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 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 method is provided. The method includes estimating adjacent channel leakage ratios respectively corresponding based on a test output signal output from a power amplifier according to a test input signal corresponding to a plurality of frequencies; selecting a test delay value corresponding to a largest value among the estimated adjacent channel leakage ratios; and providing a supply voltage to the power amplifier based on an envelope signal delayed according to the selected test delay value. For each of the plurality of test delay values, a corresponding adjacent channel leakage ratio is estimated based on a ratio of a magnitude of a component included in the test output signal and a magnitude of an inter-modulated component.

A method is provided. The method includes estimating adjacent channel leakage ratios respectively corresponding based on a test output signal output from a power amplifier according to a test input signal corresponding to a plurality of frequencies; selecting a test delay value corresponding to a largest value among the estimated adjacent channel leakage ratios; and providing a supply voltage to the power amplifier based on an envelope signal delayed according to the selected test delay value. For each of the plurality of test delay values, a corresponding adjacent channel leakage ratio is estimated based on a ratio of a magnitude of a component included in the test output signal and a magnitude of an inter-modulated component.

Apparatuses, methods, and systems for dynamically estimating a propagation time between a first node and a second node of a wireless network are disclosed. One method includes receiving, by the second node, from the first node a packet containing a first timestamp representing the transmit time of the packet, receiving, by the second node, from a local time source, a second timestamp corresponding with a time of reception of the first timestamp received from the first node, calculating a time difference between the first timestamp and the second timestamp, storing the time difference between the first timestamp and the second timestamp, calculating a predictive model for predicting the propagation time based the time difference between the first timestamp and the second timestamp, and estimating the propagation time between the first node and the second node at a time by querying the predictive model with the time.

Apparatuses, methods, and systems for dynamically estimating a propagation time between a first node and a second node of a wireless network are disclosed. One method includes receiving, by the second node, from the first node a packet containing a first timestamp representing the transmit time of the packet, receiving, by the second node, from a local time source, a second timestamp corresponding with a time of reception of the first timestamp received from the first node, calculating a time difference between the first timestamp and the second timestamp, storing the time difference between the first timestamp and the second timestamp, calculating a predictive model for predicting the propagation time based the time difference between the first timestamp and the second timestamp, and estimating the propagation time between the first node and the second node at a time by querying the predictive model with the time.

A first cellular base station transmits a configuration message to a reporting device installed on a high-speed vehicle. The configuration message specifies one or more parameters of a Doppler measurement report. The reporting device performs one or more first Doppler measurements on the first base station and/or one or more second Doppler measurements on a second base station. The reporting device transmits the Doppler measurement report to the first and/or second base stations. The Doppler measurement report may be used by the first and/or second base stations to perform Doppler pre-compensation on transmissions to the reporting device.

A first cellular base station transmits a configuration message to a reporting device installed on a high-speed vehicle. The configuration message specifies one or more parameters of a Doppler measurement report. The reporting device performs one or more first Doppler measurements on the first base station and/or one or more second Doppler measurements on a second base station. The reporting device transmits the Doppler measurement report to the first and/or second base stations. The Doppler measurement report may be used by the first and/or second base stations to perform Doppler pre-compensation on transmissions to the reporting device.

An in-situ flow detection method. When determining a transmission delay in a detection domain in a first in-situ flow detection period, a first network node may color a plurality of packets in a first service flow that are received by the first network node through a first inbound interface and determine the delay in the detection domain in the first in-situ flow detection period based on the plurality of colored packets, thereby improving the precision of the detected transmission delay in the detection domain.

A vehicle remote instruction system 100 transmits a remote instruction request from an autonomous driving vehicle 2 to a remote instruction apparatus 1, and controls travel of the autonomous driving vehicle 2 based on a remote instruction transmitted from the remote instruction apparatus 1 in response to the remote instruction request. The vehicle remote instruction system 100 includes a delay determination unit 39 configured to determine whether or not a communication delay occurs between the remote instruction apparatus 1 and the autonomous driving vehicle 2, and a rejection unit 40 configured to reject the remote instruction transmitted in response to the remote instruction request if it is determined by the delay determination unit 39 that the communication delay occurs.