Abstract: Methods and systems for over the air channel analysis through artificial intelligence (AI)/machine learning (ML) aided pattern recognition are provided. A shape of a signal (e.g., control signal) may be detected through image analysis using AI/ML and technology of signal in a channel of interest may be determined from the shape. Next, the control channel (SSB, SIB1) may be determined based on period. A user (e.g., a technician) may be provided with center frequency and bandwidth for LTE or SSB, SIB1 for NR to assist with diagnosis and maintenance of the corresponding channel. Furthermore, channel analysis (e.g., EVM, SNR, time error) may be performed without demodulation and provided to the user. Moreover, massive MIMO performance may also be measured based on power difference between broadcast beam and user beam for data throughput evaluation.

Abstract: A test device may be a portable test device that can perform over the air measurements to determine synchronization errors between clusters in a cellular network. The test device can generate a graphical user interface that can show a map of the boundary area between clusters, locations of base stations in the boundary area, locations where over the air measurements were taken by the test device, an indication of whether cells are in synchronization for each measurement location, and a table of actual measurements and derived parameters pertaining to cell phase synchronization, interference and other performance metrics.

Abstract: A modular testing device provides field technicians with resources to support multiple aspects of signal analysis. The modular testing device includes multiple modules such as a base module, a user interface module, a testing module, and a battery module. The modular testing device may include a processor, a memory to store machine readable instructions executable by the processor to capture first data associated with signal activity of a communications link for a first capture period, capture second data associated with signal activity of the communications link for one or more additional capture periods, overlay a first representation of the first data and a second representation of the second data to display a overlay representation on a graphical user interface (GUI) of a display component, and denote at least one of one or more signal characteristics and anomalies in the graphical user interface.

Abstract: Methods and systems for over the air channel analysis through artificial intelligence (AI)/machine learning (ML) aided pattern recognition are provided. A shape of a signal (e.g., control signal) may be detected through image analysis using AI/ML and technology of signal in a channel of interest may be determined from the shape. Next, the control channel (SSB, SIB1) may be determined based on period. A user (e.g., a technician) may be provided with center frequency and bandwidth for LTE or SSB, SIB1 for NR to assist with diagnosis and maintenance of the corresponding channel. Furthermore, channel analysis (e.g., EVM, SNR, time error) may be performed without demodulation and provided to the user. Moreover, massive MIMO performance may also be measured based on power difference between broadcast beam and user beam for data throughput evaluation.

Abstract: A test device may be a portable test device that can perform over the air measurements to determine synchronization errors between clusters in a cellular network. The test device can generate a graphical user interface that can show a map of the boundary area between clusters, locations of base stations in the boundary area, locations where over the air measurements were taken by the test device, an indication of whether cells are in synchronization for each measurement location, and a table of actual measurements and derived parameters pertaining to cell phase synchronization, interference and other performance metrics.

Abstract: A test device may be a portable test device that can perform over the air measurements to determine synchronization errors between clusters in a cellular network. The test device can generate a graphical user interface that can show a map of the boundary area between clusters, locations of base stations hi the boundary area, locations where over the air measurements were taken by the test device, an indication of whether cells are in synchronization for each measurement location, and a table of actual measurements and derived parameters pertaining to cell phase synchronization, interference and other performance metrics.

Abstract: A test device may be a portable test device that can perform over the air measurements to determine synchronization errors between clusters in a cellular network. The test device can generate a graphical user interface that can show a map of the boundary area between clusters, locations of base stations in the boundary area, locations where over the air measurements were taken by the test device, an indication of whether cells are in synchronization for each measurement location, and a table of actual measurements and derived parameters pertaining to cell phase synchronization, interference and other performance metrics.

Abstract: A test device is operable to detect interference in a time division duplex (TDD) cellular network. The test device can automatically detect a TDD frame structure of RF signals received over the air Eagle Eye from a cell site. The test device determines a transition period between uplink (UL) and downlink (DL) transmissions in which no UL and DL signals are being transmitted based on the TDD frame structure. An interference signal is detected during the transition period, and the test device can generate a display of the spectrum during the transition period that includes the interference signal.