation for Mobile OFDM

Zhang Nan(62427P)

OUTLINE

Basics of OFDM Channel Estimation Challenges of Channel Estimation in

Mobile OFDM Channel Estimation Techniques Performance Evaluation Conclusion

OFDM Overview

Orthogonal Frequency Division Multiplexing To split a high-rate data stream into a number of lower rate st

reams that are transmitted simultaneously over a number of subcarrier

In OFDM systems, the spectrum of individual subcarrier is overlapped with minimum frequency spacing, which is carefully designed so that each subcarrier is orthogonal to the other subcarriers. The bandwidth efficiency of OFDM is another advantage.

In the guard time , the OFDM symbol is cyclically extended to avoid intercarrier interference.

Advantages of OFDM

Immunity to delay spread Symbol duration >> channel delay spread Guard interval

Resistance to frequency selective fading Each subchannel is almost flat fading

Simple equalization Each subchannel is almost flat fading, so it only nee

ds a one-tap equalizer to overcome channel effect. Efficient bandwidth usage

The subchannel is kept orthogonality with overlap.

Challenges of OFDM (1/2)

Synchronization Symbol synchronization

Timing errors Carrier phase noise

Frequency synchronization Sampling frequency synchronization Carrier frequency synchronization

Challenges of OFDM (2/2)

High Peak to Average Power Ratio (PAPR) It increased complexity of the analog-to-di

gital and digital-to-analog converters. It reduced efficiency of the RF power amplif

ier.

OFDM System Architecture

Three Large Groups of Channel Estimation Techniques (1/3)

Channel estimation allows the receiver to approximate the effect of the channel on the signal.

Pilot Assisted It is the most straightforward way

where symbols or tones known to the receiver, called pilots.

Has a good performance in fast fading environments

Three Large Groups of Channel Estimation Techniques (2/3)

Blind (without pilots) Based on channel statistics employment

rather than on that of pilots. No Training sequences required Most existing blind channel estimation

methods are based on second- or higher order statistics. It features relatively low complexity and a very fast convergence rate.

Hard to implement on real time systems.

Three Large Groups of Channel Estimation Techniques (3/3)

Semi-Blind (with initial pilot-based channel estimation and next channel tracking) Assumes an intermediate position and

relies partly on pilots and partly on the use of channel statistics.

A semi-blind competitive neural network based method of time-varying channel estimation is tested in this work.

Alogrithm

Consider a multipath radio channel. Assume the Jakes model on each path. CNN based channel estimator

Competitive Neural Networks

One of the most famous self-organizing in the neural networks

A simple competitive network.

One common learning rule simply adds the difference between the winning neuron and the input sequence to the winning neuron.

CNN Based OFDM Channel Estimator (1/2)

The winner neuron is selected according to the Kohonen updated rule

The dynamics of others neurons non-winners are defined as

)( 1,

1,,

kwn

kn

kwn

kwn NRNN

)( 1,,

1,,

kqn

kqn

kqn

kqn NRNN

CNN Based OFDM Channel Estimator (2/2)

An estimate of the channel frequency response can be obtained from the weights of the neurons

4

1,

1

4

1ˆi

kin

ikn NjH

Simulation Result

SNR=0

-800 -600 -400 -200 0 200 400 600 800-800

-600

-400

-200

0

200

400

600

800

1000

1200Received Neuron constellation

Real part

Imag

inar

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rt

Simulation Result

SNR=5

-400 -300 -200 -100 0 100 200 300-250

-200

-150

-100

-50

0

50

100

150

200

250Received Neuron constellation

Real part

Imag

inar

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rt

Simulation Result

SNR=10

-60 -40 -20 0 20 40 60 80-60

-40

-20

0

20

40

60Received Neuron constellation

Real part

Imag

inar

y pa

rt

Simulation Result

SNR=15

-50 -40 -30 -20 -10 0 10 20 30 40-40

-30

-20

-10

0

10

20

30

40

50

60Received Neuron constellation

Real part

Imag

inar

y pa

rt

Simulation Result

SNR=20

-10 -8 -6 -4 -2 0 2 4 6 8 10-10

-8

-6

-4

-2

0

2

4

6

8Received Neuron constellation

Real part

Imag

inar

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Simulation Result

SNR=25

-4 -3 -2 -1 0 1 2 3 4 5-4

-3

-2

-1

0

1

2

3

4Received Neuron constellation

Real part

Imag

inar

y pa

rt

Simulation Result

SNR=30

-1.5 -1 -0.5 0 0.5 1 1.5 2-1.5

-1

-0.5

0

0.5

1

1.5Received Neuron constellation

Real part

Imag

inar

y pa

rt

MSE

The MSE measures the average of the square of the error which can be calculated as

N

iii HE

NMSE

1

2ˆˆ1

MSE v.s. SNR

0 5 10 15 20 25 3010

-5

10-4

10-3

10-2

10-1

100

SNR in DB

Mea

n S

quar

ed E

rror

SNR V/S MSE