CROSS-REFERENCE TO RELATED APPLICATIONS

The present Application for Patent claims the benefit of U.S. Provisional Application No. 63/060,998, entitled “NEURAL NETWORK FUNCTIONS FOR POSITIONING OF A USER EQUIPMENT,” filed Aug. 4, 2020, assigned to the assignee hereof, and expressly incorporated herein by reference in its entirety.

BACKGROUND OF THE DISCLOSURE
1. Field of the Disclosure

Aspects of the disclosure relate generally to wireless communications, and more particularly to neural network functions for positioning of a user equipment (UE).

2. Description of the Related Art

Wireless communication systems have developed through various generations, including a first-generation analog wireless phone service (1G), a second-generation (2G) digital wireless phone service (including interim 2.5G networks), a third-generation (3G) high speed data, Internet-capable wireless service and a fourth-generation (4G) service (e.g., LTE or WiMax). There are presently many different types of wireless communication systems in use, including cellular and personal communications service (PCS) systems. Examples of known cellular systems include the cellular analog advanced mobile phone system (AMPS), and digital cellular systems based on code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), the Global System for Mobile access (GSM) variation of TDMA, etc.

A fifth generation (5G) wireless standard, referred to as New Radio (NR), enables higher data transfer speeds, greater numbers of connections, and better coverage, among other improvements. The 5G standard, according to the Next Generation Mobile Networks Alliance, is designed to provide data rates of several tens of megabits per second to each of tens of thousands of users, with 1 gigabit per second to tens of workers on an office floor. Several hundreds of thousands of simultaneous connections should be supported in order to support large wireless sensor deployments. Consequently, the spectral efficiency of 5G mobile communications should be significantly enhanced compared to the current 4G standard. Furthermore, signaling efficiencies should be enhanced and latency should be substantially reduced compared to current standards.

SUMMARY

The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview relating to all contemplated aspects, nor should the following summary be considered to identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary has the sole purpose to present certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below.

In an aspect, a method of operating a user equipment (UE) includes obtaining at least one neural network function configured to derive a likelihood of at least one set of positioning measurement features being present at a candidate set of positioning estimates for the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures; obtaining positioning measurement data associated with a location of the UE; and determining a positioning estimate for the UE based at least in part upon the positioning measurement data and the at least one neural network function.

In some aspects, the at least one neural network function comprises a UE-feature processing neural network function.

In some aspects, the positioning measurement data comprises a set of positioning measurements at the UE, and wherein the determining comprises: detecting a set of positioning measurement features based on the set of positioning measurements at the UE; and deriving a likelihood of the set of positioning measurement features being present at the candidate set of positioning estimates for the UE based at least in part upon the UE-feature processing neural network function,

In some aspects, the at least one neural network function comprises at least one additional UE-feature processing neural network function.

In some aspects, the UE-feature processing neural network function is configured to derive the likelihoods based on at least one of: a clock drift at the UE, a hardware group delay at the UE, a model of UE, or a combination thereof.

In some aspects, the at least one neural network function comprises a base station (BS)-feature processing neural network function.

In some aspects, the positioning measurement data comprises a set of positioning measurement features based on a set of positioning measurements at one or more BSs, and the determining comprises: deriving a likelihood of the set of positioning measurement features being present at the candidate set of positioning estimates for the UE based at least in part upon the BS-feature processing neural network function, wherein the positioning estimate for the UE is based in part upon the derived likelihoods.

In some aspects, the at least one neural network function comprises at least one additional BS-feature processing neural network function.

In some aspects, the at least one neural network function further comprises a UE-feature processing neural network function.

In some aspects, the positioning measurement data comprises a first set of positioning measurement features based on a first set of positioning measurements at one or more BSs, and a second set of positioning measurement features based on a second set of positioning measurements at the UE, further comprising: deriving a likelihood of the first and second sets of positioning measurement features being present at the candidate set of positioning estimates for the UE based at least in part upon the UE-feature processing neural network function and the BS-feature processing neural network function, wherein the positioning estimate for the UE is based in part upon the derived likelihoods.

In some aspects, the BS-feature processing neural network function is configured to derive the likelihoods based on at least one of: a location of at least one BS, a downtilt of the at least one BS, a transmit power of the at least one BS, a clock synchronization error between two or more BSs, a clock drift of the at least one BS, a hardware group delay of the at least one BS, a base station almanac (BSA) error associated with the at least one BS, or any combination thereof.

In some aspects, the candidate set of positioning estimates is implicitly indicated to the UE in association with the at least one neural network function, or the candidate set of positioning estimates is explicitly indicated to the UE in association with the at least one neural network function.

In some aspects, the at least one neural network function is specific to: a particular base station (BS) or group of BSs, a carrier, a location region, a positioning measurement type or group of positioning measurement types, a beam or group of beams, or any combination thereof.

In some aspects, the positioning estimate comprises: a wireless wide area network (WWAN) position estimate, a wireless local area network (WLAN) position estimate, a Global Navigation Satellite System (GNSS) position estimate, a sensor-based position estimate, or any combination thereof.

In some aspects, the obtaining comprises receipt of the at least one neural network function from a base station, a server, or a combination thereof.

In some aspects, the at least one neural network function configured to facilitate the UE to derive the likelihood of the at least one set of positioning measurement features being present at the candidate set of positioning estimates for the UE in association with one or more other positioning measurement features.

In an aspect, a method of operating a base station (BS) includes obtaining at least one neural network function configured to facilitate a user equipment (UE) to derive a likelihood of at least one set of positioning measurement features being present at a candidate set of positioning estimates for the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures; and transmitting the at least one neural network function to the UE.

In some aspects, the at least one neural network function is generated dynamically at the BS or another network component.

In some aspects, the at least one neural network function comprises a UE-feature processing neural network function.

In some aspects, the at least one neural network function comprises at least one additional UE-feature processing neural network function.

In some aspects, the UE-feature processing neural network function is configured to derive the likelihoods based on at least one of: a clock drift at the UE, a hardware group delay at the UE, a model of UE, or a combination thereof.

In some aspects, the at least one neural network function comprises one or more base station (BS)-feature processing neural network functions.

In some aspects, the at least one neural network function further comprises one or more UE-feature processing neural network functions.

In some aspects, the one or more BS-feature processing neural network functions are configured to derive the likelihoods based on at least one of: a location of at least one BS, a downtilt of the at least one BS, a transmit power of the at least one BS, a clock synchronization error between two or more BSs, a clock drift of the at least one BS, a hardware group delay of the at least one BS, a base station almanac (BSA) error associated with the at least one BS, or any combination thereof.

In some aspects, the candidate set of positioning estimates is implicitly indicated to the UE in association with the at least one neural network function, or the candidate set of positioning estimates is explicitly indicated to the UE in association with the at least one neural network function.

In some aspects, the at least one neural network function is specific to: a particular base station (BS) or group of BSs, a carrier, a location region, a positioning measurement type or group of positioning measurement types, a beam or group of beams, or any combination thereof.

In some aspects, the at least one neural network function is configured to facilitate the UE to determine one or more: a wireless wide area network (WWAN) position estimate, a wireless local area network (WLAN) position estimate, a Global Navigation Satellite System (GNSS) position estimate, a sensor-based position estimate, or any combination thereof.

In some aspects, the obtaining comprises generation of the at least one neural network function at the base station, or the obtaining comprises receipt of the at least one neural network function from a core network component or an external server.

In an aspect, a user equipment (UE) includes a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: obtain at least one neural network function configured to derive a likelihood of at least one set of positioning measurement features being present at a candidate set of positioning estimates for the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures; obtain positioning measurement data associated with a location of the UE; and determine a positioning estimate for the UE based at least in part upon the positioning measurement data and the at least one neural network function.

In some aspects, the at least one neural network function comprises a UE-feature processing neural network function.

In some aspects, the positioning measurement data comprises a set of positioning measurements at the UE, and wherein the determining comprises: detect a set of positioning measurement features based on the set of positioning measurements at the UE; and derive a likelihood of the set of positioning measurement features being present at the candidate set of positioning estimates for the UE based at least in part upon the UE-feature processing neural network function,

In some aspects, the at least one neural network function comprises at least one additional UE-feature processing neural network function.

In some aspects, the UE-feature processing neural network function is configured to derive the likelihoods based on at least one of: a clock drift at the UE, a hardware group delay at the UE, a model of UE, or a combination thereof.

In some aspects, the at least one neural network function comprises a base station (BS)-feature processing neural network function.

In some aspects, the positioning measurement data comprises a set of positioning measurement features based on a set of positioning measurements at one or more BSs, and the determining comprises: derive a likelihood of the set of positioning measurement features being present at the candidate set of positioning estimates for the UE based at least in part upon the BS-feature processing neural network function, wherein the positioning estimate for the UE is based in part upon the derived likelihoods.

In some aspects, the at least one neural network function comprises at least one additional BS-feature processing neural network function.

In some aspects, the at least one neural network function further comprises a UE-feature processing neural network function.

In some aspects, the positioning measurement data comprises a first set of positioning measurement features based on a first set of positioning measurements at one or more BSs, and a second set of positioning measurement features based on a second set of positioning measurements at the UE, further comprising: derive a likelihood of the first and second sets of positioning measurement features being present at the candidate set of positioning estimates for the UE based at least in part upon the UE-feature processing neural network function and the BS-feature processing neural network function, wherein the positioning estimate for the UE is based in part upon the derived likelihoods.

In some aspects, the BS-feature processing neural network function is configured to derive the likelihoods based on at least one of: a location of at least one BS, a downtilt of the at least one BS, a transmit power of the at least one BS, a clock synchronization error between two or more BSs, a clock drift of the at least one BS, a hardware group delay of the at least one BS, a base station almanac (BSA) error associated with the at least one BS, or any combination thereof.

In some aspects, the candidate set of positioning estimates is implicitly indicated to the UE in association with the at least one neural network function, or the candidate set of positioning estimates is explicitly indicated to the UE in association with the at least one neural network function.

In some aspects, the at least one neural network function is specific to: a particular base station (BS) or group of BSs, a carrier, a location region, a positioning measurement type or group of positioning measurement types, a beam or group of beams, or any combination thereof.

In some aspects, the positioning estimate comprises: a wireless wide area network (WWAN) position estimate, a wireless local area network (WLAN) position estimate, a Global Navigation Satellite System (GNSS) position estimate, a sensor-based position estimate, or any combination thereof.

In some aspects, the obtaining comprises receipt of the at least one neural network function from a base station, a server, or a combination thereof.

In some aspects, the at least one neural network function configured to facilitate the UE to derive the likelihood of the at least one set of positioning measurement features being present at the candidate set of positioning estimates for the UE in association with one or more other positioning measurement features.

In an aspect, a base station (BS) includes a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: obtain at least one neural network function configured to facilitate a user equipment (UE) to derive a likelihood of at least one set of positioning measurement features being present at a candidate set of positioning estimates for the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures; and transmit, via the at least one transceiver, the at least one neural network function to the UE.