Method and apparatus for mitigating codebook inaccuracy when using layered beam operation in wireless communication systems

Abstract

The present disclosure relates to a communication method and system for integrating a fifth-generation (5G) communication system supporting higher data rates than a fourth-generation (4G) system with technologies for the Internet of Things (IoT). The present disclosure can be applied to smart services based on 5G communication technology and IoT-related technologies, such as smart homes, smart buildings, smart cities, smart cars, connected vehicles, healthcare, digital education, smart retail, and safety and security services. A method performed by a base station in a wireless communication system is provided. The method includes: receiving a first measurement report from a user equipment (UE), the first measurement report indicating information about at least a first wide beam and a second wide beam in a wide beam set for beam selection, the first measurement report being generated based on measurement of a first sub-narrow beam set in the first wide beam; determining, based on the first measurement report, whether a condition for requesting measurement of a second sub-narrow beam set in a second wide beam in the wide beam set is detected; determining, based on determining that the condition is detected, to request the UE to measure the second sub-narrow beam set in the second wide beam, and after receiving a second measurement report indicating information about the second sub-narrow beam set, selecting a sub-narrow beam for use from one of the first sub-narrow beam set and the second sub-narrow beam set; and selecting a sub-narrow beam for use from the first sub-narrow beam set based on determining that the condition is not detected.

Description

Technical Field

[0001] The present disclosure generally relates to codebook design in wireless communication systems. In particular, methods and apparatus are provided for mitigating codebook inaccuracies when using layered beam operation. Background Art

[0002] In order to meet the demand for wireless data services that has increased due to the deployment of 4G communication systems, efforts have been made to develop improved 5G or quasi-5G (pre-5G) communication systems. Therefore, 5G or quasi-5G communication systems are also called "super 4G networks" or "post-LTE systems". 5G communication systems are considered to be implemented in higher frequency (mmWave) bands (for example, 60GHz bands) in order to achieve higher data rates. In order to reduce the propagation loss of radio waves and increase the transmission distance, beamforming, massive multiple input multiple output (MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and massive antenna technologies are discussed in 5G communication systems. In addition, in 5G communication systems, development of system network improvements is carried out based on advanced small cells, cloud radio access networks (RANs), ultra-dense networks, device-to-device (D2D) communications, wireless backhaul, mobile networks, collaborative communications, coordinated multi-point (CoMP), receiving-end interference cancellation, etc. In 5G systems, hybrid FSK and QAM modulation (FQAM) and sliding window superposition coding (SWSC) have been developed as advanced coding modulation (ACM), and filter bank multi-carrier (FBMC), non-orthogonal multiple access (NOMA), and sparse code multiple access (SCMA) have been developed as advanced access technologies.

[0003] The Internet has evolved from a human-centric network of connected devices where humans generate and consume information to the Internet of Things (IoT), in which distributed entities such as things exchange and process information without human intervention. The Internet of Everything (IoE) has emerged, combining IoT technologies with big data processing technology, enabled by connections to cloud servers. With technological elements such as sensing technology, wired / wireless communication and network infrastructure, service interface technology, and security technology required for IoT implementation, research has recently begun on sensor networks, machine-to-machine (M2M) communication, and machine-type communication (MTC). Such IoT environments can provide intelligent Internet technology services that create new value for human life by collecting and analyzing data generated between connected objects. Through the convergence and combination of existing information technology (IT) and various industrial applications, the IoT can be applied in a variety of fields, including smart homes, smart buildings, smart cities, smart cars (connected cars), smart grids, healthcare, smart appliances, and advanced medical services.

[0004] Accordingly, various attempts have been made to apply 5G communication systems to IoT networks. For example, technologies such as sensor networks, machine-type communications (MTC), and machine-to-machine (M2M) communications can be implemented through beamforming, MIMO, and array antennas. The application of cloud radio access networks (RANs), which are the big data processing technologies described above, can also be considered as an example of the convergence of 5G and IoT technologies.

[0005] The communication system includes a downlink (DL) that carries signals from a transmission point, such as a base station (BS) or gNB, to a user equipment (UE), and an uplink (UL) that carries signals from the UE to a reception point, such as a gNB. A UE, also commonly referred to as a terminal or mobile station, can be fixed or mobile and can be a cellular phone, personal computer, or other device. A gNB, which is typically a fixed station, may also be referred to as an access point or other equivalent terminology. Summary of the Invention

[0006] Technical issues

[0007] With the development of 5G communication systems, a method is needed to address codebook inaccuracies between users and base stations when using layered beam operation.

[0008] Technical Solution

[0009] Embodiments of the present disclosure provide methods and apparatus for mitigating codebook inaccuracies when layered beam operation is proposed.

[0010] In one embodiment, a method performed by a base station in a wireless communication system is provided. The method includes: receiving a first measurement report from a user equipment (UE), the first measurement report indicating information about at least a first wide beam and a second wide beam in a wide beam set for beam selection, the first measurement report being generated based on measurement of a first sub-narrow beam set in the first wide beam; determining, based on the first measurement report, whether a condition for requesting measurement of a second sub-narrow beam set in the second wide beam in the wide beam set is detected; determining, based on determining that the condition is detected, to request the UE to measure the second sub-narrow beam set in the second wide beam; and after receiving a second measurement report indicating information about the second sub-narrow beam set, selecting a sub-narrow beam for use from one of the first sub-narrow beam set and the second sub-narrow beam set; and selecting a sub-narrow beam for use from the first sub-narrow beam set based on determining that the condition is not detected.

[0011] In another embodiment, a method performed by a user equipment (UE) in a wireless communication system is provided. The method includes: sending a first measurement report to a base station, the first measurement report indicating information about at least a first wide beam and a second wide beam in a wide beam set used for beam selection, the first measurement report being generated based on measurement of a first set of narrow sub-beams in the first wide beam; and after sending a second measurement report indicating information about a second set of narrow sub-beams in the second wide beam, receiving a request from the base station to measure the second set of narrow sub-beams. Receipt of the request is determined based on a determination, based on the first measurement report, that a condition requesting measurement of the second set of narrow sub-beams in the second wide beam in the wide beam set is detected. At the base station, a narrow sub-beam for use is selected from the first set of narrow sub-beams based on a determination that the condition is not detected.

[0012] In another embodiment, a base station in a wireless communication system is provided. The base station includes: a transceiver; and a processor operably connected to the transceiver. The processor is configured to: receive a first measurement report from a user equipment (UE) via the transceiver, the first measurement report indicating information about at least a first wide beam and a second wide beam in a wide beam set for beam selection, the first measurement report being generated based on measurement of a first sub-narrow beam set in the first wide beam; determine, based on the first measurement report, whether a condition for requesting measurement of a second sub-narrow beam set in the second wide beam in the wide beam set is detected; determine, based on determining that the condition is detected, to request the UE to measure the second sub-narrow beam set in the second wide beam; and after receiving a second measurement report indicating information about the second sub-narrow beam set, select a sub-narrow beam for use from one of the first sub-narrow beam set and the second sub-narrow beam set; and select a sub-narrow beam for use from the first sub-narrow beam set based on determining that the condition is not detected.

[0013] In yet another embodiment, a user equipment (UE) in a wireless communication system is provided. The UE includes a transceiver and a processor operably connected to the transceiver. The processor is configured to: transmit a first measurement report to a base station via the transceiver, the first measurement report indicating information about at least a first wide beam and a second wide beam in a wide beam set for beam selection, the first measurement report being generated based on measurement of a first sub-narrow beam set in the first wide beam; and after transmitting a second measurement report indicating information about a second sub-narrow beam set in the second wide beam, receive a request from the base station via the transceiver to measure the second sub-narrow beam set, the receipt of the request being determined based on a condition that a request to measure the second sub-narrow beam set in the second wide beam in the wide beam set is detected based on the first measurement report. At the base station, a sub-narrow beam for use is selected from the first sub-narrow beam set based on a determination that the condition is not detected.

[0014] Other technical features may be easily understood by those skilled in the art based on the following drawings, detailed description and claims.

[0015] Beneficial effects

[0016] According to an embodiment of the present disclosure, codebook inaccuracy can be alleviated when layered beam operation is used. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 An example wireless network according to an embodiment of the present disclosure is shown.

[0018] Figure 2 An example gNB according to an embodiment of the present disclosure is shown.

[0019] Figure 3 An example UE according to an embodiment of the present disclosure is shown.

[0020] Figure 4A A high-level diagram illustrating an OFDMA transmission path according to an embodiment of the present disclosure is shown.

[0021] Figure 4B A high-level diagram illustrating an OFDMA receive path according to an embodiment of the present disclosure is shown.

[0022] Figure 5 An example electronic device according to an embodiment of the present disclosure is shown.

[0023] Figure 6 Example hybrid beamforming according to an embodiment of the present disclosure is shown.

[0024] Figure 7An example CDF of the gap in signal strength for the hierarchical selection method for optimal beam selection according to an embodiment of the present disclosure is shown.

[0025] Figure 8 An example timing structure according to an embodiment of the present disclosure is shown.

[0026] Figure 9 A flow chart illustrating a method for alleviating the impact of codebook inaccuracy according to an embodiment of the present disclosure is shown.

[0027] Figure 10 A flow chart of a method for detecting codebook inaccuracy according to an embodiment of the present disclosure is shown.

[0028] Figure 11 A flow chart illustrating a method for alleviating the impact of codebook inaccuracy according to an embodiment of the present disclosure is shown.

[0029] Figure 12 A flowchart of a method for detecting codebook accuracy using a machine learning classifier according to an embodiment of the present disclosure is shown.

[0030] Figure 13 An example process of calculating training data samples for a selected propagation channel and an antenna orientation of a selected user according to an embodiment of the present disclosure is shown.

[0031] Figure 14 A flowchart illustrating a method for detecting codebook inaccuracy using a classifier for selecting WB2 according to an embodiment of the present disclosure is shown.

[0032] Figure 15 A flow chart illustrating a method for operating an environment according to an embodiment of the present disclosure is shown.

[0033] Figure 16 Another flow chart illustrating a method for operating an environment according to an embodiment of the present disclosure is shown.

[0034] Figure 17 Another flow chart illustrating a method for allowing temporary beam changes according to an embodiment of the present disclosure is shown.

[0035] Figure 18 An example timing structure according to an embodiment of the present disclosure is shown.

[0036] Figure 19 A flowchart illustrating a method for supporting the use of additional NB measurement opportunities according to an embodiment of the present disclosure is shown.

[0037] Figure 20 An example process for detecting the possibility of refining a beam according to an embodiment of the present disclosure is shown.

[0038] Figure 21 A flow chart illustrating a method for mitigating codebook inaccuracy when using layered beam operation according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0039] Before proceeding with the detailed description below, it may be helpful to clarify the definitions of certain words and phrases used throughout this patent document. The term "coupling" and its derivatives refer to any direct or indirect communication between two or more elements, regardless of whether those elements are in physical contact with each other. The terms "send," "receive," and "transmit," and their derivatives encompass both direct and indirect communication. The terms "include," "comprise," and their derivatives refer to unlimited inclusion. The term "or" is an open inclusion, meaning and / or. The phrase "associated with..." and its derivatives refer to including, being included within, interconnected with, including, being included within, connected to or connected with, coupled to or coupled with, communicable with, collaborating with, interwoven, juxtaposed, close to, bound to, bound to, having, having the properties of, having a relationship to, or having a relationship with, etc. The term "controller" refers to any device, system, or part thereof that controls at least one operation. Such a controller can be implemented in hardware or a combination of hardware and software and / or firmware. The functionality associated with any particular controller, whether local or remote, can be centralized or distributed. When used with a list of items, the phrase "at least one of" means that different combinations of one or more of the listed items can be used, and only one of the items in the list may be required. For example, "at least one of A, B, and C" includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A, B, and C.

[0040] Moreover, various functions as described below can be implemented or supported by one or more computer programs, each function being formed by computer-readable program code and being implemented in computer-readable medium.Term " application " and " program " refer to one or more computer programs, software components, instruction sets, rules, functions, objects, categories, examples, relevant data or its part that are suitable for being implemented with suitable computer-readable program code.Phrase " computer-readable program code " includes any type of computer code, including source code, object code and executable code.Phrase " computer-readable medium " includes any type of medium that can be accessed by a computer, such as read-only memory (ROM), random access memory (RAM), hard disk drive, compact disc (CD), digital video disc (DVD) or any other type of memory." non-transient " computer-readable medium does not include wired, wireless, optical or other communication links that convey instantaneous electric signals or other signals.Non-transient computer-readable medium includes the medium in which data can be permanently stored and the medium in which data can be stored and overwritten later such as rewritable optical disc or erasable memory device.

[0041] Definitions for certain other words and phrases are provided throughout this patent document. Those skilled in the art should understand that in many, if not most instances, such definitions apply to prior, as well as future uses of such defined words and phrases.

[0042] Discussed below Figures 1 to 21 The various embodiments used to describe the principles of the present disclosure in this patent document are for illustration only and should not be interpreted in any way as limiting the scope of the present disclosure. Those skilled in the art will understand that the principles of the present disclosure can be implemented in any suitably arranged system or device.

[0043] Aspects, features, and advantages of the present disclosure will become more apparent from the following detailed description, which is presented only by way of illustration of a number of specific embodiments and implementations, including the best mode contemplated for carrying out the present disclosure. The present disclosure is also capable of other and different embodiments, and its several details may be modified in various obvious respects without departing from the spirit and scope of the present disclosure. Therefore, the drawings and description are to be regarded as illustrative in nature and not as limiting. The present disclosure is shown by way of example in the figures of the accompanying drawings and is not intended to be limiting.

[0044] The following Figure 1-Figure 3 Various embodiments are described as being implemented in a wireless communication system and utilizing Orthogonal Frequency Division Multiplexing (OFDM) or Orthogonal Frequency Division Multiple Access (OFDMA) communication techniques. Figure 1-Figure 3The description is not meant to imply physical or architectural limitations to the manner in which different embodiments may be implemented. Different embodiments of the present disclosure may be implemented in any suitably arranged communications system.

[0045] Figure 1 An example wireless network according to an embodiment of the present disclosure is shown. Figure 1 The embodiment of the wireless network shown in FIG is for illustration only. Other embodiments of the wireless network 100 may also be used without departing from the scope of the present disclosure.

[0046] like Figure 1 As shown, the wireless network includes gNB 101 (e.g., base station, BS), gNB 102, and gNB 103. gNB 101 communicates with gNB 102 and gNB 103. gNB 101 also communicates with at least one network 130, such as the Internet, a proprietary Internet Protocol (IP) network, or other data network.

[0047] gNB 102 provides wireless broadband access to network 130 for a first plurality of UEs within gNB 102's coverage area 120. The first plurality of UEs includes: UE 111, which may be located in a small business; UE 112, which may be located in an enterprise (E); UE 113, which may be located in a WiFi hotspot (HS); UE 114, which may be located in a first residence (R); UE 115, which may be located in a second residence (R); and UE 116, which may be a mobile device (M) such as a cell phone, wireless laptop, wireless PDA, etc. gNB 103 provides wireless broadband access to network 130 for a second plurality of UEs within gNB 103's coverage area 125. The second plurality of UEs may include UE 115 and UE 116. In some embodiments, one or more gNBs 101-103 may communicate with each other and with UEs 111-116 using 5G / NR, LTE, LTE-A, WiMAX, WiFi, or other wireless communication technologies.

[0048] Depending on the network type, the term "base station" or "BS" can refer to any component (or collection of components) configured to provide wireless access to a network, such as a transmission point (TP), a transceiver point (TRP), an enhanced base station (eNodeB or eNB), a 5G / NR base station (gNB), a macrocell, a microcell, a WiFi access point (AP), or other wireless-enabled devices. A base station can provide wireless access according to one or more wireless communication protocols, for example, 5G / NR 3GPP New Radio Interface / Access (NR), Long Term Evolution (LTE), Advanced LTE (LTE-A), High Speed ​​Packet Access (HSPA), Wi-Fi 802.11a / b / g / n / ac, etc. For convenience, the terms "BS" and "TRP" in this patent document are used interchangeably to refer to network infrastructure components that provide wireless access to remote terminals. In addition, depending on the network type, the term "user equipment" or "UE" can refer to any component, such as a "mobile station," "subscriber station," "remote terminal," "wireless terminal," "receiving point," or "user device." For convenience, the terms "user equipment" and "UE" are used in this patent document to refer to a remote wireless device that wirelessly accesses a BS, regardless of whether the UE is a mobile device (such as a mobile phone or smartphone) or is generally considered to be a fixed device (such as a desktop computer or vending machine).

[0049] Dashed lines illustrate the approximate extents of coverage areas 120 and 125, which are shown as generally circular for purposes of illustration and explanation only. It should be clearly understood that coverage areas associated with a gNB, such as coverage areas 120 and 125, may have other shapes, including irregular shapes, depending on the configuration of the gNB and variations in the wireless environment associated with natural and man-made obstacles.

[0050] As described in more detail below, one or more of UEs 111-116 include circuitry, programming, or a combination thereof for mitigating codebook inaccuracies when operating with layered beams. In certain embodiments, one or more of gNBs 101-103 include circuitry, programming, or a combination thereof for mitigating codebook inaccuracies when operating with layered beams.

[0051] although Figure 1 An example of a wireless network is shown, but Figure 1Various variations are possible. For example, the wireless network can include any number of gNBs and any number of UEs in any suitable arrangement. Moreover, gNB 101 can communicate directly with any number of UEs and provide those UEs with wireless broadband access to network 130. Similarly, each gNB 102-103 can communicate directly with network 130 and provide the UEs with direct wireless broadband access to network 130. In addition, gNBs 101, 102, and / or 103 can provide access to other or additional external networks, such as an external telephone network or other types of data networks.

[0052] Figure 2 An example gNB 102 is shown according to an embodiment of the present disclosure. Figure 2 The embodiment of gNB 102 shown in FIGURE 1 is for illustration only. Figure 1 gNBs 101 and 103 may have the same or similar configurations. However, gNBs appear in a variety of configurations. Figure 2 The scope of this disclosure is not limited to any particular implementation of the gNB.

[0053] like Figure 2 As shown in FIG, gNB 102 includes multiple antennas 205a-205n, multiple RF transceivers 210a-210n, transmit (TX) processing circuitry 215, and receive (RX) processing circuitry 220. gNB 102 also includes a controller / processor 225, memory 230, and a backhaul or network interface 235.

[0054] RF transceivers 210a-210n receive incoming RF signals from antennas 205a-205n, such as signals transmitted by UEs in network 100. RF transceivers 210a-210n downconvert the incoming RF signals to generate IF or baseband signals. The IF or baseband signals are sent to RX processing circuitry 220, which generates processed baseband signals by filtering, decoding, and / or digitizing the baseband or IF signals. RX processing circuitry 220 sends the processed baseband signals to controller / processor 225 for further processing.

[0055] The TX processing circuitry 215 receives analog or digital data (such as voice data, network data, email, or interactive video game data) from the controller / processor 225. The TX processing circuitry 215 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate processed baseband or IF signals. The RF transceivers 210a-210n receive the outgoing processed baseband or IF signals from the TX processing circuitry 215 and up-convert the baseband or IF signals into RF signals that are transmitted via the antennas 205a-205n.

[0056] The controller / processor 225 may include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 225 may control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceivers 210a-210n, the RX processing circuitry 220, and the TX processing circuitry 215 in accordance with well-known principles. The controller / processor 225 may also support additional functionality, such as more advanced wireless communication functionality. For example, the controller / processor 225 may support beamforming or directional routing operations, in which incoming signals from / outgoing signals to the multiple antennas 205a-205n are weighted differently to effectively steer the outgoing signals in a desired direction. Any of a variety of other functions may be supported in the gNB 102 by the controller / processor 225.

[0057] Controller / processor 225 is also capable of executing programs and other processes, such as an OS, that reside in memory 230. Controller / processor 225 may move data into or out of memory 230 as needed to execute processes.

[0058] The controller / processor 225 is also coupled to a backhaul or network interface 235. The backhaul or network interface 235 enables the gNB 102 to communicate with other devices or systems via a backhaul connection or via a network. The interface 235 can support communication via any suitable wired or wireless connection. For example, when the gNB 102 is implemented as part of a cellular communication system (e.g., a system supporting 5G / NR, LTE, or LTE-A), the interface 235 can enable the gNB 102 to communicate with other gNBs via a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, the interface 235 can enable the gNB 102 to communicate with a larger network (such as the Internet) via a wired or wireless local area network or via a wired or wireless connection. The interface 235 includes any suitable structure that supports communication via a wired or wireless connection, such as an Ethernet or RF transceiver.

[0059] Memory 230 is coupled to controller / processor 225. A portion of memory 230 may include RAM, and another portion of memory 230 may include flash memory or other ROM.

[0060] although Figure 2 An example of gNB 102 is shown, but the Figure 2 For example, gNB 102 may include Figure 22. As a specific example, an access point may include many interfaces 235, and the controller / processor 225 may support routing functionality for routing data between different network addresses. As another specific example, while shown as including a single instance of TX processing circuitry 215 and a single instance of RX processing circuitry 220, gNB 102 may include multiple instances of each (such as one for each RF transceiver). Additionally, Figure 2 The various components in can be combined, further subdivided, or omitted, and additional components can be added according to specific needs.

[0061] Figure 3 An example UE 116 is shown in accordance with an embodiment of the present disclosure. Figure 3 The embodiment of UE 116 shown in FIGURE 1 is for illustration only, and Figure 1 UEs 111-115 may have the same or similar configurations. However, UEs may appear in a variety of configurations, and Figure 3 The scope of this disclosure is not limited to any particular implementation of the UE.

[0062] like Figure 3 As shown, UE 116 includes an antenna 305, a radio frequency (RF) transceiver 310, a TX processing circuit 315, a microphone 320, and an RX processing circuit 325. UE 116 also includes a speaker 330, a processor 340, an input / output (I / O) interface (IF) 345, a touch screen 350, a display 355, and a memory 360. The memory 360 includes an operating system (OS) 361 and one or more applications 362.

[0063] RF transceiver 310 receives incoming RF signals from antenna 305, transmitted by a gNB of network 100. RF transceiver 310 downconverts the incoming RF signals to generate an intermediate frequency (IF) or baseband signal. The IF or baseband signal is sent to RX processing circuitry 325, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. RX processing circuitry 325 sends the processed baseband signal to speaker 330 (e.g., for voice data) or to processor 340 for further processing (e.g., for web browsing data).

[0064] The TX processing circuit 315 receives analog or digital voice data from the microphone 320 or other outgoing baseband data (such as web data, email, or interactive video game data) from the processor 340. The TX processing circuit 315 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceiver 310 receives the outgoing processed baseband or IF signal from the TX processing circuit 315 and up-converts the baseband or IF signal into an RF signal that is transmitted via the antenna 305.

[0065] The processor 340 may include one or more processors or other processing devices and execute the OS 361 stored in the memory 360 to control the overall operation of the UE 116. For example, the processor 340 may control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceiver 310, the RX processing circuit 325, and the TX processing circuit 315 according to well-known principles. In some embodiments, the processor 340 includes at least one microprocessor or microcontroller.

[0066] Processor 340 is also capable of executing other processes and programs residing in memory 360, such as processes for beam management. Processor 340 can move data into or out of memory 360 as needed to execute processes. In some embodiments, processor 340 is configured to execute applications 362 based on OS 361 or in response to signals received from a gNB or operator. Processor 340 is also coupled to I / O interface 345, which provides UE 116 with the ability to connect to other devices such as laptops and handheld computers. I / O interface 345 is the communication path between these accessories and processor 340.

[0067] Processor 340 is also coupled to touch screen 350 and display 355. An operator of UE 116 may input data into UE 116 using touch screen 350. Display 355 may be a liquid crystal display, a light emitting diode display, or other display capable of presenting text and / or at least limited graphics, such as from a website.

[0068] The memory 360 is coupled to the processor 340. A portion of the memory 360 may include RAM, and another portion of the memory 360 may include flash memory or other read-only memory ROM.

[0069] although Figure 3 An example of UE 116 is shown, but the Figure 3 Various changes can be made. For example, they can be combined, further subdivided, or omitted according to specific needs. Figure 3As a specific example, processor 340 may be divided into multiple processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). Furthermore, although Figure 3 The UE 116 is shown configured as a mobile phone or smartphone, but the UE may be configured to operate as other types of mobile or stationary devices.

[0070] To meet the increased demand for wireless data services since the deployment of 4G communication systems and to enable various vertical applications, 5G / NR communication systems have been developed and are currently being deployed. 5G / NR communication systems are configured to be implemented at higher frequency (millimeter wave) bands, such as the 28 GHz or 60 GHz bands, to achieve higher data rates, or at lower frequency bands, such as 6 GHz, to achieve robust coverage and mobility support. In order to reduce radio wave propagation losses and increase transmission distances, beamforming, massive multiple-input multiple-output (MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and massive antenna technologies are discussed in 5G / NR communication systems.

[0071] Furthermore, in 5G / NR communication systems, system network improvements are being developed based on advanced small cells, cloud radio access networks (RAN), ultra-dense networks, device-to-device (D2D) communications, wireless backhaul, mobile networks, collaborative communications, coordinated multi-point (CoMP), and receiver-side interference cancellation.

[0072] Since some embodiments of the present disclosure may be implemented in 5G systems, the discussion of 5G systems and the frequency bands associated therewith is provided for reference. However, the present disclosure is not limited to 5G systems or the frequency bands associated therewith, but rather embodiments of the present disclosure may be used with respect to any frequency band. For example, aspects of the present disclosure may also be applied to 5G communication systems, 6G, or even later versions that may utilize terahertz (THz) frequency bands.

[0073] A communication system includes DL, which refers to transmission from a base station or one or more transmission points to a UE, and UL, which refers to transmission from a UE to a base station or one or more reception points.

[0074] The time unit used for DL ​​signaling or UL signaling on a cell is called a time slot and can include one or more symbols. A symbol can also serve as another time unit. A frequency (or bandwidth (BW)) unit is called a resource block (RB). An RB includes multiple subcarriers (SCs). For example, a time slot can have a duration of 0.5 milliseconds or 1 millisecond, include 14 symbols, and an RB can include 12 SCs, etc., where the spacing between SCs is 15 kHz or 30 kHz, etc.

[0075] DL signals include data signals conveying information content, control signals conveying DL control information (DCI), and reference signals (RS), also known as pilot signals. gNBs transmit data information or DCI via their respective physical DL shared channels (PDSCH) or physical DL control channels (PDCCH). PDSCH or PDCCH can be transmitted over a variable number of time slot symbols, including one time slot symbol. For simplicity, the DCI format for UEs scheduled for PDSCH reception is referred to as the DL DCI format, and the DCI format for UEs scheduled for transmission via the physical uplink shared channel (PUSCH) is referred to as the UL DCI format.

[0076] The gNB transmits one or more of several types of RS, including Channel State Information RS (CSI-RS) and Demodulation RS (DMRS). CSI-RS primarily enables the UE to perform measurements and provide Channel State Information (CSI) to the gNB. For channel measurements, non-zero power CSI-RS (NZP CSI-RS) resources are used. For interference measurement reporting (IMR), CSI interference measurement (CSI-IM) resources associated with a zero power CSI-RS (ZP CSI-RS) configuration are used. A CSI process includes both NZP CSI-RS and CSI-IM resources.

[0077] The UE can determine CSI-RS transmission parameters through DL control signaling from the gNB or higher-layer signaling such as Radio Resource Control (RRC) signaling. The CSI-RS transmission instance can be indicated by DL control signaling or configured by higher-layer signaling. DMRS is transmitted only within the bandwidth of the corresponding PDCCH or PDSCH, and the UE can use DMRS to demodulate data or control information.

[0078] Figure 4A This is a high-level diagram of a transmit path circuit. For example, the transmit path circuit may be used for OFDMA communication. Figure 4B is a high-level diagram of the receive path circuit. For example, the receive path circuit can be used for OFDMA communication. Figure 4A and Figure 4BFor downlink communications, the transmit path circuitry may be implemented in a base station (gNB) 102 or a relay station, and the receive path circuitry may be implemented in a user equipment (e.g., Figure 1 In other examples, for uplink communications, the receive path circuit 450 may be implemented in a base station (e.g., Figure 1 The transmission path circuit may be implemented in a user equipment (e.g., gNB 102) or a relay station, and the transmission path circuit may be implemented in a user equipment (e.g., Figure 1 in the user device 116).

[0079] The transmit path circuitry 200 includes a channel coding and modulation block 405, a serial-to-parallel (S-to-P) block 410, an inverse fast Fourier transform (IFFT) block of size N, a parallel-to-serial (P-to-S) block 420, an add cyclic prefix block 425, and an upconverter (UC) 430. The receive path circuitry 450 includes a downconverter (DC) 455, a remove cyclic prefix block 460, a serial-to-parallel (S-to-P) block 465, a fast Fourier transform (FFT) block of size N, a parallel-to-serial (P-to-S) block 475, and a channel decoding and demodulation block 480.

[0080] Can be implemented in software Figure 4A 400 and Figure 4B At least some of the components in 450 may be implemented by configurable hardware or a mixture of software and configurable hardware. Specifically, it is noted that the FFT block and IFFT block described in this disclosure document may be implemented as configurable software algorithms, where the value of size N may be modified depending on the implementation.

[0081] In addition, although the present disclosure relates to embodiments for realizing fast Fourier transform and inverse fast Fourier transform, this is for illustration only and should not be understood as limiting the scope of the present disclosure. It is understood that in alternative embodiments of the present disclosure, the fast Fourier transform function and the inverse fast Fourier transform function can be easily replaced by discrete Fourier transform (DFT) function and inverse discrete Fourier transform (IDFT) function respectively. It is understood that for DFT function and IDFT function, the value of N variable can be any integer (i.e., 1, 2, 3, 4, etc.), and for FFT function and IFFT function, the value of N variable can be any integer (i.e., 1, 2, 4, 8, 16, etc.) that is a power of 2.

[0082] In the transmit path circuitry 400, the channel coding and modulation block 405 receives a set of information bits, applies coding (e.g., LDPC coding), and modulates the input bits (e.g., quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM)) to produce a sequence of frequency-domain modulation symbols. The serial-to-parallel block 410 converts (i.e., demultiplexes) the serially modulated symbols into parallel data to produce N parallel symbol streams, where N is the IFFT / FFT size used in the BS 102 and UE 116. The size-N IFFT block 415 then performs an IFFT operation on the N parallel symbol streams to produce a time-domain output signal. The parallel-to-serial block 420 converts (i.e., multiplexes) the parallel time-domain output symbols from the size-N IFFT block 415 to produce a serial time-domain signal. The add cyclic prefix block 425 then inserts a cyclic prefix into the time-domain signal. Finally, upconverter 430 modulates (ie, upconverts) the output of add cyclic prefix block 425 to RF frequency for transmission via a wireless channel. The signal may also be filtered at baseband before converting it to RF frequency.

[0083] After traversing the wireless channel, the transmitted RF signal reaches UE 116, where operations reverse those performed at gNB 102. Downconverter 455 downconverts the received signal to baseband frequency, and cyclic prefix removal block 460 removes the cyclic prefix to generate a serial time-domain baseband signal. Serial-to-parallel block 465 converts the time-domain baseband signal into parallel time-domain signals. Size-N FFT block 470 then performs an FFT algorithm to produce N parallel frequency-domain signals. Parallel-to-serial block 475 converts the parallel frequency-domain signals into a sequence of modulated data symbols. Channel decoding and demodulation block 480 demodulates and then decodes the modulated symbols to recover the original input data stream.

[0084] Each of gNBs 101-103 may implement a transmit path similar to that used for transmitting in the downlink to user equipment 111-116 and may implement a receive path similar to that used for receiving in the uplink from user equipment 111-116. Similarly, each of user equipment 111-116 may implement a transmit path corresponding to the architecture used for transmitting in the uplink to gNB 101-103 and may implement a receive path corresponding to the architecture used for receiving in the downlink from gNB 101-103.

[0085] A peer-aware communication (PAC) network is a fully distributed communication network that allows direct communication between PAC devices (PDs). A wireless personal area network (WPAN), or simply a personal area network (PAN), can be a fully distributed communication network. A WPAN or PAN is a communication network that allows wireless connections between PAN devices (PDs). PAN devices and PAC devices can be used interchangeably, as a PAC network is also a PAN network, and vice versa.

[0086] A PAC network can employ several topologies, such as mesh, star, and / or point-to-point, to support interactions between PDs for various services. Although this disclosure uses PAC networks and PDs as examples to develop and illustrate this disclosure, please note that this disclosure is not limited to these networks. The general concepts developed in this disclosure can be used in various types of networks with different types of scenarios.

[0087] Figure 5 An example electronic device 501 is shown in accordance with an embodiment of the present disclosure. Figure 5 The embodiment of electronic device 501 shown in FIGURE 5 is for illustration only. Figure 5 The scope of the present disclosure is not limited to any particular embodiment. A PD may be an electronic device that may have communication and ranging capabilities. The electronic device may be referred to as a ranging device (RDEV), an enhanced ranging device (ERDEV), a secure ranging device (SRDEV), or any other similar name according to IEEE standard specifications. The RDEV, ERDEV, or SRDEV may be part of an AP, a station (STA), an eNB, a gNB, a UE, or any other communication node with ranging capabilities.

[0088] refer to Figure 5 , the electronic device 501 in the network environment 500 can communicate with the electronic device 502 via the first network 598 (e.g., a short-range wireless communication network), or communicate with the electronic device 104 or the server 508 via the second network 599 (e.g., a long-range wireless communication network). According to an embodiment, the electronic device 501 can communicate with the electronic device 504 via the server 508.

[0089] According to an embodiment, the electronic device 501 may include a processor 520, a memory 530, an input device 550, a sound output device 555, a display device 560, an audio device 570, a sensor 576, an interface 577, a haptic device 579, a camera 580, a power management device 588, a battery 589, a communication interface 590, a subscriber identification module (SIM) 596, or an antenna 597. In some embodiments, at least one of the components (e.g., the display device 560 or the camera 580) may be omitted from the electronic device 501, or one or more other components may be added to the electronic device 501. In some embodiments, some of the components may be implemented as a single integrated circuit. For example, the sensor 576 (e.g., a fingerprint sensor, an iris sensor, or an illumination sensor) may be implemented as embedded in the display device 560 (e.g., a display).

[0090] The processor 520 may execute, for example, software (e.g., program 540) to control at least one other component (e.g., hardware or software component) of the electronic device 501 coupled to the processor 520 and may perform various data processing or calculations. According to one embodiment of the present disclosure, as at least part of the data processing or calculation, the processor 520 may load a command or data received from another component (e.g., sensor 576 or communication interface 590) into the volatile memory 532, process the command or data stored in the volatile memory 532, and store the resulting data in the non-volatile memory 534.

[0091] According to an embodiment of the present disclosure, the processor 520 may include a main processor 521 (e.g., a CPU or AP) and an auxiliary processor 523 (e.g., a GPU, an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is independent of or combined with the main processor 521 in operation. Additionally or alternatively, the auxiliary processor 523 may be adapted to consume less power than the main processor 521 or to be specifically used for a designated function. The auxiliary processor 523 may be implemented separately from the main processor 521 or as part of the main processor 521.

[0092] The auxiliary processor 523 may control at least some of the functions or states related to at least one component (e.g., the display device 560, the sensor 576, or the communication interface 590) among the components of the electronic device 501 in place of the main processor 521 when the main processor 521 is in an inactive (e.g., dormant) state, or perform the above control together with the main processor 521 when the main processor 521 is in an active state (e.g., executing an application). Depending on the embodiment, the auxiliary processor 523 (e.g., an image signal processor or a communication processor) may be implemented as part of another component (e.g., the camera 580 or the communication interface 190) that is functionally related to the auxiliary processor 523.

[0093] The memory 530 may store various data used by at least one component of the electronic device 501 (e.g., the processor 520 or the sensor 576). The various data may include, for example, software (e.g., the program 540) and input data or output data for commands related thereto. The memory 530 may include a volatile memory 532 or a non-volatile memory 534.

[0094] The program 540 may be stored as software in the memory 530 and may include, for example, an OS 542 , middleware 544 , or applications 546 .

[0095] The input device 550 may receive commands or data from outside the electronic device 501 (e.g., a user) to be used by other components of the electronic device 501 (e.g., the processor 520). The input device 550 may include, for example, a microphone, a mouse, a keyboard, or a digital pen (e.g., a stylus).

[0096] The sound output device 555 can output sound signals to the outside of the electronic device 501. The sound output device 555 can include, for example, a speaker or a receiver. The speaker can be used for general purposes such as playing multimedia or playing records, and the receiver can be used for incoming calls. Depending on the embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0097] The display device 560 can visually provide information to the outside of the electronic device 501 (e.g., a user). The display device 560 may include, for example, a display, a holographic device, or a projector, and a control circuit for controlling the display, the holographic device, and the projector. Depending on the embodiment, the display device 560 may include a touch circuit adapted to detect a touch or a sensor circuit adapted to measure the strength of the force caused by the touch (e.g., a pressure sensor).

[0098] The audio device 570 can convert sound into an electrical signal, and vice versa. Depending on the embodiment, the audio device 570 can obtain sound via the input device 550, or output sound via the sound output device 555 or headphones of an external electronic device (e.g., electronic device 502) directly coupled to the electronic device 501 (e.g., using a wired line) or wirelessly coupled.

[0099] The sensor 576 can detect the operating state of the electronic device 501 (e.g., power or temperature) or the environmental state outside the electronic device 501 (e.g., the state of the user), and then generate an electrical signal or data value corresponding to the detected state. Depending on the embodiment, the sensor 576 may include, for example, a gesture sensor, a gyroscope sensor, an atmospheric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illumination sensor.

[0100] The interface 577 may support one or more designated communication protocols for coupling the electronic device 501 directly (e.g., using a wired line) or wirelessly with an external electronic device (e.g., the electronic device 502). According to an embodiment of the present disclosure, the interface 577 may include, for example, a High-Definition Multimedia Interface (HDMI), a Universal Serial Bus (USB) interface, a Secure Digital (SD) card interface, or an audio interface.

[0101] The connection end 578 may include a connector, wherein the electronic device 501 can be physically connected to an external electronic device (e.g., the electronic device 502) via the connector. Depending on the embodiment, the connection end 578 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0102] The haptic device 579 may convert the electrical signal into a mechanical stimulus (eg, vibration or motion) or an electrical stimulus that can be recognized by the user via his sense of touch or kinesthetic sense. Depending on the embodiment, the haptic device 579 may include, for example, a motor, a piezoelectric element, or an electrical stimulator.

[0103] The camera 580 may capture still images or moving images. According to an embodiment of the present disclosure, the camera 580 may include one or more lenses, image sensors, image signal processors, or flashes.

[0104] The power management device 588 can manage the power supply to the electronic device 501. According to one embodiment, the power management device 588 can be implemented as, for example, at least a portion of a power management integrated circuit (PMIC). The battery 589 can supply power to at least one component of the electronic device 501. Depending on the embodiment, the battery 589 can include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0105] The communication interface 590 may support establishing a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device 501 and an external electronic device (e.g., electronic device 502, electronic device 504, or server 508) and performing communication via the established communication channel. The communication interface 590 may include one or more communication processors that can operate independently of the processor 520 (e.g., AP) and support direct (e.g., wired) communication or wireless communication.

[0106] According to an embodiment of the present disclosure, the communication interface 590 may include a wireless communication interface 592 (e.g., a cellular communication interface, a short-range wireless communication interface, or a global navigation satellite system (GNSS) communication interface) or a wired communication interface 594 (e.g., a local area network (LAN) communication interface or a power line communication (PLC)). A corresponding communication interface among these communication interfaces may communicate with an external electronic device via a first network 598 (e.g., a short-range communication network such as Bluetooth, Wireless Fidelity (Wi-Fi) Direct, Ultra-Wideband (UWB), or Infrared Data Association) or a second network 599 (e.g., a long-range communication network such as a cellular network, the Internet, or a computer network (e.g., a LAN or a wide area network (WAN))).

[0107] These various types of communication interfaces may be implemented as a single component (e.g., a single chip), or may be implemented as multiple components (e.g., multiple chips) separated from each other. The wireless communication interface 592 may use user information (e.g., International Mobile Subscriber Identity (IMSI)) stored in the user identification module 596 to identify and authenticate the electronic device 501 in a communication network such as the first network 598 or the second network 599.

[0108] The antenna 597 can send signals or power to the outside of the electronic device 501 (e.g., an external electronic device), or receive signals or power from it. According to an embodiment, the antenna 597 may include an antenna having a radiating element, which is composed of a conductive material or a conductive pattern formed in or on a substrate (e.g., a PCB). According to an embodiment, the antenna 597 may include multiple antennas. In this case, at least one antenna suitable for a communication scheme used in a communication network such as the first network 598 or the second network 599 can be selected from the multiple antennas by the communication interface 590 (e.g., a wireless communication interface 592). Signals or power can then be sent or received between the communication interface 590 and the external electronic device via the selected at least one antenna. According to an embodiment, another component other than the radiating element (e.g., a radio frequency integrated circuit (RFIC)) can be additionally formed as part of the antenna 597.

[0109] At least some of the above components can be coupled to each other via an inter-peripheral communication scheme (e.g., a bus, general-purpose input output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and communicatively transmit signals (e.g., commands or data) therebetween.

[0110] According to an embodiment of the present disclosure, commands or data can be sent or received between electronic device 501 and external electronic device 504 via server 508 coupled to second network 599. Each of electronic devices 502 and 504 can be of the same type as electronic device 501 or a different type. Depending on the embodiment, all or some operations to be performed on electronic device 501 can be performed on one or more of external electronic devices 502, 504, or 508. For example, if electronic device 501 can perform a function or service automatically or in response to a request from a user or another device, electronic device 501 can request one or more external electronic devices to perform at least a portion of the function or service, instead of or in addition to performing the function or service. The one or more external electronic devices that receive the request can perform at least a portion of the requested function or service, or perform additional functions or services related to the request, and transmit the results of the execution to electronic device 501. Electronic device 501 can provide the results as at least part of the response to the request, either with or without further processing. To this end, for example, cloud computing technology, distributed computing technology, or client-server computing technology may be used.

[0111] The electronic device according to various embodiments may be one of various types of electronic devices. For example, the electronic device may include a portable communication device (e.g., a smart phone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a household appliance. According to an embodiment of the present disclosure, the electronic device is not limited to those described above.

[0112] As used herein, the term "module" may include units implemented in hardware, software, or firmware, and may be used interchangeably with other terms (e.g., "logic," "logic block," "portion," or "circuit"). A module may be a single integrated component adapted to perform one or more functions or the smallest unit or portion of the single integrated component. For example, depending on an embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0113] The various embodiments described herein can be implemented as software (e.g., program 540) comprising one or more instructions stored in a storage medium (e.g., internal memory 536 or external memory 538) that can be read by a machine (e.g., electronic device 501). For example, under the control of a processor (e.g., processor 520) of the machine (e.g., electronic device 501), the processor can call and execute at least one of the one or more instructions stored in the storage medium with or without one or more other components. This enables the machine to operate to perform at least one function according to the called at least one instruction. The one or more instructions can include code generated by a compiler or code that can be executed by an interpreter. The machine-readable storage medium can be provided in the form of a non-transitory storage medium. The term "non-transitory" only means that the storage medium is a tangible device and does not include signals (e.g., electromagnetic waves), but the term does not distinguish between data being semi-permanently stored in the storage medium and data being temporarily stored in the storage medium.

[0114] According to an embodiment, the method according to various embodiments of the present disclosure may be included and provided in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disk read-only memory (CD-ROM)), or may be distributed (e.g., downloaded or uploaded) online via an application store (e.g., Play Store™), or may be distributed (e.g., downloaded or uploaded) directly between two user devices (e.g., smart phones). If distributed online, at least a portion of the computer program product may be temporarily generated, or at least a portion of the computer program product may be at least temporarily stored in a machine-readable storage medium (such as a manufacturer's server, an application store's server, or a memory of a relay server).

[0115] According to various embodiments, each component (e.g., module or program) in the above-mentioned components can include a single entity or multiple entities. According to various embodiments, one or more components in the above-mentioned components can be omitted, or one or more other components can be added. As another option or in addition, multiple components (e.g., module or program) can be integrated into a single component. In this case, according to various embodiments, the integrated component can still perform one or more functions of each component in the multiple components in the same or similar manner as the corresponding component in the multiple components performed one or more functions before integration. According to various embodiments, the operations performed by a module, program or another component can be performed sequentially, in parallel, repeatedly or in a heuristic manner, or one or more operations can be performed or omitted in a different order, or one or more other operations can be added.

[0116] Figure 6 Example hybrid beamforming according to an embodiment of the present disclosure is shown. Figure 6 The embodiment of hybrid beamforming 600 shown in FIGURE 6 is for illustration only.

[0117] For mmWave bands, the number of antenna elements can be large for a given form factor. Figure 6 Due to the hardware limitations shown in (such as the feasibility of installing a large number of analog-to-digital conversion / digital-to-analog conversion (ADC / DAC) at millimeter wave frequencies), the number of digital chains is limited. In this case, one digital chain is mapped to a large number of antenna elements that can be controlled by a group of analog phase shifters. One digital chain can then correspond to a subarray that produces a narrow analog beam through analog beamforming. The analog beam can be configured to sweep over a wider angular range by changing the phase shifter group over the transmission time interval. Figure 6The hybrid beamforming architecture shown in FIG can be applied to base stations as well as UEs.

[0118] A gNB can utilize one or more transmit beams to cover the entire area of ​​a cell. The gNB can form transmit beams by applying appropriate gain and phase settings to the antenna array. Transmit gain (i.e., the amplification of the transmitted signal power provided by a transmit beam) is typically inversely proportional to the width or area covered by the beam. At lower carrier frequencies, more benign propagation losses can make it feasible for the gNB to provide coverage with a single transmit beam, i.e., to ensure adequate received signal quality for all UE locations within the coverage area through the use of a single transmit beam. In other words, at lower transmit signal carrier frequencies, the transmit power amplification provided by a transmit beam that is wide enough to cover the area can be sufficient to overcome propagation losses and ensure adequate received signal quality for all UE locations within the coverage area.

[0119] However, at higher signal carrier frequencies, the transmit beam power amplification corresponding to the same coverage area may not be sufficient to overcome the higher propagation losses, resulting in degradation of received signal quality at UE locations within the coverage area. To overcome this degradation in received signal quality, the gNB can form many transmit beams, each of which provides coverage over an area narrower than the overall coverage area, but provides transmit power amplification sufficient to overcome the higher signal propagation losses caused by the use of higher transmit signal carrier frequencies. The UE can also form receive beams to increase the signal-to-interference-and-noise ratio (SINR) at the receiver. Similarly, in the uplink, the UE can form transmit beams, and the gNB can form receive beams.

[0120] To help the UE determine its RX and / or TX beams, a beam sweeping process is employed. This involves the gNB sending a set of transmit beams to sweep the cell area, and the UE using its receive beams to measure the signal quality on different beams. To facilitate candidate beam identification, beam measurement, and beam quality reporting, the gNB configures the UE with one or more RS resources (e.g., SS blocks, periodic / aperiodic / semi-persistent CSI-RS resources, or CRI) corresponding to the set of TX beams. RS resources refer to reference signal transmissions at one or more combinations of time (OFDM symbol) / frequency (resource element) / spatial (antenna port) locations. For each RX beam, the UE reports the different TX beams received using that RX beam, ranked in order of signal strength (RSRP) and, optionally, CSI (channel quality indicator / precoding matrix indicator / rank indicator (CQI / PMI / RI)). Based on the UE's measurement report feedback, the gNB indicates to the UE one or more Transmission Configuration Indicator (TCI) states for receiving PDCCH and / or PDSCH.

[0121] In one embodiment, at these frequencies, directivity is needed to combat the more severe propagation losses of isotropic antennas. The higher the directivity (equivalently, the narrower the beamwidth), the better the gain and therefore the higher the signal strength. However, the narrower the beam, the higher the cost of finding the optimal beam (i.e., beam alignment) becomes.

[0122] In one embodiment, with appropriate beam codebook design, the hierarchical search may require an order of magnitude smaller number of beam measurements compared to a direct search on narrow beams.

[0123] In one embodiment, a hierarchical search is performed to find the best wide beam (WB), and then the best sub-beam (narrow beam; NB) of the best WB can be found. If the codebook is accurate, the NB selected by the hierarchical search is the same as the best NB (when searching all NBs as a whole and not just the sub-beam of the best WB).

[0124] Figure 7 An example cumulative distribution function (CDF) illustrating the difference in signal strength for the hierarchical selection method 700 for optimal beam selection according to an embodiment of the present disclosure. Figure 7 The embodiment of the CDF of the difference in signal strength shown in the hierarchical selection method 700 is for illustration only. Figure 7 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0125] Specifically, in one example, even in the best case scenario, it can be found that the codebook is accurate only about 85% of the time assuming users with isotropic antennas (e.g., omnidirectional users). For directional users, the accuracy of the codebook can further deteriorate to about 75%, as shown in Figure 2. Figure 7 The reason for this degradation is the coupling between the user's codebook and the base station's codebook. Figure 7 As shown in Figure 3, the difference in signal strength can be quite significant, exceeding 20 dB in this case. Furthermore, the layered accuracy depends not only on the codebook design itself, but also on the physical environment in which the layered gNB operates. For example, a codebook that is very accurate in cell A may not perform as well in cell B due to differences in the spatial signature of the propagation environment. Due to this coupling of factors, achieving a universally accurate codebook across all deployment environments is difficult, if not impossible.

[0126] like Figure 7, which shows the CDF of the difference in signal strength for the hierarchical selection approach for optimal beam selection. A difference of 0 dB means that the hierarchical structure is preserved, so for this example the hierarchical codebook is 75% accurate at all locations under the coverage of the base station.

[0127] In Table 1, an example of simulated beam management is shown. As shown in Table 1 below, note that the hierarchical base stations perform better at high speeds compared to base stations using only narrow beams (represented by pure NB in ​​this table). The performance metric shown here is the RSRP loss in dB, defined as the difference between the simulated scheme and the optimal beam pair at each time slot. In this disclosure, there are 3 representative cases that vary according to time. "Static" refers to a static UE with only device rotation, "slow" refers to a UE with a speed of 30 km / h, and "fast" refers to a UE with a speed of 60 km / h. The first column "hier" refers to the simple hierarchical beam operation and the second column "hier.enh" refers to the method following one of the embodiments described in this disclosure. It can be noted that the proposed enhancement scheme seems to strike a good balance and the proposed enhancement scheme works well at all three speed levels.

[0128] At slow speeds, the enhanced solution can search more accurately, resulting in less RSRP loss compared to simple layered operation. At fast speeds, the use of WBs helps maintain alignment more easily than using only narrow beams, resulting in greater tolerance for increased speed. The enhanced solution requires some additional delay to conduct a more accurate NB search, so its performance may degrade compared to simple layered operation. In this disclosure, however, a non-negligible degradation is only shown for a 1% tile RSRP gap.

[0129] [Table 1]

[0130] Hierarchical base stations

[0131]

[0132]

[0133] Specifically, it can be noted that under the following two conditions, it is very likely that the codebook accuracy is not applicable to this instance: (1) the difference between the measured signal strength of the best WB and the second best WB is small; and (2) the measured signal strength of the NB found by the hierarchical search is lower than the measured signal strength of the best WB.

[0134] It should be noted that these two conditions are not independent and neither condition provides 100% detection. False detection of layer inaccuracy scenarios can lead to a waste of measurement resources in one solution provided in this disclosure. This solution offers a trade-off between measurement overhead and performance (i.e., the signal strength of the beams found by the search). Operators can choose an appropriate trade-off to meet their performance requirements.

[0135] Figure 8 An example timing structure 800 is shown in accordance with an embodiment of the present disclosure. Figure 8 The embodiment of the timing structure 800 shown in FIGURE 8 is for illustration only.

[0136] In one embodiment, a description of the timing structure of a hypothetical system is provided. It can be assumed that there are periodically available measurement opportunities. Figure 8 An example diagram of timing is shown in FIG.

[0137] Figure 8 Assuming there is a round of measurement opportunities ("round" refers to the entire cycle of WB measurements and NB measurements with associated feedback and control signaling), with a gap between each opportunity. If the SS block is transmitted on the WB, the WB can be configured to be measured and reported by the UE via the configuration of the SS block measurement and the reporting of L1-RSRP and / or L1-SINR.

[0138] If CSI-RS is transmitted on WB, it is also possible to configure CSI-RS for UE to measure WB and report the measurement results. A separate CSI-RS can also be configured to the UE for measuring NB and reporting L1-RSRP, L1-SINR or CSI. This is just an example and is not required by the solution provided in this disclosure. The system can provide multiple rounds of measurement opportunities, and all opportunities can be assembled back to back. Also, please note that the NB measurement opportunity may be sufficient to measure the sub-beam of one WB, or the NB measurement opportunity may be larger and allow measurement of two or even more sub-beams of WB. For ease of description, it can be assumed that the subsequent description of this disclosure is as follows Figure 8 The timing shown in and each NB measurement opportunity is only sufficient to measure one WB sub-beam.

[0139] like Figure 8As shown in , the system provides periodic beam measurement opportunities. In this example, there is one opportunity for measuring the WB and one for measuring the NB. Feedback / control signals are also provided for the base station and user to exchange messages, allowing the base station and user to notify each other of measurement results and beam search results. In this example, gaps between these opportunities are assumed, and each gap can be used for data transmission. However, this is merely an example and is not required; all these measurement / feedback opportunities can be arranged back-to-back.

[0140] In the present disclosure, a baseline hierarchical search process is provided, and one or more embodiments provided in the present disclosure are built on top of a straightforward baseline to provide a mechanism to mitigate the effects of codebook inaccuracies. The baseline search process is set up in the downlink, and it should be understood that the same process can also be directly applied to the uplink. In the baseline search, the WB is first measured. The user then feeds back the measurement to the base station. In the baseline search, only the WB index of the strongest WB is required at the base station. Once the best WB is found, the sub-beam of the best WB (assuming the sub-beam is the NB) is measured. The user can report the measurement of the NB, and the base station can make a decision and notify the user accordingly which beam can be used for transmission. Please note that the solution provided in the present disclosure does not make any assumptions about the user's codebook (e.g., omnidirectional or directional).

[0141] Figure 9 A flow chart of a method 900 for mitigating the impact of codebook inaccuracy according to an embodiment of the present disclosure is shown. Figure 9 The embodiment of method 900 shown in FIGURE 9 is for illustration only. Figure 9 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0142] In one embodiment, building upon these basic system descriptions, such as Figure 9 The overall process is shown in FIG. In this embodiment, if a certain condition is met, up to two rounds of beam measurement are used to make the final beam selection. The condition here is designed to detect whether the codebook hierarchy is inaccurate. Details of the criteria for this detection will be provided later. When the beam search starts, the WB is measured in step 902. The user then feeds back the measurement. In this embodiment, the user is required to report the measurement results of more than one WB (both the beam index and signal strength of at least the two best WBs, for example, in terms of RSRP or SINR).

[0143] Next, the base station may sweep the sub-beams of the best WB reported by the user (referred to as WB1). Once the measurements on the sub-beams are completed in step 904, the user may feed back the best NB measurement (both beam index and signal strength, e.g., in RSRP or SINR) to the base station. Using the WB measurement report and the NB measurement report, the base station determines in step 906 whether the codebook hierarchy is likely inaccurate for the user (e.g., a "qualification condition"). If the answer is no, then in step 908 the base station makes a determination that the found NB is likely good enough, and the base station may select the best NB among the sub-beams of WB1. If the answer is yes, then the base station makes a determination that the hierarchical accuracy of the codebook is likely inaccurate for the user, and the base station may continue searching without making a final beam selection.

[0144] Due to the assumed timing structure, the second part of the search also begins at step 910 with WB measurements. Note that if the time changes are not too rapid, the WB measurements of this new set may not provide new information, and the candidate WBs may be the same as in the first part. In this case, the process first selects another WB (called WB2) as a candidate for additional search at step 912, and the base station and user can continue to measure the sub-beams of WB2 at step 914. The determination of which WB can be WB2 may depend on the eligibility criteria adopted, and details will be provided in the subsequent description. At step 916, the next best WB is selected as WB2 by simple selection. Once the measurements of the sub-beams of WB2 and the corresponding reporting are completed, the base station can make a final selection among the measurements of the sub-beams of WB1 and WB2. The NB with the strongest signal strength among the sub-beams of WB1 and WB2 is selected by simple selection criteria. Finally, method 900 ends at step 918.

[0145] In one embodiment, a criterion for detecting a "qualified condition" is provided. In the present disclosure, two conditions are found to be strongly correlated with codebook inaccuracies: (1) the difference in signal strength between the strongest WB and the second strongest WB is less than a certain threshold; and (2) the signal strength of the best sub-NB is lower than the signal strength of the best WB.

[0146] These two conditions are not independent or complementary. In fact, if the threshold used to determine condition 1 is set to a sufficiently large value, it is likely that this threshold will also capture all events that meet condition 2. This does not mean that choosing a large threshold for condition 1 is a "better" choice; by choosing a larger threshold, more cases can be detected, but there will also be more false positives detected. Moderate values ​​for the threshold are often ideal to balance the missed detection rate with the false positive rate. With a moderate threshold (e.g., between 1-5 dB), it is likely that condition 2 can help improve detection accuracy, and the two conditions can be used together. It should also be noted that for condition 2, if non-negligible noise is expected, a detection margin can be incorporated to counteract noisy measurements. In that case, the condition would be that the signal strength of the best sub-NB is lower than the signal strength of the best WB by a selected margin. This margin can be selected based on the noise level (e.g., 1 to 3 standard deviations of the noise).

[0147] Having identified these two conditions and having confirmed that they may not completely overlap, there are several options for implementation, such as using both conditions or using only one of them. There are pros and cons to each option, and the pros and cons can be selected based on the operating environment and the desired performance.

[0148] With the goal of trying to get the most accurate alignment possible, it is desirable to capture all cases where the codebook is inaccurate. For this purpose, using these two conditions in the context of an OR logical operator is very suitable. Note that it makes sense to make a moderate choice of the threshold for condition 1, because if the threshold is too large, it may already cover all cases that would be detected by condition 2. However, the disadvantage of this is that the number of false positives detecting codebook inaccuracies is likely to increase. This means that with a round of WB measurements and NB measurements (such as in Figure 8 While this is likely to cause a small or negligible effect in environments with slow temporal variations, it may become problematic in rapidly changing environments where frequent beam switching is expected.

[0149] For rapidly changing environments, stricter detection conditions than an OR condition may be desirable. Possible options include using only one of the two conditions or using both conditions in the case of an AND logical operator. At the other extreme of using both conditions, an AND logical operator is the strictest. This is likely to minimize false positives when detecting codebook inaccuracies. This can help eliminate unnecessary delayed beam switching, which is beneficial in rapidly changing environments. However, at the same time, missed detections due to codebook inaccuracies may increase. This means that at slow speeds, alignment accuracy may be affected and may become problematic.

[0150] One advantage of using condition 1 is that it provides flexibility by adjusting the threshold used for detection. The threshold can even be adjusted accordingly to the user. For example, if speed information is available from the user, the base station can choose a smaller threshold for fast speeds and a larger value for slow speeds. Note that when condition 1 is used, the determination that the codebook is inaccurate is based only on measurements of the WB alone. Therefore, in this case, Figure 9 The decision box "Is a qualifying condition detected?" (eg, step 906) is moved before the box "Measure the sub-beam of the best WB (eg, WB1)".

[0151] Figure 10 FIG. 1 is a flow chart of a method 1000 for detecting codebook inaccuracy according to an embodiment of the present disclosure. Figure 10 The embodiment of method 1000 shown in FIGURE 1 is for illustration only. Figure 10 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0152] Condition 2 can also be used. In order to achieve the goal of avoiding unnecessary delayed beam switching as much as possible, the following can be done: Figure 10 A slight modification of condition 2 is provided as shown in . Note that this can be used instead of Figure 9 , the "Qualifying Condition Detected?" decision box in FIG. Here, when initial condition 2 is met, the signal strength of the WB is once again checked against a predetermined threshold. This threshold can be selected based on a minimum required signal strength for operation (e.g., as required by the user's application). In this case, if the best WB found happens to have a signal strength exceeding this minimum threshold, then rather than continuing the search requiring delayed beam switching, it may be desirable to use that WB instead.

[0153] Note that any use of the adjustments made to Condition 1 or Condition 2 described earlier in combination with other situations is also possible. However, the desired adjustments can be set accordingly with how Condition 1 or Condition 2 is used (for example, when using Condition 1 or Condition 2 independently or when using Condition 1 or Condition 2 together).

[0154] like Figure 10 As shown in FIG, at step 1002, method 1000 determines whether the signal strength of the best sub-NB is lower than the signal strength of the best WB. If not, method 1000 outputs "No." Otherwise, method 1000 determines at step 1006 whether the best WB signal strength is less than a threshold. At step 1006, if the answer is "No," method 1000 executes step 1004. Otherwise, method 1000 outputs "Yes" at step 1008.

[0155] Finally, in Figure 10 The idea of ​​modifying condition 2 in can also be applied to the overall process. It is a good option to prioritize beam switching to avoid unnecessary delays, which may be a preferred option under rapidly changing environments. Figure 11 The modified overall process is shown in FIG.

[0156] Figure 11 A flow chart of a method 1100 for mitigating the impact of codebook inaccuracy according to an embodiment of the present disclosure is shown. Figure 11 The embodiment of method 1100 shown in FIGURE 1 is for illustration only. Figure 11 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0157] Figure 11 A modified overall process for mitigating the effects of codebook inaccuracies is shown. In one example, avoiding unnecessary delayed beam switching is prioritized by not detecting for inaccuracies if the currently found beam can already provide signal strength above a certain level.

[0158] like Figure 11As shown in , the idea is that if the beam that has been found (the best WB or the best subNB of the best WB) can provide a signal strength that exceeds the minimum requirement, then the codebook inaccuracy detection can be skipped entirely. To achieve this, in the modified overall process, after obtaining the WB measurement and the NB measurement in steps 1102 and 1104, it is determined in step 1106 whether the best WB or the best subNB of the WB is greater than a threshold. The threshold can be set to a minimum level of signal strength required to meet a certain minimum performance requirement. If the minimum signal strength has been met, the stronger of the best WB or the best subNB can be selected in step 1108, and the base station can make a beam switching decision for the selected beam in step 1112. In the modified process, delayed beam switching through enhanced beam search is only performed when the minimum signal strength cannot be met and the conditions for codebook inaccuracy detection are met. In step 1116, if no qualifying condition is detected, then in step 1118, method 1100 selects the best NB among the sub-beams of WB 1; otherwise, method 1100 measures WB in step 1120. In step 1108, if the best WB is not greater than the best sub-NB, method 1100 performs step 1118. In step 1122, method 1100 selects another WB (e.g., WB2) and measures the sub-beam of WB2 in step 1124. In step 1126, method 1100 selects the best NB among the sub-beams of WB1 and WB2, and ends this round of beam searching in step 1114.

[0159] The aforementioned embodiment uses only measurements of the best and second-best WBs. This requires minimal information to be reported from the user to the base station. If the feedback channel is flexible and allows reporting of more than two WBs, this information can be used to more accurately measure codebook accuracy. Note that limitations on this reporting may arise from specifications in the communication standard and / or from the limited data rate of the feedback / control channel.

[0160] With more WB measurement reports, a more detailed detection of codebook inaccuracies can be devised. For ease of description, it can be assumed that the user reports k WBs in each measurement round, where k ≥ 2. The report includes the index of the WBs and their signal strengths. Note that k = 2 is also allowed, and the described embodiment can be similarly applied to the earlier setup; for example, the case of 2 WB measurement reports. The solution here can be used to replace the decision box "Qualifying condition detected?" in the embodiments described so far (such as Figure 9 and Figure 11 (the ones in the decision boxes).

[0161] Figure 12 A flow chart of a method 1200 for detecting codebook accuracy using a machine learning classifier according to an embodiment of the present disclosure is shown. Figure 12 The embodiment of method 1200 shown in FIGURE 1 is for illustration only. Figure 12 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0162] exist Figure 12 Shown in Figure 9 An example of such an alternative process is shown in FIG. Regarding the selection of the k WBs to be reported, a simple criterion is to select the k strongest WBs seen by the user. It should be noted that no assumptions can be made about the user's codebook, so these k WBs can be measured by different users' beams. Other selection criteria for selecting the k beams can also be used; the only requirement is that the criterion is consistently used in the system.

[0163] Figure 12 The overall process of using machine learning classifiers to detect codebook accuracy is shown.

[0164] This detection task using k WB measurements is a binary classification problem: is a codebook inaccuracy detected or not detected? The solution here uses a machine learning approach to perform the classification.

[0165] In one embodiment, an overview of the solution is provided, followed by a description of how to obtain data for training a binary classifier. In such an embodiment, any classifier model can be used, including but not limited to K-nearest neighbors, support vector machines, random forests, or artificial neural networks. The information available when using the classifier for inference includes k WB measurements and the best sub-NB for the best WB. With this in mind, there are several options for defining the classifier model.

[0166] In one example, if condition 1 is replaced by a trained classifier, only the WB measurement is used as input. Another option is to include both the WB measurement and the WB index. If conditions 1 and 2 are replaced by a classifier, both the WB measurement and the best sub-NB measurement can be included. Again, this can be used with or without the beam index. Regarding input shaping, different options can be used depending on the choice of classifier model.

[0167] In one example, for the beam index, the actual index used by the base station can be used (i.e., the beam index is represented by an integer), or the actual index can be represented by a one-hot encoding. Similarly, there is generally no preferred choice of ranking, although it may depend on the choice of classifier model. Regarding the signal strength measurement, some normalization can have some benefits. One approach is to normalize to the strongest WB.

[0168] Assuming that the signal strength is measured using a dB (or logarithmic) scale, this normalization step will obtain the difference from the signal strength of the strongest WB. This has several advantages. As implied by the description of condition 1, what is important is the relative strength between the best WB and the second-best WB rather than their absolute signal strength. Another point is that with this normalization, the dependence on the transmit power and the dependence on the gain of the user's beam pattern can be eliminated. It can be noted that this normalization is separate from the normalization typically used in machine learning solutions to facilitate the training process. This normalization can be applied on top of the power normalization of the aforementioned embodiment, for example.

[0169] like Figure 12 As shown in FIG, method 1200 measures the WB at step 1202. At step 1204, method 1200 measures the sub-beam of the best WB (e.g., WB1). At step 1206, method 1200 uses a codebook accuracy classifier to determine whether the codebook is accurate. At step 1206, if it is accurate, method 1200 selects the best NB among the sub-beams of WB1; otherwise, method 1200 measures the WB at step 1210. At step 1212, method 1200 selects another WB (e.g., WB2) and measures the sub-beam of WB2 at step 1214. Method 1200 selects the best NB among the sub-beams of WB1 and WB2 at step 1216. At step 1218, method 1200 ends this round of beam search.

[0170] In one embodiment, how to obtain training data for training a classifier is provided. Generating the training data requires three inputs: (1) the base station's beam pattern, (2) the user's beam pattern, and (3) the propagation channel. It can be assumed that a measured beam pattern from the base station's codebook is available. If such a beam pattern is not available, an approximation made using an antenna simulation software package can be used, understanding that some degradation in performance can be expected. In any case, it can be assumed that a beam pattern is available, either from measurements or any other approximation.

[0171] Because user beam patterns are outside the control of the base station, it is desirable to have beam codebooks for several types of users, for example, with different numbers of beams. The key is to achieve some diversity in the user codebooks so that the codebooks of typical users are more likely to be captured in the training dataset. In the special case where the target user's codebook (or codebooks if there are several known targets) is known, the training set can be customized only for that codebook (or codebooks).

[0172] The beam patterns are used to generate beam measurements (both WB and NB) for some channel models. Different types of channel models can be used. For example, if a location-specific model is desirable and affordable, a model of that location can be constructed in some ray tracing simulator and the propagation channels for all points in that location under coverage calculated. If the model is expected to run in various locations, multiple of those locations can be included, or some generic locations that are readily available to be used in a ray tracing simulator can be used. The emphasis here will be on good coverage of typical locations to be deployed. Yet another possibility is to use a statistical model that can provide at least the angle of arrival (AoA) and angle of departure (AoD) as well as path gain.

[0173] Figure 13 An example method 1300 of calculating training data samples for a selected propagation channel and a selected user's antenna orientation according to an embodiment of the present disclosure is shown. Figure 13 The embodiment of method 1300 shown in FIGURE 1 is for illustration only. Figure 13 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0174] Once the three components are available, we are ready to generate data samples for training. Figure 13 The steps for obtaining training samples for the selected channel and user orientation are shown. The steps can be repeated for different channels and user orientations. Figure 13 to generate a training data set. In this process, first, in steps 1302 and 1306, a channel and a user's orientation are selected, respectively. The selection can be random. The next step is to combine them in step 1304 to obtain the received power of each base station and user beam pair. Various ways of calculating it can be found in the literature with different improvements. For example, one way would be to calculate the overall physical layer simulation from waveform generation, and modeling of radio frequency (RF) components such as power amplifiers. Another way would be to calculate the integration of the power angular distribution of the channel (by ignoring delay) and the beam pattern. For the purposes of this disclosure, any embodiment or example is acceptable as long as it reflects the relationship between AoA / AoD and the beam pattern. For each channel sample, one or more user antenna orientations can be combined.

[0175] Once the signal power of all beam pairs has been calculated, a hierarchical beam search is performed in step 1308. One approach would be to examine the signal strength of the combination of all base station WBs and all user beams and then identify the strongest pair. The base station WB in this best pair is the best WB. Then, in step 1312, the process finds the best sub-beam for the best WB. This provides features for the training sample. The features are the strongest k beam pairs (of the combination of the base station's WB and all user beams), the indices of those k WBs, the best sub-NB signal strength, and the index of the best sub-NB signal strength. For simplicity, the selection of the k WBs is described in terms of signal strength, but as mentioned previously, any selection method is acceptable as long as it is consistently used. Furthermore, with respect to user beam selection for sub-NB search, a simple choice would be to use the user's beam corresponding to the best WB.

[0176] It may be desirable to have multiple criteria for beam selection for a user and have some diversity in the training data as previously described for the user's codebook. In step 1310, method 1300 determines the base station's codebook accuracy (which is used as a label for the training sample) and outputs the training sample (e.g., features, codebook accuracy) in step 1314. A binary value indicating whether the codebook is accurate is used as the label. Codebook accuracy is determined by checking whether the best sub-NB (the strongest NB among the sub-beams of the best WB) is the best NB (the NB that provides the highest signal strength when searching across the combination of the base station's NB and the user's beam).

[0177] Similar to the previous embodiment, a machine learning solution can be used to select WB2 to maximize the likelihood of finding the best beam. The setup is similar to the classifier used to determine the accuracy of the codebook, but with some significant differences. First, there are some additional measurements of WB that are also available for use in the second round of input. For slowly changing environments, this may add little value, but in rapidly changing environments it may become significant.

[0178] In one embodiment, a slowly changing environment and a rapidly changing environment are provided.

[0179] In one embodiment of a slowly changing situation, the information from the WB measurements in the second round may have little useful information, so those measurements can be ignored. In this case, the inputs to the classifier here can be the same as those of the classifier used to determine the accuracy of the base station's hierarchical codebook in the above-mentioned embodiment. Another option is to discard the WB measurements from the previous round and replace them with new measurements. Secondly, the output is also different. In this case, the output is the beam index of the WB. For training data, in this case, the label of the class needs to be changed. Instead of a binary indicator of codebook accuracy, the label here is the WB index that contains the best NB (i.e., the NB that provides the highest signal strength when searching on a combination of the base station's NB and the user's beam).

[0180] Figure 14 A flow chart illustrating a method 1400 for detecting codebook inaccuracy using a classifier for selecting WB2 according to an embodiment of the present disclosure. Figure 14 The embodiment of method 1400 shown in FIGURE 1 is for illustration only. Figure 14 One or more components shown in the may be implemented in dedicated circuits configured to perform the functions described, or one or more components may be implemented by one or more processors executing instructions to perform the functions described. Figure 14 Figure 2 shows how this operation fits into the overall process, where the dashed arrow at the input indicates that the classifier's selection of WB2 can use measurements from the previous round. Finally, it can be mentioned that using this approach, it is straightforward to extend the entire search to more than two rounds. A separate classifier can be trained for each round until a certain condition is met, for example, when the number of NBs found exceeds a certain threshold, or when the maximum number of allowed rounds is met.

[0181] Figure 14 Shown in Figure 10 A modified overall pipeline built on the pipeline of FIG. 5 by using a classifier for selecting WB2 to be searched in the next round of measurements.

[0182] like Figure 14As shown in FIG, method 1400 measures the WB at step 1402 and measures the beamlet of the best WB (e.g., WB1) at step 1404. At step 1406, method 1400 uses a codebook accuracy classifier to determine the accuracy. If accurate, method 1400 selects the best NB among the beamlets of WB1 at step 1408 and ends the beam search at step 1418. Otherwise (inaccurate), method 1400 measures the WB at step 1410. At step 1412, method 1400 uses the trained classifier to select WB2. At step 1414, the method measures the beamlets of another WB (e.g., WB2) and selects the best NB among the beamlets of WB1 and WB2, and then ends the beam search at step 1418.

[0183] In one embodiment, an operation is provided in which the time variation between the measurement rounds may be non-negligible. Figure 15 In this case, it may be necessary to estimate the current state of the environment to determine whether the operating environment has high or low temporal variation. If no measurements exist before the first round of measurements, then the temporal variation can be set to "high" at the beginning of operation. The main idea here is to attempt codebook inaccuracy mitigation only when low temporal variation is detected. The basic principle is as follows.

[0184] Under high temporal variations, it is likely that enhancements to mitigate the effects of codebook inaccuracies may have only limited success, if not detrimental; for example, measurements from a previous round of measurements are not guaranteed to still be valid when beam change decisions are made in the next round. Figure 15 An example implementation of this concept is shown below. First, measurements are taken of the WB and the sub-NB with the best WB. Then, the time variation state is estimated to see whether the environment has high or low time variation. This step requires measurements from both the current round and the previous round.

[0185] If the time variation is high, the process can make a beam change decision based only on the measurements in the current round. If the time variation is low, the process continues to detect whether codebook inaccuracy may have occurred. If it is determined to be inaccurate, then the WB is measured and the time variation rate can be estimated. If the time variation becomes high in this round, then the process can select the best WB based on this current round (represented by WB1′, which can be the same as WB1 in the previous round or different from it). After measuring the sub-NB of WB1′, the beam change decision is made. If the time variation is still low, WB2 is selected and the sub-NB of WB2 is measured. In the same manner as described in the earlier embodiment, the beam change decision is based on the measurements of this round and the measurements of the previous round.

[0186] Figure 15 A flow chart illustrating a method 1500 for operating an environment according to an embodiment of the present disclosure is shown. Figure 15 The embodiment of method 1500 shown in FIGURE 1 is for illustration only. Figure 15 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions. Figure 15 Shows operating environments that may have high temporal variation or low temporal variation.

[0187] like Figure 15 As shown in FIG, method 1500 measures the WB at step 1502 and measures the beamlet of the best WB (e.g., WB1) at step 1504. At step 1506, method 1500 determines whether the temporal variation is high or low. If the variation is high, method 1500 selects the best NB among the beamlets of WB1 at step 1510; otherwise (if the variation is low), method 1500 determines the accuracy at step 1508. If the variation is accurate at step 1508, method 1500 performs step 1510; otherwise (if the variation is inaccurate), method 1500 measures the WB at step 1512. At step 1514, method 1500 determines whether the temporal variation is high or low. If the variation is high, method 1500 measures the beamlet of the best WB (let's call it WB1') and selects the best NB among the beamlets of WB1' and WB1' itself. If high at step 1514, method 1500 performs classification for selecting WB2 at step 1520 and performs measurements on the beamlets of another WB (e.g., WB2) at step 1522. At step 1524, the method selects the best NB among the beamlets of WB1 and WB2 and ends this round of beam searching at step 1526.

[0188] In one embodiment, a method for determining the state of temporal change is provided. Solutions based on heuristics or machine learning can be employed. For example, as a heuristic, the proximity of the measurement results reported in the previous round to the current round can be examined. Consider using WB measurements. In this case, the measurements can be superimposed into a vector. The distance between the measurement vectors from the current round and the previous round is then used.

[0189] Various distance measures may be used, including but not limited to Euclidean distance, L-infinity distance, any general Lp distance, and cosine distance. For the machine learning approach, the same inputs (i.e., the input for determining codebook inaccuracy and the input for selecting WB2) may be used as described in the previous embodiment.

[0190] The next problem will be to now get labels for the training data. The labels can be generated by trying to perform codebook inaccuracy mitigation, and if the end result is better than not mitigating, then the state is marked as "low", otherwise the state is marked as "high".

[0191] Note that technically, this classifier is not strictly limited to temporal variation, but rather it is also related to codebook accuracy, but the labeling can provide the desired results. In fact, this is achieved when using a machine learning classifier for temporal variation in Figure 15 Another alternative embodiment in

[15] could be to combine a classifier for temporal variation with a codebook accuracy classifier. The input features to the classifier remain the same as before. The labels are "should attempt mitigation" and "should not attempt mitigation." The labels can be generated by attempting codebook inaccuracy mitigation, and if the end result is better than not mitigating, the state is marked as "should attempt mitigation," otherwise the state is marked as "should not attempt mitigation."

[0192] Figure 16 Another flow chart of a method 1600 for operating an environment according to an embodiment of the present disclosure is shown. Figure 16 The embodiment of method 1600 shown in FIGURE 1 is for illustration only. Figure 16 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions. Figure 16 Shown from Figure 15 A modified embodiment wherein the time variation classifier and the codebook accuracy classifier are combined into one classifier.

[0193] like Figure 16As shown in FIG, method 1600 measures the WB at step 1602 and measures the beamlet of the best WB (e.g., WB1) at step 1604. At step 1608, method 1500 determines a time variation and codebook accuracy classifier. If the answer at step 1608 is “do not attempt mitigation,” method 1600 selects the best NB among the beamlets of WB1 at step 1610; otherwise (“do attempt mitigation,” method 1600 measures the WB at step 1612. At step 1614, method 1600 determines a time variation and codebook accuracy classifier. If the answer at step 1614 is “do not attempt mitigation,” method 1600 measures the beamlets of the best WB (e.g., WB1′) and selects the best NB among the beamlets of WB1′ and WB1′ itself. If "mitigation should be attempted" at step 1614, method 1600 performs classification for selecting WB2 at step 1620 and performs measurements on the beamlets of another WB (e.g., WB2) at step 1622. At step 1624, the method selects the best NB among the beamlets of WB1 and WB2 and ends this round of beam searching at step 1626.

[0194] Finally, as described in the previous embodiment, for simplicity, temporary beam changes are not considered when performing mitigation. If the cost of beam changes is low, it may be desirable to allow temporary beam changes during mitigation. Figure 12 A modified version of the example embodiment of that allows for temporary beam changes is Figure 17 In this case, beam selection in the current round occurs regardless of the result of the codebook accuracy classifier. If the beam selection turns out to be accurate, no correction is required. If the beam selection turns out to be inaccurate, the beam selection can be overwritten after the next round of measurements.

[0195] Figure 17 Another flow chart of a method 1700 for allowing temporary beam changes according to an embodiment of the present disclosure is shown. Figure 17 The embodiment of method 1700 shown in FIGURE 1 is for illustration only. Figure 17 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0196] like Figure 17As shown in FIG, method 1700 measures the WB at step 1702 and measures the beamlet of the best WB (e.g., WB1) at step 1704. At step 1706, method 1700 selects the best NB among the beamlets of WB1. At step 1708, method 1700 uses a codebook accuracy classifier to determine accuracy. If accurate at step 1708, method 1700 concludes the beam search at step 1718; otherwise (inaccurate), method 1700 measures the WB at step 1710. At step 1712, method 1700 performs classification for selecting WB2 and measures the beamlet of another WB (e.g., WB2) at step 1714. At step 1716, the method selects the best NB among the beamlets of WB1 and WB2 and concludes the beam search at step 1718.

[0197] In this disclosure, the aforementioned embodiments focus on mitigating codebook inaccuracies using a maximum of two rounds of measurements. This is for clarity and ease of description only. In the case of k > 2 WB reports, this can also be generalized to allow searching over more than two rounds of measurements.

[0198] In one example, condition 1 can be modified to check all WBs with signal strength less than a threshold value of the best WB as candidates for refinement. The process can then proceed as described in Figure 9 Refinement is performed in a similar manner as shown in , but the process continues with a third round of measurements on the sub-beams of the third best WB, and a fourth round, and so on, before making a final beam selection by selecting the best NB among the sub-beams of those WBs that fall within the threshold of the best WB. Note that this process can allow all k WBs, or it can limit the number of allowed candidate beams. For example, if the process allows a maximum of three candidates, there will be an additional round of measurements on the sub-beams of the third WB before making a final selection. Note that the solution described so far uses a maximum of two candidates.

[0199] In this disclosure, the use of an extension of Condition 1 is described to identify multiple candidate WBs for measurement (e.g., more than two candidates), but this can also be similarly applied to machine learning-based solutions. In this case, the classifier for selecting WB2 can be trained instead to select M candidate WBs, where M≥1 (M=1, as in Figure 17 ).

[0200] In one example, a recommender system solution can be used to make these candidate selections. Alternatively, separate classifiers can be trained to select a candidate. For example, one classifier can be used to select WB2, another classifier can be used to select WB3, and so on. Note that for the separate classifier approach, the latter will also have access to measurements of the previous candidate sub-NBs, and those measurements can also be used as input to the classifiers.

[0201] One drawback is the delayed beam variation, which can become problematic when the time variation is high. Note that this is due to the Figure 8 The timing of its assumed measurement opportunity is shown in . In other embodiments, for example Figure 18 As shown in , multiple NB measurement opportunities can be allowed in each measurement round as needed. In this case, when it is determined that the codebook is likely to be inaccurate, additional NB measurement opportunities are implemented and measurements can be made without much delay and within the same measurement round.

[0202] Figure 18 An example timing structure 1800 is shown in accordance with an embodiment of the present disclosure. Figure 18 The embodiment of timing structure 1800 shown in FIGURE 1 is for illustration only.

[0203] exist Figure 19 An example of a modified overall process to allow for this on-demand NB measurement opportunity is provided in [1]. Note that in this case, the additional NB measurement opportunity incurs additional overhead, but it can reduce the latency of beam changes when the process determines that the codebook is likely inaccurate. This can be a more ideal mode of operation for deployments with high temporal variation.

[0204] Figure 19 A flow chart of a method 1900 for supporting the use of additional NB measurement opportunities according to an embodiment of the present disclosure is shown. Figure 19 The embodiment of method 1900 shown in FIGURE 1 is for illustration only. Figure 19 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0205] like Figure 19As shown in FIG, method 1900 measures the WB at step 1902 and measures the sub-beam of the best WB (e.g., WB1) at step 1904. At step 1906, method 1900 determines whether the codebook is accurate. If so, at step 1906, method 1900 selects the best NB among the sub-beams of WB1 at step 1908 and ends the beam search round at step 1918. If not, at step 1906, method 1900 enables additional NB measurement opportunities at step 1910. Method 1900 selects another WB (e.g., WB2) at step 1912 and selects the best NB among the sub-beams of WB1 and WB2 at step 1916, and then ends the beam search round at step 1918.

[0206] In the present disclosure, the aforementioned embodiments are described from the perspective of the base station, but similar concepts can also be applied to the user side. Specifically, when a user observes similar signal strengths on the beams of multiple users, there may be some beam mismatches, and extended search can help improve signal quality. For the sake of specific description, it can be assumed that the user has directionality with respect to M beams (for simplicity, it is assumed to be non-hierarchical). The user can measure the base station beam for all users' beams according to some scheduling (for example, round-robin scheduling can be used) and maintain a measurement table.

[0207] The user can maintain a measurement table of all base station WBs and all user beams. In that case, if the user observes that there are multiple similar values ​​in the base station WB measurement table corresponding to one base station WB and multiple users' beams, the user can determine that the user's beam selection can be improved by expanding the search on the beams of multiple users. This is condition 1 as described for the base station in the aforementioned embodiment, but now also applies to the user side (e.g., UE). In this case, when the user reports the same best WB to the base station, the user can use the beams of multiple users to measure the NB (sub-beams of the reported WB) over multiple rounds. In this way, the user can refine the beam that can better match the ultimately selected base station NB.

[0208] In one example, Figure 20 , a user is shown refining beams for up to two users (the best user beam and the second best user beam). The user may choose to refine more than two beams in a similar manner as described earlier for base station beam operations. Regarding the thresholds used in this example embodiment, the same range of values ​​as described earlier for Condition 1 of the base station may be used.

[0209] Figure 20 An example method 2000 for detecting the possibility of refining a beam is shown according to an embodiment of the present disclosure. Figure 20The embodiment of method 2000 shown in FIGURE 2 is for illustration only. Figure 20 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0210] Figure 20 This example shows how a user can detect the possibility of beam refinement using the WB measurement table. If two entries in the WB measurement table belong to the same base station WB but two different user beams, and the signal strength difference (in dB) is below a certain threshold, the user can choose to first measure the sub-NB of the best base station beam using the user beam corresponding to the best entry. Then, in the next measurement round, the user selects the beam corresponding to the second-best entry in the WB measurement table. Only then does the user make a beam switching decision.

[0211] like Figure 20 As shown in FIG, method 2000 determines in step 2002 whether the best and second-best entries in the WB table belong to the same base station WB but different UE beams and their difference is within a certain threshold. If not determined (No) in step 2002, method 2000 reports the best WB in step 2006 and uses the best UE beam to measure the sub-NB. Method 2000 then provides, in step 2010, for the UE to make beam switching decisions based on sub-NB measurements using only this round. If detected (Yes) in step 2002, method 2000 reports the best WB in step 2004 and uses the best UE beam to measure the sub-NB. In step 2008, method 2000 measures the WB and updates the WB measurement table. In step 2012, method 2000 reports the same best WB as in the previous round, but uses the second-best UE beam to measure the sub-NB. In step 2014, the method provides for the UE to make beam switching decisions based on sub-NB measurements from the last two rounds.

[0212] Figure 21 A flow chart illustrating a method 2100 for mitigating codebook inaccuracy when operating with layered beams according to an embodiment of the present disclosure is shown. Figure 21 The embodiment of method 2100 shown in FIGURE 2 is for illustration only. Figure 21 One or more components shown in the drawings may be implemented in dedicated circuits configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions.

[0213] Method 2100 may be performed by a BS (e.g., Figure 1 101) shown in FIG.

[0214] like Figure 21As shown in , method 2100 starts at step 2102. In step 2102, the BS receives a first measurement report from the UE, where the first measurement report indicates information about at least a first wide beam and a second wide beam in a wide beam set used for beam selection, where the first measurement report is generated based on measurement of a first sub-narrow beam set in the first wide beam.

[0215] In one embodiment, the first measurement report includes information indicating signal strength of a wide beam in the wide beam set.

[0216] In another embodiment, the first measurement report includes information indicating the signal strength of the first wide beam and the signal strengths of the sub-narrow beams in the first set of sub-narrow beams.

[0217] In step 2104, the BS determines whether a condition requesting measurement of a second sub-narrow beam set in a second wide beam in the wide beam set is detected based on the first measurement report.

[0218] In step 2106, the BS determines to request the UE to measure a second set of narrow sub-beams in the second wide beam based on determining that the condition is detected, and after receiving a second measurement report indicating information about the second set of narrow sub-beams, selects a narrow sub-beam for use from one of the first set of narrow sub-beams and the second set of narrow sub-beams.

[0219] In step 2108, the BS selects a sub-narrow beam for use from the first set of sub-narrow beams based on determining that the condition is not detected.

[0220] In one embodiment, the BS determines that the condition is detected based on determining that a difference between signal strengths of the first wide beam and the second wide beam is less than a predetermined threshold.

[0221] In one embodiment, the BS determines that the condition is detected based on determining that a difference between a signal strength of the first wide beam and a signal strength of a best narrow beam in the first set of narrow sub-beams is greater than a predetermined threshold.

[0222] In one embodiment, the BS performs a machine learning prediction operation based on training data (the training data is generated based on at least one of the BS's beam pattern, the UE's beam pattern, and information about the propagation channel), and selects the second wide beam based on the result of the machine learning prediction operation.

[0223] In one embodiment, the BS determines the signal strength for beam selection based on the BS's beam pattern, the UE's beam pattern, a channel model, and the UE's antenna orientation.

[0224] In one embodiment, the BS generates one or more training samples based on (i) signal strength and (ii) codebook accuracy.

[0225] In one embodiment, the BS identifies the signal strength of the first wide beam and the signal strength of the best sub-narrow beam in the first set of sub-narrow beams; before determining whether the condition is detected, determines whether the larger of the signal strength of the first wide beam and the signal strength of the best sub-narrow beam is not less than a predetermined threshold; and based on determining that the larger of the signal strength of the first wide beam and the signal strength of the best sub-narrow beam is not less than the predetermined threshold, determines to skip requesting the UE to measure the second set of sub-narrow beams and select the first wide beam.

[0226] In one embodiment, the BS compares beam measurement results between a previous round of measurements of the wide beam set and a current round of measurements of the wide beam set; determines a rate of temporal variation based on the compared beam measurement results; and determines to perform codebook inaccuracy mitigation processing before beam selection for the current round of measurements based on the comparison of the rate of temporal variation with a threshold.

[0227] The above flowcharts illustrate example methods that can be implemented according to the principles of the present disclosure, and various variations can be made to the methods shown in the flowcharts herein. For example, although shown as a series of steps, the individual steps in each figure may overlap, occur in parallel, occur in a different order, or occur multiple times. In another example, a step may be omitted or replaced by another step.

[0228] Although the present disclosure has been described with respect to exemplary embodiments, various variations and modifications may be suggested to those skilled in the art. The present disclosure is intended to encompass such variations and modifications as fall within the scope of the appended claims. No description in this application should be construed as implying that any particular element, step, or function is essential to be included within the scope of the claims. The scope of the claimed subject matter is defined solely by the claims.

Claims

1. A method performed by a base station in a wireless communication system, the method comprising: receiving a first measurement report from a user equipment (UE), the first measurement report indicating information about at least a first wide beam and a second wide beam in a wide beam set for beam selection, the first measurement report being generated based on measurement of a first sub-narrow beam set in the first wide beam; determining, based on the first measurement report, whether a condition for requesting measurement of a second set of narrow sub-beams in the second wide beam in the set of wide beams is detected; Based on determining that the condition is detected, determining to request the UE to measure the second set of narrow sub-beams in the second wide beam, and after receiving a second measurement report indicating information about the second set of narrow sub-beams, selecting a narrow sub-beam for use from one of the first set of narrow sub-beams and the second set of narrow sub-beams; and Based on determining that the condition is not detected, a narrow sub-beam is selected for use from the first set of narrow sub-beams.

2. The method according to claim 1, further comprising: determining that the condition is detected based on determining that a difference between a signal strength of the first wide beam and a signal strength of the second wide beam is less than a predetermined threshold, The first measurement report includes information indicating the signal strength of the wide beam in the wide beam set.

3. The method according to claim 1, further comprising: determining that the condition is detected based on determining that a difference between a signal strength of the first wide beam and a signal strength of a best narrow beam in the first set of narrow sub-beams is greater than a predetermined threshold, The first measurement report includes information indicating the signal strength of the first wide beam and the signal strength of the sub-narrow beams in the first set of sub-narrow beams.

4. The method according to claim 1, further comprising: performing a machine learning prediction operation based on training data, the training data being generated based on at least one of a beam pattern of the base station, a beam pattern of the UE, and information of a propagation channel; selecting the second wide beam based on a result of the machine learning prediction operation; determining a signal strength for the beam selection based on a beam pattern of the base station, a beam pattern of the UE, a channel model, and an antenna orientation of the UE; as well as One or more training samples are generated based on the signal strength and the accuracy of the codebook.

5. A method performed by a user equipment (UE) in a wireless communication system, the method comprising: Sending a first measurement report to a base station, where the first measurement report indicates information about at least a first wide beam and a second wide beam in a wide beam set used for beam selection, where the first measurement report is generated based on measurement of a first sub-narrow beam set in the first wide beam; as well as After sending a second measurement report indicating information about a second set of narrow sub-beams in the second wide beam, receiving a request from the base station to measure the second set of narrow sub-beams, The reception of the request is determined according to a condition that a request to measure the second set of narrow sub-beams in the second wide beam in the set of wide beams is detected based on the first measurement report, and The base station selects a sub-narrow beam for use from the first set of sub-narrow beams based on determining that the condition is not detected.

6. The method according to claim 5, in, The first measurement report includes information indicating signal strength of a wide beam in the wide beam set, and The base station determines that the condition is detected based on determining that a difference between a signal strength of the first wide beam and a signal strength of the second wide beam is less than a predetermined threshold.

7. The method according to claim 5, in, The first measurement report includes information indicating the signal strength of the first wide beam and the signal strengths of the sub-narrow beams in the first set of sub-narrow beams, The condition is detected based on determining that a difference between a signal strength of the first wide beam and a signal strength of an optimal narrow beam in the first set of narrow sub-beams is greater than a predetermined threshold, The signal strength used for the beam selection is determined based on the beam pattern of the base station, the beam pattern of the UE, a channel model, and the antenna orientation of the UE.

8. A base station in a wireless communication system, the base station comprising: transceiver; as well as a processor operatively connected to the transceiver, the processor configured to: receiving, via the transceiver, a first measurement report from a user equipment (UE), the first measurement report indicating information about at least a first wide beam and a second wide beam in a wide beam set for beam selection, the first measurement report being generated based on measurement of a first sub-narrow beam set in the first wide beam, determining, based on the first measurement report, whether a condition requesting measurement of a second set of narrow sub-beams in the second wide beam in the set of wide beams is detected; Based on determining that the condition is detected, determining to request the UE to measure the second set of narrow sub-beams in the second wide beam, and after receiving a second measurement report indicating information about the second set of narrow sub-beams, selecting a narrow sub-beam for use from one of the first set of narrow sub-beams and the second set of narrow sub-beams; and Based on determining that the condition is not detected, a narrow sub-beam is selected for use from the first set of narrow sub-beams.

9. The base station according to claim 8, in, The first measurement report includes information indicating signal strength of a wide beam in the wide beam set, and The processor is further configured to determine that the condition is detected based on determining that a difference between a signal strength of the first wide beam and a signal strength of the second wide beam is less than a predetermined threshold.

10. The base station according to claim 8, in, The first measurement report includes information indicating the signal strength of the first wide beam and the signal strength of a narrow sub-beam in the first set of narrow sub-beams, and The processor is further configured to determine that the condition is detected based on determining that a difference between a signal strength of the first wide beam and a signal strength of a best narrow beam in the first set of narrow sub-beams is greater than a predetermined threshold.

11. The base station according to claim 8, wherein: The processor is further configured to: performing a machine learning prediction operation based on training data, wherein the training data is generated based on at least one of a beam pattern of the base station, a beam pattern of the UE, and information of a propagation channel, selecting the second wide beam based on a result of the machine learning prediction operation, determining a signal strength for the beam selection based on the beam pattern of the base station, the beam pattern of the UE, a channel model, and an antenna orientation of the UE, and One or more training samples are generated based on the signal strength and the accuracy of the codebook.

12. A user equipment (UE) in a wireless communication system, the UE comprising: transceiver; as well as a processor operatively connected to the transceiver, the processor configured to: sending, via the transceiver, a first measurement report to a base station, the first measurement report indicating information about at least a first wide beam and a second wide beam in a wide beam set for beam selection, the first measurement report being generated based on measurement of a first sub-narrow beam set in the first wide beam; as well as After transmitting a second measurement report indicating information about a second set of sub-narrow beams in the second wide beam, receiving a request to measure the second set of sub-narrow beams from the base station via the transceiver, The reception of the request is determined according to a condition that a request to measure the second set of narrow sub-beams in the second wide beam in the set of wide beams is detected based on the first measurement report, and The base station selects a sub-narrow beam for use from the first set of sub-narrow beams based on determining that the condition is not detected.

13. The UE according to claim 12, in, The first measurement report includes information indicating signal strength of a wide beam in the wide beam set, and The base station determines that the condition is detected based on determining that a difference between a signal strength of the first wide beam and a signal strength of the second wide beam is less than a predetermined threshold.

14. The UE according to claim 12, in, The first measurement report includes information indicating the signal strength of the first wide beam and the signal strength of a narrow sub-beam in the first set of narrow sub-beams, and The detection of the condition is determined based on determining that a difference between a signal strength of the first wide beam and a signal strength of a best narrow beam in the first set of narrow sub-beams is greater than a predetermined threshold.

15. The UE according to claim 14, wherein: The signal strength used for the beam selection is determined based on the beam pattern of the base station, the beam pattern of the UE, a channel model, and an antenna orientation of the UE.

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