WO2020091191A1

WO2020091191A1 - Method for transmitting and receiving discovery signal for relay node in next generation communication system and device therefor

Info

Publication number: WO2020091191A1
Application number: PCT/KR2019/008879
Authority: WO
Inventors: 김영태; 고현수; 김기준; 유향선; 윤석현; 이윤정
Original assignee: 엘지전자 주식회사
Priority date: 2018-11-01
Filing date: 2019-07-18
Publication date: 2020-05-07

Abstract

Disclosed is a method for a relay node to transmit and receive a discovery signal in a next generation wireless communication system. Specifically, the method comprises: a step for receiving synchronization signal/physical broadcast channel (SS/PBCH) block measurement timing configuration information defined by a bitmap from a network; and a step for, on the basis of the bitmap, transmitting a discovery signal for another relay node or donor node, or detecting the discovery signal transmitted from the other relay node or the donor node.

Description

A discovery signal transmission and reception method for a relay node in a next-generation communication system and a device therefor

The present invention relates to a wireless communication system, and more particularly, to a method and apparatus for transmitting and receiving a discovery signal for a relay node in a next generation communication system.

As more and more communication devices require more communication traffic according to the trend of the times, the next generation 5G system, which is an improved wireless broadband communication than the existing LTE system, is required. In the next generation 5G system, called NewRAT, communication scenarios are classified into Enhanced Mobile BroadBand (eMBB) / Ultra-reliability and low-latency communication (URLLC) / Massive Machine-Type Communications (mMTC).

Here, eMBB is a next-generation mobile communication scenario with characteristics such as High Spectrum Efficiency, High User Experienced Data Rate, High Peak Data Rate, and URLLC is a next-generation mobile communication scenario having characteristics such as Ultra Reliable, Ultra Low Latency, Ultra High Availability, etc. And (eg, V2X, Emergency Service, Remote Control), mMTC is a next-generation mobile communication scenario with low cost, low energy, short packet, and massive connectivity characteristics. (e.g., IoT).

Based on the discussion as described above, a method for transmitting and receiving a discovery signal for a relay node in a next-generation communication system and an apparatus therefor are proposed.

In a next-generation wireless communication system that is an aspect of the present invention, a method for a relay node to transmit / receive a discovery signal includes receiving SS / PBCH (Synchronization Signal / Physical Broadcast channel) block measurement timing setting information defined as a bitmap from a network; And based on the bitmap, transmitting a discovery signal for another relay node or donor node or detecting the discovery signal transmitted from the other relay node or donor node.

Meanwhile, a relay node in a next-generation wireless communication system, which is an aspect of the present invention, includes a wireless communication module; At least one processor; And at least one memory operatively connected to the at least one processor and storing instructions that, when executed, cause the at least one processor to perform a specific operation, wherein the specific operation is from a network. An operation of receiving SS / PBCH (Synchronization Signal / Physical Broadcast channel) block measurement timing setting information defined as a bitmap, and transmitting a discovery signal for another relay node or donor node based on the bitmap, or the other relay And detecting the discovery signal transmitted from the node or the donor node.

Preferably, at the timing indicated by 1 in the bitmap, the relay node may perform an operation of transmitting a discovery signal for the other relay node or the donor node. More preferably, at a timing indicated by 0 in the bitmap, the relay node may perform an operation of detecting the discovery signal transmitted from the other relay node or the donor node.

In particular, it is preferable that the relay node belongs to a specific group and the SS / PBCH block measurement timing setting information is the specific group-specific information.

Additionally, the SS / PBCH block measurement timing setting information includes a period and timing offset to which the bitmap is applied, and the bitmap can be defined by a random sequence.

According to an embodiment of the present invention, a relay node in a next-generation communication system can transmit and receive a discovery signal more efficiently.

The effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those skilled in the art from the following description. will be.

1 is a diagram illustrating a control plane and a user plane structure of a radio interface protocol between a terminal and an E-UTRAN based on a 3GPP radio access network standard.

2 is a diagram for explaining physical channels used in a 3GPP system and a general signal transmission method using them.

3 is a diagram illustrating a structure of a radio frame used in an LTE system.

4 to 6 are diagrams for explaining the structure of a radio frame and slot used in the NR system.

7 abstractly illustrates a hybrid beamforming structure from the perspective of a transceiver unit (TXRU) and a physical antenna.

8 shows a beam sweeping operation for a synchronization signal and system information in a downlink transmission process.

9 illustrates a cell of a new radio access technology (NR) system.

10 illustrates a hop time grouping technique operated in groups with different offsets according to an embodiment of the present invention.

11 illustrates a transmission pattern configured in a hierarchical structure according to an embodiment of the present invention.

12 shows an example in which a period is determined based on the number of hops according to an embodiment of the present invention.

13 illustrates another example of a hop time grouping technique operated in groups with different offsets according to an embodiment of the present invention.

14-16 illustrate a grouping technique of geographical access according to an embodiment of the present invention.

17 is a block diagram illustrating an example of components of an apparatus for implementing embodiments of the present invention.

18 to 20 are diagrams illustrating examples of an artificial intelligence (AI) system and apparatus for implementing embodiments of the present invention.

The configuration, operation, and other features of the present invention will be readily understood by embodiments of the present invention described with reference to the accompanying drawings. The embodiments described below are examples in which the technical features of the present invention are applied to a 3GPP system.

Although the present specification describes an embodiment of the present invention using an LTE system, an LTE-A system, and an NR system, as an example, the embodiment of the present invention can be applied to any communication system corresponding to the above definition.

In addition, in this specification, the name of the base station may be used as a comprehensive term including a remote radio head (RRH), an eNB, a transmission point (TP), a reception point (RP), a relay, and the like.

Artificial intelligence refers to the field of studying artificial intelligence or a methodology to create it, and machine learning (machine learning) refers to the field of studying the methodology to define and solve various problems in the field of artificial intelligence. do. Machine learning is defined as an algorithm that improves the performance of a job through steady experience.

An artificial neural network (ANN) is a model used in machine learning, and may mean an overall model having a problem-solving ability, composed of artificial neurons (nodes) forming a network through a combination of synapses. The artificial neural network may be defined by a connection pattern between neurons of different layers, a learning process for updating model parameters, and an activation function that generates output values.

The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer contains one or more neurons, and the artificial neural network can include neurons and synapses connecting neurons. In an artificial neural network, each neuron may output a function value of an input function input through a synapse, a weight, and an active function for bias.

The model parameter means a parameter determined through learning, and includes weights of synaptic connections and bias of neurons. In addition, the hyperparameter means a parameter that must be set before learning in a machine learning algorithm, and includes learning rate, number of iterations, mini-batch size, initialization function, and the like.

The purpose of training an artificial neural network can be seen as determining model parameters that minimize the loss function. The loss function can be used as an index for determining an optimal model parameter in the learning process of an artificial neural network.

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning according to the learning method.

Supervised learning refers to a method of training an artificial neural network while a label for training data is given, and a label is a correct answer (or a result value) that the artificial neural network must infer when the training data is input to the artificial neural network. Can mean Unsupervised learning may refer to a method of training an artificial neural network without a label for learning data. Reinforcement learning may mean a learning method in which an agent defined in a certain environment is trained to select an action or a sequence of actions to maximize cumulative reward in each state.

Machine learning implemented as a deep neural network (DNN) that includes a plurality of hidden layers among artificial neural networks is also referred to as deep learning (deep learning), and deep learning is part of machine learning. In the following, machine learning is used to mean deep learning.

<Robot>

A robot can mean a machine that automatically handles or acts on a task given by its own capabilities. In particular, a robot having a function of recognizing the environment and performing an operation by determining itself can be referred to as an intelligent robot.

Robots can be classified into industrial, medical, household, and military according to the purpose or field of use.

The robot may be provided with a driving unit including an actuator or a motor to perform various physical operations such as moving a robot joint. In addition, the movable robot includes a wheel, a brake, a propeller, and the like in the driving unit, so that it can travel on the ground or fly in the air through the driving unit.

<Self-Driving, Autonomous? Driving>

Autonomous driving refers to the technology of driving on its own, and autonomous driving means a vehicle that operates without a user's manipulation or with a minimum manipulation of the user.

For example, in autonomous driving, a technology that maintains a driving lane, a technology that automatically adjusts speed such as adaptive cruise control, a technology that automatically drives along a predetermined route, and a technology that automatically sets a route when a destination is set, etc. All of this can be included.

The vehicle includes a vehicle having only an internal combustion engine, a hybrid vehicle having both an internal combustion engine and an electric motor, and an electric vehicle having only an electric motor, and may include a train, a motorcycle, etc. as well as a vehicle.

At this time, the autonomous vehicle can be viewed as a robot having an autonomous driving function.

<XR: eXtended Reality>

Augmented reality refers to virtual reality (VR), augmented reality (AR), and mixed reality (MR). VR technology provides real-world objects or backgrounds only as CG images, AR technology provides CG images made virtually on real objects, and MR technology is a computer that mixes and combines virtual objects in the real world. Graphics technology.

MR technology is similar to AR technology in that it shows both real and virtual objects. However, in AR technology, a virtual object is used as a complement to a real object, whereas in MR technology, there is a difference in that a virtual object and a real object are used with equal characteristics.

XR technology can be applied to Head-Mount Display (HMD), Head-Up Display (HUD), mobile phone, tablet PC, laptop, desktop, TV, digital signage, etc. It can be called.

Now, let's look at 5G communication including the NR system.

The three main requirements areas of 5G are: (1) Enhanced Mobile Broadband (eMBB) area, (2) Massive Machine Type Communication (mMTC) area, and (3) Super-reliability and Ultra-reliable and Low Latency Communications (URLLC) domain.

Some use cases may require multiple areas for optimization, and other use cases may focus on only one key performance indicator (KPI). 5G is a flexible and reliable way to support these various use cases.

eMBB goes far beyond basic mobile Internet access, and covers media and entertainment applications in rich interactive work, cloud or augmented reality. Data is one of the key drivers of 5G, and it may not be possible to see dedicated voice services for the first time in the 5G era. In 5G, it is expected that voice will be processed as an application program simply using the data connection provided by the communication system. The main causes for increased traffic volume are increased content size and increased number of applications requiring high data rates. Streaming services (audio and video), interactive video and mobile internet connections will become more widely used as more devices connect to the internet. Many of these applications require always-on connectivity to push real-time information and notifications to users. Cloud storage and applications are rapidly increasing in mobile communication platforms, which can be applied to both work and entertainment. And, cloud storage is a special use case that drives the growth of uplink data transfer rate. 5G is also used for remote work in the cloud, requiring much lower end-to-end delay to maintain a good user experience when a tactile interface is used. Entertainment For example, cloud gaming and video streaming are another key factor in increasing demand for mobile broadband capabilities. Entertainment is essential for smartphones and tablets anywhere, including high mobility environments such as trains, cars and airplanes. Another use case is augmented reality and information retrieval for entertainment. Here, augmented reality requires very low delay and instantaneous amount of data.

In addition, one of the most anticipated 5G use cases is the ability to seamlessly connect embedded sensors in all fields, namely mMTC. It is predicted that by 2020, there will be 20.4 billion potential IoT devices. Industrial IoT is one of the areas where 5G plays a key role in enabling smart cities, asset tracking, smart utilities, agriculture and security infrastructure.

URLLC includes new services that will transform the industry through ultra-reliable / low-latency links, such as remote control of the main infrastructure and self-driving vehicles. Reliability and level of delay are essential for smart grid control, industrial automation, robotics, drone control and coordination.

Next, a number of use cases in a 5G communication system including an NR system will be described in more detail.

5G can complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS) as a means to provide streams rated at hundreds of megabits per second to gigabit per second. This fast speed is required to deliver TV in 4K (6K, 8K and higher) resolutions as well as virtual and augmented reality. Virtual Reality (VR) and Augmented Reality (AR) applications include almost immersive sports events. Certain application programs may require special network settings. For VR games, for example, game companies may need to integrate the core server with the network operator's edge network server to minimize latency.

Automotive is expected to be an important new driver for 5G, along with many use cases for mobile communications to vehicles. For example, entertainment for passengers requires simultaneous high capacity and high mobility mobile broadband. This is because future users continue to expect high-quality connections regardless of their location and speed. Another example of application in the automotive field is the augmented reality dashboard. It identifies objects in the dark over what the driver sees through the front window, and superimposes and displays information telling the driver about the distance and movement of the object. In the future, wireless modules will enable communication between vehicles, exchange of information between the vehicle and the supporting infrastructure and exchange of information between the vehicle and other connected devices (eg, devices carried by pedestrians). The safety system helps the driver to reduce the risk of accidents by guiding alternative courses of action to make driving safer. The next step will be remote control or a self-driven vehicle. This requires very reliable and very fast communication between different self-driving vehicles and between the vehicle and the infrastructure. In the future, self-driving vehicles will perform all driving activities, and drivers will focus only on traffic beyond which the vehicle itself cannot identify. The technical requirements of self-driving vehicles require ultra-low delays and ultra-high-speed reliability to increase traffic safety to levels beyond human reach.

Smart cities and smart homes, referred to as smart societies, will be embedded in high-density wireless sensor networks. The distributed network of intelligent sensors will identify the conditions for cost and energy-efficient maintenance of the city or home. Similar settings can be made for each assumption. Temperature sensors, window and heating controllers, burglar alarms and consumer electronics are all connected wirelessly. Many of these sensors are typically low data rates, low power and low cost. However, for example, real-time HD video may be required in certain types of devices for surveillance.

The consumption and distribution of energy, including heat or gas, is highly decentralized, requiring automated control of a distributed sensor network. The smart grid interconnects these sensors using digital information and communication technologies to collect information and act accordingly. This information can include supplier and consumer behavior, so smart grids can improve efficiency, reliability, economics, production sustainability and distribution of fuels like electricity in an automated way. The smart grid can be viewed as another sensor network with low latency.

The health sector has many applications that can benefit from mobile communications. The communication system can support telemedicine that provides clinical care from a distance. This can help reduce barriers to distance and improve access to medical services that are not continuously available in remote rural areas. It is also used to save lives in critical care and emergency situations. A wireless sensor network based on mobile communication can provide remote monitoring and sensors for parameters such as heart rate and blood pressure.

Wireless and mobile communications are becoming increasingly important in industrial applications. Wiring is expensive to install and maintain. Thus, the possibility of replacing cables with wireless links that can be reconfigured is an attractive opportunity in many industries. However, achieving this requires that the wireless connection operates with cable-like delay, reliability and capacity, and that management is simplified. Low latency and very low error probability are new requirements that need to be connected to 5G.

Logistics and freight tracking are important use cases for mobile communications that enable the tracking of inventory and packages from anywhere using location-based information systems. Logistics and freight tracking use cases typically require low data rates, but require wide range and reliable location information.

The 3GPP-based communication standard includes downlink physical channels corresponding to resource elements carrying information originating from an upper layer and downlink corresponding to resource elements used by the physical layer but not carrying information originating from an upper layer. Physical signals are defined. For example, a physical downlink shared channel (PDSCH), a physical broadcast channel (PBCH), a physical multicast channel (physical multicast channel, PMCH), a physical control format indicator channel (physical control) Format indicator channel (PCFICH), physical downlink control channel (PDCCH) and physical hybrid ARQ indicator channel (PHICH) are defined as downlink physical channels, and reference signals and synchronization signals Is defined as downlink physical signals. A reference signal (RS), also referred to as a pilot, refers to a signal of a predetermined special waveform known to each other by the gNB and the UE. For example, cell specific RS (UE), UE- A specific RS (UE-specific RS, UE-RS), positioning RS (positioning RS, PRS) and channel state information RS (channel state information RS, CSI-RS) are defined as downlink reference signals. The 3GPP LTE / LTE-A standard corresponds to uplink physical channels corresponding to resource elements carrying information originating from an upper layer and resource elements used by the physical layer but not carrying information originating from an upper layer. Defines uplink physical signals. For example, a physical uplink shared channel (PUSCH), a physical uplink control channel (PUCCH), a physical random access channel (physical random access channel, PRACH) as an uplink physical channel It is defined, and a demodulation reference signal (DMRS) for uplink control / data signals and a sounding reference signal (SRS) used for uplink channel measurement are defined.

In the present invention, PDCCH (Physical Downlink Control CHannel) / PCFICH (Physical Control Format Indicator CHannel) / PHICH ((Physical Hybrid automatic retransmit request Indicator CHannel) / PDSCH (Physical Downlink Shared CHannel), respectively DCI (Downlink Control Information) / CFI ( Control Format Indicator) / downlink ACK / NACK (ACKnowlegement / Negative ACK) / means a set of time-frequency resources or a set of resource elements carrying downlink data, and PUCCH (Physical Uplink Control CHannel) / PUSCH (Physical) Uplink Shared CHannel) / PRACH (Physical Random Access CHannel) means a set of time-frequency resources or a set of resource elements that carry uplink control information (UCI) / uplink data / random access signals, respectively. , PDCCH / PCFICH / PHICH / PDSCH / PUCCH / PUSCH / PRACH, or the time-frequency resource or resource element (RE) allocated to or belong to PDCCH / PCFICH / PHICH / PDSCH / PUCCH / PUSCH / PRACH RE or Referred to as PDCCH / PCFICH / PHICH / PDSCH / PUCCH / PUSCH / PRACH resource The expression that the user equipment transmits PUCCH / PUSCH / PRACH is hereinafter referred to as uplink control information / uplink on or through PUSCH / PUCCH / PRACH, respectively. It is used in the same sense as transmitting the data / random access signal, and the expression that the gNB transmits the PDCCH / PCFICH / PHICH / PDSCH, respectively, is the downlink data / control information on or through the PDCCH / PCFICH / PHICH / PDSCH. It is used in the same sense as sending it.

Hereinafter, CRS / DMRS / CSI-RS / SRS / UE-RS is assigned or configured (configured) OFDM symbol / subcarrier / RE to CRS / DMRS / CSI-RS / SRS / UE-RS symbol / carrier It is called / subcarrier / RE. For example, an OFDM symbol to which tracking RS (TRS) is assigned or configured is called a TRS symbol, and a subcarrier to which TRS is assigned or configured is called a TRS subcarrier, and to which a TRS is assigned. Or, the configured RE is called a TRS RE. Also, a subframe configured for TRS transmission is called a TRS subframe. In addition, a subframe in which a broadcast signal is transmitted is called a broadcast subframe or a PBCH subframe, and a subframe in which a synchronization signal (eg, PSS and / or SSS) is transmitted is a synchronization signal subframe or a PSS / SSS subframe. It is called. The OFDM symbols / subcarriers / REs to which PSS / SSS is assigned or configured are called PSS / SSS symbols / subcarriers / RE, respectively.

In the present invention, CRS port, UE-RS port, CSI-RS port, and TRS port are antenna ports configured to transmit CRS and antenna ports configured to transmit UE-RS, respectively. Refers to an antenna port configured to transmit CSI-RS and an antenna port configured to transmit TRS. Antenna ports configured to transmit CRSs may be distinguished by CRS ports according to positions of REs occupied by CRSs, and antenna ports configured to transmit UE-RSs are configured to UEs. Depending on the RS ports, UE-RS may be distinguished by location of REs occupied, and antenna ports configured to transmit CSI-RSs are occupied by CSI-RS according to CSI-RS ports. It can be distinguished from each other by the location of REs. Therefore, the term CRS / UE-RS / CSI-RS / TRS port is also used as a term for a pattern of REs occupied by CRS / UE-RS / CSI-RS / TRS within a certain resource region.

1 is a diagram illustrating a control plane and a user plane structure of a radio interface protocol between a terminal and an E-UTRAN based on a 3GPP radio access network standard. The control plane means a path through which control messages used by a user equipment (UE) and a network to manage a call are transmitted. The user plane refers to a path through which data generated at the application layer, for example, voice data or Internet packet data, is transmitted.

The physical layer, which is the first layer, provides an information transfer service to an upper layer using a physical channel. The physical layer is connected to the upper medium access control layer through a transmission channel. Data is moved between the medium access control layer and the physical layer through the transmission channel. Data is moved between the physical layer of the transmitting side and the receiving side through a physical channel. The physical channel uses time and frequency as radio resources. Specifically, the physical channel is modulated by OFDMA (Orthogonal Frequency Division Multiple Access) in the downlink, and modulated by Single Carrier Frequency Division Multiple Access (SC-FDMA) in the uplink.

The medium access control (MAC) layer of the second layer provides a service to a radio link control (RLC) layer, which is an upper layer, through a logical channel. The RLC layer of the second layer supports reliable data transmission. The function of the RLC layer may be implemented as a function block inside the MAC. The packet data convergence protocol (PDCP) layer of the second layer performs a header compression function that reduces unnecessary control information in order to efficiently transmit IP packets such as IPv4 or IPv6 in a narrow bandwidth wireless interface.

The radio resource control (RRC) layer located at the bottom of the third layer is defined only in the control plane. The RRC layer is responsible for controlling logical channels, transmission channels, and physical channels in connection with configuration, re-configuration, and release of radio bearers. The radio bearer means a service provided by the second layer for data transmission between the terminal and the network. To this end, the RRC layer of the terminal and the network exchanges RRC messages with each other. If there is an RRC connection (RRC Connected) between the terminal and the RRC layer of the network, the terminal is in the RRC connected state (Connected Mode), otherwise it is in the RRC idle state (Idle Mode). The NAS (Non-Access Stratum) layer above the RRC layer performs functions such as session management and mobility management.

The downlink transmission channel for transmitting data from the network to the terminal includes a broadcast channel (BCH) for transmitting system information, a paging channel (PCH) for transmitting paging messages, and a downlink shared channel (SCH) for transmitting user traffic or control messages. have. Traffic or control messages of a downlink multicast or broadcast service may be transmitted through a downlink SCH or may be transmitted through a separate downlink multicast channel (MCH). Meanwhile, the uplink transmission channel for transmitting data from the terminal to the network includes a random access channel (RACH) for transmitting an initial control message and an uplink shared channel (SCH) for transmitting user traffic or a control message. Logical channels that are located above the transmission channel and are mapped to the transmission channel include Broadcast Control Channel (BCCH), Paging Control Channel (PCCH), Common Control Channel (CCCH), Multicast Control Channel (MCCH), and Multicast (MTCH). Traffic Channel).

The terminal performs an initial cell search operation such as synchronizing with the base station when the power is turned on or newly enters the cell (S201). To this end, the terminal can receive a primary synchronization channel (P-SCH) and a secondary synchronization channel (Secondary Synchronization Channel; S-SCH) from the base station to synchronize with the base station and obtain information such as cell ID. have. Thereafter, the terminal may acquire a physical broadcast channel from the base station to obtain intra-cell broadcast information. Meanwhile, the UE may check a downlink channel state by receiving a downlink reference signal (DL RS) in an initial cell search step.

After completing the initial cell search, the UE obtains more specific system information by receiving a physical downlink control channel (PDCCH) and a physical downlink control channel (PDSCH) according to information carried on the PDCCH. It can be done (S202).

On the other hand, if the first access to the base station or there is no radio resource for signal transmission, the terminal may perform a random access procedure (RACH) to the base station (steps S203 to S206). To this end, the UE may transmit a specific sequence as a preamble through a physical random access channel (PRACH) (S203 and S205), and receive a response message for the preamble through the PDCCH and the corresponding PDSCH ( S204 and S206). In the case of a contention-based RACH, a contention resolution procedure may be additionally performed.

The UE that has performed the above-described procedure is a PDCCH / PDSCH reception (S207) and a physical uplink shared channel (PUSCH) / physical uplink control channel (Physical Uplink) as a general uplink / downlink signal transmission procedure. Control Channel (PUCCH) transmission (S208) may be performed. In particular, the terminal receives downlink control information (DCI) through the PDCCH. Here, the DCI includes control information such as resource allocation information for the terminal, and formats are different depending on the purpose of use.

Meanwhile, control information that the UE transmits to the base station through the uplink or that the UE receives from the base station includes a downlink / uplink ACK / NACK signal, a channel quality indicator (CQI), a precoding matrix index (PMI), and a rank indicator (RI). ) And the like. In the case of a 3GPP LTE system, the UE may transmit control information such as CQI / PMI / RI described above through PUSCH and / or PUCCH.

3 is a diagram illustrating a structure of a radio frame used in an LTE system.

Referring to FIG. 3, a radio frame has a length of 10 ms (327200 × Ts) and is composed of 10 equally sized subframes. Each subframe has a length of 1 ms and is composed of two slots. Each slot has a length of 0.5 ms (15360 x Ts). Here, Ts represents the sampling time and is represented by Ts = 1 / (15kHz × 2048) = 3.2552 × 10 ⁻⁸ (about 33 ns). The slot includes a plurality of OFDM symbols in the time domain and a plurality of resource blocks (RBs) in the frequency domain. In the LTE system, one resource block includes 12 subcarriers × 7 (6) OFDM symbols. The transmission time interval (TTI), which is a unit time for transmitting data, may be determined in units of one or more subframes. The above-described structure of the radio frame is only an example, and the number of subframes included in the radio frame, the number of slots included in the subframe, and the number of OFDM symbols included in the slot may be variously changed.

4 illustrates the structure of a radio frame used in NR.

In NR, uplink and downlink transmission are composed of frames. The radio frame has a length of 10 ms, and is defined as two 5 ms half-frames (HFs). The half-frame is defined by five 1ms subframes (Subframe, SF). The subframe is divided into one or more slots, and the number of slots in the subframe depends on SCS (Subcarrier Spacing). Each slot includes 12 or 14 OFDM (A) symbols according to a cyclic prefix (CP). Normally, when CP is used, each slot contains 14 symbols. When an extended CP is used, each slot includes 12 symbols. Here, the symbol may include an OFDM symbol (or CP-OFDM symbol) and an SC-FDMA symbol (or DFT-s-OFDM symbol).

In an NR system, OFDM (A) numerology (eg, SCS, CP length, etc.) may be set differently among a plurality of cells merged into one UE. Accordingly, a (absolute time) section of a time resource (eg, SF, slot, or TTI) composed of the same number of symbols (for convenience, collectively referred to as a time unit (TU)) may be set differently between merged cells.

5 illustrates the slot structure of the NR frame. A slot contains multiple symbols in the time domain. For example, in the case of a normal CP, one slot includes 7 symbols, but in the case of an extended CP, one slot includes 6 symbols. The carrier includes a plurality of subcarriers in the frequency domain. Resource block (RB) is defined as a plurality of (eg, 12) consecutive subcarriers in the frequency domain. BWP (Bandwidth Part) is defined as a plurality of contiguous (P) RBs in the frequency domain, and may correspond to one numerology (eg, SCS, CP length, etc.). The carrier may include up to N (eg, 5) BWPs. Data communication is performed through the activated BWP, and only one BWP can be activated for one terminal. Each element in the resource grid is referred to as a resource element (RE), and one complex symbol may be mapped.

6 illustrates the structure of a self-contained slot. In an NR system, a frame is characterized by a self-contained structure in which a DL control channel, DL or UL data, UL control channel, etc. can all be included in one slot. For example, the first N symbols in a slot are used to transmit a DL control channel (hereinafter, DL control region), and the last M symbols in a slot can be used to transmit an UL control channel (hereinafter, UL control region). N and M are each an integer of 0 or more. The resource region (hereinafter, the data region) between the DL control region and the UL control region may be used for DL data transmission or may be used for UL data transmission. As an example, the following configuration may be considered. Each section was listed in chronological order.

1.DL only configuration

2. UL only configuration

3. Mixed UL-DL configuration

-DL area + GP (Guard Period) + UL control area

-DL control area + GP + UL area

* DL area: (i) DL data area, (ii) DL control area + DL data area

* UL area: (i) UL data area, (ii) UL data area + UL control area

The PDCCH may be transmitted in the DL control region, and the PDSCH may be transmitted in the DL data region. PUCCH may be transmitted in the UL control region, and PUSCH may be transmitted in the UL data region. In PDCCH, downlink control information (DCI), for example, DL data scheduling information, UL data scheduling information, and the like may be transmitted. In the PUCCH, uplink control information (UCI), for example, ACK / NACK (Positive Acknowledgement / Negative Acknowledgement) information for DL data, CSI (Channel State Information) information, SR (Scheduling Request) may be transmitted. The GP provides a time gap in the process of the base station and the terminal switching from the transmission mode to the reception mode or the process from the reception mode to the transmission mode. In a subframe, some symbols at a time point of switching from DL to UL may be set as GP.

On the other hand, the NR system considers using a high ultra-high frequency band, that is, a millimeter frequency band of 6 GHz or more, to transmit data while maintaining a high transmission rate to a large number of users using a wide frequency band. In 3GPP, this is called NR, and in the present invention, it will be referred to as NR system in the future. However, the millimeter frequency band has a frequency characteristic in which signal attenuation according to distance is very rapidly due to using a frequency band that is too high. Therefore, the NR system using a band of at least 6 GHz or more narrow beams that solve the problem of reduction in coverage due to the rapid propagation attenuation by collecting and transmitting signal transmission in a specific direction rather than all directions to compensate for the rapid propagation attenuation characteristic Narrow beam) transmission technique is used. However, in the case of service using only one narrow beam, since a range of a single base station is narrowed, the base station collects a plurality of narrow beams and provides a broadband service.

In the millimeter frequency band, that is, in the millimeter wave (mmW) band, the wavelength is shortened, so that it is possible to install a plurality of antenna elements in the same area. For example, a total of 100 antenna elements can be installed in a 2-dimension arrangement at 0.5 lambda (wavelength) intervals on a 5 by 5 cm panel in a 30 GHz band having a wavelength of about 1 cm. Do. Therefore, in mmW, it is considered to increase the coverage by increasing the beamforming gain by using a plurality of antenna elements, or to increase the throughput.

As a method for forming a narrow beam in the millimeter frequency band, a beamforming method in which energy is increased only in a specific direction is mainly considered by transmitting the same signal using an appropriate phase difference to a large number of antennas in a base station or a UE. Such beamforming methods include digital beamforming, which creates a phase difference on a digital baseband signal, analog beamforming, which creates a phase difference using a time delay (ie, cyclic shift) on a modulated analog signal, digital beamforming, and analog beam. And hybrid beamforming using both forming. If a transceiver unit (TXRU) is provided to enable transmission power and phase adjustment for each antenna element, independent beamforming for each frequency resource is possible. However, there is a problem in that it is ineffective in terms of price to install the TXRU on all 100 antenna elements. That is, the millimeter frequency band needs to be used by a large number of antennas to compensate for the rapid propagation attenuation characteristics, and digital beamforming corresponds to the number of antennas, so RF components (eg, digital analog converter (DAC), mixer, power) Since an amplifier (power amplifier, linear amplifier, etc.) is required, there is a problem in that the price of a communication device increases to implement digital beamforming in the millimeter frequency band. Therefore, when a large number of antennas are required, such as a millimeter frequency band, use of an analog beamforming or hybrid beamforming method is considered. The analog beamforming method maps a plurality of antenna elements to one TXRU and adjusts the direction of the beam with an analog phase shifter. This analog beamforming method has a disadvantage in that it can make only one beam direction in the entire band and thus cannot perform frequency selective beamforming (BF). Hybrid BF is a type of digital BF and analog BF, and has a number of B TXRUs less than Q antenna elements. In the case of the hybrid BF, although there are differences depending on the connection scheme of the B TXRU and the Q antenna elements, the direction of beams that can be simultaneously transmitted is limited to B or less.

As mentioned above, digital beamforming processes signals for transmitted or received digital baseband signals, so multiple beams can be used to simultaneously transmit or receive signals in multiple directions, while analog beamforming can transmit or Since beamforming is performed while the received analog signal is modulated, signals cannot be simultaneously transmitted or received in multiple directions beyond a range covered by one beam. Normally, a base station communicates with multiple users at the same time using broadband transmission or multiple antenna characteristics. When the base station uses analog or hybrid beamforming and forms an analog beam in one beam direction, the characteristics of analog beamforming Only users included in the same analog beam direction are forced to communicate. The RACH resource allocation and resource utilization method of the base station according to the present invention, which will be described later, is proposed by reflecting the constraints caused by analog beamforming or hybrid beamforming characteristics.

When multiple antennas are used, a hybrid beamforming technique combining digital beamforming and analog beamforming is emerging. At this time, analog beamforming (or RF beamforming) refers to an operation in which a transceiver (or RF unit) performs precoding (or combining). In hard-beamed beamforming, the baseband unit and the transceiver (or RF unit) perform precoding (or combining), respectively, which results in the number of RF chains and the D / A (or A / D) converter. It has the advantage of being able to achieve performance close to digital beamforming while reducing the number of. For convenience, the hybrid beamforming structure may be represented by N TXRUs and M physical antennas. The digital beamforming for the L data layers to be transmitted by the transmitting end can be represented by an N-by-L matrix, and then the converted N digital signals are converted into analog signals through TXRU and then converted into an M-by-N matrix. The expressed analog beamforming is applied.

In FIG. 7, the number of digital beams is L and the number of analog beams is N. Furthermore, in the NR system, a base station is designed to change the analog beamforming on a symbol-by-symbol basis, and directions for supporting more efficient beamforming to UEs located in a specific area are being considered. Furthermore, when N TXRUs and M RF antennas are defined as one antenna panel, a method of introducing a plurality of antenna panels to which hybrid beamforming independent of each other is applicable is considered in the NR system. When the base station utilizes a plurality of analog beams as described above, since the analog beams advantageous for signal reception may differ for each UE, the base station is applied in a specific slot or subframe (subframe, SF) at least for synchronization signals, system information, and paging. A beam sweeping operation is considered in which a plurality of analog beams to be performed is changed for each symbol so that all UEs have a reception opportunity.

8 is a diagram illustrating a beam sweeping operation for a synchronization signal and system information in a downlink transmission process.

In FIG. 8, a physical resource or physical channel in which system information of the New RAT system is broadcast is referred to as a physical broadcast channel (xPBCH). At this time, analog beams belonging to different antenna panels within one symbol may be simultaneously transmitted, and in order to measure a channel for each analog beam, as shown in FIG. 8, to a specific antenna panel A method for introducing a beam RS (BRS), a reference signal (RS) transmitted for a corresponding single analog beam, has been discussed. The BRS may be defined for a plurality of antenna ports, and each antenna port of the BRS may correspond to a single analog beam. At this time, unlike the BRS, the synchronization signal (Synchronization signal) or xPBCH can be transmitted for all analog beams (Analog beam) included in the analog beam group (Analog beam group) so that any UE can receive well.

9 illustrates a cell of a new radio access technology (NR) system.

Referring to FIG. 9, in the NR system, a method in which a plurality of TRPs constitute one cell is being discussed, unlike a base station forming one cell in a wireless communication system such as LTE. When the cell is configured, even if the TRP serving the UE is changed, seamless communication is possible, and thus the mobility management of the UE is easy.

In the LTE / LTE-A system, PSS / SSS is transmitted omni-direction, whereas gNB applying mmWave rotates the beam direction omni-direction to signal PSS / SSS / PBCH, etc. A method of beamforming and transmitting is considered. In this way, transmitting / receiving a signal while turning the beam direction is called beam sweeping or beam scanning. In the present invention, “beam sweeping” is a transmitter-side action, and “beam scanning” is a receiver-side action. For example, assuming that the gNB can have up to N beam directions, signals such as PSS / SSS / PBCH are transmitted for the N beam directions, respectively. That is, the gNB transmits synchronization signals such as PSS / SSS / PBCH for each direction while sweeping directions that it may have or wants to support. Or, if the gNB can form N beams, several beams may be grouped to form one beam group, and PSS / SSS / PBCH may be transmitted / received for each beam group. At this time, one beam group includes one or more beams. A signal such as PSS / SSS / PBCH transmitted in the same direction may be defined as one SS block, and a plurality of SS blocks may exist in one cell. When a plurality of SS blocks exist, an SS block index may be used to distinguish each SS block. For example, when PSS / SSS / PBCH is transmitted in 10 beam directions in one system, PSS / SSS / PBCH in the same direction may constitute one SS block (SSB). It can be understood that SS blocks are present. In the present invention, the beam index may be interpreted as an SS block index.

Currently, in the standardization of 3GPP Release 16, that is, the NR system, a relay base station is being discussed for the purpose of supplementing coverage holes but reducing wired connections between base stations. This is called IAB (integrated access and backhaul), the Donor gNB (DgNB) transmits a signal to the UE through a relay base station, and a wireless backhaul link (wireless backhaul link) and DgNB for communication between the DgNB and the relay base station or between the relay base stations. And an access link for communication between the UE or the relay base station and the UE.

Signal transmission scenario through IAB is largely divided into two types. The first scenario is an in-band scenario in which the wireless backhaul link and the access link use the same frequency band, and the second scenario is out-band (out-) in which the wireless backhaul link and the access link use different frequency bands. band) scenario. In the first scenario, the interference between the wireless backhaul link and the access link must be dealt with compared to the second scenario, and thus, it may be considered that it is inferior in ease of implementation.

Hereinafter, for convenience of explanation, when RN1 and RN2 exist, RN1 is connected to RN2 through a backhaul link to relay data transmitted and received to RN2, RN1 is referred to as a parent node of RN2, and RN2 is referred to as RN1. Referred to as a child node.

Current NR system standardization assumes that nodes transmit SSB or CSI-RS in a backhaul link in order to perform a discovery procedure. Each IAB node measures or discovers these SSBs or CSI-RSs and feeds them back to the parent node or donor node, and allows the network or intermediate nodes to determine route selection based on the feedback values. do. In addition, when the intermediate nodes are responsible for the route selection, the parent node may transmit a discovery or measured feedback value to the intermediate node, and when the network targets the nodes responsible for the route selection, the parent node The node may transmit a discovery or measured feedback value to the donor node.

In this discovery operation, since IAB nodes assume a half duplex operation that cannot simultaneously transmit and receive, SSB for discovery or SSB transmitted by other nodes when transmitting CSI-RS or The CSI-RS cannot be measured or discovered. In order to solve this, it is necessary to TDM the transmission of the SSB or CSI-RS between nodes, and for this, the transmission pattern for the transmission of the SSB or the CSI-RS is stopped in the middle of the transmission pattern or transmission, and discovery or measurement of the discovery signal of another node is performed. A muting pattern may be required, and methods 1 to 9 below may be considered.

1. Hop based grouping

(A) The transmission period of SSB or CSI-RS is the same for each hop number, but the transmission time offset is different to solve the half duplex problem so that IAB nodes having different offsets can be discovered or measured.

When all of the hops have different transmission time offsets, a network having nodes with a large number of hops may have a problem that a transmission opportunity decreases because the number of time resources to be measured by each node increases. In addition, since there may be very few cases in which IAB nodes having a large number of hops need to be discovered, the number of hops can be grouped to operate by setting the transmission time offset for each group differently. For example, the nodes having even-numbered hops can be divided into groups and the nodes having odd-numbered hops can be divided into groups to operate with different time offsets for each group in two groups.

Referring to FIG. 10, SSB or CSI-RS may be transmitted with different time offsets between even and odd hops to measure or discover each other's discovery signals between even and odd hops.

(B) For every hop (or for every hop group), the same period and different time offsets are signaled for the discovery signal. The network can determine this and signal it by relaying from the donor node to the last IAB node.

Alternatively, it can be inferred from the period and time offset of the discovery signal of the donor node. The period and time offset for the discovery signal for each hop number (or for each group of hop counts) can be determined by calculating the period and time offset of the discovery signal of the donor node from the period and time offset of the discovery signal of the donor node. I can make it.

For example, a relationship function may be constructed with an odd hop group having an offset equal to twice the period between a donor node and an IAB node, and two even hop groups having an offset equal to 10 times the offset between a donor node and an IAB node. In this case, the IAB nodes in the odd hop group have a period twice the period of the donor node, set the same offset as the donor node, and transmit a discovery signal, and the IAB nodes in the even hop group are 2 in the period of the donor node It has a double period, a 10 ms offset from the donor node, and transmits a discovery signal.

This relationship function may be associated with donor nodes and IAB nodes, but also with donor nodes and specific IAB nodes. For example, you can build a relationship function with a single hop IAB node and another IAB node. The relationship function can be defined in advance, and the network can determine and signal from the donor node to the last IAB node. Also, the period and time offset of the donor node may be determined by the network and signaled by relaying from the donor node to the last IAB node.

(C) In the case of hop-based grouping, measurement or discovery is possible between different hops, but there is a problem that measurement or discovery cannot be performed between nodes having the same hop. Therefore, for this case, there is a need for a method of creating and operating resources having different muting patterns between nodes having the same hop and not transmitting a discovery signal (SSB or CSI-RS).

For example, in FIG. 10, odd groups of two groups of odd hops are shown. At the time of muting, a discovery signal of another group may be discovered or measured without transmitting a discovery signal.

Nodes having the same number of hops are grouped in N groups, and a muting period and time offset are signaled for each group. The network can determine this and signal it by relaying from the donor node to the last IAB node. The N groupings may be defined in advance or determined by the network and signaled by relaying from the donor node to the last IAB node.

Alternatively, N groups having the same number of hops are grouped to signal the same period and different time offsets for each group. These time offsets give a different value to the number of hops (or per group of hops) than the time offset already given, resulting in a different number of hops and different groups within the hops (or different groups of hops and different groups within the hops group). It can be operated at different time offsets. The time offset value at this time can be determined by the network and signaled by relaying from the donor node to the last IAB node. The time offset value at this time can be operated by building a relationship function with the time offset value of the discovery signal of the donor node. The relationship function may be defined in advance or may be determined by the network and signaled by relaying from the donor node to the last IAB node. In addition, the period and time offset of the donor node may be determined by the network and signaled by relaying from the donor node to the last IAB node. The N groupings may be defined in advance or determined by the network and signaled by relaying from the donor node to the last IAB node.

Alternatively, if the periodicity and the offset time point indicating the measurement in the SMTC (SS / PBCH Block Measurement Time Configuration) is a time point at which the discovery signal is transmitted, muting may be performed at that moment, and a discovery signal of another node may be discovered or measured.

(D) SMTC can be set up to 2 with the same time offset and different periods. When operating hop-based grouping, the measurement timing is different from the measurement period (when muting by grouping nodes with the same number of hops) It may be different from the measurement period for nodes with numbers.) And several SMTCs with different offsets must be set. Alternatively, rather than setting multiple SMTCs, one SMTC's period and offset can be set, and among these, in addition to the timings it transmits, SMTC resources can be measured or discovered. The period and offset of this one SMTC also points to the transmission period and offset of the discovery signal of the donor node (or of a specific IAB node, e.g. of a single hop node), and the IAB nodes (or specific IAB node) of the donor node. For example, by establishing the relative relationship function (see above) and the period and offset of the discovery signal (of a single hop node), it is possible to point to both the transmission pattern, the muting pattern, and the period and offset of the SMTC only by setting the SMTC.

2. Random pattern transmission

(a) The transmission pattern of the discovery signal is random for each IAB node, so that the half duplex problem can be solved.

The random pattern includes the cell IDs of the IAB nodes in the initialization function to generate a pseudo random sequence, muting whether to transmit by 0 and 1 for each discovery opportunity (opportunity) to be described later, and performing discovery or measurement. You can decide to do it. The random pattern may be initialized every predetermined time, and the predetermined time may be predefined or determined by the network, and signaled by relaying from the donor node to the last IAB node.

Provides that N (or N or less than 1 or more) discovery signals are transmitted (or muted) for every M discovery opportunities (described later), and the network determines M and N to relay from the donor node to the last IAB node. Can be signaled. Each IAB node can randomly select transmission (or muting) under this pattern rule. When defining a random sequence, there may be groups in which half duplex is not well resolved between IAB nodes. For example, it is possible to measure or discover each other's discovery signals with very few opportunities over a period of time. When this node relationship is with a very close node of the IAB node, a situation in which it is difficult to select the node may occur due to a half duplex problem. In order to solve all of these situations, it may be a difficult problem to create a random sequence among nodes, so M and N can be simply set, and based on this, each IAB node can make a random transmission or muting pattern to use. In this case, the parent node can operate without a problem with the RRM measurement by discarding N of the small values among the M of feedbacked RRM measurements and processing them as valid values for route selection.

The transmission may occur continuously only by the rule that N discovery signals are transmitted for every M discovery opportunities, or muting may occur continuously, and measurement or discovery of specific IAB nodes may be unevenly performed in a certain period. In order to prevent this, it is possible to stipulate that no more than N2 muting is continuously transmitted. (N2 value can be defined in advance, or the network can decide and relay from the donor node to the last IAB node to signal.) N2 value can be defined as M / (c * N) using M and N. have. (You can define or set the c value in advance to adjust the frequency of successive transmissions or mutings.)

Under the rule of transmitting N discovery signals for every M discovery opportunities, a donor node or a parent node or each IAB node may need to know the pattern in terms of creating a Tx / muting pattern of IAB nodes. This is because, in relation to the measurement performance, when it is unclear whether the measurement result at which timing is the result of transmission or the result of not transmission, there is a problem in interpreting the measurement result.

Therefore, this can be solved using a pseudo-random sequence. If the pseudo-random sequence is c (i), the initialization of the pseudo-random sequence generator is performed periodically at a specific time (for example, every L frame number), and the sequence generated by the pseudo-random sequence generator is log ₂ N Cut every bit. Each truncated value may be mapped to a value of 0 to N-1, and M truncated values may be selected to indicate a timing of discovery signal transmission among N opportunities. (If the same value among M is mixed, you can select the next value again to create different M values. In this case, the overlapped values are unused values. You can include the value unconditionally, or you can have a higher probability of being randomly selected again, which makes it possible for M selected numbers to occur evenly, assuming that 0 and 1 of the PN sequence are equal.) When approaching, the initialization of the pseudo-random sequence generator is the cell ID (or cell group ID, virtual cell ID, which has a different pattern for each cell or cell group, which is why IAB nodes try to solve the half duplex problem), Alternatively, a frame number or a slot number may be included.

The above approach can also be conducted from a muting point of view. It is possible to make this by using a pseudo-random sequence under the rule that the measurement timing comes out of MN discovery signals (MN can be directly set) for every M discovery opportunities. If the pseudo-random sequence is c (i), the initialization of the pseudo-random sequence generator is performed periodically at a specific time (for example, every L frame number), and the sequence generated by the pseudo-random sequence generator is log ₂ N Cut every bit. Each truncated value may be mapped to a value from 0 to N-1, and MN truncated values may be selected to indicate the timing of discovery signal measurement (or muting) among N opportunities.

If the same value among MN is mixed, the next value can be selected again to create different MN values, and in this case, the overlapped values are unused values. You can include it unconditionally, or you can make it more likely that it will be randomly selected again. This serves to ensure that M-N selected numbers are generated evenly, assuming that 0 and 1 of the PN sequence appear evenly. In this approach, the initialization of the pseudo-random sequence generator has a reason to solve the half duplex problem among IAB nodes by having different patterns for each cell ID (or cell group ID, virtual cell ID, cell or cell group). ), Or a frame number or a slot number.

Another approach is to create a pattern so that the timing of muting or measurement does not occur continuously. If the pseudo-random sequence is c (i), the initialization of the pseudo-random sequence generator is performed periodically at a specific time (for example, every L frame number), and the sequence generated by the pseudo-random sequence generator is log ₂ N Cut every bit. Each truncated value can be mapped to a value from 0 to N-1, which points to the muting moment. For example, the truncated value may mean the length from the previous muting timing to the next muting timing among N opportunities. Therefore, if the truncated values point to 3, 5, and 1 sequentially, the interval between muting timings points to 3, 5, and 1 in sequence. In this approach, the initialization of the pseudo-random sequence generator has a reason to solve the half duplex problem among IAB nodes by having different patterns for each cell ID (or cell group ID, virtual cell ID, cell or cell group). ), Or a frame number or a slot number.

Another approach is a hierarchical structure. A pseudo-random sequence is used first under the rule that a measurement timing (or muting timing) comes out for each M discovery opportunity. If the pseudo-random sequence is c (i), the initialization of the pseudo-random sequence generator is performed periodically at a specific time (for example, every L frame number), and the sequence generated by the pseudo-random sequence generator is log ₂ N Cut every bit.

The bits of each truncated value are b0, b1, b2… When it is made of .., the first bit indicates whether the N opportunity means the front N / 2 opportunity or the back N / 2 opportunity. The next bit indicates whether the first bit represents N / (2 * 2) opportunities in the front or N / (2 * 2) opportunities in the back among the N / 2 opportunities pointed to by the first bit. Again, the next bit represents the N / (2 * 2 * 2) opportunities in the front of the N / (2 * 2) opportunities pointed to by the second bit, and the N / (2 * 2 * 2) in the back You will be taught whether it means dog chance. In this way, each bit of the N chances sequentially means half chances from the previously selected chances, using log ₂ N bits until one chance remains, and defining the last one chance as muting or measurement timing. can do. In this approach, the initialization of the pseudo-random sequence generator has a reason to solve the half duplex problem among IAB nodes by having different patterns for each cell ID (or cell group ID, virtual cell ID, cell or cell group). ), Or a frame number or a slot number.

Referring to FIG. 11, if N = 8, log ₂ 8 is taken and configured as 3 bits, and it is assumed that this value is 010. Then, if 0 points to the front, and 1 points to the back, the first bit points to the first 4 opportunities, and the second bit points to the second 2 of the previous 4 opportunities. The third beat points to the first one out of the two previous ones. It can be said that it is measured at this timing.

The network may determine a period and time offset for a discovery opportunity point in time at which the discovery signal can be transmitted, and relay and signal from the donor node to the last IAB node. This discovery opportunity may have different values for each group of IAB nodes, or a donor node (or a specific IAB node, for example, an IAB node with cell ID a) has a period and offset of one discovery opportunity, It can be operated in a way that all IAB nodes share the same value.

It is possible to perform measurement or discovery without transmitting SSB or CSI-RS only at the time of muting at the period and time offset point to be measured in SMTC. The period and time offset to be measured of the SMTC mean a discovery opportunity time point in which a discovery signal can be transmitted, so that a separate transmission pattern may not be signaled. Alternatively, if the period and time offset to be measured are set by the SMTC, it is automatically recognized as a discovery opportunity, and according to a transmission / muting random pattern, if it is '1', it can be operated by muting if it is '1'. Through this, only SMTC provides a discovery opportunity for transmission and muting, thereby reducing signaling overhead.

3. Pre-defined or pre-determined by donor node

The network can be defined or determined in advance to inform the entire node whether to transmit or mute the discovery signal for every N discovery opportunities (described later). These values are signaled by relaying from the donor node to the last IAB node. This value can be signaled by mapping transmission and muting to 0 and 1 of each bit using an N bit sequence for each node. This method may have a signaling overhead.

The network may determine a period and time offset for a discovery opportunity point in time at which the discovery signal can be transmitted, and relay and signal from the donor node to the last IAB node. (This can mean a muting pattern.)

It is possible to perform measurement or discovery without transmitting SSB or CSI-RS only at the time of muting at the period and time offset point to be measured in SMTC. The period and time offset to be measured of the SMTC mean a discovery opportunity time point in which a discovery signal can be transmitted, so that a separate transmission pattern may not be signaled. Alternatively, if the period and time offset to be measured are set in the SMTC, it is automatically recognized as a discovery opportunity, and according to the transmission / muting pattern, if it is '1', it can be operated by muting if it is '1' and muting it is '0'. Through this, only SMTC provides a discovery opportunity for transmission and muting, thereby reducing signaling overhead.

4. Hop-based cycle determination

As the number of hops increases, the discovery signal transmission period increases, and the time offset may use the same value to measure or discover a discovery signal of a node having fewer hops than itself.

Referring to FIG. 12, the period of a discovery signal between a donor node and a single hop node is 2 times different, and the period of a discovery signal between a single hop node and a second hop node is again 2 times different, so that the single hop nodes are donors. It is possible to secure a time point at which the discovery signal of the node can be viewed, and second hop nodes can secure a time point at which the discovery signal of single hop nodes can be viewed.

The network may determine the period and time offset of the discovery signal of the donor node or single hop nodes, and relay and signal from the donor node to the last IAB node. Each IAB node multiplies the discovery signal period by the number of hop differences according to its number of hops, so that it can know the transmission period of its discovery signal. The time offset assumes the same value as the value reported.

A discovery signal of a node having a small number of hops among different hops can be measured or discovered, but there is a problem that a measurement or discovery cannot be performed between nodes having the same hop. Therefore, for this case, there is a need for a method of creating and operating resources having different muting patterns between nodes having the same hop and not transmitting a discovery signal (SSB or CSI-RS). For example, in FIG. 12, the first hops are grouped in two groups to show different muting patterns. At the time of muting, a discovery signal of another group may be discovered or measured without transmitting a discovery signal.

Alternatively, N groups having the same number of hops are grouped to signal the same period and different time offsets for each group. The time offset value may be determined by the network and signaled by relaying from the donor node to the last IAB node. The N groupings may be defined in advance or determined by the network and signaled by relaying from the donor node to the last IAB node.

Alternatively, if the period and offset point of time at which the SMTC points to the measurement is a time point at which the discovery signal is transmitted, muting may be performed at that moment, and a discovery signal of another node may be detected or measured.

It is possible to perform measurement or discovery without transmitting SSB or CSI-RS only at the time of muting at the period and time offset point to be measured in SMTC. The period and time offset of the SMTC to be measured mean the period and time offset of the discovery signal of the donor node or single hop nodes, so that transmission pattern signaling may not be performed separately.

5. Special case of hop-based grouping

There is a method of dividing nodes into 4 groups and operating the discovery signal with the same period and different time offsets in each group. This is due to the fact that the IAB node has a high probability that only its parent node and the node closest to both sides of the parent node and a node close to both sides having the same number of hops as itself are almost the target of discovery or measurement.

Therefore, a group is created between even and odd hops, and two groups are placed between the same hops, so that the groups are geographically alternated on the same hop, and a total of four groups are created to operate a discovery signal with the same period and different time offset. You can do it.

Referring to FIG. 13, the even and odd hops basically have different time offsets, and even within the same hop, a discovery signal is operated with a total of four time offsets by having different time offsets from nodes next to each other geographically. In this way, one IAB node can discover or measure its parent node, nodes next to the parent node, and nodes next to it.

In addition to the even-hop group and the odd-hop group, nodes having the same number of hops are alternately geographically formed to form two more groups (refer to FIG. 13), and a discovery signal for each group (or for each group of hops) out of a total of four groups For this, the same period and different time offsets are signaled. The network can determine this and signal it by relaying from the donor node to the last IAB node.

Alternatively, it can be inferred from the period and time offset of the discovery signal of the donor node. The period and time offset for the discovery signal for each of the four groups may be calculated by calculating the period and time offset of the discovery signal of the donor node from the period and time offset of the discovery signal of the donor node. The relationship function may be defined in advance, or the network may determine and relay the signaling from the donor node to the last IAB node. Also, the period and time offset of the donor node may be determined by the network and signaled by relaying from the donor node to the last IAB node. The N groupings may be defined in advance or determined by the network and signaled by relaying from the donor node to the last IAB node.

Alternatively, the measurement or discovery may be performed only at a time point in which the period to be measured and the time offset time in the SMTC are not the transmission time of the discovery signal. The period and time offset of the SMTC to be measured means a period and time offset for transmitting the discovery signal of the donor node, and may not separately signal the period and time offset of the discovery signal of the donor node. Alternatively, if the period and time offset to be measured are set by the SMTC, it is automatically recognized as a discovery opportunity, and according to a transmission / muting random pattern, if it is '1', it can be operated by muting if it is '1'. Through this, only SMTC provides a discovery opportunity for transmission and muting, thereby reducing signaling overhead.

6. Grouping

In order to solve the half duplex problem for the discovery procedure, the discovery signal must be transmitted with different offsets or periods for each IAB node. Setting different offsets and periods for all IAB nodes under one donor node can actually be inefficient. Accordingly, IAB nodes are grouped through grouping, and each group has the same period and offset, but has different periods and offsets between groups, so that half-duplex problems can be solved between groups. If it has the principle that it only needs to look at geographically close nodes, it can be operated in four groups as shown in FIG. 13 described above.

When N groups are grouped, information about grouping is determined by a network, and a donor node can relay and signal to the last IAB node. When signaling information about grouping, for each node, it can be known which group of N belongs to, but it can be operated by notifying each group of IAB nodes with which hops are mapped. The discovery signal period and offset may be signaled for each group, but muting is performed at '0', measurement or discovery is performed at each discovery opportunity through a bitmap having an aperiodic pattern for each group, and transmission is performed at '1'. You can do it. Such a bitmap is not signaled separately, but may be generated through a random sequence.

In addition, SSB or CSI-RS transmission is not performed only at the time of muting at the period and time offset point to be measured in SMTC, and measurement or discovery can be performed. The period and time offset of the SMTC to be measured means a discovery opportunity point at which a discovery signal can be transmitted. If it is '1', it can be transmitted and muted to operate. Through this, only SMTC provides a discovery opportunity for transmission and muting, thereby reducing signaling overhead.

7. Diversifying patterns from a geographical perspective

For the above-described discovery procedure, the SSB transmission pattern or muting pattern may be different between at least geographically close IAB nodes to solve the half duplex problem at least in terms of discovery or measurement between close IAB nodes.

When grouping to give the same SSB transmission pattern or muting pattern (different SSB transmission patterns or muting patterns between groups) is geographically approached, at least adjacent IAB nodes belong to different groups, and thus different SSB transmission patterns or muting It should be possible to discover or measure each other with patterns.

If you specify how to group by geographic approach, you can start with four rectangles (rectangular or square). The entire area to be grouped is grouped into squares (rectangular or square) of the same size and shape adjacent to each other. Adjust the size, shape and number of rectangles so that up to 4 IAB nodes are located in each rectangle.

Referring to FIG. 14, assuming that the entire area is divided into 8 squares and the circle is an IAB node, it can be seen that there are no more than 4 IAB nodes in each square. At this time, it can be assumed that the meaning of the IAB node included in each rectangle belongs to a rectangle in which the IAB node occupies a larger portion when the IAB nodes overlap between adjacent rectangles.

Then, each square is divided into four adjacent squares (square or rectangle). The 4 squares should be 2 horizontally and 2 vertically. (This is because, in general, in order to have different groups between adjacent nodes, it must be different groups in at least two directions.) For example, the square of FIG. 14 can be divided into four squares of the same shape in FIG. 15.

Thereafter, each of the up to four IAB nodes in each quadrangle divided by four maps one of the four rectangles closest to the center of the rectangle. If more than one IAB node is mapped to one rectangle, only the IAB node closer to the center of the rectangle is mapped, and the unmapped IAB node is placed next to the rectangle. And again, when there are more than one IAB node in one square, the operation is repeated, so that only one IAB node is mapped to one square. All IABs mapped to each of the four rectangles belong to different groups. Since the four squares are repeated, the groups appear repeatedly in the same form. For example, groups of A, B, C, and D are repeated on the four squares of FIG. 15 to fit as shown in FIG. 16. When this happens, it can be seen that adjacent rectangles always belong to different groups.

Overall, they are grouped according to the following procedure.

-Group the entire area to be grouped into squares (rectangular or square) of the same size and shape adjacent to each other.

-Adjust the size, shape and number of rectangles so that up to 4 IAB nodes are located in each rectangle.

-Each square is divided into 4 adjacent squares (square or rectangle). The 4 squares should be 2 horizontally and 2 vertically.

-Each of up to four IAB nodes in each square divided by four maps one of the four squares closest to the center of the square. If more than one IAB node is mapped to one rectangle, only the IAB node closer to the center of the rectangle is mapped, and the unmapped IAB node is placed next to the rectangle. And again, when there are more than one IAB node in one square, the operation is repeated, so that only one IAB node is mapped to one square.

-All IABs mapped to each of the 4 squares belong to different groups. Since the four squares are repeated, the groups appear repeatedly in the same form.

The above was explained under the assumption that it is divided into 4 squares, and if it is increased to N, it is as follows.

1) Group the entire area to be grouped into squares (rectangular or square) of the same size and shape.

2) Adjust the size, shape, and number of rectangles so that up to x * y = N IAB nodes are located in each rectangle.

3) Each rectangle is divided into x * y = N adjacent rectangles (square or rectangle). The N squares are formed to be x horizontal and y vertical. (x, y is an integer greater than or equal to 2)

4) Each of up to N IAB nodes in each rectangle divided by N maps one of N rectangles closest to the center of the rectangle. If more than one IAB node is mapped to one rectangle, only the IAB node closer to the center of the rectangle is mapped, and the unmapped IAB node is placed next to the rectangle. And again, if there are more than one IAB node in one square, the operation is repeated, so that only one IAB node is mapped to one square.

5) All IABs mapped to N squares belong to different groups. Since N squares are repeated, the groups appear repeatedly in the same form.

8. Network pattern setting

Unlike the above 1 to 7, the transmission / reception pattern of each IAB node can be designed to be determined and set by the network according to a deployment scenario or operation. That is, the network is free to set the pattern.

1) Transmission settings

-Set the period of discovery signal transmission opportunity

-Set the unit of transmission / reception pattern as N based on transmission opportunity

-Set A, which is the number of transmissions of discovery among N transmission opportunities (meaning that A is transmitted among N opportunities)

-Set the transmit / receive pattern according to the set N and A. (Set N bit, but 1 for each bit and 0 for the measurement timing.)

When A = 1, log ₂ N bits can be selected among N. For example, when N = 4, one of 1000, 0100, 0010, 0001 is set as a pattern. When the period of the transmission opportunity set above is 20ms, N = 4, so the SSB is transmitted once every 80ms, and the measurement is performed every 20ms, but the timing of transmission is not measured.

When A = N-1, select among N with log ₂ N bits. For example, when N = 4, one of 0111, 1011, 1101, and 1110 is set as a pattern. When the period of the transmission opportunity set above is 20ms, N = 4, so the SSB is transmitted once every 20ms, and the SSB is not transmitted every 80ms, and measurement is performed.

When A = 2 to N-2, select from (N combination A) bits with log ₂ (N combination A) bits. Depending on the value of (N combination A), a bit of a large unit may be required, so the value of A that can be selected can be limited.

Alternatively, a random pattern may be set. When the random pattern is set, A is transmitted out of N opportunities, but is randomly selected. In this case, it can be defined in advance to use one of the two random patterns.

2) Reception setting

The measurement period is set, and measurement is performed for each measurement period. When the transmission pattern value is '1', the measurement signal is transmitted without measurement. SMTC can be reused by default for reception settings.

In the transmission setting and the reception setting, it is assumed that the measurement period and the discovery opportunity period are the same, and the period can be set only in one of the transmission setting and reception setting.

Basically, the network specifies the Tx / Rx pattern with '1' and '0', but it sets how many of the discovery opportunities are transmitted through N and A. This is to enable the pattern to be selected. For example, if A = 1, or A = N-1, simply have an offset, it means that IAB nodes want to solve the half duplex, and if 2 ~ N-2, the network can flexibly pattern the It means to take a picture, and when selecting a random pattern, it is to easily operate the pattern in a situation where many IAB nodes discover each other.

9. Sharing between cycle IAB nodes

In terms of transmitting the discovery signal, a P1 period is set, and it may be assumed that all IAB nodes share. The offset value may be set differently for each IAB node. Then set the P2 period and offset on the SMTC side (ie, on the measurement or discovery side). In addition, it is assumed that an operation having a long period (that is, discovery signal transmission or discovery signal measurement) takes precedence among P1 and P2.

In terms of measurement, it is assumed that all IAB nodes transmit in the same period P1. Cells found at the timing of measurement based on P1 and P2 can know at what period they will be transmitted, so IAB nodes will not be confused to measure. In particular, this method avoids multi-transmission setup / multi-reception setup for signaling overhead in terms of measuring nodes that perform aperiodic transmission to solve the half duplex problem, and one SMTC setup that points to one periodic measurement. This is to easily solve with only one transmission setting indicating periodic transmission.

Some or all of the above-mentioned contents 1 to 9 may be used together. In addition, when measuring a discovery signal in the above 1 to 9, it is basically the same as the concept of the current RRM measurement, and it is assumed that the IAB node knows the target of the cell to measure. In addition, in the discovery of the above 1 to 9, the discovery signal of the discovery signal is matched with a sequence or signal without the IAB node knowing the target of the cell to be discovered, and, for example, RSRP exceeds a certain threshold. ) It means finding nodes.

17 illustrates an embodiment of a wireless communication device according to an embodiment of the present invention.

The wireless communication device illustrated in FIG. 17 may represent a terminal and / or a base station according to an embodiment of the present invention. However, the wireless communication device of FIG. 11 is not necessarily limited to the terminal and / or base station according to the present embodiment, and may be replaced with various devices such as a vehicle communication system or device, a wearable device, a laptop, and a smart phone. Can be. More specifically, the device is a base station, a network node, a transmitting terminal, a receiving terminal, a wireless device, a wireless communication device, a vehicle, a vehicle equipped with an autonomous driving function, a drone (Unmanned Aerial Vehicle, UAV), AI (Artificial Intelligence) Modules, robots, Augmented Reality (AR) devices, Virtual Reality (VR) devices, MTC devices, IoT devices, medical devices, fintech devices (or financial devices), security devices, climate / environment devices, or any other 4th industrial revolution It may be a field or a device related to 5G service. For example, a drone may be a vehicle that does not ride and is flying by radio control signals. For example, the MTC device and the IoT device are devices that do not require direct human intervention or manipulation, and may be smart meters, bending machines, thermometers, smart bulbs, door locks, and various sensors. For example, a medical device is a device used for the purpose of diagnosing, treating, reducing, treating or preventing a disease, a device used for examining, replacing or modifying a structure or function, medical equipment, surgical device, ( In vitro) diagnostic devices, hearing aids, surgical devices, and the like. For example, a security device is a device installed to prevent a risk that may occur and to maintain safety, and may be a camera, CCTV, black box, or the like. For example, a fintech device is a device that can provide financial services such as mobile payment, and may be a payment device, point of sales (POS), or the like. For example, a climate / environmental device may mean a device that monitors and predicts the climate / environment.

In addition, the transmitting terminal and the receiving terminal are mobile phones, smart phones (smart phones), laptop computers (laptop computers), digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), navigation, slate PCs , Tablet PC (tablet PC), ultrabook (ultrabook), wearable device (wearable device, for example, a watch-type terminal (smartwatch), glass-type terminal (smart glass), HMD (head mounted display)), foldable ( foldable) devices. For example, the HMD is a display device in a form worn on the head, and may be used to implement VR or AR.

Referring to FIG. 17, a terminal and / or a base station according to an embodiment of the present invention includes at least one processor 10, a transceiver 35, such as a digital signal processor (DSP) or a microprocessor, Power management module 5, antenna 40, battery 55, display 15, keypad 20, memory 30, subscriber identification module (SIM) card 25, speaker 45 and microphone ( 50). In addition, the terminal and / or the base station may include a single antenna or multiple antennas. Meanwhile, the transceiver 35 may also be referred to as a radio frequency module (RF module).

The processor 10 may be configured to implement the functions, procedures and / or methods described in the present invention. In at least some of the embodiments described in the present invention, the processor 10 may implement one or more protocols, such as layers of a radio interface protocol (eg, functional layers).

The memory 30 is connected to the processor 10 and stores information related to the operation of the processor 10. The memory 30 may be located inside or outside the processor 10, and may be connected to the processor through various technologies such as wired or wireless communication.

The user can input various types of information (for example, instruction information such as a telephone number) by pressing a button of the keypad 20 or by various techniques such as voice activation using the microphone 50. The processor 10 performs appropriate functions such as receiving and / or processing user information and dialing a telephone number.

In addition, data (eg, operation data) may be retrieved from the SIM card 25 or the memory 30 to perform the appropriate functions. In addition, the processor 10 may receive and process GPS information from a GPS chip to obtain location information of terminals and / or base stations such as vehicle navigation and map services, or perform functions related to location information. In addition, the processor 10 may display various types of information and data on the display 15 for the user's reference and convenience.

The transceiver 35 is connected to the processor 10 to transmit and / or receive a radio signal such as a radio frequency (RF) signal. At this time, the processor 10 may control the transceiver 35 to initiate communication and transmit wireless signals including various types of information or data such as voice communication data. The transceiver 35 may include a receiver that receives a radio signal and a transmitter that transmits it. The antenna 40 facilitates transmission and reception of radio signals. In some embodiments, upon receiving a wireless signal, the transceiver 35 may forward and convert the signal to a baseband frequency for processing by the processor 10. The processed signal can be processed according to various techniques, such as being converted into audible or readable information, and the signal can be output through the speaker 45.

In some embodiments, sensors may also be connected to the processor 10. The sensor may include one or more sensing devices configured to detect various types of information including speed, acceleration, light, vibration, and the like. By receiving and processing sensor information obtained from a sensor, such as proximity, location, and image, various functions such as collision avoidance and autonomous driving can be performed.

Meanwhile, various components such as a camera and a USB port may be additionally included in the terminal and / or the base station. For example, a camera may be further connected to the processor 10, and such a camera may be used for various services such as autonomous driving and vehicle safety services.

As such, FIG. 17 is not limited to this, as long as it is only an embodiment of devices constituting a terminal and / or a base station. For example, some components, such as keypad 20, Global Positioning System (GPS) chip, sensor, speaker 45 and / or microphone 50, may be excluded for terminal and / or base station implementation in some embodiments. It might be.

18 shows an AI device 100 capable of implementing embodiments of the present invention.

The AI device 100 is a TV, projector, mobile phone, smartphone, desktop computer, laptop, digital broadcasting terminal, PDA (personal digital assistants), PMP (portable multimedia player), navigation, tablet PC, wearable device, set-top box (STB) ), DMB receivers, radios, washing machines, refrigerators, desktop computers, digital signage, robots, vehicles, and the like.

Referring to FIG. 18, the terminal 100 includes a communication unit 110, an input unit 120, a running processor 130, a sensing unit 140, an output unit 150, a memory 170, a processor 180, and the like. It can contain.

The communication unit 110 may transmit and receive data to and from external devices such as other AI devices 100a to 100e or the AI server 200 using wired / wireless communication technology. For example, the communication unit 110 may transmit and receive sensor information, a user input, a learning model, a control signal, etc. with external devices.

At this time, the communication technology used by the communication unit 110 includes Global System for Mobile Communication (GSM), Code Division Multi Access (CDMA), Long Term Evolution (LTE), 5G, Wireless LAN (WLAN), Wireless-Fidelity (Wi-Fi). ), Bluetooth ™, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), ZigBee, Near Field Communication (NFC), and the like.

The input unit 120 may acquire various types of data.

At this time, the input unit 120 may include a camera for inputting a video signal, a microphone for receiving an audio signal, a user input unit for receiving information from a user, and the like. Here, the camera or microphone is treated as a sensor, and the signal obtained from the camera or microphone may be referred to as sensing data or sensor information.

The input unit 120 may acquire training data for model training and input data to be used when obtaining an output using the training model. The input unit 120 may obtain raw input data. In this case, the processor 180 or the learning processor 130 may extract input features as pre-processing of the input data.

The learning processor 130 may train a model composed of artificial neural networks using the training data. Here, the trained artificial neural network may be referred to as a learning model. The learning model can be used to infer a result value for new input data rather than learning data, and the inferred value can be used as a basis for determining to perform an action.

At this time, the learning processor 130 may perform AI processing together with the learning processor 240 of the AI server 200.

At this time, the learning processor 130 may include a memory integrated or implemented in the AI device 100. Alternatively, the learning processor 130 may be implemented using memory 170, external memory directly coupled to the AI device 100, or memory maintained in the external device.

The sensing unit 140 may acquire at least one of AI device 100 internal information, AI device 100 environment information, and user information using various sensors.

At this time, the sensors included in the sensing unit 140 include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and a lidar. , And radar.

The output unit 150 may generate output related to vision, hearing, or tactile sense.

At this time, the output unit 150 may include a display unit for outputting visual information, a speaker for outputting auditory information, a haptic module for outputting tactile information, and the like.

The memory 170 may store data supporting various functions of the AI device 100. For example, the memory 170 may store input data, learning data, learning models, learning history, etc. acquired by the input unit 120.

The processor 180 may determine at least one executable action of the AI device 100 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. Also, the processor 180 may control components of the AI device 100 to perform a determined operation.

To this end, the processor 180 may request, search, receive, or utilize data of the learning processor 130 or the memory 170, and perform an operation that is predicted or determined to be preferable among the at least one executable operation. It is possible to control the components of the AI device 100 to execute.

At this time, the processor 180 may generate a control signal for controlling the corresponding external device, and transmit the generated control signal to the corresponding external device when it is necessary to link the external device to perform the determined operation.

The processor 180 may acquire intention information for a user input, and determine a user's requirement based on the obtained intention information.

At this time, the processor 180 uses at least one of a Speech To Text (STT) engine for converting voice input into a string or a Natural Language Processing (NLP) engine for acquiring intention information of natural language, and a user Intention information corresponding to an input may be obtained.

At this time, at least one of the STT engine or the NLP engine may be configured as an artificial neural network at least partially learned according to a machine learning algorithm. And, at least one or more of the STT engine or the NLP engine is learned by the learning processor 130, learned by the learning processor 240 of the AI server 200, or learned by distributed processing thereof May be

The processor 180 collects history information including the user's feedback on the operation content or operation of the AI device 100 and stores it in the memory 170 or the running processor 130, or the AI server 200, etc. Can be sent to external devices. The collected history information can be used to update the learning model.

The processor 180 may control at least some of the components of the AI device 100 to drive an application program stored in the memory 170. Furthermore, the processor 180 may operate by combining two or more of the components included in the AI device 100 with each other to drive the application program.

19 shows an AI server 200 capable of implementing embodiments of the present invention.

Referring to FIG. 19, the AI server 200 may refer to an apparatus for learning an artificial neural network using a machine learning algorithm or using a trained artificial neural network. Here, the AI server 200 may be composed of a plurality of servers to perform distributed processing, or may be defined as a 5G network. At this time, the AI server 200 is included as a configuration of a part of the AI device 100, and may perform at least a part of AI processing together.

The AI server 200 may include a communication unit 210, a memory 230, a running processor 240 and a processor 260.

The communication unit 210 may transmit and receive data with an external device such as the AI device 100.

The memory 230 may include a model storage unit 231. The model storage unit 231 may store a model (or artificial neural network, 231a) being trained or trained through the learning processor 240.

The learning processor 240 may train the artificial neural network 231a using learning data. The learning model may be used while being mounted on the AI server 200 of the artificial neural network, or may be mounted and used on an external device such as the AI device 100.

The learning model can be implemented in hardware, software, or a combination of hardware and software. When part or all of the learning model is implemented in software, one or more instructions constituting the learning model may be stored in the memory 230.

The processor 260 may infer the result value for the new input data using the learning model, and generate a response or control command based on the inferred result value.

20 shows an AI system 1 according to which embodiments of the present invention can be implemented.

Referring to FIG. 20, the AI system 1 includes at least one of an AI server 200, a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e. It is connected to the cloud network 10. Here, the robot 100a to which AI technology is applied, the autonomous vehicle 100b, the XR device 100c, the smartphone 100d, or the home appliance 100e may be referred to as AI devices 100a to 100e.

The cloud network 10 may form a part of the cloud computing infrastructure or may mean a network existing in the cloud computing infrastructure. Here, the cloud network 10 may be configured using a 3G network, a 4G or a Long Term Evolution (LTE) network, or a 5G network.

That is, each device (100a to 100e, 200) constituting the AI system 1 may be connected to each other through the cloud network (10). In particular, the devices 100a to 100e and 200 may communicate with each other through a base station, but may communicate with each other directly without passing through the base station.

The AI server 200 may include a server performing AI processing and a server performing operations on big data.

The AI server 200 includes at least one or more among robots 100a, autonomous vehicles 100b, XR devices 100c, smart phones 100d, or home appliances 100e, which are AI devices constituting the AI system 1. It is connected through the cloud network 10 and can assist at least some of the AI processing of the connected AI devices 100a to 100e.

At this time, the AI server 200 may train the artificial neural network according to the machine learning algorithm in place of the AI devices 100a to 100e, and may directly store the learning model or transmit it to the AI devices 100a to 100e.

At this time, the AI server 200 receives input data from the AI devices 100a to 100e, infers a result value to the received input data using a learning model, and issues a response or control command based on the inferred result value. It can be generated and transmitted to AI devices 100a to 100e.

Alternatively, the AI devices 100a to 100e may infer a result value with respect to input data using a direct learning model and generate a response or control command based on the inferred result value.

Hereinafter, various embodiments of the AI devices 100a to 100e to which the above-described technology is applied will be described. Here, the AI devices 100a to 100e illustrated in FIG. 22 may be viewed as specific embodiments of the AI device 100 illustrated in FIG. 20.

AI technology is applied to the robot 100a, and may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, and an unmanned flying robot.

The robot 100a may include a robot control module for controlling an operation, and the robot control module may mean a software module or a chip implemented with hardware.

The robot 100a acquires state information of the robot 100a using sensor information obtained from various types of sensors, detects (recognizes) surrounding environment and objects, generates map data, or moves and travels. You can decide on a plan, determine a response to user interaction, or determine an action.

Here, the robot 100a may use sensor information acquired from at least one sensor among a lidar, a radar, and a camera in order to determine a movement route and a driving plan.

The robot 100a may perform the above operations using a learning model composed of at least one artificial neural network. For example, the robot 100a may recognize a surrounding environment and an object using a learning model, and may determine an operation using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the robot 100a or may be learned from an external device such as the AI server 200.

At this time, the robot 100a may perform an operation by generating a result using a direct learning model, but transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly. You may.

The robot 100a determines a moving path and a driving plan using at least one of map data, object information detected from sensor information, or object information obtained from an external device, and controls the driving unit to determine the determined moving path and driving plan. Accordingly, the robot 100a can be driven.

The map data may include object identification information for various objects arranged in a space in which the robot 100a moves. For example, the map data may include object identification information for fixed objects such as walls and doors and movable objects such as flower pots and desks. In addition, the object identification information may include a name, type, distance, and location.

In addition, the robot 100a may perform an operation or travel by controlling a driving unit based on a user's control / interaction. At this time, the robot 100a may acquire intention information of an interaction according to a user's motion or voice utterance, and determine an answer based on the obtained intention information to perform an operation.

The autonomous driving vehicle 100b is applied with AI technology and can be implemented as a mobile robot, a vehicle, or an unmanned aerial vehicle.

The autonomous driving vehicle 100b may include an autonomous driving control module for controlling an autonomous driving function, and the autonomous driving control module may refer to a software module or a chip implemented with hardware. The autonomous driving control module may be included therein as a configuration of the autonomous driving vehicle 100b, but may be configured and connected to a separate hardware outside the autonomous driving vehicle 100b.

The autonomous vehicle 100b acquires state information of the autonomous vehicle 100b using sensor information obtained from various types of sensors, detects (recognizes) surrounding objects and objects, generates map data, The route and driving plan may be determined, or an operation may be determined.

Here, the autonomous vehicle 100b may use sensor information obtained from at least one sensor among a lidar, a radar, and a camera, like the robot 100a, to determine a movement path and a driving plan.

In particular, the autonomous driving vehicle 100b may receive sensor information from external devices or recognize an environment or an object for an area where a field of view is obscured or a predetermined distance or more, or receive information recognized directly from external devices. .

The autonomous vehicle 100b may perform the above-described operations using a learning model composed of at least one artificial neural network. For example, the autonomous vehicle 100b may recognize a surrounding environment and an object using a learning model, and may determine a driving line using the recognized surrounding environment information or object information. Here, the learning model may be learned directly from the autonomous vehicle 100b or may be learned from an external device such as the AI server 200.

At this time, the autonomous vehicle 100b may perform an operation by generating a result using a direct learning model, but transmits sensor information to an external device such as the AI server 200 and receives the generated result accordingly. You can also do

The autonomous vehicle 100b determines a moving path and a driving plan using at least one of map data, object information detected from sensor information, or object information obtained from an external device, and controls the driving unit to determine the moving path and driving According to the plan, the autonomous vehicle 100b may be driven.

The map data may include object identification information for various objects arranged in a space (for example, a road) in which the autonomous vehicle 100b travels. For example, the map data may include object identification information for fixed objects such as street lights, rocks, buildings, and movable objects such as vehicles and pedestrians. In addition, the object identification information may include a name, type, distance, and location.

In addition, the autonomous vehicle 100b may perform an operation or travel by controlling a driving unit based on a user's control / interaction. At this time, the autonomous driving vehicle 100b may acquire intention information of an interaction according to a user's motion or voice utterance, and determine an answer based on the obtained intention information to perform an operation.

AI technology is applied to the XR device 100c, HMD (Head-Mount Display), HUD (Head-Up Display) provided in a vehicle, television, mobile phone, smart phone, computer, wearable device, home appliance, digital signage , It can be implemented as a vehicle, a fixed robot or a mobile robot.

The XR device 100c generates location data and attribute data for 3D points by analyzing 3D point cloud data or image data obtained through various sensors or from an external device, thereby providing information about surrounding space or real objects. The XR object to be acquired and output can be rendered and output. For example, the XR device 100c may output an XR object including additional information about the recognized object in correspondence with the recognized object.

The XR device 100c may perform the above operations using a learning model composed of at least one artificial neural network. For example, the XR device 100c may recognize a real object from 3D point cloud data or image data using a learning model, and provide information corresponding to the recognized real object. Here, the learning model may be directly trained in the XR device 100c or may be learned in an external device such as the AI server 200.

At this time, the XR device 100c may perform an operation by generating a result using a direct learning model, but transmits sensor information to an external device such as the AI server 200 and receives the generated result accordingly. You can also do

The robot 100a is applied with AI technology and autonomous driving technology, and can be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, and an unmanned flying robot.

The robot 100a to which AI technology and autonomous driving technology are applied may mean a robot itself having an autonomous driving function or a robot 100a that interacts with the autonomous driving vehicle 100b.

The robot 100a having an autonomous driving function may collectively refer to moving devices by moving itself or determining the moving line according to a given moving line without user control.

The robot 100a and the autonomous vehicle 100b having an autonomous driving function may use a common sensing method to determine one or more of a moving path or a driving plan. For example, the robot 100a and the autonomous vehicle 100b having an autonomous driving function may determine one or more of a moving route or a driving plan using information sensed through a lidar, a radar, and a camera.

The robot 100a interacting with the autonomous vehicle 100b exists separately from the autonomous vehicle 100b, and is connected to an autonomous vehicle function inside or outside the autonomous vehicle 100b, or the autonomous vehicle 100b ) Can perform the operation associated with the user on board.

At this time, the robot 100a that interacts with the autonomous vehicle 100b acquires sensor information on behalf of the autonomous vehicle 100b and provides it to the autonomous vehicle 100b, acquires sensor information, and obtains environment information or By generating object information and providing it to the autonomous vehicle 100b, it is possible to control or assist the autonomous vehicle driving function of the autonomous vehicle 100b.

Alternatively, the robot 100a interacting with the autonomous vehicle 100b may monitor a user on the autonomous vehicle 100b or control a function of the autonomous vehicle 100b through interaction with the user. . For example, when it is determined that the driver is in a drowsy state, the robot 100a may activate the autonomous driving function of the autonomous vehicle 100b or assist control of a driving unit of the autonomous vehicle 100b. Here, the function of the autonomous driving vehicle 100b controlled by the robot 100a may include not only an autonomous driving function, but also a function provided by a navigation system or an audio system provided inside the autonomous driving vehicle 100b.

Alternatively, the robot 100a interacting with the autonomous vehicle 100b may provide information or assist a function to the autonomous vehicle 100b from outside the autonomous vehicle 100b. For example, the robot 100a may provide traffic information including signal information to the autonomous vehicle 100b, such as a smart traffic light, or interact with the autonomous vehicle 100b, such as an automatic electric charger for an electric vehicle. An electric charger can also be automatically connected to the charging port.

The robot 100a is applied with AI technology and XR technology, and can be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, and a drone.

The robot 100a to which XR technology is applied may mean a robot that is a target of control / interaction within an XR image. In this case, the robot 100a is separated from the XR device 100c and can be interlocked with each other.

When the robot 100a, which is the object of control / interaction within the XR image, acquires sensor information from sensors including a camera, the robot 100a or the XR device 100c generates an XR image based on the sensor information. And, the XR device 100c may output the generated XR image. In addition, the robot 100a may operate based on a control signal input through the XR device 100c or a user's interaction.

For example, the user can check the XR image corresponding to the viewpoint of the robot 100a remotely linked through an external device such as the XR device 100c, and adjust the autonomous driving path of the robot 100a through interaction or , You can control the operation or driving, or check the information of the surrounding objects.

The autonomous vehicle 100b is applied with AI technology and XR technology, and may be implemented as a mobile robot, a vehicle, or an unmanned aerial vehicle.

The autonomous driving vehicle 100b to which the XR technology is applied may mean an autonomous driving vehicle having a means for providing an XR image or an autonomous driving vehicle targeted for control / interaction within the XR image. In particular, the autonomous vehicle 100b, which is the object of control / interaction within the XR image, is distinguished from the XR device 100c and may be interlocked with each other.

The autonomous vehicle 100b having a means for providing an XR image may acquire sensor information from sensors including a camera, and output an XR image generated based on the acquired sensor information. For example, the autonomous vehicle 100b may provide an XR object corresponding to a real object or an object on the screen to the occupant by outputting an XR image with a HUD.

At this time, when the XR object is output to the HUD, at least a portion of the XR object may be output so as to overlap with an actual object facing the occupant's gaze. On the other hand, when the XR object is output to a display provided inside the autonomous vehicle 100b, at least a part of the XR object may be output to overlap with an object in the screen. For example, the autonomous vehicle 100b may output XR objects corresponding to objects such as lanes, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, buildings, and the like.

When the autonomous vehicle 100b, which is the object of control / interaction within the XR image, acquires sensor information from sensors including a camera, the autonomous vehicle 100b or the XR device 100c is based on the sensor information. The XR image is generated, and the XR device 100c may output the generated XR image. In addition, the autonomous vehicle 100b may operate based on a user's interaction or a control signal input through an external device such as the XR device 100c.

The embodiments described above are those in which the components and features of the present invention are combined in a predetermined form. Each component or feature should be considered optional unless stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. It is also possible to configure an embodiment of the present invention by combining some components and / or features. The order of the operations described in the embodiments of the present invention can be changed. Some configurations or features of one embodiment may be included in other embodiments, or may be replaced with corresponding configurations or features of other embodiments. It is obvious that the claims may be combined with claims that do not have an explicit citation relationship in the claims, or may be included as new claims by amendment after filing.

In this document, a specific operation described as being performed by a base station may be performed by its upper node in some cases. That is, it is obvious that various operations performed for communication with a terminal in a network composed of a plurality of network nodes (network nodes) including a base station can be performed by a base station or other network nodes other than the base station. The base station may be replaced by terms such as a fixed station, a Node B, an eNode B (eNB), an access point, and the like.

Embodiments according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. For implementation by hardware, one embodiment of the present invention includes one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), FPGAs ( field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, and the like.

In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, function, etc. that performs the functions or operations described above. The software code can be stored in a memory unit and driven by a processor. The memory unit is located inside or outside the processor, and can exchange data with the processor by various known means.

It is apparent to those skilled in the art that the present invention may be embodied in other specific forms without departing from the characteristics of the present invention. Accordingly, the above detailed description should not be construed as limiting in all respects, but should be considered illustrative. The scope of the present invention should be determined by rational interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

Claims

In a next-generation wireless communication system, a method in which a relay node transmits and receives a discovery signal,

Receiving SS / PBCH (Synchronization Signal / Physical Broadcast channel) block measurement timing setting information defined as a bitmap from a network; And

And based on the bitmap, transmitting a discovery signal for another relay node or donor node, or detecting the discovery signal transmitted from the other relay node or donor node.

How to send and receive discovery signals.
According to claim 1,

Transmitting or detecting the discovery signal,

And transmitting a discovery signal for the other relay node or the donor node at the timing indicated by 1 in the bitmap,

How to send and receive discovery signals.
According to claim 1,

Transmitting or detecting the discovery signal,

And detecting the discovery signal transmitted from the other relay node or the donor node at a timing indicated by 0 in the bitmap.

How to send and receive discovery signals.
According to claim 1,

The relay node belongs to a specific group,

The SS / PBCH block measurement timing setting information is characterized in that the specific group-specific information,

How to send and receive discovery signals.
According to claim 1,

The SS / PBCH block measurement timing setting information includes a period and timing offset to which the bitmap is applied,

The bitmap is characterized by being defined by a random sequence,

How to send and receive discovery signals.
As a relay node in the next generation wireless communication system,

Wireless communication module;

At least one processor; And

And at least one memory operatively connected to the at least one processor and storing instructions that, when executed, cause the at least one processor to perform a particular operation,

The specific operation,

Receiving an SS / PBCH (Synchronization Signal / Physical Broadcast channel) block measurement timing setting information defined as a bitmap from a network, and transmitting a discovery signal for another relay node or donor node based on the bitmap, or And detecting the discovery signal transmitted from another relay node or the donor node.

Relay node.
The method of claim 6,

The specific operation,

And transmitting a discovery signal for the other relay node or the donor node at a timing indicated by 1 in the bitmap.

Relay node.
The method of claim 6,

The specific operation,

And detecting the discovery signal transmitted from the other relay node or the donor node at a timing indicated by 0 in the bitmap.

Relay node.
The method of claim 6,

The relay node belongs to a specific group,

The SS / PBCH block measurement timing setting information is characterized in that the specific group-specific information,

Relay node.
The method of claim 6,

The SS / PBCH block measurement timing setting information includes a period and timing offset to which the bitmap is applied,

The bitmap is characterized by being defined by a random sequence,

Relay node.