Abstract: Certain aspects of the present disclosure provide techniques for selecting a public land mobile network (PLMN) based on route information. A method that may be performed by a wireless node includes camping on a first cellular network on a first frequency band of one or more first frequency bands associated with a first mobile country code (MCC) of a planned route of the wireless node; detecting a condition with the first cellular network after the camping on the first cellular network; and searching, after detecting the condition, for service on the one or more first frequency bands and one or more second frequency bands associated with a second MCC, wherein the one or more second frequency bands are selected based on the first MCC and the planned route.

Abstract: A method of providing UE location data includes: receiving, at the UE, reference signals each being from a respective one of a plurality of reference signal sources, and each being associated with a corresponding signal strength; obtaining, based on the reference signals, a first signal source data set comprising reference signal source identifiers and corresponding signal strengths for respective ones of the reference signals; producing a second signal source data set by at least one of compressing at least one of the reference signal source identifiers, or down-selecting available identifiers based on a signal strength diversity of the corresponding signal strengths of the first signal source data set, each of the available identifiers comprising one of the reference signal source identifiers or a compressed one of the reference signal source identifiers; and transmitting, from the UE, the second signal source data set.

Abstract: In various embodiments, crowd sourcing techniques are provided to determine beacon altitudes that may then be used in 3D positioning of UE. Some techniques may crowd source beacon altitudes based on global navigation satellite system (GNSS) position fixes obtained by UE. Other techniques may crowd source beacon altitudes based on uncalibrated pressure measurements obtained by UE. Still other techniques may combine beacon altitude crowd-sourcing and pressure sensor calibration on UE. Such techniques may make inferences based on line of sight (LOS) between UE and beacons, determined using signal strength, connection status, and/or timing measurement. The techniques may be implemented separately, or as part of a combined system that determines beacon altitudes in diverse manners. Once beacon altitudes are known, that may be used to determine 3D positions of the UE (e.g., by trilateration, multilateration or other positioning techniques).

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to determine beacon altitudes that may then be used in 3D positioning of UE. Some techniques may crowd source beacon altitudes based on global navigation satellite system (GNSS) position fixes obtained by UE. Other techniques may crowd source beacon altitudes based on uncalibrated pressure measurements obtained by UE. Still other techniques may combine beacon altitude crowd-sourcing and pressure sensor calibration on UE. Such techniques may make inferences based on line of sight (LOS) between UE and beacons, determined using signal strength, connection status, and/or timing measurement. The techniques may be implemented separately, or as part of a combined system that determines beacon altitudes in diverse manners. Once beacon altitudes are known, that may be used to determine 3D positions of the UE (e.g., by trilateration, multilateration or other positioning techniques).

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that am RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., pseudoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, techniques are provided for deploying a positioning applet to a SIM (e.g., a physical SIM card or an embedded SIM (eSIM)/integrated SIM (iSIM)) via an over-the-air (OTA) update or by permanent programming (i.e. “burning in”) during manufacture. The positioning applet may run solely on a processor of the SIM, functioning without support of application, OS or baseband software executing on the CPU or baseband processor of the UE, or network deployed infrastructure support. In operation, the positioning applet collects positioning measurements from a baseband processor (e.g., a baseband chipset) of the UE (e.g., via 3GPP protocols) which are sent (e.g., as an encrypted payload) to a remote location platform that compares the positioning measurements to known positioning data in a database (e.g., a crowd sourced database) to determine UE position. The remote location platform may provide an estimated position to a designated recipient system, without involvement of the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that am RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to determine beacon altitudes that may then be used in 3D positioning of UE. Some techniques may crowd source beacon altitudes based on global navigation satellite system (GNSS) position fixes obtained by UE. Other techniques may crowd source beacon altitudes based on uncalibrated pressure measurements obtained by UE. Still other techniques may combine beacon altitude crowd-sourcing and pressure sensor calibration on UE. Such techniques may make inferences based on line of sight (LOS) between UE and beacons, determined using signal strength, connection status, and/or timing measurement. The techniques may be implemented separately, or as part of a combined system that determines beacon altitudes in diverse manners. Once beacon altitudes are known, that may be used to determine 3D positions of the UE (e.g., by trilateration, multilateration or other positioning techniques).

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., pseudoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, crowd sourcing techniques are provided to enable RTT-based positioning of UE. To address issues of discovering which beacons (e.g., Wi-Fi APs, cellular base stations, BLE transmitters, etc.) support measurement of RTT (e.g., according to IEEE 802.11mc, 3GPP Release 16, etc.), beacon RTT capabilities may be crowd-sourced from UE and maintained by a cloud-based location platform in a beacon database (or more specifically, a RTT database portion thereof). To address the issue of determining physical antenna positions, RTT measurements may be crowd-sourced from UE for those beacons that are RTT capable, and used by a trilateration algorithm (e.g., a WLS multilateration algorithm) to determine physical antenna positions, which also may be maintained in the beacon database. Accuracy of the trilateration may be enhanced by obtaining raw GNSS measurements (e.g., psuedoranges) from the UE, and performing a cloud-based RTK GNSS position fix for the UE.

Abstract: In various embodiments, techniques are provided for deploying a positioning applet to a SIM (e.g., a physical SIM card or an embedded SIM (eSIM)/integrated SIM (iSIM)) via an over-the-air (OTA) update or by permanent programming (i.e. “burning in”) during manufacture. The positioning applet may run solely on a processor of the SIM, functioning without support of application, OS or baseband software executing on the CPU or baseband processor of the UE, or network deployed infrastructure support. In operation, the positioning applet collects positioning measurements from a baseband processor (e.g., a baseband chipset) of the UE (e.g., via 3GPP protocols) which are sent (e.g., as an encrypted payload) to a remote location platform that compares the positioning measurements to known positioning data in a database (e.g., a crowd sourced database) to determine UE position. The remote location platform may provide an estimated position to a designated recipient system, without involvement of the UE.

Abstract: In an overlay, network-based, wireless location system, LMUs typically co-located with BTSs are used to collect radio signaling both in the forward and reverse channels for use in TDOA and/or AOA positioning methods. Information broadcast from the radio network and by global satellite navigation system constellations can be received by the LMU and used to reduce the difficulty of initial system configuration and reconfiguration due to radio network changes.

Abstract: In an overlay, network-based, wireless location system, passive network probes and Location Measurement Units, typically co-located with eNodeB's, are used to collect identity information and radio signaling both in the forward and reverse channels for use in power-based, timing-based and/or angle-based positioning methods in Long Term Evolution (LTE) and LTE-Advanced wireless communications networks.

Abstract: For Wireless Communications Networks (WCNs) that support soft handover, cooperator receiver selection for a TDOA, AOA, TDOA/AOA, or hybrid network-based or network-overlay Wireless Location System (WLS) must contend with one or more network base stations as a serving cell. When the active set contains more than one member, two techniques for determining a set of cooperating and demodulating receivers to use in the signal collection for location estimation is disclosed. In one embodiment, the active set members are constructively reduced to a single member that is used as a proxy serving cell. In another embodiment, the information contained in the active set membership is retained and a new set of demodulating and cooperator receivers are generated based on the entire membership of the active set.

Abstract: In an overlay, network-based, wireless location system, passive network probes and Location Measurement Units, typically co-located with eNodeB's, are used to collect identity information and radio signaling both in the forward and reverse channels for use in power-based, timing-based and/or angle-based positioning methods in Long Term Evolution (LTE) and LTE-Advanced wireless communications networks.