A method for leakage detection in an aeronautical band for a high split HFC network includes: providing a vehicle borne leak detector configured to perform substantially simultaneous upstream and downstream leakage detection; and while traversing a hub containing any quantity of high split nodes, performing a substantially simultaneous upstream leakage detection and a downstream leakage detection at about a same frequency. A system for leakage detection in an aeronautical band for a high split HFC network is also described.

A method for leakage detection in an aeronautical band for a high split HFC network includes: providing a vehicle borne leak detector configured to perform substantially simultaneous upstream and downstream leakage detection; and while traversing a hub containing any quantity of high split nodes, performing a substantially simultaneous upstream leakage detection and a downstream leakage detection at about a same frequency. A system for leakage detection in an aeronautical band for a high split HFC network is also described.

A method and a test device for testing the CSI Type 2 channel estimation capability of a DUT are provided. The method includes: a) stimulating certain variance of PMI feedback values from the DUT, especially those belonging to the finer grained Type 2 CSI, b) a statistical collection of one or more PMI reports received through CSI reporting from the DUT during the test execution, c) an identification of Type 1/Type 2 PMI feedback type based on the CSI reports received from the DUT, and d) applying a pass criterion: a minimum threshold of Type 2 specific feedback reports must have been received.

A method and apparatus are provided, where a data sequence for transmission is identified (1002) as part of evaluating transmitter performance involving multiple physical antennas. The data sequence is mapped (1004) to the multiple physical antennas to be involved in the transmission. The data sequence is then transmitted (1006) using the multiple physical antennas from which a signal quality metric of a transmitter corresponding to a difference between a received signal associated with the transmission of each respective data symbol of the data sequence and a respective ideal location of a predefined constellation point associated with the data symbol that was transmitted can be determined, wherein an error vector magnitude involving an aggregated difference associated with the data sequence involving the transmission via the multiple physical antennas is determined.

A processing system may determine a plurality of input features of a first machine learning model that is deployed in a telecommunication network for a prediction task associated with an operation of the telecommunication network and apply a time series forecast model to a historical data set of a first data source associated with at least one of the plurality of input features to generate a forecast upper bound of a first characteristic of the first data source for a first time period and a forecast lower bound of the first characteristic of the first data source for the first time period. The processing system may then detect that the first characteristic exceeds one of the forecast upper bound or the forecast lower bound during the first time period and generate an alert that an output of the first machine learning model may be faulty, in response to the detecting.

A processing system may determine a plurality of input features of a first machine learning model that is deployed in a telecommunication network for a prediction task associated with an operation of the telecommunication network and apply a time series forecast model to a historical data set of a first data source associated with at least one of the plurality of input features to generate a forecast upper bound of a first characteristic of the first data source for a first time period and a forecast lower bound of the first characteristic of the first data source for the first time period. The processing system may then detect that the first characteristic exceeds one of the forecast upper bound or the forecast lower bound during the first time period and generate an alert that an output of the first machine learning model may be faulty, in response to the detecting.

Artificial Intelligence (AI) can rapidly evaluate a faulted message in 5G or 6G, calculate a likelihood that each message element is faulted, and optionally suggest a most probable corrected version for each of the likely faulted message elements. To do so, the AI takes in numerous factors besides the message itself, such as the modulation quality of each message element, the proximity and quality of a nearest demodulation reference, a signal-to-noise ratio of the message element, a measure of current electromagnetic noise during the message element, an expected format or expected codewords based on prior messages or convention, and other factors. The AI model can then provide guidance as to mitigation, such as choosing whether to request a retransmission or attempting to vary the likely faulted message elements. The AI model can be adapted to fixed-site computers or to the more limited computers of a mobile user device.

Artificial Intelligence (AI) can rapidly evaluate a faulted message in 5G or 6G, calculate a likelihood that each message element is faulted, and optionally suggest a most probable corrected version for each of the likely faulted message elements. To do so, the AI takes in numerous factors besides the message itself, such as the modulation quality of each message element, the proximity and quality of a nearest demodulation reference, a signal-to-noise ratio of the message element, a measure of current electromagnetic noise during the message element, an expected format or expected codewords based on prior messages or convention, and other factors. The AI model can then provide guidance as to mitigation, such as choosing whether to request a retransmission or attempting to vary the likely faulted message elements. The AI model can be adapted to fixed-site computers or to the more limited computers of a mobile user device.

A system includes: a TX port and a RX port; an omnidirectional TX antenna connected to the TX port; a plurality of RX antennas connected to the RX port, wherein each RX antenna comprises a first antenna element and a second antenna element; a monitoring circuit connected to the TX antenna; a switching circuit connected to the second antenna element of each RX antenna; and a processor connected to the monitoring circuit and to the switching circuit. The monitoring circuit monitors a status of the TX antenna. In response to a failure of the TX antenna detected by the monitoring circuit: the monitoring circuit notifies the processor of the failure; and the processor controls the switching circuit to switch the second antenna element of each RX antenna to be connected to the TX port. When the failure is not detected, the second antenna element of each RX antenna is disconnected from the TX port.

A system includes: a TX port and a RX port; an omnidirectional TX antenna connected to the TX port; a plurality of RX antennas connected to the RX port, wherein each RX antenna comprises a first antenna element and a second antenna element; a monitoring circuit connected to the TX antenna; a switching circuit connected to the second antenna element of each RX antenna; and a processor connected to the monitoring circuit and to the switching circuit. The monitoring circuit monitors a status of the TX antenna. In response to a failure of the TX antenna detected by the monitoring circuit: the monitoring circuit notifies the processor of the failure; and the processor controls the switching circuit to switch the second antenna element of each RX antenna to be connected to the TX port. When the failure is not detected, the second antenna element of each RX antenna is disconnected from the TX port.