Abstract: First node(s) obtains first information relating to current locations of the wireless communication devices that are currently served by a first set of radio network nodes of the wireless communication network, located at first locations, respectively. The first node(s) determines, based on the obtained first information, different, second locations of a second set of radio network nodes for continued serving of the wireless communication devices instead of by the first set at the first locations. The first node(s) initiates activation of the second set of radio network nodes at the second locations, to thereby provide radio coverage for the continued serving.

Abstract: A method performed by a first radio node for handling a beam pair with a second radio node is provided. The first radio node receives a first information from one or more other radio nodes. The first information comprises a number of quality values related to a number of beam pairs. The first radio node predicts a time to failure for a first beam pair. The first radio node then decides whether or not there is enough time until the predicted time to failure, for performing a beam pair switch from the first beam pair to a second beam pair. When there is enough time, the first radio node switches to the second beam pair before the predicted time to failure. When there is not enough time, the first radio node prepares an upcoming beam pair failure.

Abstract: 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.

Abstract: First node(s) obtains first information relating to current locations of the wireless communication devices that are currently served by a first set of radio network nodes of the wireless communication network, located at first locations, respectively. The first node(s) determines, based on the obtained first information, different, second locations of a second set of radio network nodes for continued serving of the wireless communication devices instead of by the first set at the first locations. The first node(s) initiates activation of the second set of radio network nodes at the second locations, to thereby provide radio coverage for the continued serving.

Abstract: A network node (NN) and method therein for assisting a wireless device (UE) in updating a first instance of a machine learning model. The NN has a second instance of the model. The NN receives, from the UE, information relating to a prediction of an operation and to a result of the operation. The prediction of the operation is obtained by means of the first instance and the operation relates to a transmission over the communications interface. The NN updates one or more parameters of the second instance based on the received information. The NN transmits, to the UE, information relating to the updated parameters when a model difference between a prediction of the operation obtained by the second instance which includes the updated parameters and the prediction of the operation obtained by means of the first instance is indicative of a need of updating the first instance.

Abstract: A wireless device and a method therein for assisting a network node to perform training of a machine learning model. The wireless device collects a number of successive data samples. Further, the wireless device successively creates compressed data by associating each collected data sample to a cluster. The cluster has a cluster centroid, a cluster counter representative of a number of collected data samples determined to be normal and being associated with the cluster, and a number of outlier collected data samples associated with the cluster. Then, the wireless device updates the cluster centroid to correspond to a mean position of all normal data samples that are associated with the cluster, and increases the cluster counter by one for each normal data sample that is associated with the cluster. The wireless device transmits the compressed data to the network node.

Abstract: The present disclosure relates to radio network communication. In one of its aspects, the disclosure presented herein concerns a method for performing beam management. The method is implemented by an apparatus. According to the method, an initial coarse Beam Pair Link (BPL) is established with a device. Information from at least one sensor at the device is acquired. The acquired information is input into a machine learning model, wherein the machine learning model is trained to predict beam indices from sensor information and refined beam indices are received, from the machine learning model, wherein the machine learning model has predicted the refined beam indices from the input information. Thereafter, a refined BPL is established with the device, based on the predicted refined beam indices.

Abstract: A wireless communications system and a method therein for handling of machine learning. The system includes a central node and one or more intermediate nodes arranged between the central node and one or more leaf nodes. Further, at least one out of the nodes includes a machine learning unit. The system determines, by means of the machine learning unit and a machine learning model relating to at least one node out of the one or more intermediate nodes or the one or more leaf nodes, a prediction of a performance of the at least one node based on input data relating to the at least one node. Further, the system performs, based on the determined prediction, an operation relating to the at least one node, and communicates the determined prediction and/or information relating to the machine learning model to one or more other nodes.

Abstract: A method performed by a first radio node for handling a beam pair with a second radio node is provided. The first radio node receives a first information from one or more other radio nodes. The first information comprises a number of quality values related to a number of beam pairs. The first radio node predicts a time to failure for a first beam pair. The first radio node then decides whether or not there is enough time until the predicted time to failure, for performing a beam pair switch from the first beam pair to a second beam pair. When there is enough time, the first radio node switches to the second beam pair before the predicted time to failure. When there is not enough time, the first radio node prepares an upcoming beam pair failure.

Abstract: The present disclosure relates to a method for determining a best antenna beam. The method includes obtaining a training model, wherein the training model has been trained to provide a best antenna beam based on a plurality of feedback values, determining a first plurality of feedback values indicative of partial estimates of channels of a first plurality of wireless signals and determining a best antenna beam, based on the first plurality of feedback values and the obtained training model.

Abstract: The present disclosure relates to a method for determining a best antenna beam. The method includes obtaining a training model, wherein the training model has been trained to provide a best antenna beam based on a plurality of feedback values, determining a first plurality of feedback values indicative of partial estimates of channels of a first plurality of wireless signals and determining a best antenna beam, based on the first plurality of feedback values and the obtained training model.

Abstract: A method performed by a first radio node for adjusting a set of beams for communication with a second radio node. The first radio node obtains an indication of a first set of beams based on a beam prediction model. The first radio node sends a training symbol on each beam in the first set of beams. The first radio node receives from the second radio node, feedback relating to the sent training symbols. A second beam is identified based on the feedback and is used for transmission. The received feedback and the second beam are used to adapt the beam prediction model. Further, the first radio node decides whether to adjust the number of beams in the first set of beams based on a relationship between the first and second beam. The adjusted first set of beams is to be used for sending training symbols before an upcoming communication.

Abstract: A method performed by a first radio node for adjusting a set of beams for communication with a second radio node is provided. The first radio node obtains (206) an indication of a first set of beams based on a beam prediction model. The first radio node sends (207) a training symbol on each beam in the first set of beams. The first radio node then receives (208) from the second radio node, feedback relating to the sent training symbols. A second beam is identified (209) based on the feedback and is used for transmission. The received feedback and the second beam are used to adapt (211) the beam prediction model. Further, the first radio node decides (212) whether to adjust the number of beams in the first set of beams based on a relationship between the first and second beam. The adjusted first set of beams is to be used for sending training symbols before an upcoming communication.