# US 12,004,115 B2 · User equipment (UE)-based sidelink-aware radio frequency fingerprinting (RFFP) positioning

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- Number: US 12,004,115 B2
- Document ID: US12004115B2
- Status: Granted
- Kind: B2
- Application number: 17/648,239
- Filed: 2022-01-18
- Published: 2023-07-20
- Granted: 2024-06-04
- Earliest claimed: 2022-01-18
- Assignees: QUALCOMM Incorporated
- Applicants: QUALCOMM Incorporated
- Inventors: Mohammed Ali Mohammed Hirzallah; Srinivas Yerramalli; Rajat Prakash; Taesang Yoo; Xiaoxia Zhang; Roohollah Amiri; Marwen Zorgui
- Web page: https://patentgrep.com/patent/US12004115B2

## CPC

- G01S 5/0278: {involving statistical or probabilistic considerations (G01S5/0252, G01S5/0294 take precedence)}
- H04W 64/00: Locating users or terminals {or network equipment} for network management purposes, e.g. mobility management
- H04W 72/20: Control channels or signalling for resource management
- H04W 72/51: based on terminal or device properties
- G01S 5/0252: {Radio frequency fingerprinting}
- G06N 3/0499: Feedforward networks
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N 20/20: Ensemble learning
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

In an aspect, a first user equipment (UE) obtains one or more first radio frequency fingerprint (RFFP) measurements of one or more first downlink channels received at the first UE, one or more first sidelink channels received at the first UE, or both, and determines one or more locations of a target UE based on the one or more first RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.

## Claims

### Claim 1

A method of wireless positioning performed by a first user equipment (UE), comprising:
obtaining one or more first radio frequency fingerprint (RFFP) measurements of at least one or more first sidelink channels received at the first UE; and
determining one or more locations of a target UE based on the one or more first RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
transmitting, to at least one network node, a measurement report request for RFFP measurements; and
receiving a measurement report from the at least one network node, the measurement report including one or more second RFFP measurements obtained by the at least one network node of at least one or more second downlink channels, one or more first uplink channels, or one or more second sidelink channels received at the at least one network node.

### Claim 3 (depends on 2)

The method of claim 2, further comprising:
inputting the one or more second RFFP measurements into the machine learning module, wherein the one or more locations of the target UE are estimated further based on the one or more second RFFP measurements.

### Claim 4 (depends on 2)

The method of claim 2, wherein:
the at least one network node is a second UE, or
the at least one network node is a base station.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
receiving the RFFP measurements of the one or more downlink channels, the RFFP measurements of the one or more uplink channels, the RFFP measurements of the one or more sidelink channels, the locations of the one or more sidelink anchor UEs, the locations of the one or more base stations, or the combination thereof; and
training the machine learning module using the RFFP measurements of the one or more downlink channels, the RFFP measurements of the one or more uplink channels, the RFFP measurements of the one or more sidelink channels, the locations of the one or more sidelink anchor UEs, the locations of the one or more base stations, or the combination thereof.

### Claim 6 (depends on 5)

The method of claim 5, wherein the RFFP measurements of the one or more downlink channels, the RFFP measurements of the one or more uplink channels, the RFFP measurements of the one or more sidelink channels, the locations of the one or more sidelink anchor UEs, the locations of the one or more base stations, or the combination thereof are received from the target UE, the one or more sidelink anchor UEs, the one or more base stations, a location server, or a combination thereof.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
receiving the machine learning module.

### Claim 8 (depends on 7)

The method of claim 7, wherein:
the first UE is the target UE, and
the machine learning module is received from a location server or a sidelink anchor UE of the one or more sidelink anchor UEs.

### Claim 9 (depends on 7)

The method of claim 7, wherein:
the first UE is a sidelink anchor UE of the one or more sidelink anchor UEs, and
the machine learning module is received from the target UE or a location server.

### Claim 10 (depends on 7)

The method of claim 7, further comprising:
updating the machine learning module after reception of the machine learning module and before the one or more first RFFP measurements are input into the machine learning module.

### Claim 11 (depends on 7)

The method of claim 7, further comprising:
receiving, from a network node, a measurement report request for RFFP measurements; and
transmitting, to the network node, a measurement report including one or more second RFFP measurements obtained by the first UE of at least one or more second downlink channels received at the first UE, one or more second sidelink channels received at the first UE, or both.

### Claim 12 (depends on 11)

The method of claim 11, wherein the network node is:
a sidelink anchor UE,
a base station, or
a location server.

### Claim 13 (depends on 11)

The method of claim 11, wherein the machine learning module is trained further based on the one or more second RFFP measurements.

### Claim 14 (depends on 1)

The method of claim 1, wherein:
the first UE is the target UE, or
the first UE is a sidelink anchor UE.

### Claim 15

A first user equipment (UE), comprising:
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 one or more first radio frequency fingerprint (RFFP) measurements of at least one or more first sidelink channels received at the first UE; and
determine one or more locations of a target UE based on the one or more first RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.

### Claim 16 (depends on 15)

The first UE of claim 15, wherein the at least one processor is further configured to:
transmit, via the at least one transceiver, to at least one network node, a measurement report request for RFFP measurements; and
receive, via the at least one transceiver, a measurement report from the at least one network node, the measurement report including one or more second RFFP measurements obtained by the at least one network node of at least one or more second downlink channels, one or more first uplink channels, or one or more second sidelink channels received at the at least one network node.

### Claim 17 (depends on 16)

The first UE of claim 16, wherein the at least one processor is further configured to:
input the one or more second RFFP measurements into the machine learning module, wherein the one or more locations of the target UE are estimated further based on the one or more second RFFP measurements.

### Claim 18 (depends on 16)

The first UE of claim 16, wherein:
the at least one network node is a second UE, or
the at least one network node is a base station.

### Claim 19 (depends on 15)

The first UE of claim 15, wherein the at least one processor is further configured to:
receive, via the at least one transceiver, the RFFP measurements of the one or more downlink channels, the RFFP measurements of the one or more uplink channels, the RFFP measurements of the one or more sidelink channels, the locations of the one or more sidelink anchor UEs, the locations of the one or more base stations, or the combination thereof; and
train the machine learning module using the RFFP measurements of the one or more downlink channels, the RFFP measurements of the one or more uplink channels, the RFFP measurements of the one or more sidelink channels, the locations of the one or more sidelink anchor UEs, the locations of the one or more base stations, or the combination thereof.

### Claim 20 (depends on 19)

The first UE of claim 19, wherein the RFFP measurements of the one or more downlink channels, the RFFP measurements of the one or more uplink channels, the RFFP measurements of the one or more sidelink channels, the locations of the one or more sidelink anchor UEs, the locations of the one or more base stations, or the combination thereof are received from the target UE, the one or more sidelink anchor UEs, the one or more base stations, a location server, or a combination thereof.

### Claim 21 (depends on 15)

The first UE of claim 15, wherein the at least one processor is further configured to:
receive, via the at least one transceiver, the machine learning module.

### Claim 22 (depends on 21)

The first UE of claim 21, wherein:
the first UE is the target UE, and
the machine learning module is received from a location server or a sidelink anchor UE of the one or more sidelink anchor UEs.

### Claim 23 (depends on 21)

The first UE of claim 21, wherein:
the first UE is a sidelink anchor UE of the one or more sidelink anchor UEs, and
the machine learning module is received from the target UE or a location server.

### Claim 24 (depends on 21)

The first UE of claim 21, wherein the at least one processor is further configured to:
update the machine learning module after reception of the machine learning module and before the one or more first RFFP measurements are input into the machine learning module.

### Claim 25 (depends on 21)

The first UE of claim 21, wherein the at least one processor is further configured to:
receive, via the at least one transceiver, from a network node, a measurement report request for RFFP measurements; and
transmit, via the at least one transceiver, to the network node, a measurement report including one or more second RFFP measurements obtained by the first UE of at least one or more second downlink channels received at the first UE, one or more second sidelink channels received at the first UE, or both.

### Claim 26 (depends on 25)

The first UE of claim 25, wherein the network node is:
a sidelink anchor UE,
a base station, or
a location server.

### Claim 27 (depends on 25)

The first UE of claim 25, wherein the machine learning module is trained further based on the one or more second RFFP measurements.

### Claim 28 (depends on 15)

The first UE of claim 15, wherein:
the first UE is the target UE, or
the first UE is a sidelink anchor UE.

### Claim 29

A first user equipment (UE), comprising:
means for obtaining one or more first radio frequency fingerprint (RFFP) measurements of at least one or more first sidelink channels received at the first UE; and
means for determining one or more locations of a target UE based on the one or more first RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.

### Claim 30

A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a first user equipment (UE), cause the first UE to:
obtain one or more first radio frequency fingerprint (RFFP) measurements of at least one or more first sidelink channels received at the first UE; and
determine one or more locations of a target UE based on the one or more first RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.