Method and apparatus for antenna parameter configuration in cellular communication system

Abstract

The present disclosure relates to a communication method and system for integrating a 5th generation (5G) communication system supporting a higher data rate than a 4th generation (4G) system with an Internet of Things (IoT) technology. 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, car networking, health care, digital education, smart retail, security and safety services, etc. A method and apparatus for a BS in a wireless communication system are provided. The method and apparatus include: storing a set of beam codebooks including a plurality of angle grids, each of which is identified as a first type angle grid or a second type angle grid; identifying a set of parameters for adjusting an antenna beam pattern based on a set of beam codebooks, wherein the set of parameters includes at least one of feedback information received from a UE or a set of KPIs identified by the BS; determining the type of the angle grid as a first type angle grid or a second type angle grid, wherein the first type angle grid and the second type angle grid respectively include a first antenna gain and a second antenna gain; and selecting a beam codebook corresponding to the antenna beam pattern from a set of beam codebooks.

Description

Technical Field

[0001] The present disclosure relates generally to wireless communication systems, and more particularly, to antenna parameter configuration for cellular communication systems. Background Art

[0002] In order to meet the increased demand for wireless data traffic since the deployment of 4G communication systems, efforts have been made to develop improved 5G or pre-5G communication systems. Therefore, 5G or pre-5G communication systems are also referred to as "beyond 4G networks" or "post-LTE systems". 5G communication systems are considered to be implemented in higher (mmWave) frequency bands, such as the 60GHz band, 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-size MIMO (FD-MIMO), array antennas, analog beamforming and massive antenna technology are discussed relative to 5G communication systems. In addition, in 5G communication systems, improvements and developments are being made based on advanced small cells, cloud radio access networks (RAN), ultra-dense networks, device-to-device (D2D) communications, wireless backhaul, mobile networks, system networks, collaborative communications, collaborative multi-point (CoMP), receiving-end interference elimination, etc. In 5G systems, hybrid FSK and QAM modulation (FQAM) and sliding window superposition coding (SWSC) have been developed as advanced coded modulation (ACM), and filter bank multi-carrier (FBMC), non-orthogonal multiple access (NOMA) and sparse code multiple access (SCMA) as advanced access technologies.

[0003] The Internet is a human-centric connected network where people generate and consume information. Today, the Internet is developing into the Internet of Things (IoT), where information can be exchanged and processed by distributed entities such as objects without human intervention. The Internet of Everything (IoE) has emerged, which is a combination of IoT technology and big data processing technology achieved by connecting to cloud servers. Since the implementation of the IoT already requires corresponding technical elements such as "sensing technology", "wired / wireless communication and network infrastructure", "service interface technology" and "security technology", sensor networks, machine-to-machine (M2M) communication, machine type communication (MTC), etc. have been studied recently. Such IoT environments can provide smart Internet technology services that create new value for human life by collecting and analyzing data generated between connected objects. Through the integration and combination between existing information technology (IT) and various industrial technologies, the IoT can be applied to various fields, including smart homes, smart buildings, smart cities, smart cars or 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) as the above-mentioned big data processing technology can also be considered as an example of the fusion between 5G technology and IoT technology.

[0005] Massive Multiple Input Multiple Output (MIMO) units (MMUs) are much more expensive than traditional Remote Units (RUs). Although MMUs bring the benefits of massive MIMO to Long Term Evolution (LTE) Release 10 and later users, most user equipment available in the market are LTE Release 8 users, which only support Transmission Mode 4 (TM4) with 1D Precoding Matrix Indicator (PMI) feedback. Therefore, when MMUs are deployed, it is more important to provide enhanced user experience for LTE Release 8 and earlier users, rather than just for LTE Release 10 and later users. Dynamic or adaptive broadcast beams or common beams for transmitting cell-specific reference signals (CRS) are needed to improve the performance of TM4 users. Broadcast beams can be used to send CRS signals. Summary of the invention

[0006]

Technical issues

[0007] For an enhanced (or advanced) cellular communication system, a method for configuring antenna parameters is needed.

[0008]

Problem Solution

[0009] In one embodiment, a base station (BS) in a wireless communication network is provided. The BS includes a transceiver and a memory operably connected to the transceiver, the memory being configured to store a set of beam codebooks including a plurality of angular grids, wherein each angular grid is identified as a first type angular grid or a second type angular grid based on a cell angular region of a cell belonging to the BS. The BS also includes a processor operably connected to the transceiver and the memory, the processor being configured to: identify a set of parameters for adjusting an antenna beam pattern based on a set of beam codebooks, wherein the set of parameters includes feedback information received from a user equipment (UE) or at least one of a set of key performance indicators (KPIs) identified by the BS; determine the type of the angular grid to be a first type angular grid or a second type angular grid based on a set of parameters, wherein the first type angular grid and the second type angular grid include a first antenna gain and a second antenna gain, respectively; and select a beam codebook corresponding to the antenna beam pattern from a set of beam codebooks based on the type of the angular grid.

[0010] In another embodiment, a method for a base station in a wireless communication network is provided. The method includes: storing a set of beam codebooks including a plurality of angle grids, each of which is identified as a first type angle grid or a second type angle grid based on a cell angle region of a cell belonging to the BS; identifying a set of parameters for adjusting an antenna beam pattern based on a set of beam codebooks, wherein the set of parameters includes at least one of feedback information received from a UE or a set of KPIs identified by the BS; determining the type of the angle grid as a first type angle grid or a second type angle grid based on a set of parameters, wherein the first type angle grid and the second type angle grid include a first antenna gain and a second antenna gain, respectively; and selecting a beam codebook corresponding to the antenna beam pattern from a set of beam codebooks based on the type of the angle grid.

[0011] In another embodiment, a non-transitory computer-readable medium including program code, which, when executed by at least one processor, causes an electronic device to: store a set of beam codebooks including multiple angle grids, each of which is identified as a first type of angle grid or a second type of angle grid based on a cell angle domain of a cell belonging to a BS; identify a set of parameters for adjusting an antenna beam pattern based on a set of beam codebooks, wherein the set of parameters includes feedback information received from a UE or at least one of a set of KPIs identified by the BS; determine the type of the angle grid as a first type of angle grid or a second type of angle grid based on a set of parameters, wherein the first type of angle grid and the second type of angle grid respectively include a first antenna gain and a second antenna gain; and select a beam codebook corresponding to the antenna beam pattern from a set of beam codebooks based on the type of the angle grid.

[0012] Those skilled in the art can easily understand other technical features from the following drawings, descriptions and claims.

[0013] [Advantageous effects of the present disclosure]

[0014] Embodiments of the present disclosure provide a method and apparatus for antenna parameter configuration in a cellular communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] For a more complete understanding of the present disclosure and its advantages, reference will now be made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals represent like parts:

[0016] Figure 1 illustrates an example wireless network according to an embodiment of the present disclosure;

[0017] Figure 2 An example gNB according to an embodiment of the present disclosure is shown;

[0018] Figure 3 An example UE according to an embodiment of the present disclosure is shown;

[0019] Figure 4 illustrates an example wireless transmission path according to the present disclosure;

[0020] Figure 5 illustrates an example wireless receive path according to the present disclosure;

[0021] FIG6A shows an example of dividing an angular region into grids according to an embodiment of the present disclosure;

[0022] FIG6B shows an example of mapping an angular region into a reading cell according to an embodiment of the present disclosure;

[0023] Figure 7 A flowchart showing a method for online beam selection according to an embodiment of the present disclosure is shown;

[0024] Figure 8 illustrates example beam selection according to an embodiment of the present disclosure;

[0025] Fig. 9 showing an example coverage plane bounded by a spherical region of interest and divided into rectangles of equal size representing flow regions in accordance with an embodiment of the present disclosure;

[0026] Fig.10 A flowchart of a method for beamforming according to an embodiment of the present disclosure is shown;

[0027] Fig.11 shows an example coverage pattern according to an embodiment of the present disclosure;

[0028] Fig.12Example 3dB and 9dB cutoff profiles (red and blue) of a common beam according to an embodiment of the present disclosure are shown;

[0029] Fig.13 shows an example heat map showing a continuum of coverage based on a target coverage pattern in accordance with an embodiment of the present disclosure;

[0030] Fig.14 shows an example coverage pattern according to an embodiment of the present disclosure, wherein low traffic areas are given less importance;

[0031] Fig.15 illustrates an example coverage profile of a beam generated from a target coverage pattern according to an embodiment of the present disclosure;

[0032] Fig.16 An example coverage diagram showing beams generated from a target coverage pattern according to an embodiment of the present disclosure;

[0033] Fig.17 shows an example target coverage pattern indicating a coverage plane for uniformly distributed traffic according to an embodiment of the present disclosure;

[0034] Fig.18 shows an example coverage profile corresponding to a coverage pattern according to an embodiment of the present disclosure;

[0035] Fig.19 shows an example overlay corresponding to an overlay pattern according to an embodiment of the present disclosure;

[0036] Fig. 20 illustrates an example initial beam selection according to an embodiment of the present disclosure;

[0037] Fig.21 illustrates example beam selection according to an embodiment of the present disclosure;

[0038] Fig. 22 An example bisection beam search algorithm according to an embodiment of the present disclosure is shown;

[0039] Fig.23 Optimal beam selection based on example classification according to an embodiment of the present disclosure is shown;

[0040] Fig.24 illustrates an example MIS beam selection according to an embodiment of the present disclosure;

[0041] Fig.25 illustrates example beam selection facilitated by transfer learning enhanced classification according to an embodiment of the present disclosure;

[0042] Fig.26 illustrates example classification-based beam selection with online adjustment according to an embodiment of the present disclosure; and

[0043] Fig. 27 A flow chart of a method for antenna parameter configuration according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0044] Before the following detailed description, it may be advantageous to clarify the definitions of certain words and phrases used throughout the disclosure. The term "coupling" and its derivatives refer to any direct or indirect communication between two or more elements, whether or not these elements are in physical contact with each other. The terms "send", "receive" and "communication" and their derivatives include direct and indirect communication. The terms "include" and "comprise" and their derivatives mean including but not limited to. The term "or" is inclusive, meaning "and / or". The phrase "associated with..." and its derivatives mean to include, include therein, interconnect, include, be included therein, be connected to or connected with it, be coupled to or coupled with it, communicate with it, collaborate with it, interlace with it, be parallel with it, be close to it, be bound to or bound with it, have, have its nature, be associated with or be associated with it, or similar meaning. The term "controller" refers to any device, system or part thereof that controls at least one operation. Such controllers can be implemented with hardware or a combination of hardware and software and / or firmware. The function associated with any particular controller can be centralized or distributed locally or remotely. The phrase "at least one of" when used with a list of items means that different combinations of one or more of the listed items may be used, and that only one item in the list may be needed. 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 and B and C.

[0045] In addition, the various functions described below can be implemented or supported by one or more computer programs, each of which is formed by a computer-readable program code and implemented in a computer-readable medium. The terms "application" and "program" refer to one or more computer programs, software components, instruction sets, programs, functions, objects, classes, instances, related data or a part thereof that are suitable for implementation with appropriate computer-readable program codes. The phrase "computer-readable program code" includes any type of computer code, including source code, object code and executable code. The phrase "computer-readable medium" includes any type of medium that can be accessed by a computer, such as a read-only memory (ROM), a random access memory (RAM), a hard drive, a compact disc (CD), a digital video disc (DVD) or any other type of memory. "Non-transient" computer-readable media does not include wired, wireless, optical or other communication links that transmit transient electrical signals or other signals. Non-transient computer-readable media include media that can permanently store data and media that can store data and overwrite the data at a later stage, such as rewritable optical discs or erasable storage devices.

[0046] Definitions for certain other words and phrases are provided throughout this patent document. Those of ordinary skill in the art should understand that in many, if not most instances, such definitions apply to prior and future uses of such defined words and phrases.

[0047] Discussed below Figures 1 to 27 The various embodiments used to describe the principles of the present disclosure in this patent document are for illustrative purposes only and should not be considered to limit the scope of the present disclosure in any way. Those skilled in the art should understand that the principles of the present disclosure can be implemented in any appropriately arranged system or device.

[0048] The following Figure 1-3 Various embodiments are described that are implemented in a wireless communication system and use orthogonal frequency division multiplexing (OFDM) or orthogonal frequency division multiple access (OFDMA) communication techniques. Figures 1 to 3 The 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.

[0049] 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 is for illustration only. Other embodiments of the wireless network 100 may be used without departing from the scope of the present disclosure.

[0050] 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.

[0051] gNB 102 provides wireless broadband access to network 130 for a first plurality of UEs within coverage area 120 of gNB 102. The first plurality of UEs includes UE 111, which may be located within a small enterprise; UE 112, which may be located within an enterprise (E); UE 113, which may be located within a WiFi hotspot (HS); UE 114, which may be located in a first residence (R); UE 115, which may be located within a second residence (R); and UE 116, which may be a mobile device (M) such as a cellular phone, a wireless laptop, a wireless PDA, etc. gNB 103 provides wireless broadband access to network 130 for a second plurality of UEs within coverage area 125 of gNB 103. The second plurality of UEs includes UE 115 and UE 116. In some embodiments, one or more of 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.

[0052] Depending on the network type, the term "base station" or "BS" may refer to any component (or set of components) configured to provide wireless access to a network, such as a transmission point (TP), a transmission-reception point (TRP), an enhanced base station (eNodeB or eNB), a 5G / NR base station (gNB), a macrocell base station, a femtocell, a WiFi access point (AP), or other wireless-enabled devices. The base station may provide wireless access according to one or more wireless communication protocols, such as 5G / NR 3GPP New Radio Interface / Access (NR), 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" are used interchangeably in this patent document 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" may refer to any component, such as a "mobile station", "subscriber station", "remote terminal", "wireless terminal", "reception point", or "user equipment". For convenience, the terms "user equipment" and "UE" used in this patent document refer to a remote wireless device that wirelessly accesses a BS, whether the UE is a mobile device (such as a mobile phone or smartphone) or a device that is generally considered to be a fixed device (such as a desktop computer or a vending machine).

[0053] The dashed lines show the approximate extents of coverage areas 120 and 125, which are illustrated as nearly circular for purposes of illustration and explanation only. It should be clearly understood that coverage areas associated with gNBs, 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 radio environment associated with natural and man-made obstacles.

[0054] As described in more detail below, one or more of the UEs 111-116 include circuitry, programming, or a combination thereof for antenna parameter configuration for a cellular communication system. In some embodiments, one or more of the gNBs 101-103 include circuitry, programming, or a combination thereof for antenna parameter configuration for a cellular communication system.

[0055] although Figure 1 An example of a wireless network is shown, but Figure 1 Various changes may be made. For example, the wireless network may include any number of gNBs and any number of UEs in any suitable arrangement. In addition, gNB 101 may communicate directly with any number of UEs and provide such UEs with wireless broadband access to network 130. Similarly, each gNB 102-103 may communicate directly with network 130 and provide UEs with direct wireless broadband access to network 130. In addition, gNBs 101, 102, and / or 103 may provide access to other or additional external networks, such as an external telephone network or other type of data network.

[0056] Figure 2 An example gNB 102 is shown according to an embodiment of the present disclosure. Figure 2 The embodiment of the gNB 102 shown in FIG. 1 is for illustration purposes only, and Figure 1 gNBs 101 and 103 may have the same or similar configurations. However, gNBs have various configurations, and Figure 2 The scope of the present disclosure is not limited to any particular implementation of the gNB.

[0057] like Figure 2 As shown, 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, a memory 230, and a backhaul or network interface 235.

[0058] The RF transceivers 210a-210n receive input RF signals from antennas 205a-205n, such as signals transmitted by UEs in the network 100. The RF transceivers 210a-210n down-convert the input RF signals to generate IF or baseband signals. The IF or baseband signals are sent to the RX processing circuit 220, which generates processed baseband signals by filtering, decoding and / or digitizing the baseband or IF signals. The RX processing circuit 220 sends the processed baseband signals to the controller / processor 225 for further processing.

[0059] The TX processing circuit 215 receives analog or digital data (such as voice data, web data, email, or interactive video game data) from the controller / processor 225. The TX processing circuit 215 encodes, multiplexes, and / or digitizes the output baseband data to generate processed baseband or IF signals. The RF transceivers 210a-210n receive the output processed baseband or IF signals from the TX processing circuit 215 and up-convert the baseband or IF signals into RF signals that are transmitted via the antennas 205a-205n.

[0060] 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 output signals from multiple antennas 205a-205n or input signals to multiple antennas are weighted differently to effectively steer the output signals in a desired direction. The controller / processor 225 may support any of a variety of other functions in the gNB 102.

[0061] 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 required by the executing process.

[0062] The controller / processor 225 is also coupled to a backhaul or network interface 235. The backhaul or network interface 235 allows 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 appropriate (multiple) wired or wireless connections. For example, when the gNB 102 is implemented as part of a cellular communication system (such as a system supporting 5G / NR, LTE, or LTE-A), the interface 235 can allow 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 allow the gNB 102 to communicate via a wired or wireless local area network or via a wired or wireless connection to a larger network (such as the Internet). The interface 235 includes any appropriate structure that supports communication via a wired or wireless connection, such as an Ethernet or RF transceiver.

[0063] 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 or other ROM.

[0064] although Figure 2 An example of gNB 102 is shown, but the Figure 2 For example, gNB 102 may include any number of Figure 2 As a specific example, the access point may include multiple interfaces 235, and the controller / processor 225 may support routing functions to route data between different network addresses. As another specific example, although illustrated as including a single instance of TX processing circuitry 215 and a single instance of RX processing circuitry 220, the gNB 102 may include multiple instances of each circuit (e.g., one for each RF transceiver). In addition, Figure 2 The various components in may be combined, further subdivided, or omitted, and additional components may be added according to specific needs.

[0065] Figure 3 An example UE 116 is shown according to an embodiment of the present disclosure. Figure 3 The embodiment of UE 116 shown in FIG. 1 is for illustration only, and Figure 1 UEs 111-115 may have the same or similar configurations. However, UEs may have various configurations, and Figure 3 The scope of the present disclosure is not limited to any particular implementation of the UE.

[0066] like Figure 3As shown, UE 116 includes antenna 305, radio frequency (RF) transceiver 310, TX processing circuit 315, microphone 320 and RX processing circuit 325. UE 116 also includes speaker 330, processor 340, input / output (I / O) interface (IF) 345, touch screen 350, display 355 and memory 360. Memory 360 includes operating system (OS) 361 and one or more applications 362.

[0067] RF transceiver 310 receives an incoming RF signal from antenna 305 transmitted by a gNB of network 100. RF transceiver 310 downconverts the incoming RF signal 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 (such as for voice data) or to processor 340 for further processing (such as for web browsing data).

[0068] The TX processing circuit 315 receives analog or digital voice data from the microphone 320 or other output 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 output baseband data to generate a processed baseband or IF signal. The RF transceiver 310 receives the output processed baseband or IF signal from the TX processing circuit 315 and up-converts the baseband or IF signal to an RF signal that is transmitted through the antenna 305.

[0069] The processor 340 may include one or more processors or other processing devices and executes 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.

[0070] The processor 340 is also capable of executing other processes and programs resident in the memory 360, such as processes for beam management. The processor 340 can move data into or out of the memory 360 as needed to execute the process. In some embodiments, the processor 340 is configured to execute the application 362 based on the OS 361 or in response to a signal received from the gNB or operator. The processor 340 is also coupled to the I / O interface 345, which provides the UE 116 with the ability to connect to other devices such as laptops and handheld computers. The I / O interface 345 is the communication path between these accessories and the processor 340.

[0071] 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, light emitting diode display, or other display capable of presenting text and / or at least limited graphics, such as from a website.

[0072] Memory 360 is coupled to processor 340. A portion of memory 360 may include random access memory (RAM), and another portion of memory 360 may include flash memory or other read-only memory (ROM).

[0073] although Figure 3 An example of UE 116 is shown, but the Figure 3 Make various changes. For example, Figure 3 The various components in the embodiment may be combined, further subdivided, or omitted, and additional components may be added as required. As 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). In addition, although Figure 3 The UE 116 is illustrated as being configured as a mobile phone or smart phone, but the UE may be configured to operate as other types of mobile or stationary devices.

[0074] In order to meet the increased demand for wireless data traffic since the deployment of 4G communication systems and to realize various vertical applications, efforts have been made to develop and deploy improved 5G / NR or pre-5G / NR communication systems. Therefore, 5G / NR or pre-5G / NR communication systems are also called "beyond 4G networks" or "post-LTE systems". 5G / NR communication systems are considered to be implemented in higher frequency (mmWave) bands such as 28GHz or 60GHz bands to achieve higher data rates or in lower frequency bands such as 6GHz to achieve robust coverage and mobility support. In order to reduce the propagation loss of radio waves and increase the transmission distance, beamforming, massive multiple-input multiple-output (MIMO), full-size MIMO (FD-MIMO), array antennas, analog beamforming and massive antenna technology are discussed with respect to 5G communication systems.

[0075] In addition, in the 5G / NR communication system, improvements and development are being made based on advanced small cells, cloud radio access networks (RAN), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, mobile networks, system networks, collaborative communications, coordinated multi-point (CoMP), and receiving-end interference cancellation.

[0076] Since certain embodiments of the present disclosure may be implemented in 5G systems, the discussion of 5G systems and frequency bands associated therewith is for reference only. However, the present disclosure is not limited to 5G systems or frequency bands associated therewith, and embodiments of the present disclosure may be utilized in conjunction with any frequency band. For example, aspects of the present disclosure may also be applied to deploy 5G communication systems, 6G, or even higher versions that may use terahertz (THz) frequency bands.

[0077] A communication system may include a downlink (DL) and an uplink (UL), where DL refers to transmission from a base station or one or more transmission points to a UE and UL refers to transmission from a UE to a base station or one or more reception points.

[0078] The time unit used for DL ​​signaling or UL signaling on a cell is called a time slot and may include one or more symbols. A symbol may also serve as an additional time unit. The frequency (or bandwidth (BW)) unit is called a resource block (RB). An RB includes several subcarriers (SC). For example, a time slot may have a duration of 0.5 milliseconds or 1 millisecond, include 14 symbols, and an RB may include 12 SCs, where the spacing between SCs is 15KHz or 30KHz, etc.

[0079] DL signals include data signals conveying information content, control signals conveying DL control information (DCI), and reference signals (RS) also called pilot signals. The gNB transmits data information or DCI through the corresponding physical DL shared channel (PDSCH) or physical DL control channel (PDCCH). PDSCH or PDCCH can be transmitted on a variable number of time slot symbols including one time slot symbol. For simplicity, the DCI format received by the UE scheduling PDSCH is called DL DCI format, and the DCI format sent from the UE scheduling PUSCH is called UL DCI format.

[0080] The gNB transmits one or more of multiple types of RS, including Channel State Information RS (CSI-RS) and Demodulation RS (DMRS). CSI-RS is primarily used by UEs 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 reports (IMRs), CSI interference measurement (CSI-IM) resources associated with zero power CSI-RS (ZP CSI-RS) configurations are used. The CSI process includes both NZP CSI-RS and CSI-IM resources.

[0081] The UE can determine the CSI-RS transmission parameters through DL control signaling from the gNB or higher layer signaling, such as radio resource control (RRC) signaling. The transmission instance of CSI-RS can be indicated by DL control signaling or configured by higher layer signaling. DMRS is only transmitted in the BW of the corresponding PDCCH or PDSCH, and the UE can use DMRS to demodulate data or control information.

[0082] Figure 4 and Figure 5 Example wireless transmit and receive paths according to the present disclosure are shown. In the following description, transmit path 400 may be described as being implemented in a gNB (such as gNB 102) and receive path 500 may be described as being implemented in a UE (such as UE 116). However, it is understood that receive path 500 may be implemented in a gNB and transmit path 400 may be implemented in a UE. In some embodiments, receive path 500 is configured to support antenna parameter configuration of a cellular communication system as described in embodiments of the present disclosure.

[0083] like Figure 4 The transmit path 400 shown 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 415, a parallel to serial (P-to-S) block 420, an add cyclic prefix block 425, and an upconverter (UC) 430. Figure 5The receive path 500 shown includes a downconverter (DC) 555, a cyclic prefix removal block 560, a serial to parallel (S-to-P) block 565, a size-N fast Fourier transform (FFT) block 570, a parallel to serial (P-to-S) block 575, and a channel decoding and demodulation block 580.

[0084] As shown in diagram 400, the channel coding and modulation block 405 receives a set of information bits, applies coding (such as low-density parity check (LDPC) coding), and modulates the input bits (such as using quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM)) to generate a frequency-domain modulation symbol sequence.

[0085] The serial to parallel block 410 converts (such as demultiplexes) the serial modulation symbols into parallel data to generate N parallel symbol streams, where N is the IFFT / FFT size used in the gNB 102 and the UE 116. The size N IFFT block 415 performs an IFFT operation on the N parallel symbol streams to generate a time domain output signal. The parallel to serial block 420 converts (such as multiplexes) the parallel time domain output symbols from the size N IFFT block 415 to generate a serial time domain signal. The add cyclic prefix block 425 inserts a cyclic prefix to the time domain signal. The up-converter 430 modulates (such as up-converts) the output of the add cyclic prefix block 425 to an RF frequency for transmission via a wireless channel. The signal may also be filtered at baseband before conversion to RF frequency.

[0086] The RF signal transmitted from gNB 102 reaches UE 116 after propagating through the wireless channel, and operations opposite to those at gNB 102 are performed at UE 116.

[0087] like Figure 5 As shown, the down converter 555 down-converts the received signal to the baseband frequency, and the cyclic prefix removal block 560 removes the cyclic prefix to generate a serial time domain baseband signal. The serial to parallel block 565 converts the time domain baseband signal into a parallel time domain signal. The size N FFT block 570 performs an FFT algorithm to generate N parallel frequency domain signals. The parallel to serial block 575 converts the parallel frequency domain signals into a modulated data symbol sequence. The channel decoding and demodulation block 580 demodulates and decodes the modulated symbols to recover the original input data stream.

[0088] Each of gNBs 101-103 may implement Figure 41-16 in the downlink, and may implement a receive path 500 similar to that shown in the figure for receiving from UEs 111-116 in the uplink. Similarly, each of UEs 111-116 may implement transmit path 400 for transmitting to gNBs 101-103 in the uplink, and may implement receive path 500 for receiving from gNBs 101-103 in the downlink.

[0089] Figure 4 and Figure 5 Each component in may be implemented using hardware only or a combination of hardware and software / firmware. As a specific example, Figure 4 and Figure 5 At least some of the components in can be implemented with software, while other components can be implemented by configurable hardware or a mixture of software and configurable hardware. For example, FFT block 570 and IFFT block 515 can be implemented as a configurable software algorithm, wherein the value of size N can be modified according to the implementation.

[0090] In addition, although described as using FFT and IFFT, this is for illustration only and should not be construed as limiting the scope of the present disclosure. Other types of transforms may be used, such as discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT) functions. It will be appreciated that for DFT and IDFT functions, the value of variable N may be any integer (such as 1, 2, 3, 4, etc.), while for FFT and IFFT functions, the value of variable N may be any integer as a power of 2 (such as 1, 2, 4, 8, 16, etc.).

[0091] although Figure 4 and Figure 5 An example of a wireless transmit and receive path is shown, but the Figure 4 and Figure 5 Make various changes. For example, Figure 4 and Figure 5 The various components in may be combined, further subdivided, or omitted, and additional components may be added as required. Figure 4 and Figure 5 It is intended to illustrate examples of the types of transmit and receive paths that may be used in a wireless network. Any other suitable architecture may be used to support wireless communications in a wireless network.

[0092] In one embodiment, selection of a common beam from a predefined beam codebook is provided based on feedback from the UE. The predefined codebook is generated to improve the throughput of connected UEs and maintain coverage of idle UEs. One way to balance throughput and coverage is to divide the cell angular coverage into different angular grids, as shown in Figures 6A and 6B.

[0093] FIG6A shows an example of dividing an angular domain into a grid 600 according to an embodiment of the present disclosure. The embodiment of dividing an angular domain into a grid 600 shown in FIG6A is for illustration only. One or more components shown in FIG6A may be implemented in a dedicated circuit configured to perform the functions described, or one or more components may be implemented by one or more processors that execute instructions to perform the functions described.

[0094] FIG6B shows an example of mapping an angular domain into a read cell 650 according to an embodiment of the present disclosure. The embodiment of dividing the angular domain into a grid 650 shown in FIG6B is for illustration only. One or more components shown in FIG6B may be implemented in a dedicated circuit configured to perform the functions, or one or more components may be implemented by one or more processors that execute instructions to perform the functions.

[0095] In one embodiment, the predefined cell coverage area is composed of a horizontal angle coverage range and vertical angle coverage θ c Indicates. In one example, the horizontal angle coverage can be 120 degrees, while the vertical angle coverage can be 10 degrees. The coverage can be the beam width defined by 3dB or any other gain of the antenna. In the angular space, the cell coverage is divided into different parts. As shown in Figure 6A, one division method is to divide the horizontal angle into N equal parts and the vertical angle into M equal parts. As shown in Figure 6B, the angular domain angle grid can also be mapped into the cell coverage space.

[0096] In one embodiment, to ensure coverage and improve throughput, different weights may be assigned to different angular grids when synthesizing antenna beam patterns. For example, weight w1 may be placed on angular grids a0 and a1, while weight w2 may be placed on other angular grids. The weights here may be used to synthesize beam patterns.

[0097] In one embodiment, the beam codebook is generated based on assigning weights w1 w2 to different combinations of angular grids to synthesize the beam codebook. If there are MN grids and two weights in total, the size of the beam codebook can be 2MN.

[0098] Figure 7 A flow chart of a method 700 for online beam selection according to an embodiment of the present disclosure is shown. Figure 7 The illustrated embodiment of method 700 is for illustration only. Figure 7 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0099] In one embodiment, to select a beam from a beam codebook, angle information identified from a sounding reference signal (SRS) is provided. Figure 7 As shown, first, in step 702, the eNB collects SRS signaling from the connected UEs within a predefined time period T. Second, based on the SRS information, in step 704, the eNB identifies the arrival angles of all connected UEs. Third, in step 706, the eNB selects the best beam from the beam codebook based on the beam selection metric. These steps are summarized in the following flow chart.

[0100] Figure 8 An example beam selection 800 is shown in accordance with an embodiment of the disclosure. Figure 8 The illustrated embodiment of beam selection 800 is for illustration only. Figure 8 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0101] In the present disclosure, a digital unit (DU) may be considered. After the UE sends the SRS and measurement report to the RU and DU, the DU first calls the function in the digital signal processing (DSP) to process the SRS and measurement report to obtain the angle information of the angle of arrival (AoA). This information may then be passed to the beam selection module to select a beam from the beam codebook. Afterwards, the selected beam may be sent to the RU to configure the antenna configuration.

[0102] The network determines a set of beams based on the required spherical coverage area and UE traffic intensity. During the online beam selection phase, beams are searched and applied.

[0103] Fig. 9 An example coverage plane 900 is shown bounded by a spherical region of interest and divided into equal-sized rectangles representing flow regions in accordance with an embodiment of the present disclosure. Fig. 9 The illustrated embodiment of cover plane 900 is for illustration only. Fig. 9 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0104] In one embodiment, single beam forming is provided.

[0105] In this embodiment, in step 1, the total angular coverage area and the partitions of the angular coverage area are identified.

[0106] An example of this step is as follows. As shown in Figures 6A and 6B, the spherical coverage plane is set to cover the following ranges of interest: and in the azimuth domain Angle θ c and θ C It is called the center (boresight) elevation and azimuth and elevation, θ T is the vertical tilt angle, and θ W and φ w are the vertical and horizontal beam widths. The coverage plane is divided into VXH rectangles of equal size along the grid. Rectangles 0≤i<V, 0≤j<H cover the elevation domain. and in the azimuth domain

[0107] In this embodiment, in step 2, the traffic intensity of each corner area is determined. For the examples given in Figures 6A and 6B, each rectangle corresponds to a traffic area with different intensity levels. According to any utility, the beam pointing to the higher traffic intensity area needs to present enhanced coverage. To achieve this, the coverage plane is encoded using a set of weights that reflect the traffic intensity and thus the beamforming gain. The weights are directly related to the traffic intensity. The encoded coverage plane is called the target coverage pattern. For any coverage pattern, a single beam codebook is generated by running a K-means algorithm to optimize the utility function (e.g., average throughput or average log throughput) of the coverage plane and the corresponding weights.

[0108] In this embodiment, in step 3, beams are generated according to the determined spherical coverage and weights.

[0109] In one embodiment, the beams may be factored to maximize an objective function in the following form: where {A i} is the coverage area division, {a i} is the weight applied to each region according to the traffic intensity, f(·) is the utility function, and G(θ j ,φ j ) is the direction (θ j ,φ j ) on the beamforming gain. The utility function f(·) can be data rate, throughput, received reference signal power, etc.

[0110] In one embodiment, an alphabet of weight size n is specified, such as a i ∈{0, 1, 10}, where these weights correspond to areas of different traffic intensity and, therefore, to the target coverage.

[0111] Fig.10 A flowchart of a method 1000 for beamforming according to an embodiment of the present disclosure is shown. Fig.10 The illustrated embodiment of method 1000 is for illustration only. Fig.10One or more components shown in the figure may be implemented in a dedicated circuit configured to perform the functions, or one or more components may be implemented by one or more processors executing instructions to perform the functions. The beamforming process is summarized as follows Fig.10 shown.

[0112] In one embodiment, the above single beam forming process can be repeated to generate all possible n VH These beams are generated offline and cached for fast access.

[0113] In another embodiment, if the network only identifies a subset of the traffic density distribution and generates corresponding beams, the network may select the subset based on historical knowledge, building location information, etc.

[0114] Example simulation results are provided here to illustrate the design process. Assume that the base station has an 8X4 antenna array. The boresight direction is assumed to be (θ 0 =90°,φ 0 =180°)·. Downward tilt angle θ T It's 5 degrees.

[0115] The required angular coverage area is (90°≤θ≤100°, 135°≤θ≤255°). The entire area is divided into 8 regions. In the figure below, three different situations can be provided.

[0116] like Figure 11-13 As shown in , only two areas with high traffic density are provided. Figure 14-16 As shown, 2 high flow areas and 6 low flow areas are provided. Figure 14-16 As shown, 8 areas with similar traffic intensity are provided.

[0117] As shown in the above figures, the provided method can adjust the beam radiation pattern according to the coverage requirements and traffic intensity.

[0118] Fig.11 An example overlay pattern 1100 is shown according to an embodiment of the present disclosure. Fig.11 The illustrated embodiment of the cover pattern 1100 is for illustration only. Fig.11 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0119] Fig.12 Example 3 decibel (dB) and 9 dB cutoff profiles (red and blue) 1200 of a common beam are shown in accordance with an embodiment of the disclosure. Fig.12 The example of 3 dB and 9 dB cutoff profiles (red and blue) 1200 for a common beam is shown for illustration only. Fig.12 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0120] Fig.13 An example heat map 1300 showing a continuum of coverage based on a target coverage pattern is shown in accordance with an embodiment of the present disclosure. The embodiment of the heat map 1300 showing a continuum of coverage based on a target coverage pattern is for illustration only. Fig.13 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0121] Fig.14 An example coverage pattern 1400 is shown in which low traffic areas are given less importance, in accordance with an embodiment of the present disclosure. Fig.14 The embodiment of the coverage pattern 1400 shown in which low flow areas are given less importance is for illustration only. Fig.14 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0122] Fig.15 An example coverage profile 1500 of a beam generated from a target coverage pattern is shown according to an embodiment of the present disclosure. Fig.15 The illustrated embodiment of coverage profile 1500 of beams generated by a target coverage pattern is for illustration only. Fig.15 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0123] Fig.16 An example coverage diagram 1600 of beams generated from a target coverage pattern is shown in accordance with an embodiment of the present disclosure. Fig.16 The illustrated embodiment of the target coverage pattern generated beam coverage diagram 1600 is for illustration only. Fig.16 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0124] Fig.17 An example target coverage pattern 1700 is shown indicating a coverage plane for evenly distributed traffic according to an embodiment of the present disclosure. Fig.17The embodiment of target coverage pattern 1700 shown in FIG. 1 indicating a coverage plane for uniformly distributed traffic is for illustration only. Fig.17 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0125] Fig.18 An example coverage profile 1800 corresponding to a coverage pattern according to an embodiment of the present disclosure is shown. Fig.18 The embodiment of the coverage profile 1800 of the coverage pattern shown in FIG. 1 is for illustration only. Fig.18 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0126] Fig.19 An example overlay 1900 corresponding to an overlay pattern according to an embodiment of the present disclosure is shown. Fig.19 The embodiment of overlay map 1900 of the overlay pattern shown in FIG. 1 is for illustration only. Fig.19 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0127] In one embodiment, the eNB selects a beam from a codebook based on SRS signaling. The SRS can provide information about the angular direction (horizontal and azimuth) of each UE. Based on this information, the eNB can determine which angular directions include the UE (traffic direction) and which angular directions do not include the UE (coverage direction). One metric for selecting the beam to use is to select the beam that can generate the best average antenna gain in the traffic direction. Another metric for selecting a beam is to select a beam that can optimize the gain in the traffic direction while ensuring that the gain in the coverage direction is reduced to within a threshold or remains unchanged.

[0128] In one embodiment, the eNB selects a beam from a codebook based on SRS and channel quality indicator (CQI) feedback. The selection of the beam depends on many parameters. One of these parameters is SRS, which is the subband information sent from the UE to the eNB.

[0129] Another parameter is the CQI which is also sent from the UE to the eNB, and the CQI can be subband or wideband information. The SRS can provide information about the angular direction (horizontal and azimuth) of each UE. Based on this information, the eNB can determine which angular directions include the UE (traffic direction) and which angular directions do not include the UE (coverage direction). In addition, using the CQI information can provide the eNB with some information about UEs that are well covered by existing beams (UEs with high CQI values) and UEs that need more coverage (UEs reporting lower CQI values). Combined with this information, the eNB can select the best beam that can be used as a cell common beam.

[0130] In another embodiment, the eNB also considers other KPIs to adjust the beams. One of these KPIs is coverage guarantee, which means that even if there are certain locations without any users (no SRS is received from these angular directions), a minimum amount of coverage must be provided for these directions so that when idle users connect, these idle users can find corresponding coverage. Other KPIs (such as switching frequency, dropped calls, etc.) are also monitored and used for the decision of the selected beam.

[0131] In another embodiment, the beam selection includes an initial beam selection and a dynamic beam adaptation step. In the initial beam selection step, the eNB may select the beams according to the following steps: Fig. 20 The above process is shown to select beams based on SRS and / or CQI.

[0132] Fig. 20 An example initial beam selection 2000 is shown according to an embodiment of the disclosure. Fig. 20 The illustrated embodiment of initial beam selection 2000 is for illustration only. Fig. 20 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0133] In the beam adaptation step, after applying the new beam, the eNB may continue to monitor network KPIs, including throughput, call drop rate, coverage, and UE reports. If the KPI degradation is greater than a threshold, the eNB may reconfigure the eNB to the previously used beam. Otherwise, the eNB may select a new beam according to the beam bisection selection algorithm. In the beam bisection selection algorithm, the eNB may first select an angular grid and select a beam from it to enhance the traffic performance on the grid. If the throughput performance is enhanced, the eNB may determine the grid to be a traffic grid, otherwise the grid is determined to be a coverage grid. After deciding the first grid, the eNB may continue to decide the next grid in the angular coverage domain until all grids have been decided to be traffic grids or coverage grids. These processes are as follows: Fig.21 and22 shown.

[0134] Fig.21 An example beam selection 2100 is shown according to an embodiment of the present disclosure. Fig.21 The illustrated embodiment of beam selection 2100 is for illustration only. Fig.21 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0135] Fig. 22 An example bisection beam search algorithm 2200 is shown in accordance with an embodiment of the present disclosure. Fig. 22 The illustrated embodiment of the bisection beam search algorithm 2200 is for illustration only. Fig. 22 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0136] In one embodiment, it is described how to use machine learning techniques to select the best common beam to achieve the best tradeoff between the throughput of connected UEs and the cell coverage (including connected UEs and idle UEs).

[0137] In one example of artificial intelligence (AI) method 1, optimal beam selection based on classification is provided. As shown in the figure, the training of the classification model can be performed offline.

[0138] Fig.23 An example classification-based optimal beam selection 2300 is shown according to an embodiment of the present disclosure. Fig.23 The illustrated embodiment of classification-based best beam selection 2300 is for illustration only. Fig.23 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0139] In one embodiment of step 1-1, training model construction is provided under the assumption that a finite number of codebooks are defined. In terms of optimizing the KPI metric of interest, offline data can be used to classify UEs based on the best beam index, i.e., UE category 1 corresponds to beam index 1, UE category 2 corresponds to beam index 2, etc. A variety of classification algorithms can be used, such as ridge algorithm, SVM, random forest, etc. The input features for UE classification may be as follows but are not limited to: UE SRS measurements and statistical moments of UE SRS; and UE feedback (CQI, PMI, RI, RSRP) and statistical moments of UE feedback.

[0140] In one embodiment of step 2-1, online selection is provided to classify UEs using a classification model obtained via offline training and apply a corresponding beam index for each UE category. Note that the trained UEs may include connected UEs and / or idle UEs.

[0141] In one embodiment of AI method 2, RL learning enabled optimal beam selection (multi-independent state beam selection) is provided, as shown in the following figure.

[0142] Fig.24 An example multiple independent state (MIS) beam selection 2400 according to an embodiment of the present disclosure is shown. Fig.24 The illustrated embodiment of MIS beam selection 2400 is for illustration only. Fig.24 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0143] In one embodiment of step 2-1, offline classification is provided to classify the UE into multiple independent states using a composite function of multiple parameters. This classification can be achieved by black box (using machine learning classification algorithm) or white box (using domain knowledge to define classification metrics. Different values ​​of the classification metric correspond to different states).

[0144] The reward model is given as follows: Reward = f(State, Action), where the state is a composite function of UE feedback and / or its statistical moments, i.e., State = g(UE feedbacks , U Emeasurement ).

[0145] The action space is the candidates of all predefined beam books or a subset of beam books. The reward is the KPI of interest, such as UE throughput, capacity or coverage, predicted throughput (including both connected UEs and idle UEs).

[0146] In one embodiment of step 2-2, online reinforcement learning is provided within each state, and a multi-bandit arm (MAB) algorithm is employed to maximize the KPI metric of interest, as shown in the above equation. In each state, the action corresponds to a different beam. The beam candidates for each state can be the same or different.

[0147] In one embodiment, in an alternative to AI method 1, beam selection facilitated by transfer learning enhanced classification is provided. Due to the mismatch between offline and online data and the dynamic changes in UE / traffic distribution, transfer learning can be applied to further improve the accuracy of UE classification. An updated training model based on online data can be used to classify UEs.

[0148] Fig.25 An example beam selection 2500 facilitated by transfer learning enhanced classification is shown according to an embodiment of the present disclosure. Fig.25 The illustrated embodiment of transfer learning enhanced classification facilitated beam selection 2500 is for illustration only. Fig.25 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0149] In another embodiment, a classification-based beam selection with online adjustment is provided. Due to the mismatch between offline data and online data, the best beam offset for each class of UE can be calculated online. The best beam selection for each class of UE can then be adjusted by the offset calculated online. The offsets for each class of UE can be the same or different.

[0150] Fig.26 An example classification-based beam selection 2600 with online adjustment is shown according to an embodiment of the disclosure. Fig.26 The illustrated embodiment of classification-based beam selection with online adjustment 2600 is for illustration only. Fig.26 One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0151] In one embodiment of AI Method 2, the size of the beam codebook candidates within each state is fixed. However, the candidates can change in real time, whether it is some fixed shift of the candidates or dynamic adjustment of the candidates. In another embodiment, the size of the beam codebook candidates within each state is different. The candidates can also change dynamically. In yet another embodiment, the candidates of the beam codebook within each state can be the same or different and change over time.

[0152] Fig. 27 A flowchart of a method 2700 for antenna parameter configuration according to an embodiment of the present disclosure is shown, which may be performed by a BS (eg, Figure 1 101-103) shown in FIG. Fig. 27 The illustrated embodiment of method 2700 is for illustration only. Fig. 27One or more of the components shown in the drawings may be implemented in dedicated circuits configured to perform the functions described, or one or more of the components may be implemented by one or more processors that execute instructions to perform the functions described.

[0153] like Fig. 27 As shown, method 2700 starts at step 2702. In step 2702, the BS stores a set of beam codebooks including a plurality of angular grids, each of the plurality of angular grids being identified as a first type angular grid or a second type angular grid based on a cell angular region of a cell belonging to the BS.

[0154] In step 2704, the BS identifies a set of parameters for adjusting the antenna beam pattern based on the set of beam codebooks, wherein the set of parameters includes at least one of feedback information received from the UE or a set of KPIs identified by the BS.

[0155] In step 2704, the group of KPIs includes at least one of network throughput, call drop rate, or the number of UEs connected to RRC, and the feedback information received from the UE includes CQI, rank indicator (RI), reference signal received power (RSRP) information, reference signal received quality (RSRQ) information, or SRS.

[0156] In step 2706, the BS determines the angular grid type as a first type angular grid or a second type angular grid based on the set of parameters, wherein the first type and the second type angular grid include a first antenna gain and a second antenna gain, respectively.

[0157] In step 2708, the BS selects a beam codebook corresponding to the antenna beam pattern from a group of beam codebooks based on the type of the angular grid.

[0158] In one embodiment, the BS identifies a cell angle domain including a traffic grid with high traffic and a coverage grid with low traffic, identifies a cell azimuth angle domain including N angles, and identifies a cell elevation angle domain including M angles. In this embodiment, a total of MN grids cover the cell angle domains.

[0159] In one embodiment, the BS identifies the angular grid type based on the AoA determined based on the sounding reference signal or measurement report received from the UE, and assigns a value to each of the plurality of angular grids, the value being increased if the AoA is identified in the plurality of angular grids during a predefined time period.

[0160] In one embodiment, if the value is greater than a predefined threshold, the BS determines the corner mesh type as a traffic mesh of high traffic, or if the value is less than or equal to the predefined threshold, the BS determines the corner mesh type as a coverage mesh of low traffic.

[0161] In one embodiment, the BS identifies the beam metric based on the weighted antenna gain. In this embodiment, the beam codebook corresponds to an antenna beam pattern such that the beam metric on a first type of angular grid is maximized and the beam metric on a second type of angular grid is reduced to within a predefined threshold.

[0162] In one embodiment, the BS determines whether the set of KPIs is updated after selecting the beam codebook corresponding to the first type of angular grid, determines the first type of angular grid as a traffic grid for high traffic based on updating the set of KPIs in a first manner, and determines the first angular grid as a coverage grid for low traffic based on updating the set of KPIs in a second manner. In this embodiment, the beam codebook corresponds to an antenna beam pattern so that the antenna beam gain on the first type of angular grid is maximized.

[0163] In one embodiment, the base station determines whether the set of KPIs is updated after selecting the beam codebook corresponding to the second type angular grid and the first type angular grid; the second angular grid is determined to be a traffic grid of high traffic based on updating the set of KPIs in a first manner, and the second angular grid is determined to be a coverage grid of low traffic based on updating the set of KPIs in a second manner. In this embodiment, the beam codebook corresponds to a sky beam diagram, so that the weighted antenna gain for the second type angular grid and the first type angular grid is maximized.

[0164] The above flow charts illustrate example methods that can be implemented according to the principles of the present disclosure, and various changes can be made to the methods shown in the flow charts herein. For example, although illustrated as a series of steps, the individual steps in each figure may overlap, occur in parallel, occur in different orders, or occur multiple times. In another example, a step may be omitted or replaced by another step.

[0165] Although the present disclosure has been described using example embodiments, various changes and modifications may be proposed by those skilled in the art. The present disclosure is intended to include changes and modifications that fall within the scope of the appended claims. Any description in this application should not be construed as implying that any particular element, step, or function is an essential element that must be included within the scope of the claims. The scope of this patent subject matter is limited only by the claims.

Claims

1. A base station BS in a wireless communication system, the BS comprising: Transceiver; a memory operably connected to the transceiver, the memory configured to store a set of beam codebooks including a plurality of angular grids, wherein each of the plurality of angular grids is identified as a first type angular grid or a second type angular grid based on a cell angular region of a cell belonging to the BS, and wherein the first type angular grid and the second type angular grid are determined based on a traffic intensity of the cell; and at least one processor operably connected to the transceiver and the memory, the at least one processor configured to: identifying a set of parameters for adjusting an antenna beam pattern based on the set of beam codebooks, wherein the set of parameters comprises at least one of feedback information received from a user equipment UE or a set of key performance indicators KPIs identified by the BS, determining the type of angular grid to be the first type angular grid or the second type angular grid based on the set of parameters, wherein the first type angular grid includes a first antenna gain and the second type angular grid includes a second antenna gain, and A beam codebook is selected from the set of beam codebooks based on the angular grid type, the beam codebook corresponding to the antenna beam pattern.

2. The BS of claim 1 , wherein the at least one processor is further configured to: identifying the cell corner region including a traffic grid with high traffic and a coverage grid with low traffic, Identify a cell azimuth angle domain comprising N angles, and Identify the cell elevation angle domain including M angles, A total of MN grids cover the cell corner area of ​​the cell.

3. The BS of claim 1 , wherein the at least one processor is further configured to: identifying the angular grid type based on an angle of arrival AoA, the angle of arrival AoA being determined based on a sounding reference signal or a measurement report received from the UE, assigning a value to each of the plurality of angular grids, the value being increased if the AoA is identified in the plurality of angular grids during a predefined time period, If the value is greater than a predefined threshold, the corner grid type is determined to be a traffic grid with high traffic, or If the value is less than or equal to the predefined threshold, the corner mesh type is determined to be a coverage mesh with low traffic.

4. The BS of claim 1 , wherein the at least one processor is further configured to: identifying a beam metric based on weighted antenna gains, and selecting a beam codebook corresponding to the antenna beam pattern from the set of beam codebooks such that the beam metric on the first type angular grid is maximized and the beam metric on the second type angular grid is reduced to within a predefined threshold, and in, The set of KPIs includes at least one of network throughput, call drop rate, or number of UEs connected to Radio Resource Control (RRC); and The feedback information received from the UE includes at least one of a channel quality indicator CQI, a rank indicator RI, reference signal received power RSRP information, reference signal received quality RSRQ information or a sounding reference signal SRS.

5. The BS of claim 1 , wherein the at least one processor is further configured to: selecting a beam codebook corresponding to the antenna beam pattern from the set of beam codebooks so as to maximize the antenna beam gain on the first type of angular grid, determining whether the set of KPIs is updated after selecting a beam codebook corresponding to the first type of angular grid, Based on updating the set of KPIs in a first manner, determining the first type of corner grid as a traffic grid with high traffic, and Based on updating the set of KPIs in a second manner, the first-type corner grid is determined as a coverage grid with low traffic.

6. A method performed by a base station BS in a wireless communication network, the method comprising: storing a set of beam codebooks including a plurality of angular grids, wherein each of the plurality of angular grids is identified as a first type angular grid or a second type angular grid based on a cell angular region of a cell belonging to the BS, and wherein the first type angular grid and the second type angular grid are determined based on a traffic intensity of the cell; identifying a set of parameters for adjusting an antenna beam pattern based on the set of beam codebooks, wherein the set of parameters comprises at least one of feedback information received from a user equipment (UE) or a set of key performance indicators (KPIs) identified by the BS; determining an angular grid type as the first type angular grid or the second type angular grid based on the set of parameters, wherein the first type angular grid includes a first antenna gain and the second type angular grid includes a second antenna gain; and A beam codebook is selected from the set of beam codebooks based on the angular grid type, the beam codebook corresponding to the antenna beam pattern.

7. The method according to claim 6, further comprising: identifying the cell corner region including a traffic grid having high traffic and a coverage grid having low traffic; Identify a cell azimuth angle domain including N angles; as well as Identify the cell elevation angle domain including M angles, A total of MN grids cover the cell corner area of ​​the cell.

8. The method according to claim 6, further comprising: identifying the angular grid type based on an angle of arrival AoA, the angle of arrival AoA being determined based on a sounding reference signal or a measurement report received from the UE, assigning a value to each of the plurality of angular grids, the value being increased if the AoA is identified in the plurality of angular grids during a predefined time period, If the value is greater than a predefined threshold, the corner grid type is determined to be a traffic grid with high traffic, or If the value is less than or equal to the predefined threshold, the corner mesh type is determined to be a coverage mesh with low traffic.

9. The method according to claim 6, further comprising: Identifying beam metrics based on weighted antenna gains; and Wherein selecting the beam codebook comprises selecting a beam codebook corresponding to the antenna beam pattern from the set of beam codebooks so that the beam metric on the first type angular grid is maximized and the beam metric on the second type angular grid is reduced to within a predefined threshold.

10. The method according to claim 6, wherein: The set of KPIs includes at least one of network throughput, call drop rate, or number of UEs connected to a radio resource control (RRC) connection, and The feedback information received from the UE includes at least one of a channel quality indicator CQI, a rank indicator RI, reference signal received power RSRP information, reference signal received quality RSRQ information or a sounding reference signal SRS.

11. The method according to claim 6, wherein selecting the beam codebook comprises selecting a beam codebook corresponding to the antenna beam pattern from the set of beam codebooks so that the antenna beam gain on the first type of angular grid is maximized, the method further comprising: determining whether the set of KPIs is updated after selecting a beam codebook corresponding to the first type of angular grid; Based on updating the set of KPIs in a first manner, determining the first type of corner grid as a traffic grid with high traffic; and Based on updating the set of KPIs in a second manner, the first-type corner grid is determined as a coverage grid with low traffic.

12. The method of claim 11, wherein selecting the beam codebook comprises selecting a beam codebook corresponding to the antenna beam pattern from the set of beam codebooks such that weighted antenna beam gains for the second type angular grid and the first type angular grid are maximized, wherein the method further comprises: determining whether the set of KPIs is updated after selecting a beam codebook corresponding to the second type of angular grid and the first type of angular grid; Based on updating the set of KPIs in the first manner, determining the second type of corner grid as a traffic grid with high traffic; and Based on updating the set of KPIs in a second manner, the second-type corner grid is determined as a coverage grid with low traffic.

13. A non-transitory computer readable medium comprising instructions which, when executed by at least one processor of a base station BS, cause the BS to: A set of beam codebooks including a plurality of angular grids is stored, wherein: Each of the plurality of angular grids is identified as a first type angular grid or a second type angular grid based on a cell angular region of a cell belonging to the BS, and wherein the first type angular grid and the second type angular grid are determined based on a traffic intensity of the cell; identifying a set of parameters for adjusting an antenna beam pattern based on the set of beam codebooks, wherein the set of parameters comprises at least one of feedback information received from a user equipment UE or a set of key performance indicators KPIs identified by the BS, determining the type of angular grid to be the first type angular grid or the second type angular grid based on the set of parameters, wherein the first type angular grid includes a first antenna gain and the second type angular grid includes a second antenna gain, and A beam codebook is selected from the set of beam codebooks based on the angular grid type, the beam codebook corresponding to the antenna beam pattern.

14. The non-transitory computer readable medium of claim 13, further comprising instructions that, when executed by the at least one processor, cause the BS to: identifying the cell corner region including a traffic grid with high traffic and a coverage grid with low traffic, identifying a cell azimuth angle region comprising N angles; and Identify the cell elevation angle domain including M angles, A total of MN grids cover the cell corner area of ​​the cell.

15. The non-transitory computer readable medium of claim 13, further comprising instructions that, when executed by the at least one processor, cause the BS to: identifying the angular grid type based on an angle of arrival AoA, the angle of arrival AoA being determined based on a sounding reference signal or a measurement report received from the UE, assigning a value to each of the plurality of angular grids, the value being increased if the AoA is identified in the plurality of angular grids during a predefined time period, If the value is greater than a predefined threshold, the corner grid type is determined to be a traffic grid with high traffic, or If the value is less than or equal to the predefined threshold, the corner mesh type is determined to be a coverage mesh with low traffic.

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