Granted Patent B2

US 12,356,249 · App. 18/427,444 · Granted Jul 8, 2025

Inventors: Mehdi Alasti (Reston, VA); Kazi Bashir (Lewisville, TX); Ash Khamas (Goffstown, NM); Ashish Bansal (Frisco, TX); Siddhartha Chenumolu (Broadlands, VA)

Assignee: DISH Wireless L.L.C.

H04W28/0942H04W28/095H04W48/18

Abstract

Claims (54)

1. A system, comprising:

a memory that stores computer instructions; and

a processor that executes the computer instructions to perform actions, the actions including:

receiving network data analytics regarding a cellular telecommunication network having a plurality of User Plane Functions (UPFs), wherein:

the plurality of UPFs serve as anchor points between user equipment (UE) in the cellular telecommunication network and a data network (DN);

each UPF of the plurality of UPFs is a virtual network function responsible for interconnecting packet data unit (PDU) sessions between the user equipment (UE) and the DN by anchoring the PDU sessions on individual UPFs; and

receiving a request to anchor on a UPF a PDU session of a new UE newly appearing on the cellular telecommunication network; and

selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on network data analytics.

2. The system of claim 1 , wherein selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on network data analytics includes:

using the network data analytics to predict throughput of the UE and load on a UPF of the new UE appearing on the cellular telecommunication network based on the predicted throughput; and

selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on the predicted load of the new UE on a UPF.

3. The system of claim 2 , wherein the network data analytics is provided via a network data analytics function (NWDAF) of a 5 th generation (5G) mobile network of which the cellular telecommunication network is comprised.

4. The system of claim 2 , wherein the using the network data analytics to predict throughput of the new UE and load on a UPF of the new UE appearing on the cellular telecommunication network includes:

using artificial intelligence (AI) or machine learning (ML) algorithms to perform predictive analysis of throughput of the new UE and resulting load on a UPF of the new UE appearing on the cellular telecommunication network based on historical activity of the new UE appearing on the cellular telecommunication network.

5. The system of claim 2 , wherein the selecting a UPF of the plurality of UPFs includes:

weighting selection of a particular UPF of the plurality of UPFs based on the predicted throughput of the new UE and resulting predicted load on a UPF of the new UE by using credit/token-based weighted scheduling or probability-based weighted scheduling.

6. The system of claim 5 , wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:

weighting selection of the particular UPF to not overload other UPFs of the plurality of UPFs as compared to the particular UPF in response to the predicted load being at a particular level.

7. The system of claim 5 , wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:

weighting selection of the particular UPF in the plurality of UPFs to not overload the particular UPF beyond a threshold amount compared to other UPFs in the plurality of UPFs based on the predicted load by using credit/token-based weighted scheduling or probability-based weighted scheduling based on the predicted load.

8. A method, comprising:

electronically receiving network data analytics regarding a cellular telecommunication network having a plurality of User Plane Functions (UPFs), wherein:

the plurality of UPFs serve as anchor points between user equipment (UE) in the cellular telecommunication network and a data network (DN);

each UPF of the plurality of UPFs is a virtual network function responsible for interconnecting packet data unit (PDU) sessions between the user equipment (UE) and the DN by anchoring the PDU sessions on individual UPFs; and

electronically receiving a request to anchor on a UPF a PDU session of a new UE newly appearing on the cellular telecommunication network; and

electronically selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on network data analytics.

9. The method of claim 8 , wherein selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on network data analytics includes:

using the network data analytics to predict throughput of the UE and load on a UPF of the new UE appearing on the cellular telecommunication network based on the predicted throughput; and

selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on the predicted load of the new UE on a UPF.

10. The method of claim 9 , wherein the network data analytics is provided via a network data analytics function (NWDAF) of a 5 th generation (5G) mobile network of which the cellular telecommunication network is comprised.

11. The method of claim 9 , wherein the using the network data analytics to predict throughput of the new UE and load on a UPF of the new UE appearing on the cellular telecommunication network includes:

using artificial intelligence (AI) or machine learning (ML) algorithms to perform predictive analysis of throughput of the new UE and resulting load on a UPF of the new UE appearing on the cellular telecommunication network based on historical activity of the new UE appearing on the cellular telecommunication network.

12. The method of claim 9 , wherein the selecting a UPF of the plurality of UPFs includes:

weighting selection of a particular UPF of the plurality of UPFs based on the predicted throughput of the new UE and resulting predicted load on a UPF of the new UE by using credit/token-based weighted scheduling or probability-based weighted scheduling.

13. The method of claim 12 , wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:

weighting selection of the particular UPF to not overload other UPFs of the plurality of UPFs as compared to the particular UPF in response to the predicted load being at a particular level.

14. The method of claim 12 , wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:

weighting selection of the particular UPF in the plurality of UPFs to not overload the particular UPF beyond a threshold amount compared to other UPFs in the plurality of UPFs based on the predicted load by using credit/token-based weighted scheduling or probability-based weighted scheduling based on the predicted load.

15. A non-transitory computer-readable storage medium having computer-executable instructions stored thereon that, when executed by at least one computer processor, cause actions to be performed including:

receiving network data analytics regarding a cellular telecommunication network having a plurality of User Plane Functions (UPFs), wherein:

the plurality of UPFs serve as anchor points between user equipment (UE) in the cellular telecommunication network and a data network (DN);

each UPF of the plurality of UPFs is a virtual network function responsible for interconnecting packet data unit (PDU) sessions between the user equipment (UE) and the DN by anchoring the PDU sessions on individual UPFs; and

receiving a request to anchor on a UPF a PDU session of a new UE newly appearing on the cellular telecommunication network; and

selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on network data analytics.

16. The non-transitory computer-readable storage medium of claim 15 , wherein selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on network data analytics includes:

using the network data analytics to predict throughput of the UE and load on a UPF of the new UE appearing on the cellular telecommunication network based on the predicted throughput; and

selecting a UPF of the plurality of UPFs on which to anchor the PDU session based on the predicted load of the new UE on a UPF.

17. The non-transitory computer-readable storage medium of claim 16 , wherein the network data analytics is provided via a network data analytics function (NWDAF) of a 5 th generation (5G) mobile network of which the cellular telecommunication network is comprised.

18. The non-transitory computer-readable storage medium of claim 16 , wherein the using the network data analytics to predict throughput of the new UE and load on a UPF of the new UE appearing on the cellular telecommunication network includes:

using artificial intelligence (AI) or machine learning (ML) algorithms to perform predictive analysis of throughput of the new UE and resulting load on a UPF of the new UE appearing on the cellular telecommunication network based on historical activity of the new UE appearing on the cellular telecommunication network.

19. The non-transitory computer-readable storage medium of claim 16 , wherein the selecting a UPF of the plurality of UPFs includes:

weighting selection of a particular UPF of the plurality of UPFs based on the predicted throughput of the new UE and resulting predicted load on a UPF of the new UE by using credit/token-based weighted scheduling or probability-based weighted scheduling.

20. The non-transitory computer-readable storage medium of claim 19 wherein the weighting selection of a particular UPF based on the predicted throughput of the new UE and predicted load on a UPF of the new UE includes:

weighting selection of the particular UPF to not overload other UPFs of the plurality of UPFs as compared to the particular UPF in response to the predicted load being at a particular level.

Assignments (2)

Continuity (3)

References Cited (148)