System and method of calculating noise variance
The claimed subject matter relates to estimating noise variance associated with a transmitter. For example, the noise variance can be estimated in connection with determining performance parameters associated with a transmitter. Determining noise variance can include the acts of estimating phase alteration of a received signal through utilization of a least squares-based phase estimation algorithm. Determining noise variance can also include the act of determining an unbiased estimation of noise variance as a function of the estimated phase alteration.
1. A method for estimating noise variance associated with a transmitter, comprising:
estimating, using a processor, phase alteration of a received signal through utilization of a least squares based phase estimation algorithm;
determining, using the processor, an unbiased estimate of noise variance as a function of the estimated phase alteration; and
calculating from the unbiased estimate of noise variance, using the processor, a modulation error ratio comprising only interference caused by imperfections of a transmitter.
2. The method of claim 1 , wherein the least squares based algorithm is a first order algorithm.
3. The method of claim 1 , wherein the least squares based algorithm is a second order algorithm.
4. The method of claim 1 , wherein estimating phase alteration comprises:
segmenting the received signal into a plurality of portions; and
estimating phase alteration for each of the plurality of portions.
5. The method of claim 1 , wherein the unbiased estimate of noise variance is determined through employment of the following algorithm:
.sigma.^ zk 2=.beta. n= 0 N− 1 rk″ ( n )− h^k 2,
where {circumflex over (.sigma.)}.sub.z.sub.k is the unbiased estimate with respect to a particular subcarrier k, .beta. is a scaling factor, N is a number of symbols in the received signal, and r″.sub.k(n) and h.sub.k are in-phase and quadrature components of noise, respectively.
6. The method of claim 5 , wherein .beta.=1 N−1.5.
7. The method of claim 5 , wherein the received signal has been partitioned into a plurality of segments, each of the segments includes K symbols.
8. The method of claim 7 , wherein .beta.=1 N−1.5−K and the least squares based algorithm is a first order algorithm.
9. The method of claim 7 , wherein .beta.=1 N−0.5−1.5 K and the least squares based algorithm is a second order algorithm.
10. The method of claim 1 , further determining MER based at least in part upon the determined estimate of noise variance.
11. A receiver configured to perform the method of claim 1 .
12. A communications apparatus, comprising:
a memory that retains instructions for:
estimating phase alteration over time with respect to a received signal through employment of a least squares based phase estimation algorithm;
de-estimating noise variance of the received signal based at least in part upon the estimated phase alteration, wherein the noise variance de-estimation is calculated from a modulation error ratio comprising only interference caused by imperfections of a transmitter; and
a processor that is configured to execute the instructions retained within the memory.
13. The communications apparatus of claim 12 being a FLO receiver.
14. The communications apparatus of claim 12 , wherein the least squares based phase estimation algorithm is a first order least squares based phase estimation algorithm.
15. The communications apparatus of claim 12 , wherein the least squares based phase estimation algorithm is a second order least squares based phase estimation algorithm.
16. The communications apparatus of claim 12 , the memory retains additional instructions for segmenting the received signal into a plurality of segments and estimating phase alteration with respect to at least one of the segments, wherein the processor is configured to execute the additional instructions.
17. The communications apparatus of claim 16 , wherein each of the plurality of segments include a substantially similar number of symbols.
18. The communications apparatus of claim 12 , wherein the processor executes the following algorithm in connection with estimating noise variance:
.sigma.^ zk 2=.beta. n= 0 N− 1 rk ″( n )− h^k 2,
where {circumflex over (.phi.)}.sub.z.sub.k is the estimated noise variance with respect to a particular subcarrier k, .beta. is a scaling factor, N is a number of symbols in the received signal, and r″.sub.k(n) and h.sub.k are in-phase and quadrature components of noise, respectively.
19. The communications apparatus of claim 18 , wherein .beta.=1 N−0.5−K, where K is a number of symbols within a segment of the received signal and the least squares based phase estimation algorithm is a first order phase estimation algorithm.
20. The communications apparatus of claim 18 , wherein .beta.=1 N−0.5−1.5 K, where K is a number of symbols within a segment of the received signal and the least squares based phase estimation algorithm is a second order phase estimation algorithm.
21. A communications apparatus, comprising:
means for estimating phase alteration associated with a received signal through employment of a least squares based phase estimation algorithm;
means for determining an unbiased estimate of noise variance with respect to the received signal as a function of the estimated phase alteration and
means for calculating from the unbiased estimate of noise variance, a modulation error ratio comprising only interference caused by imperfections of a transmitter.
22. The communications apparatus of claim 21 , further comprising means for partitioning the received signal into a plurality of segments, wherein each of the plurality of segments includes a substantially similar number of symbols.
23. The communications apparatus of claim 21 being a FLO receiver.
24. The communications apparatus of claim 21 , wherein the means for determining the unbiased estimate comprises means for executing the following algorithm:
.sigma.^ zk 2=.beta. n= 0 N− 1 rk ″( n )− h^k 2,
where {circumflex over (.phi.)}.sub.z.sub.j is the unbiased estimate with respect to a subcarrier k, .beta. is a scaling factor, N is a number of symbols in the received signal, and r″.sub.k(n) and h.sub.k are in-phase and quadrature components of noise, respectively.
25. The communications apparatus of claim 24 , wherein .beta.=1 N−0.5−K, where K is a number of symbols within a segment of the received signal and the least squares based phase estimation algorithm is a first order algorithm.
26. The communications apparatus of claim 24 , wherein .beta.=1 N−0.5−1.5 K, where K is a number of symbols within a segment of the received signal and the least squares based phase estimation algorithm is a second order algorithm.
27. The communications apparatus of claim 21 , further comprising means for estimating MER as a function of the unbiased estimate of noise variance.
28. A machine-readable medium having stored thereon machine-executable instructions for:
receiving a signal;
estimating phase alteration associated with the received signal through employment of a least squares based phase estimation algorithm;
determining an unbiased estimate of noise variance with respect to the received signal based at least in part upon the estimated phase alteration and
calculating from the unbiased estimate of noise variance, a modulation error ratio comprising only interference caused by imperfections of a transmitter.
29. The machine-readable medium of claim 28 having further instructions stored thereon for partitioning the received signal into a plurality of segments, at least one of the segments includes K number of symbols.
30. The machine-readable medium of claim 29 having further instructions stored thereon for determining the unbiased estimate of noise variance with respect to the received signal through employment of the following algorithm:
.sigma.^ zk 2=.beta. n= 0 N− 1 rk ″( n )− h^k 2,
where {circumflex over (.phi.)}.sub.z.sub.j is the unbiased estimate with respect to a subcarrier k, .beta. is a scaling factor, N is a number of symbols in the received signal, and r″.sub.k(n) and h.sub.k are in-phase and quadrature components of noise, respectively.
31. The machine-readable medium of claim 30 , wherein .beta.=1 N−0.5−K and the least squares based phase estimation algorithm is a first order algorithm.
32. The machine-readable medium of claim 30 , wherein .beta.=1 N−0.5−1.5 K and the least squares based phase estimation algorithm is a second order algorithm.
33. A processor configured to execute the following instructions:
partitioning a received signal into a plurality of segments, wherein each of the segments includes K symbols;
determining an estimated phase alteration with respect to each segment of the received signal through utilization of a least squares based phase estimation algorithm;
determining an estimate of noise variance with respect to the received signal as a function of the estimated phase alteration with respect to each segment of the received signal and
calculating from the estimate of noise variance, a modulation error ratio comprising only interference caused by imperfections of a transmitter.
34. A FLO receiver that comprises the processor of claim 33 .
35. The processor of claim 33 , wherein the processor is further configured to execute instructions for determining MER in association with a transmitter that transmits the received signal.
36. The processor of claim 33 , wherein the processor is further configured to estimate noise variance through employment of the following algorithm:
.sigma.^ zk 2=.beta. n= 0 N− 1 rk ″( n )− h^k 2,
where {circumflex over (.phi.)}.sub.z.sub.j is the unbiased estimate with respect to a subcarrier k, .beta. is a scaling factor, N is a number of symbols in the received signal, and r″.sub.k(n) and h.sub.k are in-phase and quadrature components of noise, respectively.
37. The processor of claim 36 , wherein .beta.=1 N−0.5−K and the least squares based phase estimation algorithm is a first order algorithm.
38. The processor of claim 36 , wherein .beta.=1 N−0.5−1.5 K and the least squares based phase estimation algorithm is a second order algorithm.