User equipment and operation method of user equipment

Abstract

A user equipment and an operation method of the user equipment are disclosed. The user equipment may include a transceiver configured to receive a first wireless signal from a base station at a first location of the user equipment, and a first processor configured to control the transceiver, where the first processor is configured to generate first values of a plurality of first indicators based on the first wireless signal, and performing an operation according to a first positioning function that determines whether the first position corresponds to the first specific position based on the first value.

Description

[0001] This application claims priority based on and claims priority to Korean Patent Application No. 10-2024-0000499, filed with the Korean Intellectual Property Office on January 2, 2024, and Korean Patent Application No. 10-2024-0060757, filed with the Korean Intellectual Property Office on May 8, 2024. The disclosures of the Korean patent applications are incorporated herein by reference in their entirety. Technical Field

[0002] The inventive concept relates to wireless communication, and more particularly, to a user equipment supporting a positioning function for determining the position of the user equipment and an operation method of the user equipment. Background Art

[0003] In wireless communication technology, a technique for determining (or identifying) the position of a user equipment (e.g., a user's mobile device) is referred to as positioning. As an example, a method using satellite signals received from satellites and a method using wireless signals of a wireless network (e.g., Long Term Evolution (LTE), New Radio (NR), etc.) have been proposed as positioning.

[0004] The method using satellite signals determines the position of the user equipment by using multiple satellite information via a Global Navigation Satellite System (GNSS). The method using satellite signals can determine the position of the user equipment with high accuracy outdoors, but has a limitation that the method may not accurately determine the position of the user equipment indoors.

[0005] In addition, the method using wireless signals of a wireless network is referred to as Observed Time Difference of Arrival (OTDOA) or Uplink Time Difference of Arrival (UTDOA). Specifically, in the method using wireless signals of a wireless network, the user equipment receives positioning reference signals (PRSs) for position determination transmitted by multiple base stations, and transmits reference signal time difference (RSTD) information including the reception time difference of the received PRSs back to the base stations, so that the base stations determine the position of the user equipment. The base stations determine the position of the user equipment by transmitting the RSTD information received from the user equipment to a Location Management Function (LMF). However, the method using wireless signals of a wireless network is affected by the measurement performance of the RSTD information of the user equipment and has a limitation that the method may not accurately determine the position of the user equipment indoors. Summary of the Invention

[0006] The inventive concept provides a user equipment and an operation method of the user equipment, which can accurately determine the position of the user equipment with high accuracy by using characteristics of wireless signals processed by the user equipment using a neural network model.

[0007] According to an aspect of the inventive concept, there is provided a user equipment configured to support a first positioning function. The user equipment includes: a transceiver configured to receive a first wireless signal from a base station at a first position of the user equipment, and a first processor configured to control the transceiver. Wherein, the first processor is further configured to: generate a first value of a plurality of first indicators based on the first wireless signal, determine whether the first position corresponds to a first specific position based on the first value, and perform an operation according to the first positioning function based on determining that the first position corresponds to the first specific position.

[0008] According to another aspect of the inventive concept, there is provided a method of operating a user equipment that supports a positioning function based on a neural network. The method of operation includes: generating a first value of a plurality of indicators based on a first wireless signal received from a base station at a first position of the user equipment, determining whether the first position corresponds to a specific position based on the first value and a neural network model, the specific position being included as one of a plurality of specific positions, and enabling an operation mode of the user equipment based on determining that the first position corresponds to the specific position.

[0009] According to another aspect of the inventive concept, there is provided a user equipment including: a memory storing a neural network model trained using a plurality of training positions to support a positioning function, and a first processor configured to: generate a first value of a plurality of indicators based on a first wireless signal received at a first position of the user equipment, and determine whether the first position corresponds to a specific position, the specific position being included as one of a plurality of specific positions, based on the first value and the neural network model. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Embodiments will be understood more clearly from the following detailed description in conjunction with the accompanying drawings.

[0011] Figure 1 is a diagram illustrating a wireless communication system according to at least one embodiment.

[0012] Figure 2 is a block diagram illustrating a user equipment according to at least one embodiment.

[0013] Figure 3 is illustrating Figure 2 the structure of a neural network model of.

[0014] Figure 4 is a flowchart illustrating a method of operating a user equipment according to at least one embodiment.

[0015] Figure 5A and Figure 5B is a flowchart for specifically explaining Figure 4 operation S100 of.

[0016] Figure 6 is a table diagram for explaining a specific location and its corresponding relevant information according to at least one embodiment.

[0017] Figure 7A and Figure 7B is a diagram for explaining examples of a first specific location to a third specific location.

[0018] Figure 8 is a table diagram for explaining the type of a specific location and its corresponding positioning method according to at least one embodiment.

[0019] Figure 9 is a flowchart showing an operation method of a user equipment according to at least one embodiment.

[0020] Figure 10A and Figure 10B is for specifically explaining Figure 9 operations S220 and S230 of which.

[0021] Figure 11 is a flowchart showing an operation method of a user equipment according to at least one embodiment.

[0022] Figure 12A and Figure 12B is for specifically explaining Figure 11 operation S310 of which.

[0023] Figure 13A and Figure 13B is for specifically explaining Figure 11 operations S300 and S310 of which.

[0024] Figure 14A is a flowchart showing an operation method of a user equipment according to at least one embodiment, and Figure 14B is a table diagram showing a specific location and its corresponding specific mode according to at least one embodiment.

[0025] Figure 15 is a flowchart showing an operation method of a user equipment according to at least one embodiment.

[0026] Figure 16 is a block diagram showing a baseband processor according to at least one embodiment.

[0027] Figure 17A and Figure 17B is a flowchart showing an operation method of a user equipment according to at least one embodiment.

[0028] Figure 18A and Figure 18BIt is a flowchart showing an operation method of a user equipment according to at least one embodiment.

[0029] Figure 19 It is a flowchart showing an operation method of a user equipment according to at least one embodiment.

[0030] Figure 20 It is a conceptual diagram of an Internet of Things (IoT) network system to which an embodiment is applied. Detailed implementation

[0031] Figure 1 It is a diagram showing a wireless communication system 1 according to at least one embodiment. The wireless communication system 1 can provide a communication service based on at least one of a plurality of wireless networks to the user equipment 30. For example, the wireless communication system 1 can provide a communication service based on at least one of a third-generation (3G) network, a fourth-generation (4G) network, a wireless broadband (Wibro) network, a global system for mobile communications (GSM) network, a fifth-generation (5G) network, a sixth-generation (6G) network, etc.

[0032] In addition, functions and / or functional elements enabling the functions can be implemented by a processing circuit (such as hardware, software, or a combination of hardware and software). For example, the processing circuit may include, but is not limited to, a central processing unit (CPU), an application processor, an arithmetic logic unit (ALU), a graphics processing unit (GPU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a system on a chip (SoC), a programmable logic unit, a microprocessor, or an application specific integrated circuit (ASIC), etc. For example, the various functions described below can be implemented or supported by artificial intelligence technology and / or one or more computer programs, and each of the computer programs consists of computer-readable program code and is executed in a computer-readable medium. The terms "application" and "program" indicate that one or more computer programs, software components, instruction sets, processes, functions, objects, classes, instances, related data, or some of them are configured to be an implementation of appropriate computer-readable program code. The term "computer-readable program code" includes all types of computer code, including source code, object code, and executable code. The term "computer-readable medium" includes each type of medium accessible by a computer (such as a read-only memory (ROM), a random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory). A "non-transitory" computer-readable medium excludes a temporary wired communication link, a wireless communication link, an optical communication link, or other communication links through which electrical signals or other signals are transmitted. Non-transitory computer-readable media include media that permanently store data and media that store data and are later rewritten by other data (such as a rewritable optical disc or an erasable memory device).

[0033] The embodiments described below use a hardware-based access method as an example. However, since the embodiments include technologies using both hardware and software, the embodiments do not exclude software-based access methods.

[0034] Referring to Figure 1 , the wireless communication system 1 may include a base station 10 and a user equipment 30. The base station 10 may generally be referred to as a fixed station that communicates with the user equipment 30 and exchanges control information and data by communicating with the user equipment 30. For example, the base station 10 may be differently referred to as a Node B, an evolved Node B (eNB), a next-generation Node B (gNB), a sector, a site, a base transceiver system (BTS), an access point (AP), a relay node, a remote radio head (RRH), a radio unit (RU), a small cell, a wireless device, a device, etc.

[0035] The user equipment 30 may represent a fixed or mobile device that sends and receives data and / or control information by communicating with the base station 10. For example, the user equipment 30 may be referred to as a terminal, a terminal device, a mobile station (MS), a mobile terminal (MT), a user terminal (UT), a subscriber station (SS), a wireless communication device, a wireless device, a device, a handheld device, etc.

[0036] The base station 10 may be connected to the user equipment 30 within the coverage area 20 (e.g., communicate with the user equipment 30 within the coverage area 20) and provide a wireless network-based communication service to the user equipment 30. The user equipment 30 may move within the coverage area 20 of the base station 10 and continuously communicate with the base station 10 while moving.

[0037] In at least one embodiment, the user equipment 30 may support a first positioning function to autonomously and / or actively determine the position of the user equipment 30. The first positioning function may be defined as a function in which the user equipment 30 performs position determination independently of the base station 10. For example, the term "position" may cover a point, area, or space that can be recognized by the user equipment 30. In addition, "position determination" may be interchangeably referred to as "positioning". In at least one embodiment, the first positioning function may correspond to a neural network-based positioning function, and the user equipment 30 may include a neural network model that is pre-trained to support the first positioning function.

[0038] In at least one embodiment, the user equipment 30 may include a positioning circuit 32 that determines the position of the user equipment 30 according to a first positioning function. In at least one embodiment, the positioning circuit 32 may be implemented as software executed by a processor (e.g., a baseband processor) of the user equipment 30, or may be implemented as hardware within the processor of the user equipment 30. For ease of description, embodiments are described focusing on the operation of the positioning circuit 32, and the operation of the positioning circuit 32 may be understood as the operation of the user equipment 30.

[0039] Hereinafter, at least one embodiment in which the positioning circuit 32 determines whether the first position of the user equipment 30 corresponds to a first specific position SP_1 (or SP1) will be described.

[0040] In at least one embodiment, the positioning circuit 32 may determine whether the first position of the user equipment 30 corresponds to a previously registered first specific position SP_1. In some embodiments, when multiple specific positions are registered, the positioning circuit 32 may determine whether the changed position of the user equipment 30 corresponds to one of the multiple specific positions.

[0041] In at least one embodiment, when the user equipment 30 moves to the first position, the positioning circuit 32 may generate a first value of a plurality of indicators based on a first radio signal received from the base station 10 at the first position. The positioning circuit 32 may determine that the first position corresponds to the first specific position SP_1 based on the first value. As a specific example, the positioning circuit 32 may input the first value into a neural network model and determine whether the first position corresponds to the first specific position SP_1 based on the output of the neural network model. In some embodiments, when multiple specific positions are registered and the first value is input into the neural network model, the output of the neural network model may be implemented as a value "corresponding to the index indicating the first specific position SP_1".

[0042] In at least one embodiment, the plurality of indicators may include indicators suitable for determining the location of the user equipment 30. For example, the plurality of indicators may include at least one of the following: global cell identifier (ID), physical cell ID, frequency band, bandwidth, reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal-to-interference plus noise ratio (SINR), number of resource blocks, channel quality indicator (CQI), rank indicator (RI), precoding matrix indicator (PMI), modulation and coding scheme (MCS), modulation order, block error rate (BLER), transmission power, Doppler frequency, and delay spread. As an example, the user equipment 30 may selectively use some of the plurality of indicators to perform location determination. As a specific example, the user equipment 30 may perform location determination based on the value of an indicator related to the reception strength of a wireless signal. However, this is only an example, and thus, the inventive concept is not limited thereto, and various embodiments (such as the user equipment 30 using all of the many indicators for location determination or concentrating on using some of the many indicators) may be implemented.

[0043] Meanwhile, in the present specification, generating the values of the plurality of indicators based on a wireless signal may be understood as an operation of generating the values of the plurality of indicators based on a "baseband signal generated by down-converting a wireless signal".

[0044] In at least one embodiment, the user equipment 30 may display to the user information indicating that the first location of the user equipment 30 has been determined to be a first specific location SP_1. In addition, in at least one embodiment, the user equipment 30 may switch from a first operation mode to a first specific mode and operate in the first specific mode, where the first specific mode corresponds to the first specific location SP_1. As an example, the first specific mode may be a preset operation mode of the user equipment 30 such that the user equipment 30 can operate efficiently at the first specific location SP_1. As an example, the first specific mode may be set to an operation mode desired by the user at the first specific location SP_1 and / or may be set to an operation mode based on the usage pattern of the user's communication service. For example, when the first specific location SP_1 is an indoor space for the user to rest, the first specific mode may be a mode in which unwanted message alerts are minimized, which may be set by the user or the user equipment 30. In at least some embodiments, the switch from the first operation mode to the first specific mode may be initiated without additional input from the user.

[0045] Hereinafter, at least one embodiment in which the positioning circuit 32 registers the second location of the user equipment 30 as a second specific location SP_2 (or SP2) will be described.

[0046] In at least one embodiment, the positioning circuit 32 may determine whether to register the second position of the user equipment 30 as a second specific position SP_2 that is a new specific position. In at least one embodiment, the positioning circuit 32 may determine to register the second position as the second specific position SP_2 in response to a registration request received from the user for the second position (e.g., a request to register the second position as a new specific position).

[0047] In addition, in at least one embodiment, the positioning circuit 32 may perform a monitoring operation to determine whether a new specific position is registered at the second position, and register the second position as the second specific position SP_2 in response to the monitoring result satisfying an automatic registration condition. As an example, the monitoring operation of the positioning circuit 32 may include an operation of analyzing the pattern of the second wireless signal received by the user equipment 30 from the base station 10 at the second position and the pattern of the third wireless signal transmitted by the user equipment 30 to the base station 10. As an example, the automatic registration condition may be set as a condition that a "characteristic value derived from the usage pattern of the user's communication service analyzed by the positioning circuit 32" exceeds a threshold. That is, the positioning circuit 32 may predict that the user equipment 30 will operate in a specific mode at the corresponding position based on the automatic registration condition, and may actively register a new specific position without a registration request from the user.

[0048] In at least one embodiment, when the positioning circuit 32 determines to register the second position as the second specific position SP_2, the positioning circuit 32 may generate a second value of a plurality of signals (e.g., a plurality of indicators) based on the second wireless signal received from the base station 10 at the second position, and register the second specific position SP_2 based on the second value. As a specific example, the positioning circuit 32 may register the second specific position SP_2 by training a neural network model based on the second value. As an example, the neural network model may distinguish the second specific position SP_2 from other positions by extracting the features of the second value and learning the features.

[0049] In at least one embodiment, the positioning circuit 32 may provide appropriate guidance information to the user through the user interface for receiving sufficient second wireless signals to accurately register the second specific position SP_2. In some embodiments, the guidance information may vary according to the type information of the second specific position SP_2. As an example, the positioning circuit 32 may send the guidance information for receiving the second wireless signal to the user interface through the application processor 140.

[0050] Multiple specific positions may be registered to the user equipment 30 based on the above registration method of the second specific position SP_2.

[0051] Meanwhile, in at least one embodiment, the positioning circuit 32 may determine that the user equipment 30 is at the first specific location SP_1, then generate a third value of a plurality of indicators based on a third radio signal received from the base station 10, and determine whether the user equipment 30 has left the first specific location SP_1 based on the third value. As a specific example, the positioning circuit 32 may input the third value into a neural network model and determine whether the user equipment 30 has left the first specific location SP_1 based on the output of the neural network model. As an example, when the positioning circuit 32 determines that the user equipment 30 has left the first specific location SP_1, the operation mode of the user equipment 30 may be restored to the operation mode before the first specific mode.

[0052] In at least one embodiment, together with the first positioning function, the user equipment 30 may also support a second positioning function. That is, the user equipment 30 may support heterogeneous positioning functions. As an example, the user equipment 30 may support a positioning function based on the Global Navigation Satellite System (GNSS) (or a positioning function based on the Global Positioning System (GPS)) as the second positioning function. In at least one embodiment, the user equipment 30 may selectively activate one of the first positioning function and the second positioning function based on the current communication environment. As a specific example, when the current communication environment corresponds to an indoor communication environment, the user equipment 30 may activate the first positioning function and deactivate the second positioning function. In addition, when the current communication environment corresponds to an outdoor communication environment, the user equipment 30 may activate the second positioning function. In some embodiments, the user equipment 30 may activate both the first positioning function and the second positioning function, and may accurately determine the position of the user equipment 30 by complementarily using the first positioning function and the second positioning function.

[0053] In at least one embodiment, together with the first positioning function, the user equipment 30 may support an operation of determining the position of the user equipment 30 based on the base station 10. As an example, the user equipment 30 may receive positioning reference signals (PRSs) from a plurality of base stations including the base station 10, measure the time difference of arrival between the PRSs, and generate reference signal time difference (RSTD) information. The user equipment 30 may provide the RSTD information to the base station 10, and the base station 10 may determine the position of the user equipment 30 based on the RSTD information. In some embodiments, the user equipment 30 may calibrate the RSTD information by using the first positioning function and then send the calibrated RSTD information to the base station 10. As a specific example, when the positioning circuit 32 determines that the user equipment 30 is currently at the first specific location SP_1, the user equipment 30 may calibrate the RSTD information based on the first specific location SP_1.

[0054] User equipment 30 according to at least one embodiment may support a first positioning function for position determination that is "independent of base station 10 or a system for position determination (e.g., GNSS or GPS)", and supplement the limitations of position determination by base station 10 and position determination by the system, so as to accurately perform position determination on user equipment 30 that moves to various positions.

[0055] User equipment 30 according to at least one embodiment does not require additional wireless signals for position determination, and may perform position determination by only using the wireless signals received for communication with base station 10, thereby minimizing the consumption of wireless resources.

[0056] In addition, user equipment 30 according to at least one embodiment may perform position determination by using a neural network model and registering a new specific position by training the neural network model, thereby improving the accuracy of position determination.

[0057] Figure 2 is a block diagram showing user equipment 100 according to at least one embodiment.

[0058] Referring to Figure 2 , user equipment 100 may include a baseband processor 110, a transceiver 120, a memory 130, an application processor 140, a user interface 150, and a plurality of antennas 121_1 to 121_x. The baseband processor 110 may also be referred to as a first processor, and the application processor 140 may also be referred to as a second processor. Meanwhile, in Figure 2 , the baseband processor 110 and the application processor 140 are shown as separate components, but this is only an example and the inventive concept is not limited thereto, and the baseband processor 110 and the application processor 140 may be integrated, or the baseband processor 110 and the application processor 140 may perform position determination according to the inventive concept in combination with each other. As an example, the baseband processor 110 may be configured to control the transceiver 120.

[0059] The transceiver 120 may be configured to receive wireless signals transmitted from a base station and / or transmit wireless signals to a base station through the plurality of antennas 121_1 to 121_x. The transceiver 120 may down-convert the received wireless signals to generate intermediate frequency signals and / or baseband signals. The baseband processor 110 may obtain data by performing processing operations of filtering, decoding, and / or digitizing the intermediate frequency signals or baseband signals, and generate data for communication with the base station based on the obtained data.

[0060] In addition, the baseband processor 110 may encode, multiplex, and / or analogize the generated data to provide an intermediate frequency signal or a baseband signal to the transceiver 120. The transceiver 120 may up-convert the intermediate frequency signal or the baseband signal to transmit a wireless signal to a base station via a plurality of antennas 121_1 to 121_x.

[0061] In at least one embodiment, the baseband processor 110 may include a positioning circuit 112, and the memory 130 may store a neural network model 132 for supporting a first positioning function. As an example, the neural network model 132 may be executed by the baseband processor 110 and is used to perform position determination on the user equipment 100. Hereinafter, for ease of description, the operation of the positioning circuit 112 will be mainly described, but the operation of the positioning circuit 112 may be understood as the operation of the baseband processor 110, and further, the operation of the positioning circuit 112 may be understood as the operation of the user equipment 100.

[0062] In at least one embodiment, the positioning circuit 112 may be configured to: generate values of a plurality of indicators based on wireless signals received from a base station, perform training on the neural network model 132 based on the generated values, and register a plurality of specific locations to the neural network model 132. In addition, in at least one embodiment, the positioning circuit 112 may determine whether the current position of the user equipment 100 corresponds to one of the plurality of specific locations by using the neural network model 132.

[0063] In at least one embodiment, the application processor 140 may execute an application associated with the position determination of the positioning circuit 112 to perform an operation of "controlling the position determination of the positioning circuit 112".

[0064] In at least one embodiment, the application processor 140 may be configured to send a registration request for a new specific location received from a user through the user interface 150 to the positioning circuit 112. At this time, the positioning circuit 112 may, in response to the registration request for the new specific location, determine to register the current location of the user equipment 100 as the new specific location. In at least one embodiment, the application processor 140 may send type information of the new specific location received through the user interface 150 together with the registration request for the new specific location to the positioning circuit 112. That is, the user may wish to register the new specific location as one of multiple types, and the type information may indicate the type desired by the user. As an example, the type information may include information indicating whether the new specific location corresponds to one of a specific floor and a specific area. As a specific example, the amount of wireless signals required to be received for registering a specific location corresponding to a specific floor may be greater than the amount of wireless signals required to be received for registering a specific location corresponding to a specific area. The positioning circuit 112 may generate guidance information based on the type information of the new specific location, and send the guidance information to the application processor 140 to sufficiently receive wireless signals for determining the location of the user equipment 100. The application processor 140 may display the guidance information to the user through the user interface 150.

[0065] In at least one embodiment, the application processor 140 may be configured to receive information about a specific location "determined by the positioning circuit 112 to correspond to the current location of the user equipment 100" from the positioning circuit 112, and control the operation mode of the user equipment 100 based on the information. As an example, the operation mode of the user equipment 100 may include multiple specific modes, and the user equipment 100 may control the operation mode to a specific mode corresponding to the determined specific location among the multiple specific modes. As an example, one of the multiple specific modes may be an off-the-shelf mode supported by the application processor 140. As an example, one of the multiple specific modes may be a mode set by the user.

[0066] In at least one embodiment, the application processor 140 may be configured to enable information indicating that a specific location is determined to correspond to the current location of the user equipment 100 to be displayed to the user through the user interface 150. In at least one embodiment, the application processor 140 may display information indicating that the user equipment 100 operates based on a specific mode corresponding to the determined specific location. In some embodiments, the application processor 140 may notify the user of this through the user interface 150 before controlling the user equipment 100 to operate based on a specific mode corresponding to the determined specific location.

[0067] In at least one embodiment, the neural network model 132 may adopt one or more neural network (NN) architectures among the following architectures: multi-layer perceptron (MLP) architecture, convolutional neural network (CNN) architecture, region-based convolutional neural network (R-CNN) architecture, region proposal network (RPN) architecture, recurrent neural network (RNN) architecture, stacked deep neural network (S-DNN) architecture, state-space dynamic neural network (S-SDNN) architecture, deconvolution network architecture, deep belief network (DBN) architecture, restricted Boltzmann machine (RBM) architecture, fully convolutional network architecture, classification network architecture, ordinary residual network architecture, dense network architecture, hierarchical pyramid network architecture, transformer architecture, long short-term memory (LSTM) architecture, etc.

[0068] In at least one embodiment, the neural network model 132 may be trained based on various values of a plurality of indicators generated by the positioning circuit 112 at a plurality of positions of the user equipment 100 to accurately identify a plurality of specific positions. In this specification, identifying a specific position by the neural network model 132 may be understood as registering the specific position.

[0069] In addition, in at least one embodiment, the neural network model 132 may be updated based on the values of a plurality of indicators generated by the positioning circuit 112 at a plurality of registered specific positions of the user equipment 100 so as to more accurately and quickly identify the plurality of registered specific positions. For example, the neural network model 132 may be updated to include new specific positions and / or compensate for changes to existing specific positions.

[0070] Figure 3 is a diagram showing Figure 2 the structure of the neural network model 132. The neural network model 132 may be used to determine whether the position of the user equipment 100 described above corresponds to a specific position and may be effectively trained to identify a specific position based on the structure described below.

[0071] Referring to Figure 3 , the neural network model 132 may include a plurality of levels (e.g., a first level LV1 to a third level LV3). However, the inventive concept is not necessarily limited thereto, and the neural network model 132 may consist of only one level or two or more levels. Hereinafter, the first level LV1 is described, but this may also be applied to other levels included in the neural network model 132 in substantially the same manner. In addition, depending on the situation, some layers in the first level LV1 of the neural network model 132 may be omitted, or other layers may be added to the layers in the first level LV1.

[0072] The first - level LV1 may include multiple layers (e.g., the first layer L1_1 to the nth layer Ln_1 (where "n" is an integer)). A neural network model 132 with such a multi - layer architecture may be referred to as a deep neural network (DNN) or a deep - learning architecture. Each of the first layer L1_1 to the nth layer Ln_1 can be a linear layer or a non - linear layer, and in some embodiments, at least one linear layer and at least one non - linear layer can be combined into one layer. For example, a linear layer may include a convolutional layer or a fully - connected layer, and a non - linear layer may include a pooling layer or an activation layer.

[0073] For example, the first layer L1_1 can be a convolutional layer, the second layer L2_1 can be a pooling layer, and the nth layer Ln_1 can be a fully - connected layer as an output layer. The neural network model 132 may also include an activation layer and layers that perform other types of calculations.

[0074] Each of the first layer L1_1 to the nth layer Ln_1 may receive input data or a feature map generated in a previous layer as an input feature map, and may perform calculations on the input feature map to generate an output feature map. In this regard, a feature map indicates "data representing various features of the input data". The first feature map FM1_1 to the nth feature map FMn_1 may each have, for example, a two - dimensional (2D) matrix form including multiple feature values or a three - dimensional (3D) matrix (or called a tensor) form. The first feature map FM1_1 to the nth feature map FMn_1 may each have a width W1 (or called a column), a height H1 (or called a row), and a depth D1, which respectively correspond to the x - axis, y - axis, and z - axis on a coordinate system. In this regard, the depth D1 may be referred to as the number of channels CH1. Figure 3 Three channels CH1, CH2, and CH3 are shown, but the inventive concept is not limited thereto, and the neural network model 132 may have one or more channels.

[0075] The first layer L1_1 may generate a second feature map FM2_1 by convolving the first feature map FM1_1 with a weight map WM1. The weight map WM1 may have a 2D matrix form or a 3D matrix form including multiple weight values. The weight map WM1 may be referred to as a kernel. The weight map WM1 may perform filtering on the first feature map FM1_1 and may be referred to as a filter or a kernel. The same channels of the weight map WM1 and the first feature map FM1_1 may be convolved with each other. The weight map WM1 may shift as a sliding window across the first feature map FM1_1. For each shift, the weights included in the weight map WM1 may be multiplied by all the feature values in the region of the first feature map FM1_1 that overlaps with the weight map WM1, and then added together. When the first feature map FM1_1 and the weight map WM1 are convolved with each other, one channel of the second feature map FM2_1 may be generated. Although Figure 3A weight map WM1 is shown, but substantially multiple weight maps are convolved with the first feature map FM1_1, and thus, multiple channels of a second feature map FM2_1 can be generated. In other words, the number of channels of the second feature map FM2_1 can correspond to the number of weight maps.

[0076] The second layer L2_1 can generate a third feature map FM3_1 by changing the spatial dimension of the second feature map FM2_1 via pooling. Pooling can be referred to as sampling or downsampling. A 2D pooling window PW1 can be shifted on the second feature map FM2_1 in units of the size of the pooling window PW1, and the maximum value (or average value) of the feature values in the region overlapping with the pooling window PW1 can be selected. Accordingly, the third feature map FM3_1 can be generated by changing the spatial dimension of the second feature map FM2_1. The number of channels of the third feature map FM3_1 can be the same as the number of channels of the second feature map FM2_1. The n-th layer Ln_1 can classify the class CL of the input data by combining the features of the n-th feature map FMn_1. In addition, the n-th layer Ln_1 can also generate an identification signal REC corresponding to the class CL. The n-th layer Ln_1 can be omitted according to circumstances.

[0077] The neural network model 132 may include one stage, but may include multiple stages. Each stage of the multiple stages may receive a feature map generated from data input to the stage as an input feature map, and perform calculations on the input feature map to generate an output feature map or an identification signal REC. As an example, the first stage LV1 may receive a feature map generated from input data as an input feature map. The first layer L1_1 of the first stage LV1 may receive the first feature map FM1_1 generated from the input data. The second stage LV2 may receive a feature map generated from the first reconstructed data as an input feature map. The first layer L1_2 of the second stage LV2 may receive the first feature map FM1_2 generated from the first reconstructed data. The third stage LV3 may receive a feature map generated from the second reconstructed data as an input feature map. The first layer L1_3 of the third stage LV3 may receive the first feature map FM1_3 generated from the second reconstructed data. Similar to the first stage LV1, the first layer L1_2 of the second stage LV2 may generate a second feature map FM2_2 by convolving the first feature map FM1_2 with a weight map WM2, and the second layer L2_2 of the second stage LV2 may generate a third feature map FM3_2 based on the second feature map FM2_2 and a pooling window PW2. The first layer L1_3 of the third stage LV3 may generate a second feature map FM2_3 by convolving the first feature map FM1_3 with a weight map WM3, and the second layer L2_3 of the third stage LV3 may generate a third feature map FM3_3 based on the second feature map FM2_3 and a pooling window PW3. The unit of the first reconstructed data may be different from the unit of the second reconstructed data. The widths W1, W2, and W3, heights H1, H2, and H3, and depths D1, D2, and D3 of the first feature maps FM1_1, FM1_2, and FM1_3 may be different from each other.

[0078] The multiple stages may be organically connected to each other. In at least one embodiment, the feature maps output from the layers included in each of the multiple stages may be organically connected to the feature maps of different stages. For example, the neural network model 132 may perform calculations to extract the features of the third feature map FM3_1 of the first stage LV1 and the first feature map FM1_2 of the second stage LV2, and generate a new feature map. In at least one embodiment, the nth layer Ln_1 may exist in only one stage, and combine the features of the feature maps of the multiple stages to classify the category CL of the input data.

[0079] In at least one embodiment, the configuration of the layers including the neural network model 132, the number of feature maps, and the combination of the feature maps may be determined to effectively identify a specific location.

[0080] Figure 4 is a flowchart showing an operation method of a user device according to at least one embodiment. Figure 4The operations of the user equipment herein may also be understood as the operations of the positioning circuit or the baseband processor. For example, the neural network model 132 may be configured to receive data regarding the first radio signal of the input and / or a first value based on a plurality of indicators of the first radio signal. The positioning circuit 32 may be configured to determine the position of the user equipment and / or whether the user equipment is at a specific position based on the output of the neural network model 132.

[0081] Referring Figure 4 , in operation S100, the user equipment may determine to register the current position of the user equipment as a new specific position. For example, the determination may be based on user input, the monitoring operation of the positioning circuit 32, automatic registration conditions, location-specific policies, laws, and / or regulations, etc.

[0082] In operation S110, the user equipment may generate values of a plurality of indicators based on the radio signals received from the base station at the current position.

[0083] In operation S120, the user equipment may register the current position as a new specific position based on the values generated in operation S110.

[0084] Figure 5A and Figure 5B is a flowchart for specifically explaining Figure 4 operation S100.

[0085] Referring Figure 5A , in operation S101A, the user equipment may receive, through the user interface, a request from the user to register the current position of the user equipment as a new specific position.

[0086] In operation S102A, the user equipment may, in response to the registration request received in operation S101A, determine to register the current position of the user equipment as a new specific position.

[0087] As described above, the user equipment may, in response to the user's registration request, determine to register the current position of the user equipment desired by the user as a new specific position.

[0088] Referring Figure 5B , in operation S101B, the user equipment may perform a monitoring operation to determine whether to register a new specific position. In at least one embodiment, the user equipment may be in various positions due to movement and may perform a monitoring operation to analyze the patterns of radio signals periodically or periodically sent to the base station at various positions and the patterns of radio signals received from the base station.

[0089] In operation S102B, the user equipment may determine whether the monitoring result of operation S101B meets the automatic registration condition. In at least one embodiment, the automatic registration condition may be set based on at least one of the common features between the specific locations for which the user requests registration, the information received from the user, and the usage pattern of the user's communication service. That is, the automatic registration condition may be set to predict that the user will want the user equipment 30 to operate in a specific pattern at any location and actively register a new specific location without the user's registration request.

[0090] When the result of operation S102B is "yes", operation S103B is then performed, where the user equipment may determine to register the current location of the user equipment as a new specific location. In at least some embodiments, although not shown, the user equipment may monitor the location of the user equipment to determine whether the user equipment has moved, and may perform operation S101B based on determining that the user equipment has moved more than a minimum preset distance.

[0091] When the result of operation S102B is "no", operation S101B may then be performed. In at least some embodiments, a pause may occur between the operation when S102B is "no" and the subsequent operation S101B. For example, although not shown, the user equipment may monitor the location of the user equipment to determine whether the user equipment has moved, and may perform operation S101B based on determining that the user equipment has moved more than a minimum preset distance.

[0092] Figure 6 It is a table diagram for explaining a specific location and its corresponding related information according to at least one embodiment.

[0093] Referring to Figure 6 In the first table TB1 in, the user equipment may register the first specific location to the third specific locations SP_1, SP_2, and SP_3, and provide the corresponding first related information to the third related information INFO_1, INFO_2, and INFO_3.

[0094] In at least one embodiment, the first specific location SP_1 may correspond to the first related information INFO_1, and the first related information INFO_1 may include at least one of "an index indicating the first specific location SP_1" and "type information indicating the type of the first specific location SP_1".

[0095] In at least one embodiment, the second specific location SP_2 may correspond to the second related information INFO_2, and the second related information INFO_2 may include at least one of "an index indicating the second specific location SP_2" and "type information indicating the type of the second specific location SP_2".

[0096] In addition, in at least one embodiment, the third specific location SP_3 may correspond to the third related information INFO_3, and the third related information INFO_3 may include at least one of "an index indicating the third specific location SP_3" and "type information indicating the type of the third specific location SP_3".

[0097] In at least one embodiment, the index included in the related information may be information for identifying a specific location indicated by the output of the neural network model. In at least one embodiment, the type information included in the related information may be information required to determine whether the current position of the user equipment corresponds to a specific location.

[0098] In some embodiments, the related information may further include various information required for the user equipment to determine whether the current position corresponds to one of the first specific location to the third specific locations SP_1, SP_2, SP_3.

[0099] Figure 7A and Figure 7B is a diagram for explaining examples of the first specific location to the third specific locations SP_1, SP_2, SP_3.

[0100] Referring to Figure 7A , the first specific location to the third specific locations SP_1, SP_2, SP_3 may respectively correspond to the first specific floor to the third specific floor. That is, the user equipment may register a specific floor in a specific building as a specific location.

[0101] As an example, when the user equipment is located on the first specific floor, it may be determined that the user equipment is located at the first specific location SP_1 based on the first value of a plurality of first indicators generated from "wireless signals received on the first specific floor". When the user equipment is located on the second specific floor, it may be determined that the user equipment is located at the second specific location SP_2 based on the second value of a plurality of first indicators generated from "wireless signals received on the second specific floor". In addition, when the user equipment is located on the third specific floor, it may be determined that the user equipment is located at the third specific location SP_3 based on the third value of a plurality of first indicators generated from "wireless signals received on the third specific floor". In at least some embodiments, triangulation may be used to determine the position of the user equipment. For example, the user equipment 30 may receive signals from a plurality of base stations 10 and may determine the position based on the signals. For example, in at least some embodiments, the position may be determined based on the relative positions with two, three or more of the plurality of base stations 10.

[0102] Further referring to Figure 7B, the first specific position to the third specific position SP_1', SP_2' and SP_3' may respectively correspond to the first specific area to the third specific area on the Nth floor. That is, the user equipment may register the specific areas on the same floor in a specific building as specific positions.

[0103] As an example, when the user equipment is located in the first specific area, it may be determined that the user equipment is located at the first specific position SP_1' based on the first value of a plurality of second indicators generated from "the wireless signals received in the first specific area". When the user equipment is located in the second specific area, it may be determined that the user equipment is located at the second specific position SP_2' based on the second value of a plurality of second indicators generated from "the wireless signals received in the second specific area". In addition, when the user equipment is located in the third specific area, it may be determined that the user equipment is located at the third specific position SP_3' based on the third value of a plurality of second indicators generated from "the wireless signals received in the third specific area".

[0104] In at least one embodiment, Figure 7A at least one of the first indicators in Figure 7B may be different from at least one of the second indicators in

[0105] That is, at least one of the first indicators required to determine the specific position corresponding to a specific floor may be different from at least one of the second indicators required to determine the specific position corresponding to a specific area.

[0106] Figure 8 is a table diagram for explaining the type of specific position and the corresponding positioning method according to at least one embodiment.

[0107] Referring to Figure 8 in the second table TB2, the user equipment may classify the type of the specific position into one of the first type TYPE_1 and the second type TYPE_2, and perform positioning with the corresponding first positioning method PM_1 and second positioning method PM_2.

[0108] As at least one embodiment, the first type TYPE_1 may indicate the type of the specific floor described with reference to Figure 7A and the second type TYPE_2 may indicate the type of the specific area described with reference to Figure 7B described.

[0109] In at least one embodiment, the positioning method may be related to at least one of the configuration of the guiding information provided to the user and the configuration of the indicators required to determine the specific position.

[0110] In at least one embodiment, the first positioning method PM_1 may indicate at least one of "guiding information provided to the user to register a specific location corresponding to the first type TYPE_1" and "a first indicator required to determine a specific location corresponding to the first type TYPE_1".

[0111] In at least one embodiment, the second positioning method PM_2 may indicate at least one of "guiding information provided to the user to register a specific location corresponding to the second type TYPE_2" and "a second indicator required to determine a specific location corresponding to the second type TYPE_2".

[0112] However, this is only an example, and thus, the inventive concept is not limited thereto, and the user equipment may classify specific locations into more types and perform positioning operations suitable for each type, or perform general positioning operations without classifying specific locations into multiple types.

[0113] Figure 9 is a flowchart showing an operation method of a user equipment according to at least one embodiment. Figure 9 The user equipment in may include a positioning circuit 200 and a user interface 210. Figure 9 Direct signaling operations between the positioning circuit 200 and the user interface 210 are shown, but this is for ease of description, and an application processor for signaling between the positioning circuit 200 and the user interface 210 may perform specific operations.

[0114] Referring to Figure 9 , in operation S200, the positioning circuit 200 may receive, from the user through the user interface 210, type information of a new specific location and a registration request for the new specific location. As a specific example, the positioning circuit 200 may receive, from the user through the user interface 210, a registration request of "requesting to register the current location as a new specific location" and type information indicating a desired type.

[0115] In operation S210, the positioning circuit 200 may send guiding information to the user interface 210 based on the type information received in operation S200. As a specific example, guiding information may be provided to the user to receive a wireless signal sufficient to register the current location of the user equipment as a new specific location of the type desired by the user. As an example, the user may act according to the guiding information displayed through the user interface 210.

[0116] However, the reception of type information and the sending of guiding information in operations S200 and S210 are only examples, and thus, the inventive concept is not limited thereto, and the reception of type information and the sending of guiding information may be omitted. That is, the positioning circuit 200 may immediately execute operations S220 to S240 in response to a registration request for a new specific location received through the user interface 210.

[0117] In at least one embodiment, the positioning circuit 200 may, in response to a registration request received in operation S200, determine to register the current location of the user equipment as a new specific location, and perform operations S220 to S240.

[0118] In at least one embodiment, in operation S220, the positioning circuit 200 may generate values of a plurality of indicators based on wireless signals received at the current location.

[0119] In at least one embodiment, in operation S230, the positioning circuit 200 may train a neural network model based on the values of the plurality of indicators. As an example, the positioning circuit 200 may train the neural network model by inputting the values of the plurality of indicators into the neural network model and extracting features of the input values from the neural network model to identify the features as a new specific location.

[0120] In at least one embodiment, in operation S240, the positioning circuit 200 may complete the registration of the new specific location when the training in operation S230 is completed.

[0121] Figure 10A and Figure 10B are diagrams for specifically explaining Figure 9 operations S220 and S230.

[0122] Referring to Figure 10A In operation S221, the positioning circuit 200 may selectively generate values of indicators among the plurality of indicators that match the type information. As an example, when the type information indicates a type corresponding to a specific floor, the positioning circuit 200 may selectively generate the value of a first indicator among the plurality of indicators. In addition, when the type information indicates a type corresponding to a specific area, the positioning circuit 200 may selectively generate the value of a second indicator among the plurality of indicators.

[0123] In operation S231, the positioning circuit 200 may train the neural network model based on the values generated in operation S221 and the type information. As an example, the calculation method for training the neural network model may also vary according to the type of the new specific location.

[0124] Further referring to Figure 10B in the third table TB3, the user equipment may classify the type of the specific location into one of a first type TYPE_1 and a second type TYPE_2, and perform positioning by using a corresponding first indicator set IS_1 and second indicator set IS_2.

[0125] In at least one embodiment, the first type TYPE_1 may indicate referring to Figure 7Athe type of a specific floor described, and the second type TYPE_2 may indicate a reference Figure 7B the type of a specific area described.

[0126] In at least one embodiment, the first indicator set IS_1 may include first indicators required for "registering a specific location type of the first type TYPE_1 or determining a specific location of the first type TYPE_1".

[0127] In at least one embodiment, the second indicator set IS_2 may include second indicators required for "registering a specific location type of the second type TYPE_2 or determining a specific location of the second type TYPE_2".

[0128] Figure 11 is a flowchart showing an operation method of a user equipment according to at least one embodiment. In Figure 11 the operation of the user equipment, operations S300 and S310 may be understood as operations of a positioning circuit or a baseband processor, and operation S320 may be understood as an operation of an application processor.

[0129] Referring to Figure 11 , in operation S300, the user equipment may generate values of a plurality of indicators based on wireless signals received at the current location of the user equipment.

[0130] In operation S310, the user equipment may determine whether the current location corresponds to a specific location based on the values generated in operation S300. Further, when a plurality of specific locations are registered in the user equipment, the user equipment may determine whether the current location corresponds to one of the plurality of specific locations based on the generated values.

[0131] In operation S320, the user equipment may control the operation mode of the user equipment based on the determination result of operation S310. As an example, when the user equipment determines that the current location corresponds to a specific location, the user equipment may control the operation mode such that the user equipment operates in a specific mode corresponding to the specific location. In other words, the user equipment may switch from a previous operation mode to a specific mode.

[0132] Figure 12A and Figure 12B are diagrams for specifically explaining Figure 11 operation S310 of

[0133] Referring to Figure 12A , in operation S311, the user equipment may input the generated values into a neural network model.

[0134] In operation S312, the user equipment may determine whether the current location corresponds to a specific location and / or whether the automatic registration condition is satisfied based on the output of the neural network model. As an example, when the output of the neural network model has a value corresponding to the index of a specific location, the user equipment may determine that the current location corresponds to the specific location. In addition, as an example, when multiple specific locations are registered in the user equipment, the output of the neural network model may have a value corresponding to one of the indexes of the multiple specific locations, and based on this value, the user equipment may determine the specific location corresponding to the current location among the multiple specific locations.

[0135] Further referring to Figure 12B , data including values V_11, V_21, and V_31 of three indicators I_11, I_21, and I_31 may be input into the neural network model 220, and the neural network model 220 is trained for position determination of the current location of the user equipment. The neural network model 220 may perform neural network-based calculations based on the input and output data indicating whether the current location corresponds to a specific location. In at least one embodiment, the three indicators I_11, I_21, and I_31 may be indicators suitable for position determination of the user equipment.

[0136] However, Figure 12B the embodiment of the neural network model 220 of

[0137] Figure 13A and Figure 13B is only an example, and thus, the inventive concept is not limited thereto, and values of more or fewer indicators may be input. Figure 11 are diagrams for specifically explaining operations S300 and S310 of

[0138] Referring to Figure 13A , in operation S300', the user equipment may generate a value of an indicator corresponding to the type information of a specific location based on the wireless signal received at the current location. As an example, the user equipment may confirm the type of the specific location based on the type information of the specific location and then selectively generate a value of an indicator suitable for determining the specific location.

[0139] In operation S310', the user equipment may input the value generated in operation S300' and the type information into the neural network model. As an example, the neural network model may identify the input value based on the type information, and in addition, the neural network model may perform neural network calculations by using a calculation method corresponding to the type information.

[0140] In operation S320', the user equipment may determine whether the current location corresponds to a specific location based on the output of the neural network model.

[0141] Further referring to Figure 13BData including the values V_12, V_22, and V_32 of three indicators I_12, I_22, and I_32 and the type information TYPE_INFO of a specific location can be input into the neural network model 220'. The neural network model 220' is trained for location determination of the current location of the user equipment. The neural network model 220' can perform neural network-based calculations based on the input and the type information TYPE_INFO and output data indicating whether the current location corresponds to a specific location.

[0142] However, Figure 13B The embodiment of the neural network model 220' in [] is only an example. Therefore, the inventive concept is not limited thereto, and more or fewer indicator values can be input.

[0143] Figure 14A is a flowchart showing an operation method of a user equipment according to at least one embodiment. Figure 14B is a table diagram showing a specific location and a specific pattern corresponding thereto according to at least one embodiment. Figure 14A The operation of the user equipment in [] can be understood as the operation of the application processor.

[0144] Referring to Figure 14A , in operation S400, the user equipment can activate a specific pattern corresponding to "the specific location determined to correspond to the current location of the user equipment". For example, the user equipment can confirm the specific pattern corresponding to the determined specific location and then switch the previous operation pattern to the specific pattern by "deactivating the previous operation pattern and activating the confirmed specific pattern".

[0145] In operation S410, the user equipment can operate based on the specific pattern activated in operation S400.

[0146] Further referring to Figure 14B the fourth table TB4 of [], the specific locations registered in the user equipment can include a first specific location to a third specific location SP_1, SP_2, and SP_3. A first specific pattern SM_1 can correspond to the first specific location SP_1, a second specific pattern SM_2 can correspond to the second specific location SP_2, and a third specific pattern SM_3 can correspond to the third specific location SP_3.

[0147] In at least one embodiment, at least one of the first specific pattern to the third specific pattern SM_1, SM_2, and SM_3 can be an off-the-shelf pattern supported by the user equipment. In at least one embodiment, at least one of the first specific pattern to the third specific pattern SM_1, SM_2, and SM_3 can be a pattern set by the user.

[0148] In at least one embodiment, the user equipment may perform mapping between a first specific location to a third specific location SP_1, SP_2, and SP_3 and a first specific mode to a third specific mode SM_1, SM_2, and SM_3. In some embodiments, the user equipment performs mapping between a first specific location to a third specific location SP_1, SP_2, and SP_3 and a first specific mode to a third specific mode SM_1, SM_2, and SM_3 based on input or feedback received from the user.

[0149] In at least one embodiment, when it is determined that the current location corresponds to the first specific location SP_1, the user equipment may be configured to operate in the first specific mode SM_1. When the current location is determined to correspond to the second specific location SP_2, the user equipment may operate in the second specific mode SM_2. When the current location is determined to correspond to the third specific location SP_3, the user equipment may be configured to operate in the third specific mode SM_3.

[0150] Figure 15 is a flowchart showing an operation method of a user equipment according to at least one embodiment. In Figure 15 the operation of the user equipment in, operations S500 and S510 may be understood as operations of a positioning circuit or a baseband processor, and operation S520 may be understood as an operation of an application processor.

[0151] Referring to Figure 15 , in operation S500, the user equipment may generate values of a plurality of indicators based on wireless signals received at the current location.

[0152] In operation S510, the user equipment may determine whether the current location deviates from a specific location based on the values generated in operation S500.

[0153] In operation S520, the user equipment may control the operation mode of the user equipment based on the determination result of operation S510. For example, when it is determined that the current location deviates from a specific location, the user equipment may control the operation mode such that the user equipment operates in an operation mode before the specific mode.

[0154] Figure 16 is a block diagram showing a baseband processor 300 according to at least one embodiment.

[0155] Referring to Figure 16 , the baseband processor 300 may include a positioning circuit 302, a GNSS circuit 304, and a selection circuit 306.

[0156] In at least one embodiment, the positioning circuit 302 may support a first positioning function (or a neural network-based positioning function), and the GNSS circuit 304 may support a second positioning function (e.g., a GNSS-based positioning function). The baseband processor 300 may support heterogeneous positioning functions through the positioning circuit 302 and the GNSS circuit 304. Meanwhile, in some embodiments, the baseband processor 300 may include a GPS circuit that supports a GPS-based positioning function instead of the GNSS circuit 304.

[0157] In at least one embodiment, the positioning circuit 302 may generate values of a plurality of indicators based on wireless signals received at the current location of the user equipment, and determine whether the current location corresponds to a specific location based on the generated values to perform location determination.

[0158] In at least one embodiment, the GNSS circuit 304 may generate positioning information about the current location of the user equipment by using radio waves transmitted from a plurality of satellites.

[0159] In at least one embodiment, the selection circuit 306 may be controlled to select the positioning circuit 302 or the GNSS circuit 304 to perform location determination according to the first positioning function or the second positioning function.

[0160] In at least one embodiment, the selection circuit 306 may measure the current communication environment and select one of the positioning circuit 302 and the GNSS circuit 304 based on the measured current communication environment. As an example, the selected circuit may be activated, and the unselected circuit may be deactivated.

[0161] However, Figure 16 the baseband processor 300 is only an example. Therefore, the inventive concept is not limited thereto, and the positioning circuit 302 and the GNSS circuit 304 may be activated simultaneously to utilize both the first positioning function and the second positioning function for accurate location determination of the user equipment.

[0162] Figure 17A and Figure 17B are flowcharts showing an operation method of a user equipment according to at least one embodiment.

[0163] Referring to Figure 17A In operation S600, the user equipment may periodically measure the current communication environment while the neural network-based positioning function is activated.

[0164] In operation S610, the user equipment may determine whether the current communication environment measured in operation S600 satisfies a first condition. As an example, the first condition may be a condition that the measured current communication environment corresponds to an outdoor communication environment.

[0165] When operation S610 is "Yes", operation S620 is performed, where the user equipment may deactivate the neural network-based positioning function and activate the GNSS-based positioning function. In at least some embodiments, the neural network-based positioning function may be automatically activated when it is determined that the user equipment has moved and / or based on a preset timer.

[0166] When operation S610 is "No", operation S600 may be repeated.

[0167] Further referring to Figure 17B , in operation S700, the user equipment may periodically measure the current communication environment while the GNSS-based positioning function is activated.

[0168] In operation S710, the user equipment may determine whether the current communication environment measured in operation S700 meets a second condition. As an example, the second condition may be a condition that the measured current communication environment corresponds to an indoor communication environment.

[0169] When operation S710 is "Yes", operation S720 is performed, where the user equipment may deactivate the GNSS-based positioning function and activate the neural network-based positioning function.

[0170] When operation S710 is "No", operation S700 may be repeated.

[0171] Figure 18A and Figure 18B are flowcharts showing a method of operating a user equipment according to at least one embodiment.

[0172] Referring to Figure 18A , in operation S800A, the user equipment 400 may perform a positioning operation on the current location according to a first positioning function. The first positioning function is the above-mentioned neural network-based positioning function, and the positioning operation may be performed by the user equipment 400 independently of the base station 410.

[0173] In operation S810A, the user equipment 400 may receive a positioning reference signal from the base station 410, and the base station 410 includes a base station connected to the user equipment 400.

[0174] In operation S820A, the user equipment 400 may send reference signal time difference information to the base station among the base stations 410 that is connected to the user equipment 400. In at least one embodiment, the user equipment 400 may generate RSTD information by measuring the difference between the reception times of the positioning reference signals received in operation S810A.

[0175] In operation S830A, the base station among the base stations 410 that is connected to the user equipment 400 may determine the current location of the user equipment 400 based on the received RSTD information.

[0176] In at least one embodiment, operation S800A and operation S830A may be performed independently or in parallel.

[0177] Further referring to Figure 18B , in operation S800B, user equipment 400 may perform a positioning operation on the current location according to the first positioning function.

[0178] In operation S810B, user equipment 400 may receive a positioning reference signal from base station 410, where base station 410 includes a base station connected to user equipment 400.

[0179] In operation S820B, user equipment 400 may calibrate the RSTD information based on a specific location determined to be the current location. For example, when user equipment 400 measures the difference between the reception times of the positioning reference signals, there may be characteristic errors at the specific location, and user equipment 400 may calibrate the RSTD information by considering such errors.

[0180] In operation S830B, user equipment 400 may send the calibrated RSTD information to the base station among base stations 410 that is connected to user equipment 400.

[0181] In operation S840B, the base station among base stations 410 that is connected to user equipment 400 may determine the current location of user equipment 400 based on the calibrated RSTD information.

[0182] Figure 19 is a flowchart showing a method of operating user equipment 500 according to at least one embodiment.

[0183] Referring to Figure 19 , in operation S900, user equipment 500 may send information related to the first positioning function. In at least one embodiment, user equipment 500 may send capability information indicating support for the first positioning function to base station 510 in a radio resource control (RRC) connection with base station 510.

[0184] In operation S910, base station 510 may determine a positioning method for user equipment 500 based on the capability information received in operation S900. As an example, when user equipment 500 supports the first positioning function, base station 510 may determine a positioning method such that the result of positioning performed on user equipment 500 is obtained from user equipment 500, and this result is used to perform location determination on user equipment 500 at base station 510.

[0185] In operation S920, base station 510 may perform signaling with user equipment 500 according to the positioning method determined in operation S910.

[0186] Figure 20 FIG. 2 is a conceptual diagram illustrating an Internet of Things (IoT) network system 1000 to which an embodiment is applied.

[0187] Referring Figure 20 to FIG. 2, the IoT network system 1000 may include a plurality of IoT devices 1100, 1120, 1140, and 1160, an AP 1200, a gateway 1250, a wireless network 1300, and a server 1400. IoT may indicate a network among objects using wired / wireless communication.

[0188] The plurality of IoT devices 1100, 1120, 1140, and 1160 may be grouped according to their characteristics. For example, the plurality of IoT devices 1100, 1120, 1140, and 1160 may be grouped into a home appliance group 1100, a household appliance / furniture group 1120, an entertainment group 1140, and a vehicle group 1160. The IoT devices 1100, 1120, and 1140 may be connected to a communication network or another IoT device through the AP 1200. The AP 1200 may be embedded in one IoT device. The gateway 1250 may change a protocol to connect the AP 1200 to an external radio network. The IoT devices 1100, 1120, and 1140 may be connected to an external communication network through the gateway 1250. The wireless network 1300 may include the Internet and / or a public network. The plurality of IoT devices 1100, 1120, 1140, and 1160 may be connected to the server 1400 that provides a specific service through the wireless network 1300, and a user may use the specific service through at least one of the plurality of IoT devices 1100, 1120, 1140, and 1160.

[0189] According to an embodiment, each of the plurality of IoT devices 1100, 1120, 1140, and 1160 may support a neural network-based positioning function to determine whether its location corresponds to a specific location.

[0190] Although the inventive concept has been specifically shown and described with reference to embodiments of the inventive concept, it will be understood that various changes in form and detail may be made therein without departing from the spirit and scope of the appended claims.

Claims

1. A user equipment configured to support a first positioning function, the user equipment comprising: a transceiver configured to receive a first radio signal from a base station at a first location of the user equipment; and a first processor configured to control the transceiver, wherein the first processor is further configured to: generate a first value of a plurality of first indicators based on the first radio signal, determine whether the first location corresponds to a first specific location based on the first value, and perform an operation according to the first positioning function based on determining that the first location corresponds to the first specific location.

2. The user equipment according to claim 1, wherein, The first processor is configured to: input the first value into a neural network model, and determine whether the first location corresponds to the first specific location based on the output of the neural network model.

3. The user equipment according to claim 1, wherein the transceiver is further configured to: receive a second radio signal from the base station at a second location of the user equipment, and transmit a third radio signal to the base station, and the first processor is further configured to: generate a second value of the plurality of first indicators based on the second radio signal, and register the second location as a second specific location.

4. The user equipment according to claim 3, further comprising: a user interface configured to receive a registration request for the second location from a user; and a second processor configured to send the registration request to the first processor, wherein the first processor is further configured to: register the second location as the second specific location in response to the registration request.

5. The user equipment according to claim 4, wherein the second processor is further configured to: send type information of a new specific location to the first processor, the type information being received from the user interface together with the registration request, the first processor is further configured to: send, based on the type information, guidance information for receiving the second radio signal to the user interface through the second processor.

6. The user equipment according to claim 5, wherein The type information includes information indicating whether the new specific location corresponds to at least one of a specific floor and a specific area.

7. The user equipment according to claim 3, wherein, The first processor is configured to: perform a monitoring operation, and register the second location as the second specific location in response to the result of the monitoring operation satisfying an automatic registration condition.

8. The user equipment according to claim 7, wherein, The monitoring operation includes analyzing the pattern of the second radio signal and the pattern of the third radio signal.

9. The user equipment according to claim 3, wherein, The first processor is further configured to: register the second specific location by training the neural network model based on the second value.

10. The user equipment according to any one of claims 1 to 9, wherein the plurality of first indicators includes at least one of the following: global cell identifier, physical cell identifier, frequency band, bandwidth, reference signal received power, reference signal received quality, received signal strength indicator, signal-to-interference-plus-noise ratio, number of resource blocks, channel quality indicator, rank indicator, precoding matrix indicator, modulation and coding scheme, modulation order, block error rate, transmission power, Doppler frequency, and delay spread.

11. The user equipment according to claim 1, further comprising: A second processor, configured to: control the user equipment to operate in a specific mode corresponding to the first specific location based on determining that the first location corresponds to the first specific location.

12. The user equipment according to any one of claims 1 to 9, wherein, The plurality of first indicators include indicators corresponding to the type information of the first specific location.

13. The user equipment according to any one of claims 1 to 9, wherein, The first processor is further configured to: after determining that the first location corresponds to the first specific location, generate a second value of the plurality of first indicators based on a second radio signal received by the transceiver, and determine whether the user equipment has left the first specific location based on the second value.

14. The user equipment according to any one of claims 1 to 9, wherein the user equipment is configured to further support a second positioning function, and the first processor is further configured to: selectively activate the first positioning function and the second positioning function based on the current communication environment.

15. The user equipment according to claim 14, wherein, The second positioning function is a positioning function based on a global navigation satellite system.

16. The user equipment according to any one of claims 1 to 9, wherein the transceiver is further configured to receive positioning reference signals from a plurality of base stations including the base station, and the first processor is further configured to: generate reference signal time difference information based on the positioning reference signals, and send the reference signal time difference information to the base station through the transceiver.

17. The user equipment according to claim 16, wherein, The first processor is further configured to: in response to determining that the first location corresponds to the first specific location, calibrate the reference signal time difference information based on the first specific location; send the calibrated reference signal time difference information to the base station through the transceiver.

18. The user equipment according to any one of claims 1 to 9, wherein The first processor is further configured to: enable information indicating support for the first positioning function to be sent to the base station through the radio resource control connection with the base station via the transceiver.

19. An operation method of a user equipment, the user equipment supporting a positioning function based on a neural network, the operation method comprising: generating a first value of a plurality of indicators based on a first radio signal received from a base station at a first location of the user equipment; determining whether the first location corresponds to a specific location based on the first value and a neural network model, the specific location including one of a plurality of specific locations; and controlling an operation mode of the user equipment based on determining that the first location corresponds to the specific location.

20. A user equipment, comprising: a memory storing a neural network model trained using a plurality of training locations to support a positioning function; and a first processor, configured to: generate a first value of a plurality of indicators based on a first radio signal received at a first location of the user equipment, and determine whether the first location corresponds to a specific location based on the first value and the neural network model, the specific location including one of a plurality of specific locations.

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