Multiple-input and multiple-output (MIMO) detection in wireless communications

1. A method for improving noise estimation at a receiver, the method comprising:

receiving a plurality of reference signals;

for a given reference signal:

determining a noise estimation; and

determining a distance metric and a log-likelihood ratio (LLR) of the noise estimation; and

determining a final LLR based on the distance metric and the LLR of each noise estimation;

wherein determining the final LLR further comprises:

combining the LLRs of at least two noise estimations; or

selecting the LLR of a particular noise estimation based on the distance metric of the particular noise estimation;

wherein for a first reference signal of the plurality of reference signal, the noise estimation is a first noise estimation, the distance metric is a first distance metric, and the LLR is a first LLR;

for a second reference signal of the plurality of reference signals, the noise estimation is a second noise estimation, the distance metric is a second distance metric, and the LLR is a second LLR;

wherein the method further comprises:

determining a combined noise estimate, wherein determining the combined noise estimate comprises:

combining the first LLR and the second LLR; or

determining that the second distance metric is nearer to a threshold value than the first distance metric; and determining the combined noise estimate according to the second noise estimation.

2. The method of claim 1 , wherein determining the final LLR further comprises:

determining whether the distance metric is greater than a threshold value.

3. The method of claim 1 , wherein determining the distance metric and the LLR comprises:

decomposing the noise estimate based on Cholesky decomposition techniques; and

applying sphere decoding techniques.

4. The method of claim 3 , wherein the sphere decoding techniques include K-best, fixed complexity sphere decoding, and/or depth first sphere decoding.

5. The method of claim 1 , wherein combining the LLRs of the at least two noise estimations further comprises:

selecting the at least two noise estimations based on the distance metric of the at least two noise estimations.

6. The method of claim 1 , wherein determining the noise estimation comprises:

determining a covariance based on any of:

interference from a transmitter, wherein the transmitter is connected to the receiver;

interference from every transmitter within a threshold distance; and/or

interference from a particular transmitter that the receiver is not connected to.

7. The method of claim 6 , further comprising:

for a first reference signal of the plurality of reference signals, determining a first noise estimation according to a first covariance, wherein the first covariance is based on the interference from the transmitter, wherein the transmitter is connected to the receiver.

8. The method of claim 6 , further comprising:

for a second reference signal of the plurality of reference signals, determining a second noise estimate according a second covariance, wherein the second covariance is based on the interference from every transmitter with the threshold distance.

9. The method of claim 6 , further comprising:

for a third reference signal of the plurality of reference signals, determining a third noise estimate according a third covariance, wherein the third covariance is based on the interference from the particular transmitter that the receiver is not connected to.

10. The method of claim 1 , wherein the distance metric is based on a Euclidean Distance calculation and/or Manhattan Distance calculation.

11. The method of claim 10 , wherein a first distance metric of a first noise estimation is based on the Euclidean Distance calculation, wherein a second distance metric of a second noise estimation is based on the Manhattan Distance calculation.

12. The method of claim 1 , wherein the plurality of reference signals is from a transmitter that the receiver is connected to or from multiple transmitters within a threshold distance of the receiver.

13. The method of claim 1 , wherein the receiver is operating in multiple-input and multiple output (MIMO) technology.

14. A method comprising:

receiving a reference signal;

determining a noise estimation of the reference signal;

determining a distance metric and a log-likelihood ratio (LLR) of the determined noise estimation, wherein determining the distance metric and the LLR comprises:

decomposing the noise estimation based on Cholesky decomposition techniques; and

applying sphere decoding techniques; and

determining a final LLR based on the distance metric and the LLR of the noise estimation,

wherein determining the final LLR further comprises:

combining the LLR of the noise estimation with an LLR of another noise estimation; or

selecting the LLR of the noise estimation based on the distance metric of the noise estimation;

wherein the reference signal is a first reference signal, the noise estimation is a first noise estimation, the distance metric is a first distance metric, and the LLR is a first LLR, the method further comprising:

receiving a second reference signal;

determining a second noise estimation of the second reference signal;

determining a second distance metric and a second (LLR) of the second noise estimation; and

determining a combined noise estimate, wherein determining the combined noise estimate comprises:

combining the first LLR and the second LLR; or

determining that the second distance metric is nearer to a threshold value than the first distance metric; and determining the combined noise estimate according to the second noise estimation.

15. The method of claim 14 , wherein the method is performed by a receiver, and wherein the reference signal is from a transmitter that the receiver is connected to.

16. A receiver comprising:

a receiver operable to receive a plurality of reference signals; and

a processor operable to:

for a given reference signal of the plurality of reference signals:

determining a noise estimation; and

determine a distance metric and a log-likelihood ratio (LLR) of the noise estimation; and

determine a final LLR based on of the distance metric and the LLR of each noise estimation;

wherein the processor is further operable to:

combine the LLRs of at least two noise estimations; or

select the LLR of a particular noise estimation based on the distance metric of the particular noise estimation;

wherein for a first reference signal of the plurality of reference signal, the noise estimation is a first noise estimation, the distance metric is a first distance metric, and the LLR is a first LLR;

for a second reference signal of the plurality of reference signals, the noise estimation is a second noise estimation, the distance metric is a second distance metric, and the LLR is a second LLR;

wherein the processor is further operable to:

determine a combined noise estimate, wherein determining of the combined noise estimate comprises:

combining the first LLR and the second LLR; or

determining that the second distance metric is nearer to a threshold value than the first distance metric; and determining the combined noise estimate according to the second noise estimation.

17. The receiver of claim 16 , wherein the receiver is operating in multiple-input and multiple output (MIMO) technology, and the plurality of reference signals are from one or more transmitters.

18. The receiver of claim 17 , wherein the receiver is connected to the one or more transmitters.