Method and apparatus for collecting data

Abstract

Provided are a method by which a first device performs wireless communication and an apparatus supporting same. The first device: may receive, from a second device, configuration information including information related to at least one of an artificial intelligence function and a model; may perform measurement on a cluster; and may transmit, to the second device, a measurement report acquired on the basis of the measurement. For example, the measurement may be performed on the basis that the cluster is related to at least one of the artificial intelligence function and the model.

Description

Data collection methods and devices

[0001] The present disclosure relates to a wireless communication system.

[0002] 5G NR, the successor to LTE (long-term evolution), is a new clean-slate mobile communications system characterized by high performance, low latency, and high availability. 5G NR can utilize all available spectrum resources, from low-frequency bands below 1 GHz, mid-frequency bands between 1 GHz and 10 GHz, and high-frequency (millimeter wave) bands above 24 GHz.

[0003] The 6G (wireless communication) system aims to achieve (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) low energy consumption for battery-free Internet of Things (IoT) devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements as shown in Table 1 below. For example, Table 1 can represent an example of the requirements of a 6G system.

[0004] Maximum data rate per device: 1 Tbps, E2E latency: 1 ms, Maximum spectral efficiency: 100 bps / Hz, Mobility support: Up to 1000 km / hr, Satellite integration: Fully AI, Fully autonomous driving, Fully XR, Fully haptic communication

[0005] According to one embodiment of the present disclosure, a method may be provided. For example, the method may include: a first device receiving configuration information from a second device, the configuration information including information related to at least one of an artificial intelligence function or model; performing a measurement on a cluster; and transmitting a measurement report obtained based on the measurement to the second device. For example, the measurement may be performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0006] According to one embodiment of the present disclosure, a first device may be provided. For example, the first device may include at least one transceiver; at least one processor; and at least one memory connected to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the first device to: receive, from a second device, configuration information including information related to at least one of an artificial intelligence function or model; perform a measurement on a cluster; and transmit a measurement report obtained based on the measurement to the second device. For example, the measurement may be performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0007] According to one embodiment of the present disclosure, a processing device configured to control a first device may be provided. For example, the processing device may include at least one processor; and at least one memory coupled to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the first device to: receive, from a second device, configuration information including information related to at least one of an artificial intelligence function or model; perform a measurement on a cluster; and transmit a measurement report obtained based on the measurement to the second device. For example, the measurement may be performed based on the cluster being associated with at least one of the artificial intelligence function or model.

[0008] According to one embodiment of the present disclosure, a non-transitory computer-readable storage medium having instructions recorded thereon may be provided. For example, the instructions, when executed, may cause a first device to: receive, from a second device, configuration information including information related to at least one of an artificial intelligence function or model; perform a measurement on a cluster; and transmit a measurement report obtained based on the measurement to the second device. For example, the measurement may be performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0009] Figure 1 illustrates a device-to-device communication procedure according to one embodiment of the present disclosure.

[0010] FIG. 2 illustrates a radio protocol architecture according to one embodiment of the present disclosure.

[0011] FIG. 3 illustrates the structure of a wireless frame according to one embodiment of the present disclosure.

[0012] FIG. 4 illustrates a slot structure of a frame according to one embodiment of the present disclosure.

[0013] FIG. 5 illustrates an example of a BWP according to one embodiment of the present disclosure.

[0014] FIG. 6 illustrates a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure.

[0015] FIG. 7 illustrates an example of a communication scenario based on a 6G system according to one embodiment of the present disclosure.

[0016] FIG. 8 illustrates a procedure for a terminal to perform V2X or SL (sidelink) communication according to a resource allocation mode, according to one embodiment of the present disclosure.

[0017] FIG. 9 illustrates a functional framework for AI / ML (Artificial Intelligence and Machine Learning) according to one embodiment of the present disclosure.

[0018] FIG. 10 illustrates an example of license-based data collection approval according to one embodiment of the present disclosure.

[0019] FIG. 11 illustrates an example of a data collection procedure according to exception handling, according to one embodiment of the present disclosure.

[0020] FIG. 12 illustrates an example of cluster-based data collection / reporting according to one embodiment of the present disclosure.

[0021] FIG. 13 illustrates an example of a data collection failure report according to one embodiment of the present disclosure.

[0022] FIG. 14 illustrates an example of intra-group relay and / or collaboration-based data collection according to one embodiment of the present disclosure.

[0023] FIG. 15 illustrates a method for a first device to perform wireless communication according to one embodiment of the present disclosure.

[0024] FIG. 16 illustrates a method for a second device to perform wireless communication according to one embodiment of the present disclosure.

[0025] Fig. 17 illustrates a communication system (1) according to one embodiment of the present disclosure.

[0026] FIG. 18 illustrates a wireless device according to one embodiment of the present disclosure.

[0027] FIG. 19 illustrates a signal processing circuit for a transmission signal according to one embodiment of the present disclosure.

[0028] FIG. 20 illustrates a wireless device according to an embodiment of the present disclosure.

[0029] FIG. 21 illustrates a mobile device according to one embodiment of the present disclosure.

[0030] FIG. 22 illustrates a vehicle or autonomous vehicle according to one embodiment of the present disclosure.

[0031] In this disclosure, "A or B" can mean "only A," "only B," or "both A and B." In other words, "A or B" in this disclosure can be interpreted as "A and / or B." For example, "A, B or C" in this disclosure can mean "only A," "only B," "only C," or "any combination of A, B and C."

[0032] As used herein, a slash ( / ) or a comma may mean "and / or." For example, "A / B" may mean "A and / or B." Accordingly, "A / B" may mean "only A," "only B," or "both A and B." For example, "A, B, C" may mean "A, B, or C."

[0033] In the present disclosure, “at least one of A and B” may mean “only A,” “only B,” or “both A and B.” Additionally, in the present disclosure, the expressions “at least one of A or B” or “at least one of A and / or B” may be interpreted identically to “at least one of A and B.”

[0034] Additionally, in the present disclosure, “at least one of A, B and C” can mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Additionally, “at least one of A, B or C” or “at least one of A, B and / or C” can mean “at least one of A, B and C.”

[0035] Additionally, parentheses used in the present disclosure may mean "for example." Specifically, when indicated as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information." In other words, "control information" in the present disclosure is not limited to "PDCCH," and "PDCCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information."

[0036] In the following explanation, ‘when, if, in case of’ can be replaced with ‘based on’.

[0037] Technical features individually described in one drawing in this disclosure may be implemented individually or simultaneously.

[0038] In the present disclosure, higher layer parameters may be parameters set for the terminal, preset, or predefined. For example, a base station or network may transmit higher layer parameters to the terminal. For example, the higher layer parameters may be transmitted via radio resource control (RRC) signaling or medium access control (MAC) signaling.

[0039] In the present disclosure, "setting or defining" may be interpreted as being set or preset to a device through predefined signaling (e.g., SIB, MAC, RRC, DCI (downlink control information), etc.) from a base station or a network. In the present disclosure, "setting or defining" may be interpreted as being set or preset to a device through predefined signaling (e.g., MAC, RRC, SCI (sidelink control information), device-to-device signaling control information, etc.) from another device. In the present disclosure, "setting or defining" may be interpreted as being set or preset to a device.

[0040] In the present disclosure, a user equipment (UE) may refer to a device, a portable device, a wireless device, etc. In the present disclosure, a base station (BS) may refer to a radio access network (RAN) node, a non-terrestrial network (NTN) cell / node, a transmission reception point (TRP), a network, an integrated access and backhaul (IAB) node, a device, a portable device, a wireless device, etc.

[0041] The technology proposed in the present disclosure can be used in various wireless communication systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access). CDMA can be implemented with wireless technologies such as UTRA (universal terrestrial radio access) or CDMA2000. TDMA can be implemented with wireless technologies such as GSM (global system for mobile communications) / GPRS (general packet radio service) / EDGE (enhanced data rates for GSM evolution). OFDMA can be implemented with wireless technologies such as IEEE (Institute of Electrical and Electronics Engineers) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, E-UTRA (evolved UTRA), LTE (long term evolution), and 5G NR.

[0042] The technology proposed in this disclosure can be implemented with 6G wireless technology and applied to various 6G systems. For example, 6G systems can have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), artificial intelligence (AI) integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0043] FIG. 1 illustrates a device-to-device communication procedure according to one embodiment of the present disclosure. The embodiment of FIG. 1 may be combined with various embodiments of the present disclosure.

[0044] Referring to FIG. 1, in step S101, a first device and a second device can perform synchronization. For example, the first device can be a terminal and / or at least one of the devices proposed in the present disclosure. For example, the second device can be a base station, a network, a RAN node, an NTN node / cell, a TRP, a terminal and / or at least one of the devices proposed in the present disclosure. For example, the first device can perform an initial cell search operation. For example, the first device can detect at least one synchronization signal transmitted by the second device according to a predefined rule. Here, for example, the synchronization signal can include a plurality of synchronization signals classified according to a structure or purpose (e.g., a primary synchronization signal, a secondary synchronization signal, etc.). Through this, the first device can identify the boundaries of the frame, subframe, time unit, slot, and / or symbol of the second device, and the first device can obtain information about the second device (e.g., a cell identifier).

[0045] In step S103, the first device can obtain system information transmitted by the second device. For example, the system information may include information related to the properties, characteristics, and / or capabilities of the second device required to connect to the second device and use the service. For example, the system information may be classified according to content (e.g., whether it is essential for connection), transmission structure (e.g., the channel used, whether it is provided on-demand), etc. For example, the system information may be classified into a master information block (MIB) and a system information block (SIB). For example, if necessary, the first device may transmit a signal requesting system information before receiving the system information. For example, the request and provision of system information may be performed after a random access procedure described below.

[0046] In step S105, the first device and the second device can perform a random access procedure. For example, the first device can transmit and / or receive at least one message (e.g., a random access preamble, a random access response message, etc.) for the random access procedure based on information related to a random access channel of the second device obtained through system information (e.g., channel location, channel structure, structure of supported preamble, etc.). For example, the first device can transmit a preamble (e.g., Msg1) through the random access channel, the first device can receive a random access response message (e.g., Msg2), the first device can transmit a message (e.g., Msg3) including information related to the first device (e.g., identification information) to the second device using scheduling information included in the random access response message, and the first device can receive a message (e.g., Msg4) for contention resolution and / or connection establishment. For example, Msg1 and Msg3 can be sent and received as one message (e.g., MsgA), and / or Msg2 and Msg4 can be sent and received as one message (e.g., MsgB).

[0047] In step S107, the first device and the second device may perform signaling of control information. Here, for example, the control information may be defined in various layers, such as a layer that controls a connection (e.g., a radio resource control (RRC) layer), a layer that handles mapping between logical channels and transport channels (e.g., a media access control (MAC) layer), a layer that handles physical channels (e.g., a physical (PHY) layer), etc. For example, the first device and the second device may perform at least one of signaling for establishing a connection, signaling for determining settings related to communication, and / or signaling for indicating allocated resources. For example, the control information may be signaled / transmitted via a control channel. For example, the control information and / or the control channel may be used to schedule at least one of data, a data channel (e.g., a shared channel), and / or control information on the data channel.

[0048] In step S109, the first device and the second device may transmit and / or receive data. For example, the first device and the second device may process, transmit, and / or receive data based on signaling of control information. For example, when transmitting data, the first device or the second device may perform at least one of channel encoding, rate matching, scrambling, constellation mapping, layer mapping, waveform modulation, antenna mapping, and / or resource mapping on the information bits. For example, when receiving data, the first device or the second device may perform at least one of signal extraction from resources, waveform demodulation for each antenna, signal arrangement considering layer mapping, constellation demapping, descrambling, and / or channel decoding.

[0049] For example, the layers of a radio interface protocol between a first device and a second device can be divided into L1 (layer 1), L2 (layer 2), L3 (layer 3), etc. For example, a physical layer belonging to the first layer can provide an information transfer service using a physical channel, and an RRC (radio resource control) layer located in the third layer can play a role in controlling radio resources between the first device and the second device. For this purpose, for example, the RRC layer can exchange RRC messages between the first device and the second device.

[0050] FIG. 2 illustrates a radio protocol architecture according to an embodiment of the present disclosure. The embodiment of FIG. 2 can be combined with various embodiments of the present disclosure. For example, (a) of FIG. 2 may illustrate a radio protocol stack of a user plane for uplink communication or downlink communication, and (b) of FIG. 2 may illustrate a radio protocol stack of a control plane for uplink communication or downlink communication. For example, (c) of FIG. 2 may illustrate a radio protocol stack of a user plane for device-to-device communication, and (d) of FIG. 2 may illustrate a radio protocol stack of a control plane for device-to-device communication.

[0051] For example, the physical layer can provide information transmission services to upper layers using physical channels. For example, the physical layer can be connected to the upper layer, the medium access control (MAC) layer, through a transport channel. For example, data can be transmitted between the MAC layer and the physical layer through the transport channel. For example, transport channels can be classified according to how and with what characteristics data is transmitted over the wireless interface. For example, data can be transmitted between different physical layers, i.e., between the physical layers of a first device and a second device, through a physical channel. For example, the physical channel can be modulated using an orthogonal frequency division multiplexing (OFDM) scheme, and time and frequency can be utilized as radio resources.

[0052] For example, the MAC layer can provide services to the upper layer, the radio link control (RLC) layer, through logical channels. For example, the MAC layer can provide a mapping function from multiple logical channels to multiple transport channels. For example, the MAC layer can provide a logical channel multiplexing function by mapping multiple logical channels to a single transport channel. For example, the MAC sublayer can provide data transmission services on logical channels.

[0053] For example, the RLC layer can perform concatenation, segmentation, and reassembly of RLC service data units (SDUs). For example, to guarantee the various quality of service (QoS) required by radio bearers (RBs), the RLC layer can provide three operating modes: transparent mode (TM), unacknowledged mode (UM), and acknowledged mode (AM). For example, AM RLC can provide error correction through automatic repeat request (ARQ).

[0054] For example, the RRC (radio resource control) layer can be defined only in the control plane. For example, the RRC layer can be responsible for controlling logical channels, transport channels, and physical channels in relation to the configuration, re-configuration, and release of radio bearers. For example, an RB can mean a logical path provided by a first layer (e.g., a physical layer) and a second layer (e.g., a MAC layer, an RLC layer, a PDCP (packet data convergence protocol) layer, a SDAP (service data adaptation protocol) layer, etc.) for data transmission between a first device and a second device.

[0055] For example, the functions of the PDCP layer in the user plane may include forwarding of user data, header compression, and ciphering. For example, the functions of the PDCP layer in the control plane may include forwarding of control plane data and ciphering / integrity protection.

[0056] For example, establishing an RB can refer to the process of defining the characteristics of the radio protocol layer and channel to provide a specific service, and setting specific parameters and operating methods for each. For example, RBs can be divided into two types: signaling radio bearers (SRBs) and data radio bearers (DRBs). For example, SRBs can be used as a channel to transmit RRC messages in the control plane, while DRBs can be used as a channel to transmit user data in the user plane.

[0057] For example, a downlink transmission channel may include at least one of a broadcast channel (BCH) for transmitting system information, and / or a downlink shared channel (SCH) for transmitting user traffic or control messages. For example, traffic or control messages of a downlink multicast or broadcast service may be transmitted through the downlink SCH, or may be transmitted through a separate downlink multicast channel (MCH). Meanwhile, an uplink transmission channel may include at least one of a random access channel (RACH) for transmitting initial control messages, and / or an uplink shared channel (SCH) for transmitting user traffic or control messages. For example, a logical channel located above a transmission channel and mapped to the transmission channel may include at least one of a broadcast control channel (BCCH), a paging control channel (PCCH), a common control channel (CCCH), a multicast control channel (MCCH), and / or a multicast traffic channel (MTCH).

[0058] FIG. 3 illustrates the structure of a wireless frame according to an embodiment of the present disclosure. The embodiment of FIG. 3 can be combined with various embodiments of the present disclosure.

[0059] Referring to FIG. 3, for example, a radio frame may be used in uplink transmission, downlink transmission, and / or device-to-device transmission. For example, a radio frame may have a length of 10 ms and may be defined as two 5 ms half-frames (HF). For example, a half-frame may include five 1 ms subframes (SF). For example, a subframe may be divided into one or more slots, and the number of slots within a subframe may be determined according to a subcarrier spacing (SCS). For example, each slot may include 12 or 14 OFDM (A) symbols, depending on a cyclic prefix (CP).

[0060] For example, when normal CP is used, each slot can contain 14 symbols. For example, when extended CP is used, each slot can contain 12 symbols. Here, for example, the symbols can contain OFDM symbols (or CP-OFDM symbols), SC-FDMA (single carrier-FDMA) symbols (or DFT-s-OFDM (Discrete Fourier Transform-spread-OFDM) symbols).

[0061] Table 2 below shows the number of symbols per slot (N) depending on the SCS setting (u) when normal CP or extended CP is used. slot symb ), number of slots per frame (N frame,u slot ) and the number of slots per subframe (N subframe,u slot ) is an example.

[0062] CP type SCS (15*2u )N slot symb N frame,u slot N subframe,u slot Normal CP15kHz (u=0)1410130kHz (u=1)1420260kHz (u=2)14404120kHz (u=3)14808240kHz (u=4)1416016Extended CP60kHz (u=2)12404

[0063] For example, OFDM(A) numerology (e.g., SCS, CP length, etc.) may be set differently between multiple cells that are merged into a single terminal. Accordingly, the (absolute time) interval of time resources (e.g., subframes, slots, or transmit time intervals (TTIs)) composed of the same number of symbols may be set differently between the merged cells. For example, in the present disclosure, time resources such as subframes, slots, TTIs, etc. may be referred to as time units.

[0064] For example, multiple numerologies, or SCSs, may be supported to support various services. For example, a 15 kHz SCS may support wide areas in traditional cellular bands, while a 30 kHz / 60 kHz SCS may support dense urban areas, lower latency, and wider carrier bandwidth. For example, a 60 kHz or higher SCS may support bandwidths greater than 24.25 GHz to overcome phase noise.

[0065] FIG. 4 illustrates a slot structure of a frame according to an embodiment of the present disclosure. The embodiment of FIG. 4 can be combined with various embodiments of the present disclosure.

[0066] Referring to FIG. 4, for example, a slot may include multiple symbols in the time domain. For example, a carrier may include multiple subcarriers in the frequency domain. For example, a resource block (RB) may be defined as multiple consecutive subcarriers in the frequency domain. For example, a bandwidth part (BWP) may be defined as multiple consecutive (P)RBs ((physical) resource blocks) in the frequency domain, and may correspond to one numerology (e.g., SCS, CP length, etc.). For example, a carrier may include at most N BWPs (where N is a positive integer). For example, data communication may be performed through an activated BWP. For example, each element may be referred to as a resource element (RE) in the resource grid, and one complex symbol may be mapped to it.

[0067] For example, a BWP may be a contiguous set of PRBs in a given numerology. For example, a PRB may be selected from a contiguous subset of common resource blocks (CRBs) for a given numerology on a given carrier.

[0068] For example, the BWP may be at least one of an active BWP, an initial BWP, and / or a default BWP. For example, the UE may not monitor the downlink radio link quality in a DL BWP other than the active DL BWP on the PCell (primary cell). For example, the UE may not receive a physical downlink control channel (PDCCH), a physical downlink shared channel (PDSCH), or a channel state information-reference signal (CSI-RS) (except for radio resource management (RRM)) outside of the active DL BWP. For example, the UE may not trigger channel state information (CSI) reporting for an inactive DL BWP. For example, the UE may not transmit a physical uplink control channel (PUCCH) or a physical uplink shared channel (PUSCH) outside of the active UL BWP. For example, for downlink, the initial BWP can be given as a set of consecutive resource blocks (RBs) for the remaining minimum system information (RMSI) CORESET (control resource set) (set by the physical broadcast channel (PBCH)). For uplink, for example, the initial BWP can be given by the system information block (SIB) for the random access procedure. For example, the default BWP can be set by a higher layer. For example, the initial value of the default BWP can be the initial DL BWP.For energy saving, if a terminal does not detect DCI (downlink control information) for a certain period of time, the terminal may switch its active BWP to a default BWP.

[0069] FIG. 5 illustrates an example of a BWP according to an embodiment of the present disclosure. The embodiment of FIG. 5 can be combined with various embodiments of the present disclosure. In the embodiment of FIG. 5, it is assumed that there are three BWPs.

[0070] Referring to FIG. 5, for example, a common resource block (CRB) may be a carrier resource block numbered from one end of a carrier band to the other, and a PRB may be a numbered resource block within each BWP. For example, point A may indicate a common reference point for a resource block grid.

[0071] For example, BWP is point A, offset from point A (N start BWP ) and bandwidth (N size BWP ) can be set by. For example, point A can be an outer reference point of the PRB of a carrier where subcarrier 0 of all numerologies (e.g., all numerologies supported by the network on that carrier) aligns. For example, the offset can be the PRB spacing between the lowest subcarrier in a given numerology and point A. For example, the bandwidth can be the number of PRBs in a given numerology.

[0072] FIG. 6 illustrates a communication structure that can be provided in a 6G system according to an embodiment of the present disclosure. The embodiment of FIG. 6 can be combined with various embodiments of the present disclosure.

[0073] As core implementation technologies of the 6G system, technologies such as artificial intelligence (AI), THz (terahertz) communication, optical wireless technology, free-space optical transmission (FSO) backhaul networks, massive MIMO (multiple input multiple output) technology, blockchain, 3D networking, quantum communication, unmanned aerial vehicles, cell-free communication, wireless information and energy transfer (WIET), integration of sensing and communication, integration of access backhaul networks, holographic beamforming, big data analysis, and large intelligent surface (LIS) can be adopted.

[0074] - Artificial Intelligence: Incorporating AI into communications can streamline and improve real-time data transmission. AI can use numerous analytics to determine how complex target tasks should be performed. This means AI can increase efficiency and reduce processing delays. Time-consuming tasks such as handovers, network selection, and resource scheduling can be performed instantly using AI. AI can also play a crucial role in machine-to-machine (M2M), machine-to-human, and human-to-machine communications. Furthermore, AI can facilitate rapid communication in brain-computer interfaces (BCIs). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0075] - THz communication (terahertz communication): Data rates can be increased by increasing the bandwidth. This can be achieved by using sub-THz communication with wide bandwidths and applying advanced massive MIMO technology. THz waves, also known as sub-millimeter waves, typically refer to the frequency range between 0.1 THz and 10 THz, with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz to 300 GHz band (sub-THz band) is considered a key part of the THz spectrum for cellular communications. Adding the sub-THz band to the mmWave band will increase the capacity of 6G cellular communications. Among the defined THz bands, 300 GHz to 3 THz lies in the far infrared (IR) frequency band. While part of the optical band, the 300 GHz to 3 THz band lies at the boundary of the optical band, immediately following the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarities to RF. Key characteristics of THz communications include (i) the widely available bandwidth to support very high data rates and (ii) the high path loss that occurs at high frequencies (requiring highly directional antennas). The narrow beamwidths generated by highly directional antennas reduce interference. The small wavelength of THz signals allows for a significantly larger number of antenna elements to be integrated into devices and base stations operating in this band. This enables the use of advanced adaptive array technologies to overcome range limitations.

[0076] - Large-scale MIMO technology

[0077] - Hologram beamforming (HBF)

[0078] - Optical wireless technology

[0079] - Free-space optical transmission backhaul network (FSO backhaul network)

[0080] - Quantum communication

[0081] - Cell-free communication

[0082] - Integration of wireless information and power transmission

[0083] - Integration of wireless communication and sensing

[0084] - Integrated access and backhaul network

[0085] - Big data analysis

[0086] - Reconfigurable intelligent surface

[0087] - metaverse

[0088] - Block chain

[0089] Advanced Air Mobility (AAM): AAM can be a broad concept encompassing urban air mobility (UAM), regional air mobility (RAM), and uncrewed aerial systems (UAS). For example, AAM can include UAM, RAM, UAS, and uncrewed aerial vehicles (UAVs).

[0090] - Autonomous driving (self-driving): V2X (vehicle to everything), a key element in building autonomous driving infrastructure, can be a technology that allows cars to communicate and share with various elements on the road for autonomous driving, such as vehicle to vehicle (V2V) wireless communication and vehicle to infrastructure (V2I) wireless communication.

[0091] Non-terrestrial network (NTN): NTN can refer to a network or network segment that utilizes radio frequency (RF) resources mounted on satellites (or UAS platforms). NTN services may be considered to secure wider coverage or provide wireless communication services in locations where the installation of wireless communication base stations is difficult.

[0092] - Integrated sensing and communication (ISAC): Wireless sensing is a technology that uses radio frequencies to determine the instantaneous linear velocity, angle, distance (range), etc. of an object, thereby obtaining information about the characteristics of the environment and / or objects within the environment.

[0093] - Reconfigurable intelligent surface (RIS): RIS can be used to manipulate and enhance signal propagation in wireless communication environments. For example, a RIS can be composed of many small antennas, or metasurfaces, arranged on a surface, each of which can actively control the phase, amplitude, polarization, etc. of the reflected signal. For example, a RIS can improve signal reception by controlling the path, phase, and / or intensity of the propagating signal. For example, in the case of a RIS, power consumption can be very low because power is consumed only for controlling the phase and amplitude of the small antennas. For example, because a RIS can be reconfigured to suit different environments, it can meet diverse communication requirements and operate effectively in dynamic network environments.

[0094] FIG. 7 illustrates an example of a communication scenario based on a 6G system, according to an embodiment of the present disclosure. The embodiment of FIG. 7 may be combined with various embodiments of the present disclosure.

[0095] Referring to FIG. 7, NTN communication can be performed based on satellite networks, high-altitude platform stations (HAPS) as international mobile telecommunications (IMT) base stations (BS), and terminals capable of aerial communication (e.g., AAMs). For example, to improve coverage, etc., devices such as satellite networks, HIBS, and terminals capable of aerial communication (e.g., AAMs) can act as relays. For example, an AAM can communicate with a base station, a satellite network, etc., and / or an AAM can communicate directly with a terminal, another AAM, etc.

[0096] FIG. 8 illustrates a procedure for a terminal to perform V2X or SL (sidelink) communication according to a resource allocation mode, according to an embodiment of the present disclosure. The embodiment of FIG. 8 may be combined with various embodiments of the present disclosure.

[0097] Referring to (a) of FIG. 8, in resource allocation mode 1, the base station may schedule SL resources to be used by the terminal for SL transmission. For example, in step S800, the base station may transmit information related to SL resources and / or information related to UL resources to the first terminal. For example, the UL resources may include PUCCH resources and / or PUSCH resources. For example, the UL resources may be resources for reporting SL HARQ (hybrid automatic repeat request) feedback to the base station.

[0098] For example, a first terminal may receive information related to a dynamic grant (DG) resource and / or information related to a configured grant (CG) resource from a base station. For example, a CG resource may include a CG type 1 resource or a CG type 2 resource. In this specification, a DG resource may be a resource that a base station configures / allocates to the first terminal via downlink control information (DCI). In this specification, a CG resource may be a (periodic) resource that a base station configures / allocates to the first terminal via DCI and / or an RRC message. For example, in the case of a CG type 1 resource, the base station may transmit an RRC message including information related to the CG resource to the first terminal. For example, in the case of a CG type 2 resource, the base station may transmit an RRC message including information related to the CG resource to the first terminal, and the base station may transmit a DCI related to activation or release of the CG resource to the first terminal.

[0099] In step S810, the first terminal may transmit a physical sidelink control channel (PSCCH) (e.g., sidelink control information (SCI) or 1st-stage SCI) to the second terminal based on the resource scheduling. In step S820, the first terminal may transmit a physical sidelink shared channel (PSSCH) (e.g., 2nd-stage SCI, MAC PDU, data, etc.) related to the PSCCH to the second terminal. In step S830, the first terminal may receive a physical sidelink feedback channel (PSFCH) related to the PSCCH / PSSCH from the second terminal. For example, HARQ feedback information (e.g., negative acknowledgment (NACK) information or positive acknowledgment (ACK) information) may be received from the second terminal via the PSFCH. In step S840, the first terminal may transmit / report HARQ feedback information to the base station via PUCCH or PUSCH. For example, the HARQ feedback information reported to the base station may be information generated by the first terminal based on HARQ feedback information received from the second terminal. For example, the HARQ feedback information reported to the base station may be information generated by the first terminal based on a rule set in advance. For example, the DCI may be DCI for SL scheduling.

[0100] Referring to (b) of FIG. 8, in resource allocation mode 2, the terminal can determine SL transmission resources within SL resources set by the base station / network or preset SL resources. For example, the set SL resources or preset SL resources may be a resource pool. For example, the terminal can autonomously select or schedule resources for SL transmission. For example, the terminal can perform SL communication by selecting resources by itself within the set resource pool. For example, the terminal can select resources by itself within a selection window by performing sensing and resource (re)selection procedures. For example, the sensing can be performed on a subchannel basis. For example, in step S810, the first terminal that has selected resources by itself within the resource pool transmits PSCCH (e.g., sidelink control information (SCI) or 1) using the resources. st -stage SCI) can be transmitted to the second terminal. In step S820, the first terminal transmits the PSSCH (e.g., 2) related to the PSCCH. nd -stage SCI, MAC PDU, data, etc.) can be transmitted to the second terminal. In step S830, the first terminal can receive a PSFCH related to the PSCCH / PSSCH from the second terminal.

[0101] Referring to (a) or (b) of FIG. 8, for example, a first terminal may transmit an SCI to a second terminal on a PSCCH. Or, for example, the first terminal may transmit two consecutive SCIs (e.g., 2-stage SCIs) to the second terminal on the PSCCH and / or the PSSCH. In this case, the second terminal may decode the two consecutive SCIs (e.g., 2-stage SCIs) to receive the PSSCH from the first terminal. In the present specification, an SCI transmitted on a PSCCH is 1 st SCI, 1st SCI, 1st -stage SCI or 1 st -stage SCI format, and the SCI transmitted on the PSSCH is 2 nd SCI, 2nd SCI, 2 nd -stage SCI or 2 nd -It can be called a stage SCI format.

[0102] For example, 1 st -stage SCI format may include SCI format 1-A and / or SCI format 1-B, and 2 nd -stage SCI formats may include SCI Format 2-A, SCI Format 2-B, SCI Format 2-C, and / or SCI Format 2-D.

[0103] Below, an example of SCI format 1-A is described.

[0104] SCI Format 1-A is a 2-bit format on the PSSCH and PSSCH nd -stage is used for scheduling SCI.

[0105] The following information is transmitted using SCI Format 1-A.

[0106] - Priority - 3 bits

[0107] - Frequency resource allocation - If the value of the upper layer parameter sl-MaxNumPerReserve is set to 2, then ceiling (log2(N SL subChannel(N SL subChannel+1) / 2)) bits; otherwise, if the value of the upper layer parameter sl-MaxNumPerReserve is set to 3, ceiling log2(N SL subChannel(N SL subChannel+1)(2N SL subChannel+1) / 6) bits

[0108] - Time resource allocation - 5 bits if the value of the upper layer parameter sl-MaxNumPerReserve is set to 2; otherwise, 9 bits if the value of the upper layer parameter sl-MaxNumPerReserve is set to 3.

[0109] - Resource reservation cycle - ceiling (log2N) rsv_period ) bits, where N rsv_period The number of entries in the upper layer parameter sl-ResourceReservePeriodList if the upper layer parameter sl-MultiReserveResource is set; otherwise, 0 bits.

[0110] - DMRS pattern - ceiling (log2N pattern ) bits, where N pattern is the number of DMRS patterns set by the upper layer parameter sl-PSSCH-DMRS-TimePatternList.

[0111] - 2 nd -stage SCI format - 2 bits

[0112] - Beta_Offsets indicator - 2 bits as provided by the upper layer parameter sl-BetaOffsets2ndSCI

[0113] - Number of DMRS ports - 1 bit

[0114] - Modulation and coding method - 5 bits

[0115] - Additional MCS table indicator - 1 bit if one MCS table is set by the upper layer parameter sl-Additional-MCS-Table; 2 bits if two MCS tables are set by the upper layer parameter sl-Additional-MCS-Table; otherwise 0 bits

[0116] - PSFCH Overhead Indicator - 1 bit if the upper layer parameter sl-PSFCH-Period = 2 or 4; otherwise 0 bit

[0117] - Reserved bits - The number of bits determined by the upper layer parameter sl-NumReservedBits, whose value is set to 0.

[0118] Below, an example of SCI format 2-A is described.

[0119] In HARQ operation, when HARQ-ACK information contains ACK or NACK, or when HARQ-ACK information contains only NACK, or when there is no feedback of HARQ-ACK information, SCI format 2-A is used for decoding PSSCH.

[0120] The following information is transmitted via SCI Format 2-A.

[0121] - HARQ process number - 4 bits

[0122] - New data indicator - 1 bit

[0123] - Redundancy version - 2 bits

[0124] - Source ID - 8 bits

[0125] - Destination ID - 16 bits

[0126] - HARQ feedback enable / disable indicator - 1 bit

[0127] - Cast type indicator - 2 bits as defined in Table 3

[0128] - CSI request - 1 bit

[0129] Cast Type Indicator ValueCast Type00Broadcast01Groupcast if HARQ-ACK information contains ACK or NACK10Unicast11Groupcast if HARQ-ACK information contains only NACK

[0130] Below, an example of SCI format 2-B is described.

[0131] In HARQ operation, when HARQ-ACK information contains only NACK or there is no feedback of HARQ-ACK information, SCI format 2-B is used for decoding PSSCH.

[0132] The following information is transmitted via SCI Format 2-B.

[0133] - HARQ process number - 4 bits

[0134] - New data indicator - 1 bit

[0135] - Redundancy version - 2 bits

[0136] - Source ID - 8 bits

[0137] - Destination ID - 16 bits

[0138] - HARQ feedback enable / disable indicator - 1 bit

[0139] - Zone ID - 12 bits

[0140] - Communication range requirement - 4 bits determined by the upper layer parameter sl-ZoneConfigMCR-Index

[0141] Referring to (a) or (b) of FIG. 8, in step S830, the first terminal may receive a PSFCH. For example, the first terminal and the second terminal may determine PSFCH resources, and the second terminal may use the PSFCH resources to transmit HARQ feedback to the first terminal.

[0142] Referring to (a) of FIG. 8, in step S840, the first terminal may transmit SL HARQ feedback to the base station via PUCCH and / or PUSCH.

[0143] Meanwhile, with the technological advancement of AI / ML (Artificial Intelligence and Machine Learning), the node(s) and / or terminal(s) that make up the wireless communication network are becoming more intelligent / advanced. In particular, due to the intelligence of the network / base station, it is expected that various network / base station decision parameter values ​​(e.g., transmit / receive power of each base station, transmit power of each terminal, precoder / beam of the base station / terminal, time / frequency resource allocation for each terminal, duplex method of each base station, etc.) can be quickly optimized and derived / applied according to various environmental parameters (e.g., distribution / location of base stations, distribution / location / material of buildings / furniture, etc., location / movement direction / speed of terminals, climate information, etc.). In line with this trend, many standardization organizations (e.g., 3GPP, O-RAN) are considering the introduction of AI / ML, and research on this is actively underway.

[0144] For example, artificial intelligence (AI) can encompass any automation that allows machines to perform tasks previously performed by humans. Machine learning, for example, can involve machines autonomously learning patterns for decision-making based on data, without explicitly programming rules. Deep learning, for example, can be a neural network-based model that allows machines to simultaneously extract features from unstructured data and make judgments. For example, algorithms can rely on multilayer networks of interconnected nodes for feature extraction and transformation, inspired by biological nervous systems, or neural networks. For example, common deep learning network architectures include deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs).

[0145] For example, the types of AI / ML based on various criteria can be as follows:

[0146] (1) Classification by offline and online

[0147] For example, offline learning can faithfully follow the sequential process of database collection, training, and prediction. In other words, collection and training can be performed offline, and the completed program can be installed on-site and utilized for prediction tasks. This offline learning approach can be used in most situations.

[0148] For example, online learning can be a method that gradually improves performance by taking advantage of the fact that data that can be used for learning is continuously generated through the Internet, and incrementally learning with additional data that is generated.

[0149] (2) Classification according to AI / ML framework concept

[0150] For example, in centralized learning, when learning data collected from multiple different node(s) are reported to a centralized node, all data resources / storage / learning (e.g., supervised, unsupervised, reinforcement learning), etc. can be performed in one central node.

[0151] For example, federated learning allows a collective model to be built based on data across distributed data owners. Instead of importing data into a model, the AI / ML model is imported into the data source, allowing local nodes and / or individual devices to collect data and train their own copies of the model, eliminating the need to report source data to a central node. In federated learning, the parameters / weights of the AI / ML model can simply be sent back to the central node to support general model training. The advantages of federated learning include increased computational speed and improved information security. This eliminates the need to upload personal data to a central server, thereby preventing personal information leakage and misuse.

[0152] For example, distributed learning can represent the concept of machine learning processes being scaled and distributed across a cluster of nodes. Training models can be split and shared across multiple nodes operating simultaneously to speed up model training.

[0153] (3) Classification by learning method

[0154] For example, supervised learning can be a machine learning task that aims to learn a mapping function from input to output, given a labeled data set. The input data is called training data and may have known labels or outcomes. Examples of supervised learning include:

[0155] - Regression: linear regression, logistic regression

[0156] - Instance-based algorithms: k-nearest neighbor (KNN)

[0157] - Decision tree algorithms: CART

[0158] - Support vector machines (SVM)

[0159] - Bayesian algorithms: Naive Bayes

[0160] - Ensemble algorithms: extreme gradient boosting

[0161] - Bagging: Random forest

[0162] For example, supervised learning can be further grouped into regression and classification problems, where classification might be predicting a label and regression might be predicting a quantity.

[0163] For example, unsupervised learning can be a machine learning task that aims to learn features that describe hidden structures in unlabeled data. The input data may be unlabeled and have no known outcome. Some examples of unsupervised learning include K-means clustering, principal component analysis (PCA), nonlinear independent component analysis (ICA), and long-term memory (LSTM).

[0164] For example, in reinforcement learning, an agent interacts with its environment through a trial-and-error process, aiming to optimize a long-term goal. This can be goal-directed learning based on interaction with the environment. Below are some examples of reinforcement learning (RL) algorithms.

[0165] - Q-learning

[0166] - Multi-armed bandit learning

[0167] - Deep Q network

[0168] - State-action-reward-state-action (SARSA)

[0169] - Temporal difference learning

[0170] - Actor-critic reinforcement learning

[0171] - Deep deterministic policy gradient

[0172] - Monte-Carlo tree search

[0173] For example, reinforcement learning can be further categorized into model-based reinforcement learning and model-free reinforcement learning. For example, model-based reinforcement learning may be an RL algorithm that uses a predictive model to obtain transition probabilities between states using various dynamic states of the environment and a model of how these states lead to rewards. For example, model-free reinforcement learning may be an RL algorithm based on values ​​or policies that maximize future rewards. In multi-agent environments / states, it may be computationally less complex and may not require an accurate representation of the environment. For example, RL algorithms can also be categorized into value-based RL versus policy-based RL, policy-based RL versus non-policy RL, etc.

[0174] For example, representative models of deep learning may include FFNN (feed-forward neural network), RNN (recurrent neural network), CNN (convolution neural network), and autoencoder.

[0175] For example, an FFNN may consist of an input layer, a hidden layer, and / or an output layer.

[0176] For example, an RNN may be a type of artificial neural network in which hidden nodes are connected by directed edges to form a circular structure. For example, this may be a model suitable for processing sequentially appearing data such as voice and text. For example, one type of RNN may be an LSTM (long short-term memory), which may have a structure that adds a cell state to the hidden state of an RNN. For example, an input gate, a forget gate, and / or an output gate may be added to an RNN cell, and / or a cell state may be added.

[0177] For example, CNN can be used for two purposes: reducing model complexity and extracting good features by applying convolution operations commonly used in image processing or video processing.

[0178] - Kernel or filter: a unit / structure that applies weights to inputs of a specific range / unit.

[0179] - Stride: The range of movement of the kernel within the input.

[0180] - Feature map: The result of applying the kernel to the input.

[0181] - Padding: A value added to adjust the size of the feature map.

[0182] - Pooling: An operation to reduce the size of a feature map by downsampling the feature map (e.g., max pooling, average pooling)

[0183] For example, an autoencoder can be a neural network that receives a feature vector x as input and outputs the same or similar vector x'. For example, the input and output nodes of an autoencoder can have the same features. For example, an autoencoder can be a type of unsupervised learning. For example, the loss function can be defined as follows.

[0184]

[0185] In this disclosure, the following terms may be defined, for example, to describe AI / ML.

[0186] - Data collection: Data collected from network nodes, management entities, or terminals as a basis for ML model training, data analysis, and inference.

[0187] - ML model: A data-driven algorithm that applies machine learning techniques to generate a set of outputs containing predictive information based on a set of inputs.

[0188] - ML training: The online or offline process of training an ML model by learning features and patterns that best represent the data and obtain a trained ML model for inference.

[0189] - ML Inference: The process of making predictions or inducing decisions based on collected data and the ML model using a trained ML model.

[0190] FIG. 9 illustrates a functional framework for AI / ML (Artificial Intelligence and Machine Learning) according to one embodiment of the present disclosure. The embodiment of FIG. 9 may be combined with various embodiments of the present disclosure.

[0191] Referring to Figure 9, for example, data collection may be a function that provides input data to model training and model inference functions. AI / ML algorithm-specific data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) may not be performed in the data collection function. Examples of input data may include measurements from terminals or other network entities, feedback from actors, and output from AI / ML models.

[0192] For example, training data may be data required as input to an AI / ML model training function.

[0193] For example, inference data may be data required as input to the AI / ML model inference function.

[0194] For example, model training may be a function that performs ML model training, validation, and testing, which can generate model performance metrics as part of the model testing process. If necessary, the model training function may also handle data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on training data provided by the data collection function.

[0195] For example, model deployment / update can be used to initially deploy a trained, validated, and tested AI / ML model to a model inference function, or to provide an updated model to a model inference function.

[0196] For example, model inference may be a function that provides AI / ML model inference output (e.g., predictions or decisions). The model inference function may, where applicable, provide model performance feedback to the model training function. If necessary, the model inference function may also handle data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on the inference data provided by the data collection function.

[0197] For example, the output could be the inference output of an AI / ML model generated by the model inference function. Note that the details of the inference output may vary depending on the use case.

[0198] For example, model performance feedback can be used to monitor the performance of AI / ML models.

[0199] For example, an actor can be a function that receives output from a model inference function and triggers or performs corresponding actions. An actor can trigger actions on other entities or on itself.

[0200] For example, feedback may be training or inference data, or information that may be needed to derive performance feedback.

[0201] For example, in AI / ML, the definitions of training / validation / test data can be as follows. For example, training data can be a data set for training a model. For example, validation data can be a data set for validating a model that has already completed training. For example, validation data can be a data set typically used to prevent overfitting of the training data set. For example, validation data can be a data set for selecting the best model among several models trained during the training process. Therefore, it can be viewed as a type of learning. For example, test data can be a data set for final evaluation, and the test data can be unrelated to learning. For example, in the case of the above data set, if the training set is typically divided, the training and validation data can be divided in a ratio of 8:2 or 7:3 within the entire training set. If testing is included, the ratio can be 6:2:2 (training:validation:test).

[0202] For example, depending on the capability of AI / ML functions between the base station and the terminal, the collaboration level can be defined as follows, and variations due to combination or separation of the levels below may also be possible.

[0203] Category 0a) No Collaboration Framework: AI / ML algorithms are purely implementation-based and may not require any changes to the air interface. This may be the baseline.

[0204] Cat 0b) There may be no collaborative framework with air interfaces adapted to efficient implementation-based AI / ML algorithms.

[0205] Category 1) Inter-node support to improve AI / ML algorithms at each node. This can be applied to UEs receiving support from base stations (e.g., training, adaptation, etc.) and vice versa. At this level, model exchange between network nodes may not be necessary.

[0206] Category 2) Joint ML tasks between terminals and base stations. This level may require guidance or exchange of AI / ML models between network nodes.

[0207] Recently, thanks to the advancement of AI / ML (Artificial Intelligence and Machine Learning) technology, the direction of utilizing AI / ML is being actively researched in the field of mobile communications. For example, the 3GPP organization is conducting a study on the air interface using AI / ML, and major use cases include AI / ML-based CSI (channel state information) feedback, AI / ML-based beam management, and AI / ML-based positioning. Meanwhile, with the rapid development of technologies related to unmanned aerial vehicles (UAVs) and / or drones (hereinafter referred to as UAVs) and / or robots, it is expected that new types of terminals, such as UAVs and / or robots, will be introduced in the field of mobile communications. For example, the UAV may serve as a relay node that provides communication services within the mobile communication system and / or as a terminal node that receives communication services within the mobile communication system.

[0208] Furthermore, next-generation mobile communication systems are considering network topologies that support diverse connectivity beyond the links between base stations and terminals in conventional cellular networks. For example, the UAV terminals may fly in groups, supporting intra- and inter-group communication. Alternatively, the UAV terminals may be connected to both terrestrial networks, such as base stations, and non-terrestrial networks, such as satellites.

[0209] In the present disclosure, a method for collecting data related to artificial intelligence functions and / or models, taking into account the above-described new characteristics of terminals (e.g., UAVs, robots, etc.), network structures, etc., and a device supporting the same are proposed.

[0210] [Proposal #01] In a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or model from the second node, the second node can report condition information and conditional approval information for collecting specific data, and the first node can collect the data if the condition information is met. Here, for example, the condition information can include license and / or pre-collaboration information. For example, a second node provider can provide license and / or pre-collaboration information to a first node provider in advance, and a first node of a provider having the license and / or pre-collaboration information can collect related data from the second node. Here, for example, the data can be collected for one or more of the following purposes.

[0211] (1) Learning purpose of artificial intelligence functions and / or models

[0212] (2) Purpose of evaluating the performance of artificial intelligence functions and / or models

[0213] (3) For artificial intelligence functions and / or model-based reasoning purposes;

[0214] Here, for example, the condition information and conditional approval information may be transmitted together or in separate processes.

[0215] For example, in a next-generation communication system according to an embodiment of the present disclosure, assume that a first node and / or a second node support an artificial intelligence function and / or model, and that the first node can collect data related to the artificial intelligence function and / or model from the second node. Here, for example, the artificial intelligence function and / or model and the related data may correspond to the intellectual property and / or proprietary assets of the equipment supplier. Therefore, even if the first node can collect data related to the artificial intelligence function and / or model from the second node, the data may not be collected without the approval procedure of the second node. Here, for example, the approval of the second node to collect data may be conditional. For example, if the equipment supplier of the second node and the equipment supplier of the first node have entered into an agreement in advance, the first node may be approved to collect data from the second node. Accordingly, in the present disclosure, in a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or model from the second node, the second node can report conditional information and conditional approval information for collecting specific data, and the first node can collect the data if the conditional information is met. Here, for example, the conditional information may include license and / or pre-collaboration information. Here, for example, the pre-collaboration information may include information related to whether an IODT (Inter-Operability Device Test) process according to offline engineering is performed. According to the proposed method of the present disclosure, a node that is the target of data collection can selectively and / or conditionally approve data provision. In this case, there is an advantage in that approval can be flexibly granted while ensuring data ownership. Fig. 10 illustrates an example of license-based data collection approval according to one embodiment of the present disclosure.The embodiment of FIG. 10 can be combined with various embodiments of the present disclosure. The above [Proposed Method #01] can be applied in combination with other proposed methods(s) within the scope of non-conflicting proposed operations.

[0216] [Proposal #02] In a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or model from the second node, if it corresponds to a specific region and / or a specific service and / or a specific event and / or a specific terminal type and / or a specific object type, the second node may mandatorily support the collection of specific data. Here, for example, the specific terminal type may mean an unmanned terminal (e.g., a UAV) and / or a terminal under the control of a control system and / or a terminal that acts as a gateway between other terminal(s) and the underlying network. Here, for example, the specific event may be related to regional regulations. For example, if the second node has a movement path and / or a flight path, and if an area where data collection is mandatory is included in the movement path and / or flight path, the second node may be mandatorily supported to support data collection for a part and / or the entirety of the path. Here, for example, when collecting data from the second node, the terminal's approval and / or privacy information may be ignored. Here, for example, the specific data may include location information.

[0217] For example, in a next-generation communication system according to an embodiment of the present disclosure, assume that a first node and / or a second node support an artificial intelligence function and / or model, and that the first node can collect data related to the artificial intelligence function and / or model from the second node. Here, for example, when the first node collects data related to the artificial intelligence function and / or model from the second node, private information such as location information can be collected with the data. Here, for example, in typical cases, private information such as location information may be preferably collected only with the approval of the second node, which is the target of data collection. However, if the second node is an unmanned terminal such as a UAV and / or a robot, it may be relatively free from the approval and / or privacy issues of the terminal, and the approval and / or privacy verification procedure can be omitted and the private information such as the location information can be included in the data collection. Here, for example, the proposed method of the present disclosure may include a specific service or event. For example, if the second node is a terminal moving in an area with strengthened safety regulations, it may be mandated to support the collection of private information such as location information. In other words, in exceptional circumstances such as regulations, the terminal's approval and / or privacy verification procedures for data collection may be omitted or ignored. Accordingly, in the present disclosure, in a mobile communication system comprising a first node and a second node, when the first node can collect data related to artificial intelligence functions and / or models from the second node, the second node may mandate the collection of specific data if the data corresponds to a specific region, a specific service, a specific event, and / or a specific terminal type.More generally, in the process of data collection, cases for exception handling can be (pre-)configured, and when the case(s) occur, the data collection target node may be required to support data collection. The proposed method of the present disclosure has the advantage of mandating data collection in cases where safety regulations are more important than user privacy and / or where overfitting of artificial intelligence functions and / or models must be avoided. Fig. 11 illustrates an example of a data collection procedure with exception handling according to an embodiment of the present disclosure. The embodiment of Fig. 11 can be combined with various embodiments of the present disclosure. The above-mentioned [Proposed Method #02] can be applied in combination with other proposed method(s) as long as the proposed operations do not conflict.

[0218] [Proposal #03] In a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or model from the second node, the first node can set and / or instruct the second node to collect data (by cluster) for a plurality of cluster(s), and thereafter request (selective) data reporting for preferred cluster(s). Here, for example, the clusters can be defined based on one or more of the following attributes, and can be defined according to a prior agreement and / or (pre)setting between the nodes.

[0219] (1) Measurement location

[0220] (2) Measurement time

[0221] (3) Artificial intelligence functions and / or models;

[0222] Here, for example, the measurement location may include an absolute location and / or a relative location and / or a height and / or a (3D) spatial identifier (e.g., a zone ID) set by the network. Here, for example, the first node may set, to the second node, the number of samples to be measured per cluster and / or a time interval between measurement samples and / or a spatial (position / distance) interval between measurement samples. Here, for example, the second node may not support data collection in an area other than the cluster(s) for which data collection is instructed. For example, if it is a UAV terminal and data collection for a specific path is instructed, data collection according to the relevant data collection process may not be performed when it deviates from the specific path.

[0223] For example, in a next-generation communication system according to an embodiment of the present disclosure, assume that a first node and / or a second node support an artificial intelligence function and / or model, and that the first node can collect data related to the artificial intelligence function and / or model from the second node. Here, for example, if the second node is a UAV terminal, it can report its own route information to the network. Here, for example, it may be desirable that data for learning the artificial intelligence function and / or model of the first node and / or the second node be collected in accordance with the route information. For example, the second node can collect data for each waypoint on the route, and the first node can request data reporting for waypoint(s) of interest or for which the route information reported by the second node requires an update. Therefore, in the present disclosure, in a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or model from the second node, the first node can set / instruct the second node to collect data by cluster, and then request (selective) data reporting for preferred cluster(s). Here, for example, the clusters can be defined to be distinguished according to measurement location and / or measurement time and / or artificial intelligence function / model. Here, for example, the data collection can be data collection in the form of logging rather than immediate reporting. According to the proposed method of the present disclosure, a node that wishes to collect data can receive selective data reporting for the cluster(s) of interest to it, and there is an advantage in that communication costs for data exchange can be effectively reduced. Fig. 12 illustrates an example of cluster-based data collection / reporting according to an embodiment of the present disclosure. The embodiment of Fig. 12 can be combined with various embodiments of the present disclosure.The above [Proposal #03] can be applied in combination with other proposed methods(s) to the extent that the proposed actions do not conflict.

[0224] [Proposal #04] In a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or model from the second node, the second node can report to the first node data collection capability information and data collection availability / inability status information for specific data. Here, for example, the data collection capability information may mean that the second node can implementally support data collection. Here, for example, the data collection availability / inability status information may mean whether the second node is temporarily capable of data collection. Here, for example, when reporting the data collection availability status, the second node may also report information on the size and / or frequency of data that can be collected. Here, for example, when the second node is in a data collection availability status, the first node may request collection and / or reporting for specific data. Here, for example, if the second node is in a data collection unavailable state, the second node may expect the first node not to request collection and / or reporting of specific data, or may ignore the request. Here, for example, the second node may report its data collection support status to the first node upon initial connection and / or data collection state transition (e.g., data collection unavailable -> data collection available, or data collection available -> data collection unavailable).

[0225] For example, in a next-generation communication system according to an embodiment of the present disclosure, assume that a first node and / or a second node support an artificial intelligence function and / or a model, and that the first node can collect data related to the artificial intelligence function and / or the model from the second node. Here, for example, the second node may notify the first node that it has the capability to collect specific data related to the artificial intelligence function and / or the model. Here, for example, the data collection capability may mean that the collection of the corresponding data is supported from an implementation perspective. In other words, it may mean that the collection of the corresponding data is possible under certain conditions. Meanwhile, in addition to the data collection capability information, the second node may report data collection availability / inability status information indicating whether data collection is currently possible or not. For example, if the second node is a battery-operated UAV and / or unmanned robot, it may be in a state where the remaining battery power is low and other functions except for essential functions must be stopped. In the above case, the second node may reject a data collection request from the first node and transmit information regarding the availability / inability of data collection. The proposed method of the present disclosure has the advantage of enabling timely data collection / reporting procedures between the node seeking to collect data and the node targeted for data collection, thereby reducing unnecessary data collection / reporting requests. The above [Proposed Method #04] may be applied in combination with other proposed methods as long as the proposed operations do not conflict.

[0226] [Proposal #05] In a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or a model from the second node, the second node may report to the first node whether data collection has failed and / or the reason for the data collection failure when reporting the collected data. Here, for example, the reason for the data collection failure may include whether an emergency situation exists and / or whether the battery is low and / or whether the number of data samples is insufficient. Here, for example, when reporting the data collection failure, the second node may also report time information for when data collection can be resumed. For example, the time information may be transmitted in the form of timer(s), and the expiration time of the timer(s) may mean the start and / or end time for resuming data collection.

[0227] For example, in a next-generation communication system according to an embodiment of the present disclosure, assume that a first node and / or a second node support an artificial intelligence function and / or model, and the first node can collect data related to the artificial intelligence function and / or model from the second node. Here, for example, the second node can collect and report specific data related to the artificial intelligence function and / or model to the first node. Here, for example, the data collection may be at the request of the first node, and a criterion for determining the validity of the data collection may be set. For example, the criterion for determining the validity of the data collection may be determined based on the number of samples of the collected data and / or the level of measurement error. Here, for example, the second node may report whether data collection failed and / or the reason for the data collection failure when reporting data. For example, the reason for the data collection failure may include whether there is an emergency situation and / or a low battery and / or an insufficient number of data samples. Here, for example, the first node can determine whether to resume or discontinue data collection by checking the reason for the data collection failure reported by the second node. The proposed method of the present disclosure has the advantage of supporting the successful execution of a data collection procedure by checking both the node's data collection capability and / or status. Fig. 13 illustrates an example of a data collection failure report according to an embodiment of the present disclosure. The embodiment of Fig. 13 can be combined with various embodiments of the present disclosure. The above-described [Proposed Method #05] can be applied in combination with other proposed methods as long as the proposed operations do not conflict.

[0228] [Proposal #06] In a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or model from the second node, the second node can report environmental information from which the data was collected when reporting the collected data to the first node. Here, for example, the environmental information may include one or more of the following information.

[0229] (1) Information on the route and / or stopover where data was collected;

[0230] (2) Whether the data was collected within the (pre-)reported route

[0231] (3) Frequency and / or distribution of occurrence in data collection environment;

[0232] Here, for example, the second node may be a terminal capable of (in advance) reporting a movement path to the first node.

[0233] For example, in a next-generation communication system according to an embodiment of the present disclosure, assume that a first node and / or a second node support an artificial intelligence function and / or model, and that the first node can collect data related to the artificial intelligence function and / or model from the second node. Here, for example, the environment in which the data collection is performed may be an environment pre-arranged and / or (pre-)set environment between the first node and the second node, or an arbitrary environment depending on the mobility of the second node. Here, for example, the first node may not control the environment in which the second node performs data collection, and in such a case, when the second node reports the collected data to the first node, it may also transmit information about the environment in which the data was collected. Here, for example, the first node may perform tasks such as learning / inference / performance evaluation of a specific artificial intelligence function and / or model by referring to the collected data and the environment in which the data was collected. For example, if the second node is a UAV terminal, the node may report the path and / or waypoint information along which the data was collected as environmental information related to data collection. In addition, considering cases where the UAV terminal is in manual control mode or moves outside the (pre-)reported path, the second node may report whether the data was collected within the (pre-)reported path when collecting and reporting data. Alternatively, for the purpose of configuring a data distribution utilized in learning artificial intelligence functions and / or models, the second node may report, when reporting the collected data, information on the occurrence frequency and / or distribution of the data collection environment together. Here, for example, the occurrence frequency and / or distribution of the data collection environment may be measured within the section in which data collection is directed or in a separate section.According to the proposed method of the present disclosure, the second node can report to the first node, in addition to the collected data, information about the environment in which the data was collected, and utilize this environmental information to manage the data set for artificial intelligence functions and / or models according to the field situation, which has the advantage of being applicable. The above [Proposed Method #06] can be applied in combination with other proposed methods(s) as long as the proposed operations do not conflict.

[0234] [Proposal #07] In a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or a model from the second node, the first node can set up an anonymous transmission channel based on a virtual node identifier to the second node, and the second node can report data related to the artificial intelligence function and / or the model through the anonymous transmission channel. Here, for example, the anonymous transmission channel can be transmitted based on a contention-based transmission scheme.

[0235] For example, in a next-generation communication system according to an embodiment of the present disclosure, assume that a first node and / or a second node support an artificial intelligence function and / or a model, and that the first node can collect data related to the artificial intelligence function and / or the model from the second node. Here, for example, the data related to the artificial intelligence function and / or the model may include private information. Here, for example, when collecting data on sensitive information and / or private information, data collection may be performed after obtaining permission from the data collection target, but alternatively, data collection based on anonymity may be performed. For example, even if the second node reports private information to the first node, the privacy of the second node can be guaranteed if the identity of the second node is not revealed. Therefore, in the present disclosure, in a mobile communication system comprising a first node and a second node, when the first node can collect data related to an artificial intelligence function and / or a model from the second node, the first node can set up an anonymous transmission channel based on a virtual node identifier to the second node, and the second node can report data related to the artificial intelligence function and / or the model through the anonymous transmission channel. For example, the first node can allocate a virtual node identifier to support the second node to report the collected data through the anonymous transmission channel based on the virtual node identifier. Here, for example, since the anonymous transmission channel can be utilized by any node(s), a contention-based transmission scheme can be applied to avoid potential resource conflicts. According to the proposed method of the present disclosure, there is an advantage in that data collection can be performed regardless of privacy issues by supporting a data collection channel that utilizes anonymity. The above-mentioned [Proposed Method #07] can be applied in combination with other proposed methods(s) within the scope where the proposed operations do not conflict.

[0236] [Proposal #08] In a mobile communication system comprising multiple node(s), when the node(s) can collect data related to artificial intelligence functions and / or models, one or more of the following group-based data collection operations may be supported.

[0237] (1) A method in which each node performs data collection and a representative node compiles data for each node.

[0238] (2) A method in which each node collects data on distinct items among the total number of items for data collection, and a representative node compiles the entire data.

[0239] Here, for example, the representative node may be a node performing relay and / or IAB (integrated access backhaul) functions.

[0240] For example, in a next-generation communication system according to an embodiment of the present disclosure, assume that node(s) can collect data related to artificial intelligence functions and / or models. Here, for example, specific types of terminals, such as UAV terminals, can perform swarm flights (e.g., drone swarms) and maintain communication with each other during the swarm flights. Here, for example, each UAV terminal can perform data collection related to the artificial intelligence function and / or model, and the collected data may be information that needs to be transmitted to a server and / or network. Here, for example, if each UAV terminal in the swarm transmits the collected data, communication costs may be excessive and unintended interference may occur. Therefore, when multiple node(s) collect data related to artificial intelligence functions and / or models as described above, a group-based data collection operation may be introduced. For example, each node(s) can perform data collection, and a representative node can collate the data for each node and transmit it to a server and / or network. For example, the node(s) can each perform data collection on distinct item(s) among the entire item(s) for data collection, and the representative node can collate the entire data and transmit it to the server and / or network. Here, for example, the former example may mean a relay-based data collection function, and the latter example may mean a collaboration-based data collection function. Here, for example, the representative node may have functions and / or capabilities that are distinct from other node(s). For example, the representative node may be a node that performs relay and / or IAB (integrated access backhaul) functions. When following the proposed method of the present disclosure, there is an advantage in that the communication cost for transmitting the collected data can be reduced when collecting data in a group that maintains connectivity with each other.Figure 14 illustrates an example of group-based relay and / or collaboration-based data collection, according to one embodiment of the present disclosure. The embodiment of Figure 14 can be combined with various embodiments of the present disclosure. The above [Proposal #08] can be applied in combination with other proposed methods, as long as the proposed operations do not conflict.

[0241] [Proposal #09] In a mobile communication system comprising multiple node(s), when the node(s) can collect data related to artificial intelligence functions and / or models, the data collected by a specific node for a terrestrial network can be reported via a non-terrestrial network. Here, for example, the specific node may be a node that maintains connectivity with both the terrestrial network and the non-terrestrial network.

[0242] For example, in a next-generation communication system according to an embodiment of the present disclosure, assume that node(s) can collect data related to artificial intelligence functions and / or models. Here, for example, a specific type of terminal, such as a UAV terminal, can maintain connectivity with both a terrestrial network and a non-terrestrial network. Here, for example, the UAV terminal can collect data related to the artificial intelligence function and / or model for the terrestrial network. Here, for example, when the connection between the UAV terminal and the terrestrial network becomes unstable or the connection with the terrestrial network is lost, it may be difficult to report the collected data related to the artificial intelligence function and / or model. Therefore, in the present disclosure, in a mobile communication system composed of a plurality of node(s), when the node(s) can collect data related to the artificial intelligence function and / or model, the data collected by a specific node for the terrestrial network can be reported via a non-terrestrial network. Here, for example, the non-terrestrial network can transmit the collected data via a backbone network with the terrestrial network. According to the proposed method of the present disclosure, a terminal can utilize one or more networks when reporting collected data, thereby supporting smooth data collection. For example, the data may require real-time monitoring rather than logging, and may be used for evaluating the performance of artificial intelligence functions and / or models, for example. [Proposed Method #09] may be applied in combination with other proposed methods, as long as the proposed operations do not conflict.

[0243] FIG. 15 illustrates a method for a first device to perform wireless communication according to an embodiment of the present disclosure. The embodiment of FIG. 15 may be combined with various embodiments of the present disclosure.

[0244] Referring to FIG. 15, in step S1510, the first device may receive configuration information from the second device, including information related to at least one of an artificial intelligence function or model. In step S1520, the first device may perform a measurement on the cluster. In step S1530, the first device may transmit a measurement report obtained based on the measurement to the second device. For example, the measurement may be performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0245] For example, the configuration information may further include information related to the measurement location. For example, the measurement may be performed based on the cluster at the measurement location being associated with at least one of the artificial intelligence functions or models. For example, the measurement location may be at least one of an absolute location, a relative location, an elevation, or a spatial identifier.

[0246] For example, the configuration information may further include information related to a measurement time. For example, the measurement may be performed within the measurement time based on the cluster being associated with at least one of the artificial intelligence functions or models.

[0247] For example, the configuration information may include at least one of information related to the number of measurement samples for the cluster, information related to a time interval between measurement samples for the cluster, or information related to a spatial interval between measurement samples for the cluster.

[0248] For example, the first device may transmit at least one of information related to its data collection capabilities or information related to whether data collection is possible to the second device. For example, the information related to whether data collection is possible may be transmitted to the second device based on a change in the data collection status of the first device.

[0249] For example, the measurement report may include at least one of information related to the route or waypoint from which the measurement report was obtained, information related to whether the measurement report was obtained within the route reported to the second device, or information related to the frequency or distribution of occurrence of the environment from which the measurement report was obtained.

[0250] For example, the above measurement report may be transmitted on a contention basis over an anonymous transmission channel.

[0251] For example, based on the first device belonging to an area where the measurement is enforced, it may not be permissible for the first device to not perform the measurement.

[0252] For example, based on the first device being a type of device for which the measurement is enforced, it may not be permissible for the first device to not perform the measurement.

[0253] For example, the first device may receive a measurement report request for the cluster from the second device. For example, the measurement report may be transmitted to the second device in response to the measurement report request.

[0254] The proposed method can be applied to devices according to various embodiments of the present disclosure. First, the processor (102) of the first device (100) can control the transceiver (106) to receive configuration information including information related to at least one of an artificial intelligence function or model from the second device. Then, the processor (102) of the first device (100) can perform measurement on the cluster. Then, the processor (102) of the first device (100) can control the transceiver (106) to transmit a measurement report obtained based on the measurement to the second device. For example, the measurement can be performed based on whether the cluster is related to at least one of the artificial intelligence function or model.

[0255] According to one embodiment of the present disclosure, a first device may be provided. For example, the first device may include at least one transceiver; at least one processor; and at least one memory connected to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the first device to: receive, from a second device, configuration information including information related to at least one of an artificial intelligence function or model; perform a measurement on a cluster; and transmit a measurement report obtained based on the measurement to the second device. For example, the measurement may be performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0256] According to one embodiment of the present disclosure, a processing device configured to control a first device may be provided. For example, the processing device may include at least one processor; and at least one memory coupled to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the first device to: receive, from a second device, configuration information including information related to at least one of an artificial intelligence function or model; perform a measurement on a cluster; and transmit a measurement report obtained based on the measurement to the second device. For example, the measurement may be performed based on the cluster being associated with at least one of the artificial intelligence function or model.

[0257] According to one embodiment of the present disclosure, a non-transitory computer-readable storage medium having instructions recorded thereon may be provided. For example, the instructions, when executed, may cause a first device to: receive, from a second device, configuration information including information related to at least one of an artificial intelligence function or model; perform a measurement on a cluster; and transmit a measurement report obtained based on the measurement to the second device. For example, the measurement may be performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0258] FIG. 16 illustrates a method for a second device to perform wireless communication according to an embodiment of the present disclosure. The embodiment of FIG. 16 may be combined with various embodiments of the present disclosure.

[0259] Referring to FIG. 16, in step S1610, the second device may transmit configuration information including information related to at least one of the artificial intelligence function or model to the first device. In step S1620, the second device may receive a measurement report from the first device. For example, the measurement report may be obtained based on a measurement of the cluster, which is performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0260] For example, the configuration information may further include information related to the measurement location. For example, the measurement may be performed based on the cluster at the measurement location being associated with at least one of the artificial intelligence functions or models. For example, the measurement location may be at least one of an absolute location, a relative location, an elevation, or a spatial identifier.

[0261] For example, the configuration information may further include information related to a measurement time. For example, the measurement may be performed within the measurement time based on the cluster being associated with at least one of the artificial intelligence functions or models.

[0262] For example, the configuration information may include at least one of information related to the number of measurement samples for the cluster, information related to a time interval between measurement samples for the cluster, or information related to a spatial interval between measurement samples for the cluster.

[0263] For example, the second device may receive from the first device at least one of information related to data collection capabilities or information related to whether data collection is possible. For example, the information related to whether data collection is possible may be received from the first device based on a change in the data collection status of the first device.

[0264] For example, the measurement report may include at least one of information related to the route or waypoint from which the measurement report was obtained, information related to whether the measurement report was obtained within the route reported to the second device, or information related to the frequency or distribution of occurrence of the environment from which the measurement report was obtained.

[0265] For example, the above measurement report may be transmitted on a contention basis over an anonymous transmission channel.

[0266] For example, based on the first device belonging to an area where the measurement is enforced, it may not be permissible for the first device to not perform the measurement.

[0267] For example, based on the first device being a type of device for which the measurement is enforced, it may not be permissible for the first device to not perform the measurement.

[0268] For example, the second device may transmit a measurement report request for the cluster to the first device. For example, the measurement report may be received from the first device in response to the measurement report request.

[0269] The proposed method can be applied to devices according to various embodiments of the present disclosure. First, the processor (202) of the second device (200) can control the transceiver (206) to transmit configuration information including information related to at least one of the artificial intelligence function or model to the first device. Then, the processor (202) of the second device (200) can control the transceiver (206) to receive a measurement report from the first device. For example, the measurement report can be obtained based on a measurement for the cluster, which is performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0270] According to one embodiment of the present disclosure, a second device may be provided. For example, the second device may include at least one transceiver; at least one processor; and at least one memory coupled to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the second device to: transmit configuration information, including information related to at least one of an artificial intelligence function or model, to a first device; and receive a measurement report from the first device. For example, the measurement report may be obtained based on a measurement for a cluster, the measurement being performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0271] According to one embodiment of the present disclosure, a processing device configured to control a second device may be provided. For example, the processing device may include at least one processor; and at least one memory coupled to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the second device to: transmit configuration information, including information related to at least one of an artificial intelligence function or model, to a first device; and receive a measurement report from the first device. For example, the measurement report may be obtained based on a measurement for a cluster, the measurement being performed based on the cluster being related to at least one of the artificial intelligence function or model.

[0272] According to one embodiment of the present disclosure, a non-transitory computer-readable storage medium storing commands may be provided. For example, the commands, when executed, may cause a second device to: transmit configuration information, including information related to at least one of an artificial intelligence function or model, to a first device; and receive a measurement report from the first device. For example, the measurement report may be obtained based on a measurement of a cluster, the measurement being performed based on the cluster's association with at least one of the artificial intelligence function or model.

[0273] According to various embodiments of the present disclosure, terminals can efficiently perform data collection. For example, specific data collection (region-specific) may be mandated for specific terminal types and / or service types. Furthermore, data collection functions that consider the movement path of the terminal may be supported. For example, data collection by path and / or location may be instructed, and the terminal may selectively report data for specific paths and / or locations among the collected data. Furthermore, to reduce costs during the data collection process, data collection may be performed based on a representative terminal during swarm flight, such as an aerial terminal, and the representative terminal may then report the collected data to a base station (or network node). Furthermore, the aerial terminal may determine and report whether to support data collection based on its own operating information (e.g., automatic / manual operation) and battery status, etc.

[0274] The various embodiments of the present disclosure may be combined with each other.

[0275] Below, a description is given of devices to which various embodiments of the present disclosure can be applied.

[0276] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed in this document may be applied to various fields requiring wireless communication / connectivity (e.g., 5G) between devices.

[0277] Hereinafter, more specific examples will be provided with reference to the drawings. In the drawings / descriptions below, the same drawing reference numerals may represent identical or corresponding hardware blocks, software blocks, or functional blocks, unless otherwise described.

[0278] Fig. 17 illustrates a communication system (1) according to one embodiment of the present disclosure. The embodiment of Fig. 17 can be combined with various embodiments of the present disclosure.

[0279] Referring to FIG. 17, a communication system (1) to which various embodiments of the present disclosure are applied includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone) and / or an Aerial Vehicle (AV) (e.g., an Advanced Air Mobility (AAM)). The XR device may include an Augmented Reality (AR) / Virtual Reality (VR) / Mixed Reality (MR) device, and may be implemented in the form of a Head-Mounted Device (HMD), a Head-Up Display (HUD) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, etc. The portable device may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), a computer (e.g., a laptop, etc.), etc. The home appliance may include a TV, a refrigerator, a washing machine, etc. The IoT device may include a sensor, a smart meter, etc. For example, a base station and a network may also be implemented as a wireless device, and a specific wireless device (200a) may operate as a base station / network node to other wireless devices.

[0280] Here, the wireless communication technology implemented in the wireless devices (100a to 100f) of the present specification may include not only LTE, NR, and 6G, but also Narrowband Internet of Things for low-power communication. At this time, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology, and may be implemented with standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless devices (100a to 100f) of the present specification may perform communication based on LTE-M technology. At this time, for example, LTE-M technology may be an example of LPWAN technology, and may be called by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology can be implemented by at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless devices (100a to 100f) of the present specification can include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) considering low-power communication, and is not limited to the above-described names. For example, ZigBee technology can create personal area networks (PAN) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and can be called by various names.

[0281] Wireless devices (100a to 100f) can be connected to a network (300) via a base station (200). Artificial Intelligence (AI) technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) via the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, etc. The wireless devices (100a to 100f) can communicate with each other via the base station (200) / network (300), but can also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to Everything) communication). In addition, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0282] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base stations (200), and base stations (200) / base stations (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR) such as uplink / downlink communication (150a), sidelink communication (150b) (or, D2D communication), and communication between base stations (150c) (e.g., relay, IAB (Integrated Access Backhaul). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, at least some of various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and resource allocation processes can be performed based on various proposals of the present disclosure.

[0283] FIG. 18 illustrates a wireless device according to an embodiment of the present disclosure. The embodiment of FIG. 18 may be combined with various embodiments of the present disclosure.

[0284] Referring to FIG. 18, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} can correspond to {the wireless device (100x), the base station (200)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 17.

[0285] A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may further include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memories (104) and / or the transceivers (106), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (102) may process information in the memory (104) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (106). Furthermore, the processor (102) may receive a wireless signal including second information / signal via the transceiver (106), and then store information obtained from signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may perform some or all of the processes controlled by the processor (102), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals via one or more antennas (108). The transceiver (106) may include a transmitter and / or a receiver. The transceiver (106) may be used interchangeably with an RF (Radio Frequency) unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0286] A second wireless device (200) includes one or more processors (202), one or more memories (204), and may further include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memories (204) and / or the transceivers (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202) may process information in the memory (204) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206). In addition, the processor (202) may receive a wireless signal including fourth information / signals via the transceivers (206), and then store information obtained from signal processing of the fourth information / signals in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may perform some or all of the processes controlled by the processor (202), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via one or more antennas (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) may be used interchangeably with an RF unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0287] Hereinafter, the hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) can generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed herein, and provide the signals to one or more transceivers (106, 206). One or more processors (102, 202) can receive signals (e.g., baseband signals) from one or more transceivers (106, 206) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0288] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software configured to perform one or more processors (102, 202) or stored in one or more memories (104, 204) and executed by one or more processors (102, 202). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0289] One or more memories (104, 204) may be coupled to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (104, 204) may be configured as ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer-readable storage media, and / or combinations thereof. The one or more memories (104, 204) may be located internally and / or externally to the one or more processors (102, 202). Additionally, the one or more memories (104, 204) may be coupled to the one or more processors (102, 202) via various technologies, such as wired or wireless connections.

[0290] One or more transceivers (106, 206) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this document, to one or more other devices. One or more transceivers (106, 206) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this document, from one or more other devices. For example, one or more transceivers (106, 206) can be connected to one or more processors (102, 202) and can transmit and receive wireless signals. For example, one or more processors (102, 202) can control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be coupled to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein, via one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (102, 202).One or more transceivers (106, 206) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (102, 202) from baseband signals to RF band signals. For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and / or filter.

[0291] FIG. 19 illustrates a signal processing circuit for a transmission signal according to an embodiment of the present disclosure. The embodiment of FIG. 19 can be combined with various embodiments of the present disclosure.

[0292] Referring to FIG. 19, the signal processing circuit (1000) may include a scrambler (1010), a modulator (1020), a layer mapper (1030), a precoder (1040), a resource mapper (1050), and a signal generator (1060). Although not limited thereto, the operations / functions of FIG. 19 may be performed in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 18. The hardware elements of FIG. 19 may be implemented in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 18. For example, blocks 1010 to 1060 may be implemented in the processor (102, 202) of FIG. 18. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 18, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 18.

[0293] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 19. Here, the codeword is an encoded bit sequence of an information block. The information block can include a transport block (e.g., an UL-SCH transport block, a DL-SCH transport block). The wireless signal can be transmitted through various physical channels (e.g., a PUSCH or a PDSCH).

[0294] Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (1010). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (1020). The modulation method may include pi / 2-BPSK (pi / 2-Binary Phase Shift Keying), m-PSK (m-Phase Shift Keying), m-QAM (m-Quadrature Amplitude Modulation), etc. The complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (1030). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (1040) (precoding). The output z of the precoder (1040) can be obtained by multiplying the output y of the layer mapper (1030) by a precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (1040) can perform precoding after performing transform precoding (e.g., DFT transform) on complex modulation symbols. In addition, the precoder (1040) can perform precoding without performing transform precoding.

[0295] The resource mapper (1050) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. The signal generator (1060) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna. To this end, the signal generator (1060) can include an Inverse Fast Fourier Transform (IFFT) module, a Cyclic Prefix (CP) inserter, a Digital-to-Analog Converter (DAC), a frequency uplink converter, etc.

[0296] The signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (1010 to 1060) of FIG. 19. For example, a wireless device (e.g., 100, 200 of FIG. 18) can receive wireless signals from the outside through an antenna port / transceiver. The received wireless signals can be converted into baseband signals through a signal restorer. For this purpose, the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codewords can be restored to the original information blocks through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0297] FIG. 20 illustrates a wireless device according to an embodiment of the present disclosure. The wireless device may be implemented in various forms depending on the use case / service (see FIG. 17). The embodiment of FIG. 20 may be combined with various embodiments of the present disclosure.

[0298] Referring to FIG. 20, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 18 and may be composed of various elements, components, units / units, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and additional elements (140). The communication unit may include a communication circuit (112) and a transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 18. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 18. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and the additional elements (140) and controls the overall operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (130). In addition, the control unit (120) may transmit information stored in the memory unit (130) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (130).

[0299] The additional element (140) may be configured in various ways depending on the type of the wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 17, 100a), a vehicle (Fig. 17, 100b-1, 100b-2), an XR device (Fig. 17, 100c), a portable device (Fig. 17, 100d), a home appliance (Fig. 17, 100e), an IoT device (Fig. 17, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 17, 400), a base station (Fig. 17, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0300] In FIG. 20, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). In addition, each element, component, unit / part, and / or module within the wireless device (100, 200) may further include one or more elements. For example, the control unit (120) may be composed of one or more processor sets. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of a random access memory (RAM), a dynamic RAM (DRAM), a read only memory (ROM), a flash memory, a volatile memory, a non-volatile memory, and / or a combination thereof.

[0301] Below, the implementation example of Fig. 20 is described in more detail with reference to the drawings.

[0302] FIG. 21 illustrates a mobile device according to an embodiment of the present disclosure. The mobile device may include a smartphone, a smart pad, a wearable device (e.g., a smartwatch, smartglasses), or a portable computer (e.g., a laptop, etc.). The mobile device may be referred to as a Mobile Station (MS), a User Terminal (UT), a Mobile Subscriber Station (MSS), a Subscriber Station (SS), an Advanced Mobile Station (AMS), or a Wireless Terminal (WT). The embodiment of FIG. 21 may be combined with various embodiments of the present disclosure.

[0303] Referring to FIG. 21, the portable device (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a memory unit (130), a power supply unit (140a), an interface unit (140b), and an input / output unit (140c). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 20, respectively.

[0304] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (120) can control components of the mobile device (100) to perform various operations. The control unit (120) can include an AP (Application Processor). The memory unit (130) can store data / parameters / programs / codes / commands required for operating the mobile device (100). In addition, the memory unit (130) can store input / output data / information, etc. The power supply unit (140a) supplies power to the mobile device (100) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (140b) can support connection between the mobile device (100) and other external devices. The interface unit (140b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (140c) can input or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (140c) may include a camera, a microphone, a user input unit, a display unit (140d), a speaker, and / or a haptic module.

[0305] For example, in the case of data communication, the input / output unit (140c) obtains information / signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information / signals can be stored in the memory unit (130). The communication unit (110) converts the information / signals stored in the memory into wireless signals, and can directly transmit the converted wireless signals to other wireless devices or to a base station. In addition, the communication unit (110) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information / signals. The restored information / signals can be stored in the memory unit (130) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (140c).

[0306] Figure 22 illustrates a vehicle or autonomous vehicle according to one embodiment of the present disclosure. The vehicle or autonomous vehicle may be implemented as a mobile robot, a car, a train, a manned or unmanned aerial vehicle (AV), a ship, or the like. The embodiment of Figure 22 may be combined with various embodiments of the present disclosure.

[0307] Referring to FIG. 22, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 / 130 / 140a to 140d correspond to blocks 110 / 130 / 140 of FIG. 20, respectively.

[0308] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, road side units, etc.), and servers. The control unit (120) can control elements of the vehicle or autonomous vehicle (100) to perform various operations. The control unit (120) can include an ECU (Electronic Control Unit). The drive unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The drive unit (140a) can include an engine, a motor, a power train, wheels, brakes, a steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and can include a wired / wireless charging circuit, a battery, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an incline sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement a technology for maintaining a driving lane, a technology for automatically controlling speed such as adaptive cruise control, a technology for automatically driving along a set path, a technology for automatically setting a path and driving when a destination is set, etc.

[0309] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving route and driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or autonomous vehicle (100) moves along the autonomous driving route according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can irregularly / periodically acquire the latest traffic information data from an external server and can acquire surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving route and driving plan based on newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving route, driving plan, etc. to the external server. External servers can predict traffic information data in advance using AI technology or other technologies based on information collected from vehicles or autonomous vehicles, and provide the predicted traffic information data to the vehicles or autonomous vehicles.

[0310] The claims set forth in this specification may be combined in various ways. For example, the technical features of the method claims of this specification may be combined and implemented as a device, and the technical features of the device claims of this specification may be combined and implemented as a method. Furthermore, the technical features of the method claims and the technical features of the device claims of this specification may be combined and implemented as a device, and the technical features of the method claims and the technical features of the device claims of this specification may be combined and implemented as a method.

Claims

1. In the method, A step in which a first device receives, from a second device, configuration information including information related to at least one of an artificial intelligence function or model; a step of performing measurements on a cluster; and A step of transmitting a measurement report obtained based on the above measurement to the second device; including; A method wherein the above measurement is performed based on the cluster being associated with at least one of the artificial intelligence functions or models.

2. In paragraph 1, A method wherein the above setting information further includes information related to a measurement location.

3. In paragraph 2, A method wherein the measurement is performed based on the cluster at the measurement location being associated with at least one of the artificial intelligence functions or models.

4. In paragraph 2, A method wherein the above measurement position is at least one of an absolute position, a relative position, a height, or a spatial identifier.

5. In paragraph 1, A method wherein the above setting information further includes information related to the measurement time.

6. In paragraph 5, A method wherein the measurement is performed within the measurement time based on the cluster being associated with at least one of the artificial intelligence functions or models.

7. In paragraph 1, A method wherein the above configuration information includes at least one of information related to the number of measurement samples for the cluster, information related to a time interval between measurement samples for the cluster, or information related to a spatial interval between measurement samples for the cluster.

8. In paragraph 1, A method further comprising the step of transmitting at least one of information related to data collection capability or information related to whether data collection is possible to the second device.

9. In paragraph 8, A method in which information related to whether the data can be collected is transmitted to the second device based on the data collection status of the first device being switched.

10. In paragraph 1, A method wherein the measurement report includes at least one of information related to the route or waypoint from which the measurement report was obtained, information related to whether the measurement report was obtained within the route reported to the second device, or information related to the frequency or distribution of occurrence of the environment from which the measurement report is obtained.

11. In paragraph 1, The above measurement report is transmitted based on competition through an anonymous transmission channel, the method.

12. In paragraph 1, A method wherein it is not permissible for the first device to not perform the measurement based on the first device belonging to an area in which the measurement is enforced, or based on the first device being a type of device in which the measurement is enforced.

13. In paragraph 1, Further comprising: a step of receiving a measurement report request for the cluster from the second device; A method wherein the above measurement report is transmitted to the second device in response to the above measurement report request.

14. In the first device, At least one transmitter / receiver; at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said first device causes: Receive from the second device configuration information including information related to at least one of the artificial intelligence functions or models; to perform measurements on the cluster; and Transmit the measurement report obtained based on the above measurement to the second device, A first device, wherein the above measurement is performed based on the cluster being associated with at least one of the artificial intelligence functions or models.

15. In a processing device set to control the first device, at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said first device causes: Receive from the second device configuration information including information related to at least one of the artificial intelligence functions or models; to perform measurements on the cluster; and Transmit the measurement report obtained based on the above measurement to the second device, A processing device wherein the measurement is performed based on the cluster being associated with at least one of the artificial intelligence functions or models.

16. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the first device to: Receive from the second device configuration information including information related to at least one of the artificial intelligence functions or models; to perform measurements on the cluster; and Transmit the measurement report obtained based on the above measurement to the second device, A non-transitory computer-readable storage medium wherein the measurement is performed based on the cluster being associated with at least one of the artificial intelligence functions or models.

17. In the method, A step in which the second device transmits, to the first device, configuration information including information related to at least one of an artificial intelligence function or model; and A step of receiving a measurement report from the first device; A method wherein the above measurement report is obtained based on measurements for the cluster, wherein the cluster is performed based on at least one of the artificial intelligence functions or models.

18. In the second device, At least one transmitter / receiver; at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said second device causes: Transmitting to the first device configuration information including information related to at least one of the artificial intelligence functions or models; and To receive a measurement report from the first device, A second device, wherein the above measurement report is obtained based on measurements for the cluster, wherein the cluster is performed based on at least one of the artificial intelligence functions or models.

19. In a processing device set to control a second device, at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said second device causes: Transmitting to the first device configuration information including information related to at least one of the artificial intelligence functions or models; and To receive a measurement report from the first device, A processing device wherein the above measurement report is obtained based on measurements for the cluster, wherein the cluster is performed based on at least one of the artificial intelligence functions or models.

20. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the second device to: Transmitting to the first device configuration information including information related to at least one of the artificial intelligence functions or models; and To receive a measurement report from the first device, A non-transitory computer-readable storage medium, wherein the measurement report is obtained based on measurements for the cluster, wherein the cluster is performed based on at least one of the artificial intelligence functions or models.

Images