Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

To provide an interference evaluation apparatus including a station information storage unit configured to store interfering station information indicating station information related to an interfering station causing radio interference in radio communication and interfered station information indicating station information related to an interfered station receiving the interference, and a determination unit configured to perform an interference calculation for calculating an interference power, based on the interfering station information and the interfered station information stored in the station information storage unit, and determine whether the interfering station and the interfered station are usable together, based on the calculated interference power and an acceptable interference power in the interfered station.

Systems and methods for enhancing coverage of repeater systems by time-division beamforming are provided. A repeater system includes an interface configured to communicate signals with a base station and a phased antenna array. The phased antenna array includes a plurality of antenna elements, wherein the phased antenna array is configured to generate a beam in a plurality of predefined directions and wirelessly communicate signals with user equipment in a coverage area of the phased antenna array. The repeater system further includes a beamforming circuit communicatively coupled to the phased antenna array, wherein the beamforming circuit is configured to adjust a direction of the beam generated by the phased antenna array to correspond to one of the plurality of predefined directions at a particular time according to a schedule.

Systems and methods for iterative distributed beam selection include a device including at least one of a first head wearable display (HWD), a second HWD, a first console or a second console. The device detects a predefined condition. The device performs a first distributed beam selection responsive to detecting the predefined condition. Performing the first distributed beam selection includes performing beamforming to provide a first plurality of beams for a first link between the first HWD and the first console, selecting a first beam of the first link with a highest signal-to-interference-plus-noise ratio (SINR) from the first plurality of beams, performing beamforming to provide a second plurality of beams for a second link between the second HWD and the second console while the first beam of the first link is active, and selecting a second beam of the second link with a highest SINR from the second plurality of beams.

One example method includes: When a network device indicates that a reporting type is group-based reporting, channel state information reported by a terminal device to the network device includes indexes of X first reference signal resources and Y pieces of channel quality information. Channel quality information of a j.sup.th first reference signal resource is determined based on the j.sup.th first reference signal resource and a second reference signal resource that has a first association relationship with the j.sup.th first reference signal resource.

Methods, systems, and devices for wireless communications are described, including a movable relay location variance report. The movable relay location variance report may enable more robust and accurate communications between a control node (e.g., a user equipment (UE) and a base station) and a movable relay (e.g., a drone) equipped with a reconfigurable intelligent surface (RIS). In some aspects, the location variance report may characterize the variance of the drone's location and transmit the information to the UE, the base station, or both. The location variance report may influence the control node beam width, the drone location, and an angle at which the drone may position a RIS. The control node may indicate an adjusted set of parameters to the drone based on receiving the location variance report.

Methods, systems, and devices for wireless communications are described, including a movable relay location variance report. The movable relay location variance report may enable more robust and accurate communications between a control node (e.g., a user equipment (UE) and a base station) and a movable relay (e.g., a drone) equipped with a reconfigurable intelligent surface (RIS). In some aspects, the location variance report may characterize the variance of the drone's location and transmit the information to the UE, the base station, or both. The location variance report may influence the control node beam width, the drone location, and an angle at which the drone may position a RIS. The control node may indicate an adjusted set of parameters to the drone based on receiving the location variance report.

Apparatus and methods for identifying a wireless signal-emitting device are disclosed. The apparatus is configured to sense and measure wireless communication signals from signal-emitting devices in a spectrum. The apparatus is operable to automatically detect a signal of interest from the wireless signal-emitting device and create a signal profile of the signal of interest; compare the signal profile with stored device signal profiles for identification of the wireless signal-emitting device; and calculate signal degradation data for the signal of interest based on information associated with the signal of interest in a static database including noise figure parameters of a wireless signal-emitting device outputting the signal of interest. The signal profile of the signal of interest, profile comparison result, and signal degradation data are stored in the apparatus.

Apparatus and methods for identifying a wireless signal-emitting device are disclosed. The apparatus is configured to sense and measure wireless communication signals from signal-emitting devices in a spectrum. The apparatus is operable to automatically detect a signal of interest from the wireless signal-emitting device and create a signal profile of the signal of interest; compare the signal profile with stored device signal profiles for identification of the wireless signal-emitting device; and calculate signal degradation data for the signal of interest based on information associated with the signal of interest in a static database including noise figure parameters of a wireless signal-emitting device outputting the signal of interest. The signal profile of the signal of interest, profile comparison result, and signal degradation data are stored in the apparatus.