Dynamic Blocking of 5G Cells in Non-Standalone Networks

A multi-mode user equipment (UE) employing multiple radio access technologies (RATs) detects secondary cell group (SCG) failures in a cellular network. The UE implements a dynamic blocking mechanism to block the new radio (NR) cell associated with the SCG failure from being subsequently added as a secondary cell during a blocking period/cycle. In one example, the UE adds the NR cell to a block list and sets a blocking period for the NR cell. During the blocking period, the UE does not send a cell measurement report to the cellular network for the NR cell, which prevents/blocks the network from adding the NR cell as a secondary cell. The blocking period is associated with a minimum blocking duration and a maximum blocking duration. The blocking duration for the NR cell can be dynamically adjusted for the blocking period. For example, the UE dynamically increases the blocking duration for the NR cell for each instance of SCG failure detected by the UE during the blocking period. The UE unblocks the NR cell after the blocking period has expired, which configures the UE to send a cell measurement report for the NR cell upon receiving a request from the cellular network. The dynamic blocking mechanism implemented by the UE prevents the frequent addition and removal of an NR cell associated with an SCG failure, which enhances the overall user experience when accessing the cellular network

Call Audio Quality Determination and Root Cause Analysis Using Machine Learning

Accurate assessment and categorization of real-world audio quality in a call, e.g., a call over VoLTE/VoNR, is essential to provide a satisfactory call experience. However, current techniques to determine call quality do not accurately categorize the audio quality. Also, there are no techniques to determine the root cause of poor audio quality or to identify potential solutions. This disclosure describes the use of machine learning clustering techniques to cluster audio metrics and using the obtained clusters to generate a root cause table. Further, a classifier is trained to determine whether an ongoing call has unsatisfactory audio quality. The quality can be categorized and labeled, e.g., good, mildly choppy, severely choppy, and no audio. If the audio quality is unsatisfactory, the likely root cause is identified using the root cause table to identify and apply solutions while the call is in progress. The described techniques are a closed loop technique to identify solutions to audio quality problems in an audio call

Analysis of the Hydroelastic Performance of Very Large Floating Structures Based on Multimodules Beam Theory

The hydroelastic behavior of very large floating structures (VLFSs) is investigated based on the proposed multimodules beam theory (MBT). To carry out the analysis, the VLFS is first divided into multiple submodules that are connected through their gravity center by a spatial beam with specific stiffness. The external force exerted on the submodules includes the wave hydrodynamic force as well as the beam bending force due to the relative displacements of different submodules. The wave hydrodynamic force is computed based on three-dimensional potential theory. The beam bending force is expressed in the form of a stiffness matrix. The motion response defined at the gravity center of the submodules is solved by the multibody hydrodynamic control equations; then both the displacement and the structure bending moment of the VLFS are determined from the stiffness matrix equations. To account for the moving point mass effects, the proposed method is extended to the time domain based on impulse response function (IRF) theory. The method is verified by comparison with existing results. Detailed results through the displacement and bending moment of the VLFS are provided to show the influence of the number of the submodules and the influence of the moving point mass