System and method for frequency burst detection for GSM/GPRS

• US 20050254609A1
• Filed: 05/13/2004
• Published: 11/17/2005
• Est. Priority Date: 05/13/2004
• Status: Active Grant

• Current Assignee: Imagination Technologies Limited (Imagination Technologies Group Ltd.)
• Original Assignee: Hellosoft Incorporated (Imagination Technologies Group Ltd.)
• Inventors: Varma, Gottimukkala Narendra, Sahu, Usha, Charan, Garapati Prabhu
• Granted Patent: US 7,254,200 B2
• Time in Patent Office: Days
• US Class Current: 375/350
• CPC Class Codes: H04L 2007/047 using a sine signal or unmodulated carrier
• CPC Class Codes: H04L 7/041 using special codes as synchronising signal

Abstract

A system, method and computer program product for detecting a frequency burst in a received signal at a wireless receiver is disclosed. The invention predicts the current sample of the received signal by filtering the past samples of the received signal through an adaptive filter. A prediction error is further obtained by subtracting the actual current sample and the predicted current sample. The prediction error is then used to adapt the adaptive filter. Since the frequency burst is a substantially predictable signal, the adaptive filter adapts to accurately predict the samples of the frequency burst. Therefore, the prediction error decreases when the frequency burst is received at the receiver. The end of the frequency burst is identified using the prediction error at each discrete time instant.

Claims

1. A method of detecting a frequency burst in a received signal at a wireless receiver, the frequency burst having a known length, the method comprising the steps of:

a. predicting the current sample of the received signal from the past samples of the received signal using an adaptive filter;

b. obtaining the prediction error between the predicted current sample of the received signal and the actual current sample of the received signal;

c. adapting the adaptive filter using the prediction error; and

d. locating the frequency burst using the prediction error.

2. The method as recited in claim 1 wherein the step of locating the frequency burst using the prediction error comprises the steps of:

if the previous sample of the received signal is established to belong to the frequency burst, then performing the steps of;

i. analyzing the prediction error to establish whether the previous sample of the received signal is the last sample of the frequency burst; and

if the previous sample of the received signal is not established as the last sample of the frequency burst, then performing the steps of;

ii. establishing that the current sample of the received signal belongs to the frequency burst; and

if the previous sample of the received signal is not established to belong to the frequency burst, then performing the steps of;

iii. analyzing the prediction error to establish whether the current sample of the received signal belongs to the frequency burst.

3. The method as recited in claim 2 wherein the step of analyzing the prediction error to establish whether the current sample of the received signal belongs to the frequency burst comprises the steps of:

a. calculating a prediction inaccuracy parameter using the prediction error; and

if the prediction inaccuracy parameter is less than an error power threshold, then performing the step of;

i. establishing that the current sample of the received signal belongs to the frequency burst.

4. The method as recited in claim 3 wherein the step of calculating a prediction inaccuracy parameter using the prediction error is performed using the relation {overscore (E)}(N)=(1−

ρ

)*{overscore (E)}(N−

1)+ρ

*|e(N)|², where N denotes the discrete time instant of the current sample, {overscore (E)}(n) is the prediction inaccuracy parameter at n^th discrete time instant, e(n) is the prediction error at n^th discrete time instant and ρ

is a forgetting factor.

5. The method as recited in claim 3 wherein the step of calculating a prediction inaccuracy parameter using the prediction error is performed by calculating the average power of the prediction error over a predetermined number of discrete time instants preceding the discrete time instant of the current sample.

6. The method as recited in claim 2 wherein the step of analyzing the prediction error to establish whether the previous sample of the received signal is the last sample of the frequency burst comprises the steps of:

a. obtaining an error power parameter using the prediction error corresponding to the current sample;

b. calculating a prediction inaccuracy parameter using the prediction error corresponding to the current sample;

c. obtaining the ratio of the error power parameter and the prediction inaccuracy parameter corresponding to the current sample; and

if the ratio corresponding to a previously received sample established to belong to the frequency burst is less than a first ratio threshold, and the ratio corresponding to the current sample of the received signal is greater than a second ratio threshold, then performing the steps of;

i. establishing that the previous sample of the received signal is the last sample of the frequency burst.

7. The method as recited in claim 6 wherein the step of obtaining the error power parameter is performed by obtaining the absolute value of the prediction error.

8. The method as recited in claim 2 wherein the step of locating the frequency burst using the prediction error further comprises the step of locating the start of the frequency burst using the end of the frequency burst and the known length of the frequency burst.

9. The method as recited in claim 1 wherein the step of predicting the current sample of the received signal from the past samples of the received signal using the adaptive filter comprises the steps of:

a. selecting a finite set of samples of the received signal as the past samples of the received signal, wherein the finite set of samples has been received a predefined number of discrete time instants before the current sample; and

b. filtering the past samples of the received signal through the adaptive filter to obtain the predicted current sample of the received signal, whereas the adaptive filter is a finite impulse response (FIR) filter.

10. The method as recited in claim 9 wherein the step of adapting the adaptive filter using the prediction error comprises the steps of:

a. obtaining the product of the complex conjugate of the prediction error, the past sample of the received signal corresponding to the first coefficient of the adaptive filter and a convergence step-size corresponding to the first coefficient of the adaptive filter;

b. adding the obtained product to the first coefficient of the adaptive filter to obtain the adapted value of the first coefficient of the adaptive filter; and

repeating step-a and step-b for each coefficient of the adaptive filter.

11. The method as recited in claim 1 wherein the step of predicting the current sample of the received signal from the past samples of the received signal using an adaptive filter comprises the steps of:

a. selecting a set of samples of the received signal as the past samples of the received signal, wherein the set of samples has been received a predefined number of discrete time instants D before the current sample; and

b. obtaining the predicted current sample of the received signal using the past samples of the received signal and a set of previously predicted samples of the received signal, whereas the predicted current sample is obtained using an adaptive infinite impulse response (IIR) filter.

12. The method as recited in claim 11 wherein the step of obtaining the predicted current sample of the received signal y(N) using the adaptive filter is performed using the relation

y(N)=(1+s(N))w(N)y(N−

1)−

s(N)y(N−

2)+(1−

s(N))(w(N)x(N−

D)−

x(N−

D−

1)),

where N denotes the discrete time instant of the current sample, y(n) denotes the predicted sample of the received signal at n^th discrete time instant, x(n) denotes the actual sample of the received signal at n^th discrete time instant, w(n) denotes a discrete center frequency parameter of the adaptive filter at n^th discrete time instant, and s(n) denotes a bandwidth-controlling factor of the adaptive filter at the n^th discrete time instant.

13. The method as recited in claim 12 wherein the step of adapting the adaptive filter using the prediction error comprises the steps of:

a. updating the discrete center frequency parameter using the relation w(N+1)=w(N)+2μ

_w real(e(N)α

(N)*), where μ

_w is a first step-size parameter, e(n) denotes the prediction error at n^th discrete time instant and α

(n) denotes the partial derivative of the y(n) with respect to w(n) at the n^th discrete time instant;

b. updating the bandwidth-controlling factor using the relation

$s<br><br>\left(N+1\right)=s<br><br>\left(N\right)+2<br><br>{\mathrm{\mu <br><br>}}_s<br><br>\mathrm{real}<br><br>\left(\frac{1}{{\left(1-s<br><br>\left(N\right)\right)}^2}<br><br>y^2<br><br>\left(N\right)+\frac{1+s<br><br>\left(N\right)}{1-s<br><br>\left(N\right)}<br><br>y<br><br>\left(N\right)<br><br>\mathrm{\beta <br><br>}<br><br>\left(N\right)\right),$

where μ

_s is a second step-size parameter and β

(n) denotes the partial derivative of y(n) with respect to s(n) at the n^th discrete time instant, wherein the value of μ

_s is predefined; and

c. updating the first step-size parameter μ

_w using the relation

μ

_w=0.1(1−

s(N))³;

14. The method as recited in claim 13 wherein the step of updating the discrete center frequency parameter comprises the step of obtaining α

(N) using the relation

α

(N)=(1+s(N))w(N)α

(N−

1)−

s(N)α

(N−

2)+(1+s(N))y(N−

1)+(1−

s(N))x(N−

D)

15. The method as recited in claim 13 wherein the step of updating the bandwidth-controlling factor comprises the step of obtaining β

(N) using the relation

β

(N)=(1+s(N))w(N)β

(N−

1)−

s(N)β

(N−

2)−

(w(N)e(N−

1)−

e(N−

2)).

16. The method as recited in claim 1 wherein at least one of the steps is embodied in a computer program product.

17. A system for detecting a frequency burst in a received signal at a wireless receiver, the system comprising:

a. a predictor predicting the current sample of the received signal from the past samples of the received signal, the predictor comprising;

i. an adapting unit adapting the predictor using a prediction error;

b. a summation unit obtaining the prediction error using the predicted current sample of the received signal and the actual current sample of the received signal; and

c. a frequency burst detector locating the frequency burst using the prediction error.

18. The system as recited in claim 17 wherein the frequency burst detector comprises:

a. a frequency burst presence flag for recording whether the previous sample of the received signal is established to belong to the frequency burst;

b. a prediction inaccuracy calculator calculating the prediction inaccuracy parameter using the prediction error and setting the frequency burst presence flag if the prediction inaccuracy parameter is less than an error power threshold;

c. a ratio calculator calculating an error power parameter using the prediction error and obtaining the ratio of the error power parameter and the prediction inaccuracy parameter if the frequency burst presence flag is set; and

d. an end detector establishing whether the previous sample of the received signal is the last sample of the frequency burst using the obtained ratio and resetting the flag if the previous sample of the received signal is the last sample of the frequency burst.

19. The system as recited in claim 18 wherein the end detector comprises:

a. a frequency burst location flag recording whether the ratio obtained by the ratio calculator is less than a first ratio threshold;

b. a first threshold detector setting the frequency burst location flag if the ratio obtained by the ratio calculator is less than the first ratio threshold; and

c. a second threshold detector establishing that the previous sample of the received signal is the last sample of the frequency burst if the frequency burst location flag is set and the ratio obtained by the ratio calculator is greater than a second ratio threshold.

20. The system as recited in claim 17, wherein the predictor further comprises:

a. a past sample selector selecting a finite set of samples of the received signal as the past samples of the received signal, wherein the finite set of samples has been received a predefined number of discrete time instants before the current sample; and

b. an adaptive FIR filter filtering the past samples of the received signal to obtain the predicted current sample of the received signal.

21. The system as recited in claim 20 wherein the adapting unit comprises:

a. a multiplier obtaining the product of the complex conjugate of the prediction error, the past sample of the received signal corresponding to each coefficient of the adaptive filter and a convergence step-size corresponding to each coefficient of the adaptive filter; and

b. an adder adding the obtained product corresponding to each coefficient of the adaptive filter to the corresponding coefficients of the adaptive filter, to obtain the adapted values of coefficients of the adaptive filter.

22. The system as recited in claim 17 wherein the predictor further comprises:

a. a sample selector selecting a set of samples of the received signal as the past samples of the received signal, wherein the set of samples has been received a predefined number of discrete time instants D before the current sample; and

b. an adaptive IIR filter obtaining the predicted current sample of the received signal using the past samples of the received signal and a set of previously predicted samples of the received signal.

23. The system as recited in claim 22 wherein the adaptive IIR filter filters the past samples of the received signal to obtain the predicted value of the current sample of the received signal y(N) using the relation

y(N)=(1+s(N))w(N)y(N−

1)−

s(N)y(N−

2)+(1−

s(N))(w(N)x(N−

D)−

x(N−

D−

1)),

where N denotes the discrete time instant of the current sample, y(n) denotes the predicted sample of the received signal at n^th discrete time instant, x(n) denotes the actual sample of the received signal at n^th discrete time instant, w(n) denotes a discrete center frequency parameter of the adaptive filter at n^th discrete time instant, and s(n) denotes a bandwidth-controlling factor of the adaptive filter at the n^th discrete time instant.

24. The system as recited in claim 23 wherein the adapting unit comprises:

a. a discrete center frequency adapter updating the discrete center frequency parameter using the relation w(N+1)=w(N)+2μ

_w real(e(N)α

(N)*), where μ

_w is a first step-size parameter, e(n) denotes the prediction error at n^th discrete time instant and α

(n) denotes the partial derivative of the y(n) with respect to w(n) at the n^th discrete time instant;

b. a bandwidth-controlling factor adapter updating the bandwidth-controlling factor using the relation

$s<br><br>\left(N+1\right)=s<br><br>\left(N\right)+2<br><br>{\mathrm{\mu <br><br>}}_s<br><br>\mathrm{real}<br><br>\left(\frac{1}{{\left(1-s<br><br>\left(N\right)\right)}^2}<br><br>y^2<br><br>\left(N\right)+\frac{1+s<br><br>\left(N\right)}{1-s<br><br>\left(N\right)}<br><br>y<br><br>\left(N\right)<br><br>\mathrm{\beta <br><br>}<br><br>\left(N\right)\right),$

where μ

_s is a second step-size parameter and β

(n) denotes the partial derivative of y(n) with respect to s(n) at the n^th discrete time instant, wherein the value of μ

_s is predefined; and

c. a step-size parameter adapter updating the first step-size parameter μ

_w using the relation μ

_w=0.1(1−

s(N))³.

25. The system as recited in claim 24 wherein the discrete center frequency adapter comprises a first partial differentiator obtaining α

(N) using the relation

α

(N)=(1+s(N))w(N)α

(N−

1)−

s(N)α

(N−

2)+(1+s(N))y(N−

1)+(1−

s(N))x(N−

D).

26. The system as recited in claim 24 wherein the bandwidth-controlling factor adapter comprises a second partial differentiator obtaining β

(N) using the relation β

(N)=(1+s(N))w(N)β

(N−

1)−

s(N)β

(N−

2)−

(w(N)e(N−

1)−

e(N−

2))