Abstract: The invention refers to a method performed by a performance recommender for a wireless network, obtaining (2010) for a plurality of cells input data, the input data comprising actual cell configuration parameter values; applying (2030) a machine-learning model to the input data to generate, for at least a portion of the cells, one or more recommendations for changes to the cell configuration parameter values to improve uplink, UL, performance in the respective cells; and based on identifying conflicts between recommendations for different cells, partitioning (2040) the plurality of cells into a plurality of interaction areas of neighboring cells; resolving (2050) conflicts in recommendations for respective cells within each of the interaction areas and across different interaction areas; and for at least a portion of the cells, determining (2060) preferred values for the cell configuration parameters to improve UL performance in the respective cells; the invention further relates to a corresponding performanc

Abstract: The invention refers to method, performed by a network optimization function associated with a wireless network (100), of forecasting performance issues associated with cells (106, 111, 116) in the wireless network, the method comprising: for each of a plurality of cells in the wireless network, obtaining (1210) first time-series data representing a plurality of key performance indicators, KPIs, at a first plurality of past time points; applying (1220) a multi-layer forecasting model to the first time-series data to determine second time-series data representing the plurality of KPIs for each of the plurality of cells at a second plurality of future time points; and based on the second time-series data, determining (1240) respective probabilities of occurrence for a respective plurality of different performance issues, associated with the plurality of cells, during one or more future time periods that include the second plurality of future time points; the method further refers to a corresponding network opti

Abstract: A method is provided for identifying operating conditions of a system. Input data relating to operation of the system is applied to a multi-class model for classification, where the multi-class model is configured for classifying the data into one of a plurality of predefined classes, and each class corresponds to a respective operating condition of the system. A confidence level of the classification by the multi-class model is determined. If the confidence level is below a threshold confidence level, the input data is applied to a plurality of binary models, where each binary model is configured for determining whether the data is or is not in a respective one of the predefined classes. If the plurality of binary models determine that the data is not in any of the respective predefined classes, the data can be taken into consideration when updating the multi-class model.

Abstract: The present invention refers to a method and apparatus and computer program for detecting communities in a radio access network with a plurality of vertices (C, BBU), wherein the method comprises calculating (S11) relationship strengths (31, 41) for network connections between the vertices (C, BBU) by executing a connection strength calculation process by analyzing performance factors, decisive for network performance including cell coverage overlap, signal strength, and inter-cell interference. The method further creates (S12) a network graph with nodes, representing the vertices (C,BBU) in the network and edges, representing the network connections based on the calculated relationship strengths (31, 41) and applies (S13) an extended iterative disjoint community detection algorithm for clustering nodes into communities, wherein in each iteration imposed one or more constraints for clustering cooperating nodes into the same community are analyzed (S14).

Abstract: A method 300 is disclosed for generating and training a model to detect interference conditions at a cell in a wireless cellular network and to classify the impact of detected interference conditions on performance of the wireless cellular network in the cell. The method comprises, for each of a plurality of cells in the wireless cellular network (360), obtaining data representing received signal power at a base station serving the cell over a period of time (310) and obtaining data representing a plurality of performance metrics for the cell over the time period (330). The method further comprises obtaining classifications of the received signal power data into one of a plurality of cell interference conditions (320) and the performance metric data into one of a plurality of cell impact classes (340).

Abstract: Embodiments of the disclosure provide methods, apparatus and computer-readable mediums for the detection of cell conditions in a wireless cellular network, and the training of a classifier model to detect cell conditions in a wireless cellular network. In one embodiment, a method of training a classifier model to detect cell conditions in a wireless cellular network comprises: obtaining time-series data for a plurality of performance metrics for each cell of a plurality of cells of the wireless cellular network; converting the time-series data to respective image data sets for each cell of the plurality of cells; classifying the image data sets into one of a plurality of predefined cell conditions; and applying a machine-learning algorithm to training data comprising the classified image data sets to generate a classifier model for classifying image data sets into one of the plurality of predefined cell conditions.

Abstract: The present invention refers to a method and apparatus and computer program for detecting communities in a radio access network with a plurality of vertices (C, BBU), wherein the method comprises calculating (S11) relationship strengths (31, 41) for network connections between the vertices (C, BBU) by executing a connection strength calculation process by analyzing performance factors, decisive for network performance including cell coverage overlap, signal strength, and inter-cell interference. The method further creates (S12) a network graph with nodes, representing the vertices (C,BBU) in the network and edges, representing the network connections based on the calculated relationship strengths (31, 41) and applies (S13) an extended iterative disjoint community detection algorithm for clustering nodes into communities, wherein in each iteration imposed one or more constraints for clustering cooperating nodes into the same community are analyzed (S14).

Abstract: A rotary tool is provided with a head section that is selectively pivotable at a plurality of positions between an orientation where the head section is in-line with a body section to an orientation where the head section is perpendicular to the body section. The rotary tool can also swivel about an arc of 360 degrees.

Abstract: A rotary tool is provided with a head section that is selectively pivotable at a plurality of positions between an orientation where the head section is in-line with a body section to an orientation where the head section is perpendicular to the body section. The rotary tool can also swivel about an arc of 360 degrees.

Abstract: A chuck is provided with a body, a plurality of jaws and an adjustment ring. The adjustment ring is threadably engaged with the jaws. When the adjustment ring is rotated in one direction, the jaws tighten by moving closer to each other. When the adjustment ring is rotated in the opposite direction, the jaws loosen by moving away from each other. An impact member is also provided for engagement with the adjustment ring. When the drive shaft of the power tool is rotated, the adjustment ring impacts against the impact member. As a result, the adjustment ring and the body of the chuck rotate relative to each other causing the jaws of the chuck to loosen or tighten depending on the direction the drive shaft is rotating.

Abstract: A chuck is provided with a body, a plurality of jaws and an adjustment ring. The adjustment ring is threadably engaged with the jaws. When the adjustment ring is rotated in one direction, the jaws tighten by moving closer to each other. When the adjustment ring is rotated in the opposite direction, the jaws loosen by moving away from each other. An impact member is also provided for engagement with the adjustment ring. When the drive shaft of the power tool is rotated, the adjustment ring impacts against the impact member. As a result, the adjustment ring and the body of the chuck rotate relative to each other causing the jaws of the chuck to loosen or tighten depending on the direction the drive shaft is rotating.