Method and apparatus for classifying user morphology for efficient use of cell phone system resources

• US 20070049295A1
• Filed: 08/24/2005
• Published: 03/01/2007
• Est. Priority Date: 08/24/2005
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Soliman, Samir, Gaal, Peter
• Granted Patent: US 8,099,106 B2
• Time in Patent Office: Days
• US Class Current: 455/456.300
• CPC Class Codes: G01S 19/25 involving aiding data received from a cooperating element, e.g. assisted GPS
• CPC Class Codes: G01S 19/46 the supplementary measurement being of a radio-wave signal type
• CPC Class Codes: H04W 24/00 Supervisory, monitoring or testing arrangements
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A system and method for classifying the user'"'"'s morphology, and using that classification to control the UE in order to provide more efficient operation of the UE. The classification may be used to assist the user in determining its location more efficiently, without needless interruption or unnecessary use of system resources. The morphology classifier may classify the user'"'"'s morphology into at least one of the following classifications: indoor, outdoor, local coverage, no local coverage, proximate to a base station, stationary, moving, urban, suburban, rural, deep indoor, mid indoor, and indoor near window. The classification may be used by the UE or by a remote server that utilizes the classification to develop assistance data that is sent to the UE.

Claims

1. A method of classifying morphology at the location of a UE that communicates using RF signals, in order to assist the UE in efficiently performing position location operations and conserve system resources, comprising:

a) measuring a first set of signals received in said UE from RF signal sources within range of said UE, to provide a first set of data measurements;

b) responsive to said first set of data measurements, classifying a user'"'"'s morphology; and

c) responsive to said morphology classification, determining whether to perform operations including at least one of fixing position location determinations, and sending unsolicited sensitivity assistance data to the UE.

2. The method of claim 1 wherein said RF signal sources include a plurality of base stations, and said data measurements include the SNR of each of the RF signal sources, and the RSSI.

3. The method of claim 1 wherein said RF signal sources include a plurality of base stations, and said data measurements include AFLT measurements.

4. The method of claim 1 wherein said RF signal sources include a plurality of positioning satellites, and said data measurements include GPS measurements.

5. The method of claim 1 wherein said RF signal sources comprise a plurality of base stations in a CDMA network, and said data measurements include at least two of Ec/No, RSSI, RTD, the number of pilots in the found set, and the dynamic variation of energy profiles among the found pilots.

6. The method of claim 1 wherein said morphology classifications include at least one of indoor, outdoor, local coverage, no local coverage, proximate to a base station, stationary, moving, urban, suburban, rural, deep indoor, mid indoor, and indoor near window.

7. The method of claim 1 wherein said morphology classifications include at least one of indoor with local coverage, outdoor near cell, outdoor dense urban moving, high rise building close to window, outdoor dense urban stationary, outdoor suburban moving, outdoor suburban stationary, deep indoor no local coverage close to a cell site, rural area moving, rural area stationary, outdoor suburban area edge of coverage mobile, outdoor suburban area edge of coverage stationary, and indoor mid building no local coverage.

8. The method of claim 1 further comprising determining if the SNR exceeds a minimum threshold before classifying the user morphology, and if it does not exceed the minimum threshold, then repeating said measuring a first set of signals to provide a second set of data measurements before classifying the user morphology, otherwise continuing with classifying a user'"'"'s morphology.

9. The method of claim 1, further comprising determining a previously known location of the UE, and wherein said classifying the user'"'"'s morphology comprises determining said classification responsive to said previously known location in addition to said first set of data measurements.

10. The method of claim 1 wherein said determining whether to perform operations comprises generating position assistance data responsive to said classification, and making a second set of measurements responsive to said assistance data.

11. The method of claim 1 further comprising determining a level of confidence in said classification, and wherein said determining whether to perform operations is performed responsive to said level of confidence and said morphology classification.

12. A method of efficiently utilizing the system resources of a UE, comprising:

a) measuring a first set of signals received in said UE from RF signal sources within range of said UE, to provide a first set of data measurements;

b) responsive to said first set of data measurements, classifying a user'"'"'s morphology; and

c) responsive to said morphology classification, controlling said UE to efficiently perform operations and thereby efficiently utilize system resources within said UE, including at least one of enabling/disabling position location determinations, enabling/disabling sending unsolicited sensitivity assistance data to the UE, adjusting the number of signal sources to be searched, adjusting the search window size, and predicting an expected signal strength and controlling said UE responsive to said expected signal strength.

13. The method of claim 12 wherein said RF signal sources include a plurality of base stations, and said data measurements include the SNR of each of the RF signal sources, and the RSSI.

14. The method of claim 12 wherein said RF signal sources include a plurality of base stations, and said data measurements include AFLT measurements.

15. The method of claim 12 wherein said RF signal sources include a plurality of positioning satellites, and said data measurements include GPS measurements.

16. The method of claim 12 wherein said RF signal sources comprise a plurality of base stations in a CDMA network, and said data measurements include at least two of Ec/No, RSSI, RTD, the number of pilots in the found set, and the dynamic variation of energy profiles among the found pilots.

17. The method of claim 12 wherein said morphology classifications include at least one of indoor, outdoor, local coverage, no local coverage, proximate to a base station, stationary, moving, urban, suburban, rural, deep indoor, mid indoor, and indoor near window.

18. The method of claim 12 wherein said morphology classifications include at least one of indoor with local coverage, outdoor near cell, outdoor dense urban moving, high rise building close to window, outdoor dense urban stationary, outdoor suburban moving, outdoor suburban stationary, deep indoor no local coverage close to a cell site, rural area moving, rural area stationary, outdoor suburban area edge of coverage mobile, outdoor suburban area edge of coverage stationary, and indoor mid building no local coverage.

19. The method of claim 12 further comprising determining if the SNR exceeds a minimum threshold before classifying the user morphology, and if the SNR does not exceed the minimum threshold, then repeating said measuring a first set of signals to provide a second set of data measurements before classifying the user morphology, otherwise continuing with said classifying a user'"'"'s morphology.

20. The method of claim 12 further comprising determining a previously known location of the UE, and wherein said classifying a user'"'"'s morphology comprises determining said classification responsive to said previously known location in addition to said first set of measurements.

21. The method of claim 12 wherein said controlling said UE comprises generating position assistance data responsive to said classification, and making a second set of measurements responsive to said assistance data.

22. The method of claim 12 further comprising determining a level of confidence in said classification, and said controlling said UE is performed responsive to said level of confidence and said morphology classification.

23. A wireless apparatus for efficiently performing position location operations to enable system resources to be conserved, comprising:

a receiver including a system for measuring a first set of signals received in said UE from RF signal sources within range of said UE, to provide a first set of data measurements;

a morphology classifier for classifying the user'"'"'s morphology responsive to said first set of data measurements; and

a control system, responsive to said morphology classification, for determining whether to perform operations including at least one of fixing position location determinations, and sending unsolicited sensitivity assistance data to the UE.

24. The wireless apparatus of claim 23 wherein said RF signal sources comprise a plurality of base stations in a CDMA network, and said receiver includes means for taking data measurements including at least two of Ec/No, RSSI, RTD, the number of pilots in the found set, and the dynamic variation of energy profiles among the found pilots.

25. The wireless apparatus of claim 23 wherein said morphology classifier including means for classifying the user'"'"'s morphology into at least one of indoor, outdoor, local coverage, no local coverage, proximate to a base station, stationary, moving, urban, suburban, rural, deep indoor, mid indoor, and indoor near window.

26. The wireless apparatus of claim 23 further comprising means for determining a level of confidence in said classification, and said control system responsive to said level of confidence and said morphology classification.