A method and system to provide Mobility Robustness Optimization (MRO) in a NR network are described. The MRO parameters, use case, management services and information definition and procedures are described. A distributed self-organized network (D-SON) management function requests a producer of provisioning management service (MnS) to set targets, handover parameter ranges, and control information for an MRO function and then enables the MRO function for a non-enabled NR cell. The MRO function receives and analyses information from UEs to determine actions to optimize MRO performance. The D-SON management function collects and analyses MRO related performance measurements to evaluate the MRO performance, and updates the targets, handover parameter ranges, and/or control information when the MRO performance does not meet the targets.

The present disclosure relates to the field of communication technology, and provides a cell handover method and apparatus, a device, and a storage medium. The method includes: determining at least one handover information in a cell handover process based on a first AI model. Since the UE determines at least one handover information in the cell handover process based on autonomous decision-making of the AI model, it can select an appropriate handover timing or target cell autonomously to complete cell handover, thereby improving the efficiency of the cell handover, reducing the time required for the cell handover, and ensuring the handover success rate.

An SHR generation method includes receiving, by a UE, SHR trigger information configured by a network-side device, where trigger information is used for enabling an SHR or setting an SHR trigger condition, and the trigger information contains a target time parameter for SHR generation; and configuring, by the UE, a target timer based on the target time parameter and generating a target SHR in a case that the target timer expires; where a timing start point of the target timer is trigger time of a target event corresponding to the target SHR.

A radio base station receives a handover message from a network related to a handover of a terminal and determines the number of handover failures based on a number of the handover failures based on the handover message. The radio base station releases the terminal when the number of failures reaches a predetermined number.

An apparatus configured to: obtain an indication of partitions of a machine learning model corresponding to respective ones of the one or more groups; transmit, to the respective ones of the plurality of user equipments, a corresponding partition, of the partitions of the machine learning model; transmit, to the plurality of user equipments, an indication to record measurements for the first cell; receive, from at least one of the plurality of user equipments, at least one message regarding a handover failure, wherein the at least one message comprises a message generated using a first partition of the partitions of the machine learning model; and determine, with a second partition of the partitions of the machine learning model, whether the first cell is a rogue base station based, at least partially, on a plurality of detection reports.

Example embodiments may relate to controlling re-training of a machine learning (ML) model deployed at a device. A method may comprise: performing, by a device associated with a communication network, a task with a ML model to obtain an output, wherein the output is configured to be used for performance of a network operation of the communication network; receiving, from an access node of the communication network, feedback data indicative of a cause of a failure of the network operation; and determining, based on the feedback data, to perform at least one of the following: re-training the machine learning model for performing the task, updating at least one parameter of a non-machine learning algorithm associated with performance of the task with the machine learning model, refraining from re-training the machine learning model, or refraining from updating the at least one parameter of the non-machine learning algorithm.

The present disclosure relates to a pre-5.sup.th-generation (5G) or 5G communication system to be provided for supporting higher data rates beyond 4.sup.th-generation (4G) communication system such as long term evolution (LTE). A communication method and apparatus using beamforming are provided. The method includes acquiring transmission beam specific, measurement information of a base station (BS) and measuring a reference signal (RS) transmitted through transmission beams of the BS according to the transmission beam specific, measurement information. The measurement information on each transmission beam is determined according to at least one of an elevation angle of the corresponding transmission beam, an azimuth of the corresponding transmission beam, a handover urgency, information on a handover failure, and information on a radio link failure (RLF). A mobile station (MS) may perform a measurement report or a handover process according to a result of the measurement.

Embodiments include methods for a user equipment (UE) to report failure of an inter-radio access technology (RAT) mobility procedure from a source cell to a target cell. Such methods include receiving, from a source node serving the source cell, a command to perform an inter-RAT mobility procedure towards the target cell, which uses a different RAT than the source cell. Such methods include detecting a failure associated with the inter-RAT mobility procedure. The failure cause is either the UE was unable to comply with a configuration included in the command or a protocol error associated with inter-RAT information included in the command. Such methods include sending a failure report, including an indication of the failure cause, to a node serving a cell the UE connected to after detecting the failure. Other embodiments include complementary methods for network nodes, as well as UEs and network nodes configured to perform such methods.

The present disclosure contributes to the transmission/reception of various types of information between an AI-capable RAN node and another apparatus. A radio access network (RAN) node is configured as an RAN node including an Artificial Intelligence (AI) function of performing control of communication based on information received from another apparatus. The RAN node transmits first information indicating that the RAN node is an RAN node including an AI function to an RAN node. This contributes to the transmission and reception of various types of information between AI-enabled RAN nodes and other devices.

Methods, apparatuses and systems performed and configured to operate in a wireless communication network are presented. In example implementations, the methods, apparatuses and systems are configured for use in a conditional handover for a user equipment in the wireless communication network. Based on a known first location of the user equipment in a first cell of the wireless communication network at a first point in time, a second location of the user equipment at a second point in time is predicted. The prediction is based on applying a movement prediction to the user equipment relative to the known first location at the first point in time. A probability for the second location to be located in at least one of the first cell and any one of one or more second cells in the wireless convocation network is thereby obtained. The one or more second cells are different from the first cell. Based on the obtained probability, it is determined for which one or more of the one or more second cells the conditional handover is to be configured.