Abstract: A method performed by a first network node operating in a communications network comprising the set of nodes providing a segmented front-haul. The first network node determines, out of a respective first subset of nodes to serve a respective device of one or more devices, a respective second subset of nodes to serve each respective device. The determining is based on information obtained from a second network node. The information comprises respective routing information between a respective source node of respective one or more packets, and the respective device, so that a respective delivery of the respective packets to the respective device is based on an improvement of one or more metrics. The first network node also initiates sending of the respective one or more packets to the respective device via the determined respective second subset of nodes.

Abstract: A system, method and non-transitory computer readable media for effectuating ML-based fault analysis in a network (102A, 102B) comprising a plurality of nodes (104-N, 120-M). An example method (200A) comprises determining (202) that at least one of a topological configuration of the network (102 A, 102B) and one or more key performance indicator (KPI) requirements associated with the network (102A, 102B) have changed; and responsive to the determining, selecting (204) a machine language (ML) engine optimally adapted to facilitate root cause determination of any faults detected in the network (102 A, 102B) after the topological configuration or any KPI requirements of the network (102A, 102B) have changed.

Abstract: A method performed by a managing unit is provided. The method is for predicting a serving Access Point, AP, among one or more APs comprised in a subset of APs, to serve a User Equipment UE in a communications network. The UE is within a radio range of the one or more APs in the subset of APs. The managing unit obtains (302) a model associated to the subset of APs, for predicting the serving AP. The model is obtained based on training the model over a first training period. The managing unit obtains (303) a predicted AP from the subset of APs to serve the UE. The predicted AP is obtained based on invoking the model with INPUT data for prediction. The INPUT data for prediction comprises radio signal measurements from a set of reporting APs comprised in the subset of APs. The managing unit communicates (304) a first indication to at least the predicted AP.

Abstract: Methods and apparatus are provided. In an example aspect, a method in a first access point calculating interference suppression weights is provided. The method includes receiving, for each of at least one other access point, channel information for channels between the other access point and at least one User Equipment (UE), calculating, for each of the at least one other access point, the interference suppression weights based on the channel information received for the at least one other access point, wherein the interference suppression weights are for signals transmitted between the at least one other access point and the at least one UE, and sending, to each of the at least one other access point, the interference suppression weights for the other access point.

Abstract: Methods and apparatus are provided. In an example aspect, a method in a first access point (AP) of processing signals from at least one User Equipment (UE) is provided. The method includes receiving, for each of at least one other access point, information based on signals received at one or more antennas of the other access point from the at least one UE, processing the information based on the signals received at the one or more antennas of the at least one other access point to determine symbol estimates of symbols in the signals, and forwarding information identifying the symbol estimates to a processing node.

Abstract: A method performed by a network computing device in a telecommunications network for adaptively deploying an aggregated machine learning model and an output parameter in the telecommunications network to control an operation in the telecommunications network. The network computing device can aggregate client machine learning models and an output performance metric the client machine learning models to obtain an aggregated machine learning model and an aggregated output performance metric. The network computing device can train a network machine learning model with the aggregated output performance metric and at least one measurement of a network parameter to obtain an output parameter. The network computing device can send to the client computing devices the aggregated machine learning model and the output parameter of the network machine learning model. A method performed by a client computing device is also provided.

Abstract: A system, method and non-transitory computer readable media for effectuating ML-based fault analysis in a network (102A, 102B) comprising a plurality of nodes (104-N, 120-M). An example method (200A) comprises determining (202) that at least one of a topological configuration of the network (102 A, 102B) and one or more key performance indicator (KPI) requirements associated with the network (102A, 102B) have changed; and responsive to the determining, selecting (204) a machine language (ML) engine optimally adapted to facilitate root cause determination of any faults detected in the network (102 A, 102B) after the topological configuration or any KPI requirements of the network (102A, 102B) have changed.