Method and apparatus for fusing radio frequency and sensor measurements for beam management

Abstract

The disclosure relates to a communication method and system that fuse a fifth generation (5G) communication system for supporting higher data rates beyond a fourth generation (4G) system with technologies for Internet of Things (IoT). The disclosure can be applied to intelligent services based on 5G communication technologies and IoT-related technologies, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A method for beam management by a user equipment (UE) in a wireless communication system includes determining reference signal measurement results from signals received from one or more base stations. The method further includes obtaining motion information of the UE from a motion sensor. The method further includes generating beam management information for beam management based on the reference signal measurement results and the motion information.

Description

Technical Field

[0001] This disclosure generally relates to wireless communication systems. More specifically, this disclosure relates to beam selection operations at a terminal or user equipment (UE) based on a combination of radio frequency (RF) and sensor measurements. Background Technology

[0002] To meet the increased demand for wireless data services since the deployment of 4G communication systems, efforts have been made to develop improved 5G or near-5G communication systems. Therefore, 5G or near-5G communication systems are also referred to as "super-4G networks" or "post-LTE systems." 5G communication systems are considered to be implemented in higher frequency (mmWave) bands (e.g., the 60GHz band) to achieve higher data rates. To reduce radio wave propagation loss and increase transmission distance, beamforming, massive MIMO, full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and massive MIMO technologies have been discussed in 5G communication systems. Furthermore, improvements to the system network are being developed based on advanced small cells, cloud radio access networks (RAN), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, mobile networks, cooperative communication, coordinated multipoint (CoMP), and receiver interference cancellation. In 5G systems, hybrid FSK and QAM modulation (FQAM) and sliding window superposition coding (SWSC) have been developed as advanced coding and modulation (ACM), as well as filter bank multicarrier (FBMC), non-orthogonal multiple access (NOMA) and sparse code multiple access (SCMA) as advanced access technologies.

[0003] The Internet, as a human-centric network of connections where humans generate and consume information, is now evolving into the Internet of Things (IoT), a distributed network of entities such as objects that exchange and process information without human intervention. The Internet of Everything (IoE), a combination of IoT technology and big data processing technology connected to cloud servers, has emerged. Because IoT implementations have always required technological elements such as sensing technology, wired / wireless communication and network infrastructure, service interface technology, and security technology, sensor networks, machine-to-machine (M2M) communication, and machine-type communication (MTC) have recently been studied. Such an IoT environment can provide intelligent Internet technology services that create new value for human life by collecting and analyzing data generated among connected objects. IoT can be applied to a wide range of fields, including smart homes, smart buildings, smart cities, smart cars or connected cars, smart grids, healthcare, smart appliances, and advanced medical services, through the integration and combination of existing information technology (IT) with various industrial applications.

[0004] Accordingly, various attempts have been made to apply 5G communication systems to IoT networks. For example, technologies such as sensor networks, machine-type communication (MTC), and machine-to-machine (M2M) communication can be implemented through beamforming, MIMO, and array antennas. Cloud radio access networks (RAN), as an application of the aforementioned big data processing technologies, can also be considered an example of the convergence between 5G and IoT technologies. Summary of the Invention

[0005] Technical issues

[0006] 5G / NR communication systems are considered to be implemented in higher frequency (mmWave) bands (e.g., 28 GHz or 60 GHz bands) to achieve higher data rates, or in lower frequency bands such as 6 GHz to enable robust coverage and mobility support. To reduce radio wave propagation loss and increase transmission distance, beamforming, massive MIMO, full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and massive MIMO technologies are discussed in 5G / NR communication systems.

[0007] The channel may change due to UE movement, such as a user changing the UE's orientation, which could cause misalignment. The channel may also change due to UE movement, such as a user moving the UE from its current location to a new location. Therefore, the UE will need to search for the optimal beam again. However, measuring all beams can introduce significant delays when searching for the optimal beam for reception and / or transmission, which may reduce reception / transmission efficiency.

[0008] Technical solution

[0009] This disclosure provides a method and apparatus for combining reference signal measurement results and sensor measurement results for beam management.

[0010] In one embodiment, a UE for beam management is provided in a wireless communication system. The UE includes a transceiver, a motion sensor, and a processor. The transceiver is configured to receive signals from one or more base stations. The motion sensor is configured to generate motion information. The processor is operatively connected to the transceiver and the motion sensor. The processor is configured to determine a reference signal measurement result based on the signals. The processor is also configured to obtain motion information of the UE. The processor is further configured to generate beam management information for beam management based on the reference signal measurement result and the motion information. The processor is further configured to identify a beam based on the generated beam management information. The processor is also configured to perform wireless communication based on the identified beam.

[0011] In another embodiment, a method for beam management by a user equipment (UE) in a wireless communication system is provided. The method includes: determining reference signal measurement results based on signals received from one or more base stations. The method further includes: obtaining motion information of the UE from a motion sensor. The method further includes: generating beam management information for beam management based on the reference signal measurement results and the motion information. The method further includes: identifying beams based on the generated beam management information. The method further includes: performing wireless communication based on the identified beams.

[0012] Other technical features will be readily apparent to those skilled in the art based on the following figures, descriptions, and claims.

[0013] Beneficial effects

[0014] Therefore, embodiments of this disclosure utilize additional sensors of the UE to more efficiently find the optimal beam. For example, embodiments of this disclosure provide an apparatus and method for combining (or fusing) reference signal measurement results (such as RSRP information) with sensor information (such as orientation information from one or more motion sensors of the UE) to find a beam for performing wireless communication. Attached Figure Description

[0015] To gain a more complete understanding of this disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which similar reference numerals denote similar parts:

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

[0017] Figure 2 An embodiment according to this disclosure is shown. Figure 1 Example exemplary base stations in a wireless network;

[0018] Figure 3 An embodiment according to this disclosure is shown. Figure 1 Example UE in a wireless network;

[0019] Figure 4 An example network configuration according to an embodiment of this disclosure is shown;

[0020] Figure 5 An example of hybrid beamforming according to an embodiment of the present disclosure is shown;

[0021] Figure 6 An example electronic device based on rotation to select different beams according to an embodiment of the present disclosure is shown;

[0022] Figure 7aA method for generating beam management information according to embodiments of the present disclosure is illustrated;

[0023] Figure 7b A table of example parameters for generating beam management information according to embodiments of the present disclosure is shown;

[0024] Figure 8a Example particle filtering at different time points according to embodiments of the present disclosure is shown;

[0025] Figure 8b An example method for identifying a beam for performing wireless communication is shown according to an embodiment of the present disclosure;

[0026] Figure 8c Example diagrams of beam determination regions corresponding to different beams according to embodiments of the present disclosure are shown;

[0027] Figure 8d A table is shown illustrating how different states affect particle filtering according to embodiments of the present disclosure;

[0028] Figure 8e An example method for identifying a beam for performing wireless communication is shown according to an embodiment of the present disclosure;

[0029] Figure 9a An example method for identifying a beam for performing wireless communication is shown according to an embodiment of the present disclosure;

[0030] Figure 9b An example process for modifying reference signal measurement results and motion measurement results to generate input according to an embodiment of the present disclosure is shown;

[0031] Figure 9c and Figure 9d An example diagram is shown for identifying patterns in reference signal measurement results and motion measurement results according to embodiments of the present disclosure;

[0032] Figure 9e A reward system for identifying beams used in wireless communication is illustrated according to embodiments of the present disclosure; and

[0033] Figure 10 A method for beam management by a UE in a wireless communication system is illustrated according to embodiments of the present disclosure. Detailed Implementation

[0034] Before proceeding with the detailed description below, it may be advantageous to define certain terms and phrases used throughout this patent document. The term “coupled” and its derivatives refer to any direct or indirect communication between two or more elements, regardless of whether those elements are physically in contact with each other. The terms “transmit,” “receive,” and “communicate,” and their derivatives, encompass both direct and indirect communication. The terms “comprise” and “include,” and their derivatives, mean to include without limitation. The term “or” is inclusive, meaning and / or. The phrase “associated with,” and its derivatives, means to include, to be included within, to be interconnected with, to contain, to be contained within, to be connected to or connected with, to be coupled to or coupled with, to be able to communicate with, to cooperate with, to be intertwined, to be juxtaposed, to be close to, to be bound to or bound by, to have, to possess the properties of, to have a relationship with, etc. The term “controller” means any device, system, or part thereof that controls at least one operation. Such a controller may be implemented in hardware or a combination of hardware and software and / or firmware. The functionality associated with any particular controller can be centralized or distributed, whether local or remote. 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 only one item from 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 and B and C.

[0035] Furthermore, the various functions described below can be implemented or supported by one or more computer programs, each computer program being formed by computer-readable program code and embodied in a computer-readable medium. The terms "application" and "program" refer to one or more computer programs, software components, instruction sets, procedures, functions, objects, classes, instances, associated data, or portions thereof adapted for implementation in suitable computer-readable program code. 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 accessible by a computer, such as read-only memory (ROM), random access memory (RAM), hard disk drive, optical disc (CD), digital video disc (DVD), or any other type of storage. "Non-transitory" computer-readable media excludes wired, wireless, optical, or other communication links that transmit transient electrical or other signals. Non-transitory computer-readable media includes media capable of permanently storing data and media capable of storing and later overwriting data, such as rewritable optical discs or erasable storage devices.

[0036] Definitions of certain other words and phrases are provided throughout this patent document. Those skilled in the art will understand that in many (if not most) instances, such definitions apply to both the prior and future uses of the words and phrases defined herein.

[0037] The following discussion Figures 1 to 10 The various embodiments used to describe the principles of this disclosure in this patent document are for illustrative purposes only and should not be construed as limiting the scope of this disclosure in any way. Those skilled in the art will understand that the principles of this disclosure can be implemented in any suitably arranged wireless communication system or apparatus.

[0038] 5G / NR communication systems are considered to be implemented in higher frequency (mmWave) bands (e.g., 28 GHz or 60 GHz bands) to achieve higher data rates, or in lower frequency bands such as 6 GHz to enable robust coverage and mobility support. To reduce radio wave propagation loss and increase transmission distance, beamforming, massive MIMO, full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and massive MIMO technologies are discussed in 5G / NR communication systems.

[0039] In addition, in 5G / NR communication systems, development is carried out to improve the system network based on advanced small cells, cloud radio access networks (RAN), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, mobile networks, cooperative communication, coordinated multipoint (CoMP), receiver interference cancellation, etc.

[0040] The discussion of 5G systems and associated frequency bands is for reference only, as certain embodiments of this disclosure can be implemented in 5G systems. However, this disclosure is not limited to 5G systems or associated frequency bands, and embodiments of this disclosure can be utilized in conjunction with any frequency band. For example, aspects of this disclosure can also be applied to 5G communication systems, 6G deployments that may use terahertz (THz) frequency bands, or even later versions.

[0041] Embodiments of this disclosure describe a communication system. In some embodiments, the communication system may be a millimeter-wave (mmWave) wireless communication system. The communication system includes a downlink (DL) that transmits signals from a transmitting point such as a base station (BS) or eNodeB to a user equipment (UE) and an uplink (UL) that transmits signals from the UE to a receiving point such as an eNodeB. The UE, also commonly referred to as a terminal or mobile station, may be fixed or mobile and may be a cellular phone, personal computer device, etc. The eNodeB, typically a fixed station, may also be referred to as an access point or other equivalent terms.

[0042] In some embodiments, the BS can transmit multiple pilot signals. The UE can receive the transmitted pilot signals from the beam and then identify the beam with the highest received power as the optimal beam. The UE can then measure reference signals (such as Reference Signal Received Power (RSRP), Signal-to-Interference Plus Noise Ratio (SINR), Signal-to-Noise Ratio (SNR), Reference Signal Received Quality (RSRQ), etc.) one beam at a time or multiple beams at a time. For example, the UE can measure multiple reference signals to find the beam with the strongest signal. The strongest reference signal can be identified by comparing the gain of multiple reference signals or any other type of metric such as RSRP, SINR, SNR, RSRQ, etc. The beam with the best reference signal measurement results is selected and used for signal reception and / or transmission.

[0043] The embodiments of this disclosure recognize and consider that the identified optimal beam may not remain optimal when the channel changes. The channel may change due to UE movement, such as a user changing the UE's orientation, which could cause misalignment. The channel may change due to UE movement, such as a user moving the UE from its current location to a new location. Therefore, the UE will need to search for the optimal beam again. However, measuring all beams can introduce significant delays in searching for the optimal beam for reception and / or transmission, which can reduce reception / transmission efficiency. For example, if there is a 20ms interval between each beam and there are 8 beams, the UE may spend up to 160ms searching for the optimal new beam. During this 160ms period, using a suboptimal beam can lead to rate loss and experience degradation.

[0044] Therefore, embodiments of this disclosure utilize additional sensors of the UE to more efficiently find the optimal beam. For example, embodiments of this disclosure provide an apparatus and method for combining (or fusing) reference signal measurement results (such as RSRP information) with sensor information (such as orientation information from one or more motion sensors of the UE) to find a beam for performing wireless communication.

[0045] In some embodiments, the UE may include an orientation sensor that indicates whether the UE has rotated, and if so, indicates the direction and magnitude of the rotation. For example, if an optimal beam is identified from a first direction, and the UE subsequently rotates, the UE may determine a new direction of the optimal beam based on rotation information from a motion sensor, by combining the direction of the previous optimal beam with the rotation information.

[0046] Figures 1-4 The following describes various embodiments implemented in wireless communication systems using orthogonal frequency division multiplexing (OFDM) or orthogonal frequency division multiple access (OFDMA) communication technologies. Figures 1-4The description is not intended to imply physical or architectural limitations in the manner in which different embodiments may be implemented. Different embodiments of this disclosure may be implemented in any suitably arranged communication system.

[0047] Figure 1 An example wireless network 100 according to an embodiment of the present disclosure is shown. Figure 1 The embodiment of wireless network 100 shown is for illustrative purposes only. Other embodiments of wireless network 100 can be used without departing from the scope of this disclosure.

[0048] like Figure 1 As shown, the wireless network 100 includes gNB 101 (e.g., a 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.

[0049] Depending on the network type, the term "base station" or "BS" can refer to any component (or set of components) configured to provide wireless access to a network, such as a transmitting point (TP), a transmitting-receiving point (TRP), an enhanced base station (eNodeB or eNB), a 5G / NR base station (gNB), a macro cell, a femtocell, a WiFi access point (AP), or other wireless-enabled device. A base station can provide wireless access according to one or more of the following wireless communication protocols: for example, 5G / NR 3GPP New Radio Interface / Access (NR), Long Term Evolution (LTE), LTE-Advanced (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. Additionally, depending on the network type, the term "user equipment" or "UE" can refer to any component such as "mobile station," "user 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 the 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).

[0050] gNB 102 provides wireless broadband access to network 130 to a first plurality of user equipments (UEs) within its coverage area 120. The first plurality of UEs includes: UE 111, which may be located in a small business (SB); 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 mobile phone, wireless laptop, wireless PDA, etc. gNB 103 provides wireless broadband access to network 130 to a second plurality of UEs within its coverage area 125. The second plurality of UEs includes UE 115 and UE 116. In some embodiments, one or more of the gNBs 101-103 may use 5G / NR, LTE, LTE-A, WiMAX, WiFi or other wireless communication technologies to communicate with each other and with the UEs 111-116.

[0051] The dashed lines indicate the approximate extent of coverage areas 120 and 125, which are shown as generally circular for illustrative purposes only. It should be clearly understood that, depending on the configuration of the gNB and variations in the wireless environment associated with natural and man-made obstacles, the coverage areas associated with the gNB (such as coverage areas 120 and 125) may have other shapes, including irregular shapes.

[0052] As described in more detail below, one or more of UEs 111-116 include circuitry, programming, or a combination thereof for efficient beam selection. In some embodiments, one or more of gNBs 101-103 include circuitry, programming, or a combination thereof for efficient beam selection. In some embodiments, the wireless network 100 may be a 5G communication system, wherein electronics such as UE 116 are capable of determining a specific beam for transmitting and / or receiving with BS 102 or BS 103 based on reference signal measurements and motion information from one or more sensors of UE 116.

[0053] although Figure 1 An example of a wireless network is shown, but more can be found on... Figure 1Various modifications can be made. For example, the wireless network can include any number of gNBs and any number of UEs in any suitable configuration. Additionally, 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 UEs with direct wireless broadband access to network 130. Furthermore, gNBs 101, 102, and / or 103 can provide access to other or additional external networks, such as external telephone networks or other types of data networks.

[0054] Figure 2 An example gNB 102 according to an embodiment of the present disclosure is shown. Figure 2 The embodiment of gNB102 shown is for illustrative purposes only, and Figure 1 gNBs 101 and 103 can have the same or similar configurations. However, gNBs come in a wide variety of configurations, and Figure 2 This disclosure is not intended to limit the scope to any particular implementation of gNB.

[0055] 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.

[0056] RF transceivers 210a-210n receive incoming RF signals, such as signals transmitted by the UE in network 100, from antennas 205a-205n. RF transceivers 210a-210n down-convert the incoming reference signal to generate an IF signal or a baseband signal. The IF signal or baseband signal is sent to RX processing circuitry 220, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband signal or IF signal. RX processing circuitry 220 sends the processed baseband signal to controller / processor 225 for further processing.

[0057] 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 outgoing baseband data to generate a processed baseband signal or IF signal. RF transceivers 210a-210n receive the outgoing processed baseband signal or IF signal from the TX processing circuit 215 and up-convert the baseband signal or IF signal into an RF signal transmitted via antennas 205a-205n.

[0058] The controller / processor 225 can include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 225 can control the reception of forward channel signals and the transmission of reverse channel signals via RF transceivers 210a-210n, RX processing circuitry 220, and TX processing circuitry 215, according to known principles. The controller / processor 225 can also support additional functions, such as more advanced wireless communication functions. For example, the controller / processor 225 can support beamforming or directional routing operations, wherein outgoing signals from multiple antennas 205a-205n and incoming signals to multiple antennas 205a-205n are weighted differently to effectively steer the outgoing signals in a desired direction. Any of a wide variety of other functions can be supported in the gNB 102 via the controller / processor 225. In some embodiments, the controller / processor 225 includes at least one microprocessor or microcontroller.

[0059] The controller / processor 225 is also capable of executing programs and other processes, such as an OS, residing in memory 230. The controller / processor 225 is capable of moving data into or out of memory 230 as required by the executing process.

[0060] The controller / processor 225 is also coupled to the 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 over a network. 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 (such as a cellular communication system supporting 5G / NR, LTE, or LTE-A), interface 235 enables 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, interface 235 enables 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. Interface 235 includes any suitable architecture supporting communication via a wired or wireless connection, such as an Ethernet or RF transceiver.

[0061] The memory 230 is coupled to the controller / processor 225. A portion of the memory 230 may include RAM, and another portion of the memory 230 may include flash memory or other ROM.

[0062] although Figure 2 An example of gNB 102 is shown, but it is possible to compare it with other models. Figure 2 Various changes were made. For example, gNB 102 was able to include... Figure 2Each component can be any number shown. As a particular example, an access point can include a plurality of interfaces 235, and a controller / processor 225 can support routing functions to route data between different network addresses. As another particular example, although shown as a single instance including TX processing circuitry 215 and a single instance including RX processing circuitry 220, the gNB 102 can include multiple instances of each (e.g., one per RF transceiver). Additionally, Figure 2 The various components can be combined, further subdivided, or omitted, and additional components can be added as needed.

[0063] Figure 3 An example UE 116 according to an embodiment of the present disclosure is shown. Figure 3 The embodiment of UE116 shown is for illustrative purposes only, and Figure 1 UEs 111-115 can have the same or similar configurations. However, UEs appear in a wide variety of configurations, and Figure 3 This disclosure is not intended to limit the scope to any particular implementation of the UE.

[0064] 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 a receive (RX) processing circuit 325. UE 116 also includes a speaker 330, a processor 340, an input / output (I / O) interface (IF) 345, an input device 350 (such as a touchscreen or keypad), a display 355, memory 360, and sensors 365. Memory 360 includes an operating system (OS) 361 and one or more applications 362.

[0065] RF transceiver 310 receives incoming RF signals transmitted by a gNB of network 100 from antenna 305. RF transceiver 310 down-converts the incoming RF signals to generate an intermediate frequency (IF) signal or a baseband signal. The IF signal or baseband signal is sent to RX processing circuitry 325, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband signal 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).

[0066] 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 signal or IF signal. The RF transceiver 310 receives the outgoing processed baseband signal or IF signal from the TX processing circuit 315 and up-converts the baseband signal or IF signal into an RF signal transmitted via the antenna 305.

[0067] Processor 340 may include one or more processors or other processing devices and execute OS 361 stored in memory 360 to control the overall operation of UE 116. For example, processor 340 may control the reception of forward channel signals and the transmission of reverse channel signals through RF transceiver 310, RX processing circuitry 325 and TX processing circuitry 315 according to known principles. In some embodiments, processor 340 includes at least one microprocessor or microcontroller.

[0068] Processor 340 is also capable of executing other processes and programs residing in memory 360, such as processes for beam management. Processor 340 is capable of moving data into or out of memory 360 as required by the executing processes. In some embodiments, processor 340 is configured to execute application 362 based on OS 361 or in response to signals received from 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 laptop computers and handheld computers. I / O interface 345 is the communication path between these accessories and processor 340.

[0069] Processor 340 is also coupled to input device 350. An operator of UE 116 can use input device 350 to input data into UE 116. Input device 350 may be a keyboard, touchscreen, mouse, trackball, voice input, or other device that serves as a user interface enabling a user to interact with UE 116. For example, input device 350 may include voice recognition processing, allowing the user to input voice commands. In another example, input device 350 may include a touch panel, (digital) pen sensor, key, or ultrasonic input device. Touch panel can recognize touch input such as capacitive, pressure-sensitive, infrared, or ultrasonic input. Input device 350 can be associated with sensor 365 and / or camera by providing additional input to processor 340. In some embodiments, sensor 365 includes one or more inertial measurement units (IMUs) (such as accelerometers, gyroscopes, and magnetometers), motion sensors, optical sensors, cameras, pressure sensors, heart rate sensors, altimeters, etc. Input device 350 may also include control circuitry. In the capacitive solution, the input device 350 is able to recognize touch or proximity.

[0070] The processor 340 is also coupled to the display 355. The display 355 may be a liquid crystal display, a light-emitting diode display, or other display capable of displaying text and / or at least limited graphics (such as from a website).

[0071] The memory 360 is coupled to the processor 340. A portion of the memory 360 may include random access memory (RAM), while another portion of the memory 360 may include flash memory or other read-only memory (ROM).

[0072] The processor 340 is also coupled to one or more sensors 365, which are capable of measuring physical quantities and converting the measured or detected information into electrical signals. For example, the sensor 365 may include one or more buttons for touch input, a camera, a gesture sensor, an IMU sensor (such as a gyroscope or gyro sensor and accelerometer), an eye-tracking sensor, a barometric pressure sensor, a magnetic sensor or magnetometer, a grip sensor, a proximity sensor, a color sensor, a biophysical sensor, a temperature / humidity sensor, a light sensor, an ultraviolet (UV) sensor, an electromyography (EMG) sensor, an electroencephalography (EEG) sensor, an electrocardiography (ECG) sensor, an IR sensor, an ultrasound sensor, an iris sensor, a fingerprint sensor, a color sensor (such as a red-green-blue (RGB) sensor), etc. The sensor 365 may also include control circuitry for controlling any of the included sensors. Any of these sensors 365 may be located within the UE 116, within an auxiliary device operatively connected to the UE 116, within a headset configured to hold the UE 116, or within a single device including the headset in the UE 116.

[0073] As described in more detail below, UE 116 is capable of receiving multiple beams and determining the specific beam for transmitting and / or receiving with the BS based on reference signal measurements and motion information generated via one of the sensors 365 (such as an IMU).

[0074] although Figure 3 An example of UE 116 is shown, but it is possible to modify it. Figure 3 Make various changes. For example, Figure 3 The various components can be combined, further subdivided, or omitted, and additional components can be added as needed. As a specific example, processor 340 can 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 UE116 is shown configured as a mobile phone or smartphone, but the UE can be configured to operate as other types of mobile or fixed devices.

[0075] Figure 4 An example network configuration according to an embodiment of this disclosure is shown. Figure 4 The network configuration examples shown are for illustrative purposes only. Implementation can be achieved in dedicated circuitry configured to perform the indicated functions. Figure 4 The one or more components shown, or the one or more components that can be implemented by one or more processors that execute instructions to implement the indicated functions.

[0076] Figure 4 This is a block diagram illustrating an electronic device 401 in a network environment 400 according to various embodiments. (Refer to...) Figure 4 In network environment 400, electronic device 401 can communicate with electronic device 402 via a first network 498 (e.g., a short-range wireless communication network), or with electronic device 404 or server 408 via a second network 499 (e.g., a long-range wireless communication network). According to an embodiment, electronic device 401 can communicate with electronic device 404 via server 408. According to an embodiment, electronic device 401 may include a processor 420, memory 430, input device 450, sound output device 455, display device 460, audio module 470, sensor module 476, interface 477, connection terminal 478, haptic module 479, camera module 480, power management module 488, battery 489, communication module 490, user identification module (SIM) 496, or antenna module 497. In some embodiments, at least one of the components (e.g., display device 460 or camera module 480) may be omitted from electronic device 401, or one or more other components may be added to electronic device 401. In some embodiments, some of the components may be implemented as a single integrated circuit. For example, the sensor module 476 (e.g., a fingerprint sensor, an iris sensor, or an illuminance sensor) may be implemented as embedded in the display device 460 (e.g., a display).

[0077] Processor 420 may run software (e.g., program 440) to control at least one other component (e.g., hardware or software component) of electronic device 401 connected to processor 420, and may perform various data processing or calculations. According to one embodiment, as at least part of the data processing or calculation, processor 420 may load commands or data received from another component (e.g., sensor module 476 or communication module 490) into volatile memory 432, process the commands or data stored in volatile memory 432, and store the resulting data in non-volatile memory 434. According to embodiments, processor 420 may include a main processor 421 (e.g., central processing unit (CPU) or application processor (AP)) and an auxiliary processor 423 (e.g., graphics processing unit (GPU), image signal processor (ISP), sensor hub processor, or communication processor (CP)) that is operationally independent of or combined with the main processor 421. Additionally or alternatively, auxiliary processor 423 may be adapted to consume less power than the main processor 421, or adapted specifically for a given function. The auxiliary processor 423 can be implemented separately from the main processor 421, or it can be implemented as part of the main processor 421.

[0078] When the main processor 421 is inactive (e.g., in sleep mode), the auxiliary processor 423 may control at least some of the functions or states associated with at least one component of the electronic device 401 (other than the main processor 421) (e.g., display device 460, sensor module 476, or communication module 490). Alternatively, when the main processor 421 is active (e.g., running an application), the auxiliary processor 423 may work with the main processor 421 to control at least some of the functions or states associated with at least one component of the electronic device 401 (e.g., display device 460, sensor module 476, or communication module 490). According to embodiments, the auxiliary processor 423 (e.g., an image signal processor or a communication processor) may be implemented as part of another component (e.g., camera module 480 or communication module 490) functionally associated with the auxiliary processor 423.

[0079] The memory 430 may store various data used by at least one component of the electronic device 401 (e.g., processor 420 or sensor module 476). The various data may include, for example, software (e.g., program 440) and input or output data for commands associated with it. The memory 430 may include volatile memory 432 or non-volatile memory 434.

[0080] The program 440 may be stored as software in the memory 430, and the program 440 may include, for example, an operating system (OS) 442, middleware 444, or application 446.

[0081] Input device 450 can receive commands or data from outside electronic device 401 (e.g., a user) that will be used by other components of electronic device 401 (e.g., processor 420). Input device 450 may include, for example, a microphone, mouse, keyboard, or digital pen (e.g., stylus).

[0082] The sound output device 455 can output sound signals to the outside of the electronic device 401. The sound output device 455 may 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. According to embodiments, the receiver may be implemented separately from the speaker or as part of the speaker.

[0083] Display device 460 can visually provide information to the outside of electronic device 401 (e.g., to a user). Display device 460 may include, for example, a display, a holographic device, or a projector, and control circuitry for controlling a respective one of the display, holographic device, and projector. According to an embodiment, display device 460 may include touch circuitry adapted to detect touch or sensor circuitry (e.g., a pressure sensor) adapted to measure the intensity of the force caused by touch.

[0084] The audio module 470 can convert sound into electrical signals and vice versa. According to an embodiment, the audio module 470 can obtain sound via the input device 450, or output sound via the sound output device 455 or headphones of an external electronic device (e.g., electronic device 402) that is directly (e.g., wired) or wirelessly connected to the electronic device 401.

[0085] Sensor module 476 can detect the operating state of electronic device 401 (e.g., power or temperature) or the environmental state outside electronic device 401 (e.g., user state), and then generate an electrical signal or data value corresponding to the detected state. According to embodiments, sensor module 476 may include, for example, a gesture sensor, gyroscope sensor, atmospheric pressure sensor, magnetic sensor, accelerometer, grip sensor, proximity sensor, color sensor, infrared (IR) sensor, biometric sensor, temperature sensor, humidity sensor, or illuminance sensor.

[0086] Interface 477 may support one or more specific protocols used to enable electronic device 401 to connect directly (e.g., wired) or wirelessly to external electronic device (e.g., electronic device 402). According to embodiments, interface 477 may include, for example, a High Definition Multimedia Interface (HDMI), a Universal Serial Bus (USB) interface, a Secure Digital Card (SD) interface, or an audio interface.

[0087] Connection end 478 may include a connector, through which electronic device 401 can be physically connected to an external electronic device (e.g., electronic device 402). According to embodiments, connection end 478 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0088] The tactile module 479 can convert electrical signals into mechanical stimulation (e.g., vibration or motion) or electrical stimulation that can be recognized by a user through his touch or kinesthesia. According to embodiments, the tactile module 479 may include, for example, a motor, a piezoelectric element, or an electrical stimulator.

[0089] Camera module 480 can capture still or moving images. According to embodiments, camera module 480 may include one or more lenses, an image sensor, an image signal processor, or a flash.

[0090] The power management module 488 manages the power supply to the electronic device 401. According to an embodiment, the power management module 488 may be implemented as at least a portion of, for example, a power management integrated circuit (PMIC).

[0091] Battery 489 can power at least one component of electronic device 401. According to an embodiment, battery 489 may include, for example, a non-rechargeable primary battery, a rechargeable rechargeable battery, or a fuel cell.

[0092] Communication module 490 can support the establishment of a direct (e.g., wired) or wireless communication channel between electronic device 401 and external electronic devices (e.g., electronic device 402, electronic device 404, or server 408), and perform communication via the established communication channel. Communication module 490 may include one or more communication processors capable of operating independently of processor 420 (e.g., application processor (AP)) and supporting direct (e.g., wired) or wireless communication. According to embodiments, communication module 490 may include wireless communication module 492 (e.g., cellular communication module, short-range wireless communication module, or Global Navigation Satellite System (GNSS) communication module) or wired communication module 494 (e.g., local area network (LAN) communication module or power line communication (PLC) module). One of these communication modules can communicate with an external electronic device via a first network 498 (e.g., a short-range communication network such as Bluetooth, Wi-Fi Direct, or Infrared Data Association (IrDA)) or a second network 499 (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))). These various types of communication modules can be implemented as a single component (e.g., a single chip) or as multiple components (e.g., multiple chips) that are separate from each other. The wireless communication module 492 can identify and verify the electronic device 401 in the communication network (such as the first network 498 or the second network 499) using user information (e.g., the International Mobile Subscriber Identity (IMSI)) stored in the user identification module 496.

[0093] Antenna module 497 can transmit or receive signals or power to or from the exterior of electronic device 401 (e.g., external electronic device). According to an embodiment, antenna module 497 may include an antenna comprising a radiating element formed of a conductive material or conductive pattern formed in or on a substrate (e.g., a PCB). According to an embodiment, antenna module 497 may include multiple antennas. In this case, at least one antenna suitable for a communication scheme used in a communication network (such as a first network 498 or a second network 499) can be selected from the multiple antennas by, for example, communication module 490 (e.g., wireless communication module 492). Signals or power can then be transmitted or received between communication module 490 and external electronic device via the selected at least one antenna. According to an embodiment, additional components besides the radiating element (e.g., a radio frequency integrated circuit (RFIC)) may be additionally incorporated into antenna module 497.

[0094] At least some of the aforementioned components can be interconnected and communicate signals (e.g., commands or data) between them via an inter-peripheral communication scheme (e.g., bus, general purpose input / output (GPIO), serial peripheral interface (SPI), or mobile industrial processor interface (MIPI)).

[0095] According to an embodiment, commands or data can be sent or received between electronic device 401 and external electronic device 404 via server 408 connected to a second network 499. Each of electronic devices 402 and 404 can be a device of the same type as electronic device 401, or a device of a different type. According to an embodiment, all or some operations that would be performed on electronic device 401 can be performed on one or more of external electronic devices 402, external electronic devices 404, or server 408. For example, if electronic device 401 is required to automatically perform a function or service, or is required to perform a function or service in response to a request from a user or another device, electronic device 401 may request the one or more external electronic devices to perform at least a portion of the function or service, instead of running the function or service, or electronic device 401 may request the one or more external electronic devices to perform at least a portion of the function or service in addition to running the function or service.

[0096] Upon receiving the request, the one or more external electronic devices may perform at least a portion of the requested function or service, or perform additional functions or services related to the request, and transmit the result of the execution to electronic device 401. Electronic device 401 may provide the result as at least a partial response to the request, either with further processing or without further processing. For this purpose, technologies such as cloud computing, distributed computing, or client-server computing may be used.

[0097] Figure 5 An example hybrid beamforming 500 according to an embodiment of the present disclosure is shown. Figure 5 The embodiment of hybrid beamforming 500 shown is for illustrative purposes only. It can be implemented in dedicated circuitry configured to perform the indicated functions. Figure 5 The one or more components shown, or the one or more components that can be implemented by one or more processors that execute instructions to implement the indicated functions.

[0098] For the mmWave band, the number of antenna elements can be large for a given form factor. However, due to factors such as Figure 5 Due to hardware constraints (such as the feasibility of installing a large number of ADCs / DACs at mmWave frequencies), the number of digital chains may be limited. In this case, a digital chain is mapped onto a large number of antenna elements that can be controlled by an analog phase shifter group. A digital chain can then correspond to a subarray that generates a narrow analog beam through analog beamforming. This analog beam can be configured to sweep over a wider range of angles by changing the phase shifter group across transmission time intervals. Applications such as... can be performed at the base station and at the UE. Figure 5 The hybrid beamforming architecture shown.

[0099] A gNB can use 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, the amplification factor of the transmitted signal power provided by the transmit beam, is generally inversely proportional to the width or area covered by the beam. At lower carrier frequencies, better propagation loss makes it feasible for a gNB to provide coverage with a single transmit beam; that is, by using a single transmit beam, adequate received signal quality is ensured at all UE locations within the coverage area.

[0100] In other words, at lower transmit carrier frequencies, the transmit power amplification provided by a transmit beam wide enough to cover the area may be sufficient to overcome propagation loss and ensure adequate received signal quality at all UE locations within the coverage area. However, at higher carrier frequencies, the transmit beam power amplification corresponding to the same coverage area may be insufficient to overcome the higher propagation loss, resulting in a degradation in received signal quality at UE locations within the coverage area.

[0101] To overcome this degradation in received signal quality, the gNB can form multiple transmit beams, each providing coverage over an area narrower than the overall coverage area, but with a transmit power amplification sufficient to overcome the higher signal propagation loss caused by the use of higher transmit carrier frequencies. The UE can also form receive beams to increase the signal-to-interference-plus-noise ratio (SINR) at the receiver. Similarly, in the uplink, the UE can form transmit beams and the gNB can form receive beams.

[0102] To assist the UE in identifying RX and / or TX beams, a beam scanning procedure is employed, consisting of the following operations: the gNB transmits a set of transmit beams to scan the cell area, and the UE uses receive beams to measure 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 a set of TX beams. RS resources refer to reference signal transmissions over a combination of one or more time (OFDM symbol) / frequency (resource element) / spatial (antenna port) domain locations. For each RX beam, the UE reports different TX beams received using that RX beam, ordered by signal strength (RSRP) and optionally CSI (CQI / PMI / RI). Based on the UE's measurement report feedback, the gNB configures the UE with a set of TX-RX beam pairs for receiving PDCCH and / or PDSCH.

[0103] In some embodiments, the UE is equipped with multiple antenna elements. It is also possible to mount one or more antenna modules on the UE, each module having one or more antenna elements. Beamforming is an important factor when the UE attempts to establish a connection with a BS station. To compensate for the narrow analog beamwidth in mmWave, analog beam scanning can be used to achieve a wider signal reception or transmission coverage for the UE. The beamcodebook includes a set of codewords, where each codeword is a set of analog phase shift values ​​or a set of amplitude plus phase shift values ​​applied to the antenna elements to form an analog beam.

[0104] For example, in mmWave systems based on directional beamforming, to optimize a specific performance metric (e.g., received signal power), the UE typically performs an exhaustive search across all candidate beamcodes in the beamcodebook and selects the candidate beamcode that produces the best performance metric (e.g., highest received signal power) to receive data. If the number of candidate beamcodes in the beamcodebook is large, the exhaustive search may take a very long time to converge, resulting in significant latency for the UE connecting to the network.

[0105] In some embodiments, the UE is also equipped with one or more IMUs. The IMUs can include accelerometers, gyroscopes, etc., for measuring and reporting the subject's orientation and angular rate, etc. The IMU configuration can include one or more accelerometers, gyroscopes, and / or magnetometers per axis for each of the three main axes (i.e., pitch, roll, and yaw). The measurement rate for determining the time between two consecutive measurements from the IMUs varies depending on the device. Measurements from the sensors are subject to errors, and the error level on each main axis can differ and varies depending on the device.

[0106] Exhaustive beam scanning or beam search can introduce significant latency and greatly increase power consumption for UE access to the network. Furthermore, this beam search, especially on the UE side, occurs in many different deployment scenarios and / or protocol states, such as inter-cell measurements, initial access, handover, and Transmission Configuration Indicator (TCI) state transitions. Therefore, a fast and efficient beam selection method needs to be designed on the user terminal side to reduce access latency and implementation complexity.

[0107] Figure 6 An example electronic device 602 based on rotation to select different beams is shown according to an embodiment of the present disclosure. The electronic device 602 is similar to... Figure 1 UE 111-116 Figure 3 UE 116 Figure 4 Any of the electronic devices 401. Figure 6 The embodiments described are for illustrative purposes only, and other embodiments may be used without departing from the scope of this disclosure.

[0108] Figure 6Example electronic device 602 is shown at time 600a and time 600b. Time 600a occurs before time 600b. As shown, electronic device 602 is in a first orientation at time 600a, while its orientation is different at time 600b. At time 600a, electronic device 602 measures multiple beams such as beams 604a-604f and selects beam 604b as the beam for transmission and reception. At time 600b, the UE's orientation has changed. Therefore, at time 600b, electronic device 602 again measures multiple beams such as beams 604a-604f and selects beam 604a as the beam for transmission and reception.

[0109] although Figure 6 Electronic device 602 and various beams are shown, but it is capable of […]. Figure 6 Make the necessary changes. Figure 6 This disclosure is not intended to be limited to any particular method or apparatus.

[0110] Figure 7a A method 700 for generating beam management information according to an embodiment of the present disclosure is shown. Figure 7b Table 710 shows example parameters for generating beam management information according to embodiments of the present disclosure. The steps of method 700 can be performed by... Figure 1 UE 111-116 Figure 4 UE 116 Figure 4 The electronic device 401 performs the operation. Figure 7a and Figure 7b The embodiments described are for illustrative purposes only, and other embodiments may be used without departing from the scope of this disclosure.

[0111] Figure 7a Method 700 illustrates an example process for determining how to generate beam management information. Beam management information is used to determine which beam to select for performing wireless communication. In step 702, UE 116 determines reference signal measurements and obtains motion information. UE 116 is capable of receiving signals transmitted from one or more base stations. UE 116 then determines reference signal measurements, such as power, based on the received signals. The reference signal measurements can be based on one or more metrics such as RSRP, SINR, SNR, RSRQ, etc. UE 116 also obtains its own motion information, for example, from motion sensors (such as...). Figure 3 The motion sensor (365) is capable of detecting the motion of the UE 116. In some embodiments, the motion sensor is an IMU such as an accelerometer or gyroscope, which is capable of detecting and measuring the motion of the UE 116.

[0112] In step 704, UE 116 determines whether to combine the reference signal measurement results with motion information when generating beam management information. Figure 7b Table 710 provides example parameters that UE 116 can use when determining whether to combine reference signal measurement results with motion information. For example, if the rotation of UE 116, as detected by the motion sensor, is slow (such as less than a threshold), then UE 116 determines beam management information based on the reference signal measurement results (rather than a combination of the reference signal measurement results and motion measurement results). Figure 7a Step 706). Alternatively, if the rotation of UE 116 detected by the motion sensor is rapid (e.g., greater than a threshold), then UE 116 determines beam management information based on a combination of reference signal measurements and motion measurements. Figure 7a Step 708).

[0113] In another example, when determining whether to combine one or more reference signal measurements with motion information to generate beam management information, UE 116 compares an error level associated with motion information detected by a motion sensor with a threshold. For example, when the motion sensor error level is below the threshold, UE 116 determines to combine one or more reference signal measurements with motion information to generate beam management information. Figure 7a (Step 708). Alternatively, when the error level of the motion sensor is higher than a threshold, UE 116 determines to use one or more reference signal measurements to generate beam management information. Figure 7a Step 706).

[0114] In another example, when determining whether to combine one or more reference signal measurements with mobility information to generate beam management information, UE 116 compares the error level associated with the reference signal measurements with a threshold. For example, when the error level of the reference signal measurements is below the threshold, processor 340 determines to combine one or more reference signal measurements with mobility information to generate beam management information. Figure 7a (Step 708). Alternatively, when the error level of the reference signal measurement results is higher than a threshold, the processor 340 determines to use one or more reference signal measurement results to generate beam management information. Figure 7a Step 706).

[0115] In another example, when determining whether to combine one or more reference signal measurements with mobility information to generate beam management information, UE 116 compares the update rate of the reference signal measurements with the update rate of the mobility measurements. For example, when the update rate of the reference signal measurements is less than the update rate of the mobility information, processor 340 determines to combine one or more reference signal measurements with the mobility information to generate beam management information. Figure 7a Step 708). Alternatively, when the update rate of the reference signal measurement results is greater than the update rate of the motion information, the processor 340 determines to use one or more reference signal measurement results to generate beam management information. Figure 7a Step 706).

[0116] although Figure 7a and Figure 7b Example methods and tables are shown, but more can be found on... Figure 7a and Figure 7b Make various changes. For example, although Figure 7a Method 700 is shown as a series of steps, but these steps can overlap, occur in parallel, occur in different orders, or occur multiple times. In another example, steps can be omitted or replaced with other steps. Furthermore, the various conditions and contents used to generate beam management information can be different.

[0117] Figure 8a Example particle filters 802 and 804 at different time points according to embodiments of the present disclosure are shown. Figure 8b An example method 810 for identifying a beam for performing wireless communication is shown according to an embodiment of the present disclosure. Figure 8c Example Figure 830 shows beam determination regions corresponding to different beams according to embodiments of the present disclosure. Figure 8d Table 835 shows how different states affect particle filtering according to embodiments of the present disclosure. Figure 8e An example method 840 for identifying a beam for performing wireless communication is illustrated according to an embodiment of the present disclosure. The steps of methods 810 and 840 can be performed by… Figure 1 UE 111-116 Figure 4 UE 116 Figure 4 The electronic device 401 performs the operation. Figures 8a-8e The embodiments described are for illustrative purposes only, and other embodiments may be used without departing from the scope of this disclosure.

[0118] Embodiments of this disclosure provide systems and methods for combining reference signal measurements with motion information from a motion sensor associated with UE 116 to identify a specific beam for performing wireless communication. Filtering can be used to combine the reference signal measurements with the motion information. In some embodiments, particle filtering is used to combine the reference signal measurements and motion measurements to identify the beam for wireless communication. Figure 8a Example particle filters 802 and 804 are shown. As illustrated, particle filter 802 represents the particles when the UE is in the first orientation at time T, while particle filter 804 represents the particles when the UE is in the second orientation at time T+1. When combining reference signal measurements and motion measurements to identify the beam used for wireless communication, other tracking filters such as extended Kalman filters, unscented Kalman filters, etc., can be used to track the beam's angle of arrival (AoA).

[0119] Figure 8b Method 810 uses particle filtering (such as...) Figure 8a Particle filtering is used to combine reference signal measurements and directional measurements to identify beams used for wireless communication. Particle filtering uses a set of particles representing the distribution of the beam. Figure 8a Particle filter 802 is shown at time T. The positions of the particles are updated at subsequent time steps (e.g., at time T+1), as shown in particle filter 804. The particles are able to move based on the movement of UE 116 and changes in the environment between the UE and BS. Particle 806 is one of many particles in particle filters 802 and 804. The high particle concentration in regions 808a and 808b corresponds to the specific beam selected for performing wireless communication based on reference signal measurements and directional measurements.

[0120] like Figure 8b As shown, method 810 is typically used to identify the beam to be used for wireless communication based on the beam's reference signal measurement results and the motion information of UE 116. In step 812, UE 116 determines which beam to measure, and then measures the determined beam (in step 816). For example, UE 116 can determine which beam to measure in part based on codebook 814. Codebook 814 includes a set of codewords, where the codewords are a set of analog phase shift values ​​or a set of amplitude plus phase shift values ​​applied to antenna elements to form an analog beam. In step 816, UE 116 measures the beam (using...) Figure 3 The RF transceiver 310 determines the reference signal measurement result based on the measured beam. The UE 116 may use codebook 814 when determining the reference signal measurement result based on the measured beam. In some embodiments, the reference signal measurement result is the RSRP at time t, z tIn other embodiments, the reference signal measurement results are SINR, SNR, RSRQ, etc. In some embodiments, steps 812 and 816 can be performed for multiple beams.

[0121] In step 818, UE 116 from, such as Figure 3 The sensor 365 obtains motion measurement results. In some embodiments, the sensor is an IMU that detects the motion and orientation of the UE 116. For example, the motion information obtained by the motion sensor can identify the direction and magnitude of the detected motion. The UE 116 can obtain the motion measurement result s at time t. t .

[0122] In step 820, UE 116 uses the reference signal measurement results (from step 816) and the motion sensor measurement results (from step 818), along with various input information (such as input 822), to update the particles in the particle filter. The various input information (such as input 822) can include the number of particles M, the number of new particles N in each iteration, and the UE beamcodebook C. B Sensor noise level σ α σ β σ γ and the noise level σ of the signal measurement results RF (such as RSRPσ) RSRP )wait.

[0123] The particle filter is generated before the particles in the particle filter are updated. To generate the particle filter, UE 116 initializes various items and parameters (such as input 822). For example, to generate the particle filter at a specific time step, the number of particles, the new number of particles, the error / noise associated with the transceiver and IMU sensors, the codebook, the beam gain, the reference signal measurement results, motion information (or measurement results) from the motion sensor, etc., are initialized.

[0124] The particle count (denoted as M) is set for particle filtering. For example, M can be set to 1000, indicating that there are 1000 particles in the particle filter. Any other particle count can be used. Each particle represents a candidate channel path and includes the angle of arrival (or AoA) in the UE coordinate system (such as a Cartesian or spherical coordinate system). It is possible for some angles to have a high particle concentration, while other angles have a low particle concentration. For example, as shown in particle filters 802 and 804, there is a high particle concentration in regions 808a and 808b, while other regions of particle filters 802 and 804 have very few particles.

[0125] Each particle in the particle filter (such as the m-th particle p) mThe position (based on X, Y, Z, Cartesian or spherical coordinates) and gain (G) are used to describe the m-th particle p. m It is {X} m ,Y m Z m G m Each particle (e.g., the m-th particle p) m It is possible to obtain a unit vector d at a given time T. m =[X m ,Y m Z m ] T and gain G m To represent. Polar coordinates Can be used to represent unit vector d m =[X m ,Y m Z m ] T .expression Indicator beam in direction Gain on.

[0126] At each iteration (time step), a number of new particles (denoted as N) are inserted into the particle filter. In some embodiments, the number of new particles inserted into the particle filter at a given iteration is 150. It is possible to insert any other number of new particles into the particle filter at a given iteration. For example, the determined number of new particles is based on a sample such as the difference between M and N (e.g., when M = 1000 and N = 150, as mentioned above, then the number of particles is 850). In some embodiments, the determined number of particles is based on an importance sample. Particles will converge into small regions (e.g., Figure 8a As shown in regions 808a and 808b, however, if the angle changes suddenly, the UE may lose transmission. Therefore, many new particles N are included and uniformly spaced to avoid transmission loss.

[0127] From sensors (such as Figure 3 The motion measurements obtained by the sensor (365) can include noise. The sensor noise level σ represents various directional motions in three dimensions and can be described as σ α σ β σ γ The amount of noise may vary for different sensors. For one example, the noise can be described as σ. α =2°, σ β =1° and σ γ =1°. Similarly, from the receiver (such as Figure 3 RF transceiver 310 or Figure 4The reference signal measurement results of the wireless communication module 492 can include noise. For example, when the reference signal measurement results are based on RSRP, the noise can be described as σ. RSRP The amount of noise can vary for different receivers and / or transceivers. For example, noise can be described as σ. RSRP =4.4.

[0128] Beamcodebook C b Indicates candidate beams. In some embodiments, narrow beam codebooks and wide beam codebooks may exist. The number of words in the codebook can vary. For example, a wide beam codebook can include eight words, while a narrow beam codebook can include twenty-eight words.

[0129] Able to direct beam i in direction The gain on is expressed as As discussed above, the reference signal measurement result z can be measured at time t. t Similarly, motion information s is measured at time t. t =[α t ,β t ,γ t ] T Optimal beam i * It is the beam identified for performing wireless communication. The rotation matrix describing the orientation of the UE based on motion information is a function of α, β, and γ and is described in the following equation (1):

[0130] Equation (1)

[0131] R(α,β,γ)=R z (α)×R y (β)×R x (γ)

[0132] in:

[0133]

[0134]

[0135]

[0136] Once the particle filter is initialized, the particles are updated (step 820). In some embodiments, new reference signal measurements and new motion measurements are available at each time step t. The updated reference signal measurement information and updated motion information are used to update particle p. m Subsequently, particles are used to predict the optimal beam index i. * The syntax (1) below describes an example of updating the particles in the particle filter.

[0137] Grammar (1)

[0138] make A set of M particles

[0139]

[0140]

[0141] In some embodiments, the new reference signal measurement or the new motion measurement is available at time t. The updated reference signal measurement information or the updated motion information is used to update particle P. m For example, if the motion sensor measurement is unavailable at time t, step 4 of syntax (1) is not executed, and d is made to... t [m] =d t-1 [m] and g t [m] =g t-1 [m] For another example, if the reference signal measurement is unavailable at time t, then step 5 of syntax (1) is not performed, such that w t [m] =w t-1 [m] Regardless of whether the IMU measurement result or the reference signal measurement result is unavailable at time t, steps 6 to 11 are still executed. In step 12, the new particle number N can be adapted by time step based on whether the updated reference signal measurement result or motion information is available in a given time step. Subsequently, in step 824, UE 116 uses the particles to identify the optimal beam index i for wireless communication based on codebook 814. * .

[0142] The angle of the particle can be initialized based on points uniformly distributed on a sphere, such as using a Fibonacci grid or random sampling of points in space. The gain of the particle in the initial iteration is described in Equation (2).

[0143] Equation (2)

[0144]

[0145]

[0146]

[0147] here, It is the perturbation in particle m, which is based on the known sensor error statistics σ α σ β σ γThe generated particles, once updated, allow for the selection of the beam used to perform wireless communication based on the particles and codebook 814.

[0148] In some embodiments, the angle of the new particle is based on a point insertion that is uniformly distributed on the sphere (such as using a Fibonacci grid and / or random sampling of points from space). The channel gain of the new particle can be determined based on reference signal measurements (including but not limited to the most recent measurements).

[0149] In some embodiments, to identify the optimal beam (step 824), UE 116 identifies an average direction based on the gain and angle of all particles. The identified average direction is then compared with the beam determination region of the beam. Figure 8c Figure 830 shows Table 832 and Map 834. Map 834 depicts the gain corresponding to eight different beams representing example beam determination regions of a codebook (such as codebook 814). Table 832 identifies the different beams within Map 834 by different colors (or shading). The numbers next to Table 832 identify the beam index for each color within Map 834. The eight beams are located in different regions of example Map 834, where each region represents the gain associated with one of the eight beams. For example, the beam determination region of beam j is the angular region where the gain of beam j is greater than any other beam in the codebook. If the average direction of particle detection lies within the determination region of beam j, then in step 824, beam j is declared as the optimal beam, i.e., i.e., i.e. * =j. That is, UE 116 compares the identified average direction with different regions of the beam map. UE 116 then identifies the region of the beam determination map corresponding to the identified average direction, where the identified region represents the gain of the beam selected for performing wireless communication.

[0150] In some embodiments, to identify the optimal beam (step 824), the UE 116 counts the number of particles in the determination region corresponding to each beam (such as shown in the beam determination region of map 834). The beam with the highest number of particles in its determination region is declared the optimal beam. For example, the UE 116 counts the particles in each of the eight different beam determination regions of map 834 and identifies a specific region of map 834 corresponding to a particular beam that includes the highest number of particles. The beam corresponding to the region of map 834 that includes the highest number of particles is selected for performing wireless communication.

[0151] In some embodiments, UE 116 uses a wide-beam codebook in steps 812 and 816 but identifies the beam used to perform wireless communication from a narrow-beam codebook (step 824). UE 116 can use particle filtering (or extended Kalman filtering or unscented Kalman filtering, etc.) to combine reference signal measurements and sensor measurements. Since a smaller number of wide beams are typically required to cover a spherical region, the number of codewords in the wide-beam codebook is less than the number of codewords in the narrow-beam codebook. Therefore, when the channel state changes (e.g., from line-of-sight (LOS) to non-line-of-sight (NLOS), and the AoA changes completely), viewing the entire sphere using reference signal measurements based on the wide-beam codebook takes less time than viewing the entire sphere using the narrow-beam codebook. By using the wide-beam codebook to measure the reference signal but using the narrow-beam codebook to identify the beam, a fast estimate of the optimal narrow beam for performing wireless communication is provided.

[0152] For example, given the gain and angle of all particles, the average direction is first found, which can be considered an estimate of AoA. This average direction is then compared with the beam determination region of the beam in the narrow beam codebook (such as...). Figure 8c (As shown) for comparison. The beam determination region of beam j in the narrow beam codebook is the angular region on a unit sphere where the gain of beam j is greater than any other beam in the narrow beam codebook. If the average direction of particle detection lies within the determination region of beam j, then beam j is declared as the optimal beam, i.e., i * =j.

[0153] For another example, the decision region for each beam in a narrow beamcodebook (such as...) Figure 8c The number of particles (as shown) is counted. The beam with the highest number of particles in its decision region is declared as the best beam.

[0154] In some embodiments, UE 116 adaptively, rather than sequentially, determines the beams to be measured. For example, beams are ordered based on the distance to the average direction of the particles or the concentration of the particles. UE 116 can then select to measure either the beam with the highest probability of being the optimal beam or a subset of beams with the highest probability. When a large codebook exists, measuring only a subset of beams reduces search overhead.

[0155] Figure 8dTable 835 illustrates how different states affect particle filtering according to embodiments of this disclosure. The number of particles and the new number of particles at each time step can vary based on the speed the UE is traveling at and / or the LOS / NLOS channel. The speed of UE 116 can be detected from a motion sensor such as an accelerometer. The speed of UE 116 can be detected by a GPS receiver capable of receiving signals from a Global Positioning System (GPS). The LOS / NLOS channel state can be detected from channel impulse response estimation. The channel impulse response estimation can be from a wireless modem.

[0156] For example, in LOS state, UE 116 can use low values ​​for the particle count M and the new particle count N. Similarly, when the UE speed is considered slow (e.g., slower than 10 km / h), UE 116 can use low values ​​for the particle count M and the new particle count N. The low value for M can be 800 and the low value for N can be 100. Note that other numbers can be used for M and N. When the UE speed is considered medium (e.g., between 10 km / h and 30 km / h), UE 116 can use intermediate values ​​for the particle count M and the new particle count N. The intermediate value for M can be 1000 and the intermediate value for N can be 150. Note that other numbers can be used for M and N.

[0157] For another example, in NLOS state, UE 116 can use high values ​​for both the particle number M and the new particle number N. Similarly, when the UE speed is considered high (e.g., above 30 km / h), UE 116 can use high values ​​for both the particle number M and the new particle number N. The high value for M can be 1200 and the high value for N can be 200. Note that other numbers can be used for M and N.

[0158] In another example, when the channel state of UE 116 changes from LOS to NLOS or vice versa, UE 116 can increase the values ​​of M and N through several iterations and then return to the previously determined particle number M and the new particle number N. For example, M can be set to 2000 and N can be set to 400 for multiple time steps. The number of time steps can be set to 20. Note that other numbers can be used for M and N, and for the number of time steps in which M and N are increased.

[0159] The state of the particle filter provides information that can help tune other UE operations. For example, the particle concentration can provide information about the channel state. In some embodiments, the particle concentration can be calculated using angular spread measurements. If the spread is low, i.e., more concentrated, the channel state is likely to be LOS; however, if the spread is high, i.e., less concentrated, the channel state is likely to be NLOS. This understanding of the channel state can then be used for other tasks, such as selecting a codebook optimized for the detected channel state. Similarly, abrupt changes in particle concentration can indicate a channel state change, which can be used as a trigger for certain operations, for example, in hierarchical beam search, such a trigger can be used to change from beam refinement to full beam search. Finally, the rate of change in particle weights can indicate the rate of channel change and can be used to adjust the RF information rate, i.e., the number of RF measurements made per second.

[0160] Figure 8e A method 840 for identifying a beam for wireless communication according to an embodiment of the present disclosure is shown. For example, in method 840, UE 116 uses a beam determined from reference signal measurement result information and uses motion sensor information to rotate and overlap to the determination region of the determined beam to find the optimal beam.

[0161] Specifically, the UE determines which beam to measure (step 842), measures the reference signal measurement results (step 846), and determines the optimal intermediate beam. (Step 484). For example, in step 842, UE 116 determines which beam to measure, and then measures the determined beam. For example, UE 116 can determine which beam to measure in part based on codebook 844. Codebook 844 includes a set of codewords, wherein the codewords are a set of analog phase shift values ​​or a set of amplitude plus phase shift values ​​applied to antenna elements to form an analog beam. In step 846, UE 116 measures the beam (using...) Figure 3 The RF transceiver 310 determines the reference signal measurement result based on the measured beam. The UE 116 is able to use codebook 844 when determining the reference signal measurement result based on the measured beam. In some embodiments, the reference signal measurement result is the RSRP at time t, z t In other embodiments, the reference signal measurement results are SINR, SNR, RSRQ, etc. In step 848, the UE determines the intermediate beam based on the reference signal measurement results of step 846. For example, the intermediate beam can be based on the beam among multiple beams with the highest RSRP.

[0162] In step 850, UE 116 receives data from a motion sensor (such as...) Figure 3The motion sensor 365 obtains motion information. In some embodiments, the sensor is an IMU that detects motion and the direction and magnitude of the detected motion. The UE 116 is able to obtain the motion sensor measurement results s at time t. t .

[0163] In step 852, UE 116 then bases the measurement on the optimal beam. The directional change from the current time to the center beam is used to achieve the optimal beam. The beam determination area rotates. UE 116 is able to achieve the optimal intermediate beam. Codebook 844 is used when the decision region is rotated. The rotated decision region then overlaps with the unrotated decision regions of all beams, and the beam with the highest degree of overlap is identified as the beam for performing wireless communication (step 854). UE 116 is able to use codebook 844 when identifying the beam for performing wireless communication.

[0164] although Figures 8a to 8e Example methods and diagrams are shown, but more can be found on... Figures 8a to 8e Various changes can be made. For example, although methods 810 and 840 are shown as a series of steps, the steps can overlap, occur in parallel, occur in different orders, or occur multiple times. In another example, steps can be omitted or replaced with other steps.

[0165] Figure 9a An example method 900 for identifying a beam for performing wireless communication is shown according to an embodiment of the present disclosure. Figure 9b An example process 910 is shown, which modifies reference signal measurement results and motion measurement results to generate input according to an embodiment of the present disclosure. Figure 9c and Figure 9d Example figures 906a and 906b, respectively, are shown for identifying patterns in reference signal measurement results and motion measurement results according to embodiments of the present disclosure. Figure 9e A reward system 906c for identifying beams used for wireless communication is illustrated according to an embodiment of the present disclosure. The steps of method 900 can be performed by... Figure 1 UE 111-116 Figure 4 UE 116 Figure 4 The electronic device 401 performs the operation. Figures 9a to 9d The embodiments described are for illustrative purposes only, and other embodiments may be used without departing from the scope of this disclosure.

[0166] Embodiments of this disclosure provide systems and methods for identifying beams for wireless communication based on reference signal measurements or motion measurements using supervised learning methods. As discussed above, the wireless channel undergoes changes due to orientation variations of the UE 116 or as the UE moves. Therefore, the beams for wireless communication are periodically updated. In addition... Figure 8b In addition to method 810, it is also possible to use Figure 9a Method 900.

[0167] like Figure 9a As shown, in step 902, method 900 determines both the reference signal measurement result and the motion measurement result of UE 116. For example, UE 116 measures the beam (using...) Figure 3 The RF transceiver 310 determines the reference signal measurement result based on the measured beam. The UE 116 is able to use codebook 814 when determining the reference signal measurement result based on the measured beam. In some embodiments, the reference signal measurement result is RSRP. In other embodiments, the reference signal measurement result is SINR, SNR, RSRQ, etc. Similarly, the UE 116 obtains the reference signal measurement result from sources such as... Figure 3 The sensor 365 obtains motion sensor measurement results. In some embodiments, the sensor is an IMU that detects the motion and orientation of the UE 116. For example, the motion information obtained by the motion sensor can identify the direction and magnitude of the detected motion. The UE 116 is able to obtain motion measurement results.

[0168] In step 904, UE 116 modifies the reference signal measurement results and motion measurement results. In some embodiments, UE 116 uses a preprocessing engine (such as...) Figure 9b The preprocessing engine 904a) modifies the reference signal measurement results and motion measurement results. Modifying the reference signal measurement results and motion measurement results preprocesses the data to prepare it and converts it into the format subsequently used in step 906.

[0169] In step 906, UE 116 identifies patterns in the reference signal measurement results and motion measurement results. UE 116 is able to stream the processed data from step 904 and detect underline patterns in the data to accurately identify the optimal beam index for wireless communication (step 908). In some embodiments, UE 116 uses, for example... Figure 9c and Figure 9d The neural network shown is used to detect patterns in the data in order to identify beams used for wireless communication (step 908).

[0170] It can obtain training data for supervised learning from synthetic / simulated data. It can obtain training data for different UE conditions, channel conditions, and UE measurement behaviors such as: different movement speeds (5km / h, 20km / h, or 60km / h), different UE orientations or variations in UE orientation, different sensor error characteristics, RF measurement patterns over time (including periodic or intermittent measurements with various intervals), and the total number of UE beams. It can compare the identified beams used for communication with beams identified based on reference signal measurements to determine the accuracy of the identified patterns. The table size can be equal to the total number of UE beams. The table can be updated after each measurement for a beam.

[0171] Figure 9b Process 910 describes how UE 116... Figure 9a As discussed in step 904, the reference signal measurement results and motion measurement results are modified to generate input 915 for the neural network. Specifically, the preprocessing engine 904a receives the reference signal measurement results 902a and motion measurement result information 902b. The preprocessing engine 904a then modifies the input information (902a and 902b) so that the neural network can recognize patterns within the information. The reference signal measurement results 902a and motion measurement result information 902b can come from... Figure 9a Step 902. The preprocessing engine 904a can be an application, such as... Figure 3 One of the 362 applications.

[0172] To generate the RF table 912, the preprocessing engine 904a generates a table (called the T table) based on the received reference signal measurement result information 902a. To generate the rotation matrix 914, the preprocessing engine 904a generates a matrix (called the R matrix) based on the received motion measurement result information 902b. For example, at time step t, the reference signal measurement result value P of beam index i∈[0,B-1] is updated. t And measure the motion measurement results information I t = (α,β,γ).

[0173] To generate RF table 912, preprocessing engine 904a will P t (It is the reference signal measurement result information 902a) converted into T as a one-dimensional (1D) vector. t The value at index i is set to P. t Meanwhile, other values ​​at other indices are set to 0. Equation (3) is an example table where index i = 1 and beam number B = 5. Equation (3) is

[0174] Equation (3)

[0175]

[0176] Here, a reference signal measurement result is obtained at a given time t. If reference signal measurement results for more than one beam are obtained at a given time t, then T... t It includes the reference signal measurement result value at the index corresponding to the beam used for measurement.

[0177] To generate rotation matrix 914, preprocessing engine 904a is based on I... t (It is the motion measurement result information 902b) to generate a 3x3 matrix R. The matrix R is a function of the orientation (α, β, γ) obtained from the motion sensor. Equation (4) describes how to generate the R matrix. The R matrix can be flattened to become a 1D vector input of size nine.

[0178] Equation (4)

[0179] R(α,β,γ)=R z (α)×R y (β)×R x (γ)

[0180] in.

[0181]

[0182]

[0183]

[0184] The preprocessing engine 904a then combines the T table and the R matrix to form a 1D vector input x of size B+9. t This generates input 915. Input 915 can be provided to the neural network to identify patterns (such as...) based on reference signal measurements and motion measurement results. Figure 9a As discussed in step 906).

[0185] In other words, preprocessing engine 904a converts the reference signal measurement results into a list format, which includes both beam index information and the reference signal measurement results. Preprocessing engine 904a also transforms the motion measurement results into a rotation matrix describing the orientation of UE 116. In some embodiments, preprocessing engine 904a can also generate metadata indicating the UE's temperature, power consumption, and geographical location. In some embodiments, the metadata can be combined with the reference signal measurement results and motion measurement results.

[0186] Figure 9c and Figure 9d It shows the method for using based Figure 9aStep 906 is used to identify patterns in the reference signal measurement results and motion measurement results, as shown in Figures 906a and 906b. A recurrent neural network (RNN) is shown in Figure 906a. An RNN is an artificial neural network (ANN) that processes time-series data. The RNN can be implemented as a Long Short-Term Memory (LSTM) 907a. Along with the LSTM 907a, the RNN includes a pair of fully connected (FC) layers (907b and 907c). These layers (907b and 907c) are responsible for identifying nonlinear patterns based on feature vectors extracted from the LSTM 907a to generate beam index information. The first FC layer 907b includes 2×B neurons, where B is the number of beams. It is followed by a ReLU activation function. The second FC layer 907c includes B neurons followed by a Softmax activation function to generate the probability of the beam index.

[0187] The purpose of using the LSTM 907a is to learn the temporal relationships between each data frame in a time series. The LSTM 907a can identify patterns in the time domain. The LSTM 907a is designed to avoid long-term dependency problems. For example, the LSTM 907a includes its cell states, activation functions (sigmoid and tanh), and various logic gates (pointwise addition and multiplication). The cell states act as a transport path, transferring relevant information down the sequence chain. The cell states can be thought of as the network's memory. They can carry relevant information throughout the processing of the sequence. Therefore, even information from earlier time steps can enter later time steps, reducing the impact of short-term memory loss. As the cell state continues its journey, information is added to or removed from the cell state via network gates. Gates are essentially tiny neural networks that determine what information is allowed at the cell state. The gates can learn what information is relevant to retention or forgetting during training.

[0188] Figure 9c Figure 906a illustrates the overall process for pattern recognition. For example, at time step t, the preprocessed input x... t (Input 915) is provided to the LSTM 907a. The LSTM 907a uses the encoding vector and the hidden state h generated from the previous time step. t-1 (If t = 0, then it is blank) to derive the optimal beam index o at time t. t and the hidden state h t ,like Figure 9d As shown in Figure 906b, the hidden state h is a feature vector that encapsulates the memory of the ANN. That is, each hidden state h, as shown in Figure 906b, is maintained in the RNN in subsequent time steps. This enables the learning of underlying patterns to generate predictions for identifying beams used in wireless communication.

[0189] Figure 9e The reward system 906c is typically used to identify beams for wireless communication. Specifically, the reward system 906c includes a reinforcement learning (RL) process with an incentive effect to approach the problem in different learning paradigms rather than supervised learning as described above. For example, it is able to... Figure 9a Step 906 uses the reward system 906c.

[0190] like Figure 9e As shown, the reward system 906c includes sensors 902c. One of the sensors 902c includes an IMU 932. The IMU 932 provides orientation measurements {α,β,γ} such as context 942. For example, context 942 could be in... Figure 9a The motion information obtained in step 902. Another component in sensor 902c is wireless channel 934. Wireless channel 934 provides beam measurement results (reference signal measurement results), such as context 944. Agent 930 provides the selection of the beam to be measured b. m And select beam b for actual communication. c Recommendation 946. Regret function r t 940 is using beam b c The reward / regret function collected after communication.

[0191] Techniques such as Thompson sampling or policy gradients can be used to train and make decisions (online learning) to adapt to specific situations and environments. Using RL methods, a reward function that rewards or punishes agent 930 based on its decisions can be used to maximize the cumulative (or averaged) measurement value of the reference signal. Regret function 940 rewards or punishes agent 930 based on its choices. An example regret function 940 is described in equation (5) below.

[0192] Equation (5)

[0193] R = 20 × (log 10 y-log 10 p)

[0194] Here, y is the reference signal measurement of the optimal beam at time step t in a linear scaling manner, and p is the reference signal measurement of the beam used for communication at time step t in a linear scaling manner. Thus, the regret value R is a regret function 940 that rewards or punishes the agent 930 based on its choices.

[0195] Another example of the regret function 940 is described in Equation (6) below. Equation (6) is based on calculating the power difference in dB between two consecutive time steps of the communication beam c. Equation (6) as described below shows that if the optimal beam cannot be selected, the next appropriate beam can be used.

[0196] Equation (6)

[0197] R = 20 × (log 10 c t -log 10 c t-1 )

[0198] In some embodiments, the reward system 906c can be used to make beam adjustment decisions based on reference signal measurements (from wireless channel 934), motion measurements (from IMU 932), and one or more other criteria such as power consumption, temperature, avoiding frequent beam changes, and maintaining a certain signal quality (reference signal measurements above a threshold). For example, instead of focusing on obtaining the optimal beam, the reward system 906c can be designed to avoid frequent beam changes (which helps save power). Therefore, the reward system 906c can be designed as an increasing function of the duration for which the selected beam can maintain a certain signal quality (such as reference signal measurements above a threshold).

[0199] In some embodiments, UE 116 may include a scheduling unit to systematically measure beams based on a cyclic method to illustrate prior beam management solutions. For example, after initially using the cyclic method, UE 116 may determine whether the reference signal measurement results have been improved. When the reference signal measurement results have been improved, the scheduling unit will schedule at index i = i c Restart at -1, where i c This is the beam index selected for previous wireless communications. If the reference signal measurement results are not improved, the scheduling unit will continue to measure the next beam in that round.

[0200] although Figures 9a to 9e Example methods and diagrams are shown, but more can be found on... Figures 9a to 9e Various changes can be made. For example, although method 900 is shown as a series of steps, the steps can overlap, occur in parallel, occur in different orders, or occur multiple times. In another example, steps can be omitted or replaced with other steps.

[0201] Figure 10 A method 1000 for beam management by a UE in a wireless communication system, according to an embodiment of the present disclosure, is illustrated. Method 1000 is capable of being performed by... Figure 1 Any UE execution and including with Figure 3 UE 116 and Figure 4 The internal components are similar to those of the electronic device 401. For ease of illustration, method 1000 is executed by a processor such as processor 340 of UE 116.

[0202] In step 1002, the processor 340 determines the reference signal measurement result based on the signal. The UE 116 is capable of receiving signals from one or more base stations. The UE 116 then determines the reference signal measurement result, such as power, based on the received signal.

[0203] In step 1004, the processor 340 obtains motion information of the UE 116. For example, motion sensors (such as...) Figure 3 The motion sensor (365) is capable of detecting the motion of the UE 116. In some embodiments, the motion sensor is an IMU such as an accelerometer or gyroscope, which is capable of detecting and measuring the motion of the UE 116.

[0204] In step 1006, processor 340 generates beam management information. The beam management information is based on one or more reference signal measurements from step 1002 and the motion information from step 1004. In some embodiments, processor 340 identifies one or more parameters associated with the one or more reference signal measurements and the motion information. Based on said one or more parameters, processor 340 determines whether to use one or more reference signal measurements or to combine one or more reference signal measurements with the motion information to generate the beam management information.

[0205] In some embodiments, when determining whether to use one or more reference signal measurements or combine one or more reference signal measurements with motion information to generate beam management information, the processor 340 compares the rotation speed of the UE 116 based on motion sensor information with a threshold. For example, when the UE's rotation speed is higher than the threshold, the processor 340 determines to combine one or more reference signal measurements with motion information to generate beam management information. Alternatively, when the UE's rotation speed is lower than the threshold, the processor 340 determines to use one or more reference signal measurements to generate beam management information.

[0206] In some embodiments, when determining whether to use one or more reference signal measurements or combine one or more reference signal measurements with motion information to generate beam management information, the processor 340 compares an error level associated with the motion sensor's movement to a threshold. For example, when the motion sensor's error level is below the threshold, the processor 340 determines to combine one or more reference signal measurements with motion information to generate beam management information. Alternatively, when the motion sensor's error level is above the threshold, the processor 340 determines to use one or more reference signal measurements to generate beam management information.

[0207] In some embodiments, when determining whether to use one or more reference signal measurements or combine one or more reference signal measurements with motion information to generate beam management information, the processor 340 compares an error level associated with the reference signal measurements with a threshold. For example, when the error level of the reference signal measurements is below the threshold, the processor 340 determines to combine one or more reference signal measurements with motion information to generate beam management information. Alternatively, when the error level of the reference signal measurements is above the threshold, the processor 340 determines to use one or more reference signal measurements to generate beam management information.

[0208] In some embodiments, when determining whether to use one or more reference signal measurement results or combine one or more reference signal measurement results with motion information to generate beam management information, the processor 340 compares the update rate of the reference signal measurement results with the update rate of the motion measurement results. For example, when the update rate of the reference signal measurement results is less than the update rate of the motion information, the processor 340 determines to combine one or more reference signal measurement results with the motion information to generate beam management information. Alternatively, when the update rate of the reference signal measurement results is greater than the update rate of the motion information, the processor 340 determines to use one or more reference signal measurement results to generate beam management information.

[0209] After determining that beam management information is generated based on reference signal measurement results (not a combination of one or more reference signal measurement results and motion information), processor 340 generates beam management information based on RSRP, SINR, SNR, RSRQ, etc.

[0210] After determining that beam management information is to be generated based on a combination of one or more reference signal measurements and motion information, processor 340 can use various types of filters to perform the combination. For example, particle filtering is an example filter that can be used to combine one or more reference signal measurements with motion information to generate beam management information. For instance, processor 340 identifies multiple particles associated with the particle filter. These particles can be based on angle of arrival and gain. Processor 340 then updates the multiple particles based on the reference signal measurements and motion information. After updating the multiple particles, processor 340 identifies one or more new particles to be included in the particle filter. The updated particle filter is the generated beam management information. Note that the generated beam management information is continuously updated at predefined intervals.

[0211] In step 1008, processor 340 identifies the beam based on beam management information. When the beam management information is based on reference signal measurements (not a combination of one or more reference signal measurements and motion information), processor 340 identifies the beam based on RSRP, SINR, SNR, RSRQ, etc. When the beam management information is based on a combination of one or more reference signal measurements and motion information, processor 340 identifies the beam based on an updated particle filter. For example, processor 340 can identify the beam based on one or more new particles included in the particle filter.

[0212] In some embodiments, to identify the beam, when beam management information is based on a combination of measurements from one or more reference signals and motion information, processor 340 identifies the average orientation of multiple particles. Processor 340 then compares the identified average orientation with a beam map (such as...). Figure 8c The processor 340 compares regions of the beammap (824). Regions of the beammap represent the gain of different beams. The processor 340 is able to identify regions of the beammap corresponding to the identified average direction. The identified region corresponds to a specific beam.

[0213] In other embodiments, processor 340 identifies a first beam among one or more beams, wherein the first beam corresponds to a region in the particle filter where the number of particles is greater than the number of particles in any other part of the particle filter.

[0214] After identifying the beam, processor 340 performs wireless communication based on the identified beam in step 1010. In some embodiments, when beam management information is based on reference signal measurement results, processor 340 identifies the beam to perform wireless communication based on RSRP, SINR, SNR, RSRQ, etc. In other embodiments, when beam management information is based on a combination of one or more reference signal measurement results and motion information, processor 340 identifies the beam for wireless communication based on particle filtering. For example, processor 340 can identify the beam to be used for wireless communication based on a beam determination map. For another example, processor 340 can identify the beam to be used for wireless communication based on the concentration of particles in the region of the beam map associated with the identified beam.

[0215] although Figure 10 Example methods and diagrams are shown, but more can be found on... Figure 10 Various changes can be made. For example, although shown as a series of steps, the steps can overlap, occur in parallel, occur in different orders, or occur multiple times. In another example, steps can be omitted or replaced with other steps.

[0216] The flowcharts above illustrate example methods that can be implemented according to the principles of this disclosure and various modifications can be made to the methods shown in the flowcharts herein. For example, although shown as a series of steps, the various steps in each diagram can overlap, occur in parallel, occur in different orders, or occur multiple times. In another example, steps can be omitted or replaced with other steps.

[0217] Although the figures illustrate different examples of user equipment, various modifications can be made to the figures. For example, the user equipment can include any number of each component in any suitable arrangement. In general, the figures do not limit the scope of this disclosure to any particular configuration. Furthermore, while the figures illustrate operating environments in which the various user equipment features disclosed in this patent document can be used, these features can be used in any other suitable system.

[0218] Although this disclosure has been described with reference to exemplary embodiments, various changes and modifications may be suggested to those skilled in the art. This disclosure is intended to include such changes and modifications as fall within the scope of the appended claims. The descriptions 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 the claimed subject matter is defined by the claims.

Claims

1. A user equipment (UE) for beam management in a wireless communication system, the UE comprising: a transceiver configured to receive signals from one or more base stations; a motion sensor configured to obtain motion information; and a processor operably connected to the transceiver and the motion sensor, the processor configured to: determine reference signal measurements from the signals, obtain motion information of the UE, identify one or more parameters associated with at least one of the reference signal measurements and the motion information, determine, based at least in part on comparing the one or more parameters to at least one threshold, whether to combine the reference signal measurements and the motion information to generate beam management information for beam management, in response to determining to combine the reference signal measurements and the motion information: generate the beam management information by combining the reference signal measurements and the motion information, identify a beam based on the generated beam management information, and perform wireless communication based on the identified beam, and in response to determining not to combine the reference signal measurements and the motion information: identify the beam based on the reference signal measurements, and perform wireless communication based on the identified beam. combine the reference signal measurements and the motion information to generate the beam management information using particle filtering; and 2. The UE of claim 1, wherein, the processor is further configured to: identify a plurality of particles associated with the particle filtering based on an angle of arrival and a channel gain, update the plurality of particles based on the reference signal measurements and the motion information, identify one or more new particles to be included in the particle filtering, and identify the beam for the wireless communication based on the plurality of particles and the one or more new particles. to further identify the beam, the processor is configured to:

3. The UE of claim 2, wherein, identify an average direction of the plurality of particles and the one or more new particles; compare the identified average direction to regions of a beam decision map, wherein each of the regions represents a gain associated with one of a plurality of beams; identify a region of the beam decision map corresponding to the identified average direction, wherein the identified region represents a gain of a first beam of the plurality of beams; and identify the first beam as the beam for performing the wireless communication. the processor is further configured to:

4. The UE of claim 2, wherein, identify a first beam from one or more beams, wherein the first beam corresponds to a region in the particle filtering in which a number of particles is greater than a number of particles in any other of the one or more beams; and identify the first beam as the beam for performing the wireless communication. the comparing includes at least one of:

5. The UE of claim 1, wherein, comparing a rotational velocity of the UE based on the motion information to a first threshold; comparing an error level associated with the motion information based on the motion sensor to a second threshold; and ​ ​ comparing an error level associated with the reference signal measurements to a third threshold.

6. The UE of claim 5, wherein: when a rotational speed of the UE is above the first threshold, the processor is configured to determine to combine the reference signal measurements and the motion information; when the rotational speed of the UE is below the first threshold, the processor is configured to determine not to combine the reference signal measurements and the motion information; when the error level associated with the motion information is below the second threshold, the processor is configured to determine to combine the reference signal measurements and the motion information; when the error level associated with the motion information is above the second threshold, the processor is configured to determine not to combine the reference signal measurements and the motion information; when the error level associated with the reference signal measurements is below the second threshold, the processor is configured to determine to combine the reference signal measurements and the motion information; and when the error level associated with the reference signal measurements is above the second threshold, the processor is configured to determine not to combine the reference signal measurements and the motion information. the processor is configured to:

7. The UE of claim 1, wherein, modify a format of the reference signal measurements and the motion information into a vector; identify a pattern from the reference signal measurements and the motion information using a neural network; and identify the beam for the wireless communication based on the pattern.

8. The UE of claim 7, wherein: to modify the format of the reference signal measurements and the motion information into the vector, the processor is configured to: convert the reference signal measurements into a one-dimensional vector, transform the motion information into a rotation matrix, and generate the vector corresponding to one time step, wherein the vector is based on a combination of the one-dimensional vector and the rotation matrix; and to identify the pattern, the processor is configured to: identify a first beam based on the vector using a long short-term memory (LSTM), the vector corresponding to a first time step, perform the wireless communication based on the first beam at the first time step, generate a second vector corresponding to a subsequent time step, identify a second beam based on the second vector and the first beam using the LSTM, and perform the wireless communication based on the second beam at the subsequent time step. the processor is configured to:

9. The UE of claim 7, wherein, generate feedback of a beam adjustment decision based on a criterion, wherein the criterion comprises at least one of a communication quality, a power consumption, a temperature, an avoidance of frequent beam changes, and a maintenance of a certain signal quality, wherein the feedback provides a reward or a penalty to the neural network based on whether the identified beam meets the criterion; rank one or more beams to be measured at a subsequent time step based on the feedback from a previous time step; and recommend one or more beams for measurement at the subsequent time step based on the ranking. ​ 10.A method for beam management, the method being implemented by a user equipment (UE) in a wireless communication system according to any one of claims 1 to 9.

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