Apparatus and method for beam selection using artificial intelligence / machine learning-based beam management and positioning in wireless communication system

Abstract

The present disclosure generally relates to a wireless communication system, and more specifically to an apparatus and method for beam selection considering AI / ML-based beam management and positioning in a wireless communication system. A method of operation of user equipment (UE) in a wireless communication system includes a process of predicting the current location and future location of the UE, and a process of transmitting the predicted current location and future location to a base station using UE assistance information (UAI), wherein the UAI may be transmitted periodically when the UE is in a connected mode.

Description

Technology Field

[0001] The present disclosure generally relates to wireless communication systems, and more specifically to an apparatus and method for beam selection considering AI / ML-based beam management and positioning in wireless communication systems. Background Technology

[0002] Regarding the application of AI / ML to radio access network (RAN) systems, conventional technology has primarily been applied for (1) automation based on statistical data from various network domains including base stations in operation administration maintenance (OAM) servers (RAN Automation) and (2) operation optimization based on data collected from base station equipment (centralized unit / distributed unit, CU / DU) for specific purposes (RAN Operation). However, recently, the “Study on Artificial Intelligence (AI) / Machine Learning (ML) for NR air interface” was established as a RAN1 SI (Study Item) in 3GPP Rel-18 and is currently underway.

[0003] Some manufacturers are conducting research to replace specific processing blocks within the Air Interface with AI / ML technology, and research is underway to apply AI / ML technology to the Air Interface between base stations and terminals, as AI is emerging as a key topic in future 6G. The problem to be solved

[0004] Based on the discussion above, the present disclosure provides an apparatus and method for beam selection considering AI / ML (artificial intelligent / machine learning)-based beam management and positioning.

[0005] In addition, the present disclosure provides an apparatus and method for low-complexity beam selection that reflects the mobility of a terminal in a wireless communication system.

[0006] In addition, the present disclosure provides an apparatus and method for providing location information of a terminal to consider the mobility of the terminal using UAI (UE assistance information) in a wireless communication system.

[0007] In addition, the present disclosure provides an apparatus and method for adaptively selecting a beam pair set based on the current location and future location of a terminal in a wireless communication system.

[0008] In addition, the present disclosure provides an apparatus and method for performing partial measurements based on the current location and future location of a terminal in a wireless communication system and adaptively selecting a set of beam pairs. means of solving the problem

[0009] According to various embodiments of the present disclosure, a method of operation of user equipment (UE) in a wireless communication system includes a process of predicting the current location and future location of the UE, and a process of transmitting the predicted current location and future location to a base station using UE assistance information (UAI), wherein the UAI may be transmitted periodically when the UE is in a connected mode.

[0010] According to various embodiments of the present disclosure, a method of operation of a base station (BS) in a wireless communication system may include receiving a predicted current location and future location from user equipment (UE) using UE assistance information (UAI), predicting a beam pair set based on the current location and future location, selecting a beam having the largest received signal received power (RSRP) among the beam pair set, and transmitting information regarding the selected beam to the UE.

[0011] According to various embodiments of the present disclosure, in a wireless communication system, user equipment (UE) includes a transceiver and a control unit operably connected to the transceiver, and the control unit predicts the current location and future location of the UE, transmits the predicted current location and future location to a base station using UE assistance information (UAI), and the UAI may be transmitted periodically when the UE is in a connected mode.

[0012] According to various embodiments of the present disclosure, a wireless communication system comprises a transceiver and a control unit operably connected to the transceiver. The control unit receives a predicted current location and a future location from user equipment (UE) using UE assistance information (UAI), predicts a beam pair set based on the current location and the future location, selects a beam having the largest received signal received power (RSRP) among the beam pair set, and transmits information regarding the selected beam to the UE. Effects of the invention

[0013] The apparatus and method according to various embodiments of the present disclosure enable adaptive selection of a beam pair set by using positioning information of a terminal for selecting a beam pair set.

[0014] 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

[0015] FIG. 1 illustrates a beam selection method based on full search according to various embodiments of the present disclosure. FIG. 2 illustrates an example of performing beam prediction using partial measurements according to various embodiments of the present disclosure. FIG. 3 illustrates a signal flow diagram between a base station and a terminal for AI / ML-based beam selection according to one embodiment of the present disclosure. FIG. 4 illustrates an example of an AI / ML model operation on the terminal side according to an embodiment of the present disclosure. FIG. 5 illustrates an example of base station-side AI / ML model operation according to an embodiment of the present disclosure. FIG. 6 illustrates an example of an operation for selecting an AI / ML-based beam pair set of a base station according to an embodiment of the present disclosure. FIG. 7 illustrates an example of beam selection based on a partial measurement-based beam prediction method according to an embodiment of the present disclosure. FIG. 8 illustrates a configuration diagram of a base station in a wireless communication system according to various embodiments of the present disclosure. FIG. 9 illustrates a configuration diagram of a terminal in a wireless communication system according to various embodiments of the present disclosure. Specific details for implementing the invention

[0016] The terms used in this disclosure are used merely to describe specific embodiments and are not intended to limit the scope of other embodiments. A singular expression may include a plural expression unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as generally understood by those skilled in the art described in this disclosure. Terms used in this disclosure that are defined in a general dictionary may be interpreted as having the same or similar meaning as they have in the context of the relevant technology, and are not to be interpreted in an ideal or overly formal sense unless explicitly defined in this disclosure. In some cases, even terms defined in this disclosure are not to be interpreted to exclude the embodiments of this disclosure.

[0017] In the various embodiments of the present disclosure described below, a hardware-based approach is described as an example. However, since the various embodiments of the present disclosure include techniques using both hardware and software, the various embodiments of the present disclosure do not exclude a software-based approach.

[0018] Additionally, in the detailed description and claims of the present disclosure, “at least one of A, B, and C” may mean “only A,” “only B,” “only C,” or “any combination of A, B, and C.” Additionally, “at least one of A, B, or C” or “at least one of A, B, and / or C” may mean “at least one of A, B, and C.”

[0019] The present disclosure generally relates to wireless communication systems, and more specifically, to an apparatus and method for beam selection considering AI / ML-based beam management and positioning in a wireless communication system. Specifically, the present disclosure describes a technique for low-complexity beam selection that reflects the mobility of a terminal by considering beam management and positioning together in a wireless communication system.

[0020] Terms referring to signals, channels, control information, network entities, and device components used in the following description are examples provided for the convenience of explanation. Accordingly, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may be used.

[0021] Additionally, the present disclosure describes various embodiments using terms used in some communication standards (e.g., 3GPP (3rd Generation Partnership Project)), but this is merely illustrative. Various embodiments of the present disclosure can be easily modified and applied to other communication systems.

[0022] [3GPP Rel-18 RAN1 AI / ML SI Status]

[0023] As part of the AI / ML-related SI being discussed in 3GPP Rel-18 RAN1, the definition of terms as an AI / ML framework, the definition of types and collaboration levels of AI / ML models between base stations and terminals, life cycle management, and evaluation methodologies are being discussed, and the types of AI / ML models between base stations and terminals are classified as shown in Table 1 according to the subject of inference.

[0024] category definition One-sided Model AI / ML models where inference is performed exclusively on either the terminal or the base station side (e.g., UE-side or NW-side models) Two-sided Model AI / ML models where inference is performed simultaneously on both the terminal and the base station

[0025] In addition, to support AI / ML between base stations and terminals, the level of collaboration was discussed from a signaling perspective, and three levels were defined as shown in Table 2.

[0026] level of collaboration definition Level x No collaboration between base station and terminal, pure implementation-based AI / ML algorithms (Proprietary AI) Level y Collaboration between base station and terminal based on signaling defined within 3GPP No Model Transfer between base station and terminal Level z Collaboration between base station and terminal based on signaling defined within 3GPP; existence of base station-terminal Model Transfer.

[0027] Rel-18 defined three use cases regarding the application of AI / ML technology to air interfaces as shown in Table 3, and for each use case, there are sub-use cases depending on the specific application purpose / method.

[0028] Use Case Sub-use Case definition CSI Feedback CSI Compression CSI compression in the spatial-frequency domain using a two-sided model CSI Prediction CSI prediction in the time domain using cross-sectional models Beam Management BM-case1 Spatial Domain DL Beam Set Prediction Using Cross-Sectional Models BM-case2 Time-domain DL Beam Set prediction using cross-sectional models Positioning Accuracy Direct AI / ML-based direct positioning using cross-sectional models AI / ML Assisted AI / ML-based auxiliary positioning utilizing cross-sectional models

[0029] First, beam management use cases are basically considered for cross-sectional models and DL (downlink) cases, and ① there is BM-case 1, which predicts beam set A based on measurement results for a specific beam set B in the spatial domain, and considers cases where (1) beam set B is completely different from set A or (2) set B is a subset of set A. Additionally, ② there is BM-case 2, which predicts beam set A based on previous measurement results of beam set B in the time domain, and considers cases where (1) beam set B is completely different from set A, (2) set B is a subset of set A, or (3) set B and A are identical. L1-RSRP (Layer-1 reference signal received power), CIR (channel impulse response), and other assistance information for set B are discussed as inputs to the AI / ML model, and predicted beam identifier, L1-RSRP, and beam angle are discussed as outputs.

[0030] Next, the positioning accuracy use case is divided into two sub-use cases: ① when the output of the AI / ML model is the direct location of the terminal, and ② when it is assistance information of the existing non-AI-based positioning method. Five detailed cases were defined according to the positioning method, the type of AI / ML model, and the method of utilizing AI / ML.

[0031] [Positioning Technology]

[0032] Positioning refers to determining the location of an object using various information, such as wireless information, and the factors determining location accuracy are largely positioning input and technology. Based on our company's standards, a representative deep learning (DL)-based positioning technique utilizing Call-Log data is employed, which uses customer connection base station information (serving & neighbor) and respective signal strength information from service-related logs collected from base stations. Additionally, 3GPP Rel-16 discussed positioning techniques based on the round trip time (RTT), angle of arrival, angle of departure, and time difference of arrival of a signal by defining and utilizing a positioning reference signal (PRS) as a separate reference signal in multi- / single-cell environments.

[0033] [Beam Management Technology]

[0034] In general, high-frequency bands offer advantages in terms of transmission capacity and speed due to their wide available bandwidth; therefore, research on the utilization of high-frequency bands has continued as generations of communication systems evolve. However, while high-frequency bands improve the directivity of radio waves, they also have a critical disadvantage: reduced transmission coverage due to poor propagation characteristics caused by path and transmission losses, as well as weakened diffraction. In 5G, digital beamforming technology based on large-scale array antennas has been actively developed, primarily in the 3.5GHz band, as a core technology to compensate for this.

[0035] However, when utilizing high-frequency bands in the 28GHz band and future 6G, analog beamforming is used, and beam management technology is important because the width of the beam used for communication becomes narrower and the number of beams increases. One of the important aspects of beam management is the process of selecting the optimal beam pair among multiple beam pairs between the transmitter and receiver.

[0036] FIG. 1 illustrates a beam selection method based on full search according to various embodiments of the present disclosure.

[0037] Referring to Figure 1, it is assumed that the base station (BS) and user equipment (UE) each have N and M beam sets, respectively. Therefore, the search space for the entire beam pair set will be N x M, and generally, the beam pair with the highest RSRP value among them will be finally selected. This exhaustive search-based selection method is fundamentally vulnerable to environments such as terminal mobility, where high complexity and relatively frequent updates are required. Furthermore, these limitations can become more severe as the frequency band increases, as beams generally become more subdivided and numerous.

[0038] To address this problem, conventional technology proposed reducing the search space by utilizing GPS (global positioning system) location information acquired from a receiver to reduce the overhead of Exhaustive Search, thereby extracting K sets of beam pairs smaller than N x M based on AI / ML.

[0039] However, this method fails to adequately address receivers in mobility environments by merely considering the receiver's current location. In particular, given that the location of receivers in high-mobility environments changes significantly within a short period, the beam pair selected through the beam selection procedure based on the current location may actually become meaningless. Furthermore, since GPS-based location information is updated at a long interval of at least one second, it suffers from lower accuracy and is not suitable for applications requiring relatively frequent beam updates.

[0040] FIG. 2 illustrates an example of performing beam prediction using partial measurements according to various embodiments of the present disclosure.

[0041] Referring to Fig. 2, a method is proposed to reduce overhead by 2D imaging the entire beam pair set in the spatial domain to perform partial measurement and predicting the remaining unmeasured area based on AI / ML technology to find the optimal beam pair.

[0042] However, the rationale for selecting the set of beam pairs to perform partial measurements was not mentioned, and complexity still increases when the number of beams increases. Furthermore, this method also fails to reflect the mobile environment of the terminal.

[0043] To solve the aforementioned problem, the present disclosure states that while research on the application of AI / ML technology to an Air Interface is currently underway as it is set up as a RAN1 SI in 3GPP Rel-18, it is important to identify use cases suitable for the application of AI / ML technology as it is in the early stages of research.

[0044] The present disclosure proposes a low-complexity beam selection method that reflects terminal mobility through AI / ML-based collaboration between a base station and a terminal by considering both beam management and positioning together.

[0045] Specifically, in the case of beam management, the primary goal is to transmit and receive with an optimal beam pair for a specific terminal, and therefore the location of the terminal is very important. In this disclosure, we propose an efficient beam selection utilizing AI / ML-based terminal positioning. The base station and the terminal each have AI / ML cross-sectional models for different purposes, and the level of collaboration is assumed to be Level y.

[0046] Use cases related to beam management discussed at 3GPP focus simply on predicting a different (or the same) beam set based on measurement results of a portion of the current entire beam set in space or a previous beam set in time. On the other hand, the present disclosure focuses on a beam selection method that infers the current and future locations of an AI / ML-based terminal together to reduce the search space for an optimal beam pair set while reflecting the terminal's mobility environment.

[0047] Furthermore, conventional technology fails to adequately respond to receivers in mobility environments by merely considering the receiver's current location. Additionally, it performs learning and inference based on the receiver's simple GPS location; since GPS is updated at a minimum interval of 1 second, it suffers from poor accuracy. Moreover, it does not address the method for the transmitter to acquire the receiver's location. The present disclosure basically assumes a DL situation and proposes a method for AI / ML-based positioning, location prediction, and collaboration with a base station (transmitter) by utilizing RF and sensor information measured by the terminal (receiver). Through this, the present disclosure infers the terminal's current and future locations together, reducing the search space for the optimal beam pair set while simultaneously reflecting the terminal's mobility environment.

[0048] FIG. 3 illustrates a signal flow diagram between a base station and a terminal for AI / ML-based beam selection according to one embodiment of the present disclosure.

[0049] Referring to FIG. 3, the present disclosure proposes that the base station and the terminal collaborate with each other using AI / ML cross-sectional models for different purposes.

[0050] AI / ML models are broadly divided into two stages: training and inference. Training is fundamentally assumed to be offline, and the discussion focuses on inference. The level of collaboration is assumed to be Level y, assuming a connected mode scenario. These assumptions are not binding, as the inputs, outputs, and algorithms of the AI / ML model may vary depending on the implementation. However, examples are provided to aid the explanation.

[0051] The signaling between the base station and the terminal for the proposed AI / ML-based Beam Selection is as shown in Fig. 3.

[0052] The terminal utilizes various information from cell data, signal strength data, and sensors to provide the base station with AI / ML-based predicted information regarding the current and future locations.

[0053] The base station utilizes location information acquired from the terminal to reduce the search space for an AI / ML-based beam pair set, reflects the terminal's mobility, selects an optimal beam pair, and notifies the terminal. According to one embodiment, information regarding the optimal beam pair may be transmitted in the form of a MAC-CE (media access control - control element).

[0054] [On-device AI-based device location positioning and prediction]

[0055] FIG. 4 illustrates an example of an AI / ML model operation on the terminal side according to an embodiment of the present disclosure.

[0056] Referring to FIG. 4, the terminal can use on-device AI to predict the current location and future location using various sensors and RF information and transmit them to the base station. The sensors embedded in the terminal may mainly include motion sensors, location sensors, or environment sensors.

[0057] In addition to RF information such as RSRP, serving cell, or neighbor cell information from existing positioning methods, the terminal can calculate its current location and predict its future location by utilizing motion or location-related sensors.

[0058] The operation of the terminal-side AI / ML model is as shown in Fig. 4. Since location information is inferred directly at the terminal side using on-device AI and reported to the base station, overhead in terms of signaling can be reduced.

[0059] UAI (UE assistance information) can be utilized for signaling to inform the base station of location information from the terminal. UAI is a concept introduced in 3GPP Rel-16 and has been discussed primarily in terms of power consumption and heat generation. However, since the terminal is a source of information that is difficult for the base station to grasp, such as the customer's environment and intent, there are various possibilities for its utilization.

[0060] The present disclosure proposes transmitting on-device AI-based location information of a terminal to a UAI, and the UAI can be transmitted when the terminal desires in connected mode. The present disclosure assumes that the terminal transmits current and future location information through the UAI. According to one embodiment, the terminal may periodically transmit current and future location information through the UAI.

[0061] [Base Station AI / ML-based Beam Pair Set Selection]

[0062] FIG. 5 illustrates an example of base station-side AI / ML model operation according to an embodiment of the present disclosure.

[0063] FIG. 6 illustrates an example of an operation for selecting an AI / ML-based beam pair set of a base station according to an embodiment of the present disclosure.

[0064] Referring to FIGS. 5 and 6, the base station calculates a specific beam pair set based on AI / ML using location information received from the terminal. At this time, since the current or future location information through the terminal's periodic UAI reflects the terminal's mobility, the AI / ML model predicts the beam pair set by reflecting the terminal's mobility during the UAI transmission period.

[0065] This reduces complexity by shrinking the location-based search space, while simultaneously enabling more accurate and adaptive predictions by reflecting the device's mobility.

[0066] The base station selects the beam with the largest RSRP among the corresponding beam pair sets and notifies the terminal. There may be various methods for notifying the terminal, but in this invention, MAC-CE is assumed.

[0067] The operation of the base station-side AI / ML model is as shown in Fig. 5, and the overall operation structure is as shown in Fig. 6.

[0068] FIG. 7 illustrates an example of beam selection based on a partial measurement-based beam prediction method according to an embodiment of the present disclosure.

[0069] Referring to FIG. 7, the present disclosure can be applied together with a partial measurement-based beam prediction method.

[0070] The rationale for selecting the beam pair set to perform partial measurements was not mentioned, and the terminal's mobility environment was not reflected. When applied in conjunction with the present invention, selecting the beam pair set to perform partial measurements based on the terminal's location information can further reduce the partial measurement area and thereby reduce complexity; furthermore, since the terminal's mobility environment is reflected, more accurate and adaptive beam selection becomes possible.

[0071] In accordance with the aforementioned disclosure, the present disclosure will accelerate the discovery of more suitable use cases at the current early stage of research on AI / ML applications within air interfaces by presenting a structure in which AI / ML models for different uses can collaborate between a base station and a terminal.

[0072] Furthermore, complexity can be reduced by shrinking the search space of the beam pair set through the combined use of the terminal's direct current and future location aids during the process of finding the optimal beam pair between the base station and the terminal. Additionally, since the predicted future location is utilized along with the current location, more accurate beam selection can be performed by reflecting the terminal's mobility environment.

[0073] The present disclosure may be very useful when considering the use of analog beamforming and beam segmentation when utilizing high-frequency bands in future 6G systems. Specifically, signaling overhead between the base station and the terminal can be reduced through terminal-side on-device AI-based positioning, thereby ensuring more real-time beam management.

[0074] FIG. 8 illustrates a configuration diagram of a base station in a wireless communication system according to various embodiments of the present disclosure. The configuration exemplified in FIG. 8 can be understood as a configuration of a base station (800). Terms such as ‘… unit’, ‘… unit’ used below refer to a unit that processes at least one function or operation, and this may be implemented in hardware or software, or a combination of hardware and software.

[0075] A base station may be a network infrastructure that provides wireless access to terminals. A base station may have coverage defined as a specific geographical area based on the distance at which it can transmit signals. In addition to being a base station, the base station (110) may be referred to as an 'access point (AP)', 'eNodeB (eNB)', '5G node (5th generation node)', 'wireless point', 'transmission / reception point (TRP)', and 'next generation Node B (gNodeB, gNB)' or other terms having an equivalent technical meaning.

[0076] Referring to FIG. 8, the base station may include a wireless communication unit (810), a backhaul communication unit (820), a storage unit (830), and a control unit (840).

[0077] The wireless communication unit (810) can transmit and receive wireless signals through a wireless channel. For example, the wireless communication unit (810) can perform a conversion function between a baseband signal and a bit sequence according to the physical layer specifications of the system. In addition, when transmitting data, the wireless communication unit (810) can generate complex symbols by encoding and modulating the transmitted bit sequence. When receiving data, the wireless communication unit (810) can restore the received bit sequence by demodulating and decoding the baseband signal.

[0078] The wireless communication unit (810) can up-convert a baseband signal into an RF (radio frequency) band signal and transmit it through an antenna, and down-convert an RF band signal received through an antenna into a baseband signal. To this end, the wireless communication unit (810) may include a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, a DAC (digital to analog converter), and an ADC (analog to digital converter).

[0079] The wireless communication unit (810) may include a plurality of transmission and reception paths, and the wireless communication unit (210) may include at least one antenna array composed of a plurality of antenna elements.

[0080] In terms of hardware, the wireless communication unit (810) may include a digital unit and an analog unit, and the analog unit may include a plurality of sub-units depending on operating power, operating frequency, etc. The digital unit may be implemented with at least one processor (e.g., a digital signal processor (DSP)).

[0081] The wireless communication unit (810) can transmit and receive wireless signals as described above. Accordingly, all or part of the wireless communication unit (810) may be referred to as a 'transmitter', 'receiver', or 'transceiver'. In addition, in the following description, transmission and reception performed through a wireless channel may include processing as described above being performed by the wireless communication unit (810).

[0082] The backhaul communication unit (820) can provide an interface for communicating with other nodes within the network. That is, the backhaul communication unit (820) can convert a bit sequence transmitted from a base station to another node, e.g., another connection node, another base station, an upper node, and a core network, etc., into a physical signal, and can convert a physical signal received from another node into a bit sequence.

[0083] The storage unit (830) can store data such as basic programs, application programs, and configuration information for the operation of the base station. The storage unit (830) may be composed of volatile memory, non-volatile memory, or a combination of volatile memory and non-volatile memory. Additionally, the storage unit (830) can provide the stored data upon request from the control unit (840).

[0084] The control unit (840) can control the overall operations of the base station. For example, the control unit (840) can transmit and receive signals through the wireless communication unit (810) or through the backhaul communication unit (820). In addition, the control unit (840) can write and read data to and from the storage unit (830). Furthermore, the control unit (840) can perform the functions of the protocol stack required by the communication standard.

[0085] To this end, the control unit (840) may include at least one processor.

[0086] According to various embodiments of the present disclosure, the control unit (840) can control the base station to perform operations according to various embodiments performed by the base station described above.

[0087] FIG. 9 illustrates a configuration diagram of a terminal in a wireless communication system according to various embodiments of the present disclosure. The configuration exemplified in FIG. 9 can be understood as a configuration of a terminal (900). Terms such as ‘… unit’, ‘… unit’ used below refer to a unit that processes at least one function or operation, and this may be implemented in hardware or software, or a combination of hardware and software.

[0088] In addition to "terminal," the terminal may be referred to as "user equipment (UE)," "mobile station," "subscriber station," "remote terminal," "wireless terminal," or "user device," or any other term having an equivalent technical meaning.

[0089] Referring to FIG. 9, the terminal may include a communication unit (910), a storage unit (920), and a control unit (930).

[0090] The communication unit (910) can perform functions for transmitting and receiving signals through a wireless channel. For example, the communication unit (910) can perform conversion functions between a baseband signal and a bit sequence according to the physical layer specifications of the system. For example, when transmitting data, the communication unit (910) can generate complex symbols by encoding and modulating the transmitted bit sequence. When receiving data, the communication unit (910) can restore the received bit sequence by demodulating and decoding the baseband signal. Additionally, the communication unit (910) can up-convert the baseband signal into an RF band signal and transmit it through an antenna, and down-convert the RF band signal received through the antenna into a baseband signal. For example, the communication unit (910) may include a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, a DAC, an ADC, etc.

[0091] Additionally, the communication unit (910) may include a plurality of transmission and reception paths. Furthermore, the communication unit (910) may include at least one antenna array composed of a plurality of antenna elements. In terms of hardware, the communication unit (910) may be composed of digital circuits and analog circuits (e.g., RFIC (radio frequency integrated circuit)). Here, the digital circuits and analog circuits may be implemented as a single package. Additionally, the communication unit (310) may include a plurality of RF chains. Furthermore, the communication unit (910) may perform beamforming.

[0092] The communication unit (910) transmits and receives signals as described above. Accordingly, all or part of the communication unit (910) may be referred to as a 'transmitter', a 'receiver', or a 'transceiver'. Additionally, in the following description, transmission and reception performed via a wireless channel may be used to include the processing performed by the communication unit (910) as described above.

[0093] The storage unit (920) can store data such as basic programs, application programs, and setting information for the operation of the terminal. The storage unit (920) may be composed of volatile memory, non-volatile memory, or a combination of volatile memory and non-volatile memory. Additionally, the storage unit (920) can provide the stored data upon request from the control unit (930).

[0094] The control unit (930) can control the overall operations of the terminal. For example, the control unit (930) can transmit and receive signals through the communication unit (910). Additionally, the control unit (930) can write and read data from the storage unit (920). The control unit (930) can perform the functions of the protocol stack required by the communication standard. To this end, the control unit (930) may include at least one processor or microprocessor, or be part of a processor. Additionally, part of the communication unit (910) and the control unit (930) may be referred to as a communication processor (CP).

[0095] According to various embodiments, the control unit (930) can control the terminal to perform operations according to various embodiments performed by the terminal described above.

[0096] Base stations and terminals can transmit and receive radio signals in the millimeter wave (mmWave) band (e.g., 28 GHz, 30 GHz, 38 GHz, 60 GHz). In this case, to improve channel gain, base stations and terminals can perform beamforming. Here, beamforming may include transmit beamforming and receive beamforming. That is, base stations and terminals can impart directivity to the transmit signal or the receive signal. To this end, base stations and terminals can select serving beams through beam search or beam management procedures. After serving beams are selected, subsequent communication can be performed through a resource that is in a quasi-co-located (QCL) relationship with the resource that transmitted the serving beams.

[0097] If large-scale characteristics of the channel transmitting the symbol on the first antenna port can be inferred from the channel transmitting the symbol on the second antenna port, the first antenna port and the second antenna port may be evaluated to have a QCL relationship. For example, the large-scale characteristics may include at least one of a delay spread, a Doppler spread, a Doppler shift, an average gain, an average delay, and a spatial receiver parameter.

[0098] 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.

[0099] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to the embodiments described in the claims or specification of this disclosure.

[0100] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, read-only memory (ROM), 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.

[0101] Additionally, the program may be stored on an attachable storage device that can be accessed via a communication network such as the Internet, Intranet, LAN (local area network), WAN (wide area network), or SAN (storage area network), 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.

[0102] In the specific embodiments of the present disclosure described above, the components included in the disclosure 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, and even if a component is expressed in the singular form, it may be composed of a plural form.

[0103] 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 operation of user equipment (UE) in a wireless communication system comprises: a process of acquiring the current location of said UE and predicting the future location of said UE and a prediction time at which said future location is expected to be reached; and a process of transmitting to a base station using UE assistance information (UAI) including the predicted current location, said future location, and said prediction time, wherein said UAI is transmitted periodically when said UE is in a connected mode. Claim 2 The method of claim 1, wherein the current position and the future position are determined based on a motion sensor, a position sensor, or an environment sensor embedded in the UE. Claim 3 The method of claim 2, wherein the current position and the future position are additionally determined based on RSRP (received signal received power), information regarding the serving cell, or information regarding the neighboring cell. Claim 4 The method of claim 1, wherein the UE determines the current location and the future location based on on-device AI. Claim 5 A method of operation of a base station (BS) in a wireless communication system, comprising: receiving using UE assistance information (UAI) including a predicted current location, a future location, and a prediction time at which the future location is expected to be reached from user equipment (UE); predicting a beam pair set based on the current location, the future location, and the prediction time; selecting a beam having the largest received signal received power (RSRP) among the beam pair set; and transmitting information regarding the selected beam to the UE. Claim 6 In claim 5, the process of transmitting information regarding the selected beam to the UE comprises the process of transmitting information regarding the selected beam to the UE using a MAC-CE (medium access control - control element). Claim 7 In claim 5, the process of predicting a beam pair set based on the current position and the future position comprises the process of partially measuring a beam pair set based on the current position and the future position. Claim 8 In claim 5, the method wherein the UAI is periodically received when the UE is in connected mode. Claim 9 A user equipment (UE) in a wireless communication system comprises a transceiver and a control unit operably connected to the transceiver, wherein the control unit obtains the current location of the UE, predicts the future location of the UE and a prediction time at which the future location is expected to be reached, and transmits to a base station using UE assistance information (UAI) including the predicted current location, the future location, and the prediction time, wherein the UAI is transmitted periodically when the UE is in a connected mode. Claim 10 The device of claim 9, wherein the current position and the future position are determined based on a motion sensor, a position sensor, or an environment sensor embedded in the UE. Claim 11 The apparatus of claim 10, wherein the current position and the future position are additionally determined based on RSRP (received signal received power), information regarding a serving cell, or information regarding a neighboring cell. Claim 12 In claim 10, the device wherein the UE determines the current location and the future location based on on-device AI. Claim 13 A device comprising, in a base station (BS) of a wireless communication system, a transceiver and a control unit operably connected to the transceiver, wherein the control unit receives UE assistance information (UAI) including a predicted current location, a future location, and a prediction time at which the future location is expected to be reached from user equipment (UE), predicts a beam pair set based on the current location, the future location, and the prediction time, selects a beam having the largest received signal received power (RSRP) among the beam pair set, and transmits information regarding the selected beam to the UE. Claim 14 In claim 13, the control unit transmits information regarding the selected beam to the UE using a MAC-CE (medium access control - control element) in order to transmit information regarding the selected beam to the UE. Claim 15 The apparatus of claim 13, wherein the control unit partially measures a beam pair set based on the current position and the future position in order to predict a beam pair set based on the current position and the future position. Claim 16 In claim 13, the device wherein the UAI is periodically received when the UE is in connected mode.

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