Method and device for scheduling in wireless communication system

Abstract

The present disclosure relates to a 5G or 6G communication system for supporting a data transmission rate higher than that of a 4G communication system such as LTE. A base station according to one embodiment of the present disclosure collects channel information of cells belonging to the base station, generates a channel distribution information (CDI) feature variable, and transmits, to a UE, system information including the CDI feature variable. The CDI feature variable includes a variable related to a channel distribution type and a variable related to setting a channel measurement variable for each channel distribution type. The base station and the UE classify a channel distribution type according to the CDI feature variable when making channel measurements , and measure channel-measurement variables according to the classified type. The measured CDI is collected in the base station, and the base station generates a collected-CDI-based scheduling performance prediction model so as to accurately predict the performance of candidate scheduling groups generated during scheduling, thereby determining the optimal scheduling and providing same to a terminal.

Description

Method and device for scheduling in a wireless communication system

[0001] The present disclosure relates to a wireless communication system, and more particularly, to a method and apparatus for ensuring scheduling performance and improving resource efficiency in a wireless communication system supporting various channel environments.

[0002] Looking back at the evolution of wireless communication over successive generations, technologies have primarily been developed for human-facing services such as voice, multimedia, and data. With the commercialization of 5G (5th-generation) communication systems, an explosive increase in connected devices is 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 equipment, and factory equipment. Mobile devices are expected to evolve into diverse form factors, including augmented reality glasses, virtual reality headsets, and holographic devices. In the 6th-generation (6G) era, efforts are being made to develop improved 6G communication systems to connect hundreds of billions of devices and objects and provide diverse services. For this reason, 6G communication systems are often referred to as "beyond 5G."

[0003] The 6G communication system, expected to be realized around 2030, will have a maximum transmission speed of terabytes per second (i.e., 1,000 gigabits per second) and a wireless latency of 100 microseconds (μsec). In other words, compared to 5G, the transmission speed in a 6G communication system will be 50 times faster, while the wireless latency will be reduced to one-tenth.

[0004] To achieve these high data rates and ultra-low latency, 6G communication systems are being considered for implementation in the terahertz band (e.g., from 95 gigahertz (GHz) to 3 terahertz (THz)). Compared to the millimeter wave (mmWave) band introduced in 5G, the terahertz band is expected to experience more severe path loss and atmospheric absorption, making it more crucial to ensure signal reach, or coverage, in this band. Key technologies to ensure coverage include radio frequency (RF) components, antennas, new waveforms that offer better coverage than OFDM (orthogonal frequency division multiplexing), beamforming, and multiple antenna transmission technologies such as massive multiple-input and multiple-output (MIMO), full-dimensional MIMO (FD-MIMO), array antennas, and large-scale antennas. In addition, new technologies such as metamaterial-based lenses and antennas, high-dimensional spatial multiplexing using orbital angular momentum (OAM), and reconfigurable intelligent surfaces (RIS) are being discussed to improve the coverage of terahertz band signals.

[0005] In addition, in order to improve frequency efficiency and system network, 6G communication systems are developing full duplex technology that utilizes the same frequency resources for uplink and downlink at the same time; network technology that integrates satellites and high-altitude platform stations (HAPS); network structure innovation technology that supports mobile base stations and enables optimization and automation of network operation; dynamic spectrum sharing technology through collision avoidance based on spectrum usage prediction; AI-based communication technology that utilizes artificial intelligence (AI) 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 with complexity that exceeds the limits of terminal computing capabilities by utilizing ultra-high-performance communication and computing resources (mobile edge computing (MEC), cloud, etc.). In addition, efforts are being made to further strengthen connectivity between devices, further optimize networks, promote softwareization of network entities, and increase the openness of wireless communications 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 use of data, and the development of technologies for maintaining privacy.

[0006] Research and development of these 6G communication systems are expected to enable a new level of hyper-connected experience through the hyper-connectivity of 6G communication systems, which encompass not only connections between things but also connections between people and things. Specifically, 6G communication systems are expected to enable services such as truly immersive extended reality (Truly Immersive XR), high-fidelity mobile holograms, and digital replicas. Furthermore, services such as remote surgery, industrial automation, and emergency response, which are provided through enhanced security and reliability, will find application in diverse fields such as industry, healthcare, automotive, and home appliances.

[0007] Embodiments of the present disclosure are intended to ensure scheduling performance and improve resource efficiency in a wireless communication system supporting various channel environments.

[0008] In a wireless communication system according to one embodiment, a method of a base station may include the steps of collecting channel information of cells belonging to the base station and generating a channel distribution information (CDI) feature variable; and transmitting system information including the CDI feature variable to a user equipment (UE). The CDI feature variable may include a variable indicating a channel distribution type and a variable relating to setting channel measurement parameters for each channel distribution type.

[0009] In a wireless communication system according to one embodiment, a method of a UE may include the step of receiving, from a base station, system information including channel distribution information (CDI) characteristic variables for measuring terminal-side channel distribution information. The CDI characteristic variables are configured by the base station by collecting channel information of cells belonging to the base station, and may include variables indicating a channel distribution type and variables relating to setting channel measurement parameters for each channel distribution type.

[0010] In a wireless communication system according to one embodiment, a base station may include a memory storing one or more commands and at least one processor. The at least one processor may collect channel information of cells belonging to the base station, generate channel distribution information (CDI) characteristic variables, and transmit system information including the CDI characteristic variables to a user equipment (UE). The CDI characteristic variables may include variables indicating a channel distribution type and variables related to setting channel measurement parameters for each channel distribution type.

[0011] In a wireless communication system according to one embodiment, a UE includes a memory storing one or more commands and at least one processor, wherein the at least one processor is capable of receiving system information including channel distribution information (CDI) characteristic variables from a base station. The CDI characteristic variables are generated by the base station by collecting channel information of cells belonging to the base station, and may include variables indicating channel distribution types and variables relating to setting channel measurement parameters for each channel distribution type.

[0012] As a technical means for achieving the above-described technical task, a computer-readable recording medium disclosed may have stored thereon a program for executing at least one of the embodiments of the disclosed method on a computer.

[0013] Other technical features will become readily apparent to those skilled in the art from the following drawings, descriptions and claims.

[0014] By considering the configuration of channel distribution information (CDI) for various channel distributions in a wireless communication system supporting various channel environments through embodiments of the present disclosure, scheduling performance suitable for various channel environments can be guaranteed and resource efficiency can be improved.

[0015] However, the effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0016] Figure 1 is a drawing for explaining the technical field and purpose of the present disclosure.

[0017] FIG. 2a is a diagram for explaining a method for performing scheduling in a wireless communication system.

[0018] FIG. 2b is a diagram for explaining a method of performing scheduling in a wireless communication system.

[0019] Figure 2c is a diagram for explaining a method for performing scheduling in a wireless communication system.

[0020] FIG. 3 is a flowchart illustrating a method for a base station to provide CDI feature variables to a terminal in a wireless communication system according to one embodiment of the present disclosure.

[0021] FIG. 4 is a flowchart illustrating a method for a base station or a terminal to acquire CDI feature variables in a wireless communication system according to one embodiment of the present disclosure.

[0022] FIG. 5 is a diagram for explaining a method for a base station or a terminal to perform channel measurement based on CDI characteristic variables in a wireless communication system according to one embodiment of the present disclosure.

[0023] FIG. 6 is a diagram for explaining a method in which a base station performs scheduling using a scheduling performance prediction model based on a terminal-side CDI and a base station-side CDI in a wireless communication system according to one embodiment of the present disclosure.

[0024] FIG. 7a is a diagram illustrating a specific example of performing scheduling optimization based on CDI in a wireless communication system according to one embodiment of the present disclosure.

[0025] FIG. 7b is a diagram illustrating a specific example of performing scheduling optimization based on CDI in a wireless communication system according to one embodiment of the present disclosure.

[0026] FIG. 7c is a diagram for explaining a specific example of performing scheduling optimization based on CDI in a wireless communication system according to one embodiment of the present disclosure.

[0027] FIG. 7d is a diagram for explaining a specific example of performing scheduling optimization based on CDI in a wireless communication system according to one embodiment of the present disclosure.

[0028] FIG. 8A is a diagram illustrating a specific example of learning and inference of a CDI-based scheduling performance prediction model in a wireless communication system according to one embodiment of the present disclosure.

[0029] FIG. 8b is a diagram illustrating a specific example of learning and inference of a CDI-based scheduling performance prediction model in a wireless communication system according to one embodiment of the present disclosure.

[0030] FIG. 8c is a diagram illustrating a specific example of learning and inference of a CDI-based scheduling performance prediction model in a wireless communication system according to one embodiment of the present disclosure.

[0031] FIG. 8d is a diagram illustrating a specific example of learning and inference of a CDI-based scheduling performance prediction model in a wireless communication system according to one embodiment of the present disclosure.

[0032] FIG. 9 is a block diagram of a base station according to one embodiment of the present disclosure.

[0033] FIG. 10 is a block diagram of a UE according to one embodiment of the present disclosure.

[0034] The present disclosure is susceptible to various modifications and embodiments. Specific embodiments are illustrated in the drawings and described in detail herein. However, the various embodiments discussed below, as well as those used to illustrate the principles of the present disclosure in this specification, are merely examples and should not be construed as limiting the scope of the present disclosure in any way. Those skilled in the art will appreciate that the principles of the present disclosure can be implemented in any appropriately arranged system or device. Those skilled in the art will also appreciate that the principles of the present disclosure can be implemented in any appropriately configured wireless communication system.

[0035] For the same reason, some components in the attached drawings are exaggerated, omitted, or schematically depicted. Furthermore, the dimensions of each component do not entirely reflect its actual size. Identical or corresponding components in each drawing are assigned the same reference numbers.

[0036] Additionally, the numbers used in the description of the specification (e.g., 1st, 2nd, etc.) are merely identifiers to distinguish one component from another.

[0037] The advantages and features of the present disclosure, and methods for achieving them, will become clearer with reference to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided only to ensure that the disclosure of the present 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. Like reference numerals designate like elements throughout the specification. In addition, when describing the present disclosure, if a specific description of a related function or configuration is determined to unnecessarily obscure the gist of the present disclosure, the detailed description thereof will be omitted. In addition, the terms described below are terms defined in consideration of the functions of the present disclosure, and these may vary depending on the intention or custom of the user or operator. Therefore, their definitions should be made based on the contents throughout the specification.

[0038] Hereinafter, a base station (BS) is an entity that performs resource allocation of a terminal, and may be at least one of an NG-RAN, a gNode B, an eNode B, a Node B, or an xNode B (where x is an alphabet including g and e), a radio access unit, a base station controller, a satellite, an airborn, or a node on a network. A distributed BS may be separated into a centralized unit (CU) and a distributed unit (DU). The CU provides support for upper protocol layers such as service data adaptation protocol (SDAP), radio resource control (RRC), and packet data convergence protocol (PDCP), and the DU may provide support for lower protocol layers such as radio link control (RLC), medium access control (MAC), and physical layer (PHY). Each gNodeB may have a single CU, and multiple DUs may be connected to each CU. The DU includes both baseband processing and RF functions and may support various mobility scenarios.

[0039] Hereinafter, a terminal (user equipment, UE) may include a mobile station (MS), a vehicle, a satellite, an airborne, a cellular phone, a smartphone, a computer, or a multimedia system capable of performing a communication function.

[0040] In addition, although LTE, LTE-A, or 5G systems may be described below as examples, embodiments of the present disclosure may also be applied to other communication systems having similar technical backgrounds or channel types. For example, this may include 5G-Advance or NR-Advance, or 6th generation mobile communication technology (6G) developed after 5G mobile communication technology (or new radio, NR), and the 5G described below may also include existing LTE, LTE-A, and other similar services. In addition, the present disclosure may be applied to other communication systems with some modifications within a range that does not significantly deviate from the scope of the present disclosure, as determined by a person having skilled technical knowledge.

[0041] At this time, it will be understood that each block of the processing flowchart drawings and combinations of the flowchart drawings can be performed by computer program instructions. These computer program instructions can be installed in a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, so that the instructions executed by the processor of the computer or other programmable data processing equipment create a means for performing the functions described in the flowchart block(s). These computer program instructions can also be stored in a computer-available or computer-readable memory that can direct a computer or other programmable data processing equipment to implement the functions in a specific manner, so that the instructions stored in the computer-available or computer-readable memory can also produce a manufactured item that includes an instruction means for performing the functions described in the flowchart block(s). Since the computer program instructions may be installed on a computer or other programmable data processing device, a series of operational steps may be performed on the computer or other programmable data processing device to create a computer-executable process, and the instructions that cause the computer or other programmable data processing device to perform the steps for performing the functions described in the flowchart block(s) may also provide steps for performing the functions described in the flowchart block(s).

[0042] Additionally, each block may represent a module, segment, or portion of code that contains one or more executable instructions for performing a specific logical function(s). It should also be noted that in some alternative implementations, the functions described in the blocks may occur out of order. For example, two blocks depicted in succession may actually be executed substantially concurrently, or the blocks may sometimes be executed in reverse order, depending on their respective functions.

[0043] Here, the term '~ part' used in this embodiment means software or hardware components such as FPGA (Field Programmable Gate Array) or ASIC (Application Specific Integrated Circuit), and the '~ part' performs certain roles. However, the '~ part' is not limited to software or hardware. The '~ part' may be configured to be on an addressable storage medium or may be configured to play one or more processors. Therefore, as an example, the '~ part' includes components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, 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'. Additionally, the components and '~parts' may be implemented to activate one or more CPUs within a device or secure multimedia card. In addition, in an embodiment, the '~parts' may include one or more processors.

[0044] In the following description, terms referring to broadcast information, terms referring to control information, terms related to communication coverage, terms referring to state changes (e.g., events), terms referring to network entities, terms referring to messages, terms referring to device components, etc. are examples for convenience of explanation. Therefore, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may be used.

[0045] For convenience of explanation, the present invention uses terms and names defined in the LTE and NR standards, the most recent standards defined by the 3rd Generation Partnership Project (3GPP) among the existing communication standards. However, the present invention is not limited to these terms and names and can be equally applied to systems conforming to other standards.

[0046] Figure 1 is a drawing for explaining the technical field and purpose of the present disclosure.

[0047] Referring to FIG. 1, system 100 is an example of an MU X-MIMO system (100) using the upper mid-band. The MU X-MIMO system (100) may include a base station (10), UEs (21-23) included in an MU-MIMO group, and other UEs (24-26). The base station (10) may communicate with the UEs (21-26) in the upper mid-band using X-MIMO technology, and the UEs (21-26) may also communicate with the UEs (21-26) in the upper mid-band using X-MIMO technology. The UEs (21-23) included in the MU-MIMO group are included in the same scheduling group and may be assigned scheduling from the base station (10) through a common control channel. A scheduling group is a logical group for managing a plurality of UEs in a network and efficiently allocating resources, and it may be predicted that the UEs 21-23 have similar channel conditions.

[0048] The upper mid-band refers to the frequency band corresponding to 7.125 - 24.25 GHz, which is currently being discussed as a new candidate frequency band for 6G. The upper mid-band is being actively researched because it has the advantage of achieving a balanced balance of cell coverage and transmission capacity compared to the sub-6 GHz band or millimeter band in 5G. In addition, it is expected that the upper mid-band will be applied with an extreme MIMO (X-MIMO) system that uses a larger number of antennas with 256-512 TRX, compared to the massive MIMO (Multi Input Multi Output) that uses 64 TRX in 5G. However, the upper mid-band can have a much wider range of channel characteristics than the 3.5 GHz band, such as correlated Rayleigh channels, correlated Rician channels, as well as Rician-mixture channels, nWDP (n-wave with diffuse power) channels, double scattering channels, and non-Gaussian channels. Therefore, research on multi-user (MU) X-MIMO technology that takes these characteristics into account is necessary.

[0049] Meanwhile, among the services to be supported by utilizing the X-MIMO system, there is the HRLLC (hyper reliable low-latency communication) or URLLC (ultra-reliable low-latency communication) service, which is expected to become increasingly important and ultimately account for the majority in communication-computing convergence services. The HRLLC or URLLC service aims to support new mission-critical services such as VR / AR, autonomous vehicles, artificial intelligence, IoT, smart factories, healthcare, and public safety that could not be provided by 5G. [Table 1] shows the requirements required for these services.

[0050] [Table 1]

[0051]

[0052]

[0053] For services that require the most difficult combination of performance requirements in [Table 1], which are 1) sporadic packet generation and 2) very high reliability and very low latency (approximately 0.2 ms or less), efficient support is possible with GFMA (grant-free multiple access) technology.

[0054] For HRLLC / URLLC services in the mid-latency or low-latency range where delay requirements are relatively less stringent, it is appropriate to provide services using GBMA (grant-based multiple access) technology. For example, in the delay model of the wireless section, assuming that the alignment delay is 1 transmission time interval (TTI), the grant delay required for scheduling is 4 TTI, and the propagation delay and decoding delay are approximately 2 TTI, an overhead of 7 TTI occurs when only one transmission is allowed in GBMA, and an overhead of 11 TTI occurs when one transmission and additional retransmissions are allowed. In NR, when the subcarrier spacing (SCS) is 120 kHz, the expected short TTI is 17.86 (i.e. 2 symbols / mini-slot) or 62.5 (i.e., 7 symbols / mini-slot), it is possible to utilize GBMA with an overhead of about 20-60% for cases where latency of 0.2 ms to 2 ms is required.

[0055] However, the current MU massive MIMO technology of NR has the following limitations. In the current MU massive MIMO system of NR, the sounding reference signal (SRS) resource for uplink channel estimation is limited in the frequency domain, time domain, and code domain. Therefore, it is difficult to properly utilize the increased degree of freedom (DoF), i.e., the increased number of transmitter antennas and receiver antennas, in the MU X-MIMO system. Accordingly, it may be difficult to obtain accurate channel state information (CSI), and scheduling performance may be degraded, especially from the perspective of performance guarantee. In addition, the above problems become more serious in the MU X-MIMO system using the upper mid-band by not considering the various channel characteristics of the upper mid-band.

[0056] In order to solve such problems, the present disclosure aims to enable a UE and a base station to accurately and efficiently obtain channel distribution information (CDI) from parameters of a size that can be stably estimated in a MIMO system with large transmitter and receiver antenna sizes, and to ensure accuracy of channel measurement and scheduling performance by utilizing the obtained CDI.

[0057] Specifically, in one embodiment of the present disclosure, a method is proposed to collect channel information of cells belonging to a base station to generate CDI characteristic variables, and perform CDI measurement on the UE side and the base station side based on the CDI characteristic variables in order to obtain accurate and efficient CDI.

[0058] Specifically, in one embodiment of the present disclosure, a method is proposed for predicting scheduling performance according to channel distribution types and channel measurements by type, by categorizing channel distributions, generating an effective channel measurement method for each channel type, performing channel measurements for each channel distribution type at a UE and a base station, and collecting channel measurements for each channel distribution type between a UE and a base station requiring scheduling.

[0059] Below, in FIGS. 2A to 2C, before a detailed description of the embodiments proposed in the present disclosure is given, a method for performing scheduling in a wireless communication system is described.

[0060] FIGS. 2A to 2C are diagrams for explaining a scheduling performance method in a wireless communication system.

[0061] To support high reliability, low latency, high capacity, and aperiodic / random traffic patterns generated from UEs in the uplink, the following three steps are performed. First, the UE receives cell system information from a base station capable of receiving communication services. The base station and the UE collect long-term channel measurement information related to the wireless channel configuration information included in the provided cell system information. Based on the long-term collected channel information, the base station performs optimized resource allocation scheduling for the UE. The operations of each of the three steps are described in detail in FIGS. 2A through 2C .

[0062] Referring to Fig. 2a, Fig. 2a illustrates a process for providing a cell synchronization signal and cell system information. The process for providing a cell synchronization signal and cell system information is a process that occurs when a UE (20) first connects to and registers with a base station (10), such as when the UE (20) is powered on. The series of processes illustrated in Fig. 2a may refer to the procedures defined in 3GPP TS 38.211, 3GPP TS 38.213, 3GPP TS 38.304, and 3GPP TS 38.331.

[0063] In S210a, the base station (10) constantly broadcasts a cell search support signal, and a UE (20) that is to perform wireless access to the cell (10) for the first time receives the cell search support signal from the base station (10). The cell search support signal is a signal that provides information necessary for the UE (20) to search for and synchronize a cell, and includes a cell identifier, a PSS (Primary Synchronization Signal), an SSS (Secondary Synchronization Signal), etc.

[0064] In S220a, the UE (20) performs cell synchronization based on the cell search support signal. The UE (20) performs timing synchronization and frequency synchronization, and prepares to perform communication according to the timing of the base station (10). Once cell synchronization is complete, the UE (20) is ready to receive cell system information (system information block, SIB) through a broadcast channel.

[0065] In S230a and S240a, the UE (20) receives cell system information from the base station (10) and stores it. The cell system information is provided in the form of system information blocks (SIBs) and includes cell configuration, resource allocation, frequency information, timing information, etc. The UE (20) can perform random access through the cell system information, and thereafter perform RRC (radio resource control) connection setup and PDU (protocol data unit) session to ensure QoS (Quality of Service). In addition, the UE (20) can perform cell selection, cell reselection, handover, etc. through the cell system information.

[0066] Referring to Fig. 2b, Fig. 2b illustrates a process for collecting terminal-side channel measurement information and base station-side channel measurement information according to cell system information. The series of processes illustrated in Fig. 2a may refer to procedures defined in 3GPP TS 38.211, 3GPP TS 38.213, 3GPP TS 38.214, 3GPP TS 38.215, and 3GPP TS 38.331.

[0067] At S210b, the UE (20) receives a downlink reference signal via the downlink. The downlink reference signal may include a channel state information reference signal (CSI-RS) or a synchronization signal block (SSB). At S220b, the UE (20) collects terminal-side channel measurement information by performing channel measurement based on the CSI-RS or SSB. The UE (20) analyzes the received downlink reference signal to measure downlink channel quality, signal strength, interference, etc., and thereby evaluates communication performance with the base station (10) and determines appropriate transmission parameters. The channel measurement information may include a reference signal received power (RSRP), a received signal strength indicator (RSSI), a reference signal received quality (RSRQ), a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), etc.

[0068] At S230b, the base station (10) receives an uplink reference signal via the uplink. The uplink reference signal may include a sounding reference signal (SRS). At S240b, the base station (10) collects base station-side channel measurement information by performing channel state measurement based on the SRS. The base station (10) analyzes the received uplink reference signal to measure uplink channel quality, signal strength, interference, etc., and thereby evaluates communication performance with the UE (20) and determines appropriate transmission parameters. The channel measurement information may include RSRP (reference signal received power), RSSI (received signal strength indicator), RSRQ (reference signal received quality), CQI (channel quality indicator), PMI (precoding matrix indicator), RI (rank indicator), etc.

[0069] At S250b, the base station (10) receives terminal-side channel measurements from the UE (20). Accordingly, at S260b, channel measurements collected from both the UE (20) and the base station (10) can be stored in the base station (20) so that they can be used later for network status monitoring, optimization, scheduling, etc.

[0070] Referring to Fig. 2c, Fig. 2c illustrates a scheduling execution process according to channel measurement information. The series of processes illustrated in Fig. 2c may refer to procedures defined in 3GPP TS 38.213, 3GPP TS 38.214, 3GPP TS 38.321, and 3GPP TS 38.331.

[0071] The base station (10) holds channel measurement information from the UE (10) and channel measurement information at the base station (10). In S210c, information associated with the UE is received from the UE (20) along with a scheduling request message. The information associated with the UE may include quality of service (QoS) requirements, a buffer status report, a power headroom report, and the like.

[0072] The scheduling performed by a base station ultimately provides scheduling information that informs terminals of the communication resources available for data transmission. Scheduling can be categorized into dynamic scheduling and configured scheduling. Both are performed prior to terminal data transmission and must guarantee subsequent terminal data transmission performance.

[0073] In S220c, the base station (10) generates a scheduling candidate group based on the information associated with the received UE and the network status.

[0074] In S230c, the base station (10) predicts the performance of each scheduling candidate group based on the UE-side channel measurement information and the base station-side channel measurement information it possesses. The base station (10) analyzes the performance of each scheduling candidate group through various scheduling algorithms to evaluate the quality of service (QoS) and resource efficiency that each scheduling candidate group can provide.

[0075] In S240c, the base station (10) performs scheduling optimization. Based on the predicted performance, the base station (10) selects the most effective scheduling method and optimizes network resources.

[0076] In S250c, the base station (10) provides the determined scheduling information to the UE (20). The scheduling information may include allocated resource blocks, transmission timing, transmission power, etc.

[0077] As mentioned in FIG. 1 of the present disclosure, recently, there has been widespread research on using the upper mid-band of 7.125 - 24.25 GHz as a communication frequency band, which has a shorter wavelength than the existing sub-6 GHz frequency band, thus allowing the use of more antennas, while also providing wider coverage than the millimeter wave frequency band. In order to maximize the advantages of the upper mid-band frequency band, it is expected that extreme MIMO (X-MIMO) systems, which use a larger number of antennas with 256-512 TRXs compared to the 64 TRXs (transceivers) of massive MIMO (multi-input multi-output), will be applied. However, the upper mid-band band has much more diverse channel characteristics than the 3.5 GHz band. Compared to the conventional channel characteristics, which were mainly modeled as uncorrelated Rayleigh, the upper mid-band can have various channel characteristics such as correlated Rician fading channel, Rician-mixture fading, and nWDP (n-wave with diffuse power) fading.

[0078] In these diverse channel environments, accurate channel distribution information (CDI) is required to ensure scheduling performance and optimize resource efficiency to support high-reliability, low-latency, high-capacity, and aperiodic / random traffic patterns in the uplink. However, because SRS resources for channel estimation in the uplink are limited in the frequency, time, and code domains, it is difficult to accurately perform channel state estimation for the increased spatial dimension in X-MIMO.

[0079] For example, the estimated size of a line-of-sight (LoS) channel is equal to the product of the antenna sizes of the transmitter and receiver, and the estimated size of a non-LoS (NLoS) channel correlation matrix is ​​equal to the square of the product of the antenna sizes of the transmitter and receiver. Therefore, the size of the channel distribution information that needs to be estimated in X-MIMO increases significantly, while the communication resources required for channel estimation are limited, making accurate channel state estimation difficult. Accordingly, resource efficiency is greatly reduced due to inaccurate CDI acquisition, and it may also become difficult to provide the targeted QoS.

[0080] The current NR standard does not address the issue of diverse channel distributions and the increasing size of channel distribution information required for estimation, which are related to channel information measurement at terminals and base stations. Furthermore, with regard to scheduling performance guarantees, current scheduling research only considers channel path loss, making it inapplicable to diverse channel characteristic environments. Therefore, an analysis of the minimum performance required to guarantee scheduling performance in diverse channel characteristic environments is necessary.

[0081] In the present disclosure, in order to ensure scheduling performance in a MIMO system with large antenna sizes of a transmitter or receiver, a base station generates a CDI feature variable based on the result of collecting channel information of cells, and a method is provided for efficiently and accurately obtaining a CDI based on the CDI feature variable at the base station and a UE.

[0082] For example, when a UE initially accesses a cell from a base station, the base station provides the CDI characteristic variables of the cells belonging to the base station. Based on the provided CDI characteristic variables, the UE obtains the channel distribution types existing in the cells and the configuration of channel distribution parameters (CDI parameters, CDIP), which are channel distribution measurement variables according to the channel distribution types. The base station internally obtains the CDI characteristic variables. Accordingly, the UE and the base station classify the channel distribution types when collecting channel state measurement information based on the CDI characteristic variables and perform channel measurement according to the configuration of channel distribution parameters suitable for each channel distribution type. The base station collects the CDI measurement results measured by the UE and the base station. The base station prepares a scheduling performance prediction model suitable for the collected results in advance, and performs scheduling that can guarantee the required performance when receiving scheduling requests and terminal information from UEs.

[0083] Hereinafter, with reference to FIGS. 3 to 8, a method for ensuring scheduling performance suitable for various channel environments and improving resource efficiency by considering the configuration of various channel distribution information (CDI) by a base station or UE according to an embodiment of the present disclosure will be described in detail.

[0084] FIG. 3 is a flowchart illustrating a method in which a base station (10) provides channel distribution information (CDI) feature variables to a UE (20) in a wireless communication system according to one embodiment of the present disclosure.

[0085] Referring to FIG. 3, in S310, a base station (10) according to one embodiment of the present disclosure can collect channel information of cells belonging to the base station (10) and generate a channel distribution information (CDI) feature variable.

[0086] In one embodiment, channel information of cells belonging to a base station (10) may include, but is not limited to, cell environment information (e.g., information about the terrain around the cell, information about the radio frequency to be used in the cell, information about the mobility of the cell and UE (20), etc.) or immediate channel measurement information of the cells.

[0087] In one embodiment, the base station (10) can generate CDI feature variables from the channel information of the collected cells by applying an explicit algorithm for a known channel distribution model, an explicit algorithm for an arbitrary channel distribution, or a generative artificial intelligence-based feature extraction for an arbitrary channel distribution.

[0088] In one embodiment, the generated CDI feature variables may represent a type of channel distribution, may represent a configuration of channel distribution parameters that determine the channel distribution type, or may represent both a channel distribution type and a configuration of channel distribution parameters.

[0089] In one embodiment, the CDI feature variables may include a CDI type variable indicating the type of channel distribution and a CDIP configuration variable indicating the configuration of channel distribution parameters.

[0090] A CDI type variable may contain information about a channel distribution type (i.e., a set of channel distribution types) and may additionally contain information about how to classify the channel distribution types.

[0091] Information about the channel distribution type may mean information indicating what channel distribution characteristics cells belonging to the base station (10) can have / support. For example, the channel distribution type may include, but is not limited to, a correlated Rayleigh channel, a correlated Rician channel, a Rician-mixture channel, an nWDP (n-wave with diffuse power) channel, a double scattering channel, a non-Gaussian channel, etc. In the present disclosure, the channel distribution type may be referred to as a CDI type or a CDI class.

[0092] Information about how to classify a channel distribution type may mean information that instructs a UE or a base station to determine which distribution type to classify a surrounding channel into, based on information about the channel distribution type (i.e., a set of channel distribution types).

[0093] The CDIP configuration variables may include information indicating the configuration of channel distribution parameters for accurately and efficiently measuring each channel distribution for each channel distribution type. The base station (10) may set CDIP configuration variables indicating the configuration of channel distribution parameters for accurately and efficiently measuring each channel distribution for each channel distribution type. The CDIP configuration variables may additionally include information regarding how the UE or the base station appropriately measures the channel, depending on the configuration of the channel measurement variables corresponding to each channel distribution type.

[0094] In S320, the base station (10) according to one embodiment of the present disclosure can provide the generated CDI feature variables to the UE (20).

[0095] In one embodiment, the base station (10) may transmit CDI feature variables to the UE (20) together with or including system information. The system information may correspond to a system information block (SIB). In one embodiment, the base station (10) may transmit the CDI feature variables together with various configuration information necessary for the UE to measure CDI according to the present disclosure.

[0096] A UE (20) according to one embodiment of the present disclosure can receive CDI characteristic variables from a base station (10) and store the received CDI characteristic variables. The UE (20) can then utilize the stored CDI characteristic variables when performing channel state measurement based on a downlink reference signal.

[0097] According to one embodiment of the present disclosure, a base station or UE can effectively manage characteristics that various channel distributions may have by representing them as CDI characteristic variables. In addition, according to one embodiment of the present disclosure, since the CDI characteristic variables can represent variables regarding the types of channel distributions and variables regarding the configuration of channel distribution parameters for each type, when a base station or UE collects channel measurements based on the channel distribution characteristic variables, accurate channel distribution information of various channel distributions according to various channel environments can be accurately obtained even when a relatively small number of channel measurement variables are configured and collected, thereby precisely guaranteeing scheduling performance and improving resource efficiency in a wireless communication system in various environments.

[0098] However, the effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0099] FIG. 4 is a flowchart illustrating a method for a base station (10) or a UE (20) to acquire CDI feature variables in a wireless communication system according to one embodiment of the present disclosure.

[0100] Regarding the contents illustrated in Fig. 4 that overlap with the contents described in Fig. 2a and Fig. 3, reference is made to the contents described in Fig. 2a and Fig. 3, and the description thereof is omitted here.

[0101] Referring to FIG. 4, in S410, a base station (10) according to an embodiment of the present disclosure may collect channel information of cells belonging to the base station (10) and configure a CDI feature variable. The CDI feature variable may be used to measure channel distribution information (CDI) on the UE side or the base station side. The CDI feature variable may include a CDI type variable and a CDIP configuration variable, as described in FIG. 3, and specifically, may include information on a channel distribution type, information on a method for classifying a channel distribution type, information on a configuration of channel distribution parameters for each channel distribution type, and information on a method for measuring a channel according to a configuration of channel distribution parameters corresponding to each classified channel distribution type.

[0102] In S420, the base station (10) constantly broadcasts a cell search support signal, and a UE (20) that is trying to perform wireless connection to the cell (10) for the first time can receive the cell search support signal from the base station (10).

[0103] In S430, a UE (20) according to an embodiment of the present disclosure may perform a cell synchronization process according to a cell search support signal when attempting an initial connection to a cell.

[0104] In S440, when cell synchronization is completed, the UE (20) can receive CDI feature variables in the process of receiving system information from the base station (10).

[0105] In S450, the UE (20) can receive CDI feature variables from the base station (10) and store them. The base station (10) can internally obtain the CDI feature variables.

[0106] A base station or UE according to one embodiment of the present disclosure can effectively manage the characteristics of various channel distributions by representing them as CDI characteristic variables. A base station or UE according to one embodiment of the present disclosure can guarantee scheduling performance and improve resource efficiency in a wireless communication system supporting various channel environments by categorizing various channel distribution characteristics into a finite set and sharing channel measurement methods for each channel distribution type.

[0107] However, the effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0108] FIG. 5 is a diagram for explaining a method in which a base station (10) or UE (20) performs channel measurement based on CDI characteristic variables in a wireless communication system according to one embodiment of the present disclosure.

[0109] Regarding the content illustrated in Fig. 5 that overlaps with the content described in Fig. 2b, reference is made to the content described in Fig. 2b, and the description thereof is omitted here.

[0110] Referring to FIG. 5, at S510, a UE (20) according to an embodiment of the present disclosure may receive a downlink reference signal via downlink. The downlink reference signal may include a channel state information reference signal (CSI-RS) or a synchronization signal block (SSB).

[0111] At S520, the UE (20) can collect terminal-side CDI by performing terminal-side channel measurement based on the CSI-RS or SSB and the CDI feature variables acquired at S440 and S450 of FIG. 4. The terminal-side CDI can include measurements of terminal-side channel distribution types and terminal-side channel distribution parameters corresponding to the terminal-side channel distribution types.

[0112] In one embodiment, the UE (20) collects downlink channel estimation data based on a downlink reference signal such as CSI-RS in a physical layer, and classifies the terminal-side channel distribution type of the collected channel estimation data according to information on the channel distribution type included in the CDI feature variable and information on the method for classifying the channel distribution type. Based on the classified terminal-side channel distribution type, the UE (20) can determine a channel distribution parameter configuration corresponding to the terminal-side channel distribution type and a channel measurement method according to the channel distribution parameter configuration. The UE (20) can measure channel distribution parameters corresponding to the terminal-side channel distribution type according to the determined channel measurement method.

[0113] In S530, the base station (10) can receive an uplink reference signal from the UE (20). The uplink reference signal can include a sounding reference signal (SRS).

[0114] In S540, the base station (10) can collect base station-side CDI by performing base station-side CDI measurement based on the SRS and the CDI characteristic variables generated in S410 of FIG. 4. The base station-side CDI can include measurements of the base station-side channel distribution type and base station-side channel distribution parameters corresponding to the base station-side channel distribution type.

[0115] In one embodiment, the base station (10) collects uplink channel estimation data based on an uplink reference signal such as an SRS in a physical layer, and classifies the base station-side channel distribution type of the collected channel estimation data according to information on the channel distribution type included in the CDI feature variable and information on the method for classifying the channel distribution type. Based on the classified base station-side channel distribution type, the base station (10) can determine a channel distribution parameter configuration corresponding to the terminal-side channel distribution type and a channel measurement method according to the channel distribution parameter configuration. The base station (10) can measure channel distribution parameters corresponding to the base station-side channel distribution type according to the determined channel measurement method.

[0116] In S550, the UE (20) can transmit / report the terminal-side CDI measured in S520 to the base station (10). The base station (10) can obtain the terminal-side CDI measured by each UE from one or more UEs.

[0117] In S560, the base station (10) can store the terminal-side CDI acquired from one or more UEs (20) in S550 and the base station-side CDI measured in S540. The base station (10) can acquire the channel distribution type of the surrounding channel environment from the channel distribution types collected from each of one or more UEs (20) and the base station (10). For example, the base station (10) can know the channel distribution type of the surrounding channel environment having various statistical characteristics, such as a correlated Rayleigh channel, a correlated Rician channel, a Rician-mixture channel, an nWDP (n-wave with diffuse power) channel, a double scattering channel, a non-Gaussian channel, etc. The base station (10) can generate a CDI-based scheduling performance prediction model from the channel distribution type of the surrounding channel environment acquired in this way and the measurements of channel distribution parameters corresponding to the channel distribution type.

[0118] According to one embodiment of the present disclosure, a base station or UE, when measuring channel estimation data based on a reference signal, can ensure scheduling performance and improve resource efficiency in a wireless communication system supporting various channel environments by measuring according to a channel distribution type included in a CDI feature variable and a channel measurement method for the corresponding channel distribution type.

[0119] However, the effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0120] FIG. 6 is a diagram for explaining a method for a base station (10) to perform scheduling using a CDI-based scheduling performance prediction model based on terminal-side CDI and base station-side CDI in a wireless communication system according to one embodiment of the present disclosure.

[0121] Regarding the content illustrated in Fig. 6 that overlaps with the content described in Fig. 2c, reference should be made to the content described in Fig. 2c, and the description thereof will be omitted here.

[0122] Referring to FIG. 6, in S610, a base station (10) according to one embodiment of the present disclosure has a terminal-side CDI and a base station-side CDI. The base station (10) can generate a CDI-based scheduling performance prediction model based on the terminal-side CDI and the base station-side CDI.

[0123] The terminal-side CDI may be information stored in S560 of Fig. 5. Among the base station-side CDIs, the measurement values ​​of channel distribution parameters corresponding to the base station-side channel distribution type may be measured from SRSs received from all scheduling target UEs, or may be measured from SRSs received from sampled UEs among them.

[0124] The CDI-based scheduling performance prediction model can be learned or generated in the base station (10) and loaded into the base station (10). The CDI-based scheduling performance prediction model pre-loaded in the base station (10) is learned or generated based on collected and stored terminal-side CDI and base station-side CDI, and can be updated together as the terminal-side CDI and base station-side CDI are additionally provided thereafter. In order to minimize errors in the AI ​​model in learning / generating the CDI-based scheduling performance prediction model, various learning model structures, learning methods, and preprocessing and postprocessing management methods of learning data can be used.

[0125] The learning / generation and inference processes of the CDI-based scheduling performance prediction model are described in more detail in FIGS. 8a to 8d of the present disclosure.

[0126] In S620, the base station (10) may receive information associated with the UE (20) along with a scheduling request message. The information associated with the UE may include quality of service (QoS) requirements, a buffer status report, a power headroom report, and the like. In one embodiment, the UE (20) may optionally or at the request of the base station report, together with the scheduling request message, report the current channel distribution type of the UE and measurements of channel distribution parameters corresponding to the current channel distribution type.

[0127] Afterwards, at the MAC (medium access control) layer, the scheduler of the base station can perform the following S630 to S650 operations.

[0128] In S630, the base station (10) can generate a scheduling candidate group based on the information associated with the received UE and the network status.

[0129] In S640 and S650, the base station (10) can predict the performance of each scheduling candidate group using a CDI-based scheduling performance prediction model installed in the base station, and can perform scheduling optimization based on the results of predicting the performance of each scheduling candidate group.

[0130] In one embodiment, the base station (10) may perform scheduling optimization by using a CDI-based scheduling performance prediction model to predict the minimum value of common target performance for each scheduling candidate group, determine scheduling group parameters that guarantee the minimum value of common target performance set for each scheduling candidate group, and select an optimal combination of scheduling candidate groups that maximizes overall scheduling performance. This is described in more detail in FIGS. 8A to 8D of the present disclosure.

[0131] In S660, the base station (10) can provide optimized scheduling information to the UE (20). The scheduling information can include allocated resource blocks, transmission timing, transmission power, etc.

[0132] A base station or UE according to one embodiment of the present disclosure can guarantee scheduling performance and improve resource efficiency in a wireless communication system supporting various channel environments by optimizing scheduling by considering channel distribution using a CDI-based scheduling performance prediction model.

[0133] However, the effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0134] FIGS. 7A to 7D are diagrams for explaining specific examples of performing scheduling optimization based on CDI in a wireless communication system according to one embodiment of the present disclosure.

[0135] Referring to FIG. 7A, according to one embodiment of the present disclosure, a system model 700 performing scheduling optimization based on CDI may be assumed. However, the system model 700 is merely an exemplary system model for specifically explaining embodiments of the present disclosure, and the present disclosure is not limited thereto.

[0136] System model 700 of Fig. 7a A base station (10) having a dog antenna and a single antenna It includes UEs (71 to 76), and their service QoS (quality of service) flows are is defined identically to , and reliability , delay [ms], burst data rate [bits / packet], arrival rate Quality-of-service parameter (QoSP) in [packets / sec] has

[0137] A consistent subchannel is defined for all UEs (71 to 76), and the subchannel has a subband bandwidth and subframe period and symbols for communication . N represents the number of symbols.

[0138] UE For , the energy constraint per subchannel is , and large path loss , and user energy information (UEI) is is defined as UE A single user channel vector for is defined as a user group For , the multi-user channel vector is is defined as , and its probability density function (PDF) is is displayed as .

[0139] Below, an example of a configured scheduling (CS) model based on CDI that can be performed in the system model 700 of FIG. 7A is described. However, the CS model below is merely an exemplary scheduling model for specifically explaining embodiments of the present disclosure, and the present disclosure is not limited thereto.

[0140] Configured scheduling (CS) is a user grouping , subband allocation , pilot assignment , power control It consists of is indicated as . User grouping is , the base station (10) is defined as a set of users cast It divides the user into scheduling groups and performs space division multiple access (SDMA) on consistent subchannels. User grouping is satisfies. Subband allocation is is defined as , where Is is the number of subbands allocated to the pilot allocation. , a unit vector of length L uniquely assigned to each UE. is defined as a set of user groups. The maximum pilot cross-correlation in is defined as. Power control is , target reception power vector is a set of , where is the user's transmission power that controls the target reception power of the pilot signal, is the user's transmission power that controls the target reception power of the data.

[0141] The CS group parameter (CSGP) is , which represents user grouping, pilot characteristics (e.g., maximum pilot cross-correlation) and power control characteristics. represents the parameter space ( ).

[0142] User Group CSGP in UEs with pilot symbols Target receive power for pilot signal , and transmits data symbols at the target reception power for the data signal. is transmitted according to the target reception power of the pilot signal. and The instantaneous estimated channel vector using , and the channel estimation error vector is am. The covariance matrix of is is defined as, The covariance matrix of is is defined as each UE For , the instantaneous signal to interference and noise ratio (SINR) of the data symbol is the target data reception power. and is determined using a maximal ratio combining (MRC) receiver. Accordingly, the user group The minimum SINR can be determined as in [Mathematical Formula 1].

[0143] [Mathematical Formula 1]

[0144]

[0145] Here, the signal, interference and noise power terms are all Since it is a function of , The distribution of has as a distribution parameter. Therefore, The PDF of is displayed as . And The reliability criterion is expressed as in [Mathematical Formula 2].

[0146] [Equation 2]

[0147]

[0148] According to [Equation 2], The reliability criteria conditions are essentially Without requiring knowledge of, only requires that only the marginal distribution of a particular confidence value be satisfied.

[0149] Therefore, in the above example, in order to guarantee QoS performance in the CS model of system model 700, scheduling group Target SINR for (710) By setting This is done so that the transmission rate function By defining the subband allocation For all user groups Burst data rate of To secure, It must be done so that it becomes possible.

[0150] As seen in Figure 7a, for QoS-guaranteed scheduling, Accurate knowledge of the specific Q reliability of the channel is essential, as well as information on the overall channel distribution. It is not necessary to obtain. Therefore, below, This paper describes an example of application of CDI management and CS optimization method based on CDI for efficient and accurate acquisition.

[0151] In the application examples described below, user groups Multi-user channel distribution for is a set of channel distributions classified as in [Mathematical Formula 3]. can be assumed as an element of .

[0152] [Equation 3]

[0153]

[0154] Here is the CDI class (corresponding to the channel distribution type), is the number of CDI classes, is a CDI parameter (CDI parameter, CDIP) (corresponding to a channel measurement variable), is a CDIP space, is a channel distribution classified according to the CDI class. The CDI of the present disclosure is is defined as is the channel distribution type is a channel measurement method corresponding to CDIP is associated with.

[0155] Next, this application example includes the following assumptions:

[0156] ● Number of CDI classes is finite.

[0157] ● CDIP is the base station antenna size or user group size It has a low number of parameters that are not affected by .

[0158] ● CDI is the UE side CDI and BS side CDI , which is obtained from the peripheral distribution of the channel observed separately by the UE and the base station, respectively.

[0159] ● And, represents a set of parametric distributions defined as in [Mathematical Formula 4].

[0160] [Equation 4]

[0161]

[0162] Here, is the SINR distribution parameter (SINR-DP), is a CDI class represents the SINR-DP space. According to these assumptions, [Equation 5] can be established.

[0163] [Equation 5]

[0164]

[0165] According to [Mathematical Formula 5], the relationship is Established, here This may mean a CDI-based scheduling performance prediction model of the present disclosure (or may be referred to as a SINR-DP estimation model).

[0166] In this application example, to optimize CS for QoS performance guarantee and resource efficiency, the optimal scheduling candidate A set of scheduling candidate groups to obtain and generates each scheduling candidate group. Trust about Target SINR that satisfies , and predict this target SINR To maximize each scheduling candidate group Determine the optimal performance parameters and, accordingly, the optimal scheduling candidates. The overall scheduling should be optimized by selecting . In this application example, the target SINR is is obtained as , where the inverse cumulative distribution function (ICDF) is Is It is defined as follows. Hereinafter, with reference to FIGS. 7b to 7d, the process of performing the CS optimization process based on CDI in this application example will be described.

[0167] Referring to FIGS. 7b to 7d, in one embodiment, the base station (10) may include a base station-side CDI manager module (11) that manages CDI on the base station side, and the UE (20) may include a terminal-side CDI manager module (12) that manages CDI on the terminal side. The base station-side CDI manager module (11) may be included in the processor 920 of the base station (900) illustrated in FIG. 9, and the terminal-side CDI manager module (12) may be included in the processor 1020 of the UE (1000) illustrated in FIG. 10.

[0168] Referring to FIG. 7b, in S710b, the base station (10) sets the channel distribution type set C of cells belonging to the base station and the channel measurement method for each channel distribution type. , that is, CDI characteristic variables can be generated. The base station does not increase according to the number of UEs and the number of base station antennas, and can generate a configuration of channel measurement variables composed of small-sized parameters and a channel measurement method according to the configuration. Through such CDI settings, the UE and the base station can measure the channel distribution characteristics separately from each other. S710b of FIG. 7b can operate similarly to S310 of FIG. 3 and S410 of FIG. 4.

[0169] In S720b and S730b, the UE (20) can receive a cell search support signal from the base station (10) when attempting initial access to a cell, perform a cell synchronization process according to the cell search support signal, and receive cell system information from the base station (10) upon completion of cell synchronization. S720b and S730b of FIG. 7b can operate similarly to S420 and S430 of FIG. 4.

[0170] In S740b, the base station (10) provides a set of cell-specific channel distribution types C and channel measurement method information for each channel distribution type. , i.e., CDI feature variables { } can be provided to the CDI manager (12) of the UE (20). The CDI manager (11) of the base station (10) can provide the generated CDI feature variable { } can be internally obtained. S740b of Fig. 7b can operate similarly to S320 of Fig. 3 and S440 of Fig. 4.

[0171] Referring to FIG. 7c, in S710c, the UE (20) can collect downlink channel estimation data based on CSI-RS in the physical layer, and the UE-side CDI manager (12) can obtain the internally collected downlink channel estimation data. Thereafter, the UE-side CDI manager (12) receives the CDI feature variables, cell-specific channel distribution type set C, and channel measurement method information for each channel distribution type from the base station (10). And based on the acquired downlink channel estimation data, the terminal side channel distribution type Determine / classify and channel measurement method corresponding to the channel distribution type CDI parameters according to can be measured. S710c of Fig. 7c can operate similarly to S510 and S520 of Fig. 5.

[0172] In S720c, the UE-side CDI manager (12) is the terminal-side CDI can report / transmit to the base station (10). The base station side CDI manager (11) can be stored in the UE-side CDI storage unit (13). The UE-side CDI storage unit (13) can be included in the memory 930 of the base station (900) illustrated in FIG. 9. S720c of FIG. 7c can operate similarly to S550 and S560 of FIG. 5.

[0173] In S730c, the base station (10) can collect uplink channel estimation data based on SRS in the physical layer, and the base station-side CDI manager (11) can obtain the internally collected uplink channel estimation data. Thereafter, the base station-side CDI manager (11) can obtain the cell-specific channel distribution type set C, which is a previously generated CDI feature variable, and channel measurement method information for each channel distribution type. And based on the acquired uplink channel estimation data, the base station side channel distribution type and classify the channel measurement method corresponding to the channel distribution type. CDI parameters according to can be measured. The base station side CDI manager (11) is the base station side CDI can be stored in the memory 930 of the base station (900) illustrated in FIG. 9. S730c of FIG. 7c can operate similarly to S530, S540, and S560 of FIG. 5.

[0174] In S740c, the base station side CDI manager (11) collects from each of the UE (20) and the base station (10). and The CDI class C of the surrounding channel environment is determined from the base station (10). The SINR-DP estimation model is based on the CDI class C of the surrounding channel environment. can be generated. S740c of Fig. 7c can operate similarly to S610 of Fig. 6.

[0175] Referring to 7d, in S710d, the scheduler (14) of the base station (10) has a channel distribution type of the surrounding channel environment. , QoS parameters , UEI ,SINR-DP estimation model can be entered. Channel distribution type of surrounding channel environment is the terminal side CDI and base station side CDI Based on the terminal side CDI The measurement values ​​stored in the UE side CDI storage unit (13) can be used, and the base station side CDI The base station (10) can use measurements measured from SRSs received from sampled UEs (30) among all scheduling target UEs. The scheduler (14) of the base station (10) can perform scheduling through a CS optimization process by utilizing the optimal scheduling algorithm using the above input values.

[0176] In S720d, the scheduler (14) of the base station (10) schedules groups of scheduling candidates. can be generated. S720d of FIG. 7d can operate similarly to S630 of FIG. 6.

[0177] In S730d, the scheduler (14) of the base station (10) can predict performance for each scheduling candidate group. S730d of Fig. 7d can operate similarly to S640 of Fig. 6.

[0178] In S740d, the scheduler (14) of the base station (10) can perform scheduling optimization based on the performance prediction results. S740d of FIG. 7d can operate similarly to S650 of FIG. 6.

[0179] As a result, the scheduler (14) of the base station (10) performs optimal scheduling can be determined. SINR-DP estimation model of this application example The learning / generation and inference process is described in more detail in Figures 8a to 8d below.

[0180] FIGS. 8A to 8D are diagrams for explaining specific examples of training and inference of a CDI-based scheduling performance prediction model in a wireless communication system according to one embodiment of the present disclosure.

[0181] Figure 8a is the SINR-DP estimation model of this application example. An example of the learning process (800a) is illustrated. Referring to Fig. 8a, The learning / generation process can be comprised of two stages: a learning data set generation stage (810a) and a model learning stage (820a).

[0182] In the learning data set generation step (810a), the base station (10) generates a channel distribution type (CDI class) for the surrounding channel environment. As a candidate for CDIP and CSCP, The base station (10) can generate an input data set according to the input data set. , and causes By generating empirical data, SINR-DP data can be generated. According to the assumptions of this application example, Therefore, this distribution is SINR-DP can be mapped to a distribution determined according to the base station (10) and the base station (10) fits the empirical distribution. The output data can be obtained. As a result, the base station (10) You can obtain a learning data set like this.

[0183] In the model learning step (820a), the base station (10) according to the above learning data set Supervised learning can be performed on neural network models to satisfy .

[0184] For example, the base station (10) can use a neural network learning and regression method in an integrated manner, thereby achieving high accuracy of the model while reducing the amount of learning of the neural network. From the analysis, About A possible regression function from And, and According to The input that determines the SINR-DP source Define as. Is , and the SINR-DP estimation model The output that the neural network wants to learn becomes the CDIP preprocessing. The input parameters for the neural network are preprocessed to reduce the size of the neural network model and provide a normalized input form. , CSGP preprocessing According to the preprocessed CDIP and preprocessed CSGP . Therefore, the neural network is used. If you say so, Learning can be achieved through repeated forward and backward propagation. As a result, the SINR-DP estimation model is a regression function as in [Mathematical Formula 6] Wow neural network may include.

[0185] [Equation 6]

[0186]

[0187] Figure 8b shows the SINR-DP estimation model of this application example. An example of the inference process (800b) is illustrated. Referring to Fig. 8b, the SINR-DP estimation model generated through the process described in Fig. 8a , can be used for CS performance prediction in the base station (10) scheduler (14).

[0188] The base station (10) scheduler (14) selects several candidates from a set of scheduling groups. and performs CS optimization by setting the minimum value of common target performance for each generated scheduling candidate group. In the application example of the present disclosure, for each scheduling candidate group, Determine CS group parameters that guarantee the target performance and perform optimization for some CSGPs to maximize the target performance.

[0189] For example, depending on the channel distribution type , each group and go Given according to generation. Is The fixed CSGP and the corresponding space are indicated by denotes the decision CSGP and the corresponding space to perform optimization. Accordingly, the target SINR is as in [Mathematical Formula 7]. can be maximized.

[0190] [Equation 7]

[0191]

[0192] Here is a specific application example when the channel distribution type is a correlated Rayleigh channel.

[0193] For the correlated Rayleigh channel, and the entire correlation matrix By , the channel distribution type c=1 is determined. is assumed, where is the observable correlation on the UE side (base station antenna correlation), is the observable correlation (multi-user correlation) on the BS side. Here, is the Kronecker product. and As a correlation property, CDIP is the eigenvalue of the matrices and , and is defined as a non-decreasing index i. and Defined as, Small like is defined as follows. The CDIP for this channel scenario is a normalized space, as in [Equation 8].

[0194] [Equation 8]

[0195]

[0196] Here, If we leave it as , for large antenna correlation Rayleigh channel, and The gamma distribution can be approximated by the scaled ratio of two independent gamma distributions. Since follows the F-distribution, [Equation 9] can be derived.

[0197] [Equation 9]

[0198]

[0199] Here is the ICDF of the F-distribution. Therefore, SINR-DP determines the distribution.

[0200] In this channel scenario, the training data set for the SINP-DP estimation model is And, here, CDIP is And CSGP is Therefore, the generated data According to SINR-DP am.

[0201] With the above training data set, SINP-DP estimation model can be trained. Preprocessed CDIP (ratios of max-min eigenvalues, normalized ranks) and preprocessed CSGP (MU pilot interference) and and Using simple polynomial regression for can be expressed as in [Mathematical Formula 10].

[0202] [Equation 10]

[0203]

[0204] Here Accordingly, the target SINR performance and target power can be optimized to perform CS optimization.

[0205] For optimization of and , and energy constraints are defined as can be defined as . Also, the target SINR performance and target power can be expressed as in [Mathematical Formula 12].

[0206] [Equation 12]

[0207]

[0208] cast In order to maximize [Equation 13], [Equation 13] must be maximized.

[0209] [Equation 13]

[0210]

[0211] Here Based on the coefficient and can be obtained.

[0212] Referring to FIGS. 8c and 8d , when applying the CDI management and performance guarantee scheduling according to an embodiment of the present disclosure in a correlated Rayleigh channel environment, as in this application example, there is an advantage in that scheduling reliability suitable for the required QoS can be accurately guaranteed for multiple channel types that may use large-scale antennas and have various characteristics (outage prob. target vs. evaluated outage prob. performance graph shown in FIG. 8c ), and resource efficiency that optimizes the resources required to guarantee the required QoS can also be achieved at the same time (required bandwidth vs. target reliability performance graph shown in FIG. 8d ).

[0213] By following the CS performance prediction and maximization process as shown in these application examples, scheduling performance can be guaranteed and resource efficiency can be secured in communication systems with various channel characteristics based on CDI.

[0214] However, the examples described in FIGS. 7a to 7d and 8a to 8d are only one application example of the CDI-based scheduling optimization method according to one embodiment of the present disclosure, and the present disclosure is not limited thereto.

[0215] Figure 9 is a block diagram of a base station (900) according to one embodiment. Base station 900 may correspond to base station 10 of the present disclosure.

[0216] Referring to FIG. 9, the base station (900) may be configured with a transceiver (910), a processor (920), and a memory (930). Depending on the communication method of the base station (900) described above, the transceiver (910), the processor (920), and the memory (930) of the base station (900) may operate. However, the components of the base station (900) are not limited to the examples described above. For example, the base station (900) may include more or fewer components than the components described above. In one embodiment, the transceiver (910), the processor (920), and the memory (930) may be implemented in the form of a single chip. In addition, the processor (920) may include one or more processors.

[0217] The transceiver (910) is a general term for the receiver of the base station (900) and the transmitter of the base station (900), and can transmit and receive signals with a network entity including a UE (1000). The signals transmitted and received with the network entity including the UE (1000) may include control information and data. To this end, the transceiver (910) may be configured with an RF transmitter that up-converts and amplifies the frequency of a transmitted signal, and an RF receiver that low-noise amplifies and frequency-down-converts a received signal. However, this is only one embodiment of the transceiver (910), and the components of the transceiver (910) are not limited to the RF transmitter and the RF receiver.

[0218] Additionally, the transceiver (910) can perform functions for transmitting and receiving signals via a wireless channel. For example, the transceiver (910) can receive a signal via a wireless channel, output it to the processor (1320), and transmit the signal output from the processor (920) via the wireless channel.

[0219] The memory (930) can store programs and data required for the operation of the base station (900). In addition, the memory (930) can store control information or data included in a signal acquired from the base station (900). The memory (930) can be configured as a storage medium such as a ROM, a RAM, a hard disk, a CD-ROM, a DVD, or a combination of storage media. In addition, the memory (930) may not exist separately but may be configured as included in the processor (920). The memory (930) can be configured as a volatile memory, a nonvolatile memory, or a combination of volatile memory and nonvolatile memory. In addition, the memory (930) can provide stored data according to a request of the processor (920). The memory (930) can store a computer program, code, or instructions that can be executed by the processor (920). According to one embodiment, the computer program, code, or instructions executable by the processor (920) may be stored in one memory device or may be separately distributed and stored in two or more memory devices. The processor (920) may perform various functions according to embodiments of the present disclosure by executing the instructions stored in the memory (930). According to one embodiment of the present disclosure, the operation of the base station (900) may be caused to be performed based on at least one processor (or processing circuit) configured to individually or collectively or in any combination perform the features of the present disclosure based on the execution of the instructions (or computer program or code) stored in the memory (930), based on processing circuitry not configured to execute instructions, and / or based on components of a processing circuitry not configured to execute instructions.

[0220] The processor (920) may control a series of processes so that the base station (900) can operate according to the above-described embodiment of the present disclosure. For example, the processor (920) may receive control signals and data signals through the transceiver (910) and process the received control signals and data signals. The processor (920) may transmit the processed control signals and data signals through the transceiver (910). In addition, the processor (920) may write or read data to or from the memory (930). The processor (920) may perform functions of a protocol stack required by a communication standard. For this purpose, the processor (920) may include at least one processor or microprocessor. In one embodiment, a part of the transceiver (910) or the processor (920) may be referred to as a CP (communication processor).

[0221] The processor (920) may be composed of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a DSP (Digital Signal Processor), a graphics-only processor such as a GPU, a VPU (Vision Processing Unit), or an artificial intelligence-only processor such as an NPU. For example, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model. The processor (920) may include at least one processor (or processor circuitry), and at least one processor may perform the following operations individually, collectively, or in any combination. In a specific embodiment, at least a portion of the processor (920) may be included in one chip, and another portion of the processor (920) may be included in a separate chip. Alternatively, at least one processor may be included within another component, for example, a transceiver (910) or a memory (930). The processor (920) may perform, cause, or control operations of the base station to perform at least one or a combination of methods according to embodiments of the present disclosure. To this end, the processor (920) may control other components of the base station (900) to perform various operations by executing computer programs, codes, and instructions stored in the memory (930).

[0222] In one embodiment, the base station (900) can collect channel information of cells belonging to the base station and generate channel distribution information (CDI) feature variables by executing instructions stored in the memory (930) by the processor (920) alone or in combination. In one embodiment, the base station (900) can transmit system information including CDI feature variables to the UE (1000) by executing instructions stored in the memory (930) by the processor (1320) alone or in combination.

[0223] In one embodiment, the CDI feature variables may include variables relating to channel distribution types and variables relating to channel distribution parameter configurations for each channel distribution type.

[0224] In one embodiment, the processor (920) executes instructions stored in the memory (930) alone or in combination, so that the base station (900) can perform channel measurement based on the generated CDI characteristic variables. In one embodiment, the processor (920) executes instructions stored in the memory (930) alone or in combination, so that the base station (900) can store a base station-side CDI according to the channel measurement result. The base station-side CDI can include a base station-side channel distribution type and measurements of channel distribution parameters corresponding to the channel distribution type.

[0225] In one embodiment, the base station (900) can obtain a UE-side CDI from at least one UE by executing instructions stored in the memory (930) by the processor (920), alone or in combination. The processor (920) can store the obtained UE-side CDI. The UE-side CDI is measured at the UE according to CDI characteristic variables. The UE-side CDI can include measurements of a UE-side channel distribution type and channel distribution parameters corresponding to the channel distribution type.

[0226] In one embodiment, the base station (900) can generate a CDI-based scheduling performance prediction model based on the stored UE-side CDI and the stored base station-side CDI by executing instructions stored in the memory (930) by the processor (920), alone or in combination.

[0227] In one embodiment, the base station (900) can receive a scheduling request from the UE (1000) by the processor (920) executing instructions stored in the memory (930) alone or in combination. In one embodiment, the base station (900) can generate scheduling candidate groups by the processor (920) executing instructions stored in the memory (930) alone or in combination. In one embodiment, the base station (900) can perform scheduling optimization by predicting scheduling performance for each scheduling candidate group using a CDI-based scheduling performance prediction model by the processor (920) executing instructions stored in the memory (930) alone or in combination. In one embodiment, the base station (900) can transmit optimized scheduling information to the UE (1000) by the processor (920) executing instructions stored in the memory (930) alone or in combination.

[0228] In one embodiment, the base station (900) can predict a minimum value of common target performance for each scheduling candidate group by the processor (920) executing instructions stored in the memory (930), either alone or in combination. In one embodiment, the base station (900) can determine scheduling group parameters that guarantee a minimum value of the predicted common target performance for each scheduling candidate group by the processor (920) executing instructions stored in the memory (930), either alone or in combination. In one embodiment, the base station (900) can perform scheduling optimization based on the determination by the processor (920) executing instructions stored in the memory (930), either alone or in combination.

[0229] FIG. 10 is a block diagram of a UE (1000) according to one embodiment of the present disclosure. UE 1000 may correspond to UE 20 of the present disclosure.

[0230] Referring to FIG. 10, the UE (1000) may be configured with a transceiver (1010), a processor (1020), and a memory (1030). Depending on the communication method of the UE (1000) described above, the transceiver (1010), the processor (1020), and the memory (1030) of the UE (1000) may operate. However, the components of the UE (1000) are not limited to the examples described above. For example, the UE (1000) may include more or fewer components than the components described above. In one embodiment, the transceiver (1010), the processor (1020), and the memory (1030) may be implemented in the form of a single chip. In addition, the processor (1020) may include one or more processors.

[0231] The transceiver (1010) is a general term for the receiver of the UE (1000) and the transmitter of the UE (1000), and can transmit and receive signals with a network entity including the base station (900). The signals transmitted and received with the network entity including the base station (900) may include control information and data. To this end, the transceiver (1410) may be configured with an RF transmitter that up-converts and amplifies the frequency of a transmitted signal, and an RF receiver that low-noise amplifies and frequency-down-converts a received signal. However, this is only one embodiment of the transceiver (1010), and the components of the transceiver (1010) are not limited to the RF transmitter and the RF receiver.

[0232] Additionally, the transceiver (1010) can perform functions for transmitting and receiving signals via a wireless channel. For example, the transceiver (1010) can receive a signal via a wireless channel, output it to the processor (1020), and transmit the signal output from the processor (1020) via the wireless channel.

[0233] The memory (1030) can store programs and data required for the operation of the UE (1000). In addition, the memory (1030) can store control information or data included in a signal acquired from the UE (1000). The memory (1030) can be configured as a storage medium or a combination of storage media, such as a ROM, a RAM, a hard disk, a CD-ROM, and a DVD. In addition, the memory (1030) may not exist separately but may be configured as included in the processor (1020). The memory (1030) can be configured as a volatile memory, a nonvolatile memory, or a combination of volatile memory and nonvolatile memory. In addition, the memory (1030) can provide stored data according to a request of the processor (1020). The memory (1030) can store a computer program, code, or instructions that can be executed by the processor (1020). According to one embodiment, a computer program, code, or instruction executable by the processor (1020) may be stored in one memory device or may be separately distributed and stored in two or more memory devices. The processor (1020) may perform various functions according to embodiments of the present disclosure by executing instructions stored in the memory (1030). According to one embodiment of the present disclosure, the operation of the terminal (1000) may be caused to be performed based on at least one processor (or processing circuit) configured to individually or collectively or in any combination perform the features of the present disclosure based on the execution of instructions (or computer program or code) stored in the memory (1030), based on processing circuitry not configured to execute instructions, and / or based on components of a processing circuitry not configured to execute instructions.

[0234] The processor (1020) may control a series of processes so that the UE (1000) may operate according to the above-described embodiment of the present disclosure. For example, the processor (1020) may receive control signals and data signals through the transceiver (1010) and process the received control signals and data signals. The processor (1020) may transmit the processed control signals and data signals through the transceiver (1010). In addition, the processor (1020) may write or read data to or from the memory (1030). The processor (1020) may perform functions of a protocol stack required by a communication standard. For this purpose, the processor (1020) may include at least one processor or microprocessor. In one embodiment, a part of the transceiver (1010) or the processor (1020) may be referred to as a communication processor (CP).

[0235] The processor (1020) may be composed of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a DSP (Digital Signal Processor), a graphics-only processor such as a GPU, a VPU (Vision Processing Unit), or an artificial intelligence-only processor such as an NPU. For example, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model. The processor (1020) may include at least one processor (or processing circuitry), and at least one processor may perform the following operations individually, collectively, or in any combination. For example, the processor (1020) may include a CP (communication processor) that controls communication operations and an AP (application processor) that controls the execution of a higher layer (e.g., an application layer). In certain embodiments, at least a portion of the processor (1020) may be included in one chip, and another portion of the processor (1020) may be included in a separate chip. Alternatively, at least one processor may be included in another component, for example, a transceiver (1010) or a memory (1030). To this end, the processor (1020) may control other components of the terminal (1000) to perform various operations by executing computer programs, codes, or instructions stored in the memory (1030).

[0236] In one embodiment, the processor (1020) may execute instructions stored in the memory (1030) alone or in combination, so that the UE (1000) may receive system information including channel distribution information (CDI) characteristic variables from the base station (900). The CDI characteristic variables may be generated by the base station (900) by collecting channel information of cells belonging to the base station.

[0237] In one embodiment, the CDI feature variables may include variables relating to channel distribution types and variables relating to channel distribution parameter configurations for each channel distribution type. The CDI feature variables may further include information relating to a method for classifying channel distribution types and information relating to a method for measuring channels according to a channel measurement variable configuration corresponding to each classified channel distribution type.

[0238] In one embodiment, the processor (1020) executes instructions stored in the memory (1030) alone or in combination, so that the UE (1000) can perform channel measurement based on CDI characteristic variables received from the base station (900). In one embodiment, the processor (1020) executes instructions stored in the memory (1030) alone or in combination, so that the UE (1000) can transmit a UE-side CDI to the base station (900) based on the channel measurement result. The UE-side CDI can include measurements of a UE-side channel distribution type and channel distribution parameters corresponding to the channel distribution type.

[0239] In one embodiment, the UE (1000) can transmit a scheduling request to the base station (900) by the processor (1020) executing instructions stored in the memory (1030) alone or in combination. In one embodiment, the UE (1000) can receive optimized scheduling information from the base station (900) by the processor (1020) executing instructions stored in the memory (1030) alone or in combination. The optimized scheduling information can be based on optimized scheduling performance according to scheduling performance for each scheduling candidate group predicted by the base station (900) using a CDI-based scheduling performance prediction model.

[0240] In one embodiment, the CDI-based scheduling performance prediction model may be generated by the base station (900) based on the UE-side CDI stored in the base station and the base station-side CDI measured at the base station.

[0241] The specific examples used to explain embodiments according to the present disclosure are merely one combination of each criterion, method, detailed method, and operation, and through a combination of at least two or more of the various techniques described, the base station and the UE can guarantee scheduling performance and improve resource efficiency in a wireless communication system supporting various channel environments. In addition, at this time, the operation may be performed according to a method determined through one or a combination of at least two or more of the above-described techniques. For example, it may be possible to perform a portion of the operation of one embodiment in combination with a portion of the operation of another embodiment.

[0242] A device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, the term "non-transitory storage medium" simply means a tangible device that does not contain signals (e.g., electromagnetic waves). This term does not distinguish between cases where data is permanently stored in the storage medium and cases where data is temporarily stored. For example, a "non-transitory storage medium" may include a buffer in which data is temporarily stored.

[0243] According to one embodiment, the method according to various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., a downloadable app) may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0244] While the detailed description of this disclosure has described specific embodiments, it should be understood that various modifications are possible without departing from the scope of this disclosure. Therefore, the scope of this disclosure should not be limited to the described embodiments, but should be defined not only by the scope of the claims described below, but also by equivalents thereof.

Claims

1. In a method of a base station in a wireless communication system, A step of collecting channel information of cells belonging to the base station and generating a channel distribution information (CDI) feature variable; and A method comprising: transmitting system information including the CDI feature variables to a UE (user equipment).

2. In paragraph 1, The above CDI characteristic variables include a variable indicating a channel distribution type and a variable regarding setting channel measurement parameters for each channel distribution type.

3. In paragraph 2, A step of performing channel measurement based on the generated CDI feature variables; and Further comprising a step of storing the base station side CDI according to the channel measurement results, A method wherein the base station side CDI includes measurements of a base station side channel distribution type and channel distribution parameters corresponding to the channel distribution type.

4. In paragraph 3, A step of obtaining a UE-side CDI from at least one UE; and Further comprising a step of storing the UE-side CDI obtained above, A method wherein the UE-side CDI is measured at the UE according to the CDI characteristic variable, and the UE-side CDI includes measurements of a UE-side channel distribution type and channel distribution parameters corresponding to the channel distribution type.

5. In paragraph 4, A method further comprising the step of generating a CDI-based scheduling performance prediction model based on the stored UE-side CDI and the stored base station-side CDI.

6. In paragraph 5, A step of receiving a scheduling request from the UE; Step of generating scheduling candidate groups; A step of performing scheduling optimization by predicting scheduling performance for each scheduling candidate group using the above CDI-based scheduling performance prediction model; and A method further comprising: a step of transmitting scheduling information optimized for the UE; 7. In the 6th paragraph, the step of performing scheduling optimization using the CDI-based scheduling performance prediction model is as follows: A step of predicting the minimum value of common target performance for each of the above scheduling candidate groups; A step of determining scheduling group parameters that guarantee the minimum value of the predicted common target performance for each of the above scheduling candidate groups; and A method comprising the step of performing scheduling optimization based on the above decision.

8. In a method of UE (user equipment) in a wireless communication system, A step of receiving system information including a channel distribution information (CDI) feature variable from a base station; and A method wherein the above CDI feature variable is generated by collecting channel information of cells belonging to the base station by the base station.

9. In paragraph 8, A method wherein the above CDI characteristic variables include variables indicating channel distribution types and variables relating to channel distribution parameter settings for each channel distribution type.

10. In paragraph 9, A step of performing channel measurement based on the received CDI characteristic variables; and Further comprising a step of transmitting a UE-side CDI to the base station according to the channel measurement result, A method wherein the UE-side CDI includes measurements of a UE-side channel distribution type and channel distribution parameters corresponding to the channel distribution type, and the UE-side CDI is stored in the base station.

11. In paragraph 10, a step of transmitting a scheduling request to the base station; and further comprising a step of receiving optimized scheduling information from the base station; The above optimized scheduling information is a method based on optimized scheduling performance according to scheduling performance for each scheduling candidate group predicted by the base station using a CDI-based scheduling performance prediction model.

12. In paragraph 11, The above CDI-based scheduling performance prediction model is generated by the base station based on the UE-side CDI stored in the base station and the base station-side CDI measured at the base station.

13. In a base station in a wireless communication system, One or more transceivers; One or more processors communicatively coupled to said one or more transceivers; one or more memories communicatively coupled to said one or more processors; The one or more memories store instructions that the one or more processors can execute alone or in combination, the instructions configured to cause the base station to perform the following: Collecting channel information of cells belonging to the above base station and generating a CDI (channel distribution information) feature variable; and A base station that transmits system information including the CDI feature variables to a UE (user equipment).

14. In paragraph 13, The above CDI characteristic variables include a variable indicating a channel distribution type and a variable related to setting channel distribution parameters for each channel distribution type.

15. In the UE (user equipment) of a wireless communication system, One or more transceivers; One or more processors communicatively coupled to said one or more transceivers; one or more memories communicatively coupled to said one or more processors; The one or more memories store instructions that the one or more processors can execute alone or in combination, the instructions being configured to cause the UE to perform the following: Receive system information including a CDI (channel distribution information, CDI) feature variable from a base station, and The above CDI feature variable is generated by the UE by collecting channel information of cells belonging to the base station.

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