In a control device 22 having a control unit 31 that controls an NR measurement device 20 and an LTE measurement device 21 and a display unit 33 that displays control contents of the control unit 31, the control unit 31 controls the display unit 33 such that the display unit 33 displays an NR simulation parameter display area 33c1 or an LTE simulation parameter display area 33c2 in accordance with the communication standard of the selected NR or LTE, in a case where the selection of either NR or LTE is received on the communication standard identification tabs 41a and 41b of the main screen 33a.

A detection method includes: obtaining at least one feature of a first access point (AP), where the at least one feature includes a quantity of target terminals that request to access the first AP within a time period with duration being first duration, and the target terminal is a terminal whose access status is abnormal; and detecting, based on the at least one feature, whether the first AP is a logical edge AP, where the logical edge AP is an AP whose signal coverage area reaches an edge of a signal coverage area of a wireless local area network (WLAN) in which the AP is located.

Various embodiments describe communication systems for implementing high-speed transmission systems using waveguide-mode transmission over wires. In certain examples, a communication system uses wire pairs as “waveguides” that transmit data at high frequencies and speeds. The data is transmitted through wave propagation that takes various forms, such as surface waves and Total Internal Reflection (TIR) waves.

Apparatus and methods for monitoring a wireless network such as a WLAN to characterize a venue or other area. In one embodiment, the network comprises a WLAN which includes one or more access points (APs) in data communication with a controller, which in turn communicates with managed network entities via a backhaul connection. The controller s is configured to monitor the operation of the network components including the APs, as well as one or more fixed or mobile sensors. In one variant, the sensors provide data relating to wireless signal performance at their current location, which can be provided to a cloud-based evaluation process for enhanced characterization of the venue in conjunction with the AP-derived data. In the exemplary embodiment, logic operative to run on the system includes automated seating allocation suggestions, thereby providing end users with a better quality experience.

A measuring device 1 performs control to perform a reception sensitivity test of measuring a throughput of a signal under measurement, and repeating the measurement while changing an output level of a test signal at each measurement position corresponding to a plurality of orientations. The integrated control device 10 that performs the control includes a measurement situation display control unit 18d that displays a measurement progress display screen having a first display area for displaying a result of the reception sensitivity test up to the measurement position where the reception sensitivity test is completed, and a second display area for displaying a progress situation of the measurement of the reception sensitivity test at the measurement position at which the reception sensitivity test is started.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over RF channels. In some implementations, information is obtained. An encoder network is used to process the information and generate a first RF signal. The first RF signal is transmitted through a first channel. A second RF signal is determined that represents the first RF signal having been altered by transmission through the first channel. Transmission of the first RF signal is simulated over a second channel implementing a machine-learning network, the second channel representing a model of the first channel. A simulated RF signal that represents the first RF signal having been altered by simulated transmission through the second channel is determined. A measure of distance between the second RF signal and the simulated RF signal is calculated. The machine-learning network is updated using the measure of distance.

A machine learning apparatus learns a radio wave propagation state between a first radio device and a second radio device. The machine learning apparatus includes an acquisition unit and a learning unit. The acquisition unit acquires first information in order to obtain at least one state variable. The first information is related to something between the first radio device and the second radio device. The learning unit learns the state variable and the radio wave propagation state in association with each other.

Embodiments provide a method, apparatus and device of reconstructing non-Kronecker structured channels, applicable to communications. A weight matrix is determined for emulating link characteristics of a reconstructed channel, and includes a weight corresponding to each ray mapped to a probe antenna. In each cluster, rays mapped to each probe antenna have different weights with each other. For each cluster, a time-varying fading channel impulse response of each ray of the cluster mapped to a probe antenna is calculated using the weight matrix. The time-varying fading channel impulse response includes a transition equation for each probe antenna describing mapping of rays of the cluster to the probe antenna. A transition matrix from each probe antenna to receiving antennas of a device under test is determined. A product of the time-varying fading channel impulse response of the cluster multiplied by the transition matrix serves as a channel impulse response of the cluster.

The technologies described herein are generally directed to modeling radio wave propagation in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include based on a graphical representation of a layout of a geographic area, identifying, by equipment comprising a processor, a feature of the geographic area relevant to propagation of a signal propagated from a signal point on the layout, resulting in an identified feature. The method can further comprise based on the identified feature and the signal point, generating, by the equipment, a feature map for the geographic area by employing a neural network, wherein the feature map comprises a map depicting estimates of the propagation of the signal at locations within the geographic area.

An apparatus (100) for determining a high-resolution QoS prediction map for a first radio communications network of a first environment is provided. The apparatus (100) comprises: a first input unit being configured to determine or provide environment information characterizing the first environment; a second input being configured to determine or provide a low-resolution QoS map associated with the first radio communications network of the first environment or a second radio communications network of a second environment; and a determination unit being configured to propagate the low-resolution QoS map and the environment information through a trained artificial deep neural network, wherein low-resolution QoS map and the environment information are provided as input parameters in an input section of the trained artificial deep neural network, and wherein the high-resolution QoS prediction map for the first radio communications network is provided in an output section of the trained artificial deep neural network.