A UE may receive a BFD-RS and calculate, based on the BFD-RS, one or more of a value of an RSSI or at least one of a value of an SNR or a value of an RSRP, such that the UE may report a BFI based on the calculated values. The UE may initiate at least one of a total BFI timer or a consecutive BFI timer when a first BFI of a plurality of BFIs is stored, and increment at least one of a total BFI counter or a consecutive BFI counter when each of the plurality of BFIs is stored. A BFD procedure may be performed if at least one of the total BFI counter is greater than or equal to a maximum total count or the consecutive BFI counter is greater than or equal to a maximum consecutive count prior to an expiration of respective BFI timers.

A mechanism for identifying a low performing radio branch at a radio transceiver device. A method is performed by the radio transceiver device that comprises transmitting a reference signal for at least some of the N radio branches in a respective test period. The reference signal in each test period is transmitted according to a test configuration that specifies that during each test period the reference signal is mapped to only one of the N radio branches such that in test period k, where k=1, . . . , N, the reference signal is only transmitted from radio branch k. The method comprises receiving at least one report from another radio transceiver device relating to measurements made by this so-called another radio transceiver device on the reference signal transmitted for these at least some of the N radio branches to identify which of the N radio branches is the low performing one.

A mechanism for identifying a low performing radio branch at a radio transceiver device. A method is performed by the radio transceiver device that comprises transmitting a reference signal for at least some of the N radio branches in a respective test period. The reference signal in each test period is transmitted according to a test configuration that specifies that during each test period the reference signal is mapped to only one of the N radio branches such that in test period k, where k=1, . . . , N, the reference signal is only transmitted from radio branch k. The method comprises receiving at least one report from another radio transceiver device relating to measurements made by this so-called another radio transceiver device on the reference signal transmitted for these at least some of the N radio branches to identify which of the N radio branches is the low performing one.

A method and an apparatus for fault mitigation in a base station are disclosed. According to an embodiment, a faulty antenna element in an antenna array is detected. The antenna array transmits a first beam covering a predetermined range of directions. A target direction in which radiation power dropped due to a fault of the detected faulty antenna element is determined. A second beam pointing to the determined target direction is generated.

A method and an apparatus for fault mitigation in a base station are disclosed. According to an embodiment, a faulty antenna element in an antenna array is detected. The antenna array transmits a first beam covering a predetermined range of directions. A target direction in which radiation power dropped due to a fault of the detected faulty antenna element is determined. A second beam pointing to the determined target direction is generated.

A test and measurement system includes a machine learning system, a test and measurement device including a port configured to connect the test and measurement device to a device under test (DUT), and one or more processors, configured to execute code that causes the one or more processors to: acquire a waveform from the device under test (DUT),transform the waveform into a composite waveform image, and send the composite waveform image to the machine learning system to obtain a bit error ratio (BER) value for the DUT. A method of determining a bit error ratio for a device under test (DUT), includes acquiring one or more waveforms from the DUT, transforming the one or more waveforms into a composite waveform image, and sending the composite waveform image to a machine learning system to obtain a bit error ratio (BER) value for the DUT.

A user equipment (UE) may simulate transmissions received from a BS to perform on-device testing of the UE. For example, the UE may be configured to loopback uplink data from the UL data path and input the uplink data as simulated downlink data for processing in the DL data path. The uplink data may include data related to a video call or network diagnostics. The user application data generated by the application and proceeding through the UL data path may be used to validate the DL data path. Downlink control information (DCI) may be determined by the UE and provided to the DL data path to accompany the uplink data. The DCI may include simulated uplink grants and/or simulated downlink scheduling assignments. The simulated downlink scheduling assignments may be determined based on availability of the uplink data in the UE's memory.

A method for fault diagnosis in a communication network is to be implemented by a processor. The method includes obtaining key performance indicator (KPI) data related to the communication network, performing a deep-learning-based classification algorithm by using the KPI data as input to a deep neural network model, and determining, based on output of the deep neural network model after performing the deep-learning-based classification algorithm, at least one type of network condition the communication network currently satisfies, and a severity level of the at least one type of network condition when the output of the deep neural network model contains information related to severity levels of the at least one type of network condition.

A method for fault diagnosis in a communication network is to be implemented by a processor. The method includes obtaining key performance indicator (KPI) data related to the communication network, performing a deep-learning-based classification algorithm by using the KPI data as input to a deep neural network model, and determining, based on output of the deep neural network model after performing the deep-learning-based classification algorithm, at least one type of network condition the communication network currently satisfies, and a severity level of the at least one type of network condition when the output of the deep neural network model contains information related to severity levels of the at least one type of network condition.

Methods and systems for automated testing of extremely-high frequency devices are disclosed. A device under test (DUT) is set in a simultaneous transmit and receive mode. The DUT receives a lower frequency radio frequency (RF) signal from a test unit and up-converts the lower frequency RF signal to a higher frequency RF signal. The DUT transmits the higher frequency RF signal using a first antenna, and receives the higher frequency RF signal using a second antenna. The DUT down-converts the received higher frequency RF signal to a received test RF signal and provides the received test RF signal to the test unit for comparing measurements derived from the received test signal to a design specification for the DUT.