Method and device for cell access based on image sensing information in wireless communication system

Abstract

The present disclosure relates to a terminal detection and cell allocation method using image sensing data acquired from a plurality of small base stations (SBSs). A method performed by a macro base station (MBS) of the present disclosure may comprise the steps of: receiving at least one piece of image sensing data from at least one SBS; generating voxel data on the basis of the at least one piece of image sensing data; identifying, on the basis of the voxel data, at least one SBS having a line of sight (LoS) path to at least one terminal; and transmitting, to the identified at least one SBS, information about the at least one terminal having the LoS path.

Description

Method and apparatus for image sensing information-based cell access in a wireless communication system

[0001] The present disclosure relates to a wireless communication system, and more specifically, to a method and apparatus for performing image sensing information-based cell access in an ultra-dense network environment of a wireless communication system.

[0002] Looking back at the evolution of wireless communication through successive generations, technologies have been developed primarily for human-oriented services, such as voice, multimedia, and data. Following the commercialization of 5G (5th Generation) communication systems, connected devices, which have been increasing explosively, are expected to be connected to communication networks. Examples of networked objects include vehicles, robots, drones, home appliances, displays, smart sensors installed in various infrastructures, construction machinery, and factory equipment. Mobile devices are expected to evolve into various form factors, such as augmented reality glasses, virtual reality headsets, and holographic devices. In the 6G (6th Generation) era, efforts are underway to develop improved 6G communication systems to connect hundreds of billions of devices and objects to provide diverse services. For this reason, 6G communication systems are being referred to as "beyond 5G" systems.

[0003] In the 6G communication system predicted to be realized around 2030, the maximum transmission speed is tera (i.e., 1,000 gigabit) bps (bit per second), and the wireless latency is 100 microseconds (μsec). In other words, compared to the 5G communication system, the transmission speed in the 6G communication system is 50 times faster, and the wireless latency is reduced to one-tenth.

[0004] To achieve such high data transmission speeds and ultra-low latency, 6G communication systems are being considered for implementation in the terahertz (THz) band (e.g., the 95 gigahertz (GHz) to 3 terahertz (3THz) band). Due to more severe path loss and atmospheric absorption phenomena compared to the millimeter wave (mmWave) band introduced in 5G, the importance of technologies capable of guaranteeing signal reach, or coverage, is expected to increase in the terahertz band. As key technologies to ensure coverage, new waveforms, beamforming, and multi-antenna transmission technologies such as massive Multiple-Input and Multiple-Output (MIMO), Full Dimensional MIMO (FD-MIMO), array antennas, and large-scale antennas, which are superior in terms of coverage compared to RF (Radio Frequency) devices, antennas, and OFDM (Orthogonal Frequency Division Multiplexing), must be developed. In addition, new technologies such as metamaterial-based lenses and antennas, high-dimensional spatial multiplexing technology using Orbital Angular Momentum (OAM), and Reconfigurable Intelligent Surface (RIS) are being discussed to improve the coverage of terahertz band signals.

[0005] In addition, to improve frequency efficiency and system network, development is underway in 6G communication systems for full duplex technology, in which uplink and downlink simultaneously utilize the same frequency resources at the same time; network technology that integrates satellites and HAPS (High-Altitude Platform Stations); network structure innovation technology that supports mobile base stations and enables network operation optimization and automation; dynamic spectrum sharing technology through collision avoidance based on spectrum usage prediction; AI-based communication technology that utilizes AI (Artificial Intelligence) from the design stage and internalizes end-to-end AI support functions to realize system optimization; and next-generation distributed computing technology that realizes services of complexity exceeding the limits of terminal computing capabilities by utilizing ultra-high performance communication and computing resources (Mobile Edge Computing (MEC), cloud, etc.). In addition, attempts are continuing to further strengthen connectivity between devices, further optimize networks, promote the softwareization of network entities, and increase the openness of wireless communication through the design of new protocols to be used in 6G communication systems, the implementation of hardware-based security environments, the development of mechanisms for the safe utilization of data, and the development of technologies regarding privacy maintenance methods.

[0006] Due to the research and development of such 6G communication systems, it is expected that a new dimension of hyper-connected experience will become possible through the hyper-connectivity of 6G communication systems, which encompasses not only connections between objects but also connections between people and objects. Specifically, it is projected that 6G communication systems will enable the provision of services such as truly immersive eXtended Reality (XR), high-fidelity mobile holograms, and digital replicas. Furthermore, services such as remote surgery, industrial automation, and emergency response, which are provided through 6G communication systems with enhanced security and reliability, will be applied in various fields including industry, healthcare, automotive, and home appliances.

[0007] The present disclosure aims to provide a cell connection method and apparatus that resolves overhead and delay caused by pilot signal transmission, channel state information measurement, and feedback in an ultra-dense network environment.

[0008] A method performed by a macro base station (MBS) of the present disclosure for solving the above-described problem may include: receiving at least one image sensing data from at least one small base station (SBS); generating voxel data based on at least one image sensing data; identifying at least one SBS having a line of sight (LoS) path with at least one terminal based on the voxel data; and transmitting information about at least one terminal having a line of sight path to the identified at least one SBS.

[0009] The macro base station (MBS) of the present disclosure comprises: a transceiver; and a control unit coupled to the transceiver. The control unit may be configured to receive at least one image sensing data from at least one small base station (SBS), generate voxel data based on at least one image sensing data, identify at least one SBS having a LoS path with at least one terminal based on the voxel data, and transmit information about at least one terminal having a LoS path to the identified at least one SBS.

[0010] According to one embodiment of the present disclosure, accurate terminal locations can be identified in an ultra-dense network environment, enabling efficient cell allocation and minimizing connection interruptions during handover.

[0011] According to one embodiment of the present disclosure, traffic can be evenly distributed across multiple cells to improve overall network performance.

[0012] According to one embodiment of the present disclosure, signal quality can be improved by minimizing angle error and positioning error.

[0013] FIG. 1 illustrates a beamforming communication method according to one embodiment.

[0014] FIG. 2 is a diagram illustrating an ultra-dense network (UDN) according to one embodiment.

[0015] FIG. 3 illustrates a terminal location identification and LoS path identification procedure according to one embodiment.

[0016] FIG. 4 is a diagram illustrating voxelization according to one embodiment.

[0017] FIG. 5 is a diagram illustrating a cell allocation step according to one embodiment.

[0018] FIG. 6 illustrates a cell connection method performed by MBS according to one embodiment.

[0019] FIG. 7 is a flowchart of a procedure performed by MBS according to one embodiment.

[0020] FIG. 8 is a flowchart of a procedure performed by MBS according to one embodiment.

[0021] FIG. 9 is a diagram illustrating the performance of a terminal detection and cell connection method based on image sensing data according to one embodiment.

[0022] FIG. 10 is a diagram illustrating the position identification error performance and angle error performance of a cell allocation method according to one embodiment.

[0023] FIG. 11 is a graph showing the average sum speed according to the number of terminals (K) according to one embodiment.

[0024] FIG. 12 is a graph showing the average throughput according to the number (M) of SBSs according to one embodiment.

[0025] FIG. 13 illustrates the configuration of a terminal according to one embodiment.

[0026] FIG. 14 illustrates the configuration of a base station according to one embodiment.

[0027] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0028] In describing the embodiments, technical details that are well known in the technical field to which this disclosure belongs and are not directly related to this disclosure are omitted. This is intended to convey the essence of this disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0029] For the same reason, some components in the attached drawings have been exaggerated, omitted, or schematically depicted. Additionally, the dimensions of each component do not entirely reflect their actual dimensions. Identical or corresponding components in each drawing have been assigned the same reference numbers.

[0030] The advantages and features of the present disclosure, and the methods for achieving them, will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure is complete and to fully inform those skilled in the art of the scope of the disclosure, and the present disclosure is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components. Furthermore, in describing the present disclosure, if it is determined that a detailed description of related functions or configurations might unnecessarily obscure the essence of the present disclosure, such detailed description is omitted. Additionally, the terms described below are defined considering their functions in the present disclosure, and these may vary depending on the intentions or conventions of the user or operator. Therefore, their definitions should be based on the content throughout the entire specification.

[0031] Hereinafter, a base station is an entity that performs resource allocation for terminals and may be at least one of a gNode B, eNode B, Node B, BS (Base Station), wireless access unit, base station controller, or a node on a network. A terminal may include a UE (User Equipment), MS (Mobile Station), cellular phone, smartphone, computer, or a multimedia system capable of performing communication functions. In this disclosure, a downlink (DL) refers to a wireless transmission path of a signal transmitted by a base station to a terminal, and an uplink (UL) refers to a wireless transmission path of a signal transmitted by a terminal to a base station. Furthermore, while LTE, LTE-A, or 5G systems may be described as examples below, embodiments of this disclosure may also be applied to other communication systems having similar technical backgrounds or channel types. For example, 5th generation mobile communication technology (5G, new radio, NR) developed after LTE-A may be included therein, and the 5G below may be a concept that includes existing LTE, LTE-A, and other similar services. In addition, the present disclosure may be applied to other communication systems with some modifications made at the discretion of a person with skilled technical knowledge, without significantly departing from the scope of the present disclosure.

[0032] At this point, it will be understood that each block of the process flow diagrams and combinations of the flow diagrams can be executed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment create means to perform the functions described in the flow diagram block(s). Since these computer program instructions can also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement the function in a specific way, the instructions stored in computer-available or computer-readable memory can also produce a manufactured item containing instruction means to perform the function described in the flow diagram block(s). Since computer program instructions can be loaded onto a computer or other programmable data processing equipment, instructions that perform a series of operation steps on the computer or other programmable data processing equipment to create a process executed by the computer can also provide steps for executing the functions described in the flowchart block(s).

[0033] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specific logical function(s). It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For example, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order according to their corresponding functions.

[0034] In this embodiment, the term "part" refers to a software or hardware component such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit), and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or configured to run one or more processors. Accordingly, as an example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." In addition, the components and 'parts' may be implemented to utilize one or more CPUs within the device or secure multimedia card. Furthermore, in the embodiment, the 'part' may include one or more processors.

[0035] Wireless communication systems are evolving from providing early voice-oriented services to broadband wireless communication systems that provide high-speed, high-quality packet data services, such as communication standards like 3GPP’s HSPA (High Speed ​​Packet Access), LTE (Long Term Evolution or E-UTRA (Evolved Universal Terrestrial Radio Access)), LTE-Advanced (LTE-A), LTE-Pro, 3GPP2’s HRPD (High Rate Packet Data), UMB (Ultra Mobile Broadband), and IEEE’s 802.16e.

[0036] As a representative example of the above-mentioned broadband wireless communication system, the LTE system employs the Orthogonal Frequency Division Multiplexing (OFDM) method for the downlink (DL) and the Single Carrier Frequency Division Multiple Access (SC-FDMA) method for the uplink (UL). The uplink refers to a wireless link through which a terminal (User Equipment (UE) or Mobile Station (MS)) transmits data or control signals to a base station (eNode B, gNode B, or base station (BS)), and the downlink refers to a wireless link through which a base station transmits data or control signals to a terminal. The above-mentioned multiple access method can distinguish the data or control information of each user by allocating and operating time-frequency resources to be sent for each user so that they do not overlap, that is, so that orthogonality is established.

[0037] As a future communication system following LTE, that is, a 5G communication system, it must be able to freely reflect the diverse requirements of users and service providers, and therefore, services that satisfy various requirements simultaneously must be supported. Services being considered for the 5G communication system include enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), and Ultra Reliability Low Latency Communication (URLLC).

[0038] eMBB aims to provide data transmission speeds that are superior to those supported by existing LTE, LTE-A, or LTE-Pro. For example, in a 5G communication system, eMBB must be able to provide a peak data rate of 20 Gbps in the downlink and 10 Gbps in the uplink from the perspective of a single base station. Furthermore, while providing these peak data rates, the 5G communication system must also provide an increased user-perceived data rate. To satisfy these requirements, it necessitates improvements in various transmission and reception technologies, including enhanced Multi-Input Multi-Output (MIMO) transmission technology. Additionally, while LTE transmits signals using a maximum bandwidth of 20 MHz in the 2 GHz band, the 5G communication system can meet the data transmission speeds required by using a frequency bandwidth wider than 20 MHz in frequency bands of 3–6 GHz or above 6 GHz.

[0039] Simultaneously, mMTC is being considered to support application services such as the Internet of Things (IoT) in 5G communication systems. To efficiently provide IoT, mMTC requires support for a large number of terminal connections within a cell, improved terminal coverage, enhanced battery life, and reduced terminal costs. Since IoT devices are attached to various sensors and equipment to provide communication functions, the system must be able to support a large number of terminals within a cell (e.g., 1,000,000 terminals / km²). Furthermore, due to the nature of the service, terminals supporting mMTC are likely to be located in dead zones not covered by cells, such as building basements; therefore, they may require wider coverage compared to other services provided by 5G communication systems. Terminals supporting mMTC must consist of low-cost devices, and since it is difficult to frequently replace terminal batteries, a very long battery life of 10 to 15 years may be required.

[0040] Finally, URLLC is a mission-critical cellular-based wireless communication service. Examples include services used for remote control of robots or machinery, industrial automation, unmanned aerial vehicles, remote health care, and emergency alerts. Therefore, the communication provided by URLLC must offer very low latency and very high reliability. For instance, services supporting URLLC must satisfy an air interface latency of less than 0.5 milliseconds and simultaneously require a packet error rate of 10^-5 or less. Consequently, for services supporting URLLC, 5G systems must provide a Transmission Time Interval (TTI) smaller than other services, and design considerations may be required to allocate wide resources within the frequency band to ensure the reliability of the communication link.

[0041] The three 5G services, namely eMBB, URLLC, and mMTC, can be multiplexed and transmitted within a single system. In this case, different transmission and reception techniques and parameters may be used between the services to satisfy the different requirements of each service. Of course, 5G is not limited to the three services mentioned above.

[0042] Ultra-dense networks (UDNs) are a core technology for 5G and subsequent generations of wireless communication systems. By deploying small base stations at very high densities, UDNs can significantly increase network capacity per unit area and enhance spectrum efficiency by maximizing frequency reuse. UDNs were introduced to meet requirements such as increased system capacity, faster data transmission speeds, and improved energy efficiency, and they can effectively support three 5G services: eMBB, URLLC, and mMTC. Unlike existing macro base station (MBS)-centric networks, UDNs can be composed of multiple small base stations (SBSs) covering a narrow area. In UDN technology, MBSs provide extensive basic coverage, while SBSs increase capacity in specific regions and eliminate dead zones, playing a complementary role.

[0043] Ultra-dense networks can contribute to improved signal quality by mitigating high attenuation losses in the mmWave and THz bands to enhance coverage, and by facilitating the implementation of advanced beamforming techniques utilizing multiple SBS antennas. Channel state information (CSI) measurement-based SBS identification and cell access methods are widely used to identify the SBS capable of securing high signal strength among multiple SBSs. However, CSI-based SBS identification and cell access methods involve a lengthy process of transmitting pilot signals, measuring channel state information, and measuring feedback; the resulting signaling latency can exceed the coherence time of the mmWave and THz bands. Consequently, CSI-based SBS identification and cell access methods in the mmWave and THz bands suffer from a serious problem of utilizing invalid or inaccurate channel state information.

[0044] The present disclosure can provide a new cell connection method capable of identifying an SBS having a line-of-sight (LOS) path with a user terminal by acquiring image sensing data through image sensors mounted on a plurality of base stations and generating voxel data related to the three-dimensional position of the terminal based thereon.

[0045] Hereinafter, a method and apparatus for cell connection according to the present disclosure will be described with reference to the drawings.

[0046] FIG. 1 illustrates a beamforming communication method according to one embodiment.

[0047] FIG. 1 illustrates a base station (110) and terminals (120, 130) as part of nodes utilizing a wireless channel in a wireless communication system. FIG. 1 illustrates one base station and two terminals, but this is merely an example. For example, the wireless communication system of FIG. 1 may further include other base stations identical or similar to the base station (110) and other terminals.

[0048] A base station (110) is a network infrastructure that provides wireless access to terminals (120, 130). The base station (110) has coverage defined as a certain geographical area based on the distance at which it can transmit signals. In addition to being a base station, the base station (110) may be referred to as an 'access point (AP)', 'evolved Node B (eNB)', 'next generation node B (gNB)', '5G node (5th generation node)', 'wireless point', 'transmission / reception point (TRP)', or other terms having an equivalent technical meaning.

[0049] A terminal (120, 130) is a device used by a user and can perform communication with a base station (110) via a wireless channel. The terminal (120, 130) can be operated without user involvement. For example, the terminal (120, 130) may be a device that performs machine type communication (MTC) and may not be carried by a user. The terminal (120, 130) may be referred to as 'user equipment (UE)', 'mobile station', 'subscriber station', 'customer premises equipment (CPE)', 'remote terminal', 'wireless terminal', 'electronic device', or 'user device', or other terms having an equivalent technical meaning.

[0050] The base station (110) and terminal (120) of the present disclosure may each be a transmitting apparatus, a transmitting node, a receiving apparatus, and / or a receiving node. For example, the base station (110) may transmit a radio frequency (RF) signal to the terminal (120). The base station (110) may receive an RF signal from the terminal (120). As another example, the terminal (120) may transmit an RF signal to the base station (110) or another network entity of the wireless communication system. The terminal (120) may receive an RF signal from the base station (110) or another network entity.

[0051] The base station (110) and terminals (120, 130) can transmit and / or receive wireless signals in the millimeter wave (mmWave) band (e.g., 28 GHz, 30 GHz, 38 GHz, 60 GHz). At this time, to improve channel gain, the base station (110) and / or terminals (120, 130) can perform beamforming.

[0052] Beamforming may include transmission beamforming and / or reception beamforming. That is, the base station (110) and / or terminal (120, 130) may give directivity to the transmission signal or the reception signal. To give directivity to the reception signal, the base station (110) and / or terminal (120, 130) may select serving beams through a beam search, beam management, or beam optimization procedure.

[0053] According to one embodiment, in order to select serving beams, a base station (110) may transmit a synchronization signal block (SSB) to a terminal (120, 130) using beam sweeping. The SSB may include a primary synchronization signal (PSS) for time synchronization between a cell associated with the base station (110) and the terminal (120, 130), a secondary synchronization signal (SSS) providing additional synchronization information, a physical broadcast channel (PBCH) providing essential system information required for initial connection, and a demodulation reference signal (PBCH DMRS) which is a reference signal used for channel estimation to accurately demodulate the PBCH. The terminal (120, 130) may identify the symbol timing and frame boundary of the cell based on the PSS. The additional synchronization information included in the SSS may include a cell ID associated with the base station (110) within a cell group, and the terminal (120, 130) may identify the cell associated with the base station (110) from other cells based on the SSS. The PBCH may include a master information block (MIB) containing essential system information such as subcarrier spacing.

[0054] According to one embodiment of the present disclosure, a base station (110) may transmit an SSB using eight different beams. Of course, it is not limited to the above example, and the base station (110) may transmit an SSB using more beams, such as 32, 128, or 256. A terminal (120, 130) that receives an SSB may detect the presence of the base station (110), perform time and frequency synchronization, and obtain essential information necessary to connect to the base station (110). The terminal (120, 130) may measure the received power (e.g., RSRP) of the received SSB and report the index of the beam with the highest received power to the base station (110).

[0055] In a codebook-based beam search procedure, the base station (110) receives a beam index from the terminal (120, 130) and can identify a beam corresponding to the beam index. The base station (110) can perform additional beam sweeping based on the identified beam. The base station (110) can perform beam sweeping with narrower beams in a narrower range associated with the identified beam. The terminal (120, 130) can report to the base station (110) the beam with the highest received power among the narrower beams. For example, in FIG. 1, the terminal (120) can report the index of beam (112) to the base station (110), and the terminal (130) can report the index of beam (113).

[0056] The base station (110) receives a report containing information regarding the beam index of the terminal (120, 130) and can repeatedly transmit to the terminal using the reported beam. The terminal (120) can tune the terminal's receiver based on the beam that the base station repeatedly transmits. For example, in FIG. 1, the terminal (120) can determine the receiving beam (121) based on the beam that the base station (110) repeatedly transmits, and the terminal (130) can determine the receiving beam (131).

[0057] After serving beams are selected, subsequent communication can be performed through a resource that is in a quasi-co-located (QCL) relationship with the resource that transmitted the serving beams.

[0058] FIG. 2 is a diagram illustrating an ultra-dense network (UDN) according to one embodiment.

[0059] The ultra-dense network environment (200) illustrated in FIG. 2 includes MBS (201) that provide extensive coverage and SBSs (210 to 230) that increase the capacity of specific areas. The SBSs (210 to 230) can be deployed at high density to significantly increase network capacity per unit area.

[0060] The MBS (201) can provide extensive coverage that covers an ultra-dense network environment (200). UE 5 may be located in a position where the wireless signal of the MBS (201) cannot properly reach due to obstacles such as buildings. To resolve the dead zone of the MBS (201) where UE 5 is located and to increase the data rate, the coverage of the MBS (201) can be supplemented by the first SBS (210) providing communication services. Specifically, the MBS (201) can control multiple SBSs (210, 220, 230) included in the wide coverage area to provide communication services to multiple UEs, thereby preventing the occurrence of dead zones within the coverage of the MBS (201) and controlling so that many UEs are not assigned to a single BS, thereby increasing the data rate of the entire cell.

[0061] In a hyper-dense network environment (200), the MBS (201) may need to provide a terminal (e.g., UE 2) with a connection to an appropriate SBS. For this purpose, a cell connection method based on channel state information (CSI) commonly used may involve a long delay. According to the channel state information-based cell connection method, the MBS (201) and SBSs (210 to 230) broadcast a reference signal or pilot signal to the terminal, and the terminal receives the signal and measures channel state information (CSI), such as the strength of the received signal (e.g., reference signal received power (RSRP)) and / or quality (signal-to-interference plus noise ratio, SINR), and the terminal feeds back a measurement report message containing the signal measurement results of the MBS (201) and SBSs (210 to 230) to the base station (MBS or SBS) to which the terminal is currently connected, and the MBS (201), having obtained the signal measurement results through the terminal or the serving base station, determines whether the signal strength and / or quality of the base station to which the terminal is currently connected is below a predetermined threshold and determines the need for a handover. If UE 2 feeds back channel state information at a period of 5ms, a delay of at least 20ms may occur to perform cell connection. If the terminal moves at a speed of 30 km / h, the 20 ms delay exceeds the correlation time of 9 ms, and the MBS cannot keep the channel state information it acquires up to date. Due to this delay, the connection between the terminal and the base station relies on past channel state information and does not reflect real-time network conditions, and the terminal experiences intermittent communication interruptions.

[0062] The present disclosure may provide a novel cell connection method that does not rely on CSI measurement and feedback. According to one embodiment of the present disclosure, the MBS may provide a connection between the appropriate SBS and the UE without CSI measurement and feedback by providing a connection between the UE and the corresponding SBS based on image sensing information obtained from the SBS.

[0063] Specifically, according to a cell connection method according to one embodiment, each of the SBSs (210 to 230) constituting the ultra-dense network environment (200) may include an image sensor for acquiring two-dimensional image sensing data. For example, an image sensor may be mounted or attached to each of the SBSs (210 to 230). According to one embodiment of the present disclosure, the image sensor may be a device capable of acquiring data such as images using a camera, radar, lidar, etc.

[0064] In addition, according to one embodiment of the present disclosure, image sensing data may be data acquired at a plurality of points in time, and may be data acquired from SBS processed into a predetermined form using computer vision.

[0065] According to one embodiment of the present disclosure, image sensing data acquired by each of the SBSs (210 to 230) may be transmitted to the MBS (201). The image sensing data may be provided via a backhaul or provided from the SBSs (210 to 230) to the MBS through a predetermined interface. Since the image sensing data from the SBSs received by the MBS (201) may include images of objects viewed from multiple viewpoints (e.g., a terminal, a person carrying the terminal, a vehicle where the terminal is located), the MBS (201) can use the image sensing data received from the SBSs to identify the three-dimensional location of the terminal and the SBSs that have a LoS path with the terminal. Specifically, since the image sensing data received from the SBSs is two-dimensional data, three-dimensional data may be generated based thereon, and based on the three-dimensional data, it may be determined whether a LoS path exists between a predetermined SBS and a predetermined UE. The three-dimensional data may include three-dimensional visual feature data for voxels. In the present disclosure, whether or not a LoS path exists may be referred to as a LoS condition.

[0066] According to FIG. 2, since UE 1 is located within the field of view (FOV) of the first SBS (210), the image sensing data acquired by the first SBS (210) through an image sensor may include UE 1. Referring to the image sensing data (225) acquired by the second SBS (220), the field of view of the second SBS (220) for UE 1 may be blocked by an obstacle such as a tree. The MBS (201) receives image sensing data from the first SBS (210) and the second SBS (220), and based on the integration of the image sensing data of the first SBS (210) and the image sensing data of the second SBS (220), can identify the location of UE 1 as shown in FIG. 2 and can identify that the first SBS (210) has a LoS path for UE 1, whereas the second SBS (220) does not have a LoS path for UE 1. MBS (201) transmits information of UE 1 to a first SBS (210) that has a LoS path with UE 1, and UE 1 can perform a cell connection procedure or a handover procedure with the first SBS (210).

[0067] Additionally, since UE 3 is located within the field of view of the third SBS (230), the image sensing data (235) obtained through the image sensor of the third SBS (230) may include UE 3. Since the field of view of the first SBS (210) for UE 3 is blocked by an obstacle, the image sensing data obtained by the first SBS (210) may not include UE 3. The MBS (201) receives image sensing data from the third SBS (230) and the first SBS (210), and based on the integration of the image sensing data of the third SBS (230) and the image sensing data of the first SBS (210), can identify the location of UE 3 shown in FIG. 2 and can identify that the third SBS (230) has a LoS path for UE 3, whereas the first SBS (210) does not have a LoS path for UE 3. MBS (201) transmits information of UE 3 to a third SBS (230) that has a LoS path with UE 3, and UE 3 can come to the third SBS (230) to perform a cell connection procedure or a handover procedure.

[0068] According to one embodiment of the present disclosure, the MBS (201) can identify the locations of a plurality of terminals located in the ultra-dense network environment (200) and identify an SBS having a LoS path for each terminal by collecting more image sensing data from more SBSs located in the ultra-dense network environment (200) and integrating the collected image sensing data. For example, the MBS (201) can receive image sensing data with various viewpoints from SBSs (210 to 230) located in the ultra-dense network environment (200), extract two-dimensional visual feature data based on each of the image sensing data with various viewpoints, and integrate the two-dimensional visual feature data with various viewpoints to generate three-dimensional visual feature data. The three-dimensional visual feature data may include visual features for voxels. MBS (201) can identify at least one SBS with a LoS path existing with at least one terminal located in a hyper-dense network environment (200) based on three-dimensional visual feature data for a voxel. MBS (201) can transmit information about at least one terminal with a LoS existing to the identified SBS so that the SBS can perform a cell connection procedure with the terminal.

[0069] According to one embodiment, the MBS (201) can generate three-dimensional visual feature data based on at least one image sensing data. According to one embodiment, three-dimensional visual feature data can be generated using only one two-dimensional image sensing data. However, if only one two-dimensional image sensing data is used, only visual feature data for one viewpoint can be obtained, so the accuracy and precision of the three-dimensional visual feature data for an area that is partially obscured by another object may be significantly reduced. For example, if image sensing data for a limited viewpoint is used, three-dimensional visual feature data indicating the presence of a terminal in an area that is partially obscured by another object when there is no terminal there, or three-dimensional visual feature data indicating the absence of a terminal when there is a terminal there. The more image sensing data from multiple viewpoints is used, the more accurate and precise three-dimensional visual feature data can be generated. According to one embodiment, image sensing data is obtained from multiple SBSs, and accurate and precise three-dimensional visual feature data can be generated based on visual feature data from multiple viewpoints based on image sensing data from multiple viewpoints.

[0070] FIG. 3 illustrates a terminal location identification and LoS path identification procedure according to one embodiment.

[0071] According to one embodiment, the MBS can acquire multiple image sensing data at different times, acquire two-dimensional visual feature data from each of the acquired multiple image sensing data, aggregate the two-dimensional visual feature data to acquire three-dimensional visual feature data, and then perform terminal location identification and LoS path identification by comparing the two-dimensional visual feature data and the three-dimensional visual feature data.

[0072] Specifically, an MBS in a hyper-dense network environment can receive multiple image sensing data at different viewpoints from multiple SBSs. The MBS can generate two-dimensional visual feature data from each of the multiple image sensing data using a feature extractor. According to one embodiment, the two-dimensional visual feature data may include multiple scales of visual features.

[0073] In addition, the MBS can generate three-dimensional visual feature data by integrating two-dimensional visual feature data using a voxelizer. The MBS can identify the presence and location of a terminal by using a 3D detection head to identify three-dimensional visual feature data associated with the terminal among the three-dimensional visual feature data. Voxelization using a voxelizer is explained in more detail in Figure 4 below. The MBS can identify whether a LoS path exists between each SBS and the terminal by comparing two-dimensional visual feature data obtained from the image sensing data of the SBS with three-dimensional visual feature data associated with the terminal. At this time, the two-dimensional visual feature data obtained by the MBS from each image sensing data and the three-dimensional visual feature data generated based on the two-dimensional visual feature data can be expressed in the form of vectors.

[0074] According to one embodiment, an MBS can acquire at least one image sensing data from at least one SBS deployed in an ultra-dense network environment. According to one embodiment, the MBS can receive image sensing data from the SBS via a wired or wireless backhaul link. The image sensor includes an RGB sensor, and the image sensing data may include an RGB (red-green-blue) image.

[0075] Referring to FIG. 3, the MBS can acquire multiple image sensing data (305) at different viewpoints. As the MBS acquires image sensing data at various viewpoints, the MBS can acquire more visual information and use more visual information to acquire more accurate three-dimensional visual feature data, and accordingly, the accuracy and precision of terminal location identification and LoS path identification can be improved.

[0076] In the present disclosure, visual information may include color information such as the overall color distribution of an image extracted from image sensing data, texture information such as the material characteristics of an object, shape information such as the contour and shape of an object, spatial information such as the relative position and arrangement of objects, object information regarding identifiable faces, people, and buildings, and scene information regarding the overall scene such as indoor / outdoor, urban / nature.

[0077] In the present disclosure, the fact that the two image sensing data have different viewpoints may mean that the physical locations of the image sensors that generated each image sensing data are different. Even if the image sensing data with different viewpoints captured the same space, they may contain different visual information.

[0078] For example, image sensing data (315) is captured from the right side of Person 1 holding UE 1, and image sensing data (325) is captured from the left side of Person 1, so their viewpoints are different. Image sensing data (315) contains visual information about the overall outline, shape, and color of UE 1, but image sensing data (325) contains visual information about only a very small part of the shape of UE 1. Image sensing data (315) can provide relatively more visual information about UE 1 than image sensing data (325). Also, since image sensing data (315) and image sensing data (325) have different viewpoints, there may be differences in the visual information that each can provide. For example, image sensing data (315) can provide more visual information about UE 2, UE 3, person 2, person 3, and tree compared to image sensing data (325), and image sensing data (325) can provide more visual information about building B2 compared to image sensing data (315). The MBS can acquire more visual information related to the terminal by using both image sensing data (315) and image sensing data (325). In this way, the MBS can collect multiple image sensing data (305) having different viewpoints in order to acquire as much visual information related to the terminal as possible.

[0079] According to one embodiment, the MBS may use an object detector or a feature extractor to obtain information related to the terminal from image sensing data.

[0080] The feature extractor illustrated in FIG. 3 may include a backbone that extracts multi-scale visual features from a plurality of image sensing data (305) and a feature pyramid network (FPN) that combines the multi-scale visual features extracted by the backbone to generate two-dimensional visual feature data.

[0081] Visual features refer to meaningful information or patterns that can be identified in an image, and may include, for example, local visual features such as edges, curves, and textures, and global visual features such as shape and color.

[0082] Referring to FIG. 3, the backbone of the feature extractor may include a patch partition and a plurality of transformer blocks. The feature pyramid network (FPN) or neck of the feature extractor may include a plurality of convolution layers. The patch partition divides the input image into small square regions, and the transformer blocks can extract visual features from the input image and represent them as vectors.

[0083] Specifically, the transformer block includes an attention block, which can quantify the correlation between multiple pixel values ​​included in a patch sequence, such as attention scores. The attention block can generate weighted pixel values ​​by multiplying the pixel values ​​by the attention scores. Based on the weights of the pixel values, the backbone can extract low-level visual features, such as local visual features (e.g., edges, curves) and global visual features (e.g., color), from adjacent pixels of the image sensing data.

[0084] Each Transformer block can output a transformed visual feature when inputting a visual feature that is the output of the previous block. Visual features progressively transformed through multiple Transformer blocks can include high-level visual features such as objects (e.g., terminals, people, cars).

[0085] Each convolutional layer of the feature pyramid network may be input with visual features output by the backbone and / or visual features output by the previous convolutional layer. Each convolutional layer of the feature pyramid network may generate 2D visual feature data by combining visual features of multiple scales.

[0086] A feature extractor according to one embodiment can extract multi-scale visual features individually for each of a plurality of image sensing data (305) and generate two-dimensional visual feature data including multi-scale visual features.

[0087] For example, when the MBS receives image sensing data from M SBSs, the multiple image sensing data (305) may include M image sensing data at different times (image sensing data of the 1st SBS (e.g., image sensing data (315)), image sensing data of the 2nd SBS (e.g., image sensing data (325)), ..., image sensing data of the mth SBS, ..., image sensing data of the Mth SBS).

[0088] The backbone of the feature extractor can extract multi-scale visual features from each of M image sensing data (305) with different viewpoints. Specifically, the patch partition of FIG. 3 can input image sensing data of the m-th SBS and output the image sensing data divided into small square areas (i.e., patches). When the divided patches, which are the outputs of the patch partition, are input to the transformer block, the transformer block can extract visual features for each of the divided patches and generate information in the form of vectors (i.e., feature maps). The feature pyramid network of the feature extractor illustrated in FIG. 3 can combine the multi-scale visual features of the image sensing data of the m-th SBS extracted by the backbone to generate two-dimensional visual feature data of the image sensing data of the m-th SBS.

[0089] For example, for m=1, 2, ..., M, the MBS can generate two-dimensional visual feature data from each of the image sensing data of the Mth SBS. The MBS can generate two-dimensional visual feature data of the image sensing data of the 1st SBS (e.g., image sensing data (315)) and two-dimensional visual feature data of the image sensing data of the 2nd SBS (e.g., image sensing data (325)). ... The MBS can generate two-dimensional visual feature data of the image sensing data of the Mth SBS.

[0090] In this case, the 2D visual feature data of the image sensing data of the m-th SBS is vector F m It may be referred to as. For example, two-dimensional visual feature data generated based on image sensing data of the first SBS (e.g., image sensing data (315)) may be referred to as visual feature vector F1. Two-dimensional visual feature data generated based on image sensing data of the second SBS (e.g., image sensing data (325)) may be referred to as visual feature vector F2. Two-dimensional visual feature data generated based on image sensing data of the Mth SBS is visual feature vector F M It may be referred to as. Thus, a feature extractor according to one embodiment comprises M visual feature vectors F based on each of the image sensing data of M SBSs. m Can output (m=1, ... or M). M visual feature vectors F m (m=1, ... or M) is a visual feature matrix for M viewpoints It can be referred to as.

[0091] The two-dimensional visual feature data obtained by the MBS using a feature extractor may include information about objects associated with the terminal. Objects associated with the terminal may include the terminal, a person likely to possess the terminal, and a vehicle likely to have the terminal. Specifically, the feature extractor may perform classification based on semantic information of global visual features among multi-scale visual features extracted from image sensing data, and may perform location identification or positioning based on spatial detail of local visual features among multi-scale visual features.

[0092] According to one embodiment of the present disclosure, classification means determining one of the predefined classes as the class of an object in object detection, and positioning may mean localization, which identifies the location of an object in object detection.

[0093] MBS can extract global and local visual features of image sensing data from the m-th SBS using a backbone. MBS can combine the extracted visual features using a feature pyramid network to generate 2-dimensional visual feature data associated with the m-th SBS (or associated with the m-th time point). Based on the 2-dimensional visual feature data associated with the m-th time point, MBS can detect objects associated with the terminal and determine the class and location of the objects. In this case, objects associated with the terminal may include the terminal, a person likely to be carrying the terminal, and a vehicle likely to have the terminal inside.

[0094] According to one embodiment, the two-dimensional visual feature data generated by MBS using a backbone and a feature pyramid network is a vector F m It can be expressed as. Visual feature vector F m It may include visual information of an object associated with the terminal that can be detected in the image sensing data of the m-th SBS. MBS is vector F m A visual feature matrix that is a set of (m=1, ..., M) Based on this, objects associated with K terminals can be detected from the image sensing data of M SBSs.

[0095] The voxelizer or voxelization network illustrated in FIG. 3 can reconstruct two-dimensional visual feature data extracted from a feature extractor into three-dimensional visual feature data. Specifically, the voxelizer can aggregate all two-dimensional visual feature data from M viewpoints to generate three-dimensional visual feature data that encompasses visual features of a hyper-dense network environment obtainable from M viewpoints. In the present disclosure, the three-dimensional visual feature data may include visual feature data for a voxel having a unit volume and may be referred to as voxel data. Alternatively, the three-dimensional visual feature data may be expressed in a vector form and may be referred to as a voxel feature vector. Alternatively, since the three-dimensional visual feature data includes visual features extracted from image sensing data from multiple viewpoints, it may be referred to as three-dimensional image data.

[0096] The voxelizer can partition a hyperdense network environment into at least one voxel and index each voxel. For example, a voxel centered at a point (x, y, z) in a 3D Cartesian coordinate system can be indexed as [u, v, w]. The voxelizer uses M visual feature vectors F m By aggregating (m=1, 2, ..., M), three-dimensional visual feature data for multiple voxels constituting an ultra-dense network environment can be generated. In the present disclosure, the three-dimensional visual feature data for voxels [u, v, w] is a vector It can be expressed as follows. The 3D visual feature data for a voxel containing the k-th terminal among multiple K terminals is a vector It can be referred to as.

[0097] Specifically, the voxelizer can map or voxelize two-dimensional visual features into three-dimensional voxels using a feature fusor and bilinear sampling. The three-dimensional visual feature data can effectively represent the three-dimensional shape and spatial relationships of an object. For example, the three-dimensional visual feature data, which integrates the two-dimensional visual feature data of each of the multiple image sensing data (305), may include information covering the shape and spatial relationships of objects existing in a hyper-dense network environment, such as trees identified in the image sensing data (315), information about UE 2 located around building B1, information about UE 3 located near UE 1, information about building B2 identified in the image sensing data (325). Additionally, the three-dimensional visual feature data may further include classification and positioning information performed by the feature extractor.

[0098] The 3D sensing head illustrated in FIG. 3 can perform classification and positioning using 3D visual feature data. The 3D sensing head can generate a class score for each voxel, which is the probability that the object is included in a designated class. For example, the designated classes may include terminals, people, and automobiles. In FIG. 3, the 3D sensing head generates a person class score for voxel 1 containing person 1, and classifies the class of voxel 1 as a person class based on the fact that the person class score of voxel 1 exceeds a predetermined threshold. The 3D sensing head can determine the center coordinates (3, 1.5, 5) of voxel 1 classified as a person class as the 3D position of the person.

[0099] A 3D sensing head can acquire channel geometric information of the propagation path between the SBS and the terminal based on a 3D position. For example, the geometric information may include angles of departure (AoD) and / or angle of arrival (AoA) of the LoS path from the m-th SBS to the k-th terminal. The angles of departure and / or arrival may include azimuth angles and / or elevation angles.

[0100] A 3D sensing head according to one embodiment can identify the presence and location of multiple terminals by identifying 3D visual feature data associated with a terminal among 3D visual feature data for multiple voxels corresponding to an ultra-dense network environment. For example, the MBS uses the 3D sensing head to identify 3D visual feature data or vectors of voxels containing objects associated with a terminal. It can generate. Specifically, MBS can use a 3D sensing head to identify K voxels with high terminal class scores among a plurality of voxels generated based on an ultra-dense network environment, and determine that the selected voxels contain a terminal. Among the plurality of voxels in the ultra-dense network environment, the 3D visual feature data for the K voxels with high terminal class scores can be determined as 3D visual feature data for the terminal. In the present disclosure, the 3D visual feature data of the voxel where the k-th terminal exists among the K terminals is 3D visual feature data for the k-th terminal or a vector It can be said that MBS is a vector for each of the K terminals among the 3D visual feature data of voxels for an ultra-dense network environment. Can identify (k=1, 2, ..., K). MBS is a vector for K terminals Based on (k=1, 2, ..., K), a LoS path between K terminals and at least one SBS can be identified.

[0101] FIG. 4 is a diagram illustrating voxelization according to one embodiment.

[0102] Referring to FIG. 4, the MBS can generate three-dimensional visual feature data based on at least one image sensing data. The three-dimensional visual feature data may be referred to as two-dimensional image data, voxel data, or voxel feature vectors. Specifically, a voxelizer or voxelization network can divide a hyper-dense network environment into multiple voxels (400) and index each voxel. A voxel is a basic element in which a three-dimensional space is divided, and may have visual features such as color, transparency, and material along with positional information. A voxel with center coordinates (x, y, z) can be indexed as [u, v, w]. For each of the multiple voxels (400), the voxelizer generates a matrix which is two-dimensional visual feature data of multiple image sensing data (305). It takes as input a vector that is the 3D visual feature data of each voxel [u, v, w]. It can extract.

[0103] In the present disclosure, the voxel corresponding to the k-th terminal is [u k , v k , w k It can be referred to as ]. For example, a voxel (410) containing visual information for UE 1 can be indexed as voxel [u1, v1, w1]. According to one embodiment, for a voxel containing visual information of a terminal, the center coordinates of the voxel can be determined as the three-dimensional position of the terminal. For example, as described in FIG. 3, the MBS can determine the three-dimensional position of UE 1 as (3, 1.5, 5) based on the center coordinates (3, 1.5, 5) of the voxel (410).

[0104] According to one embodiment, the designated class may include mobile phones, tablets, and laptops. As described above, a 3D sensing head can identify a voxel containing a terminal, and to identify the voxel, the 3D sensing head can generate a class score for a given voxel, which is the probability that the voxel contains an object belonging to a designated class. A 3D sensing head according to one embodiment generates a terminal class score, which is the probability of containing a terminal, and can identify a voxel containing a terminal based on the terminal class score.

[0105] According to one embodiment, the MBS utilizes three-dimensional visual feature data that integrates visual features from multiple viewpoints, thereby enabling more accurate and precise detection of terminals. If two-dimensional image sensing data includes visual information regarding a terminal that is partially obscured, the terminal detection performance may be degraded or the reliability of terminal detection may be reduced because sufficient information regarding the partially obscured terminal is not provided. In particular, since mobile terminals such as mobile phones, tablets, and laptops are small in size, a partial occlusion phenomenon is likely to occur where part or all of the mobile terminal is obscured by a person's body or clothing when viewing the mobile terminal at a specific viewpoint. In this case, two-dimensional image data captured at a specific viewpoint may only show a part of the mobile terminal or fail to show the entire mobile terminal. As such, two-dimensional image data captured at a specific viewpoint may provide only limited information about the terminal. Therefore, if only two-dimensional image data is considered, due to the limited information, errors may occur such as incorrectly determining that a non-existent terminal exists (false positive) or incorrectly determining that a terminal that actually exists does not exist (false negative). These errors can impair the reliability and efficiency of an object detection system. Therefore, the reliability and efficiency of an object detection system can be improved by collecting and voxelizing two-dimensional image data in the present disclosure.

[0106] Specifically, the present disclosure may provide a method for collecting visual information at various points in time and generating three-dimensional visual feature data based thereon. Since the three-dimensional visual feature data generated according to one embodiment integrates information provided by image sensing data from multiple points in time, even if it is difficult to detect the terminal based on two-dimensional image data at a specific point in time because a part of the terminal is blocked at a specific point in time, it may ultimately be possible to accurately detect the terminal because three-dimensional visual feature data for the voxel is generated by integrating information on the overall shape of the terminal at other points in time or information on a part that was blocked at a specific point in time.

[0107] For example, UE 1 illustrated in FIG. 4 is held in the hand of Person 1, and at the time the image sensing data (315) is captured, most of the terminal is exposed as in the enlarged view (415), so the image sensing data (315) can provide visual information such as the overall outline, color, and material of UE 1. On the other hand, at the time the image sensing data (325) is captured, most of the terminal is blocked by the hand as in the enlarged view (425), so the image sensing data (325) can provide only limited visual information such as the outline, color, and material of a part of UE 1. If one attempts to detect the terminal from the image sensing data (325), there is a possibility that UE 1 will not be detected, but if the visual information obtainable from the image sensing data (315) being searched is considered together, the probability of detecting the terminal may be higher. 3D feature vector of the voxel (410) illustrated in FIG. 4 It is generated based on the two-dimensional feature vector F1 of UE 1 extracted from the image sensing data (315) obtained from the first SBS and the two-dimensional feature vector F2 of UE 1 extracted from the image sensing data (325) obtained from the second SBS, and thus can include relatively accurate visual information about the shape of UE 1 extracted from the image sensing data (315).

[0108] As another example, the image sensing data (315) illustrated in FIG. 4 does not contain visual information about building B2, but the image sensing data (325) contains visual information about building B2. Among the plurality of voxels (400) illustrated in FIG. 4, the three-dimensional visual feature data (e.g., vector) of the voxel corresponding to building B2 ) is generated based on two-dimensional visual feature data (e.g., vector F2) of building B2 extracted from image sensing data (325), and thus may include visual information about building B2 extracted from image sensing data (325).

[0109] The voxelizer can voxelize 2D visual feature data by utilizing a feature fuser to project the points (xyz) of the voxel's center onto the camera plane associated with the m-th image sensing data. The feature fuser consists of M 2D visual feature vectors F as shown in Equation 1. m Integrating voxels to create 3D visual feature vectors It can generate.

[0110] [Mathematical Formula 1]

[0111]

[0112] According to one embodiment, terminal positioning may include a process of finding a mapping relationship between three-dimensional visual feature data obtained from M SBSs.

[0113] The voxelizer can calculate the terminal class score for each voxel [u, v, w] and determine the 3D location of the terminal based on the location (x, y, z) corresponding to the voxel [u, v, w] with the higher terminal class score. For example, if the terminal class score of the voxel (410) exceeds a predefined threshold, the voxel (410) can be determined to contain the terminal. The MBS can generate location information of the 1st terminal (e.g., terminal UE 1 of FIG. 4) based on the location (x1, y1, z1) corresponding to the voxel (410). The MBS can determine the location of the voxel [u] containing the k-th terminal. k , v k , w k If the terminal class score of ] exceeds a predefined threshold, voxel [u k , v k , w k ] can determine that it includes the k-th terminal and generate location information for the k-th terminal. The k-th terminal may refer to the k-th terminal among K terminals existing in an ultra-dense network environment. The location of the terminal identified using a 3D sensing head can be used by the SBS to determine the beamforming angle. For example, the beamforming angle may include the azimuth and / or elevation angles of the departure angle and / or arrival angle, and and / or It can be expressed as.

[0114] According to one embodiment, the MBS is a three-dimensional visual feature vector for a voxel including the k-th terminal and a set of 2D visual feature vectors obtained from image sensing data at M different viewpoints (i.e., a matrix By comparing ), it is possible to determine whether an LoS path exists between the k-th terminal and the m-th SBS. To this end, the MBS can utilize a LoS identification network (LIN). The MBS uses a LoS identification network f LIN 3D visual feature vector for the voxel containing the k-th terminal as shown in Equation 2 using The feature vector of the LoS identification network for the k-th terminal by comparing the 2D visual feature matrix obtained from image sensing data with M different viewpoints Can be obtained. Feature vector of the LoS identification network It may include visual information for distinguishing between LoS and NLoS states, and the visual information may include information such as detection information about the terminal and terminal class score. Feature vector of the LoS identification network is a 3D visual feature vector for a voxel containing the k-th terminal It may include information included in and information regarding whether a LoS path exists between the k-th terminal and M SBSs. The information regarding whether a LoS path exists between the k-th terminal and M SBSs may include at least some of the information regarding whether a LoS path exists between the k-th terminal and the 1st SBS, information regarding whether a LoS path exists between the k-th terminal and the 2nd SBS, ..., information regarding whether a LoS path exists between the k-th terminal and the M-th SBSs.

[0115] [Mathematical Formula 2]

[0116]

[0117] In mathematical equation 2, the matrix is M vectors F m The set of (m=1, 2, ..., M), that is ={F1, F2, ..., F m , ..., F M It can mean}. Vector F m may be 2D visual feature data extracted from image sensing data acquired by the m-th SBS. Vector F m can be obtained based on the image sensing data of the m-th SBS. Referring to Equation 2, the LoS identification network f LIN is the 3D visual feature vector of the voxel associated with the k-th terminal and a matrix that is a set of 2D visual feature vectors ={F1, F2, ..., F m , ..., F M By comparing}, it is possible to identify whether an LoS path exists for the k-th terminal (or, LoS condition). The LoS condition for the k-th terminal may include whether an LoS path exists between the k-th terminal and the 1st SBS, whether an LoS path exists between the k-th terminal and the 2nd SBS, ..., whether an LoS path exists between the k-th terminal and the M-th SBS. is a learnable parameter of the LoS identification network (LIN). In the LoS identification process according to one embodiment, the feature vector of the voxel associated with the k-th terminal The object being compared is a set of 2D feature matrices ={F1, F2, ..., F m , ..., F M Since this, MBS can determine the LoS status for all SBSs (i.e., 1st SBS, 2nd SBS, ..., Mth SBS) existing in the ultra-dense network environment through this process, and MBS can identify all SBSs that can be connected to terminal k and connect the most suitable SBS to the terminal.

[0118] MBS is a feature vector of the LoS identification network using Equation 2 above for K terminals (k=1, 2, ..., K) existing in an ultra-dense network environment. ..., ..., can decide.

[0119] MBS uses a fully connected network to determine the probability of an LoS path existing for the k-th terminal as shown in Equation 3. It can determine. For example, the probability that an LoS path exists for the k-th terminal. is a value assigned per voxel, and assuming that a maximum of one terminal can exist in one voxel, the probability It can have a value between 0 and 1.

[0120] [Mathematical Formula 3]

[0121]

[0122] f in mathematical equation 3 fc means a fully connected neural network. At this time is the feature vector of the LoS identification network for the k-th terminal, and MBS is obtained for K terminals (k=1, 2, ..., K) ..., ..., Using mathematical formula 3 for ..., ..., It is possible to obtain [this]. In this way, in the LoS identification process according to one embodiment, the LoS status for all terminals (i.e., the 1st terminal, the 2nd terminal, ..., the Kth terminal) can be determined. Through this process, the MBS can determine the LoS status for multiple terminals existing in an ultra-dense network environment, and since it can determine the connection between the terminal and the SBS in the subsequent cell allocation process by considering the LoS status information for all terminals, it is possible to prevent the connection of terminals from being concentrated on a specific SBS, and as a result, it is possible to prevent traffic from being concentrated on a specific SBS.

[0123] FIG. 5 is a diagram illustrating a cell allocation step according to one embodiment.

[0124] In the LoS identification step, the MBS identifies at least one SBS for which a LoS path exists with the terminal, and then in the cell allocation step, the MBS can connect the terminal with the SBS selected from among the at least one SBS. In the cell allocation step according to one embodiment, the MBS can select an SBS using a transformer-based cell association network (CAN). In addition, according to one embodiment, for the selection of an SBS, the MBS may additionally consider not only the LoS path but also the minimum transmission speed required to support services to the terminal and the maximum number of terminals that can be simultaneously connected to the SBS (hereinafter, the maximum number of simultaneous connections).

[0125] Specifically, the MBS can determine an SBS based on the length of the LoS path among at least one SBS having a LoS path for the k-th terminal. For example, the MBS can select an SBS having a LoS path shorter than a predetermined threshold among at least one SBS having a LoS path for the k-th terminal and assign it to the terminal. As another example, the MBS can select and assign to the terminal the SBS having the shortest LoS path among at least one SBS having a LoS path for the k-th terminal. This is because superior signal strength and signal quality are expected as the LoS path becomes shorter.

[0126] Additionally, MBS has an LoS path for the k-th terminal, and the minimum transmission rate requested by the k-th terminal Among at least one SBS satisfying [condition], the SBS to be connected to the k-th terminal can be determined. The terminal [determines] the minimum transmission rate to the SBS and / or MBS. By transmitting, minimum transmission speed It can request to be connected to an SBS that satisfies [the condition]. For example, before handover, the terminal [requests] the minimum transmission rate from the currently serving cell. It can transmit. As another example, the terminal [can transmit] the minimum transmission speed to the beam report corresponding to the SSB received from the base station via beam sweeping. It can be transmitted by including.

[0127] Additionally, the MBS can determine which SBS to connect to the k-th terminal among at least one SBS that has a LoS path with the k-th terminal and is connected to terminals with a maximum number of concurrent connections. The maximum number of concurrent connections for each SBS can be set for each SBS and / or for the MBS. The MBS can prevent a terminal from connecting to a congested SBS by assigning one of the SBSs connected to terminals with a maximum number of concurrent connections to the terminal.

[0128] Alternatively, MBS is the LoS state for the k-th terminal, the length of the LoS path, and the minimum transmission rate requested by the k-th terminal. The cell to be connected to the k-th terminal can be determined based on at least one of the maximum number of simultaneous connections. For example, among at least one SBS that has a LoS path with the k-th terminal, the MBS has the minimum transmission speed of the terminal You can connect to the terminal by selecting an SBS that satisfies the condition and does not exceed the maximum number of simultaneous connections set for the SBS.

[0129] According to one embodiment, SBS can roughly estimate the location of the UE through an AoA-based terminal location estimation method. Therefore, the requested minimum transmission speed By estimating the location of the terminal that requested it, the corresponding voxel can be identified. By using multiple attention blocks included in the transformer block of the cell connection network to measure the similarity of the LoS state between the k-th terminals, the cell connection network can collect identification information of congested SBSs and exclude congested SBSs.

[0130] Referring to Fig. 5, the cell association network (CAN) is the feature vector of the LoS identification network of the k-th terminal. Probability that an LoS path exists for the k-th terminal Minimum transmission speed requested by the k-th terminal Receives as input, and cell assignment vector It can output.

[0131] According to one embodiment, to simplify the learning of a cell connection network, a feature vector of a LoS identification network Minimum transmission speed Pre-calculated probability together with can be input. Probability Feature vector of this LoS identification network Based on this, by being acquired in advance before training the cell network, the amount of training for the cell connection network can be reduced. In addition, the probability Feature vector of the LoS identification network including additional voxel and visual information along with By using as input, the performance or reliability of the cell connection network can be improved.

[0132] vector Probability from Since it can be obtained, according to one embodiment, unlike FIG. 5, a vector in the cell connection network and speed is input and cell assignment vector ...may be output. In this case, the cell connection network is a vector Probability from It can perform an additional role in calculating. However, this may make learning more complex.

[0133] In addition, according to one embodiment, in order to further simplify the computation or learning of the cell connection network, unlike FIG. 5, the probability in the cell connection network overspeed is input and cell assignment vector It may be output. Probability Silver, feature vector of the LoS identification network Unlike, it may not include voxel information and visual information.

[0134] Specifically, the cell connection network is a feature vector of the LoS identification network for the k-th terminal (k=1, 2, ..., K). Probability speed When input is received, a concatenation operation can be performed to output a sequence. For example, in the concatenation operation, ..., A sequence including, ..., A sequence including and ... A sequence containing can be generated.

[0135] The cell connection network further includes a fully-connected layer before the transformer block, and the fully-connected layer can transform a sequence of K terminals into a dimension that the transformer block can process. Additionally, the cell connection network further includes a fully-connected layer after the transformer block, and the fully-connected layer can transform the output of the transformer block into a format for transmission to the SBS.

[0136] Referring to FIG. 5, the cell connection network may include a transformer block that determines the connection relationship between a terminal and a base station. The transformer block ..., A sequence including, ..., A sequence including and ... It may further include layer normalization that outputs a normalized sequence when a sequence containing is input, and multi-head attention that enables direct connections between all positions within the sequences.

[0137] Specifically, the transformer block is the feature vector of the input LoS identification network Based on this, the priority of the LoS path between the k-th terminal and all SBSs can be determined, and the correlation value of each LoS can be output.

[0138] Cell connection networks use fully connected layers to create cell connection vectors Can generate cell connection vector It can represent the connection relationships for M SBSs.

[0139] MBS is the cell connection vector of the k-th terminal Obtain (k=1, 2, ..., K) and the cell connection matrix It can be obtained. For example, in the case where there are 5 terminals and 4 SBSs in an ultra-dense network environment (i.e., K=5, M=4), the cell connection matrix It can be expressed as shown in the following mathematical formula 4.

[0140] [Mathematical Formula 4]

[0141]

[0142] Referring to Equation 4, the cell connection vector for the k-th terminal About The element in the first column can indicate whether it is connected to the 1st SBS, the element in the second column whether it is connected to the 2nd SBS, ..., the element in the m-th column whether it is connected to the m-th SBS. For example, if the value of an element is 1, it can be set to be connected to an SBS, and if it is 0, it can be set not to be connected to an SBS. For example, the cell connection vector for the 1st terminal in Equation 4 About It can be represented as such, and since the element of column 1 is 1, the 1st terminal can be connected to the 1st SBS.

[0143] MBS are generated cell connection vectors It can be transmitted to the m-th SBS. The cell connection vector that the MBS transmits to the m-th SBS is It can be represented as follows.

[0144] FIG. 6 illustrates a cell connection method performed by MBS according to one embodiment.

[0145] In step 610, the MBS can acquire multiple image sensing data. Since the multiple image sensing data are acquired from different SBSs and have different viewpoints, they can provide different visual information.

[0146] In step 620, the MBS can use a voxelizer to aggregate two-dimensional visual feature data obtained from each of the multiple image sensing data to obtain three-dimensional visual feature data. The MBS can use a three-dimensional object detector to identify K terminals from the three-dimensional visual feature data and obtain three-dimensional visual feature data of the voxels where the terminals are located. At this time, classification based on terminal class scores can be performed. In step 620, the MBS can perform LoS path identification by comparing the two-dimensional visual feature data and the three-dimensional visual feature data. In the present disclosure, the three-dimensional visual feature data may be referred to as three-dimensional image data, voxel data, and voxel feature vector.

[0147] In step 630, the MBS can determine whether to associate with an SBS for each terminal whose location has been identified, based on distance information between the terminal and the SBS and LoS state information. The MBS can generate a cell connection vector by determining whether to associate with K terminals and M SBSs existing in the network environment. The MBS can transmit the cell connection vector to each SBS.

[0148] In step 640, each SBS that receives the cell connection vector can perform a coordinate conversion to convert the location of the selected terminal based on the cell connection vector into a spherical coordinate system or an orthogonal coordinate system. The SBS can determine the beamforming angle ( based on the cell connection vector) , ) can be determined. In step 650, the SBS can transmit a pilot signal based on the determined beamforming angle.

[0149] FIG. 7 is a flowchart of a procedure performed by MBS according to one embodiment.

[0150] In step 710, the MBS can receive at least one image sensing data from at least one SBS included in the ultra-dense network environment. The MBS can provide extensive coverage covering the ultra-dense network environment, and the SBS can complement the coverage of the MBS by eliminating dead zones. To allocate an optimal cell to a terminal in the ultra-dense network environment, the MBS can find an SBS capable of providing a strong RF signal of high strength and quality to the terminal.

[0151] Specifically, image sensing data received from the SBS can provide visual information that can be obtained at the viewpoint of the SBS. The visual information may include color information, texture information, shape information, spatial information, and object information extracted from the image sensing data.

[0152] At least one image sensing data may be acquired using an image sensor attached to at least one SBS. The MBS may acquire image sensing data from at least one SBS via a wireless or wired communication link. As the MBS acquires image sensing data from various SBSs, it may acquire visual information at various viewpoints.

[0153] In step 720, the MBS can generate three-dimensional visual feature data by collecting more image sensing data from more SBSs located in an ultra-dense network environment and integrating the visual information that can be obtained through them. In FIG. 7, the three-dimensional visual feature data can be referred to as voxel data.

[0154] Specifically, MBS can extract visual features from each of at least one image sensing data at different viewpoints and combine the visual features generated based on each image sensing data to generate two-dimensional visual feature data for each image sensing data. The two-dimensional visual feature data generated based on each image sensing data is two-dimensional image data and may include multi-scale visual features extracted from each image sensing data. MBS can generate three-dimensional visual feature data for voxels by integrating multiple two-dimensional visual feature data extracted from multiple image sensing data. The process of integrating two-dimensional visual feature data may include a process of mapping two-dimensional visual feature vectors to three-dimensional voxels. In the present disclosure, the three-dimensional visual feature data may be referred to as three-dimensional image data, voxel feature vectors, and / or voxel data.

[0155] In step 730, the MBS can identify at least one terminal located in a hyper-dense network environment and at least one SBS where a LoS path exists, based on three-dimensional visual feature data. Specifically, the MBS can generate a class score for each voxel based on three-dimensional visual feature data using a three-dimensional object detector or a three-dimensional detection head. The class score may represent the probability of containing an object of a specified class in the space corresponding to each voxel. Based on the terminal class score, the MBS can determine index information or location information for K voxels containing the terminal. Based on the three-dimensional visual feature data of the voxels obtained in step 720, the MBS can determine K three-dimensional visual feature data (e.g., feature vectors) for the voxels containing the terminal. You can obtain ).

[0156] In step 730, MBS is a feature vector for the voxel and 2D visual feature data obtained from image sensing data with M different viewpoints (e.g., matrix Feature vector of the network for LoS identification for the k-th terminal by comparing ) You can obtain.

[0157] In step 740, the MBS may transmit information about at least one terminal where a LoS exists to the SBS identified in step 730, so that the SBS may allocate a new communication channel to the terminal. To this end, the MBS uses a transformer-based cellular connection network (CAN) to form a cellular connection matrix for K terminals and M SBSs. It can generate. MBS is a cell connection matrix Cell connection vector representing the cell connection relationship for the m-th SBS Can be transmitted to the m-th SBS. Cell connection vector It may include index information for terminals that the m-th SBS can connect to.

[0158] According to one embodiment, the MBS can transmit information about the terminal's location information and visual features, along with information about the cell connection vector, to the SBS.

[0159] FIG. 8 is a flowchart of a procedure performed by MBS according to one embodiment.

[0160] In step 810, the MBS can generate two-dimensional visual feature data based on each image sensing data. In this case, the two-dimensional visual feature data may include two-dimensional feature vectors. The two-dimensional visual feature data may be represented as vectors by combining or combining multi-scale visual features extracted from each image sensing data.

[0161] MBS generates 2D visual feature data based on image sensing data from the 1st SBS, generates 2D visual feature data based on image sensing data from the 2nd SBS, ..., generates 2D visual feature data based on image sensing data from the Mth SBS, thereby obtaining M 2D visual feature data for M viewpoints.

[0162] In step 820, the MBS can classify objects contained in each image sensing data based on a two-dimensional feature vector obtained from each image sensing data. Specifically, the MBS can perform classification based on semantic information of global visual features among the multi-scale visual features extracted from the image sensing data. In object detection, classification may mean determining an object as one of the predefined classes. The predefined classes may include classes for objects associated with a terminal, such as a terminal, a person likely to possess a terminal, or a car likely to have a terminal inside. Of course, this is merely an example, and it is obvious that the predefined classes in this disclosure are not limited to classes for objects associated with a terminal. For example, the predefined classes may include classes for all object types that are helpful in analyzing the scene of the image, even if they are not objects associated with a terminal, such as buildings, trees, and traffic lights.

[0163] In step 830, the MBS can identify the two-dimensional location of the terminal based on the classification result. Specifically, the MBS can perform location identification or positioning based on the spatial detail of local visual features among the multi-scale visual features extracted from the image sensing data. Positioning may refer to localization, which identifies the location of an object in object detection. Identifying the two-dimensional location of the terminal may include identifying the terminal in each image sensing data.

[0164] In step 840, the MBS can identify three-dimensional visual feature data associated with the terminal based on the two-dimensional position of the terminal. The three-dimensional visual feature data associated with the terminal may include three-dimensional visual feature data of a voxel containing the terminal. The three-dimensional visual feature data may be referred to as three-dimensional image data, a voxel feature vector, voxel data, and / or a three-dimensional feature vector. Identifying three-dimensional visual feature data associated with the terminal based on the two-dimensional position of the terminal involves the voxel [u containing the k-th terminal among the three-dimensional visual feature data for a plurality of voxels. k , v k , w k It may include identifying three-dimensional visual feature data for ].

[0165] Specifically, MBS can acquire 3D visual feature data by voxelizing 2D visual feature data from multiple viewpoints. MBS provides 3D visual feature data of voxels (e.g., feature vector j u,v,w By using ), the probability that each voxel contains an object of a specific class (e.g., terminal) can be obtained. MBS is, voxel [u k , v k , w k If the probability of including an object of this terminal class exceeds the threshold, voxel [uk , v k , w k ] can be determined to include this terminal. MBS is a voxel [u determined to include the terminal in the 3D visual feature data for a plurality of voxels. k , v k , w k 3D visual feature data for ] can be identified. In this case, 3D visual feature data can be referred to as 3D feature vectors.

[0166] FIG. 9 is a diagram illustrating the performance of a terminal detection and cell connection method based on image sensing data according to one embodiment.

[0167] Referring to FIG. 9, the true positive rate (TPR), which is the rate at which a terminal is detected to exist in a location where the terminal actually exists, and the false positive rate (FPR), which is the rate at which a terminal is incorrectly determined to exist in a location where the terminal does not actually exist, were evaluated. A low TPR may indicate the number of undetected UEs. Experimental data (910) refers to a case based on a 3D object detection method that detects terminals based on multi-viewpoint image sensing data from a plurality of SBSs according to one embodiment of the present disclosure described above, and may show high detection performance with a TPR of 100% and an FPR of 3%. This shows a very large difference from the comparison data (920), where the TPR was evaluated as 5% and the FPR as 98%. Comparison data (920) may represent terminal detection performance when using a 2D object detector. In a method based on image sensing data according to one embodiment, the MBS acquires image sensing data through a wired link with the SBS, so it can acquire necessary information even for terminals that are not connected to the MBS located within the network, and since multiple image sensing data are used, even if the visual information of the terminal is blocked at a specific point in time, visual information at another point in time can be used, and thus high terminal detection performance can be achieved.

[0168] In addition, referring to FIG. 8, the true positive rate (TPR), which is the rate at which an actual cell connection is possible between the terminal that instructed the cell connection and the SBS, and the false positive rate (FPR), which is the rate at which an actual cell connection is impossible between the terminal that instructed the cell connection and the SBS, were evaluated. Referring to the experimental data (910), when a cell connection vector is determined based on a plurality of image sensing data according to one embodiment, a high connection performance of 92% TPR and 5% FPR can be exhibited. Since the image sensing data-based cell connection method according to one embodiment utilizes information about the LoS state, it can provide a more accurate and faster cell connection than a CSI-based cell connection method.

[0169] FIG. 10 is a diagram illustrating the positioning error performance and angle error performance of a cell allocation method according to one embodiment. Referring to experimental data (1010), when the position of a terminal is determined using the cell allocation method according to one embodiment, the average position error may be 7 cm or less, and the average angle error may be 0.2 degrees or less. Referring to comparison data 1 (1020) of a 2D object detector, the average angle error is 0.25 degrees or less. In the case of experimental data (1010), since 3D image data is generated based on multiple image sensing data to detect the position of the terminal, it can exhibit more accurate positioning performance than a 2D object detector. Comparison data 2 (1030) may indicate a beam mismatch error when using codebook-based beam sweeping. When comparing experimental data (1010) and comparison data 2 (1030), according to the cell connection method according to one embodiment, beamforming can be performed more accurately than a codebook-based beamforming method.

[0170] FIG. 11 is a graph showing the average sum speed according to the number of terminals (K) according to one embodiment.

[0171] Referring to FIG. 11, a cell allocation method according to one embodiment can achieve a higher summing speed than the existing 5G-NR CSI-based beamforming method and the 2D object detector-based cell allocation method. When comparing experimental data (1100) of the cell allocation method according to one embodiment with comparison data (1120 to 1150) of the existing 5G-NR CSI-based beamforming method, the CSI-based beamforming method suffers from a decrease in summing speed due to beam alignment mismatch because it relies on codebook-based beam sweeping, whereas the cell allocation method according to one embodiment can improve overall throughput by enabling precise beamforming based on accurate location information extracted from 3D image data. When comparing experimental data (1100) of the cell allocation method according to one embodiment with comparison data (1110) of the existing 2D object detector-based cell allocation method, the existing 2D object detector-based cell allocation method can hinder overall throughput by detecting the location of a terminal and determining cell connection based on limited visual information of 2D image data having a single viewpoint.

[0172] FIG. 12 is a graph showing the average throughput according to the number of SBSs (M) according to one embodiment. Similar to FIG. 11, the cell allocation method according to one embodiment can achieve a higher summing speed than the existing 5G-NR CSI-based beamforming method and the 2D object detector-based cell allocation method, regardless of the number of SBSs. In other words, in the case of the cell allocation method according to one embodiment, the number of SBSs required to achieve the same summing speed is smaller. For example, to achieve a summing speed of 0.8 bps / GHz, the cell allocation method according to one embodiment in experimental data (1100) requires 3 SBSs for an ultra-dense network environment, whereas the CSI-based beamforming method in comparison data 2 (1120) requires 4 SBSs. Referring to the slopes of the experimental data (1100) and comparison data 2 (1120) in FIG. 11, the performance difference between the cell allocation method according to one embodiment and the existing 5G-NR CSI-based beamforming method may increase as the number of SBSs increases.

[0173] In FIGS. 11 and 12, the cell connection method according to one embodiment can achieve a high average summation speed because all UEs are connected to the SBS via a LoS link based on accurate positioning and cell allocation.

[0174] Meanwhile, the cell connection method according to one embodiment can significantly reduce the computation time and complexity of the cell connection vector. When the MBS generates the cell connection vector, for terminals and base stations where no LoS ​​path exists, the minimum transmission speed Cell connection vectors can be determined without additionally considering the maximum number of simultaneous connections. For example, when generating a cell connection vector such as Equation 4, MBS can determine the element values ​​of the cell connection vector for terminals and base stations where no LoS ​​path exists to be 0. Therefore, since calculations for terminals and base stations where no LoS ​​path exists are excluded from the calculation process for MBS to generate cell connection vectors, the calculation time and complexity of the cell connection vector can be significantly reduced.

[0175] FIG. 13 illustrates the configuration of a terminal according to one embodiment.

[0176] As illustrated in FIG. 13, the terminal of the present disclosure may include a control unit (control unit) (1330), a transceiver (1310), and a storage unit (memory) (1320). However, the components of the terminal are not limited to the examples described above. For example, the terminal may include more components or fewer components than the components described above. Furthermore, the control unit (1330), the transceiver (1310), and the storage unit (1320) may be implemented in the form of a single chip. Additionally, the control unit (1330) of FIG. 13 may include at least one processor or controller.

[0177] According to one embodiment, the control unit (1330) can control a series of processes that allow the terminal to operate according to the embodiments of the present disclosure described above. For example, according to the embodiments of the present disclosure, the components of the terminal can be controlled to perform a transmission and reception method of the terminal depending on whether the base station mode is a base station energy saving mode or a base station general mode. The control unit (1330) may be one or a plurality of units, and the control unit (1330) can perform a transmission and reception operation of the terminal in a wireless communication system applying the carrier band of the present disclosure described above by executing a program stored in the storage unit (1320).

[0178] The transceiver (1310) can transmit and receive signals with a base station. The signals transmitted and received with the base station may include control information and data. The transceiver (1310) may be composed of an RF transmitter that up-converts and amplifies the frequency of a transmitted signal, and an RF receiver that amplifies a received signal with low noise and down-converts the frequency. However, this is merely an example of the transceiver (1310), and the components of the transceiver (1310) are not limited to an RF transmitter and an RF receiver. Additionally, the transceiver (1310) can receive a signal through a wireless channel and output it to a control unit (1330), and transmit the signal output from the control unit (1330) through a wireless channel.

[0179] According to one embodiment, the storage unit (1320) may store programs and data necessary for the operation of the terminal. Additionally, the storage unit (1320) may store control information or data included in signals transmitted and received by the terminal. The storage unit (1320) may be composed of a storage medium such as ROM, RAM, hard disk, CD-ROM, and DVD, or a combination of storage media. Additionally, the storage unit (1320) may be a plurality of units. According to one embodiment, the storage unit (1320) may store a program for performing the transmission and reception operation of the terminal depending on whether the base station mode, which is one of the embodiments of the present disclosure described above, is a base station energy saving mode or a base station general mode.

[0180] The terminal of FIG. 13 may correspond to the terminal (120) and terminal (130) of FIG. 1, UE 1 to UE 5 of FIG. 2, UE 1 to UE 3 of FIG. 3, and UE 1 of FIG. 4.

[0181] FIG. 14 illustrates the configuration of a base station according to one embodiment.

[0182] As illustrated in FIG. 14, the base station of the present disclosure may include a control unit (1430), a transceiver (1410), and a storage unit (memory) (1420). However, the components of the base station are not limited to the examples described above. For example, the base station may include more components or fewer components than the components described above. Furthermore, the control unit (1430), the transceiver (1410), and the storage unit (1420) may be implemented in the form of a single chip. The control unit (1430) of FIG. 14 may include at least one processor or controller.

[0183] The control unit (1430) can control a series of processes to enable the base station to operate according to the embodiments of the present disclosure described above. For example, according to the embodiments of the present disclosure, the components of the base station can be controlled to perform a method of scheduling a terminal depending on whether the base station mode is a base station energy saving mode or a base station general mode. The control unit (1430) may be one or a plurality of units, and the control unit (1430) can perform a method of scheduling a terminal depending on whether the base station mode of the present disclosure described above is a base station energy saving mode or a base station general mode by executing a program stored in the storage unit (1420).

[0184] The transceiver (1410) can transmit and receive signals with a terminal. The signals transmitted and received with the terminal may include control information and data. The transceiver (1410) may be composed of an RF transmitter that up-converts and amplifies the frequency of a transmitted signal, and an RF receiver that low-noise amplifies a received signal and down-converts the frequency. However, this is merely an example of the transceiver (1410), and the components of the transceiver (1410) are not limited to an RF transmitter and an RF receiver. Additionally, the transceiver (1410) can receive a signal through a wireless channel and output it to a control unit (1430), and transmit the signal output from the control unit (1430) through a wireless channel.

[0185] According to one embodiment, the storage unit (1420) may store programs and data necessary for the operation of the base station. Additionally, the storage unit (1420) may store control information or data included in signals transmitted and received by the base station. The storage unit (1420) may be composed of a storage medium such as ROM, RAM, hard disk, CD-ROM, and DVD, or a combination of storage media. Additionally, the storage unit (1420) may be a plurality of units. According to one embodiment, the storage unit (1420) may store a program for performing a method of scheduling a terminal according to whether the base station mode, which is one of the embodiments of the present disclosure described above, is a base station energy saving mode or a base station general mode.

[0186] The base station of FIG. 14 may correspond to the base station (110) of FIG. 1, an MBS (e.g., MBS (201) of FIG. 2) and an SBS (e.g., the first SBS (210), the second SBS (220) and / or the third SBS (230) of FIG. 2) according to one embodiment.

[0187] Methods according to the embodiments described in the claims or specification of the present invention may be implemented in the form of hardware, software, or a combination of hardware and software.

[0188] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to embodiments described in the claims or specification of the present invention.

[0189] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, ROM (Read Only Memory), Electrically Erasable Programmable Read Only Memory (EEPROM), magnetic disc storage devices, Compact Disc-ROM (CD-ROM), Digital Versatile Discs (DVDs), or other forms of optical storage devices, magnetic cassettes. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0190] In addition, the above program may be stored on an attachable storage device that can be accessed via a communication network such as the Internet, Intranet, Local Area Network (LAN), Wide LAN (WLAN), or Storage Area Network (SAN), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present invention through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present invention.

[0191] In the specific embodiments of the present invention described above, the components included in the invention are expressed in a singular or plural form according to the specific embodiments presented. However, the singular or plural expression is selected to suit the situation presented for convenience of explanation, and the present invention is not limited to singular or plural components; even if a component is expressed in the plural form, it may be composed in the singular form, or even if a component is expressed in the singular form, it may be composed in the plural form.

[0192] Meanwhile, although specific embodiments have been described in the detailed description of the present invention, it is understood that various modifications are possible within the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined by the claims set forth below as well as equivalents thereof.

Claims

1. In a method performed by an MBS (macro base station), A step of receiving at least one image sensing data from at least one SBS (small base station); A step of generating voxel data based on at least one image sensing data; A step of identifying at least one SBS in which a line of sight (LoS) path exists with at least one terminal based on the above voxel data; and A method comprising the step of transmitting information about at least one terminal where the LoS path exists to at least one identified SBS.

2. In Paragraph 1, The above image sensing data is two-dimensional image data, and A method in which the above voxel data is three-dimensional image data.

3. In Paragraph 1, The step of generating the above voxel data is, A step of classifying objects included in the image sensing data based on a two-dimensional feature vector of the image sensing data; A step of identifying the two-dimensional location of the terminal based on the above classification result; and A method comprising the step of identifying a three-dimensional feature vector associated with the terminal based on the above two-dimensional position.

4. In Paragraph 3, The step of identifying at least one SBS having at least one terminal and an LoS path based on the above voxel data is: A step of determining whether there exists a terminal where a first SBS and a LoS path exist based on the set of the above 2D feature vectors and the above 3D feature vectors; and A method comprising the step of determining whether there exists a terminal where a second SSB and an LoS path exist based on the set of the above-mentioned two-dimensional feature vectors and the above-mentioned three-dimensional feature vectors.

5. In Paragraph 1, The above method is, The method further includes a step of controlling the above-mentioned at least one SBS to perform a cell connection procedure with the above-mentioned at least one terminal, and The above-mentioned controlling step is, A step of selecting one of the at least one SBS; and A method of controlling the selected SBS to perform a cell connection procedure with at least one terminal.

6. In Paragraph 5, A method in which the selected SBS includes the SBS having the shortest LoS path with the terminal.

7. In Paragraph 5, A method in which the selected SBS includes an SBS that satisfies the transmission amount requirement of the terminal.

8. In Paragraph 6, The above method is, A method of receiving a transmission amount requirement of a terminal from a first SBS among at least one SBS.

9. In the case of an MBS (macro base station), Transmitter / receiver; and It includes a control unit coupled to the above-mentioned transmitting and receiving unit, and the control unit, Receiving at least one image sensing data from at least one SBS (small base station), and Generates voxel data based on at least one image sensing data, and Based on the above voxel data, identify at least one SBS where a line of sight (LoS) path exists with at least one terminal, and MBS configured to transmit information about at least one terminal where the LoS path exists to at least one identified SBS.

10. In Paragraph 9, The above image sensing data is two-dimensional image data, and The above voxel data is 3D image data, MBS.

11. In Paragraph 9, The above control unit is, Based on the two-dimensional feature vector of the image sensing data, the objects included in the image sensing data are classified, and Based on the above classification result, the two-dimensional location of the terminal is identified, and MBS configured to identify a 3D feature vector associated with the terminal based on the above 2D position.

12. In Paragraph 11, The above control unit is, Based on the set of the above 2D feature vectors and the above 3D feature vectors, it is determined whether there exists a terminal where the first SBS and LoS path exist, and MBS configured to determine whether there exists a terminal where a second SSB and an LoS path exist based on the set of the above-mentioned two-dimensional feature vectors and the above-mentioned three-dimensional feature vectors.

13. In Paragraph 9, The above control unit is, Select one of the above at least one SBS, and MBS further configured to control the selected SBS to perform a cell connection procedure with at least one terminal.

14. In Paragraph 13, The above-mentioned selected SBS is an MBS that includes the SBS having the shortest LoS path to the terminal.

15. In Paragraph 13, The above-mentioned selected SBS includes an SBS that satisfies the transmission amount requirement of the terminal, an MBS.

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