Method and apparatus for resource scheduling considering latency requirement in wireless communication system

Abstract

The present disclosure relates to a 5G or 6G communication system for supporting higher data transmission rates than a 4G communication system such as LTE. A method of a base station in a wireless communication system according to one embodiment of the present disclosure may include: identifying information regarding the total number of resource blocks and whether at least one UE to which resources are to be allocated is a latency-critical UE; performing a first resource block allocation for said at least one UE; and, if said at least one UE is a latency-critical UE, performing a second resource block allocation based on the latency requirements of said at least one UE.

Description

Technology Field

[0001] The present disclosure relates to a wireless communication system, and more specifically, to a resource scheduling method and apparatus that take into account delay requirements. Background Technology

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

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

[0004] To achieve such high data transmission speeds and ultra-low latency, 6G communication systems are being considered for implementation in the terahertz band (e.g., the 95 GHz to 3 terahertz (3 THz) band). In the terahertz band, due to more severe path loss and atmospheric absorption compared to the millimeter wave (mmWave) band introduced in 5G, the importance of technology capable of guaranteeing signal reach, or coverage, is expected to increase. As key technologies to ensure coverage, radio frequency (RF) devices, antennas, new waveforms that offer better coverage than orthogonal frequency division multiplexing (OFDM), beamforming, and multi-antenna transmission technologies such as massive multiple-input and multiple-output (massive MIMO), full-dimensional MIMO (FD-MIMO), array antennas, and large-scale antennas must be developed. In addition, new technologies such as metamaterial-based lenses and antennas, high-dimensional spatial multiplexing technology using orbital angular momentum (OAM), and reconfigurable intelligent surface (RIS) are being discussed to improve coverage of terahertz band signals.

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

[0006] Due to the research and development of such 6G communication systems, it is expected that a new dimension of hyper-connected experience will become possible through the hyper-connectivity of 6G communication systems, which encompasses not only connections between objects but also connections between people and objects. Specifically, it is projected that 6G communication systems will enable the provision of services such as truly immersive extended reality (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 6G communication systems with enhanced security and reliability, will be applied in various fields including industry, healthcare, automotive, and home appliances.

[0007] Meanwhile, emerging applications such as sensor data, virtual reality (VR), high-quality video streaming, and cloud gaming—as well as video and audio provided by Mobile Edge Computing (MEC) platforms—require low latency for service quality. Accordingly, active research is being conducted on methods for allocating network resources to satisfy the low-latency requirements of cellular networks. The problem to be solved

[0008] The present disclosure relates to a resource scheduling method and apparatus that considers delay requirements in a wireless communication system.

[0009] The present disclosure relates to a resource scheduling method and apparatus that considers fair resource allocation between a latency-critical UE and a latency-non-critical UE in a wireless communication system. means of solving the problem

[0010] A method of a base station in a wireless communication system according to one embodiment of the present disclosure comprises: identifying information regarding the total number of resource blocks and whether at least one UE to which resources are to be allocated is a latency-critical UE; performing a first resource block allocation for the at least one UE; and, if the at least one UE is a latency-critical UE, performing a second resource block allocation based on the latency requirements of the at least one UE.

[0011] According to one embodiment of the present disclosure, a base station in a wireless communication system comprises: a transceiver; and at least one processor; wherein the at least one processor identifies information regarding the total number of resource blocks and whether at least one UE to which resources are to be allocated is a latency-critical UE, performs a first resource block allocation for the at least one UE, and if the at least one UE is a latency-critical UE, performs a second resource block allocation based on the latency requirements of the at least one UE. Effects of the invention

[0012] The method and apparatus according to the embodiment of the present disclosure have the effect of improving user experience and communication performance by scheduling resources between terminals in consideration of delay requirements, thereby satisfying the demand for low delay for delay-critical terminals and efficiently allocating resources.

[0013] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below. Brief explanation of the drawing

[0014] Figure 1 is a diagram showing an example of resource allocation for terminals in a mobile edge computing (MEC) platform of a wireless communication system. Figure 2 is a diagram showing an example of resource allocation for terminals in a mobile edge computing (MEC) platform of a wireless communication system. Figure 3 is a diagram showing an example of resource allocation for terminals in a mobile edge computing (MEC) platform of a wireless communication system. FIG. 4 is a diagram illustrating an example of a hierarchical resource allocation scheduling operation and a data transmission operation for a latency-critical UE according to an embodiment of the present disclosure. FIG. 5 is a flowchart illustrating an example of hierarchical resource allocation scheduling and data transmission operations for a latency non-critical UE according to one embodiment of the present disclosure. FIG. 6 is a diagram illustrating an example of a hierarchical resource allocation scheduling operation and a data transmission operation for a latency-critical UE performing real-time video analysis according to an embodiment of the present disclosure. FIG. 7 is a diagram illustrating an example of a hierarchical resource allocation scheduling operation and a data transmission operation for a latency-critical UE performing robot control according to one embodiment of the present disclosure. FIG. 8 is a diagram showing the results of simulating the throughput and SLO (service level objective) satisfaction of a delay-critical terminal according to one embodiment of the present disclosure. FIG. 9 is a flowchart illustrating the operation of a base station according to one embodiment of the present disclosure. FIG. 10 is a structural diagram illustrating an example of the structure of a base station according to one embodiment of the present disclosure. Specific details for implementing the invention

[0015] Hereinafter, embodiments of the present invention will be described in detail together with the accompanying drawings.

[0016] In describing the embodiments, technical details that are well known in the technical field to which the present invention belongs and are not directly related to the present invention are omitted. This is intended to convey the essence of the present invention more clearly without obscuring it by omitting unnecessary explanations.

[0017] For the same reason, some components in the attached drawings have been emphasized, omitted, or depicted schematically. Additionally, the dimensions of each component do not fully reflect their actual dimensions. Identical or corresponding components in each drawing have been assigned the same reference numbers.

[0018] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but can be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components.

[0019] At this time, it will be understood that each block of the process flow diagrams and combinations of the flow diagrams can be executed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the computer also creates means for the instructions executed through the processor of other programmable data processing equipment to perform the functions described in the flow diagram block(s). Since these computer program instructions can also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement the function in a specific way, the instructions stored in such computer-available or computer-readable memory can also produce a manufactured item containing instruction means that perform the function described in the flow diagram block(s). Since computer program instructions can be loaded onto a computer or other programmable data processing equipment, instructions that perform a series of operation steps on the computer or other programmable data processing equipment to create a process executed by the computer can also provide steps for executing the functions described in the flowchart block(s).

[0020] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specified logical function(s). It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For instance, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may be executed in reverse order according to their corresponding functions.

[0021] In this embodiment, the term "part" refers to a software or hardware component, such as an FPGA or ASIC, and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or configured to operate one or more processors. Accordingly, as an example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." Furthermore, the components and "parts" may be implemented to operate one or more CPUs within a device or secure multimedia card.

[0022] In the present disclosure, each of the phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B or C,” “at least one of A, B and C,” and “at least one of A, B, or C” may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as “first,” “second,” or “first” or “second” may be used simply to distinguish a corresponding component from another corresponding component and do not limit the corresponding components in other aspects (e.g., importance or order).

[0023] Hereinafter, a base station is an entity that performs resource allocation for terminals and may be at least one of a gNode B (gNB), eNode B (eNB), Node B, BS (Base Station), wireless access unit, base station controller, or a node on a network. A terminal may include a UE (User Equipment), MS (Mobile Station), cellular phone, smartphone, computer, or a multimedia system capable of performing communication functions. In this disclosure, a downlink (DL) refers to a wireless transmission path of a signal transmitted by a base station to a terminal, and an uplink (UL) refers to a wireless transmission path of a signal transmitted by a terminal to a base station. Furthermore, while LTE, LTE-A, or 5G systems may be described as examples below, embodiments of this disclosure may also be applied to other communication systems having similar technical backgrounds or channel types. For example, 5th generation mobile communication technology (5G, new radio, NR) developed after LTE-A may be included therein, and the 5G below may be a concept that includes existing LTE, LTE-A, and other similar services. In addition, the present disclosure may be applied to other communication systems with some modifications made at the discretion of a person with skilled technical knowledge, without significantly departing from the scope of the present disclosure.

[0024] Terms used in the following description to identify connection nodes, terms referring to network entities, terms referring to messages, terms referring to interfaces between network entities, terms referring to various identification information, etc., are examples provided for the convenience of explanation. Accordingly, the present disclosure is not limited to the terms described below, and other terms referring to objects having equivalent technical meanings may be used.

[0026] Hereinafter, various embodiments are described in detail with reference to the attached drawings. It should be noted that identical components in the attached drawings are indicated by the same reference numerals whenever possible. Furthermore, it should be noted that the drawings of the present invention attached below are provided to aid in understanding the present invention, and that the present invention is not limited to the forms or arrangements exemplified in the drawings. Additionally, detailed descriptions of known functions and configurations that may obscure the essence of the present invention will be omitted. It should be noted that in the following description, only the parts necessary for understanding the operation according to various embodiments of the present invention will be explained, and descriptions of other parts will be omitted so as not to distract from the essence of the present invention.

[0027] Figure 1 is a diagram showing an example of resource allocation for terminals in a mobile edge computing (MEC) platform of a wireless communication system.

[0028] Referring to Fig. 1, in the current MEC platform, terminals using latency-critical applications and terminals using latency-non-critical applications share the same network resources, and various resource management technologies can be used to meet the diverse requirements of these applications.

[0029] First, as a fairness-based scheduling method, the Popular Fairness (PF) scheduler aims to distribute network resources fairly. However, since the PF scheduler does not operate according to application characteristics and simply aims for fair resource distribution to all UEs, it is difficult to satisfy the latency requirements of latency-critical applications.

[0030] Next, mobile communication networks (e.g., 5G networks) utilize network virtualization technology to configure multiple virtual network slices to allocate resources tailored to specific services or applications; this method is referred to as RAN slicing. However, RAN slicing has limitations in the flexible allocation of resources due to a fixed resource allocation method that struggles to respond to variability in network conditions. For instance, if a specific service or application experiences a surge in traffic, it is difficult to dynamically redistribute resources, which poses a risk of resource shortages or a degradation of QoS (Quality of Service).

[0031] Figure 2 is a diagram showing an example of resource allocation for terminals in a mobile edge computing (MEC) platform of a wireless communication system.

[0032] Figure 2 illustrates a flow control method of OutRAN, which is one of the resource management technologies for meeting the various requirements of applications on an MEC platform.

[0033] OutRAN is a scheduling framework designed to optimize flow completion times in wireless networks, aiming to balance latency-sensitive applications with resource usage. However, because OutRAN is optimized for specific use cases, it faces challenges in handling diverse data flows and applications. In particular, it is difficult to guarantee performance in applications requiring continuous data flow, such as video streaming, and guaranteeing latency in real-time applications may be impossible.

[0034] Figure 3 is a diagram showing an example of resource allocation for terminals in a mobile edge computing (MEC) platform of a wireless communication system.

[0035] Figure 3 illustrates a system designed to maximize the performance of latency-sensitive real-time video analysis applications by synchronizing the processing power of the 5G Radio Access Network (RAN) and MEC, which is one of the resource management technologies for meeting the diverse demands of applications on an MEC platform. However, the method illustrated in Figure 3 is inefficient in terms of network throughput and may degrade overall network performance, particularly due to a lack of consideration for latency-non-critical UEs that do not have clear latency deadlines. Additionally, there may be cases where excessive resources are allocated to specific UEs to meet hard latency deadlines, which poses a risk of reducing the overall network throughput.

[0036] The resource management technologies in MEC platforms described earlier lack flexibility in the allocation and management of network resources for latency-critical applications, or suffer from a lack of fairness in resource distribution. In particular, they fail to meet the demands of latency-critical applications and cannot adequately respond to network variability, which can lead to performance degradation in real-time applications and a failure to guarantee latency.

[0037] Accordingly, the present disclosure proposes an app-aware hierarchical resource block (RB) scheduling method and apparatus that ensures the fairness of resource allocation between latency-critical UEs and latency-non-critical UEs while guaranteeing end-to-end latency requirements for latency-critical applications. Through this, the performance of real-time applications can be optimized and network resource usage can be managed efficiently.

[0038] In one embodiment, the present disclosure proposes a resource management method that utilizes a two-stage scheduling technique to efficiently meet the requirements of latency-critical applications while maintaining fairness across the entire network.

[0039] The first stage, the outer scheduler, can distribute resources fairly among all UEs. For example, the outer scheduler can perform fair resource scheduling between a group of latency-critical UEs and a group of latency-noncritical UEs by using a proportional fairness (PF) scheduling algorithm for all UEs.

[0040] This allows for allocating more resources to latency-critical UEs while maintaining fairness at the overall network level.

[0041] The second stage, the inner scheduler, can coordinate granular resource block (RB) allocation within latency-critical UE groups by reflecting the latency requirements and channel status of each application.

[0042] In one embodiment, the operation of the inner scheduler can be performed based on a utility function.

[0043] The above utility function can be used to dynamically determine the priority of resource allocation by reflecting the application characteristics, latency requirements, channel status, etc., of each UE. For example, in the case of a real-time video analysis application, the Utility value is calculated by considering the video frame size, channel status, latency deadline, etc., thereby enabling optimal resource allocation.

[0044] In addition, the operation of the inner scheduler is Depending on the value setting, it can affect the resource allocation method between latency-critical UEs and latency-non-critical UEs.

[0045] In one embodiment, the above A higher value enables granular resource redistribution among latency-critical UEs, increasing the likelihood that a specific UE will receive additional RB allocation in the next slot while maintaining a high PF factor. However, this may reduce the resources allocated to latency-non-critical UEs.

[0046] The present disclosure proposes a resource management solution that meets the performance requirements of latency-critical applications while maintaining fairness of the entire network through the interaction of an outer scheduler and an inner scheduler and the utilization of a utility function.

[0047] FIG. 4 is a diagram illustrating an example of a hierarchical resource allocation scheduling operation and a data transmission operation for a latency-critical UE according to an embodiment of the present disclosure.

[0048] Referring to FIG. 4, app recognition signaling can be performed between the UE (400), base station (BS) (410), and server (420).

[0049] In one embodiment, the base station (410) can receive and store input regarding at least one of the total number of resource blocks (RB) capable of resource allocation scheduling and the spectral efficiency per UE.

[0050] The above-mentioned spectral efficiency per UE is a performance indicator representing whether frequency bandwidth can be used efficiently in a wireless communication system, and it refers to a numerical representation of how much data can be transmitted within a given bandwidth. An example of a mathematical formula representing the above-mentioned spectral efficiency per UE is as shown in Mathematical Formula 1 below.

[0051] [Mathematical Formula 1]

[0052]

[0053] Throughput (bps): Data transmission rate

[0054] Bandwidth (Hz): Used frequency bandwidth

[0055] In one embodiment, the base station (410) may receive and store input regarding UE group information, such as whether each UE is a latency-critical UE and a latency noncritical UE. In one embodiment, the base station (410) may receive and store input regarding transmission time interval (TTI) information.

[0056] In one embodiment, the server (420) can receive and store input for at least one of the application type and the deadline for the application per UE.

[0057] In step 430, the UE (400) may perform a preparation operation for the data to be transmitted. In one embodiment, the data preparation operation may include capturing and / or compressing the data to be transmitted.

[0058] In step 435, the UE (400) can transmit data-related information (e.g., data size and / or metadata) to the server (420).

[0059] In step 440, the server (420) can transmit information related to the data transmitted by the terminal and at least one application-specific parameter to the base station (410).

[0060] In one embodiment, the server (420) can transmit information related to data transmitted by the terminal and at least one application-specific parameter to the base station (410) through the RIC (RAN intelligent controller) API (application programming interface).

[0061] In one embodiment, the at least one application-specific parameter may include at least one piece of information among a UE-specific application type and a UE-specific application deadline.

[0062] In step 450, the base station (410) can perform an outer scheduler operation in the outer scheduler. In one embodiment, the base station (410) can distribute resources fairly for all UEs. For example, the outer scheduler of the base station (410) can perform fair resource scheduling between a group of latency-critical UEs and a group of latency-noncritical UEs using a proportional fairness (PF) scheduling algorithm for all UEs.

[0063] In one embodiment, the outer scheduler of the base station (410) has a PF coefficient p for each UE(u) for each RB(b). u,b Calculate and the highest PF coefficient p u,b A UE having can be selected. The PF coefficient p for each UE(u) with respect to the above RB(b) u,b An example of a mathematical formula for calculating is mathematical formula 2 below.

[0064] [Mathematical Formula 2]

[0065]

[0066] : Instantaneous data transfer rate that UE u can obtain at RB b

[0067] : Long-term average data transfer rate of UE u

[0068] In one embodiment, the above can be calculated using the spectral efficiency for each UE shown in the above mathematical formula 1. In one embodiment, the above

[0069] can be determined by the channel state of UE u in RB r.

[0070] In step 455, the base station (410) moves the selected highest PF coefficient p from the outer scheduler to the inner scheduler. u,b Information about a UE having can be transmitted.

[0071] In step 460, the base station (410) can perform an inner scheduler operation in the inner scheduler.

[0072] In one embodiment, if the UE (400) to be scheduled is a UE belonging to the latency non-critical UE group, the base station (410) may assign the corresponding RB.

[0073] In one embodiment, if the UE (400) to be scheduled is a UE belonging to the latency critical UE group, the base station (410) selects the highest PF coefficient p u,b A subgroup of UEs having a PF coefficient similar to that of a UE having can be identified. In one embodiment, the base station (410) can identify a subgroup of UEs having a similar PF coefficient within the 1-ε range.

[0074] In one embodiment, the base station (410) can calculate a latency utility value for at least one UE in the identified subgroup using a utility function corresponding to each application. In one embodiment, the base station (410) can assign an RB to the UE having the maximum latency utility value based on the calculated latency utility value.

[0075] In one embodiment, determining the UE subgroup As the value is adjusted, the likelihood of resource allocation in the outer scheduler being swapped in the inner scheduler may increase. For example, As the value increases, the range of the above UE subgroup widens, and the inner scheduler may allocate resources to a second UE having a lower PF coefficient rather than a first UE having the highest PF coefficient based on the above delay utility value. That is, As the value increases, the range of subgroups corresponding to latency-critical UEs widens, which may reduce the throughput of latency-non-critical UEs.

[0076] In step 465, the base station (410) can transmit the resource allocation result to the UE (400) according to the scheduling of the inner scheduler.

[0077] In step 470, the UE (400) can transmit data to the server (420) based on the result of the resource allocation.

[0078] In step 480, the base station (410) can perform the resource allocation operation for the UE allocated per RB time slot.

[0079] An example of the hierarchical resource block scheduling algorithm described above is shown in Table 1.

[0080] [Table 1]

[0081]

[0083] FIGS. 5 to 7 illustrate the operation of performing RB scheduling for each UE within an RB scheduler according to the application type of the terminal. FIG. 5 illustrates a latency non-critical UE using a file upload application, FIG. 6 illustrates a latency critical UE using a real-time video analysis application, and FIG. 7 illustrates the RB scheduling operation for a latency critical UE performing robot control.

[0084] FIG. 5 is a flowchart illustrating an example of hierarchical resource allocation scheduling and data transmission operations for a latency non-critical UE according to one embodiment of the present disclosure.

[0085] Referring to FIG. 5, app recognition signaling can be performed between the UE (500), the base station (BS) (510), and the server (520). Descriptions in FIG. 5 that overlap with FIG. 4 have been omitted, and the configuration of FIG. 5 can be referenced from the description in FIG. 4 for the corresponding configuration.

[0086] The UE (500) of Fig. 5 may correspond to a latency non-critical UE using a file upload application.

[0087] In one embodiment, the base station (510) can receive and store input regarding at least one of the total number of resource blocks (RB) capable of resource allocation scheduling and the spectral efficiency per UE.

[0088] The above-mentioned spectrum efficiency per UE is a performance indicator representing whether frequency bandwidth can be used efficiently in a wireless communication system, and can refer to a numerical representation of how much data can be transmitted within a given bandwidth.

[0089] In one embodiment, the base station (510) may receive and store input regarding UE group information, such as whether each UE is a latency-critical UE and a latency noncritical UE. In one embodiment, the base station (510) may receive and store input regarding transmission time interval (TTI) information.

[0090] In one embodiment, the server (520) can receive and store input regarding application type information for each UE.

[0091] In step 530, the UE (500) can perform data compression on the data to be transmitted.

[0092] In step 540, the base station (510) can perform an outer scheduler operation in the outer scheduler. In one embodiment, the base station (510) can distribute resources fairly for all UEs. For example, the outer scheduler of the base station (510) can perform fair resource scheduling between a latency-critical UE group and a latency noncritical UE group using a proportional fairness (PF) scheduling algorithm for all UEs.

[0093] In one embodiment, if the UE (500) to be scheduled is a UE belonging to the latency non-critical UE group, the base station (510) may assign the corresponding RB.

[0094] In one embodiment, if the UE (500) to be scheduled is a UE belonging to the latency non-critical UE group, the base station (510) may not perform an inner scheduler (550) operation.

[0095] In step 545, the base station (510) can transmit the resource allocation result to the UE (500) according to the scheduling of the inner scheduler.

[0096] In step 555, the UE (500) can transmit data to the server (520) based on the result of the resource allocation.

[0097] In step 560, the base station (510) can perform the resource allocation operation for the UE allocated per RB time slot.

[0098] FIG. 6 is a diagram illustrating an example of a hierarchical resource allocation scheduling operation and a data transmission operation for a latency-critical UE performing real-time video analysis according to an embodiment of the present disclosure.

[0099] Referring to FIG. 6, app recognition signaling can be performed between the UE (600), the base station (BS) (610), and the server (620). Descriptions in FIG. 6 that overlap with FIG. 4 have been omitted, and the configuration of FIG. 6 can be described by referring to the description of the corresponding configuration in FIG. 4.

[0100] The UE (600) of Fig. 6 may correspond to a latency-critical UE using a real-time video analysis application.

[0101] In one embodiment, the base station (610) can receive and store input regarding at least one of the total number of resource blocks (RB) capable of resource allocation scheduling and the spectral efficiency per UE.

[0102] In one embodiment, the base station (610) may receive and store input regarding UE group information, such as whether each UE is a latency-critical UE and a latency noncritical UE. In one embodiment, the base station (610) may receive and store input regarding transmission time interval (TTI) information.

[0103] In one embodiment, the server (620) can receive and store input for at least one of the application type and the deadline for the application per UE.

[0104] In step 630, the UE (600) can encode video frames for video analysis.

[0105] In step 635, the UE (600) can transmit information about the encoded video frame (e.g., encoded frame size information) to the server (620).

[0106] In step 640, the server (620) may transmit at least one of information about the encoded video frame transmitted by the terminal to the base station (610) (e.g., encoded frame size information), UE-specific application type information, UE-specific deadline information, and buffer information.

[0107] In one embodiment, the server (620) may transmit at least one of the information to the base station (610) through the RIC (RAN intelligent controller) API (application programming interface) in step 640.

[0108] In step 650, the base station (610) can perform an outer scheduler operation in the outer scheduler. In one embodiment, the base station (610) can distribute resources fairly for all UEs. For example, the outer scheduler of the base station (610) can perform fair resource scheduling between a group of latency-critical UEs and a group of latency-noncritical UEs using a proportional fairness (PF) scheduling algorithm for all UEs.

[0109] In one embodiment, the outer scheduler of the base station (610) has a PF coefficient p for each UE(u) for each RB(b). u,b Calculate and the highest PF coefficient p u,b A UE having can be selected. The PF coefficient p for each UE(u) with respect to the above RB(b) u,b An example of a mathematical formula for calculating is the above mathematical formula 2.

[0110] In step 655, the base station (610) moves the selected highest PF coefficient p from the outer scheduler to the inner scheduler. u,b Information about a UE having can be transmitted.

[0111] In step 660, the base station (610) can perform an inner scheduler operation in the inner scheduler.

[0112] In one embodiment, since the UE (600) to be scheduled is a UE belonging to the latency critical UE group, the base station (610) [uses] the selected highest PF coefficient p u,b A subgroup of UEs having a PF coefficient similar to that of a UE having can be identified. In one embodiment, the base station (610) can identify a subgroup of UEs having a similar PF coefficient within the 1-ε range.

[0113] In one embodiment, the base station (610) can calculate a latency utility value for at least one UE in the identified subgroup using a utility function corresponding to each application. In one embodiment, the base station (610) can assign an RB to the UE having the maximum latency utility value based on the calculated latency utility value.

[0114] In one embodiment, a real-time video analysis application has a utility function that considers spectral efficiency, frame size, remaining data size in the buffer, time remaining until delay deadline, etc. It can be used. Utility functions used in the above real-time video analysis application An example of expressing it as a mathematical formula is as shown in Mathematical Formula 3 below.

[0115] [Mathematical Formula 3]

[0116]

[0117] : Spectral efficiency of the UE (varies depending on channel conditions)

[0118] : Frame size (total size of data to be transmitted)

[0119] : Size of remaining data in the buffer (amount of data to be transmitted)

[0120] : Time remaining until the delayed deadline (real-time requirements)

[0121] : Constant to prevent negative values ​​(prevents issues when the delayed deadline is exceeded)

[0122] This optimizes video streaming quality and meets latency requirements. During this process, optimal resource allocation is possible by analyzing the channel and buffer status of each UE in real time.

[0123] In step 665, the base station (610) can transmit the resource allocation result to the UE (600) according to the scheduling of the inner scheduler.

[0124] In step 670, the UE (600) can transmit video frames to the server (620) based on the result of the resource allocation.

[0125] In step 680, the base station (610) can perform the resource allocation operation for the UE allocated per RB time slot.

[0126] FIG. 7 is a diagram illustrating an example of a hierarchical resource allocation scheduling operation and a data transmission operation for a latency-critical UE performing robot control according to one embodiment of the present disclosure.

[0127] Referring to FIG. 7, app recognition signaling can be performed between the UE (700), the base station (BS) (710), and the server (720). Descriptions in FIG. 7 that overlap with FIG. 4 have been omitted, and the configuration of FIG. 7 can be described by referring to the description of the corresponding configuration in FIG. 4.

[0128] The UE (700) in Fig. 7 may correspond to a latency-critical UE that performs robot control.

[0129] In one embodiment, the base station (710) can receive and store at least one piece of information regarding the total number of resource blocks (RB) capable of resource allocation scheduling and the spectral efficiency per UE.

[0130] In one embodiment, the base station (710) may receive and store input regarding UE group information, such as whether each UE is a latency-critical UE and a latency noncritical UE. In one embodiment, the base station (710) may receive and store input regarding transmission time interval (TTI) information.

[0131] In one embodiment, the server (720) can receive and store input for at least one of the application type and the deadline for the application per UE.

[0132] In step 730, the UE (700) can capture sensor data for robot control.

[0133] In step 735, the UE (700) can transmit information about the sensor data (e.g., sensor status and / or action time) to the server (720).

[0134] In step 740, the server (720) may transmit at least one of information about the sensor data transmitted by the terminal to the base station (710) (e.g., sensor status and / or action time), application type information per UE, deadline information per UE, and distance information.

[0135] In one embodiment, the server (720) may transmit at least one of the information to the base station (710) through the RIC (RAN intelligent controller) API (application programming interface) in step 740.

[0136] In step 750, the base station (710) can perform an outer scheduler operation in the outer scheduler. In one embodiment, the base station (710) can distribute resources fairly for all UEs. For example, the outer scheduler of the base station (710) can perform fair resource scheduling between a group of latency-critical UEs and a group of latency-noncritical UEs using a proportional fairness (PF) scheduling algorithm for all UEs.

[0137] In one embodiment, the outer scheduler of the base station (710) has a PF coefficient p for each UE(u) for each RB(b). u,b Calculate and the highest PF coefficient p u,b A UE having can be selected. The PF coefficient p for each UE(u) with respect to the above RB(b) u,b An example of a mathematical formula for calculating is the above mathematical formula 2.

[0138] In step 755, the base station (710) moves the selected highest PF coefficient p from the outer scheduler to the inner scheduler. u,b Information about a UE having can be transmitted.

[0139] In step 760, the base station (710) can perform an inner scheduler operation in the inner scheduler.

[0140] In one embodiment, since the UE (700) to be scheduled is a UE belonging to the latency critical UE group, the base station (710) [uses] the selected highest PF coefficient p u,b A subgroup of UEs having a PF coefficient similar to that of a UE having can be identified. In one embodiment, the base station (710) can identify a subgroup of UEs having a similar PF coefficient within the 1-ε range.

[0141] In one embodiment, the base station (710) can calculate a latency utility value for at least one UE in the identified subgroup using a utility function corresponding to each application. In one embodiment, the base station (710) can assign an RB to the UE having the maximum latency utility value based on the calculated latency utility value.

[0142] In one embodiment, a robot control application provides a utility function based on the robot's sensor status, current action time, remaining distance to the goal, etc. It can be used. Utility functions used in the above robot control application An example of expressing it as a mathematical formula is as shown in Mathematical Formula 4 below.

[0143] [Mathematical Formula 4]

[0144]

[0145] : Robot sensor status (sensor reliability and accuracy)

[0146] : Robot's current action time (e.g., move, rotate)

[0147] : Distance remaining to the goal (including distance to obstacles)

[0148] : Task deadline (time to respond in real time)

[0149] : Constant to prevent negative values

[0150] Through this, the robot's real-time behavior can be optimized, and tasks such as obstacle avoidance can be performed in real time. In this process, resources can be allocated by calculating the robot's action time and the remaining time until reaching the goal in real time.

[0151] In step 765, the base station (710) can transmit the resource allocation result to the UE (700) according to the scheduling of the inner scheduler.

[0152] In step 770, the UE (700) can transmit sensor values ​​to the server (720) based on the result of the resource allocation.

[0153] In step 780, the base station (710) can perform the resource allocation operation for the UE allocated per RB time slot.

[0154] FIG. 8 is a diagram showing the results of simulating the throughput of a delay-insensitive terminal and the service level objective (SLO) satisfaction of a delay-sensitive terminal according to one embodiment of the present disclosure.

[0155] Figure 8 illustrates the SLO satisfaction of a latency-critical terminal (VA UE) performing video analytics (VA) and the throughput of a terminal that does not perform video analytics (non-VA UE) (non-VA UE total tput).

[0156] Referring to FIG. 8, it can be seen that when the hierarchical RB scheduling method of the present disclosure is performed (Proposed) compared to when only PF scheduling is performed, the SLO satisfaction (VA UE SOL satisfaction) of the delay-critical terminal (VA UE) performing video analytics (VA) increases.

[0157] Accordingly, the method proposed in this disclosure enables the efficient utilization and fair allocation of network resources while satisfying the End-to-End Latency requirements of latency-critical applications. This ensures optimal performance even in latency-sensitive applications such as video streaming, robot control, and real-time speech translation, and significantly improves the quality of real-time services (PF vs Proposed(ε=0.2)).

[0158] Furthermore, by maintaining fairness between latency-critical and latency-non-critical UEs, network performance degradation that may result from excessive resource allocation to specific applications can be prevented. In addition, by providing resource management techniques that can flexibly respond to network state variability, efficient and stable resource allocation can be realized across diverse application and service environments (Proposed(ε=0.2,0.4,0.6,0.8)). This enables simultaneous improvement of overall MEC platform performance and user experience.

[0159] FIG. 9 is a flowchart illustrating the operation of a base station according to one embodiment of the present disclosure.

[0160] In step 900, the base station can identify information regarding the total number of resource blocks and whether at least one UE to which the resource is to be allocated is a latency-critical UE.

[0161] In step 910, the base station may perform a first resource block allocation for at least one UE.

[0162] In step 920, if the base station is a latency-critical UE, the base station may perform a second resource block allocation based on the latency requirements of the at least one UE.

[0163] In one embodiment, the delay requirement of the at least one UE may be associated with application-related information for the at least one UE.

[0164] In one embodiment, the base station may receive application-related information for the at least one UE from the server.

[0165] In one embodiment, the application-related information for the at least one UE may include at least one of the application type information of the at least one UE and the application-related deadline information used by the at least one UE.

[0166] In one embodiment, when performing the first resource block allocation, the base station may use a proportional fairness (PF) scheduling algorithm to perform resource block allocation for at least one UE.

[0167] In one embodiment, when performing the second resource block allocation, the base station may determine a UE subgroup including the at least one UE to perform the second resource block allocation when the at least one UE is a latency-critical UE, calculate a latency requirement for the at least one UE included in the UE subgroup, and perform the second resource block allocation for the at least one UE included in the UE subgroup based on the calculated latency requirement.

[0168] In one embodiment, when the base station calculates the delay requirement for at least one UE included in the UE subgroup, If the application used by at least one UE included in the above UE subgroup is real-time video analysis, the delay requirement can be calculated based on at least one of the UE's spectral efficiency, frame size, remaining data size of the buffer, and time remaining until delay deadline.

[0169] In one embodiment, when a base station calculates a delay requirement for at least one UE included in the UE subgroup, if the application used by at least one UE included in the UE subgroup is a robot control application, the delay requirement can be calculated based on at least one of the robot's sensor status, the robot's current action time, the remaining distance to the target, and the task completion time.

[0170] FIG. 10 is a structural diagram illustrating an example of the structure of a base station according to one embodiment of the present disclosure.

[0171] Referring to FIG. 10, a base station may include a transceiver (1002) referring to a base station receiver and a base station transmitter, a memory (1003), and a processor (1001, or a base station control unit or processing unit). According to the communication method of the base station described above, the transceiver (1002), memory (1003), and base station processor (1001) of the base station may operate. However, the components of the base station are not limited to the examples described above. For example, the base station may include more components or fewer components than the components described above. In addition, the transceiver, memory, and processor may be implemented in the form of a single chip.

[0172] The transceiver (1002) can transmit and receive signals with a terminal. Here, the signal may include control information and data. To this end, the transceiver may be composed of an RF transmitter that up-converts and amplifies the frequency of a transmitted signal, and an RF receiver that low-noise amplifies a received signal and down-converts the frequency. However, this is merely one embodiment of the transceiver, and the components of the transceiver are not limited to an RF transmitter and an RF receiver.

[0173] Additionally, the transceiver (1002) can receive a signal through a wireless channel and output it to a processor, and transmit the signal output from the processor through a wireless channel.

[0174] The memory (1003) can store programs and data necessary for the operation of the base station. Additionally, the memory (1003) can store control information or data included in signals transmitted and received by the base station. The memory may be composed of a storage medium or a combination of storage media such as ROM, RAM, hard disk, CD-ROM, and DVD. Additionally, there may be multiple memories.

[0175] The processor (1001) can control a series of processes to enable the base station to operate according to at least one of the embodiments described above. The processor (1001) may include at least one processor.

[0176] Methods according to the embodiments described in the claims or specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software. When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute the methods according to the embodiments described in the claims or specification of the present disclosure.

[0177] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, ROM (Read Only Memory), Electrically Erasable Programmable Read Only Memory (EEPROM), magnetic disc storage devices, Compact Disc-ROM (CD-ROM), Digital Versatile Discs (DVDs), or other forms of optical storage devices, magnetic cassettes. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0178] Additionally, the above program may be stored on an attachable storage device that can be accessed via a communication network such as the Internet, Intranet, Local Area Network (LAN), Wide LAN (WLAN), or Storage Area Network (SAN), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present disclosure through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present disclosure.

[0179] In the specific embodiments of the present disclosure described above, the components included in the invention are expressed in a singular or plural form according to the specific embodiments presented. However, the singular or plural expression is selected to suit the situation presented for convenience of explanation, and the present disclosure is not limited to singular or plural components; even if a component is expressed in the plural form, it may be composed of a singular form, or even if a component is expressed in the singular form, it may be composed of a plural form.

[0180] Meanwhile, although specific embodiments have been described in the detailed description of the present disclosure, it is understood that various modifications are possible within the scope of the present disclosure. Therefore, the scope of the present disclosure should not be limited to the described embodiments, but should be defined by the claims set forth below as well as equivalents thereof.

Claims

Claim 1 A method of a base station in a wireless communication system, comprising: identifying information regarding the total number of resource blocks and whether at least one UE (user equipment) to which resources are to be allocated is a latency-critical UE; performing a first resource block allocation for said at least one UE; and, if said at least one UE is a latency-critical UE, performing a second resource block allocation based on the latency requirements of said at least one UE. Claim 2 A method according to claim 1, characterized in that the delay requirement of the at least one UE is associated with application-related information for the at least one UE. Claim 3 A method characterized by further including, in paragraph 2, the step of receiving application-related information for at least one UE from a server. Claim 4 A method according to paragraph 3, wherein the application-related information for the at least one UE comprises at least one of the application type information of the at least one UE and the application-related deadline information used by the at least one UE. Claim 5 A method according to claim 1, wherein the step of performing the first resource block allocation; comprises the step of performing resource block allocation for at least one UE using a proportional fairness (PF) scheduling algorithm. Claim 6 A method according to claim 1, wherein the step of performing the second resource block allocation comprises: determining a UE subgroup including the at least one UE to perform the second resource block allocation when the at least one UE is a latency-critical UE; calculating a latency requirement for the at least one UE included in the UE subgroup; and performing the second resource block allocation for the at least one UE included in the UE subgroup based on the calculated latency requirement. Claim 7 A method according to claim 6, wherein the step of calculating a delay requirement for at least one UE included in the above-mentioned UE subgroup comprises, when the application used by at least one UE included in the above-mentioned UE subgroup is real-time video analysis, the step of calculating the delay requirement based on at least one of the UE's spectral efficiency, frame size, remaining data size of the buffer, and time remaining until delay deadline. Claim 8 A method according to claim 6, wherein the step of calculating a delay requirement for at least one UE included in the above-mentioned UE subgroup; wherein, when the application used by at least one UE included in the above-mentioned UE subgroup is a robot control application, the delay requirement is calculated based on at least one of the robot's sensor status, the robot's current action time, the remaining distance to the goal, and the task completion time. Claim 9 A base station in a wireless communication system comprises: a transceiver; and at least one processor; wherein the at least one processor is configured to identify information regarding the total number of resource blocks and whether at least one UE to which resources are to be allocated is a latency-critical UE, perform a first resource block allocation for the at least one UE, and, if the at least one UE is a latency-critical UE, perform a second resource block allocation based on the latency requirements of the at least one UE. Claim 10 A base station according to claim 9, characterized in that the delay requirement of the at least one UE is associated with application-related information for the at least one UE. Claim 11 A base station characterized by further including, in claim 10, the step of receiving application-related information for at least one UE from a server. Claim 12 A base station according to claim 11, wherein the application-related information for the at least one UE comprises at least one of the application type information of the at least one UE and the application-related deadline information used by the at least one UE. Claim 13 A base station according to claim 9, wherein the at least one processor is configured to perform resource block allocation for the at least one UE using a proportional fairness (PF) scheduling algorithm. Claim 14 A base station according to claim 9, wherein the at least one processor is configured to determine a UE subgroup including the at least one UE to perform the second resource block allocation when the at least one UE is a latency-critical UE, calculate a latency requirement for the at least one UE included in the UE subgroup, and perform the second resource block allocation for the at least one UE included in the UE subgroup based on the calculated latency requirement. Claim 15 A base station according to claim 14, wherein the at least one processor is configured to calculate the delay requirement based on at least one of the UE's spectrum efficiency, frame size, remaining data size of the buffer, and time remaining until delay deadline, when the application used by at least one UE included in the UE subgroup is real-time video analysis. Claim 16 A base station according to claim 14, wherein the at least one processor is configured to calculate the delay requirement based on at least one of the robot's sensor status, the robot's current action time, the remaining distance to the target, and the task completion time, when the application used by at least one UE included in the UE subgroup is a robot control application.

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