Telefonaktiebolaget Lm Ericsson (publ)

Sholeh Yasini

Torbjörn Wigren

Iana Siomina

Juhani Kauppi

Richard Middleton

Jonas Lennart Wiorek

Patrik Persson

Peiliang Chang

Euhanna Ghadimi

Richard Wirén

A method of determining the position of a moving unmanned aerial vehicle (UAV) is provided. The method comprises obtaining, for each of three or more base stations, one or more measurements of a carrier frequency offset (CFO) for one or more signals sent between a moving UAV and the respective base station. The method further comprises inputting the CFO measurements into a model to determine a position of the UAV, in which inputs to the model do not include range measurements of the UAV with respect to the three or more base stations.

Methods and apparatus for positioning of a moving unmanned aerial vehicle

A method of monitoring a kinematic state of an unmanned aerial vehicle (UAV) is provided. The method comprises obtaining one or more predicted pathlosses between a UAV and one or more base stations at a first time instance, wherein the predicted pathlosses are determined using an estimate of a kinematic state of the UAV at the first time instance and one or more pathloss models developed using a machine-learning process. The method further comprises obtaining one or more measurements of a pathloss between each of the one or more base stations and the UAV at the first time instance, and re-determining the estimate of the kinematic state of the UAV at the first time instance based on the one or more predicted pathlosses and the one or more measurements of the pathloss.

Methods and apparatus for monitoring a kinematic state of an unmanned aerial vehicle

Embodiments include methods for estimating movement of an aerial user equipment (UE) by a first RAN node serving the aerial UE in a cell of the RAN. Such methods include determining initialization parameters for an interacting multiple-model (IMM) for movement of the aerial UE in the cell. The initialization parameters include a plurality of neighbor cells, in the RAN, in which positioning measurements should be performed for the aerial UE, and/or for at least one movement mode of the IMM, an initial state comprising a plurality of initial position estimates for the aerial UE. Such methods include determining a movement state for the aerial UE at a first time based on the initialization parameters and positioning measurements of the aerial UE that are performed in the cell and in at least a portion of the neighbor cells. Other embodiments include complementary methods for a second RAN node.

Initializing state estimation for aerial user equipment (UES) operating in a wireless network

A method performed by a node in a wireless communication network. The method comprises obtaining measurement data for one or more measurements performed, by a wireless device in the wireless communication network, on at least one first radio signal transmitted by one or more base stations in the wireless communication network. The method further comprises determining whether or not the wireless device is airborne by inputting the measurement data to a model developed using a machine learning algorithm.

Methods and apparatus and machine-readable media relating to airborne wireless devices

A method for kinematic state estimation of a UE connected to a wireless communication network. The method comprises obtaining (S) of measurement information related to a kinematic measurement concerning the UE. The kinematic measurement is achieved at a measuring time. The kinematic measurement belongs to a set of kinematic measurements. It is further determined (S) whether the measurement information is consistent with a measurement prediction for the UE valid for the measuring time based on a kinematic state estimation of the UE. The kinematic state estimation is created using kinematic measurements of the set of kinematic measurements. If the measurement information is determined not to be consistent, the measurement information is discarded (S). If the measurement information is determined to be consistent, the kinematic state estimation of the UE is updated (S) with the measurement information.

Method and apparatus for kinematic state estimation of a user equipment

A method for state estimation of a UE connected to a wireless communication network, comprising obtaining (S) of measurement information related to a 5 range-related measurement concerning a UE. The measurement information comprises data of the range-related measurement, a time at which the range-related measurement was performed, an identity of the UE and data enabling determination of a measuring position. The range-related measurement consists of data defining a distance between the UE and the measuringposition. A state estimation of the UE is performed (S) based on the measurement information. Methods for propagating the measurement information through the network are also disclosed as well as network nodes performing such method.

User equipment state estimation

Embodiments include methods for managing a bidirectional measurement for an uplink/downlink pair of links between one or more first nodes and one or more second nodes in a wireless network. Methods include obtaining a transmit timing of a first signal on a first link of the pair during a first time resource, obtaining a receive timing of a second signal on a second link of the pair during a second time resource, and determining the bidirectional measurement based on the obtained transmit timing and obtained receive timing. In this arrangement, at least one of the following applies: the first and second links are between one first node and two respective second nodes; at least one of the first link and the second link are associated with a non-serving carrier frequency; and at least one of the first link and the second link are managed via one or more beams.

Methods for detecting and managing bidirectional timing measurements in NR

A method for kinematic state estimation of a user equipment connected to a wireless communication network includes obtaining range rate measurement data defining a change rate of a distance between the user equipment and a range rate measuring position and obtaining range measurement data defining a distance between the user equipment and a range measuring position. A kinematic state estimation of the user equipment is performed based on at least the range rate measurement data and the range measurement data. The kinematic state estimation includes interacting-multiple-model filtering using three interacting models. The interacting-multiple-model filtering includes a three-dimensional constant velocity movement Wiener process, a three-dimensional constant acceleration movement Wiener process, and a three-dimensional constant position Wiener process.

User equipment kinematic state estimation

A method for type state estimation of a user equipment connected to a wireless communication network. The method comprises updating, recursively, of a type state estimate. The type state estimate is a probability for the user equipment to be a drone conditioned on obtained kinematic state estimate updates concerning the user equipment. The user equipment is assigned to be a drone as a response on the type state estimate exceeding a threshold.

Device type state estimation

A method for sharing user equipment state estimates between nodes within a wireless communication network comprises initiating of transmission of at least one of obtained user equipment kinematic state estimate information and obtained user equipment type state estimate information to a receiving network node as a response to an obtained indication of a need for sharing user equipment state estimates. The obtained user equipment kinematic state estimate information comprising a latest kinematic state update time, as well as mode state vector estimates, mode covariance matrices and mode probabilities for at least one user equipment kinematic mode. The obtained user equipment type state estimate information comprising a latest type state update time and a type state probability estimate. A method for receiving and propagating the user equipment state estimates, and devices for both methods are also disclosed.

Sharing of user equipment states

A framework for transfer learning for radio resource management-related tasks in radio access networks is defined in terms of functional entities of the network operator and associated signaling. A source learner () provides parameters or policies that incorporate knowledge related to another context than the actual context in which the RRM-related task is to be executed. A target learner () converts the source policies and parameters into models or target policies relevant to the actual context of the RRM-related task. An actor () receiving target learner's output is enabled to determine an RRM-related action for accomplishing the RRM-related task.

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Sundbyberg, Sweden

Sholeh Yasini