Methods, apparatuses, systems, and non-transitory computer-readable medium are disclosed relating to abnormal transmission identification. One method comprises, at a receiving device, receiving a V2X message from a transmitting device. The method further comprises determining a signal propagation context for the receiving device and obtaining an RSSI value and a distance value for the V2X message. The method further comprises generating an adjusted RSSI value based on (1) the RSSI value and (2) the signal propagation context for the receiving device. The method further comprises obtaining a predetermined RSSI-to-distance relationship model and comparing an adjusted RSSI-to-distance data pair, comprising the adjusted RSSI value and the distance value, to the predetermined RSSI-to-distance relationship model. The method further comprises, in response to determining that the adjusted RSSI-to-distance data pair fails a criterion for conforming to the predetermined RSSI-to-distance relationship model, identifying the V2X message as an abnormal transmission.

An interference detection system in a network identifies a first wireless station that has experienced radio frequency (RF) interference from an unknown source and identifies one or more second wireless stations that have experienced similar interference. A plurality of estimated interference source locations are scored based on a comparison of estimated interference to observed interference at the one or more second wireless stations. A predicted interference source location is identified based on the scored plurality of estimated interference source locations. It is determined whether the unknown interference source is a persistent interference source over a selected time period, wherein the predicted interference source location is identified for each interval in the selected time period. The predicted interference source locations for each interval in the selected time period are retrieved and an aggregated predicted interference source location is calculated based on the retrieved predicted interference source locations.

Systems, methods, and devices to reduce the channel estimation overhead by collecting data from many UEs and building a location-based mathematical model are disclosed. During building of the model, a reference signal is used to collect location- and signal-related data from connected UEs. Once the model is successfully built, it is then transmitted and/or downloaded to each new UE that connects to the base station. The UEs and/or the base stations then use this model to determine their own transmission parameter values. The UEs also report their location to the base stations, which use the model to estimate channel conditions and adapt transmission parameters for themselves.

A test apparatus includes: a communication interface for connection to a enodeb of a mobile telecommunication network provided with beamforming functionality, the communication interface being configured to receive communication traffic between the enodeb and a mobile terminal coupled in communication to the enodeb (3); a beamforming-testing unit, configured to receive communication-channel-quality signals (RSRPS) corresponding to each communication beam (B1, B2, . . . , BN), the communication-channel-quality signals (RSRPS) representing a signal power received by the mobile terminal. The beamforming-testing unit checks whether communication settings of the enodeb in relation to selection of the communication beams (B1, B2, . . . , BN) are consistent with the communication-channel-quality signals (RSRPS).

One general aspect of the present disclosure includes a device configured to operate in a wireless system. The device including: a transceiver configured with a plurality of E-UTRA operating bands; and a processor operably connectable to the transceiver. The processer may be configured to: control the transceiver to transmit an uplink signal via at least two bands among the plurality of E-UTRA operating bands; and control the transceiver to receive a downlink signal via three bands among the plurality of E-UTRA operating bands, wherein pre-configured MSD value is applied to a reference sensitivity for receiving the downlink signal based on the E-UTRA operating band 2.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learning estimation networks in a communications system. One of the methods includes: processing first information with ground truth information to generate a first RF signal by altering the first information by channel impairment having at least one channel effect, using a receiver to process the first RF signal to generate second information, training a machine-learning estimation network based on a network architecture, the second information, and the ground truth information, receiving by the receiver a second RF signal transmitted through a communication channel including the at least one channel effect, inferring by the trained estimation network the receiver to estimate an offset of the second RF signal caused by the at least one channel effect, and correcting the offset of the RF signal with the estimated offset to obtain a recovered RF signal.

The present invention provides an emissions control apparatus for an entity. The emissions control apparatus comprises: storage means for storing transmission data relating to an entity; a controller configured to calculate a propagation profile for the entity based on at least the transmission data; and a display for displaying the propagation profile. The present invention also provides a method of controlling emissions.

This disclosure provides a method for calculating the installation number and installation positions of wireless base stations in accordance with the positions of wireless terminal stations within an installation area. First, when a predetermined number of wireless base stations are temporarily installed within the installation area, a wireless base station accommodating each wireless terminal station in a communication area is determined and the number of wireless base stations accommodating each wireless terminal station is calculated as a “number of conflicts”. Next, wireless base stations successively accommodating starting with an un-accommodated wireless terminal station of a small number of conflicts are successively selected as objects of installation. Then, the installation number and installation positions of the wireless base stations are calculated, with the temporarily installed wireless base stations other than the wireless base stations serving as the objects of installation being excluded.

A technique detects when a computer simulation controller such as a computer game controller is idle and, thus, that the simulation (game) should be paused immediately without waiting for an “AwayFromKeyboard” timer to time out by detecting whether the user has laid the controller down and gone away or simply is not responding.

Systems and methods for detecting anomalies in antenna systems (e.g., air traffic control surveillance systems), include a processor receiving antenna status information. A variational autoencoder receives and optimizes the antenna status information and determines whether it qualifies as an anomaly. Optimized antenna status information is compared to either non-anomalous or anomalous antenna status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the antenna status information by generating a plurality of probabilistic models of the antenna status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the antenna status information prior to optimization.