UE capability for support of machine-learning (ML) based channel environment classification may be reported by a user equipment to a base station, where the channel environment classification classifies a channel environment of a channel between the UE and a base station based on one or more of UE speed or Doppler spread, UE trajectory, frequency selectivity or delay spread, coherence bandwidth, coherence time, radio resource management (RRM) metrics, block error rate, throughput, or UE acceleration. The user equipment may receive configuration for ML based channel environment classification, including at least enabling/disabling of ML based channel environment classification. When ML based channel environment classification is enabled, UE assistance information for ML based channel environment classification, and/or an indication of the channel environment (which may be a pre-defined channel environment associated with a lookup table), may be transmitted by the user equipment to the base station.

The computer-implemented method includes simulating, by a processor, using an electromagnetic solver including ray launching or ray tracing, multiple rays that reach a vicinity of a receiver of a wireless channel, determining locations of interactions of the rays with an environment of the wireless channel, post-processing, using one or more of the multiple rays, information about received signal at the receiver to obtain temporal variations therein, and determining a characteristic of the wireless channel using results of the post-processing.

The computer-implemented method includes simulating, by a processor, using an electromagnetic solver including ray launching or ray tracing, multiple rays that reach a vicinity of a receiver of a wireless channel, determining locations of interactions of the rays with an environment of the wireless channel, post-processing, using one or more of the multiple rays, information about received signal at the receiver to obtain temporal variations therein, and determining a characteristic of the wireless channel using results of the post-processing.

The present development details a method and apparatus for performing channel emulation of doubly selective scenarios, where the simulation and emulation duration is arbitrarily long for a stationary or non-stationary channel, with non-separable dispersion which is achieved by combining the techniques of channel orthogonalization, decomposition of the correlation tensor in the Doppler domain into frequency-dependent correlation matrices, followed by a matrix factorization of each of the mentioned matrices and, finally, the use of the windowing method to generate arbitrarily long achievements which thereby allows the concatenation of channel realizations coming from the same or different NSSF, thus achieving reproduction of stationary or non-stationary channels, respectively.

The present development details a method and apparatus for performing channel emulation of doubly selective scenarios, where the simulation and emulation duration is arbitrarily long for a stationary or non-stationary channel, with non-separable dispersion which is achieved by combining the techniques of channel orthogonalization, decomposition of the correlation tensor in the Doppler domain into frequency-dependent correlation matrices, followed by a matrix factorization of each of the mentioned matrices and, finally, the use of the windowing method to generate arbitrarily long achievements which thereby allows the concatenation of channel realizations coming from the same or different NSSF, thus achieving reproduction of stationary or non-stationary channels, respectively.

A system and method of propagating signal links by using artificial neural networks using a relay link selection protocol to predict an optimal link or path, providing a reliable mechanism to meet 5G-new radio requirements. The artificial neural networks used in the method classify training and testing datasets into sufficient signal strengths and insufficient signal strengths, such that paths are evaluated for predicted propagation links, and such that the strongest propagation link can be selected. Specifically, a multilayer perceptron method is used to identify and characterize new link candidates using the path loss parameter or the received signal strength, such that optimal links can be selected and updated. To determine the sufficiency of a signal, a threshold energy strength is determined (for example, a threshold of −120 dBm can be used; any energy strength below the threshold is considered a poor propagation and is classified as an insufficient signal).

Systems and methods of the present disclosure relate to calibration of a resistivity tool. A calibration method comprises deploying a transmitter in a known formation with a known resistivity property with a physical tilted angle θ relative to a longitudinal axis of the tool; deploying receivers in the known formation, wherein a physical tilted angle of a first receiver is θ relative to the longitudinal axis of the tool, and wherein a physical tilted angle of a second receiver is −θ, relative to the longitudinal axis of the tool; transmitting signals with the transmitter and measuring the signals at the receivers; combining measurements at two receivers with respect to a transmitter signal in the known formation; producing synthetic responses of the tool in the known formation using forward modeling; and calculating an effective tilted angle θ′ from real measurements and the synthetic responses.

An obstacle recognition method and apparatus, and a related device are provided. The method includes: obtaining an actual path loss value between a first AP and a second AP, path loss value pairs between a terminal and a plurality of AP pairs, and path loss values of the plurality of AP pairs; obtaining, based on the path loss value pairs between the terminal and the plurality of AP pairs, an AP pair similar to an AP pair formed by the first AP and the second AP; obtaining a second path loss value between the first AP and the second AP based on a path loss value of the similar AP pair; and comparing the second path loss value between the first AP and the second AP with the actual path loss value, to determine whether an obstacle exists between the first AP and the second AP.

Systems and methods model earth stations and other captive terrestrial sites as simulated cell sites in a radio access network (RAN) to identify potential cellular network interferers with the earth stations. A computing device selects an earth station within a geographic area of a RAN segment and model the earth station as a cell within the RAN segment, wherein the modeling creates a simulated earth station cell. The computing device obtains sector carrier data for cells in the RAN segment and scores, based on the sector carrier data, neighboring cells to the simulated earth station cell. The scoring indicates a level of potential interference of the neighboring cells with the earth station based on geo-spatial relevance. The computing device identifies projected mobility interference in neighboring cells to the earth station and provides prioritization recommendations for interference mitigation for the earth station based on the scoring and the identifying.

Systems and methods model earth stations and other captive terrestrial sites as simulated cell sites in a radio access network (RAN) to identify potential cellular network interferers with the earth stations. A computing device selects an earth station within a geographic area of a RAN segment and model the earth station as a cell within the RAN segment, wherein the modeling creates a simulated earth station cell. The computing device obtains sector carrier data for cells in the RAN segment and scores, based on the sector carrier data, neighboring cells to the simulated earth station cell. The scoring indicates a level of potential interference of the neighboring cells with the earth station based on geo-spatial relevance. The computing device identifies projected mobility interference in neighboring cells to the earth station and provides prioritization recommendations for interference mitigation for the earth station based on the scoring and the identifying.