Pulse or digital communications – Testing – Signal noise
Reexamination Certificate
2002-10-11
2004-07-06
Chin, Stephen (Department: 2634)
Pulse or digital communications
Testing
Signal noise
C375S346000
Reexamination Certificate
active
06760370
ABSTRACT:
FIELD OF THE INVENTION
The invention relates to processing of communications signals. More particularly, the invention relates to the estimation of the communication signal power in terms of signal-to-noise ratio.
BACKGROUND OF THE INVENTION
In the field of communications, various types of systems use algorithms that depend on a signal-to-noise ratio (SNR) estimate for proper operation. Code division multiple access (CDMA) systems, such as time division duplex CDMA (TDD/CDMA) and time division synchronous CDMA (TDSCDMA) and frequency division duplex CDMA (FDD/CDMA) and CDMA 2000, use SNR estimation for power control to maintain the required link quality while using the minimum transmitted power. An asymmetric digital subscriber loop (ADSL) system uses SNR for the bit allocation algorithm to select the maximum transmission data rate. In turbo decoders, both the determined signal power and noise power are required. Rate adaptive transmission systems often use SNR to dynamically adapt the modulation scheme or the coding rate.
Several algorithms are known for performing SNR estimation. One such algorithm, the received data-aided (RXDA) estimation, is based on the following equation:
SNR
=
(
1
N
∑
k
=
1
N
&LeftBracketingBar;
r
k
&RightBracketingBar;
)
2
1
N
∑
k
=
1
N
r
k
2
-
(
1
N
∑
k
=
1
N
&LeftBracketingBar;
r
k
&RightBracketingBar;
)
2
Equation
1
where r is the received signal vector and N is the number of sample points read by the receiver for the vector r.
Another known algorithm is the transmitted data-aided (TXDA) algorithm, which is represented by the equation:
SNR
=
(
1
N
∑
k
=
1
N
r
k
a
k
)
2
1
N
-
3
∑
k
=
1
N
r
k
2
-
1
N
(
N
-
3
)
(
∑
k
=
1
N
r
k
a
k
)
2
Equation
2
A third known algorithm for SNR estimation is represented as:
SNR
=
N
2
(
∑
k
=
1
N
/
2
(
&LeftBracketingBar;
r
2
k
-
1
&RightBracketingBar;
-
&LeftBracketingBar;
r
2
k
&RightBracketingBar;
)
2
r
2
k
-
1
2
+
r
2
k
2
)
-
1
Equation
3
The algorithms for Equations 1 and 3 are performed blind without any pilot signal. In contrast, the TDXA algorithm uses a pilot signal with known training sequences, which provides enhanced performance. The drawback of TDXA is that additional equipment is required to process the training sequence. Although the RXDA and Equation 3 algorithms work well when the SNR is high, their performance suffers at low and negative SNRs, where they are known to have a high bias. This is problematic for various communication systems. For example, turbo code applications are known to experience negative ratios of symbol energy to noise density. In CDMA systems, the chip energy to noise density is often negative. Hence, there is a need to develop a blind SNR estimation method that works well at low and negative values without the benefit of a training sequence.
SUMMARY
A method for low bias estimation of small or negative signal-to-noise ratio (SNR) for a communication signal is presented. The method is iterative and comprises choosing an initial minimum and maximum estimate of the signal amplitude and determining the mean thereof. Next, associated minimum and maximum noise variances are calculated based on the amplitude values. Using probability density, maximum likelihood estimates of the minimum, maximum and mean amplitudes are then derived. Based on whether the mean amplitude estimate increases or decreases, the initial minimum or maximum estimate is set equal to the maximum likelihood mean amplitude, and the resolution between the new minimum and maximum estimates is determined. The method is repeated until the resolution is within the acceptable limit, at which point the SNR is calculated from the mean amplitude.
REFERENCES:
patent: 5870393 (1999-02-01), Yano et al.
patent: 6442495 (2002-08-01), Fleming-Dahl
patent: 6611794 (2003-08-01), Fleming-Dahl
R. Matzner et al., “An SNR Estimation Algorithm Using Fourth-Order Moments,” IEEE Int. Symp. Inform. Theory, Trondheim, Norway, Jun. 1996, p. 119.
J.G. Proakis, “Digital Communications,” NY, McGraw Hill, 1983, pp. 162-165.
A. Wiesel et al., “Non-Data-Aided Signal-to-Noise-Ratio Estimation,”IEEE, 2002.
David R. Pauluzzi et al., “A Comparison of SNR Estimation Techniques for the AWGN Channel,” IEEE Transactions on Communications, vol. 48, No. 10, Oct. 2000, pp. 1681-1691.
Norman C. Beaulieu et al., “Comparison of Four SNR Estimators for QPSK Modulations,” IEEE Communications Letters, vol. 4, No. 2, Feb. 2000, pp. 43-45.
DiFazio Robert A.
Li Bin
Zeira Ariela
InterDigital Technology Corporation
Odom Curtis
Volpe and Koenig P.C.
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