Method and apparatus for measuring sensing service quality index

Abstract

A method by which a first device performs wireless communication, and an apparatus for supporting same are provided. The method may comprise the steps of: acquiring a first threshold value related to the accuracy or the reliability of sensing; acquiring information related to the accuracy or the reliability of an object on the basis of performing the sensing; and acquiring a missed detection probability on the basis of a comparison between the first threshold value and the information related to the accuracy or the reliability. For example, the missed detection probability can be obtained on the basis of the number of missed-detected objects and the number of non-missed-detected objects.

Description

Method and device for measuring sensing service quality indicators

[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] Per device peak data rate 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 vehicle Fully XR Fully haptic communication Fully

[0005] In one embodiment, a method for a first device to perform wireless communication is provided. The method may include: obtaining a first threshold value related to the accuracy or reliability of sensing; obtaining information related to the accuracy or reliability of an object based on performing the sensing; and obtaining a missing detection probability based on a comparison of the information related to the accuracy or reliability with the first threshold value. For example, the missing detection probability may be obtained based on the number of objects that were missed and the number of objects that were not missed.

[0006] In one embodiment, a first device configured to perform wireless communication is provided. 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, when executed by the at least one processor, may cause the first device to: obtain a first threshold associated with accuracy or reliability of sensing; obtain information related to the accuracy or reliability of an object based on performing the sensing; and obtain a missing detection probability based on a comparison of the information related to the accuracy or reliability with the first threshold. For example, the missing detection probability may be obtained based on the number of objects that were missed and the number of objects that were not missed.

[0007] In one embodiment, a processing device configured to control a first device is provided. The processing device comprises at least one processor; and at least one memory coupled to the at least one processor and storing instructions, wherein the instructions, when executed by the at least one processor, cause the first device to: obtain a first threshold associated with accuracy or reliability of sensing; obtain information associated with the accuracy or reliability of an object based on performing the sensing; and obtain a missing detection probability based on a comparison of the information associated with the accuracy or reliability with the first threshold. For example, the missing detection probability may be obtained based on the number of objects that were missing detected and the number of objects that were not missing detected.

[0008] In one embodiment, a non-transitory computer-readable storage medium having instructions recorded thereon is provided. The instructions, when executed, may cause a first device to: obtain a first threshold associated with the accuracy or reliability of sensing; obtain information associated with the accuracy or reliability of an object based on performing the sensing; and obtain a missing detection probability based on a comparison of the information associated with the accuracy or reliability with the first threshold. For example, the missing detection probability may be obtained based on the number of objects that were missed and the number of objects that were not missed.

[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 an example of a sensing operation according to one embodiment of the present disclosure.

[0017] FIG. 9 illustrates a method for calculating a missing detection probability or an error detection probability based on the accuracy or reliability of sensing, according to one embodiment of the present disclosure.

[0018] FIG. 10 illustrates a method for calculating a miss detection probability or an error detection probability based on the intensity or SINR of a sensing signal, according to one embodiment of the present disclosure.

[0019] FIG. 11 illustrates a method for a first device to perform wireless communication according to one embodiment of the present disclosure.

[0020] FIG. 12 illustrates a method for a second device to perform wireless communication according to one embodiment of the present disclosure.

[0021] FIG. 13 illustrates a communication system (1) according to one embodiment of the present disclosure.

[0022] FIG. 14 illustrates a wireless device according to an embodiment of the present disclosure.

[0023] FIG. 15 illustrates a signal processing circuit for a transmission signal according to one embodiment of the present disclosure.

[0024] FIG. 16 illustrates a wireless device according to one embodiment of the present disclosure.

[0025] FIG. 17 illustrates a mobile device according to one embodiment of the present disclosure.

[0026] 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."

[0027] 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."

[0028] 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.”

[0029] 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.”

[0030] 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."

[0031] In the following explanation, ‘when, if, in case of’ can be replaced with ‘based on’.

[0032] Technical features individually described in one drawing in this disclosure may be implemented individually or simultaneously.

[0033] 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.

[0034] 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) from a base station or a network. In the present disclosure, "setting or defining" may be interpreted as being preset to a device. 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 preset to a device.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0039] 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).

[0040] 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., channel used, whether 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 prior to receiving the system information. For example, the request and provision of system information may be performed after a random access procedure described below.

[0041] 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).

[0042] 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.

[0043] 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.

[0044] 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.

[0045] FIG. 2 illustrates a radio protocol architecture according to an embodiment of the present disclosure. The embodiment of FIG. 2 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted. 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.

[0046] 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 (e.g., 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.

[0047] 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.

[0048] 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).

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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).

[0053] FIG. 3 illustrates the structure of a wireless frame according to an embodiment of the present disclosure. The embodiment of FIG. 3 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0054] 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).

[0055] 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).

[0056] 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.

[0057] CP type SCS (15*2 u )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

[0058] 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.

[0059] 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.

[0060] FIG. 4 illustrates a slot structure of a frame according to an embodiment of the present disclosure. The embodiment of FIG. 4 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] FIG. 5 illustrates an example of a BWP according to an embodiment of the present disclosure. The embodiment of FIG. 5 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted. In the embodiment of FIG. 5, it is assumed that there are three BWPs.

[0065] 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.

[0066] 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.

[0067] 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 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0068] 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.

[0069] - 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. For example, 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. AI can also 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.

[0070] - 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.

[0071] - Large-scale MIMO technology

[0072] - Hologram beamforming (HBF)

[0073] - Optical wireless technology

[0074] - Free-space optical transmission backhaul network (FSO backhaul network)

[0075] - Quantum communication

[0076] - Cell-free communication

[0077] - Integration of wireless information and power transmission

[0078] - Integration of wireless communication and sensing

[0079] - Integrated access and backhaul network

[0080] - Big data analysis

[0081] - Reconfigurable intelligent surface

[0082] - metaverse

[0083] - Blockchain

[0084] 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).

[0085] - 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.

[0086] 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.

[0087] - Integrated sensing and communication (ISAC)

[0088] - 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.

[0089] 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, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0090] 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.

[0091] Below, the integrated sensing and communication (ISAC) mentioned above is described in detail.

[0092] Integrated Sensing and Communications (ISAC) 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 environment and / or the characteristics of objects within the environment. Because radio frequency sensing does not require a device to connect to the object through a network, it can provide services for object positioning without a device. The ability to obtain range, velocity, and angle information from radio frequency signals can enable a wide range of new capabilities, such as various object detection, object recognition (e.g., vehicles, humans, animals, UAVs), and high-precision localization, tracking, and activity recognition. Wireless sensing services can provide information to a variety of industries (e.g., unmanned aerial vehicles, smart homes, V2X, factories, railways, public safety, etc.), enabling applications such as intruder detection, assisted vehicle steering and navigation, trajectory tracking, collision avoidance, traffic management, and health and traffic management. In some cases, wireless sensing can utilize non-3GPP type sensors (e.g., radar, cameras) to further support 3GPP-based sensing. For example, the operation of a wireless sensing service (e.g., sensing operation) may depend on the transmission, reflection, and scattering processing of wireless sensing signals. Therefore, wireless sensing may provide an opportunity to enhance existing communication systems from communication networks to wireless communication and sensing networks. FIG. 8 illustrates an example of a sensing operation according to an embodiment of the present disclosure. The embodiment of FIG. 8 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted. Specifically, FIG. 8 (a) illustrates an example of sensing using a sensing receiver and a sensing transmitter located at the same location (e.g., monostatic sensing), and FIG. 8 (b) illustrates an example of sensing using a separated sensing receiver and a sensing transmitter (e.g., bistatic sensing).

[0093] The present disclosure may be applied to operations for positioning and / or operations for integrated sensing and communications (ISAC).

[0094] In this disclosure, the following terms may be used.

[0095] - Sensing RS (reference signal): Reference signal used for measurement for sensing purposes

[0096] - BS-BS sensing: Sensing in which BS#1 transmits a sensing RS and BS#2 receives the sensing RS. For example, if BS#1 and BS#2 are separate BSs, this may mean a BS-BS bi-static sensing operation. For example, if BS#1 and BS#2 are the same BS, this may mean a BS-BS mono-static sensing operation. For example, the BS may be a base station or a transmission and reception point (TRP). For example, if BS#1 and / or BS#2 are one or more BSs, this may mean a BS-BS multi-static sensing operation.

[0097] - BS-UE sensing: Sensing in which a BS transmits a sensing RS and a UE receives the sensing RS. For example, the BS may be a base station or a transmission and reception point (TRP). For example, if the BS and / or the UE are one or more BSs and / or one or more UEs, this may refer to a BS-UE multi-static sensing operation.

[0098] - UE-BS sensing: Sensing in which a UE transmits a sensing RS and a BS receives the sensing RS. For example, the BS may be a base station or a transmission and reception point (TRP). For example, if the BS and / or the UE are one or more BSs and / or one or more UEs, this may refer to a UE-BS multi-static sensing operation.

[0099] - UE-UE sensing: Sensing in which UE#1 transmits a sensing RS and UE#2 receives the sensing RS. For example, if UE#1 and UE#2 are separate UEs, this may mean a UE-UE bi-static sensing operation. For example, if UE#1 and UE#2 are the same UE, this may mean a UE-UE mono-static sensing operation. For example, the BS may be a base station or a transmission and reception point (TRP). For example, if UE#1 and / or UE#2 are one or more UEs, this may mean a UE-UE multi-static sensing operation.

[0100] - LMF: Location Management Function

[0101] - SMF: Sensing Management Function

[0102] - TSA: Target Sensing Area

[0103] - TP (transmission point): A set of geographically co-located transmitting antennas (e.g., an antenna array composed of one or more antenna elements) for a cell, a portion of a cell, or a DL PRS-only TP. A transmission point may include a base station (e.g., ng-eNB or gNB) antenna, a remote radio head, a remote antenna of a base station, an antenna of a DL PRS-only TP, etc. A cell may include one or more transmission points. In case of a homogeneous deployment, each transmission point may correspond to one cell.

[0104] - Reception point (RP): A set of geographically co-located transmitting antennas (e.g., an antenna array composed of one or more antenna elements) for a cell, a portion of a cell, or a UL SRS-only RP. The transmission point may include a base station (e.g., ng-eNB or gNB) antenna, a remote radio head, a remote antenna of the base station, an antenna of a UL SRS-only RP, etc. A cell may include one or more reception points. In a homogeneous deployment, each reception point may correspond to one cell.

[0105] - PRS-only TP: A TP that transmits only PRS signals for PRS-based TBS positioning and is not related to a cell.

[0106] - TRP (transmission-reception point): An antenna (e.g., an antenna array consisting of one or more antenna elements) geographically co-located to support TP and / or RP functions.

[0107] - SRS-only RP: An RP that receives only SRS signals for UL-only positioning and is not related to a cell.

[0108] In the present disclosure, the TRP and the base station may be replaced and used as the same entity.

[0109] In the present disclosure, the sensing signal and the sensing RS (reference signal) can be interpreted and used interchangeably.

[0110] In the embodiments of the present disclosure, “a specific threshold” may mean a threshold defined in advance or set (in advance) by a network or a base station or a higher layer of a terminal (e.g., including an application layer).

[0111] In the embodiments of the present disclosure, “specific setting value” may mean a value defined in advance or set (in advance) by a network or a base station or a higher layer of a terminal (e.g., including an application layer).

[0112] In the embodiments of the present disclosure, “configured by the network / base station” may mean an operation in which the base station configures (in advance) the UE via higher layer signaling (e.g., RRC signaling), configures / signals the UE via MAC CE, or signals the UE via DCI (downlink control information).

[0113] In embodiments of the present disclosure, “message” may be interpreted as being replaced with a control message or a data message or a signal or a data signal or a control signal.

[0114] Below, communication QoS is explained.

[0115] In a communications system, Quality of Service (QoS) refers to the quality of communication service. It refers to the ability to guarantee a certain level of performance (e.g., latency and data loss) for data transmission. For example, it can manipulate the operation of a wireless network to suit essential operations, such as traffic-generating applications.

[0116] In LTE communication systems, QoS can be configured based on QoS Class Identifiers (QCIs). The LTE network can assign a specific QCI value to a UE for each EPS bearer. The assigned QCI value can be assigned to the UE by the network through the Activate default EPS bearer context request message and the Activate dedicated EPS bearer context request message.

[0117] According to TS 23.303, QCI values ​​can be distinguished based on characteristics such as resource type, priority, packet delay budget, packet loss rate, maximum data size, and average data rate.

[0118] The contents of the standards related to the above are as follows.

[0119] - Standardized QCI characteristics

[0120] The standardized characteristics associated with the standardized QCI values ​​are as follows. These characteristics describe the edge-to-edge packet forwarding processing between the UE and the PCEF, where the SDF aggregate receives the packets, in terms of the following performance characteristics:

[0121] (1) Resource type (GBR or non-GBR);

[0122] (2) Priority;

[0123] (3) Packet delay budget;

[0124] (4) Packet error loss rate;

[0125] (5) Maximum data burst volume (for some GBR QCIs);

[0126] (6) Data rate averaging window (for some GBR QCIs)

[0127] For LTE sidelink, according to TS 23.285 and TS 23.303, in V2X communication using LTE PC5, the V2X application layer of the UE can configure two parameters: ProSe Per-Packet Priority (PPPP) and ProSe Per-Packet Reliability (PPPR). The PPPP and PPPR values ​​can be configured in the Sidelink Radio Bearer (SLRB).

[0128] In 5G communication systems, the QoS model is based on QoS flows. A specific QoS flow is identified by a QoS Flow ID (QFI). QFIs are used in all PDU session types. A QoS flow has a QoS profile. A QoS profile contains QoS parameters, which can be communicated to the RAN.

[0129] According to TS 23.501, QoS parameters consist of several parameters, including 5QI (5G QoS Identifier), ARP, RQA, GFBR, and MFBR.

[0130] The contents of the standards related to the above are as follows.

[0131] - QoS profile

[0132] A QoS flow can be GBR or non-GBR, depending on its QoS profile. The QoS profile of a QoS flow is transmitted to (R)AN and contains the QoS parameters described below:

[0133] For example, for each QoS flow, the QoS profile may contain the following QoS parameters:

[0134] - 5QI (5G QoS Identifier);

[0135] - ARP (Allocation and Retention Priority)

[0136] For example, for each non-GBR QoS flow only, the QoS profile may include the following QoS parameters:

[0137] - RQA(Reflective QoS Attribute)

[0138] For example, for each GBR QoS flow only, the QoS profile may contain the following QoS parameters:

[0139] - Guaranteed Flow Bit Rate (GFBR) - UL and DL;

[0140] - MFBR (Maximum Flow Bit Rate) - UL and DL;

[0141] For example, for GBR QoS flows only, the QoS profile may include one or more of the following QoS parameters:

[0142] - Notification control

[0143] - Maximum Packet Loss Rate - UL and DL

[0144] Note that in this release, maximum packet loss rates (UL, DL) are only provided for GBR QoS flows belonging to voice media.

[0145] For example, each QoS profile may have one corresponding QoS Flow identifier (QFI) that is not included in the QoS profile itself.

[0146] For example, the use of dynamically allocated 5QIs for QoS flows may require additional signaling of the overall 5G QoS characteristics as part of the QoS profile.

[0147] For example, if standardized or preset 5QIs are used for QoS flows, some 5G QoS characteristics may be signaled as part of the QoS profile.

[0148] The 5QI values ​​standardized in TS 23.501 are shown in Table 3 below.

[0149] 5QI Value Resource Type Default Priority Level Packet Delay Budget (NOTE 3) Packet Error Rate Default Maximum Data Burst Volume (NOTE 2) Default Averaging Window Example Service 1 GBR 20 100 ms (NOTE 11,NOTE 13) 10 -2N / A2000 ms Conversational Voice2(NOTE 1)40150 ms(NOTE 11,NOTE 13)10 -3 N / A2000 msInteractive voice (live streaming)33050 ms(NOTE 11,NOTE 13)10 -3 N / A 2000 ms Real-time gaming, V2X messaging (see TS 23.287). Electricity distribution - medium voltage, process automation monitoring 450 300 ms (NOTE 11, NOTE 13) 10 -6 N / A2000 msNon-interactive video (buffered streaming)65 (NOTE 9, NOTE 12)775 ms (NOTE 7, NOTE 8)10 -2 N / A 2000 ms Mission-critical user plane push-to-talk voice (e.g., MCPTT) 66 (NOTE 12) 20 100 ms (NOTE 10,NOTE 13) 10 -2 N / A 2000 ms Non-mission critical user plane Push-to-talk Voice 67 (NOTE 12) 15 100 ms (NOTE 10,NOTE 13) 10 -3 N / A2000 msMission Critical Video User Plane75(NOTE 14)2550 ms(NOTE 13)10 -2 N / A2000 msV2X message (see TS 23.287).A2X message (see TS 23.256).7156150 ms(NOTE 11, NOTE 13, NOTE 15).10 -6 N / A2000 msLive uplink streaming (e.g., TS 26.238)7256300 ms(NOTE 11, NOTE 13, NOTE 15)10 -4 N / A2000 msLive uplink streaming (e.g., TS 26.238)7356300 ms(NOTE 11, NOTE 13, NOTE 15)10 -8 N / A2000 msLive uplink streaming (e.g., TS 26.238)7456500 ms(NOTE 11, NOTE 15)10 -8N / A2000 msLive uplink streaming (e.g., TS 26.238)7656500 ms (NOTE 11, NOTE 13, NOTE 15)10 -4 N / A 2000 ms Live uplink streaming (e.g., TS 26.238) 5 Non-GBR 10 100 ms (NOTE 10,NOTE 13) 10 -6 N / AN / AIMS signal 6 (NOTE 1) 60300 ms (NOTE 10,NOTE 13) 10 -6 N / AN / A video (buffered streaming), TCP-based (e.g., www, email, chat, FTP, P2P file sharing, progressive video, etc.), AI / ML model download for image recognition (e.g., for model topology) (see TS 22.261) 770 100 ms (NOTE 10,NOTE 13) 10 -3 N / AN / A AI / ML model download (e.g., model weighting factors) for speech, video (live streaming), interactive games, and image recognition (see TS 22.261) 880300 ms (NOTE 10, NOTE 13) 10 -6 N / AN / A video (buffered streaming), TCP-based (e.g., www, email, chat, FTP, P2P file sharing, progressive video, etc.) 990 video, etc. 1090 1100 ms (NOTE 10, NOTE 13, NOTE 17) 10 -6 N / AN / A Video (buffered streaming) TCP-based (e.g., www, email, chat, FTP, P2P file sharing, progressive video, etc.) and all services available via satellite connection types with these characteristics69 (NOTE 9, NOTE 12)560 ms (NOTE 7, NOTE 8)10 -6 N / AN / A Mission-critical delay-sensitive signals (e.g., MC-PTT signals) 70 (NOTE 12) 55200 ms (NOTE 7,NOTE 10) 10 -6 N / AN / A Mission Critical Data (e.g., example service is equivalent to 5QI 6 / 8 / 9) 796550 ms (NOTE 10,NOTE 13) 10 -2N / AN / AV2X message (see TS 23.287) 806810 ms (NOTE 5,NOTE 10) 10 -6 N / AN / A Low-latency eMBB applications Augmented reality 82 Latency-critical GBR 19 10 ms (NOTE 4) 10 -4 255 bytes 2000 ms Discrete Automation (see TS 22.261) 832210 ms (NOTE 4) 10 -4 1354 bytes (NOTE 3) 2000 ms Discrete Automation (see TS 22.261); V2X Messages (UE-RSU Platooning, Advanced Driving: Cooperative Lane Changing with Low LoA, see TS 22.186, TS 23.287) 842430 ms (NOTE 6) 10 -5 1354 bytes (NOTE 3) 2000 ms Intelligent Transport Systems (see TS 22.261) 85215 ms (NOTE 5) 10 -5 255 bytes 2000 ms Electricity distribution - High voltage (see TS 22.261), V2X messages (remote driving, see TS 22.186, NOTE 16, TS 23.287) Split AI / ML inference - DL Split AI / ML image recognition (see TS 22.261) 86 185 ms (NOTE 5) 10 -4 1354 bytes 2000 ms V2X message (Advanced driving: collision avoidance, platooning using high LoA. See TS 22.186, TS 23.287) 87255 ms (NOTE 4) 10 -3 500 bytes 2000 ms Interactive Service - Motion Tracking Data (see TS 22.261) 882510 ms (NOTE 4) 10 -3 1125 bytes 2000 ms Interactive Services - Motion Tracking Data (see TS 22.261), Segmentation AI / ML Inference - UL Segmentation AI / ML Image Recognition (see TS 22.261) 892515 ms (NOTE 4) 10 -4 17000 bytes 2000 ms Visual content for cloud / edge / split rendering (see TS 22.261) 902520 ms (NOTE 4) 10-4 63000 bytes 2000 ms Visual content for cloud / edge / split rendering (see TS 22.261)

[0150] In 5G NR sidelink, NR PC5, like Uu link, is based on PC5 QoS flows according to TS 23.287. A PC5 QoS flow is associated with PC5 QoS rules and PC5 QoS parameters. The UE manages a PC5 QoS context for each communication mode (e.g., broadcast, groupcast, unicast) of a specific destination, identified by a destination layer-2 ID, or for each PC5 QoS flow. This is identified by a PC5 QoS Flow Identifier (PFI). Therefore, a PC5 QoS context includes a PFI and PC5 QoS parameters. A PQI (PC5 5QI), similar to the 5G QoS Identifier (5QI), is used for PC5 QoS parameters.

[0151] The PQI values ​​standardized in TS 23.287 are shown in Table 4 below.

[0152] PQI Value Resource Type Default Priority Level Packet Delay Budget Packet Error Rate Default Maximum Data Burst Volume Default Averaging Windows Example Service 21GBR 320 ms 10 -4 N / A 2000 ms UE-to-UE platooning - higher level of automation; UE-to-RSU platooning - higher level of automation 22 (NOTE 1) 450 ms 10 -2 N / A2000 msSensor sharing - Higher level of automation233100 ms10 -4 N / A2000 msInformation sharing for automated driving - between UEs or between UEs and RSUs - Higher level of automation55Non-GBR310 ms10 -4 N / AN / A Cooperative Lane Change - Higher Level of Automation 56620 ms10-1 N / AN / A Platooning Information Exchange - Low-Level Automation; Platooning - Information Sharing with RSUs 57525 ms10 -1 N / AN / A Cooperative Lane Change - Low Automation Level 584100 ms10 -2 N / AN / A sensor information sharing - low automation level 596500 ms10 -1 N / AN / A Platooning - Report to RSU 90 Delay is critical GBR310 ms10 -4 2000 bytes 2000 ms Cooperative collision avoidance; Sensor sharing - higher level of automation; Video sharing - higher level of automation 91 (NOTE 1) 23 ms 10 -5 2000 bytes 2000 ms Emergency trajectory alignment; Sensor sharing - High level of automation Note: GBR and delay-critical GBR PQIs are only available for unicast PC5 communications.

[0153] For reference, for the mapping of standardized PQI and QoS characteristics, Table 4 above will be extended / updated to support service requirements for other identified V2X services.

[0154] For reference, PQI can also be used for services other than V2X.

[0155] Note that PQI can be used with application-specified priorities, which can override the PQI's default priority level.

[0156] Below, we explain positioning QoS.

[0157] In a positioning system, QoS refers to the accuracy of the position acquired for the target UE. It also includes the maximum required response time. For example, positioning QoS requests that the UE's position be determined with the minimum (or best effort) accuracy within the time required by the positioning service. Based on this QoS, LMFs, base stations, UEs, etc. request and configure positioning methods, resource allocation, etc.

[0158] According to TR 22.872, conventional Uu positioning KPIs (Key Performance Indicators) include indicators related to accuracy (e.g., position, speed, bearing, timestamp, etc.) and indicators related to latency (e.g., latency, TTTF, update rate). These are utilized as performance indicators (e.g., KPIs) and quality indicators (e.g., QoS) of positioning systems.

[0159] For example, the following KPIs can be applied to the definition of use case location requirements:

[0160] Position accuracy: This represents the proximity between the UE's measured position and its actual position. This accuracy can describe absolute or relative positioning accuracy. It can also be subdivided into horizontal positioning accuracy (e.g., positioning error along a two-dimensional reference or horizontal plane) and vertical positioning accuracy (e.g., positioning error along the vertical axis or elevation).

[0161] - Speed ​​accuracy: Indicates the closeness between the measured value of the UE's speed and the actual UE's speed value.

[0162] - Bearing accuracy: This represents the proximity between the measured bearing of the UE and its actual bearing. Both the measured and actual bearings are defined in a common reference frame using the aircraft's principal axes (yaw, pitch, and roll). For a moving UE, bearing indicates the direction of velocity, and this KPI can be combined with velocity accuracy to express it as velocity accuracy.

[0163] Timestamp accuracy: Location-related data (e.g., position, velocity) is typically associated with a timestamp indicating the point in time at which the data was determined. Timestamp accuracy indicates the proximity of the timestamp value to the actual point in time at which the data was calculated.

[0164] - Availability: The percentage of time that the positioning system can provide the required position-related data within performance goals or requirements.

[0165] Latency: The time between the occurrence of an event triggering a location-related data decision and the time when the location-related data becomes available at the positioning system interface. For positioning system initialization, this delay is also defined as the Time to First Fix (TTFF).

[0166] - TTFF (Time to First Fix): The time from the occurrence of an event that first triggers a determination of position-related data to the time when that data becomes available on the positioning system interface. TTFF is greater than or equal to the latency.

[0167] - Update rate: The frequency with which the positioning system generates position-related data. This is defined as the reciprocal of the elapsed time between two consecutive position-related data points.

[0168] - Power consumption: The amount of power (usually in mW) used by the positioning system to generate location-related data.

[0169] Energy per fix (EPI): The amount of energy (typically measured in millijoules per fix) used by the positioning system to generate position-related data. This represents the overall power consumed by the positioning system during the required processing interval, including the energy consumed during idle periods between consecutive fixes. This KPI is advantageously used as a replacement for power consumption in situations where the positioning system is not continuously operational (e.g., device tracking).

[0170] - System scalability: The number of devices from which a positioning system can determine position-related data within a given time unit or at a given update rate.

[0171] According to TS 22.071, the QoS of the entire positioning system (e.g., LCS) is defined. This includes horizontal accuracy, vertical accuracy, response time, and LCS QoS classes.

[0172] According to TS 29.171, horizontal accuracy, vertical accuracy, and response time are defined as LCS QoS.

[0173] The contents of the standards related to the above are as follows.

[0174] - LCS QoS

[0175] This parameter provides the QoS required for LCS requests. QoS can include horizontal accuracy, vertical accuracy, and acceptable response time.

[0176] Below, Table 5 shows the LCS QoS.

[0177] IE / Group NameScopeIE Type and ReferenceSemantics DescriptionHorizontal AccuracyOINTEGER(0..127)bit 8 = 0bits 7-1 = 7-bit uncertainty code defined in 3GPP TS 23.032. The horizontal position error shall be less than the error indicated by the uncertainty code with 67% confidence.VerticalRequestedOENUMERATED(Vertical Coordinate Is Not Requested (0),Vertical Coordinate Is Requested (1))Default value if this IE is absent: Vertical coordinate is not requested (0)Vertical AccuracyOINTEGER(0..127)bit 8 = 0bits 7-1 = 7-bit vertical uncertainty code defined in 3GPP TS 23.032. The vertical position error shall be less than the error indicated by the uncertainty code with 67% confidence.Or the requested vertical IE does not exist. If present with a value of 0, the vertical accuracy is ignored even if it exists. Response time OENUMERATED (Low Delay (0), Delay Tolerant (1), ...) See 3GPP TS 22.071 for details.

[0178] According to TS 23.273, LCS QoS classes are additionally defined, and are divided into three classes (i.e., best-effort class, multiple QoS class, and assured class). These are used as higher-layer QoS, not on the RAN side.

[0179] The contents of the standards related to the above are as follows.

[0180] - LCS QoS

[0181] LCS QoS is used to characterize location requests. This can be determined by the operator or through negotiation with the LCS client or AF. It is optional for the LCS client or AF to provide LCS QoS to location requests.

[0182] LCS QoS information consists of three key attributes:

[0183] (1) LCS QoS class defined below

[0184] (2) Accuracy: Horizontal accuracy (see clause 4.3.1 of TS 22.071) and vertical accuracy (see clause 4.3.2 of TS 22.071)

[0185] (3) Response time: For example, no delay, low delay, or delay tolerant.

[0186] Note that when the LCS QoS class is set to multiple QoS classes, a position request may provide one or two horizontal / vertical accuracy QoS values, and may additionally include a preferred accuracy.

[0187] LCS QoS classes define the extent to which location services adhere to other QoS parameters (e.g., accuracy) when requested. 5G systems must attempt to satisfy these other QoS parameters regardless of whether a QoS class is used. LCS QoS classes are divided into three categories:

[0188] (1) Best-effort class: This class defines the most lenient requirements for the QoS achieved for a location request. Even if the obtained location estimate does not meet other QoS requirements, it must be returned with an appropriate indication that the requested QoS is not met. If no location estimate is obtained at all, an appropriate error cause is transmitted.

[0189] (2) Multiple QoS classes: This class defines moderately stringent QoS requirements. If the acquired position estimate fails to meet the most stringent (e.g., basic) QoS requirements, the Location Management Function (LMF) can re-perform position estimation targeting less stringent QoS requirements. This process can be repeated until the minimum QoS requirements are met. If even the minimum QoS requirements are not met, the position estimate is discarded and an appropriate error cause is sent.

[0190] Note that AF can provide location requests with multiple QoS classes via NEF. If an LCS client wishes to provide requests with multiple QoS classes, it may need to implement the Le interface that supports this.

[0191] Note that multiple QoS classes are only applicable to the deferred 5GC-MT-LR procedure in this release.

[0192] (3) Assured class: This class defines the most stringent accuracy requirements for location requests. If the obtained location estimate fails to meet other QoS requirements, it is discarded and an appropriate error cause is sent.

[0193] Note that how the LMF selects the positioning method is not predefined by QoS criteria and may vary depending on the implementation. The LCS client may indicate the accuracy defined in Tables 6.1.6.3.2-1 and 6.1.6.3.5-1 of TS 29.572. The AF may indicate the accuracy defined in Table 6.1.6.3.2-1 of TS 29.572, or a specific value such as the PLMN ID defined in Table 5.3.2.4.7-1 of TS 29.122.

[0194] According to TS 37.355, the QoS for the RAN aspect of conventional Uu positioning is defined. Typical QoS parameters include accuracy (e.g., horizontal, vertical) and response time, which are quality indicators for absolute positioning. Additionally, velocity can be requested.

[0195] According to TS 38.355, sidelink positioning QoS includes new accuracy metrics for range, azimuth, and elevation. These are new quality metrics for relative positioning (i.e., ranging and / or relative positioning).

[0196] Below, sensing QoS is explained.

[0197] QoS in a sensing system can be defined by the accuracy of the position / velocity of the detected / detected target object, and the latency. Here, the latency is the time difference from the time when sensing is triggered (e.g., the time when the sensing signal is reflected from the target object) to the sensing result (e.g., the time when the sensing result is reported).

[0198] According to TR 22.837 (ISAC SA1 study document), ISAC sensing KPIs such as accuracy (positioning estimate, velocity estimate), max sensing service latency, and refreshing rate are similar to the positioning KPIs defined in TR 22.872 and the positioning QoS defined in TS 29.171 / TS 37.355.

[0199] Additionally, TR 22.837 defines confidence level, missed detection probability, and false alarm probability.

[0200] The following KPIs apply to defining use cases for sensing quantitative requirements:

[0201] - Accuracy of positioning estimate: This represents the proximity between the measured sensing result (e.g., position) of a target object and the actual position value. This can be derived from horizontal sensing accuracy (e.g., sensing error in a two-dimensional reference or horizontal plane) and vertical sensing accuracy (e.g., sensing error in the vertical axis or elevation).

[0202] - Accuracy of velocity estimate: This indicates the closeness between the measured sensing result (e.g., velocity) of the target object's velocity and the actual velocity.

[0203] - Confidence level: When considering accuracy, it represents the percentage of all possible sensing measurement results that are expected to contain actual sensing results.

[0204] - Sensing resolution: This refers to the minimum difference in the size (e.g., distance, speed) of a measured target object that is allowed to detect objects of different sizes.

[0205] Missed detection probability: This is the conditional probability of not detecting a target object or environment despite its actual presence. This probability is expressed as the ratio of the number of events incorrectly identified as negative to the total number of events with a positive status. This applies only to binary sensing results.

[0206] For reference, an event with a positive status means that a characteristic of the target object or environment exists, and includes events that are incorrectly identified as negative and events that are correctly identified as positive.

[0207] False alarm probability: This is the conditional probability of incorrectly detecting a target object or environment when it does not actually exist. This probability is expressed as the ratio of the number of events incorrectly identified as positive to the total number of events with a negative status. This applies only to binary sensing results.

[0208] For reference, an event with a negative status means that the characteristic of the target object or environment does not exist, and includes events that are incorrectly identified as positive and events that are correctly identified as negative.

[0209] - Max sensing service latency: The time elapsed between the occurrence of an event triggering a decision on a sensing result and the time until the sensing result becomes available at the sensing system interface.

[0210] - Refreshing rate: The rate at which the sensing system generates sensing results. This is the reciprocal of the time interval between two consecutive sensing results.

[0211] Meanwhile, sensing performance indicators (e.g., Key Performance Indicators (KPIs)) can be divided into instantaneous KPIs and statistical KPIs, unlike conventional communication and positioning KPIs. For example, instantaneous KPIs may include values ​​such as the aforementioned accuracy and confidence. Furthermore, for example, statistical KPIs may include the "missed detection probability" and the "false alarm probability." Here, the "missed detection probability" refers to the number of cases in which a target object is not detected despite its actual existence. Conversely, the "false alarm probability" refers to the number of cases in which a target object is incorrectly judged to have been detected despite its actual nonexistence.

[0212] Meanwhile, the need to introduce statistical sensing performance indicators (e.g., KPIs) as parameters for sensing service quality indicators (e.g., QoS) has not yet been discussed. However, unlike conventional communications or positioning (e.g., in conventional positioning, target objects are clearly and necessarily present), sensing has the potential for target objects to be absent. Therefore, unlike conventional positioning QoS, "missing detection probability" and / or "error detection probability" should be introduced as statistical sensing quality indicators.

[0213] Additionally, the calculation / measurement of the missed detection probability and / or false alarm probability can be performed at the sensing server or sensing node (e.g., sensing transmitter / receiver). Here, a standardized calculation method is required to calculate statistical QoS across multiple distributed sensing nodes. However, the current standard does not specify such a statistical calculation method. Therefore, a clear and unified standard method is needed in this area.

[0214] In summary, conventional communication and positioning systems lack methods for quantitatively comparing sensor node performance or efficiently selecting appropriate sensing nodes based on environmental changes. Furthermore, despite the dynamic changes in sensing accuracy and reliability due to environmental conditions, location, and time, there is no standardized method for calculating statistical sensing quality indicators (e.g., sensing QoS) that reflect these changes. This reduces the reliability of sensing quality assessment or sensing node selection. In particular, sensing is inherently prone to the absence of target objects, requiring a quality assessment system based on statistical quality indicators such as the probability of missing detection and the probability of false detection. However, no clear standards or methods have been defined for this purpose to date.

[0215] In this disclosure, a method for measuring / reporting statistical sensing QoS parameters and a device supporting the same are proposed.

[0216] For example, a method for calculating missed detection probability and false alarm probability using accuracy and / or confidence may be as follows.

[0217] For example, accuracy and / or confidence thresholds can be used as a method for calculating missed detection probability and false alarm probability.

[0218] For example, objects with accuracy and / or confidence values ​​below a certain threshold may be considered as missed / falsely detected objects. In other words, objects detected with low accuracy or confidence may have a higher probability of being missed or falsely detected.

[0219] For example, a sensing node can determine whether a target object is missed by comparing the accuracy and / or confidence of all detected objects with a certain threshold, and can statistically accumulate and calculate the result. For example, one calculation method can calculate the probability of missed detection by calculating the ratio of the number of target objects above the threshold to the number of target objects below the threshold. For example, another calculation method can calculate the average value of the accuracy and / or confidence and express it as a percentage in the maximum / minimum accuracy / confidence range.

[0220] For example, for omission detection, accuracy and / or confidence can be set. For example, thresholds for accuracy and confidence can be set independently. For example, if both accuracy and confidence are set, no omissions / false detections can be determined only if both are above the threshold.

[0221] For example, for statistical calculations, at least one of the following may be included:

[0222] - Start and / or end points of accumulation

[0223] - Minimum and / or maximum time to accumulate

[0224] - Minimum number of samples to accumulate and / or maximum number of samples

[0225] For example, the corresponding setting can be set as a parameter when establishing a sensing session. Furthermore, for example, the required setting can be delivered at the required moment using a specific message. For example, various methods can be used in the form of messages. For example, between a base station and a UE, messages such as radio resource control (RRC), medium access control (MAC), control element (CE), downlink control information (DCI) / uplink control information (UCI) can be used. Alternatively, signaling between base stations or between a base station and the core network can be used, for example.

[0226] For example, the measured miss detection and false detection probability values ​​may be transmitted differently from the accuracy and / or confidence. For example, accuracy and confidence may be calculated per (single) target object, respectively, whereas the miss / false detection probability may be a QoS quality calculated per sensing server / node. Therefore, for example, unlike accuracy and confidence, which are reported per target object, the miss / false detection probability may be reported per sensing server / node. In addition, for example, sensors may be distinguished as 3GPP sensors or non-3GPP sensors, and non-3GPP sensors may be calculated and reported separately for various sensor types (e.g., Radar, LiDAR, camera, etc.).

[0227] For example, the reporting cycle and number of statistical QoS values ​​can be set. For example, parameters related to reporting can be set independently, separate from the calculation of statistical QoS values.

[0228] For example, statistical QoS values ​​could always be included in messages reporting the detection of a target object. Alternatively, they could be reported independently, for example, when a certain time, a certain number of values, etc. are met. For example, statistical QoS values ​​typically do not change very rapidly, so reporting them on a regular basis could increase power consumption and signaling overhead. Therefore, it may be more efficient to report them when a significant change occurs or at a specific interval.

[0229] For example, a statistical QoS value may be reported only if it is above or below a certain value (e.g., a certain threshold). For example, a statistical QoS value may be reported only if it is very good or very bad.

[0230] For example, a change in a statistical QoS value could be reported only if it changes by a certain value (e.g., a certain threshold) compared to a previously reported value.

[0231] Alternatively, for example, a method for calculating the missed detection probability and false alarm probability using the sensing signal strength (e.g., reflected sensing signal strength) and / or signal to interference plus noise ratio (SINR) may be as follows.

[0232] For example, as a method of measuring miss detection and error detection, wireless parameters such as signal strength and / or signal to interference plus noise ratio (SINR) of a reflected and measured sensing signal (e.g., a reflected / echo sensing signal) can be used or set. For example, a threshold of signal strength and / or SINR can be used.

[0233] For example, objects with signal strength and / or SINR values ​​below a certain threshold may be considered as missed / falsely detected objects. In other words, objects with low signal strength or SINR values ​​may have a higher probability of being potentially missed or falsely detected.

[0234] For example, the method of operation when using a threshold of signal strength and / or SINR can be used similarly to the method using a threshold of accuracy and / or confidence.

[0235] Alternatively, for example, sensing node selection and sensor type selection using missed detection probability and false alarm probability may be as follows.

[0236] For example, sensing node selection can be performed based on missed detection probability and false alarm detection probability. For example, sensing nodes with high missed detection probability and false alarm probability can be assigned lower in priority during sensing node selection.

[0237] Therefore, for example, a sensing node can store these statistical QoS values ​​and provide the information during a capability exchange with an entity (e.g., a sensing session management function) that can determine the sensing node when establishing a session for each sensing service. Therefore, for example, statistical QoS values ​​can be calculated and stored separately for each sensing node, each sensing service, and each sensor type, and this can be utilized as information for selecting a sensing node.

[0238] Additionally, for example, the statistical sensing QoS information thus generated / stored / reported can be utilized for sensor type selection. For example, sensor types with high missed detection probability and false alarm probability can be assigned lower priority when determining sensor types.

[0239] FIG. 9 illustrates a method for calculating a missing detection probability or an error detection probability based on the accuracy or reliability of sensing, according to one embodiment of the present disclosure. The embodiment of FIG. 9 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0240] Referring to FIG. 9, in step S910, a UE (or sensing entity) may obtain configuration information related to sensing from a sensing server (or base station). For example, the configuration information related to sensing may include QoS requirements of a sensing service (or QoS parameters of a sensing service). Specifically, for example, the configuration information related to sensing may include a threshold related to accuracy of sensing or reliability of sensing. Or, for example, the configuration information related to sensing may include a minimum or maximum time for which sensing must be performed for a sensing service. Or, for example, the configuration information related to sensing may include a minimum or maximum number of objects for which sensing must be performed for a sensing service. For example, the configuration information related to sensing may be transmitted to the UE based on an RRC message, MAC CE, or DCI.

[0241] In step S920, the UE may perform sensing. For example, the sensing of the UE may be performed based on the transmission and reception of sensing signals (e.g., a 3GPP sensor or a communication sensor). Alternatively, for example, the sensing of the UE may be performed based on at least one sensor (e.g., a non-3GPP sensor or a non-communication sensor).

[0242] In step S930, the UE may obtain a value related to the accuracy or reliability of sensing for a target object or target sensing area based on the sensing performed. For example, if the UE performs sensing for multiple target objects, the UE may obtain a value related to the accuracy or reliability of sensing for each of the multiple target objects.

[0243] In step S940, the UE may obtain a missed detection probability or a false detection probability based on comparing a value related to the accuracy or reliability of sensing with a threshold value. For example, the missed detection probability may be calculated based on the number of missed-detected objects and the number of objects not missed-detected. For example, if the value related to the accuracy or reliability of sensing is below the threshold value, the object may be determined to have been falsely detected as not existing even though it actually exists (a missed-detected object). For example, if the value related to the accuracy or reliability of sensing is greater than the threshold value, the object may be determined to have been correctly detected as existing even though it actually exists (a not missed-detected object). Alternatively, for example, the false detection probability may be calculated based on the number of falsely detected objects and the number of objects not missed-detected. For example, if the value related to the accuracy or reliability of sensing is below the threshold value, the object may be determined to have been falsely detected as existing even though it actually does not exist (a falsely detected object). For example, if a value related to the accuracy or reliability of sensing is greater than a threshold, it can be judged that an object that does not actually exist was correctly detected as a non-existent object (an object that was not detected incorrectly).

[0244] In step S950, the UE may report information related to the missing detection probability or the error detection probability to the sensing server. For example, the information related to the missing detection probability or the error detection probability may be utilized by the sensing server to determine whether to select the UE as a sensing node for a sensing service. For example, the information related to the missing detection probability or the error detection probability may be reported when there is a difference greater than a threshold value compared to a previously reported missing detection probability or error detection probability. Alternatively, for example, the information related to the missing detection probability or the error detection probability may be reported based on a regular cycle. Alternatively, for example, the information related to the missing detection probability or the error detection probability may be reported based on whether sensing was performed for a regular period of time and a value related to the accuracy or reliability of the sensing was obtained. Alternatively, for example, the information related to the missing detection probability or the error detection probability may be reported based on whether sensing was performed for a regular number of objects and a value related to the accuracy or reliability of the sensing was obtained.

[0245] FIG. 10 illustrates a method for calculating a missing detection probability or a false detection probability based on the strength or SINR of a sensing signal, according to an embodiment of the present disclosure. The embodiment of FIG. 10 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0246] Referring to FIG. 10, in step S1010, a UE (or sensing entity) may obtain configuration information related to sensing from a sensing server (or base station). For example, the configuration information related to sensing may include QoS requirements of a sensing service (or QoS parameters of a sensing service). Specifically, for example, the configuration information related to sensing may include a threshold related to accuracy of sensing or reliability of sensing. Or, for example, the configuration information related to sensing may include a minimum or maximum time for which sensing must be performed for a sensing service. Or, for example, the configuration information related to sensing may include a minimum or maximum number of objects for which sensing must be performed for a sensing service. For example, the configuration information related to sensing may be transmitted to the UE based on an RRC message, MAC CE, or DCI.

[0247] In step S1020, the UE may perform sensing. For example, sensing by the UE may be performed based on transmission and reception of sensing signals. For example, in the case of monostatic sensing, the UE may perform sensing by transmitting a sensing signal to a target object or a target sensing area and receiving a sensing signal reflected from the target object or the target sensing area. Alternatively, for example, in the case of bistatic sensing, the UE may perform sensing by receiving a signal reflected from a target object or the target sensing area of ​​a sensing signal transmitted by another UE or a base station.

[0248] In step S1030, the UE may obtain a value related to the strength or SINR of the received sensing signal. For example, if the UE receives multiple sensing signals, the UE may obtain a value related to the strength or SINR of the sensing signal for each of the multiple sensing signals.

[0249] In step S1040, the UE may obtain a missed detection probability or a false detection probability based on comparing a value related to the intensity or SINR of the sensing signal with a threshold. For example, the missed detection probability may be calculated based on the number of objects that were missed and the number of objects that were not ... For example, if a value related to the intensity of the sensing signal or SINR is greater than a threshold, an object that does not actually exist can be judged to have been correctly detected as a non-existent object (an object that was not falsely detected).

[0250] In step S1050, the UE may report information related to the missing detection probability or the error detection probability to the sensing server. For example, the information related to the missing detection probability or the error detection probability may be utilized by the sensing server to determine whether to select the UE as a sensing node for a sensing service. For example, the information related to the missing detection probability or the error detection probability may be reported when there is a difference greater than a threshold value compared to a previously reported missing detection probability or error detection probability. Alternatively, for example, the information related to the missing detection probability or the error detection probability may be reported based on a regular cycle. Alternatively, for example, the information related to the missing detection probability or the error detection probability may be reported based on whether sensing was performed for a regular period of time and a value related to the strength of the sensing signal or the SINR was obtained. Alternatively, for example, the information related to the missing detection probability or the error detection probability may be reported based on whether sensing was performed for a regular number of objects and a value related to the strength of the sensing signal or the SINR was obtained.

[0251] FIG. 11 illustrates a method for a first device to perform wireless communication according to an embodiment of the present disclosure. The embodiment of FIG. 11 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0252] Referring to FIG. 11, in step S1110, the first device can obtain a first threshold value related to the accuracy or reliability of sensing. In step S1120, the first device can obtain information related to the accuracy or reliability of the object based on performing the sensing. In step S1130, the first device can obtain a missing detection probability based on a comparison of the information related to the accuracy or reliability with the first threshold value. For example, the missing detection probability can be obtained based on the number of objects that were missingly detected and the number of objects that were not missingly detected.

[0253] For example, the number of objects that are missed and detected may be counted based on whether the accuracy or the reliability for the objects is less than or equal to the first threshold. For example, the number of objects that are not missed and detected may be counted based on whether the accuracy or the reliability for the objects is greater than the first threshold. For example, the probability of missed detection may be a ratio of the number of objects that are missed and detected to the sum of the number of objects that are missed and not detected.

[0254] For example, the above-mentioned missing detection probability can be obtained by converting an average value obtained based on a value related to the accuracy or the reliability for each of a plurality of objects including the object into a ratio on an interval defined by a minimum value and a maximum value among the values ​​related to the accuracy or the reliability for each of the plurality of objects.

[0255] For example, the sensing may be performed repeatedly over a certain period of time. For example, information related to the accuracy or reliability may be obtained based on the sensing results accumulated over the certain period of time.

[0256] For example, the sensing may be performed for each of a plurality of objects including the object. For example, information related to the accuracy or reliability may be obtained based on whether the number of accumulated sensing results for each of the plurality of objects falls within a threshold range.

[0257] Additionally, for example, the first device may report information related to the missed detection probability. For example, whether the first device is selected as a sensing node may be determined based on information related to the missed detection probability. For example, the higher the missed detection probability, the lower the probability that the first device will be selected as the sensing node. For example, the target for which information related to the missed detection probability is reported may be determined based on the type of sensor related to the sensing. For example, the type of sensor may be at least one of a communication sensor based on transmitting and receiving a sensing signal or a non-communication sensor including a radar, LiDAR, or a camera. For example, information related to the missed detection probability may be reported based on whether the missed detection probability is greater than a second threshold. For example, information related to the missed detection probability may be reported based on whether a difference between the missed detection probability and a previously reported missed detection probability is greater than the second threshold.

[0258] Additionally, for example, the first device may obtain an error detection probability based on a comparison of the information related to the accuracy or the reliability with the first threshold. For example, the error detection probability may be obtained based on the number of objects that were detected in error and the number of objects that were not detected in error. For example, the number of objects that were detected in error may be counted based on the accuracy or the reliability for the objects being lower than the threshold. For example, the number of objects that were not detected in error may be counted based on the accuracy or the reliability for the objects being higher than the threshold.

[0259] Additionally, for example, the first device can obtain a second threshold value related to the strength or signal-to-interference-plus-noise ratio (SINR) of the sensing signal. For example, the first device can obtain information related to the strength or SINR of the sensing signal reflected from the object based on performing the sensing. For example, the missed detection probability can be obtained based on a comparison of the information related to the strength or SINR of the sensing signal with the second threshold value.

[0260] 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 obtain a first threshold value related to the accuracy or reliability of sensing. Then, the processor (102) of the first device (100) can obtain information related to the accuracy or reliability of an object based on performing the sensing. Then, the processor (102) of the first device (100) can obtain a missing detection probability based on a comparison of the information related to the accuracy or reliability with the first threshold value. For example, the missing detection probability can be obtained based on the number of objects that were missed and the number of objects that were not missed.

[0261] According to one embodiment of the present disclosure, a first device configured to perform wireless communication 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: obtain a first threshold associated with the accuracy or reliability of sensing; obtain information related to the accuracy or reliability of an object based on performing the sensing; and obtain a missing detection probability based on a comparison of the information related to the accuracy or reliability with the first threshold. For example, the missing detection probability may be obtained based on the number of objects that were missed and the number of objects that were not missed.

[0262] 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: obtain a first threshold associated with the accuracy or reliability of sensing; obtain information related to the accuracy or reliability of an object based on performing the sensing; and obtain a missing detection probability based on a comparison of the information related to the accuracy or reliability with the first threshold. For example, the missing detection probability may be obtained based on the number of objects that were missing detected and the number of objects that were not missing detected.

[0263] 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: obtain a first threshold associated with the accuracy or reliability of sensing; obtain information associated with the accuracy or reliability of an object based on performing the sensing; and obtain a missing detection probability based on a comparison of the information associated with the accuracy or reliability with the first threshold. For example, the missing detection probability may be obtained based on the number of objects that were missed and the number of objects that were not missed.

[0264] FIG. 12 illustrates a method for a second device to perform wireless communication according to an embodiment of the present disclosure. The embodiment of FIG. 12 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0265] Referring to FIG. 12, in step S1210, the second device may transmit information including a threshold value related to the accuracy or reliability of sensing to the first device. In step S1220, the second device may receive, from the first device, information related to a missing detection probability obtained based on the threshold value. In step S1230, the second device may determine whether to select the first device as a sensing node based on the information related to the missing detection probability.

[0266] 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 information including a threshold value related to the accuracy or reliability of sensing to the first device. Then, the processor (202) of the second device (200) can control the transceiver (206) to receive information related to a missed detection probability obtained based on the threshold value from the first device. Then, the processor (202) of the second device (200) can determine whether to select the first device as a sensing node based on the information related to the missed detection probability.

[0267] According to one embodiment of the present disclosure, a second device configured to perform wireless communication may be provided. For example, the second 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 second device to: transmit, to a first device, information including a threshold value related to the accuracy or reliability of sensing; receive, from the first device, information related to a missed detection probability obtained based on the threshold value; and determine, based on the information related to the missed detection probability, whether to select the first device as a sensing node.

[0268] 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, to a first device, information including a threshold value related to the accuracy or reliability of sensing; receive, from the first device, information related to a missed detection probability obtained based on the threshold value; and determine, based on the information related to the missed detection probability, whether to select the first device as a sensing node.

[0269] 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 second device to: transmit information including a threshold value related to the accuracy or reliability of sensing to a first device; receive information related to a missed detection probability obtained based on the threshold value from the first device; and determine whether to select the first device as a sensing node based on the information related to the missed detection probability.

[0270] According to various embodiments of the present disclosure, a missed detection probability or an error detection probability can be calculated based on information related to the accuracy or reliability of sensing. In this case, the following effects can be expected. For example, the performance of a sensor node can be quantitatively evaluated based on the missed detection probability or the error detection probability, and the quality of sensing can be statistically managed. Alternatively, for example, a statistically reliable quality index reflecting sensing performance that dynamically changes according to time, location, environmental conditions, etc. can be calculated. Alternatively, for example, the reliability of sensing quality evaluation and sensing node selection can be improved. Alternatively, for example, the configuration of sensing nodes for sensing services can be dynamically adjusted according to environmental or node performance changes. Alternatively, for example, since the missed detection probability or the error detection probability can be differentially considered depending on the sensing method (e.g., communication-based or sensor-based such as radar), flexible sensing node selection and sensing type priority setting can be enabled according to the network operator's policy.

[0271] The above methods proposed in this disclosure can be combined with each other.

[0272] Although the present disclosure has been described using a 5G wireless communication system as an example, it can be equally applied and utilized in a 6G wireless communication system, etc.

[0273] The various embodiments of the present disclosure may be combined with each other.

[0274] Below, a description is given of devices to which various embodiments of the present disclosure can be applied.

[0275] 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.

[0276] 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.

[0277] Fig. 13 illustrates a communication system (1) according to one embodiment of the present disclosure. The embodiment of Fig. 13 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0278] Referring to FIG. 13, 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.

[0279] 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.

[0280] 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).

[0281] 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.

[0282] FIG. 14 illustrates a wireless device according to an embodiment of the present disclosure. The embodiment of FIG. 14 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0283] Referring to FIG. 14, 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. 13.

[0284] 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.

[0285] 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.

[0286] 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.

[0287] 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.

[0288] 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.

[0289] 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.

[0290] FIG. 15 illustrates a signal processing circuit for a transmission signal according to an embodiment of the present disclosure. The embodiment of FIG. 15 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0291] Referring to FIG. 15, 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. 15 may be performed in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 14. The hardware elements of FIG. 15 may be implemented in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 14. For example, blocks 1010 to 1060 may be implemented in the processor (102, 202) of FIG. 14. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 14, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 14.

[0292] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 15. 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).

[0293] 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.

[0294] 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.

[0295] 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. 15. For example, a wireless device (e.g., 100, 200 of FIG. 14) 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.

[0296] Figure 16 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 Figure 13). The embodiment of Figure 16 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0297] Referring to FIG. 16, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 14 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 an additional element (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. 14. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 14. 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).

[0298] 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. 13, 100a), a vehicle (Fig. 13, 100b-1, 100b-2), an XR device (Fig. 13, 100c), a portable device (Fig. 13, 100d), a home appliance (Fig. 13, 100e), an IoT device (Fig. 13, 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. 13, 400), a base station (Fig. 13, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0299] In FIG. 16, 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.

[0300] Below, the implementation example of Fig. 16 is described in more detail with reference to the drawings.

[0301] FIG. 17 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. 17 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0302] Referring to FIG. 17, 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. 16, respectively.

[0303] 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.

[0304] 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).

[0305] 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 of the first device obtaining a first threshold value related to the accuracy or reliability of sensing; A step of obtaining information related to the accuracy or reliability of an object based on performing the sensing; and A step of obtaining a missing detection probability based on a comparison of the information related to the accuracy or the reliability with the first threshold value; including; The above missing detection probability is obtained based on the number of missing detected objects and the number of not missing detected objects.

2. In paragraph 1, The number of the above-detected missing objects is counted based on whether the accuracy or the confidence for the object is less than or equal to the first threshold, and A method wherein the number of objects not detected above is counted based on whether the accuracy or the confidence for the object is greater than the first threshold value.

3. In paragraph 2, The above-mentioned missing detection probability is a method in which the number of the missing detected objects is a ratio of the number of the missing detected objects to the sum of the number of the missing detected objects and the number of the not-missed detected objects.

4. In paragraph 1, The above-mentioned missing detection probability is obtained by converting an average value obtained based on a value related to the accuracy or the reliability for each of a plurality of objects including the object into a ratio on an interval defined by a minimum value and a maximum value among the values ​​related to the accuracy or the reliability for each of the plurality of objects.

5. In paragraph 1, The above sensing is performed repeatedly for a certain period of time, and A method in which the information related to the accuracy or the reliability is obtained based on the sensing results accumulated over the above-mentioned period of time.

6. In paragraph 1, The sensing is performed for each of a plurality of objects including the above object, and A method in which the information related to the accuracy or the reliability is obtained based on the number of accumulated sensing results for each of the plurality of objects being within a threshold range.

7. In paragraph 1, A step of reporting information related to the above missing detection probability; further comprising: A method in which whether the first device is selected as a sensing node is based on information related to the missing detection probability.

8. In paragraph 7, A method wherein the higher the above-mentioned missing detection probability, the lower the probability that the first device is selected as the sensing node.

9. In paragraph 7, The target for which information related to the above missing detection probability is reported is determined based on the type of sensor related to the above sensing, and The method according to claim 1, wherein the type of the sensor is at least one of a communication sensor based on transmission and reception of a sensing signal or a non-communication sensor including a radar, LiDAR, or camera.

10. In paragraph 7, A method in which information related to the above-mentioned missing detection probability is reported based on the above-mentioned missing detection probability being greater than a second threshold value.

11. In paragraph 7, A method in which information related to the missing detection probability is reported based on a difference between the above missing detection probability and a previously reported missing detection probability being greater than a second threshold.

12. In paragraph 1, A step of obtaining an error detection probability based on a comparison of the information related to the accuracy or the reliability and the first threshold value; including; The above error detection probability is obtained based on the number of objects with error detection and the number of objects with no error detection, The number of objects detected as errors is counted based on whether the accuracy or the reliability for the objects is lower than the threshold value, and The number of objects for which the above error is not detected is counted based on whether the accuracy or the reliability for the object is greater than or equal to the threshold value.

13. In paragraph 1, A step in which the first device acquires a second threshold value related to the strength or SINR (signal to interference plus noise ratio) of the sensing signal; and A step of obtaining information related to the intensity or SINR of the sensing signal reflected from the object based on performing the sensing; further comprising: A method in which the above-mentioned missing detection probability is obtained based on a comparison of information related to the intensity or SINR of the sensing signal with the second threshold value.

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: Obtain a first threshold related to the accuracy or reliability of sensing; Based on performing the above sensing, information related to the accuracy or reliability of the object is obtained; and Based on the comparison of the above information related to the above accuracy or the above reliability with the above first threshold, a missing detection probability is obtained. The above-mentioned missing detection probability is obtained based on the number of missing detected objects and the number of not missing detected objects, the first device.

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: Obtain a first threshold related to the accuracy or reliability of sensing; Based on performing the above sensing, information related to the accuracy or reliability of the object is obtained; and Based on the comparison of the above information related to the above accuracy or the above reliability with the above first threshold, a missing detection probability is obtained. The above-mentioned missing detection probability is obtained based on the number of missing detected objects and the number of objects not missing detected.

16. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the first device to: Obtain a first threshold related to the accuracy or reliability of sensing; Based on performing the above sensing, information related to the accuracy or reliability of the object is obtained; and Based on the comparison of the above information related to the above accuracy or the above reliability with the above first threshold, a missing detection probability is obtained. A non-transitory computer-readable storage medium, wherein the above-mentioned missing detection probability is obtained based on the number of objects that are missing and the number of objects that are not missing.

17. In the method, A step in which the second device transmits information including a threshold value related to the accuracy or reliability of sensing to the first device; A step of receiving information related to a missing detection probability obtained based on the threshold value from the first device; and A method comprising: a step of determining whether to select the first device as a sensing node based on information related to the above-mentioned missing detection probability; 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: To transmit information including a threshold value related to the accuracy or reliability of sensing to the first device; Receive information related to the missing detection probability obtained based on the threshold value from the first device; and A second device that determines whether to select the first device as a sensing node based on information related to the above-mentioned missing detection probability.

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: To transmit information including a threshold value related to the accuracy or reliability of sensing to the first device; Receive information related to the missing detection probability obtained based on the threshold value from the first device; and A processing device that determines whether to select the first device as a sensing node based on information related to the above-mentioned missing detection probability.

20. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the second device to: To transmit information including a threshold value related to the accuracy or reliability of sensing to the first device; Receive information related to the missing detection probability obtained based on the threshold value from the first device; and A non-transitory computer-readable storage medium for determining whether to select the first device as a sensing node based on information related to the above-mentioned missing detection probability.

Images