Abstract:
Apparatuses and methods for identifying network anomalies. A method includes determining a cumulative anomaly score over a predefined time range based on a subset of historical PM samples and determining an anomaly ratio of a first time window and a second time window, based on the cumulative anomaly score. The method also includes determining one or more anomaly events coinciding with CM parameter changes based on the anomaly ratio; collating the PM, alarm, and CM data into a combined data set based on matching fields and timestamps; generating a set of rules linking one or more CM parameter changes and the collated data to anomaly events; and generating root cause explanations for CM parameter changes that are linked to anomaly events.
Abstract:
A method for operating a base station is provided. The method includes in response to a triggering event, fetching information on a base station (BS) configuration parameters comprising a location, a height, an antenna pattern, and topographical details surrounding the BS; determining the BS configuration parameters that are error prone and require re-estimation; obtain measurement reports created by at least one user equipment (UE); determining an audit method to perform an audit correction, the audit correction based on the one or more of the BS configuration parameters to re-estimate, available BS information and the measurement reports; performing the audit correction, to obtain a result based on a computed score for each candidate value of the BS configuration parameters; generating, based on the result, one or more corrective actions; and adjusting at least one of the BS configuration parameters based on the one or more corrective actions.
Abstract:
Apparatuses and methods for identifying network anomalies. A method includes determining a cumulative anomaly score over a predefined time range based on a subset of historical PM samples and determining an anomaly ratio of a first time window and a second time window, based on the cumulative anomaly score. The method also includes determining one or more anomaly events coinciding with CM parameter changes based on the anomaly ratio; collating the PM, alarm, and CM data into a combined data set based on matching fields and timestamps; generating a set of rules linking one or more CM parameter changes and the collated data to anomaly events; and generating root cause explanations for CM parameter changes that are linked to anomaly events.
Abstract:
A method for mitigating interference in a wireless communication system includes receiving a signal transmitted from a mobile station, subtracting a target signal within a target bandwidth (BW) from the received signal to obtain a resultant signal, wherein the target signal is constructed by estimating a target channel and target symbols from the signal, determining a subspace blind interference sensing (BIS) BW by extending resource blocks (RBs) prior to a starting RB and after an ending RB of the target bandwidth using energy detection in each RB, determining a set of candidate interfering BWs in the subspace BIS BW by determining the number of interferers in each RB in the subspace BIS BW, and determining a set of candidate interfering DMRS sequences based on the set of candidate interfering BWs by performing DMRS detection for each candidate interfering BW.
Abstract:
A base station includes a transceiver, and a processor operatively coupled to the transceiver. The processor is configured to estimate a mobility level of a user equipment (UE), and determine whether the estimated mobility level of the UE exceeds a speed threshold. The processor is also configured to generate, from a channel response prediction model, a future channel response prediction based on the estimated mobility level of the UE and whether the estimated mobility of the UE exceeds the speed threshold.
Abstract:
Methods and apparatuses for automating configuration management in cellular networks. A method of a computing device comprises: assigning, based on a correlation analysis, contexts to different time intervals of data, wherein the correlation analysis is performed based on historic time-series data; grouping, based on the assigned contexts, the historic time-series data; identifying context and compute an anomaly score comparing new data and the grouped historic-time series data of the context; indicating an event of anomaly based on a determination that the computed anomaly score exceeds a first threshold that is identified based on a function of per-context data; and computing, based on the event of the anomaly, an aggregate anomaly score or indicate using a value of mean or moving average of a set of latest anomaly scores, for a context-based multivariate anomaly detection.
Abstract:
Methods and apparatuses for automating configuration management in cellular networks. A method of a UE comprises: training, based on historical samples, a regression model y using samples obtained from a set of parameters, wherein the regression model y comprises a function of a first term X and a second term h; and predicting, based on the regression model y, a target KPI to capture parameter impacts corresponding to the second term h.
Abstract:
Methods and apparatuses for voice over long-term evolution/voice over new radio (VoLTE/VoNR) performance for a cellular communication system. A method of operating a base station includes receiving, from a user equipment (UE), uplink (UL)signals; identifying, based on the UL signals, a first and second parameters; determining first and second UL power control parameters based on the first and second parameters, respectively; determining a first time period for the first UL power control parameter and a second time period for the second UL power control parameter, wherein the first time period is longer than the second time period; updating the first UL power control parameter based on the first time period and the second UL power control parameter based on the second time period; and transmitting, to the UE, the updated first and second UL power control parameters for an UL transmit power of the UE.
Abstract:
A core network entity (CNE) can predict received signal strength values for base station (BS). In response to a triggering event, the CNE fetches information on BS configuration parameters, including at least one of: a BS class, a BS location, a BS height, an orientation of the BS, a BS antenna pattern, and topographical details surrounding the BS. The BS obtains, processes and forwards to the CNE, measurement reports created by a user equipment (UE) including a signal strength value and a location of the UE. The CNE pools the measurement reports based on the BS class and, in response to another triggering event, recalibrates signal strength prediction tools, which can predict received signal strength values from the BS to a location in a vicinity of the BSs. The CNE also pools and stores the measurement reports and corresponding BS configuration parameters, after post processing and compression.
Abstract:
A multistage beamforming circuit includes a data unit that implements a frequency domain beamforming stage and a remote radio head that implements a time-domain broadband beamforming stage. The data unit implements the frequency domain beamforming stage by converting K received data streams into M precoding output streams in a frequency-domain. The data unit is configured to transform the M output streams to M OFDM time-domain signals. The remote radio head, or integrated radio unit is configured to implement a time-domain broadband beamforming stage by converting the M OFDM time-domain signals into N transmit streams of time-domain samples. The remote radio head, or integrated radio unit includes a transmit antenna array configured to transmit the N transmit streams that together form broadcast beams and user-specific beams. The antenna array includes a plurality of physical antennas. The number N of transmit streams is greater than the number M of precoding output streams.