Artificial intelligence/machine learning (AI/ML)-based positioning measurement prediction

1 . A method of wireless communication performed by a user equipment (UE), comprising:

obtaining, during a positioning procedure with a location server, one or more positioning measurements of one or more positioning reference signal (PRS) resources transmitted by one or more network nodes;

applying a machine learning model to at least the one or more positioning measurements to obtain one or more predicted future positioning measurements associated with the one or more network nodes; and

transmitting positioning information to the location server, wherein the positioning information comprises the one or more positioning measurements and the one or more predicted future positioning measurements, a current position of the UE determined based, at least in part, on the one or more positioning measurements and a predicted future position of the UE determined based, at least in part, on the one or more predicted future positioning measurements, or both.

2 . The method of claim 1 , further comprising:

transmitting, to the location server, a capability message indicating one or more capabilities of the UE to obtain the one or more predicted future positioning measurements, the predicted future position of the UE, or both.

3 . The method of claim 2 , wherein the one or more capabilities include:

an indication of whether the UE can apply Kalman smoothing to the one or more positioning measurements, the current position of the UE, or both based on the one or more predicted future positioning measurements,

an indication of whether the UE can accept the machine learning model from the location server or other network entity,

an indication of whether the UE has already been configured with the machine learning model,

an indication of how far into the future the UE can predict future positioning measurements, future positions of the UE, or both,

an indication of whether measurement gaps are required to obtain the one or more predicted future positioning measurements,

processing requirements of the UE to obtain the one or more predicted future positioning measurements, or

any combination thereof.

4 . The method of claim 2 , further comprising:

receiving, from the location server, a request for the one or more capabilities of the UE.

5 . The method of claim 4 , wherein:

the request for the one or more capabilities of the UE is received in a Long-Term Evolution (LTE) positioning protocol (LPP) Request Capabilities message, and

the capability message is an LPP Provide Capabilities message.

6 . The method of claim 1 , further comprising:

receiving assistance information for the positioning procedure from the location server, wherein the assistance information is related to at least reporting the one or more predicted future positioning measurements, the predicted future position of the UE, or both.

7 . The method of claim 6 , wherein the assistance information comprises:

an indication of how to obtain the machine learning model,

a periodicity of reporting positioning measurements, predicted future measurements, or both,

a quantity of reporting positioning measurements, predicted future positioning measurements, or both,

an indication of whether to apply Kalman smoothing to the one or more positioning measurements, the current position of the UE, or both,

any combination thereof.

8 . The method of claim 7 , wherein the quantity of reporting the positioning measurements, the predicted future positioning measurements, or both comprises:

individual quantity reporting of the positioning measurements, the predicted future positioning measurements, or both, or

batch quantity reporting of the positioning measurements, the predicted future positioning measurements, or both.

9 . The method of claim 6 , wherein the assistance information is received in an LPP Provide Assistance Data message.

10 . The method of claim 1 , further comprising:

receiving, from the location server, a request to report the one or more predicted future positioning measurements, the predicted future position of the UE, or both.

11 . The method of claim 10 , wherein:

the request to report the one or more predicted future positioning measurements, the predicted future position of the UE, or both is an LPP Request Location Information message, and

the positioning information is transmitted in an LPP Provide Location Information message.

12 . The method of claim 1 , further comprising:

applying Kalman smoothing to the one or more positioning measurements based on the one or more predicted future positioning measurements.

13 . The method of claim 1 , further comprising:

applying the machine learning model to one or more other measurements of the one or more PRS resources to obtain the one or more predicted future positioning measurements associated with the one or more network nodes.

14 . The method of claim 13 , wherein the one or more other measurements comprise:

one or more channel impulse response (CIR) measurements,

one or more channel energy response (CER) measurements,

one or more channel frequency response (CFR) measurements,

one or more power delay profile (PDP) measurements,

one or more delay profile (DP) measurements, or

any combination thereof.

15 . The method of claim 1 , wherein:

the positioning procedure is a downlink time-difference of arrival (DL-TDOA) positioning procedure,

the one or more positioning measurements comprise one or more reference signal time difference (RSTD) measurements, one or more RSTD per path measurements, or both, and

the one or more predicted future positioning measurements comprise one or more predicted future RSTD measurements, one or more predicted future RSTD per path measurements, or both.

16 . The method of claim 1 , wherein:

the positioning procedure is a downlink angle of departure (DL-AoD) positioning procedure,

the one or more positioning measurements comprise one or more reference signal received power (RSRP) measurements, one or more reference signal received path power (RSRPP) measurements, or both, and

the one or more predicted future positioning measurements comprise one or more predicted future RSRP measurements, one or more predicted future RSRPP measurements, or both.

17 . The method of claim 1 , wherein the one or more network nodes comprise:

one or more transmission-reception points (TRPs),

one or more other UEs, or

any combination thereof.

18 . A method of wireless communication performed by a network node, comprising:

obtaining one or more positioning measurements of one or more uplink reference signal resources transmitted by a user equipment (UE);

applying a machine learning model to at least the one or more positioning measurements to obtain one or more predicted future positioning measurements associated with the UE; and

transmitting, to a location server engaged in a positioning procedure with the UE, the one or more positioning measurements and the one or more predicted future positioning measurements.

19 . The method of claim 18 , further comprising:

transmitting, to the location server, a capability message indicating one or more capabilities of the network node to obtain the one or more predicted future positioning measurements.

20 . The method of claim 19 , wherein the one or more capabilities include:

an indication of whether the network node can apply Kalman smoothing to the one or more positioning measurements based on the one or more predicted future positioning measurements,

an indication of whether the network node can accept the machine learning model from the location server or other network entity,

an indication of whether the network node has already been configured with the machine learning model,

an indication of how far into the future the network node can predict future positioning measurements,

processing requirements of the network node to obtain the one or more predicted future positioning measurements, or

any combination thereof.

21 . The method of claim 19 , further comprising:

receiving, from the location server, a request for the one or more capabilities of the network node.

22 . The method of claim 18 , further comprising:

receiving assistance information from the location server, wherein the assistance information is related to at least reporting the one or more predicted future positioning measurements.

23 . The method of claim 22 , wherein the assistance information comprises:

an indication of how to obtain the machine learning model,

a periodicity of reporting positioning measurements, predicted future measurements, or both,

a quantity of reporting positioning measurements, predicted future positioning measurements, or both,

an indication of whether to apply Kalman smoothing to the one or more positioning measurements,

any combination thereof.

24 . The method of claim 18 , further comprising:

applying Kalman smoothing to the one or more positioning measurements based on the one or more predicted future positioning measurements.

25 . The method of claim 18 , further comprising:

applying the machine learning model to one or more other measurements of the one or more uplink reference signal resources to obtain the one or more predicted future positioning measurements associated with the UE.

26 . The method of claim 25 , wherein the one or more other measurements comprise:

one or more channel impulse response (CIR) measurements,

one or more channel energy response (CER) measurements,

one or more channel frequency response (CFR) measurements,

one or more power delay profile (PDP) measurements,

one or more delay profile (DP) measurements, or

any combination thereof.

27 . The method of claim 18 , wherein:

the positioning procedure is an uplink time-difference of arrival (UL-TDOA) positioning procedure,

the one or more positioning measurements comprise one or more relative time of arrival (RTOA) measurements, one or more RTOA per path measurements, or both, and

the one or more predicted future positioning measurements comprise one or more predicted future RTOA measurements, one or more predicted future RTOA per path measurements, or both.

28 . The method of claim 18 , wherein:

the positioning procedure is an uplink angle of arrival (UL-AOA) positioning procedure,

the one or more positioning measurements comprise one or more reference signal received power (RSRP) measurements, one or more reference signal received path power (RSRPP) measurements, or both, and

the one or more predicted future positioning measurements comprise one or more predicted future RSRP measurements, one or more predicted future RSRPP measurements, or both.

29 . A user equipment (UE), comprising:

one or more memories;

one or more transceivers; and

one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to:

obtain, during a positioning procedure with a location server, one or more positioning measurements of one or more positioning reference signal (PRS) resources transmitted by one or more network nodes;

apply a machine learning model to at least the one or more positioning measurements to obtain one or more predicted future positioning measurements associated with the one or more network nodes; and

transmit, via the one or more transceivers, positioning information to the location server, wherein the positioning information comprises the one or more positioning measurements and the one or more predicted future positioning measurements, a current position of the UE determined based, at least in part, on the one or more positioning measurements and a predicted future position of the UE determined based, at least in part, on the one or more predicted future positioning measurements, or both.

30 . A network node, comprising:

one or more memories;

one or more transceivers; and

one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to:

obtain one or more positioning measurements of one or more uplink reference signal resources transmitted by a user equipment (UE);

apply a machine learning model to at least the one or more positioning measurements to obtain one or more predicted future positioning measurements associated with the UE; and

transmit, via the one or more transceivers, to a location server engaged in a positioning procedure with the UE, the one or more positioning measurements and the one or more predicted future positioning measurements.