IP Library › Granted Patent US 12,532,192
Granted Patent B2
US 12,532,192 · App. 17/617,505 · Granted Jan 20, 2026

Proactive mobile network optimization

Inventors: Ali Imran (Bixby, OK); Hasan Farooq (Tulsa, OK); Ahmad Asghar (Seattle, WA)
Assignee: The Board of Regents of the University of Oklahoma
H04W24/02H04B17/309H04W36/322
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Quick Facts
Patent No.
US 12,532,192
App. No.
17/617,505
Granted
Jan 20, 2026
Kind
B2
Abstract

An apparatus comprises: a memory; and a processor coupled to the memory and configured to: build a prediction model that predicts next cells of UEs in a future time step of a mobile network; map the next cells to future user locations; determine future loads of BSs in the mobile network based on the future user locations; determine an optimization of the mobile network using the future loads; and implement the optimization by instructing the BSs to adjust a parameter in the future time step.

Claims (38)

1 . An apparatus comprising:

a memory; and

a processor coupled to the memory and configured to:

build a prediction model that predicts next cells of user equipments (UEs) in a future time step of a mobile network, wherein the prediction model is a semi-Markov model based on user sojourn times in cells and based on next cell handover times;

map the next cells to future user locations that comprise location coordinates;

determine future loads of base stations (BSs) in the mobile network based on the future user locations;

determine an optimization of the mobile network using the future loads; and

implement the optimization by instructing the BSs to adjust a parameter in the future time step.

2 . The apparatus of claim 1 , wherein the processor is further configured to build the prediction model using at least one of handover reports, call detail records (CDRs), or UE measurements.

3 . The apparatus of claim 2 , wherein the UE measurements comprise at least one of a reference signal received power (RSRP), a reference signal received quality (RSRQ), or a received signal strength (RSS).

4 . The apparatus of claim 1 , wherein the processor is further configured to map the next cells to the future user locations using most-probable landmarks of the UEs.

5 . The apparatus of claim 1 , wherein the processor is further configured to map the next cells to the future user locations using at least one of a reference signal received power (RSRP), a reference signal received quality (RSRQ), or a received signal strength (RSS).

6 . The apparatus of claim 1 , wherein the location coordinates are Global Positioning System (GPS) coordinates.

7 . The apparatus of claim 1 , wherein the future loads are a fraction of total resources in cells required to achieve a required rate of all users of those cells.

8 . The apparatus of claim 1 , wherein the optimization is for energy saving.

9 . The apparatus of claim 8 , wherein the parameter is an on/off state and cell individual offset (CIO).

10 . The apparatus of claim 1 , wherein the optimization is for load balancing.

11 . The apparatus of claim 10 , wherein the parameter is at least one of an antenna tilt, an antenna azimuth, an antenna beam width, or an antenna transmission power.

12 . The apparatus of claim 1 , wherein the apparatus is a centralized self-organizing network (C-SON) controller.

13 . The apparatus of claim 1 , wherein the processor is further configured to further implement the optimization by instructing switching off of underutilized small cells.

14 . A method comprising:

building a prediction model that predicts next cells of user equipments (UEs) in a future time step of a mobile network, wherein the prediction model is a semi-Markov model based on user sojourn times in cells and based on next cell handover times;

mapping the next cells to future user locations that comprise location coordinates;

determining future loads of base stations (BSs) in the mobile network based on the future user locations;

determining an optimization of the mobile network using the future loads; and

implementing the optimization by instructing the BSs to adjust a parameter in the future time step.

15 . The method of claim 14 , wherein the optimization is for energy saving, and wherein the parameter is an on/off state and cell individual offset (CIO).

16 . The method of claim 14 , wherein the optimization is for load balancing, and wherein the parameter is at least one of an antenna tilt, an antenna azimuth, an antenna beam width, or an antenna transmission power.

17 . The method of claim 14 , further comprising building the prediction model using at least one of handover reports, call detail records (CDRs), or UE measurements.

18 . The method of claim 17 , wherein the UE measurements comprise at least one of a reference signal received power (RSRP), a reference signal received quality (RSRQ), or a received signal strength (RSS).

19 . The method of claim 14 , further comprising mapping the next cells to the future user locations using most-probable landmarks of the UEs.

20 . The method of claim 14 , further comprising mapping the next cells to the future user locations using at least one of a reference signal received power (RSRP), a reference signal received quality (RSRQ), or a received signal strength (RSS).

21 . A computer program product comprising instructions that are stored on a non-transitory computer-readable medium and that, when executed by a processor, cause an apparatus to:

build a prediction model that predicts next cells of user equipments (UEs) in a future time step of a mobile network, wherein the prediction model is a semi-Markov model based on user sojourn times in cells and based on next cell handover times;

map the next cells to future user locations that comprise location coordinates;

determine future loads of base stations (BSs) in the mobile network based on the future user locations;

determine an optimization of the mobile network using the future loads; and

implement the optimization by instructing the BSs to adjust a parameter in the future time step.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 1, 2022
From: IMRAN, ALI; FAROOQ, HASAN; ASGHAR, AHMAD
To: THE BOARD OF REGENTS OF THE UNIVERSITY OF OKLAHOMA
Reel/Frame 059139/0301 →
Continuity (2)
Provisional Application 62875841 · Jul 18, 2019
Related Publication 20220225127A1 · Jul 14, 2022
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