Abstract:
The present invention relates to a 5th-generation (5G) or pre-5G communication system to be provided for supporting a data transmission rate higher than that of a 4th-generation (4G) communication system, such as long term evolution (LTE), and subsequent systems. The present invention provides a method by which a mobile station (MS) operates a beam in a communication system supporting a hybrid multiple-input multiple-output (MIMO) mode, the method comprising the steps of: receiving, from a base station (BS), information related to the number of beams to be used, by the BS, for a beam training process; receiving, from the BS, a downlink reference signal (RS); performing a channel estimation process on the basis of the downlink RS; and transmitting, to the BS, information related to the number of beams to be used by the MS, after performing the channel estimation process.
Abstract:
The present invention relates to a method and apparatus for estimating a road surface type by using an ultrasonic signal and, more particularly, to a method for estimating a road surface type by using an artificial neural network model machine-learned with respect to a reflected ultrasonic signal and an apparatus for performing same. According to the present invention, provided are a method and apparatus for providing highly accurate road surface information at low cost, by machine-learning both characteristics of an ultrasonic signal reflected from a road surface and a road surface state, establishing a model between the two, and then estimating the type of the road surface by utilizing the model. In particular, even a road surface where thin ice, that is, black ice, is formed, which was not detectable in the conventional method for estimating a road-surface friction coefficient, may be accurately estimated, thereby contributing to safer driving.
Abstract:
A method and apparatus for operating an analog beam is provided. The apparatus includes a hybrid beam-forming structure having a small number of digital chains provided in a digital stage and a signal is transmitted and received in a Time Division Multiple Access (TDMA) scheme. The apparatus includes an analog beam-forming operating unit configured to change the analog beam for a counterpart device for which use of a next frame is to be permitted, upon receiving a request for using the next frame from at least one of multiple devices that transmit and receive signals in units of frames and a communication unit configured to transmit information indicating that the analog beam is changed to permit the use of the next frame by the counterpart device to the multiple devices.
Abstract:
The present invention relates to a 5th-generation (5G) or pre-5G communication system to be provided for supporting a data transmission rate higher than that of a 4th-generation (4G) communication system, such as long term evolution (LTE), and subsequent systems. The present invention provides a method by which a mobile station (MS) operates a beam in a communication system supporting a hybrid multiple-input multiple-output (MIMO) mode, the method comprising the steps of: receiving, from a base station (BS), information related to the number of beams to be used, by the BS, for a beam training process; receiving, from the BS, a downlink reference signal (RS); performing a channel estimation process on the basis of the downlink RS; and transmitting, to the BS, information related to the number of beams to be used by the MS, after performing the channel estimation process.
Abstract:
The present invention relates to a method and apparatus for estimating a road surface type by using an ultrasonic signal and, more particularly, to a method for estimating a road surface type by using an artificial neural network model machine-learned with respect to a reflected ultrasonic signal and an apparatus for performing same. According to the present invention, provided are a method and apparatus for providing highly accurate road surface information at low cost, by machine-learning both characteristics of an ultrasonic signal reflected from a road surface and a road surface state, establishing a model between the two, and then estimating the type of the road surface by utilizing the model. In particular, even a road surface where thin ice, that is, black ice, is formed, which was not detectable in the conventional method for estimating a road-surface friction coefficient, may be accurately estimated, thereby contributing to safer driving.