Using geographical features to reduce in-field propagation experimentation

• US 9,913,146 B2
• Filed: 01/06/2017
• Issued: 03/06/2018
• Est. Priority Date: 01/06/2016
• Status: Active Grant

• Current Assignee: Southern Methodist University
• Original Assignee: Southern Methodist University
• Inventors: Tonnemacher, Matthew, Rajan, Dinesh, Camp, Joseph
• Primary Examiner(s): Nguyen, Simon
• Application Number:
  US15/399,940
• Publication Number:
  US 20170195892A1
• Time in Patent Office: 424 Days
• Field of Search: 455 6711, 455 6716, 455423
• CPC Class Codes: H04B 17/318 Received signal strength
• CPC Class Codes: H04W 16/22 Traffic simulation tools or models
• CPC Class Codes: H04W 24/02 Arrangements for optimising operational condition
• CPC Class Codes: H04W 24/04 Arrangements for maintaining operational condition
• CPC Class Codes: H04W 24/08 Testing, supervising or monitoring using real traffic
• CPC Class Codes: H04W 4/02 Services making use of location information

Abstract

The present invention includes an apparatus and method for determining cell coverage in a region with reduced in-field propagation measurements comprising: obtaining geographical features of the region; predicting the number of measurements required to accurately characterize its path loss; determining the path loss prediction accuracy of wardriving and crowdsourcing by oversampling a suburban and a downtown region from cell measurements that comprise signal strength and global positioning system coordinates; and using statistical learning to build a relationship between these geographical features and the measurements required, thereby reducing the number of measurements needed to determine path loss accuracy.

Claims

1. An apparatus for determining cell coverage in a region with reduced in-field propagation measurements comprising:

a cell phone or equivalent device comprising a code segment that obtains wireless signal strength and global positioning system coordinates;

a data base that comprises geographical features of the region; and

a processor that comprises a non-transitory computer readable medium, comprising instructions stored thereon, that when executed by a computer having a communications interface, one or more databases and one or more processors communicably coupled to the interface and one or more databases, perform the steps comprising;

predicting a number of measurements required to accurately characterize a path loss in the region by obtaining RSSI readings in terms of dBm, wherein the measurements are obtained when an API reports RSSI in terms of Arbitrary Strength Units (ASU), which quantizes obtained RSSI values in the equation;

P_rx(dBm)=2*P_rx(ASU)−

113 

 

(1)

P_rx(ASU)=[0,31] 

 

(2)

to determine relative path loss accuracy;

oversampling a suburban and a downtown region from cell measurements that comprise signal strength and global positioning system coordinates to determine the path loss prediction accuracy of wardriving and crowdsourcing; and

using a statistical learning framework to build a relationship between the geographical features and the measurements, thereby reducing the number of measurements needed to determine path loss accuracy.

2. The apparatus of claim 1, wherein the measurements from crowdsourcing are obtained by loading an application on one or more cell phones, obtaining permission to use the data captured by the cell phone, and obtaining the wireless signal strength and the global positioning system coordinates from the one or more cell phones.

3. The apparatus of claim 1, further comprising the step of optimizing drive-testing routes to prevent over- and under-sampling of a region.

4. The apparatus of claim 1, further comprising the step of using the cell coverage to improve spatial reuse, intercell interference, and smooth handoffs between cells.

5. The apparatus of claim 1, wherein the step of using the statistical learning framework is defined further as comprising the steps of situationally predicting the number of measurements required to meet a specified path loss characterization precision wherein the framework comprises a geographical feature distribution input that is used to suggest measurement collection requirements in a grid-like fashion over a target region.

6. The apparatus of claim 1, wherein the step of determining the path loss prediction accuracy of wardriving is defined further as comprising the step of collecting high-density wardriving measurements from two or more distinct region types in a major metropolitan area.

7. The apparatus of claim 1, wherein the step of determining the path loss prediction accuracy is defined further as comprising clustering in crowdsourcing and frequency versus distance based wardriving.

8. The apparatus of claim 1, further comprising the steps of determining the effect of land use on path loss characterization, showing how geographical feature diversity plays a large role in determining regional measurement requirements, wherein a correlation between the number of measurements required to accurately characterize the path loss in a region and a quantity of small, medium, large buildings, and foliage in an area.

9. The apparatus of claim 1, further comprising the step of validating the framework by comparing a predicted signal strength measurement distribution to a uniformly spread wardriving measurement approach, and showing improved path loss characterization precision using 23% to 29% fewer measurements.

10. The apparatus of claim 1, wherein the step of determining a high resolution discrete map of the path loss exponent (PLE) and number of measurements required for path loss convergence (MR) metrics over a region by using the algorithm:

11. A method to determine cell coverage in a region with reduced in-field propagation measurements comprising:

obtaining geographical features of the region;

predicting a number of measurements required to accurately characterize a path loss by obtaining RSSI readings in terms of dBm, wherein the measurements are obtained when an API reports RSSI in terms of Arbitrary Strength Units (ASU), which quantizes obtained RSSI values in the equation;

P_rx(dBm)=2*P_rx(ASU)−

113 

 

(1)

P_rx(ASU)=[0,31] 

 

(2)

to determine relative path loss accuracy;

determining the path loss prediction accuracy of wardriving and crowdsourcing by oversampling a suburban and a downtown region from cell measurements that comprise signal strength and global positioning system coordinates; and

using a statistical learning framework to build a relationship between the geographical features and the measurements required, thereby reducing the number of measurements needed to determine path loss accuracy.

12. The method of claim 11, wherein the measurements from crowdsourcing are obtained by loading an application on one or more cell phones, obtaining permission to use the data captured by the cell phone, and obtaining the wireless signal strength and the global positioning system coordinates from the one or more cell phones.

13. The method of claim 11, further comprising the step of optimizing drive-testing routes to prevent over- and under-sampling of a region.

14. The method of claim 11, further comprising the step of using the cell coverage to improve spatial reuse, intercell interference, and smooth handoffs between cells.

15. The method of claim 11, wherein the step of using the statistical learning framework is defined further as comprising the steps of situationally predicting the number of measurements required to meet a specified path loss characterization precision wherein the framework comprises a geographical feature distribution input that is used to suggest measurement collection requirements in a grid-like fashion over a target region.

16. The method of claim 11, wherein the step of determining the path loss prediction accuracy of wardriving is defined further as comprising the step of collecting high-density wardriving measurements from two or more distinct region types in a major metropolitan area.

17. The method of claim 11, wherein the step of determining the path loss prediction accuracy is defined further as comprising clustering in crowdsourcing and frequency versus distance based wardriving.

18. The method of claim 11, further comprising the steps of determining the effect of land use on path loss characterization, showing how geographical feature diversity plays a large role in determining regional measurement requirements, wherein a correlation between the number of measurements required to accurately characterize the path loss in a region and a quantity of small, medium, large buildings, and foliage in an area.

19. The method of claim 11, further comprising the step of validating the framework by comparing a predicted signal strength measurement distribution to a uniformly spread wardriving measurement approach, and showing improved path loss characterization precision using 23% to 29% fewer measurements.

20. The method of claim 11, further the step of determining a high resolution discrete map of the path loss exponent (PLE) and number of measurements required for path loss convergence (MR) metrics over a region by using the algorithm:

21. A computerized method for determining cell coverage in a region with reduced in-field propagation measurements, the method comprising:

obtaining geographical features of the region;

predicting a number of measurements required to accurately characterize a path loss using a processor;

determining the path loss prediction accuracy of wardriving and crowdsourcing by oversampling a suburban and a downtown region from cell measurements that comprise signal strength and global positioning system coordinates with the processor; and

using statistical learning to build a relationship between the geographical features and the measurements required, thereby reducing the number of measurements needed to determine path loss accuracy by obtaining RSSI readings in terms of dBm, wherein the measurements are obtained when an API reports RSSI in terms of Arbitrary Strength Units (ASU), which quantizes obtained RSSI values in the equation;

P_rx(dBm) =2*P_rx(ASU)−

113 

 

(1)

P_rx(ASU)=[0,31] 

 

(2)

to determine relative path loss accuracy.

22. A non-transitory computer readable medium for determining cell coverage in a region with reduced in-field propagation measurements, comprising instructions stored thereon, that when executed by a computer having a communications interface, one or more databases and one or more processors communicably coupled to the interface and one or more databases, perform the steps comprising:

obtaining geographical features of the region;

predicting a number of measurements required to accurately characterize a path loss using a processor by obtaining RSSI readings in terms of dBm, wherein the measurements are obtained when an API reports RSSI in terms of Arbitrary Strength Units (ASU), which quantizes obtained RSSI values in the equation;

P_rx(dBm) =2*P_rx(ASU)−

113 

 

(1)

P_rx(ASU)=[0,31] 

 

(2)

to determine relative path loss accuracy;

determining the path loss prediction accuracy of wardriving and crowdsourcing by oversampling a suburban and a downtown region from cell measurements that comprise signal strength and global positioning system coordinates with the processor; and

using statistical learning to build a relationship between the geographical features and the measurements required, thereby reducing the number of measurements needed to determine path loss accuracy; and

at least one of storing or displaying the results obtained thereby.