Embodiments of the present disclosure are directed towards devices and methods for identifying preferred access networks based at least in part on access network information including access network assistance information, steering policies, or access commands. In some embodiments, conflicts between access network information and access network discovery and selection function (ANDSF) policies may be rectified in identifying a preferred access network.

A method and a device for anticipating a reconnection, to a second access point, of a mobile station connected to a first access point that forms a wireless telecommunication network. The physical bit rate between the first access point and mobile station with respect to a reception power of a signal sent by a mobile station and received by the first access point from measurements of reception power of signals sent by the mobile station and received by the first access point, calculates physical bit rates between access points and mobile station. A rate of variation of the physical bit rates between access points and mobile stations from measurements obtained and from the modelling, an anticipation of physical bit rate between access points and the mobile stations and determines whether a reconnection of the station to the second access point must be made among other things from the calculated anticipations.

A method and system for optimizing network access of a device on a cellular network are disclosed. The method includes receiving connectivity information from a device; compiling the received connectivity information as device analytics; specifying rules to maintain connectivity of the device to a network based on the device analytics; and automatically configuring the device in response to the rules to maintain connectivity of the device to the network. The system includes a back end system for receiving connectivity information from a device; an analytic system for compiling the received connectivity information as device analytics; a rules engine for specifying rules to maintain connectivity of the device to a network based on the device analytics; and a configuration module, wherein the configuration module automatically configures the device in response to the rules to maintain connectivity of the device to the network.

Disclosed is a method comprising obtaining a plurality of handover parameter values, using a first machine learning model to select a subset of handover parameter values from the plurality of handover parameter values, obtaining historical information of a plurality of terminal devices, determining a first set of optimal handover parameter values for the plurality of terminal devices from the subset of handover parameter values, tagging the first set of optimal handover parameter values with the historical information of the plurality of terminal devices to obtain a labelled dataset, and training a second machine learning model with the labelled dataset, wherein the trained second machine learning model is capable of predicting a second set of optimal handover parameter values for a first terminal device based on historical information of the first terminal device.

A method is disclosed for improved tracking area planning and handling, comprising: assigning a single tracking area code to a plurality of eNodeBs at a messaging concentrator gateway, the messaging concentrator gateway situated in a network between the plurality of eNodeBs and the core network; storing, at the messaging concentrator gateway, at least one indicator of a last known location of a user equipment (UE) other than the single tracking area code; receiving a paging message from the core network at the messaging concentrator gateway for a UE; and performing a paging sequence using the at least one indicator to identify a set of eNodeBs to page the UE, thereby allowing larger tracking area list sizes to be used without increasing signaling traffic between the radio access network and the core network.

According to certain embodiments, a method for use in a network node for predicting handover includes training a first sequential time-based machine learning model using radio link monitoring measurements for a user equipment (UE) from a plurality of geographic positions within a first cluster of cells, times of handover of the UE to target cells of the first cluster of cells, and cell identifiers of the target cells of the first cluster of cells for each handover. The method further includes: predicting a time for a UE handover to a target cell using the first sequential time-based machine learning model, radio link monitoring measurements for the UE, and geographic positions associated with the radio link monitoring measurements; determining whether enough time exists to perform the UE handover before the predicted handover time; and upon determining enough time exists, performing the UE handover to the target cell.

A processing method includes: obtaining a current movement trajectory; matching the current movement trajectory with history movement trajectories and obtaining matching degrees; determining a target cell according to a history movement trajectory having the matching degree greater than a preset matching threshold and a correspondence between the history movement trajectory and a cell; and sending a handover request to a source base station, the handover request comprising a determined target cell identifier of the target cell.

The invention refers to a wireless communication system including a plurality of base-stations covering different communication areas and a coordinator unit. The coordinator unit compiles an expectation list with at least one base-station serving as a coordinated base-station for a possible handover concerning a user equipment based on information about the user equipment. The coordinator unite provides the at least one base-station of the expectation list with handover information concerning the possible handover. The at least one base-station of the expectation list goes into an attention mode after receiving the handover information from the coordinator unit. The invention also refers to a corresponding method.

A method for identifying user equipment for real-time handover trigger in open-radio access network (O-RAN) environment includes steps to obtain a serving cell handover parameter with respect to a neighbour cell handover parameter, to determine user equipment from the plurality of user equipment positioned in the serving cell, to identify the user equipment positioned in the serving cell and transmit the serving cell handover parameter with respect to the neighbour cell handover parameter associated with the neighbour cell of a plurality of neighbour cells to the identified user equipment.

A method for identifying a user equipment for a real-time handover and selecting a target cell by a Radio access network (RAN) controller in a radio access network (RAN) environment includes obtaining a serving cell load parameter with respect to a neighbour cell load parameter associated with at least one neighbour cell, identifying the at least one user equipment positioned in a serving cell based on a channel quality index (CQI) and a throughput value, transmitting the serving cell load parameter with respect to the neighbour cell load parameter to the identified user equipment and selecting the target cell from the neighbour cells for the user equipment.