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1. (WO2007002417) ESTIMATING BIT ERROR PROBABILITY (BEP) IN AN EDGE WIRELESS SYSTEM
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CLAIMS

1. A method comprising:
(a) receiving I and Q samples, wherein the I and Q samples exhibit a bit error rate (BER); and
(b) using distribution parameter mapping to estimate the BER.

2. The method of claim 1, wherein the I and Q samples have been modulated and coded with a modulation and coding scheme (MCS) that conforms to a standard for Enhanced Data rates for GSM Evolution (EDGE).

3. The method of claim 1, further comprising, between (a) and (b):
(c) demodulating the I and Q samples to obtain demodulated I and Q samples; and
(d) equalizing the demodulated I and Q samples to obtain soft decision bits, wherein the soft decision bits have a statistical distribution, and wherein the using the distribution parameter mapping in (b) involves determining a type of the statistical distribution.

4. The method of claim 3, wherein the type of the statistical distribution is taken from the group consisting of: a Gaussian distribution, a Rice distribution, a Rayleigh distribution, a Poisson distribution and a Laplace distribution.

5. A method comprising:
(a) equalizing demodulated I and Q samples to obtain a plurality of multi-bit soft decisions, wherein the demodulated I and Q samples exhibit a bit error probability (BEP), wherein the plurality of multi-bit soft decisions has a distribution, and wherein the distribution has a mean and a variance;
(b) determining a type of the distribution;
(c) calculating the mean and the variance of the distribution; and
(d) estimating the BEP based on the mean and the variance of the distribution.

6. The method of claim 5, wherein calculating the mean and the variance in (c) is performed based on the type of the distribution.

7. The method of claim 5, wherein the type of the distribution is taken from the group consisting of: a Gaussian distribution and a Rician distribution.

8. The method of claim 5, further comprising:
(e) deinterleaving the plurality of multi-bit soft decisions; and
(f) convolutionally decoding the deinterleaved plurality of multi-bit soft decisions to obtain single-bit hard decisions.

9. The method of claim 5, wherein the multi-bit soft decisions comprise symbols, and wherein each symbol has three bits.

10. The method of claim 5, wherein a frame payload comprises the plurality of multi-bit soft decisions.

11. The method of claim 5, wherein a radio block is comprised of four pluralities of multi-bit soft decisions.

12. The method of claim 5, wherein the estimating the BEP in (d) involves finding the BEP in a lookup table using a ratio equaling the mean divided by the variance.

13. The method of claim 5 , further comprising before (a) :
(e) demodulating I and Q samples to obtain the demodulated I and Q samples, wherein the demodulating involves a modulation scheme taken from the group consisting of: Gaussian minimum shift keying (GMSK) and octal phase shift keying (8-PSK).

14. The method of claim 5, further comprising:
(e) equalizing second demodulated I and Q samples to obtain a second plurality of multi-bit soft decisions, wherein the second demodulated I and Q samples exhibit a second BEP;

(f) determining a mean BEP, wherein the mean BEP is an average of a plurality of bit error probabilities, and wherein the plurality of bit error probabilities includes at least the BEP and the second BEP; and
(g) filtering the mean BEP to obtain a filtered mean BEP.

15. The method of claim 14, further comprising:
(h) quantizing the filtered mean BEP.

16. A processor-readable medium for storing instructions operable in a wireless device to:
(a) equalize demodulated I and Q samples to obtain a plurality of multi-bit soft decisions, wherein the demodulated I and Q samples exhibit a bit error probability (BEP), wherein the plurality of multi-bit soft decisions has a distribution, and wherein the distribution has a mean and a variance;
(b) determine a type of the distribution;
(c) calculate the mean and the variance of the distribution; and
(d) estimate the BEP based on the mean and the variance of the distribution.

17. The processor-readable medium of claim 16, wherein the mean and the variance are calculated in (c) based on the type of the distribution.

18. The processor-readable medium of claim 16, and further for storing instructions operable in the wireless device to:
(e) deinterleave the plurality of multi-bit soft decisions; and
(f) convolutionally decode the deinterleaved plurality of multi-bit soft decisions to obtain single-bit hard decisions.

19. The processor-readable medium of claim 16, wherein the BEP is estimated in (d) by finding the BEP in a lookup table using a ratio equaling the mean divided by the variance.

20. The processor-readable medium of claim 16, and further for storing instructions operable in the wireless device to:
(e) demodulate I and Q samples to obtain the demodulated I and Q samples, wherein the I and Q samples are demodulated using a modulation scheme taken from the group consisting of: Gaussian minimum shift keying (GMSK) and octal phase shift keying (8-PSK).

21. The processor-readable medium of claim 16, and further for storing instructions operable in the wireless device to:
(e) equalize second demodulated I and Q samples to obtain a second plurality of multi-bit soft decisions, wherein the second demodulated I and Q samples exhibit a second BEP;
(f) determine a mean BEP, wherein the mean BEP is an average of a plurality of bit error probabilities, and wherein the plurality of bit error probabilities includes at least the BEP and the second BEP; and
(g) filter the mean BEP to obtain a filtered mean BEP.

22. The processor-readable medium of claim 21, and further for storing instructions operable in the wireless device to:
(h) quantize the filtered mean BEP.

23. A device comprising:
(a) means for equalizing demodulated I and Q samples to obtain a plurality of multi-bit soft decisions, wherein the demodulated I and Q samples exhibit a bit error probability (BEP), wherein the plurality of multi-bit soft decisions has a distribution, and wherein the distribution has a mean and a variance;
(b) means for determining a type of the distribution;
(c) means for calculating the mean and the variance of the distribution; and
(d) means for estimating the BEP based on the mean and the variance of the distribution.

24. The device of claim 23, wherein the means in (c) calculates the mean and the variance based on the type of the distribution.

25. The device of claim 23 , further comprising:
(e) means for deinterleaving the plurality of multi-bit soft decisions; and
(f) means for convolutionally decoding the deinterleaved plurality of multi-bit soft decisions to obtain single-bit hard decisions.

26. The device of claim 23, further comprising:
(e) means for demodulating I and Q samples to obtain the demodulated I and Q samples, wherein the means in (e) demodulates the I and Q samples using a modulation scheme taken from the group consisting of: Gaussian minimum shift keying (GMSK) and octal phase shift keying (8-PSK).

27. The device of claim 23, further comprising:
(e) means for equalizing second demodulated I and Q samples to obtain a second plurality of multi-bit soft decisions, wherein the second demodulated I and Q samples exhibit a second BEP;
(f) means for determining a mean BEP, wherein the mean BEP is an average of a plurality of bit error probabilities, and wherein the plurality of bit error probabilities includes at least the BEP and the second BEP; and
(g) means for filtering the mean BEP to obtain a filtered mean BEP.

28. A circuit comprising:
a distribution analyzer that receives a distribution of multi-bit soft decisions, wherein the distribution of multi-bit soft decisions exhibits a distribution type, and wherein the distribution analyzer determines the distribution type;
a bit error probability estimator that receives the distribution of multi-bit soft decisions, wherein the bit error probability estimator calculates statistical parameters of the distribution of multi-bit soft decisions; and
a lookup table, wherein the bit error probability estimator determines a bit error probability (BEP) by mapping the statistical parameters to the BEP in the lookup table.

29. The circuit of claim 28, wherein the statistical parameters of the distribution of multi-bit soft decisions approximate a signal-to-noise ratio of the multi-bit soft decisions, and wherein the lookup table correlates the statistical parameters of the distribution of multi-bit soft decisions to bit error probabilities of the multi-bit soft decisions.

30. The circuit of claim 28, wherein the distribution type is Rician, wherein the statistical parameters include a mean (A) and a variance (sigma), and wherein the bit error probability estimator determines the BEP by mapping a quotient A/sigma to the BEP in the lookup table.

31. The circuit of claim 28, wherein the distribution type is Gaussian, wherein the statistical parameters include a mean (mu) and a variance (sigma), and wherein the bit error probability estimator determines the BEP by mapping a quotient mu/sigma to the BEP in the lookup table.

32. The circuit of claim 28, wherein the distribution analyzer and the bit error probability estimator are dedicated hardware in a digital baseband processor.

33. The circuit of claim 28, wherein the bit error probability estimator is a processor executing instructions stored on a processor-readable medium.

34. The circuit of claim 28, further comprising:
an equalizer that outputs the distribution of multi-bit soft decisions.

35. The circuit of claim 28, further comprising:
a convolutional decoder that outputs hard decision bits based on the multi-bit soft decisions.

36. A circuit comprising:
an equalizer that receives demodulated I and Q samples and outputs multi-bit soft decisions; and
means for estimating a bit error probability (BEP) based on the multi-bit soft decisions.

37. The circuit of claim 36, wherein the means estimates the BEP based on a statistical distribution of the multi-bit soft decisions.

38. The circuit of claim 36, wherein the demodulated I and Q samples are demodulated using a modulation and coding scheme (MCS) that conforms to a standard for Enhanced Data rates for GSM Evolution (EDGE).