Method and system for unsupervised user clustering and power allocation in non-orthogonal multiple access (NOMA)-aided massive multiple input-multiple output (MIMO) networks

Patent metadata

Field	Value
Publication number	US-12301320-B2
Application number	US-202118252096-A
Country	US
Kind code	B2
Filing date	Dec 1, 2021
Priority date	Dec 1, 2020
Publication date	May 13, 2025
Grant date	May 13, 2025

Abstract

Embodiments of a method and network node for managing wireless devices (WDs) in a network are disclosed. In some embodiments, the method comprises grouping the plurality of WDs into clusters based at least in part on correlations between channels of the WDs. The method also includes determining beam forming weights for each cluster, the grouping into clusters and the determining of beam forming weights being performed to minimize a total transmit power to the plurality of WDs.

First claim

The invention claimed is:

1. A method in a network node configured to communicate wirelessly with a plurality of wireless devices, WDs, the method comprising:
grouping the plurality of WDs into clusters based at least in part on correlations between channels of the WDs; and
determining beam forming weights for each cluster, the grouping into clusters and the determining of beam forming weights being performed to minimize a total transmit power to the plurality of WDs,
wherein the grouping of the plurality of WDs into clusters is performed by an unsupervised machine learning process, wherein the unsupervised machine learning process includes a mean-shift algorithm.

2. The method of claim 1 , further comprising determining a power allocated to each WD of the plurality of WDs that minimizes the total transmit power.

3. The method of claim 1 , wherein the unsupervised machine learning process is configured to maximize a total number of users in each group cluster based at least in part on the correlations.

4. The method of claim 1 , wherein the correlations between the channels are derived from channel state information, CSI, that is one of reported by the plurality of WDs and estimated by the network node.

5. The method of claim 1 , wherein beam forming weights for a cluster are determined based at least in part on a centroid of a function of channel state information, CSI, for the WDs in the cluster.

6. The method of claim 1 , wherein beam forming weights for a cluster are determined based at least in part on a zero-forcing process.

7. The method of claim 1 , further comprising allocating power to WDs within a cluster by application of a non-orthogonal multiple access, NOMA, sequential interference cancellation, SIC, process.

8. The method of claim 7 , wherein the grouping of the plurality of WDs into clusters and the allocating of power to the WDs within a cluster are performed subject to constraints on signal to interference plus noise ratios, SINRs, measured at each WD of the plurality of WDs.

9. The method of claim 1 , wherein the grouping into clusters and the determining of beam forming weights for each cluster is performed in a distributed massive multiple input multiple output, MIMO, non-orthogonal multiple access, NOMA, architecture.

10. The method of claim 1 , wherein the grouping into clusters and the determining of beam forming weights for each cluster is performed in a collocated massive multiple input multiple output, MIMO, non-orthogonal multiple access, NOMA, architecture.

11. A network node configured to communicate wirelessly with a plurality of wireless devices, WDs, the network node comprising processing circuitry configured to:
group the plurality of WDs into clusters based at least in part on correlations between channels of the WDs; and
determine beam forming weights for each cluster, the grouping into clusters and the determining of beam forming weights being performed to minimize a total transmit power to the plurality of WDs,
wherein the grouping of the plurality of WDs into clusters is performed by an unsupervised machine learning process, wherein the unsupervised machine learning process includes a mean-shift algorithm.

12. The network node of claim 11 , wherein the processing circuitry is further configured to determine a power allocated to each WD of the plurality of WDs that minimizes the total transmit power.

13. The network node of claim 11 , wherein the unsupervised machine learning process is configured to minimize the total transmit power to the plurality of WDs.

14. The network node of claim 11 , wherein the correlations between the channels are derived from channel state information, CSI, reported by the plurality of WDs.

15. The network node of claim 11 , wherein beam forming weights for a cluster are determined based at least in part on a centroid of the cluster.

16. The network node of claim 11 , wherein beam forming weights for a cluster are determined based at least in part on a zero-forcing process.

17. The network node of claim 11 , wherein the processing circuitry is further configured to allocate power to WDs within a cluster by application of a non-orthogonal multiple access, NOMA, sequential interference cancellation, SIC, process.

18. The network node of claim 11 , wherein the grouping of the plurality of WDs into clusters and the allocating of power to the WDs within a cluster are performed subject to constraints on signal to interference plus noise ratios, SINRs, measured at each WD of the plurality of WDs.

19. The network node of claim 11 , wherein the grouping into clusters and the determining of beam forming weights for each cluster is performed in a distributed massive multiple input multiple output, MIMO, non-orthogonal multiple access, NOMA, architecture.

20. The network node of claim 11 , wherein the grouping into clusters and the determining of beam forming weights for each cluster is performed in a collocated massive multiple input multiple output, MIMO, non-orthogonal multiple access, NOMA, architecture.

21. A method in a network node configured to communicate wirelessly with a plurality of wireless devices, WDs, the method comprising:
grouping the plurality of WDs into clusters based at least in part on correlations between channels of the WDs; and
determining beam forming weights for each cluster, the grouping into clusters and the determining of beam forming weights being performed to minimize a total transmit power to the plurality of WDs,
wherein beam forming weights for a cluster are determined based at least in part on a centroid of a function of channel state information, CSI, for the WDs in the cluster.

Assignees

• Ericsson Telefon Ab L M

Inventors

• Kiani Sharareh
• Shahbazpanahi Shahram
• Bavand Majid
• Boudreau Gary
• Dong Min

Classifications

• H04W52/42

  in systems with time, space, frequency or polarisation diversity · CPC title

• H04W52/346

  distributing total power among users or channels · CPC title

• H04B7/0626

  Channel coefficients, e.g. channel state information [CSI] · CPC title

• H04W52/143

  Downlink power control · CPC title

• H04L5/0023

  Time-frequency-space · CPC title