Systems and methods for calculating frequency-localized adaptive beamformer weights using sliding windows across subcarriers
Examples described herein include sliding window methods for calculating frequency-localized weights for adaptive beamformers. A window of subcarriers may be used to calculate a cross-correlation vector, inverse covariance matrix, or other components used in a weight calculation for a particular subcarrier. In some examples, a next window of subcarriers may include additional subcarriers, and may not include other subcarriers. The previously-calculated cross-correlation vector, inverse covariance matrix, or other components may be updated in accordance with updates and downdates associated with the added and removed subcarriers. The updated components may be used to generate weights for a subcarrier in the next window.
1. A receiver comprising:
a processor coupled to memory;
a plurality of antennas in communication with the processor, the plurality of antennas configured to receive data streams in a frequency band spanning multiple subcarrier frequencies; and
a beamformer coupled to the plurality of antennas and in communication with the processor, the beamformer configured to receive the data streams, the beamformer comprising a weight processor configured to calculate, for each subcarrier of the frequency band, a respective plurality of weights used to generate signals for transmission by or combine the signals received at respective ones of the plurality of antennas;
wherein the weight processor is configured to calculate weights for a particular subcarrier at least in part by:
utilizing a window of subcarrier frequencies centered at the particular subcarrier; and
calculating an inverse covariance matrix for the particular subcarrier based on a previously-calculated inverse covariance matrix for a different window of subcarrier frequencies incorporating a different subcarrier of the window of subcarrier frequencies; and
wherein the weight processor is further configured to provide the respective plurality of weights for the particular subcarrier.
2. The receiver of claim 1 , wherein the different subcarrier being different than any subcarrier of the different window of subcarrier frequencies.
3. The receiver of claim 1 , wherein the weight processor is configured to calculate weights for the particular subcarrier at least in part by:
calculating a least squares solution for the particular subcarrier using the inverse covariance matrix to provide the respective plurality of weights for the particular subcarrier.
4. The receiver of claim 3 , wherein calculating the least squares solution for the particular subcarrier using the inverse covariance matrix to provide the weights for the particular subcarrier comprises performing a recursive least squares algorithm with the inverse covariance matrix for the particular subcarrier.
5. The receiver of claim 4 , wherein the recursive least squares algorithm comprises combining a previously-calculated covariance matrix with the different subcarrier of the window of subcarrier frequencies to provide the respective plurality of weights for subcarriers in the window of subcarrier frequencies.
6. The receiver of claim 4 , wherein the recursive least squares algorithm comprises an inverse covariance calculation for subcarriers in the window of subcarrier frequencies.
7. The receiver of claim 6 , wherein calculating the least squares solution for the particular subcarrier using the inverse covariance matrix comprises an update of the different subcarrier of the window of subcarrier frequencies and a downdate of another subcarrier of the different window of subcarrier frequencies.
8. The receiver of claim 6 , wherein the weight processor is configured to calculate weights for the particular subcarrier at least in part by:
sliding, to the window of subcarrier frequencies, from the different window of subcarrier frequencies by a subcarrier to incorporate the different subcarrier of the window of subcarrier frequencies.
9. The receiver of claim 1 , wherein calculating the inverse covariance matrix for the particular subcarrier based on a previously-calculated covariance matrix for the different window of subcarrier frequencies incorporating the different subcarrier of the window of subcarrier frequencies comprise performing a direct matrix inversion (DMI) algorithm with the previously-calculated covariance matrix for the different window of subcarrier frequencies incorporating the different subcarrier of the window of subcarrier frequencies.
10. The receiver of claim 9 , wherein performing the DMI algorithm comprises performing a QR-decomposition of the previously-calculated covariance matrix or a Modified Gram-Schmidt Orthogonalization (MGSO) of the previously-calculated covariance matrix.
11. The receiver of claim 1 , wherein the window of subcarrier frequencies corresponds to a subband of a frequency band, the subband centered at the particular subcarrier.
12. The receiver of claim 11 , wherein the subband corresponds to a range of subcarriers for which a frequency response of a channel is flat.
13. The receiver of claim 1 , wherein the receiver is implemented in a processor of a remote node of a wireless network, the processor configured to execute instructions, from a non-transitory computer-readable medium, that implements the beamformer.
14. The receiver of claim 1 , wherein the processor, coupled to the memory, and in communication with the plurality of antennas and the beamformer, is configured to execute instructions encoded in the memory, wherein the instructions when executed cause the processor to perform calculations using the weights processor, the calculations comprising calculating the weights for the particular subcarrier.
15. A method comprising:
receiving data streams at a plurality of antennas in a range of subcarrier frequencies;
at a beamformer, for each subcarrier frequency in the range of subcarrier frequencies, calculating for each subcarrier of the range of subcarrier frequencies, a respective plurality of weights:
utilizing a window of subcarrier frequencies centered at a particular subcarrier; and
calculating an inverse covariance matrix for the particular subcarrier based on a previously-calculated covariance matrix for a different window of subcarrier frequencies incorporating a different subcarrier of the window of subcarrier frequencies; and
at the beamformer provide the respective plurality of weights for the particular subcarrier.
16. The method of claim 15 , wherein the different subcarrier being different than any subcarrier of the different window of subcarrier frequencies.
17. The method of claim 15 , further comprising:
calculating a least squares solution for the particular subcarrier using the inverse covariance matrix to provide the respective plurality of weights for the particular subcarrier.
18. The method of claim 15 , further comprising:
sliding, to the window of subcarrier frequencies, from the different window of subcarrier frequencies by a subcarrier to incorporate the different subcarrier of the window of subcarrier frequencies.
19. The method of claim 18 , wherein sliding, to the window of subcarrier frequencies, from the different window of subcarrier frequencies comprises adding an adjacent subcarrier to the different window of subcarrier frequencies and subtracting another adjacent subcarrier from the different window of subcarrier frequencies.
20. The method of claim 15 , wherein utilizing the window of subcarrier frequencies centered at the particular subcarrier comprises:
identifying a subband to include subcarrier frequencies with training data based on a time-bandwidth product of the subcarrier frequencies centered at the particular subcarrier; and
centering the window of subcarrier frequencies based on the subband including the subcarrier frequencies with the training data.
21. The method of claim 20 , wherein the subband corresponds to a range of subcarriers for which a frequency response of a channel is flat.
22. The method of claim 15 , further comprising:
calculating an orthogonal frequency division multiplexing (OFDM) Fast Fourier Transform (FFT) for each data stream to generate a corresponding representation of the data stream in a frequency domain; and
providing the corresponding representation of the data stream in the frequency domain to a respective input of the beamformer.
23. The method of claim 22 , wherein the data streams include training data to generate respective pluralities of weights for respective inputs of the beamformer.
24. The method of claim 15 , further comprising:
receiving additional data streams; and
applying respective pluralities of weights for each respective subcarrier to each additional data stream.
25. The method of claim 15 , further comprising:
calculating the inverse covariance matrix for the particular subcarrier using a linear transformation of the data streams, such that the data streams comprise an identity spatial covariance matrix of dimension K, where K is a number of antennas of the plurality of antennas.