Context-based precoding matrix computations for radio access network for 5G or other next generation network

Patent metadata

Field	Value
Publication number	US-10979112-B2
Application number	US-202016993604-A
Country	US
Kind code	B2
Filing date	Aug 14, 2020
Priority date	Mar 29, 2019
Publication date	Apr 13, 2021
Grant date	Apr 13, 2021

Abstract

Precoding matrix computations for a large number of antenna arrays can be used to generate efficiencies within a wireless network. Utilizing network topology and context data in conjunction with known available network resources and mobile device measurements can facilitate gains in power and spectral efficiency and reduction in computation complexity posed by current procedures. To take advantage of multiple paths, the precoding matrix can be known at the radio units for each mobile device.

First claim

What is claimed is:

1. A method, comprising:
based on model data representative of a precoding prediction model, generating, by network equipment comprising a processor, recommendation data representative of a precoding matrix to be sent to a distributed unit device via a network;
in response to generating the recommendation data, sending, by the network equipment, the recommendation data to the distributed unit device;
in response to sending the recommendation data, receiving, by the network equipment, performance data associated with a performance of a user equipment;
performing, by the network equipment, multiple-input multiple-output precoding determinations associated with neighboring remote unit devices to reduce intracellular interference between a group of user equipment that communicate via the network, the group of user equipment comprising the user equipment; and
based on a result of performing the multiple-input multiple-output precoding determinations, combining, by the network equipment, multiple-input multiple-output channels currently configured for use by the group of user equipment to reduce a number of the multiple-input multiple-output channels, the combining resulting in a reduced number of multiple-input multiple-output channels.

2. The method of claim 1 , further comprising:
receiving, by the network equipment, the model data and resource data, representative of available network resources.

3. The method of claim 2 , further comprising:
in response to receiving the network resource data, modifying, by the network equipment, the precoding matrix.

4. The method of claim 1 , wherein the precoding prediction model is based on historical data associated with usage of the user equipment device.

5. The method of claim 1 , comprising:
receiving, by the network equipment from the user equipment, channel state information associated with a frequency division duplex.

6. The method of claim 1 , wherein the performance data comprises spectrum data associated with a spectrum utilized by the user equipment via the network.

7. The method of claim 1 , wherein the performance data comprises power data associated with a power of an antenna with which the user equipment communicates via the network.

8. Network equipment, comprising:
a processor; and
a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:
based on a precoding prediction model associated with a user equipment of a group of user equipment, generating recommendation data representative of a precoding matrix to be sent to a distributed unit device via a network;
in response to generating the recommendation data, transmitting the recommendation data to the distributed unit device;
in response to transmitting the recommendation data, receiving performance data associated with a performance of the user equipment device;
determining multiple-input multiple-output precoding information associated with neighboring remote unit devices to reduce an intracellular interference between the group of user equipment; and
based on a result of performing multiple-input multiple-output precoding, combining multiple-input multiple-output channels currently configured for use by the user equipment devices to reduce a number of the multiple-input multiple-output channels, resulting in a reduced number of multiple-input multiple-output channels.

9. The network equipment of claim 8 , wherein the operations further comprise:
selecting the precoding prediction model based on historical data associated with usage of the user equipment.

10. The network equipment of claim 8 , wherein the precoding prediction model is based on a detected pattern associated with the user equipment.

11. The network equipment of claim 8 , wherein the reduced number of the multiple-input multiple-output channels are in accordance with a service level agreement associated with the network.

12. The network equipment of claim 8 , wherein the operations further comprise:
based on a covariance eigenspace of a channel usable by the mobile devices for communication via the network, partitioning the group of user equipment into sub-groups of user equipment.

13. The network equipment of claim 12 , wherein, as a result of the partitioning, covariance eigenspaces of the sub-groups are orthogonal.

14. The network equipment of claim 12 , wherein the precoding matrix is a first precoding matrix, and wherein the operations further comprise:
based on the user equipment device determined to belong to a sub-group of the sub-groups, recommending a second precoding matrix to the distributed unit device.

15. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
based on mobile device data associated with precoding prediction data representative of a precoding prediction model, generating recommendation data representative of a precoding matrix to be sent to distributed unit network equipment;
in response to generating the recommendation data, transmitting the recommendation data to the distributed unit network equipment;
in response to transmitting the recommendation data, receiving performance data associated with a performance of a mobile device; and
performing multiple-input multiple-output precoding calculations associated with neighboring remote unit devices to minimize intracellular interference between mobile devices, comprising the mobile device; and
based on a result of performing the multiple-input multiple-output precoding calculations, combining multiple-input multiple-output channels currently configured for use by the mobile device to reduce a number of the multiple-input multiple-output channels, resulting in a reduced number of multiple-input multiple-output channels.

16. The non-transitory machine-readable medium of claim 15 , wherein the mobile device data comprises location data associated with a previous location of the mobile device.

17. The non-transitory machine-readable medium of claim 15 , wherein the mobile device data comprises usage data representative of a previous usage pattern of the mobile device.

18. The non-transitory machine-readable medium of claim 15 , wherein the mobile device is a first mobile device, and wherein the mobile device data comprises co-located mobile device data representative of a second mobile device determined to have been co-located to the first mobile device.

19. The non-transitory machine-readable medium of claim 15 , wherein the operations further comprise:
detecting a pattern associated with the mobile device data, resulting in a detected pattern.

20. The non-transitory machine-readable medium of claim 15 , wherein the operations further comprise:
based on the mobile device data, receiving precoding prediction data representative of the precoding prediction model, wherein the precoding prediction model is determined from the precoding prediction data based on a detected pattern associated with the mobile device data.

Assignees

• At & T Ip I Lp

Inventors

• Jana Rittwik
• Jana Shraboni
• Tofighbakhsh Mostafa
• Sharma Deva-Datta
• Ungureanu Bogdan

Classifications

• H04B7/0478 Primary

  Special codebook structures directed to feedback optimisation · CPC title

• H04B7/0456 Primary

  Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting · CPC title

• Y02D30/70

  in wireless communication networks · CPC title

• H04B7/0434

  using multiple eigenmodes · CPC title

• H04B7/0639

  Using selective indices, e.g. of a codebook, e.g. pre-distortion matrix index [PMI] or for beam selection · CPC title