A method for predicting a structure of an indoor space using radio propagation channel analysis through deep-learning is disclosed. Channel data of radio signals are collected for various indoor spaces, and radio channel parameter data such as PDP, AoA, and AoD are extracted therefrom. A large amount of propagation channel parameter data is input to an artificial neural network together with vertex coordinate data of the corresponding indoor space and deep-learning is performed in advance. The propagation channel parameter data are extracted from the indoor space to be predicted, the best matching indoor space is detected based on the trained artificial neural network. The best matching indoor space is predicted as the structure of the indoor space.

A test and measurement instrument has an input configured to receive a signal from a device under test, a memory, a user interface to allow the user to input settings for the test and measurement instrument, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to: acquire a waveform representing the signal received from the device under test; generate one or more tensor arrays based on the waveform; apply machine learning to the one or more tensor arrays to produce equalizer tap values; and apply equalization to the waveform using the equalizer tap values to produce an equalized waveform; and perform a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test. A method of testing a device under test includes acquiring a waveform representing a signal received from the device under test, generating one or more tensor arrays based on the waveform, applying machine learning to the one or more tensor arrays to produce equalizer tap values, applying the equalizer taps values to the waveform to produce an equalized waveform, performing a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test.

A device may receive sequential digital signals generated by a radio unit based on receipt of sequential radio frequency waveforms provided to ports of the radio unit by a signal generator and analyzer. The device may calculate uplink direction beamforming performance of the radio unit based on the sequential digital signals. The device may provide data identifying the uplink direction beamforming performance for display.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for processing communications signals using a machine-learning network are disclosed. In some implementations, pilot and data information are generated for a data signal. The data signal is generated using a modulator for orthogonal frequency-division multiplexing (OFDM) systems. The data signal is transmitted through a communications channel to obtain modified pilot and data information. The modified pilot and data information are processed using a machine-learning network. A prediction corresponding to the data signal transmitted through the communications channel is obtained from the machine-learning network. The prediction is compared to a set of ground truths and updates, based on a corresponding error term, are applied to the machine-learning network.

Disclosed is a 5G or pre-5G communication system for supporting a data transmission rate higher than that of a 4G communication system such as LTE. The present invention relates to a method by which a simulator analyzes a radio wave environment in a wireless communication system, and the method of the present invention comprises the steps of: allowing a simulator to receive geographic information and position information by which a transmitter and a receiver can be positioned in the geographic information; generating, by the transmitter of the simulator arranged at a random position in accordance with the position information, radio waves for at least one direction of a sphere having a fixed radius; grouping into at least one group on the basis of a traveling route of the generated radio waves; setting each group as an operation unit (Warp/Wavefront) for a graphics processing unit (GPU); and analyzing a radio wave environment by using the GPU in which the operation unit is set.

The disclosure provides a wireless channel scenario identification method and system. The method includes: simulating different wireless channel scenarios to obtain a channel scenario baseband signal y(t).sub.pq; extracting a feature parameter of y(t).sub.pq, extracting an autocorrelation function A.sub.h(t).sub.pq and performing a Fourier transform thereon to obtain a power spectral density function S(t).sub.pq; normalizing S(t).sub.pq to obtain a normalized channel scenario power spectral density function S(t).sub.pq; designing a deep learning network and inputting S(t).sub.pq and a category label pair to train the deep learning network; and for a system with a channel scenario to be identified, collecting a passband signal at its receiving end, obtaining the normalized scenario power spectral density function Ŝ(t).sub.pq, and using Ŝ(t).sub.pq as an input of the trained classifier, the output of the classifier being a label sequence of the channel scenario, and the channel scenario is effectively determined.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a device may receive layout information that identifies a configuration of an antenna array of antennas, wherein the antenna array is to include a plurality of antenna subarrays and a plurality of antenna chips, wherein each antenna chip is communicatively coupled to antennas of an associated antenna subarray; determine, based at least in part on a phase shift characteristic associated with the antennas, a set of phase differences between antenna subarrays; determine, based at least in part on the set of phase differences, a chip position of each antenna chip relative to the associated antenna subarray; and generate, based at least in part on the chip position of each antenna chip, a layout of an antenna package to receive the antenna array and the plurality of antenna chips. Numerous other aspects are provided.

The present invention discloses a system and method for simulating radio frequency (RF) signal propagation through a plurality of mediums using a plurality of configurable digital representations of building information model in two dimensional (2D) or augmented reality (AR) or virtual reality (VR) environments. The system comprising: a client interface, a processor and a memory. The processor comprising a building information model (BIM) generation unit, RF equipment conversion unit and a RF propagation prediction unit. The BIM generation unit is configured to translate 2D graphics into BIM data-sets, by reference to administrator input, to act as one or more parameters to control a generation of 2D or AR or VR environments. The RF equipment conversion unit is configured to transform adjunct data related to a physical configuration of one or more RF equipment into data models. The RF propagation prediction unit is configured to utilize the one or more parameters and the transformed data related to the physical configuration of the one or more RF equipment for simulating the RF signal propagation pattern predictions through the plurality of mediums.

An interference detection system in a network identifies a first wireless station that has experienced radio frequency (RF) interference from an unknown source on at least one physical resource block (PRB) and identifies one or more second wireless stations that have experienced similar interference on the at least one PRB. A plurality of estimated interference source locations are determined based at least on geographic locations of the first wireless station and the one or more second wireless stations. The plurality of estimated interference source locations are scored based on a comparison of estimate interference to observed interference at the one or more second wireless stations and a geographical map is generated based on the scored plurality of estimated interference source locations, wherein the geographical map includes indicia indicative of the relative scores of the plurality of estimated interference source locations.

One or more computing devices, systems, and/or methods are provided. In an example, a method includes instantiating a first user equipment (UE) module on a first computing device and instantiating a second UE module on a second computing device connected to the first computing device by a wired network. A first base station (BS) module associated with the first UE module is instantiated on a third computing device coupled to the wired network and a second BS module associated with the second UE module is instantiated on a fourth computing device coupled to the wired network. A first resource allocation map is sent from the first BS module to the second BS module. An interference metric is generated based on the first resource allocation map. A data transmission between the second UE module and the second BS module is modulated based on the interference metric.