[PPT] - Blind Chip Rate Estimation in Multirate Blind Chip Rate Estimation PowerPoint Presentation

SLIDE 1

Blind Chip Rate Estimation in Multirate Blind Chip Rate Estimation in Multirate CDMA Transmissions Using Multirate Sampling at Slow Flat Fading Channels

A U T H O R S S i h G h i

Sampling at Slow Flat Fading Channels

A U T H O R S : S i a v a s h G h a v a m i B a h m a n A b o l h a s s a n i D e p a r t m e n t o f E l e c t r i c a l E n g i n e e r i n g D e p a r t m e n t o f E l e c t r i c a l E n g i n e e r i n g I r a n U n i v e r s i t y o f S c i e n c e a n d T e c h n o l o g y T e h r a n , I r a n N o v e m b e r 2 0 0 8

SLIDE 2

Outlines

 Motivations  System Model  Estimation of Channel Impulse Response

p p

 Blind Chip Rate Estimation  Blind Bit Rate Estimation  Simulation Results  Conclusions

SLIDE 3

Motivations

 3G cellular systems

bili f i i i d di i k

 capability of supporting transmission data as diverse as voice, packet

data, low-resolution video, and compressed audio.

 heterogeneous services produce

 digital information streams with different data rates  digital information streams with different data rates

 Multi Rate Transmission

 variable spreading length (VSL) technique where all users employ

sequences with the same chip period sequences with the same chip period

 the data rate is tied to the length of the spreading code of each user.  Another way to view a multirate CDMA transmission is to consider a

constant spreading length where users employ sequences with diff hi i d different chip periods.

 In general, chip time and spreading sequence length of users can be

selected variable.

SLIDE 4

Motivations

 In

the literature, different methods have been presented for chip time estimation.

 many number of those methods are based on cyclic cumulant

method, which have been presented in literatures [3-7]. , p [3 7]

 Cyclic cumulant method in multi-rate and multi-user

system doesn’t exhibit good performance because

li f i f diff l

 cyclic frequencies of different users overlap.  chip time estimation of different users is not possible.

 Since, In this paper blind chip time estimation

Since, In this paper blind chip time estimation

 based on channel impulse response estimation using singular

value decomposition of estimated received signal covariance matrix is proposed. matrix is proposed.

SLIDE 5

Motivations

 For

spreading sequence estimation in spectrum surveillance systems over slow flat Rayleigh fading channel plus AWGN, length

f

code must be

determined. It needs to
determined. It needs to

 Bit time  Fluctuations of received signal covariance matrix

hi ti

 chip time  Multi-rate sampling of channel impulse response.

 This needs no prior knowledge about transmitter in

p g the receiver side; it is typically the case in blind signals interception in the military field

r

in spectrum surveillance spectrum surveillance.

SLIDE 6

System Model

 Down link scenario of a multi-rate DS-CDMA

system.

 Channel model is slow flat Rayleigh fading plus

Additive white Gaussian Noise (AWGN)

 Signal power is lower than noise power in receiver

(SNR i dB) d t i k l d b t (SNR<0 in dB) due to no prior knowledge about spreading sequences

 A symbol time is equal to the spreading sequence  A symbol time is equal to the spreading sequence

(Short Spreading Code)

 Symbols have zero mean and are uncorrelated  Symbols have zero mean and are uncorrelated

SLIDE 7

Estim ation of Channel Im pulse Response

 Estimation of Received Signal Covariance Matrix

C i t i

g

R i d Si l Received Signal N i Covariance matrix Covariance matrix

f Noise

     

H H H r x n

E rr E xx E nn R R      R

 Expansion of Received Signal Covariance Matrix

Received Signal d S g without noise Noise Covariance matrix

f Signal

p g

 

1 * * 2 1 1

(1 ) ( ) ( )

k k k k K k n k k k

R    

   

     v v v v I

f

 In Downlink scenario, users are synchronous, hence

Delay of kth user SNR of kth user Eigen vectors of kth user Noise Variance

, y ,

 

1 2 1

* { }

K n k k k k

R  

 

   v v I

k





SLIDE 8

Blind Chip Rate Estim ation

 Channel impulse response

p p

s c

m F R 

1/ 2 c , ,

( ) . ( )

i

mL m k i k i j

p T h t N c t j m

  

 



l i f h i fil h l fil d i fil Sampling Frequency Chip Rate

 Normalized Channel impulse response

1/ 2 c c

sgn( ( )) ( 1) ( ) ( )

i

mL m

T T h t N t j t j

 

    



convolution of the transmitter filter , channel filter and receiver filter

1/ 2 c c , ,

sgn( ( )) ( 1) , ( ) ( )

k i k i j

h t N c t j t j m m u u



        



Step Function

 Differential of Normalized Channel impulse response

sgn( )) (

i

mL m k

d h t T

 , 1/ 2 c ,

sgn( )) ) ( [ ] (

i

k i k i j

d h t T N c m t j dt m 

 

  

Impulse Function

SLIDE 9

Blind Chip Rate Estim ation

Over Sampling Ratio = 1 Over Sampling Ratio = 4

SLIDE 10

Blind Chip Rate Estim ation

 It is obvious that in sampling frequencies above the Nyquist rate, by

increasing the sampling frequency number of zero crossings on differential increasing the sampling frequency number of zero crossings on differential

f spreading sequence remains constant. Since

Number of zero crossings L th f diff ti t d S ZCR 

can be used for determining oversampling ratio and then chip rate estimation.

Length of diffrentiated Sequence

SLIDE 11

Blind Chip Rate Estim ation

 Sampling frequencies less than Nyquist rate

p g q yq

 loss some of data samples and  decrease length of differential of sequence,

i i f li f l h h

 it causes ZCR increases for sampling frequency lower than the

Nyquist rate.

SLIDE 12

Blind Chip Rate Estim ation

 Since, by weighting ZCR, it is possible to reduce ZCR

, y g g , p for sampling frequencies less than the Nyquist rate.

 we propose weighted zero crossing ratios, which is

defined as

The Number of zero crossings WZCR ZCR   g

SLIDE 13

Effect of code length increament

 Increasing the code length

g g

 increases

the computational complexity

f

subspace decomposition of the received signal covariance matrix due to increasing the dimension of covariance matrix increasing the dimension of covariance matrix,

 instead, output SNR in output of covariance matrix estimator

increases by the factor of

1 2

2

N N SNR SNR

ut
ut



1

ut

SLIDE 14

Blind Bit Rate Estim ation

 For blind bit rate estimation,

fl i f we use fluctuation

f

correlation estimator [9].

 To compute the fluctuations,

di id h i d i l we divide the received signal into M temporal windows with duration of TF for each window window.

dt t r t r T R

F

T m m F m r



 

*

) ( ) ( 1 ) ( ˆ  

Fluctuations of correlation estimator

 Taking expectation of above

equation over M window

ˆ ( ˆ ˆ ) ( ) ) (

n s r

R R R     

 

1 2 2

1 ˆ ˆ ˆ ( ) ( ) ( )

M r r

E R R M   



  



( ) ( ) ) (

n s r

,

1

ˆ ( ) ( )

i k i

K S s s i k

R R  

  

 

 

m

M



SLIDE 15

Spreading Sequence Length

 Code length of each user is determined using their

g g corresponding

 chip times

bi i

 bit times.

 Estimated parameter using this method is useful

and applicable and applicable

 spectrum surveillance in very low SNR (negative SNR in dB).  Modern wireless systems such as cognitive radios.

y g

SLIDE 16

Estimated Channel Impulse Response

Estimated eigenvector corresponding to the maximum eigenvalue

f the received signal covariance matrix

g Exact and estimated spreading sequence of user 1 The differential of the estimated eigenvector. The differential of the estimated eigenvector.

SLIDE 17

Zero crossing

Number of Zero crossings in terms of sampling frequency Zero crossing ratio in terms of sampling frequency weighted zero crossing ratio in terms of sampling frequency all weighted zero crossing ratio in terms of sampling frequency, all

f them are related to user1

SLIDE 18

Chip Rate Detection Performance

97.5% Probability of chip rate detection related to the first user for 10000 times Monte Carlo Test. 97% Probability of chip rate detection related to the d f ti M t C l T t 97% second user for 10000 times Monte Carlo Test.

SLIDE 19

Conclusions

 This paper considered the problem of blind chip rate

estimation of direct sequence spread spectrum (DS-SS) signals in multi-rate and multiuser direct-sequence code division multiple accesses (DS-CDMA) division multiple accesses (DS-CDMA).