Sensor-assisted beam tracking

Abstract

The present disclosure relates to "sensor-assisted beam tracking." This application relates to apparatuses and components, including devices, systems, and methods for determining a beam for communication between a user equipment and a base station. For example, an angle of arrival estimate can be used to determine a beam for communication.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 240,852, filed September 3, 2021, entitled “Sensor Assisted Beam Tracking,” the disclosure of which is incorporated herein by reference in its entirety for all purposes. Background Technology

[0003] 3GPP networks provide base stations that can utilize beamforming to transmit signals to user equipment (UE) on a beam. The UE can determine the direction from which it receives the beam. In some implementations, the UE can use the determined direction to correctly identify the received beam and / or may not need to monitor the direction from which it does not receive a beam. Attached Figure Description

[0004] Figure 1 An exemplary system arrangement according to some implementation schemes is shown.

[0005] Figure 2 An exemplary state machine for a sensor-assisted beamforming framework according to some embodiments is shown.

[0006] Figure 3 An exemplary system arrangement according to some implementation schemes is shown.

[0007] Figure 4 A block diagram of a beam quality-based angle of arrival (AoA) estimation method according to some implementation schemes is shown.

[0008] Figure 5 A block diagram of another beam quality-based AoA estimation method according to some implementation schemes is shown.

[0009] Figure 6 Examples of methods for transforming local AoA estimates into global AoA estimates according to some implementation schemes are shown.

[0010] Figure 7 An exemplary AoA estimation method based on beam quality is shown according to some implementation schemes.

[0011] Figure 8 A method for determining the probability of AoA based on beam measurement according to some implementation schemes is shown.

[0012] Figure 9A The first part of an exemplary process for identifying a beam used for communication, according to some implementation schemes, is shown.

[0013] Figure 9BThe second part of an exemplary process for identifying a beam used for communication, according to some implementation schemes, is shown.

[0014] Figure 10A The first part of another exemplary process for identifying a beam used for communication, according to some implementation schemes, is shown.

[0015] Figure 10B The second part of an exemplary process for identifying a beam used for communication, according to some implementation schemes, is shown.

[0016] Figure 11 An exemplary process for determining a beam for communication, according to some implementation schemes, is shown.

[0017] Figure 12 An exemplary beamforming circuit according to some implementation schemes is shown.

[0018] Figure 13 Exemplary user equipment (UE) according to some implementation schemes is shown.

[0019] Figure 14 An exemplary next-generation node B (gNB) according to some implementation schemes is shown. Detailed Implementation

[0020] The following detailed description relates to the accompanying drawings. The same reference numerals may be used in different drawings to identify the same or similar elements. In the following description, specific details, such as particular structures, architectures, interfaces, technologies, etc., are set forth for illustrative and non-limiting purposes to provide a thorough understanding of various aspects of the various embodiments. However, it will be apparent to those skilled in the art that various aspects of the various embodiments may be practiced in other examples departing from these specific details. In some cases, descriptions of well-known devices, circuits, and methods have been omitted so as not to obscure the description of the various embodiments with unnecessary detail. For the purposes of this document, the phrase "A or B" means (A), (B), or (A and B).

[0021] The following is a glossary of terms that may be used in this disclosure.

[0022] As used herein, the term "circuit" refers to, is part of, or includes the following: hardware components such as electronic circuits, logic circuits, processors (shared, dedicated, or grouped) or memories (shared, dedicated, or grouped), application-specific integrated circuits (ASICs), field-programmable devices (FPDs) (e.g., field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), complex PLDs (CPLDs), high-capacity PLDs (HCPLDs), structured ASICs, or programmable system-on-a-chip (SoCs)), digital signal processors (DSPs), etc. In some embodiments, a circuit may execute one or more software or firmware programs to provide at least some of the said functions. The term "circuit" may also refer to a combination of one or more hardware elements and program code for performing the functions (or a combination of circuits used in an electrical or electronic system). In these embodiments, the combination of hardware elements and program code may be referred to as a particular type of circuit.

[0023] As used herein, the term "processor circuit" means, is part of, or includes the following: a circuit capable of sequentially and automatically performing a series of arithmetic or logical operations or recording, storing, or transmitting digital data. The term "processor circuit" may also refer to an application processor, baseband processor, central processing unit (CPU), graphics processing unit, single-core processor, dual-core processor, triple-core processor, quad-core processor, or any other device capable of executing or otherwise operating computer-executable instructions (such as program code, software modules, and / or functional procedures).

[0024] As used herein, the term "interface circuit" refers to, is part of, or includes a circuit that enables the exchange of information between two or more components or devices. The term "interface circuit" can refer to one or more hardware interfaces, such as buses, I / O interfaces, peripheral component interfaces, network interface cards, etc.

[0025] As used herein, the term "user equipment" or "UE" refers to equipment of a remote user that has radio communication capabilities and can describe network resources in a communication network. Furthermore, the term "user equipment" or "UE" can be considered synonymous and can be referred to as a client, mobile phone, mobile device, mobile terminal, user terminal, mobile unit, mobile station, mobile user, subscriber, user, remote station, access agent, user agent, receiver, radio equipment, reconfigurable radio equipment, reconfigurable mobile device, etc. Additionally, the term "user equipment" or "UE" can include any type of wireless / wired equipment or any computing device that includes a wireless communication interface.

[0026] As used herein, the term "computer system" means any type of interconnected electronic device, computer device, or component thereof. Additionally, the term "computer system" or "system" may refer to the various components of a computer that are communicatively coupled to each other. Furthermore, the term "computer system" or "system" may refer to multiple computer devices or multiple computing systems that are communicatively coupled to each other and configured to share computing resources or network resources.

[0027] As used herein, the term "resource" refers to physical or virtual devices, physical or virtual components within a computing environment, or physical or virtual components within a specific device, such as computer equipment, mechanical equipment, memory space, processor / CPU time, processor / CPU utilization, processor and accelerator load, hardware time or utilization, power supply, input / output operations, port or network sockets, channel / link allocation, throughput, memory utilization, storage, network, databases and applications, units of workload, etc. "Hardware resource" can refer to computing, storage, or networking resources provided by physical hardware components. "Virtualized resource" can refer to computing, storage, or networking resources provided by virtualization infrastructure to applications, devices, systems, etc. The terms "network resource" or "communication resource" can refer to resources that computer equipment / systems can access via a communication network. The term "system resource" can refer to any kind of shared entity providing services and can include computing or network resources. System resources can be considered as a coherent set of functions, network data objects, or services accessible through a server, wherein such system resources reside on a single host or multiple hosts and are clearly identifiable.

[0028] As used herein, the term "channel" refers to any tangible or intangible transmission medium used for transmitting data or data streams. The term "channel" may be synonymous or equivalent with "communication channel," "data communication channel," "transmission channel," "data transmission channel," "access channel," "data access channel," "link," "data link," "carrier," "radio frequency carrier," or any other similar term indicating a path or medium through which data is transmitted. Additionally, as used herein, the term "link" refers to a connection between two devices used for transmitting and receiving information.

[0029] As used in this article, the terms "instantiate" and "instantiate" refer to the creation of an instance. "Instance" also refers to the concrete occurrence of an object, which may occur, for example, during the execution of program code.

[0030] The term "connection" can mean that two or more elements at a common communication protocol layer have an established signaling relationship with each other through a communication channel, link, interface, or reference point.

[0031] As used herein, the term "network element" refers to physical or virtualized equipment or infrastructure used to provide wired or wireless communication network services. The term "network element" may be considered synonymous with or referred to as networked computers, network hardware, network equipment, network nodes, virtualized network functions, etc.

[0032] The term "information element" refers to a structural element that contains one or more fields. The term "field" refers to the individual content of an information element, or the data element that contains that content. An information element may include one or more additional information elements.

[0033] Compared to systems with wider bandwidth, centimeter / millimeter wave (mmWave) communication systems can provide significantly higher data rates given ultra-wide bandwidth. Interest in and commercial deployment of mmWave networks continues to grow, including the 3rd Generation Partnership Project (3GPP) 5th Generation (5G) New Radio (NR) (Frequency Range 2 or FR2). Analog beamforming has been an important technique for compensating for the short-range coverage of mmWave systems. Sensor information can be used to improve beam determination in mmWave devices to reduce latency and increase reliability. The method described in this paper fuses sensor information with radio beamforming.

[0034] Receivers (such as UE 1300) Figure 13 The angle of arrival (AoA) of the input signal can be determined using super-resolution algorithms, which requires detailed antenna pattern measurements and calibration. Even with super-resolution algorithms, the AoA may still be ambiguous to the receiver. For example, using a linear array instead of a two-dimensional (2D) array or other constraints on antenna placement can lead to an ambiguous AoA.

[0035] Motion sensor data can accurately provide information about a device's rotation, thus aiding beam tracking. For example, once the device has rotated by a certain angle, an alternative beam can be selected. However, the device can lock its orientation (AoA) to the initial beam (and maintain this lock through beam tracking). Without relying on AoA estimation algorithms, sensor-assisted beam tracking can present problems in resolving AoA ambiguity or improving AoA resolution.

[0036] Figure 1 An exemplary system arrangement 100 according to some implementation schemes is shown. Specifically, system arrangement 100 illustrates an exemplary portion of a radio access network (RAN) where beamforming can be implemented. In some implementation schemes, the RAN may be a 5G network.

[0037] System deployment 100 may include user equipment (UE) 102, such as UE 1300 ( Figure 13Users can access the RAN using user equipment. For example, UE 102 can communicate with components within the RAN to provide services to users, such as voice calls, text messages, and / or other data-based services. UE 102 may include one or more sensor devices 104. Sensor devices 104 may include motion sensor devices (such as accelerometers) that can determine the motion of UE 102. One or more processors of UE 102 can receive data from sensor devices 104 and determine the orientation of UE 102 based on the date from sensor devices 104.

[0038] System deployment 100 may also include base station 106 (such as gNB 1400). Figure 14 The base station 106 and core network 108 (such as a 5th generation core network (5GC)) can be combined to provide services to the UE 102. The base station 106 can be coupled to the CN 108 via a backend that provides communication between the base station 106 and the CN 108.

[0039] Base station 106 may include antenna array 110. Antenna array 110 may include one or more antennas that allow base station 106 to communicate wirelessly with a UE (such as UE 102). Antennas of antenna array 110 or portions thereof may transmit beams 112 that can carry signals for communicating with the UE. In some cases, beam 112 may include training signals that the UE can use to determine which beams can provide services to the UE. Beam 112 or portions thereof may include pencil beams with narrow bandwidth.

[0040] UE 102 can detect beam 112 from antenna array 110 and communicate with base station 106 using beam 112. When beam 112 is narrow (such as in the case of pencil beams), the signal may degrade significantly if the detection by UE 102 is not correctly aligned with the serving beam. Since UE 102 may not be static, allowing UE 102 to quickly adapt to the beam based on its movement can reduce the chance of significant signal degradation. When UE 102 moves, UE 102 can determine the different AoA of the received beam and / or determine the different beam to utilize based on the movement of UE 102. UE 102 can detect training signals received from base station 106 and determine which beam to use to communicate with base station 106 and the AoA of the beam. For example, UE 102 can generate an AoA estimate for the AoA. According to the method described throughout this disclosure, UE 102 can utilize data from sensor device 104 to maintain the AoA. Compared to non-sensor-based AoA determination methods, the use of data from sensor device 104 can solve AoA reliability issues and improve AoA resolution.

[0041] Figure 2 An exemplary state machine 200 for a sensor-assisted beamforming framework according to some embodiments is shown. The sensor-assisted beamforming framework can define a UE (e.g., UE 102). Figure 1 ) and / or UE 1300 ( Figure 13 The operation of the sensor-assisted beamforming framework is used to determine the AoA of the beam for use by the UE. For example, state machine 200 shows states that can be implemented by the UE, which define the UE's operation regarding the determination of the AoA of the beam. The sensor-assisted beamforming framework can be a state machine with various operational phases and sensor states.

[0042] State machine 200 may include two operational phases. For example, state machine 200 may include a sensor-based AoA estimation state 202. In some embodiments, the UE may initiate the process of reaching the sensor-based AoA estimation state 202. While in the sensor-based AoA estimation state 202, the UE may monitor data from a base station (such as base station 106). Figure 1 ) and / or gNB 1400 ( Figure 14 The UE may use the training signal to determine the AoA of the beam, for example, by a method for determining AoA based on the training signal, as further described throughout this disclosure. The UE may then generate an AoA estimate based on this determination. Sensor-based AoA estimation state 202 may include sensor-assisted AoA acquisition to initialize subsequent beam tracking.

[0043] State machine 200 may also include sensor-assisted beam tracking state 204. When in sensor-assisted beam tracking state 204, the UE can utilize signals from sensor devices (such as sensor device 104). Figure 1 The UE uses data from sensor devices to maintain the AoA of the beam. For example, the UE may have previously determined that the AoA estimate for the beam has reached reliability. The UE can use data from sensor devices to maintain the AoA estimate as the UE rotates. For example, when determining the reliability of the AoA estimate, the UE can generate a local anchor that indicates a local AoA estimate (which may be an AoA estimate about the UE). As the UE rotates, the UE can update the local AoA estimate based on the local anchor and a change in the UE's orientation that can be determined based on data from sensor devices. Therefore, the UE can maintain an AoA estimate about the UE based on data from sensor devices. Sensor-assisted beam tracking state 204 may include sensor readings for assisting beam determination.

[0044] State machine 200 may include a transition between sensor-based AoA estimation state 202 and sensor-assisted beam tracking state 204, based on the reliability determination of the AoA estimate. Specifically, state machine 200 may include a sensor-locked transition 206 and a sensor-reliable transition 208. The UE may determine whether to implement sensor-locked transition 206 or sensor-reliable transition 208 based on the reliability determination of the UE's AoA estimate at that time.

[0045] The UE can determine whether to implement sensor lock transition 206 based on an AoA estimate that has not reached reliability. For example, the UE can determine that the reliability of the AoA estimate is less than a threshold and can determine that sensor lock transition 206 should be implemented. The UE can determine whether the AoA estimate has reached reliability when the UE is in sensor-based AoA estimation state 202 and / or sensor-assisted beam tracking state 204. For example, when an AoA estimate is generated during sensor-based AoA estimation state 202 and / or when a maintained AoA estimate is verified in sensor-assisted beam tracking state 204, the UE can determine whether the AoA estimate has reached reliability, as further described throughout this disclosure. In some embodiments, the time between verifications of AoA estimates maintained in sensor-assisted beam tracking state 204 may be greater than the time between AoA estimates generated in sensor-based AoA estimation state 202.

[0046] Sensor lock transition 206 can be implemented by the UE when AoA information is unknown (e.g., during initialization) or when AoA information is stagnant (e.g., due to AoA changes). Sensor lock transition 206 may indicate the need to relock AoA. Based on the implemented sensor lock transition 206, the UE can transition to a sensor-based AoA estimation state 202, where operations associated with the sensor-based AoA estimation state 202 can be performed. For example, when sensor lock transition 206 is implemented from sensor-based AoA estimation state 202, the UE can remain in sensor-based AoA estimation state 202. When sensor lock transition 206 is implemented from sensor-assisted beam tracking state 204, the UE can transition to sensor-based AoA estimation state 202.

[0047] The UE can determine whether to implement sensor reliability transition 208 based on an AoA estimate that has achieved reliability. For example, the UE can determine that the reliability of the AoA estimate is greater than or equal to a threshold, and can determine whether to implement sensor reliability transition 208. The UE can determine whether the AoA estimate has achieved reliability when the UE is in sensor-based AoA estimation state 202 and / or sensor-assisted beam tracking state 204. For example, the UE can determine whether the AoA estimate has achieved reliability when an AoA estimate is generated during sensor-based AoA estimation state 202 and / or when a maintained AoA estimate is verified in sensor-assisted beam tracking state 204. In some embodiments, the time between the verification of the maintained AoA estimate in sensor-assisted beam tracking state 204 may be greater than the time between AoA estimates generated in sensor-based AoA estimation state 202.

[0048] When AoA information is reliable, the UE can implement sensor reliability transition 208, enabling the application of sensor data to update the beam. Once sensor reliability transition 208 is achieved (e.g., during AoA estimation) or maintained (e.g., during beam tracking), sensor-assisted beam tracking state 204 can be executed. Based on the implemented sensor reliability transition 208, the UE can transition to sensor-assisted beam tracking state 204, where operations associated with sensor-assisted beam tracking state 204 can be performed. For example, when sensor reliability transition 208 is implemented from sensor-based AoA estimation state 202, the UE can transition to sensor-assisted beam tracking state 204. When sensor reliability transition 208 is implemented from sensor-assisted beam tracking state 204, the UE can remain in sensor-assisted beam tracking state 204.

[0049] Figure 3 An exemplary system arrangement 300 according to some embodiments is shown. System arrangement 300 illustrates exemplary coordinate system operations for AoA determination. For example, system arrangement 300 illustrates a coordinate system that can be implemented by UE 302 to determine the AoA of a beam 304 received from base station 306. UE 302 may include UE 102 ( Figure 1 One or more of the features of ). Furthermore, base station 306 includes base station 106 ( Figure 1 One or more of the features of ).

[0050] System arrangement 300 may include a global coordinate system (GCS) 308 (which may be referred to as a reference system), as shown by the axes. GCS 308 may have an orientation that references the physical world. For example, in some embodiments, GCS 308 may have an orientation in which the z-axis of GCS 308 extends substantially away from the Earth (within 10 degrees) with a radius from the Earth's center, and the x-axis and y-axis extend in directions perpendicular to each other and extend to the z-axis. The AoA is typically constant over large timescales in GCS 308, such as when the AoA is used for user equipment used by pedestrians. This is likely true for a typical use case for UE 302, which is an mmWave communication system used by pedestrians. For pedestrian users, the AoA in GCS 308 can typically remain constant over long periods, even when the UE 302 rotates.

[0051] System arrangement 300 may also include a local coordinate system (LCS) 310 (which may be referred to as the device system), as shown by axes. LCS 310 may have an orientation fixed relative to the body of UE 302. For example, in some embodiments, LCS 310 may have an orientation in which the z-axis of LCS 310 extends perpendicular to the top of UE 302, the x-axis extends perpendicular to the sides of UE 302, and the y-axis extends perpendicular to both the z-axis and x-axis (such as perpendicular to the screen extension of UE 302). Due to user behaviors (such as rotation of UE 302, flipping of UE 302, and / or other movements of UE 302 that may be exerted by the user), AoA may be more variable in LCS 310 than in GCS 308. Specifically, AoA in the LCS may vary due to device rotation. The variability of LCS 310 may be helpful and can be used to improve global AoA estimation.

[0052] Radio-based beam measurements can provide beam quality for a predetermined set of beams, such as an AoA, including beam 304. When the AoA is based on a single observation, it may be ambiguous (e.g., for a linear array) or low-resolution (e.g., for a small codebook or other practical constraints). Once the UE 302 is in motion (e.g., rotation of the UE 302), multiple (independent) beam measurements can be used together with sensor data during observation to improve the AoA estimate, such as the AoA estimate made by the UE 302.

[0053] Orientation rotation can be used to transform AoA in different coordinate systems. For example, the global AoA in GCS 308 can be transformed into a local AoA in LCS 310 using orientation rotation of UE 302, and the local AoA in LCS 310 can be transformed into the global AoA in GCS 308 using orientation rotation of the UE. AoA can be transformed between GCS 308 and LCS 310 using quaternions, rotation matrices, Euler angles, and / or other methods for transforming points between different coordinate systems. For example, in GCS 308, coordinates (φ, θ) can be transformed. GCS The global AoA (where φ is the azimuth and θ is the elevation in GCS 308) can be converted to coordinates in LCS 310. The local AoA (where in LCS 310) It's the azimuth. (where t is the elevation angle and t is time), and vice versa.

[0054] One or more sensor devices (such as motion sensors) can provide LCS-GCS orientation rotation information each time. For example, sensor inputs can be used to determine the coordinate system orientation rotation, such as the quaternion q(t). One or more sensor devices (such as sensor device 104) Figure 1 This provides information related to the orientation of UE 102. Based on information from the sensor device, UE 102 can determine the orientation of LCS 310. UE 302 can further determine the rotation difference between GCS 308 and LCS 310 based on the orientation of LCS 310. Due to possible limitations, the determined orientation of LCS 310 and the determined rotation difference can be estimates based on information from the sensor device.

[0055] It can track the AoA (and then the beam) of the LCS 310 across time, anchored to the GCS AoA. For example, UE 302 can determine the global AoA of the beam in GCS 308 and set the global AoA as the anchor. Based on the rotation difference between GCS 308 and LCS 310, UE 302 can apply the rotation difference to the anchor to determine the local AoA in LCS 310. As UE 102 rotates over time, UE 102 can continue to determine the rotation difference at the current time and apply different rotations to the anchor to update the local AoA in LCS 310.

[0056] Figure 4 A block diagram of a beam quality-based AoA estimation method 400 according to some embodiments is shown. For example, the beam quality-based AoA estimation method 400 may be a beam quality-based AoA estimation method for a single measurement.

[0057] The beam quality-based AoA estimation method 400 may include a codebook 402. For example, a direction set and / or codebook 402 may be defined for the beam. Different steering directions of the phase array may be used to define and / or calibrate the beam set. The beam set for which the direction set and / or codebook 402 is defined may be defined as W = {w1, w...} 2, …,w K}

[0058] Beam measurement 404 can be performed on the beam provided by codebook 402. For example, UE (such as UE102) Figure 1 The UE can perform beam measurements to generate a reference signal received power (RSRP) and / or signal-to-interference-plus-noise ratio (SINR) for each beam. The UE may be able to determine the strength of each beam based on its RSRP and / or SINR. The response to the quality of each beam can be defined as γ(w1;t),…,γ(w K ;t), where w * t is the beam at reference * and t is the time reference.

[0059] Beam / steering directions can be mapped to one or more AoA, such as through AoA grid 406. For example, a UE can map each of the beam / steering directions to one or more AoA within AoA grid 406. For a linear array, beams can be mapped to the same azimuth angle but different elevation angles. For example, the mapping can be represented as... w k This indicates the beam set. This represents the mapping set of the beam at reference k. It is the azimuth angle of the beam component at reference k in the AoA grid 404, and This is the elevation angle of the beam component at reference k in the AoA grid 406. Furthermore, the mapped value can be expressed as... in This is a complete mesh set. The UE can quantize the field of view of this phase array as a two-dimensional (2D) LCS AoA. For example, in It is the mapped azimuth angle of the beam at the reference point * in the LCS. It is the mapped elevation angle of the beam at the reference point in the LCS, and FOV is the field of view.

[0060] The AoA grid 406 can be defined as representing the entire sphere in the LCS. This sphere can be divided into sub-parts on the sphere. Each beam can cover the LCS AoA range within the AoA grid. For example, a subset of the sphere can be... k cover.

[0061] The UE can generate an AoA estimate 408 based on beam measurement 404 and / or AoA grid 406, where the AoA estimate 408 may be ambiguous. In some embodiments, the AoA estimate 408 may be a local AoA estimate. Once beam measurement 404 has been performed, the UE can determine the reliability and / or probability of the AoA estimate 408 generated based on the AoA grid 406.

[0062] In some implementations, the UE can determine whether the AoA estimate 408 has reached reliability based on hard decision. For example, the UE can determine which beam has the strongest beam measurement and then determine whether the AoA estimate 408 is within a subset of the AoA grid 406 covered by the beam with the strongest beam measurement. For example, hard decision can be expressed as...

[0063] In some implementations, the UE can determine whether the AoA estimate 408 has reached reliability based on soft decision-making. For example, the UE can determine the probability of each AoA in the AoA grid 406. Specifically, the UE can determine the probability that an AoA is within each subset within the AoA grid 406. Then, the UE can determine whether the subset corresponding to the AoA estimate 408 has a probability that the AoA located in that subset is greater than a threshold. For example, the soft decision-making can be expressed as...

[0064] The UE can further transform the probabilities of the AoA grid 406 in the LCS into probabilities in the GCS. In some implementations, the UE can perform a quaternion operation with local probabilities in the LCS corresponding to the AoA grid 406 to generate global probabilities in the GCS. The transformation can be represented as... in Let P(φ,θ) be the probability of AoA in the LCS subset, and let P(φ,θ) be the probability of AoA in the GCS subset. The beam quality-based AoA estimation method 400 can produce a coarse or fuzzy estimate of AoA.

[0065] Figure 5 A block diagram of another beam-quality-based AoA estimation method 500 according to some embodiments is shown. The beam-quality-based AoA estimation method 500 can be a beam-quality-based AoA estimation method for multiple measurements. In the illustrated embodiment, two measurements are performed using the beam-quality-based AoA estimation method 500.

[0066] The beam quality-based AoA estimation method 500 may include performing beam measurements at multiple different times (such as two different times in the illustrated embodiment). For example, the UE may perform beam measurements at multiple different times. The UE may perform beam measurements according to the beam quality-based AoA estimation method 400 ( Figure 4The method described in the related section for generating local AoA estimates generates one or more local AoA estimates each time based on beam measurements. The UE can generate local AoA estimates based on the location corresponding to local AoA estimates that have reached a certain reliability (such as a corresponding reliability exceeding a threshold).

[0067] In the illustrated embodiment, the beam quality-based AoA estimation method 500 includes a first LCS diagram 502. The first LCS diagram 502 may show local AoA estimates generated by the UE based on beam measurements performed by the UE at a first time. In the illustrated embodiment, the first LCS diagram 502 includes a first local AoA estimate 504 at a first location and a second local AoA estimate 506 at a second location. The first location may correspond to a first subset of the grid arrangement in the LCS, and the second location may correspond to a second subset of the grid arrangement in the LCS.

[0068] Furthermore, the beam quality-based AoA estimation method 500 includes a second LCS diagram 508 in the illustrated embodiment. The second LCS diagram 508 may show local AoA estimates generated by the UE based on beam measurements performed by the UE at a second time. In some cases, the second time may be after the first time. In the illustrated embodiment, the second LCS diagram 508 includes a third local AoA estimate 520 at a first location and a fourth local AoA estimate 510 at a second location. The first location may correspond to a third subset of the grid arrangement in the LCS, and the second location may correspond to a fourth subset of the grid arrangement in the LCS. In some cases, the third and fourth subsets may be the same subsets as the first and second subsets, or parts of each subset may be the same subset.

[0069] The beam quality-based AoA estimation method 500 includes a GCS diagram 512 in the illustrated embodiment. The GCS diagram 512 may show a global AoA estimate generated based on local AoA estimates from a first LCS diagram 502 and a second LCS. For example, the UE may convert local AoA estimates in the LCS into a global AoA estimate. The rotation (e.g., q(t)) of the UE from the LCS at each opportunity may be provided by a sensor device. Specifically, the sensor device (such as sensor device 104) Figure 1 The system can provide information to the UE, and the UE can determine the rotation between the LCS and GCS each time a measurement is performed. The UE can use the rotation between the LCS and GCS each time to convert the local AoA estimate in the first LCS diagram 502 and the local AoA estimate in the second LCS diagram 508 into a global AoA estimate in the GCS. Therefore, the GCS AoA (which may be referred to as the global AoA) may be estimated fuzzily each time.

[0070] GCS Figure 512 may include a first global AoA estimate 514, a second global AoA estimate 516, and a third global AoA estimate 518 in the illustrated embodiment. The UE may have generated the first global AoA estimate 514 by transforming the first local AoA estimate 504 into the GCS. Furthermore, the UE may have generated the third global AoA estimate 518 by transforming the fourth local AoA estimate 510 into the GCS. In the illustrated embodiment, the transformations of the second local AoA estimate 506 and the third local AoA estimate 520 may produce global AoA estimates in the same subset. Therefore, the GCS transformations of the second local AoA estimate 506 and the third local AoA estimate 520 may overlap to produce the second global AoA estimate 516. For example, the UE may have transformed the second local AoA estimate 506 and the third local AoA estimate 520 into the GCS to produce the second global AoA estimate 516.

[0071] The UE may attempt to determine the optimal beam for operation based on the global AoA estimate presented in GCS Figure 512. The UE may assume that the actual GCS AoA has not changed. Therefore, the ambiguity of the GCS estimate can be resolved by selecting the GCS with the most overlap among the estimates. For example, the UE may generate a second global AoA estimate 516 based on the transformation between the second local AoA estimate 506 and the third local AoA estimate 520, and determine that the second global AoA estimate 516 has the most overlap in the illustrated embodiment. Therefore, the UE may determine that the second global AoA estimate 516 corresponds to the optimal beam, and the second global AoA estimate 516 will be used as the global AoA estimate based on the overlap transformation.

[0072] Once the global AoA estimate is determined, the UE can use the global AoA estimate to determine whether to implement a sensor reliability transition (such as sensor reliability transition 208). Figure 2 Or implement sensor lock-in transition (such as sensor lock-in transition 206). Figure 2 In the UE's sensor-based AoA estimation state (such as sensor-based AoA estimation state 202), Figure 2In the case of operation under these conditions, when a global AoA estimate is generated, the UE can determine whether the global AoA estimate has reached a reliable level. For example, the probability that the AoA can be at the second global AoA estimate 516 can be determined based on the probabilities that the AoA is at the second local AoA estimate 506 and the AoA is at the third local AoA estimate 520. If the probability corresponding to the second global AoA estimate 516 is determined to be greater than or equal to a threshold, the UE can determine to implement a sensor reliability transition based on the global AoA estimate. If the probability corresponding to the second global AoA estimate 516 is determined to be less than a threshold, the UE can determine to implement a sensor locking transition.

[0073] When the UE is in sensor-assisted beam tracking state (such as sensor-assisted beam tracking state 204) Figure 2 In the case of operation under the following conditions, when a global AoA estimate is generated, the UE can determine whether the global AoA estimate matches the AoA estimate maintained during the sensor-assisted beam tracking state. For example, the UE can compare the AoA of the second global AoA estimate 516 with the AoA of the AoA estimate maintained during the sensor-assisted beam tracking state. If the AoA of the second global AoA estimate 516 matches the AoA of the maintained AoA estimate, the UE can determine to implement a sensor reliability switch. If the AoA of the second global AoA estimate 516 does not match the maintained AoA estimate, the UE can determine to implement a sensor lock switch. In some embodiments where the probability of the second global AoA estimate 516 is below a threshold, the UE can remain in the sensor-assisted beam tracking state without performing a comparison.

[0074] Figure 6 An example of a method 600 for converting a local AoA estimate to a global AoA estimate according to some implementations is shown. For example, method 600 may show a conversion from a local AoA estimate from a first grid arrangement of an LCS to a global AoA estimate within a second grid arrangement of a GCS.

[0075] An LCS AoA grid can be defined, allowing the beam to be mapped to one or more AoA grid points. Implementing an LCS AoA grid can reduce computational / calibration complexity and improve robustness. The conversion from LCS AoA to GCS AoA can be arbitrary for any sensor reading. There may not be a common GCS AoA across all measurement opportunities (because the LCS AoA is quantized). Therefore, a GCS AoA grid is also defined, such that, given a sensor reading, the LCS AoA is converted and quantized to GCS AoA grid points.

[0076] For example, the UE can generate a mesh arrangement in the LCS, where the mesh arrangement can form a sphere with multiple subgroups in the LCS. Alternatively, a mesh arrangement can be generated in the GCS (by the UE or base station), where the mesh arrangement can form a sphere with multiple subgroups in the LCS. The mesh arrangement in the LCS can differ from the mesh arrangement in the GCS, such that the positions of the subgroups in the mesh arrangement are not perfectly aligned. In these implementations, converting a local AoA estimate from the mesh arrangement in the LCS to a global AoA estimate in a second mesh arrangement in the GCS can result in the converted local AoA estimate being quantized to the nearest subgroup defined in the mesh arrangement of the GCS.

[0077] In the illustrated embodiment, method 600 may include an LCS diagram 602 and a GCS diagram 604. The LCS diagram 602 may show one or more local AoA estimates. In the illustrated embodiment, the transformation from the LCS mesh arrangement to the GCS mesh arrangement is illustrated with reference to a local AoA estimate 606. The local AoA estimate 606 may be located within the center of a first subgroup 608 of the LCS mesh arrangement. The location of the local AoA estimate 606 may be defined as... in It is the azimuth of the local AoA estimate of 606, and It is the elevation angle of the local AoA estimate of 606.

[0078] The UE can then transform the local AoA estimate 606 into the GCS according to the method described throughout this disclosure. The transformation of the local AoA estimate 606 produces a position 610 in the GCS, which can be defined as (φ′, θ′), where φ′ is the azimuth angle of position 610 and θ′ is the elevation angle of position 610. As can be seen from GCS diagram 604, position 610 does not correspond to any global AoA estimate (as indicated by the + symbol). However, position 610 may be located within a second subgroup 612 of the LCS grid arrangement. The UE can determine that position 610 will be represented by the nearest global AoA estimate, which is the closest global AoA estimate in the illustrated embodiment represented by (φ′, θ′). m ,θ m The global AoA estimate is 614, where φ represents the value of the global AoA estimate. m It is the azimuth angle of the global AoA 614 estimate, and θ m This is the elevation angle of the global AoA estimate of 614. For example, the UE can base it on... The global AoA estimate within the GCS mesh layout is determined based on the local AoA estimate within the LCS mesh layout.

[0079] Figure 7An exemplary beam quality-based AoA estimation method 700 according to some implementations is shown. Specifically, the beam quality-based AoA estimation method 700 can be implemented in an implementation where two or more measurements have been performed.

[0080] The beam quality-based AoA estimation method 700 may include performing beam measurement 702 at a first time and performing beam measurement 704 at a second time. For example, a UE (such as UE 102) Figure 1 It can execute from the base station (such as base station 106) in the first instance. Figure 1 The UE performs beam measurement 702 on the received beam and can perform beam measurement 704 on the beam at a second time. The UE can determine one or more responses from the beam measurement 702, where these responses can be represented as γ(w1; t1), ..., γ(w K ;t1), where w * The reference beam is located at *, and t1 is the first time. Furthermore, the UE can determine one or more responses from beam measurement 704, where these responses can be represented as γ(w1; t2), ..., γ(w K ;t2), where w * It is the beam at reference *, and t2 is the second time.

[0081] The beam quality-based AoA estimation method 700 may further include determining, based on the results of beam measurement 702, the probability 706 of a first LCS AoA at a first time and the probability 708 of a second LCS AoA at a second time. For example, LCS AoA estimation derived from beam quality can be performed. Specifically, the UE can utilize the results of beam measurement 702 to determine the probability of the true AoA corresponding to each local AoA estimate within the LCS at a first time, which can be expressed as... Where n is a reference for the local AoA estimate. It is the azimuth angle of the local AoA estimate, and This is the elevation angle of the local AoA estimate. The UE can use the results of beam measurement 704 to determine the probability of the true AoA corresponding to each local AoA estimate within the LCS at the second time, which can be expressed as...

[0082] The beam quality-based AoA estimation method 700 may further include converting the probability of LCS to the probability of GCS and combining these probabilities in GCS AoA 712. For example, an LCS AoA to GCS AoA mapping may be performed. Specifically, the UE may obtain the AoA from one or more sensor devices 710 (such as sensor device 104). Figure 1The system receives information about the UE's orientation and can determine the difference between the LCS and GCS at a first time and the difference between the LCS and GCS at a second time. The UE can use the difference between the LCS and GCS at the first time to transfer the probability from the first LCS AoA 706 to the GCS, which can be expressed as... Where φ m It is the azimuth angle converted to GCS, and θ m This is the elevation angle converted to the GCS. The UE can use the difference between the LCS and GCS at the second time to convert the probability from the second LCS AoA 708 to the GCS, which can be expressed as P(φ). m ,θ m ;t2).

[0083] The UE can then combine the probability of the global AoA estimate from the first LCS AoA 706 with the probability of the global AoA estimate from the second LCS AoA 708. In some implementations, the probability can be combined with additional probabilities from AoA estimates performed at other times, as shown in input 714. The additional probability can be expressed as P(φ m ,θ m ;t i ), where t i It is the time at position i. The combination probability provides the long-term probability (e.g., joint probability) of the GCS AoA (such as a global AoA estimate), which can be expressed as P(φ). m ,θ m ;t1,t2,…)=C norm ·∏ t P(φ m ,θ m ;t), where P(φ) m ,θ m ;t) is φ m ,θ m The instantaneous GCS probability at time t, and P(φ) m ,θ m ; t1, t2, ...) are the long-term probabilities of multiple measurements of this GCS grid point. Then, the UE can determine that the global AoA estimate to be used is the global AoA estimate with the highest probability, which can be expressed as φ * , Where φ * θ is the azimuth angle of the global AoA estimate to be used, and θ * This is the elevation angle of the global AoA estimate to be used.

[0084] Once the global AoA estimate is determined, the UE can use the global AoA estimate to determine whether to implement a sensor reliability transition (such as sensor reliability transition 208). Figure 2 Or implement sensor lock-in transition (such as sensor lock-in transition 206). Figure 2 In the UE's sensor-based AoA estimation state (such as sensor-based AoA estimation state 202), Figure 2 In the case of operation under the following conditions, when a global AoA estimate is generated, the UE can determine whether the global AoA estimate has reached a reliability threshold. If the probability corresponding to the global AoA estimate is greater than or equal to a threshold, the UE can determine whether to implement a sensor reliability transition based on the global AoA estimate. If the probability corresponding to the global AoA estimate is less than a threshold, the UE can determine whether to implement a sensor locking transition.

[0085] When the UE is in sensor-assisted beam tracking state (such as sensor-assisted beam tracking state 204) Figure 2 In the case of operation under the following conditions, when a global AoA estimate is generated, the UE can determine whether the global AoA estimate matches the AoA estimate maintained during sensor-assisted beam tracking (SABST) state. For example, the UE can compare the AoA of the global AoA estimate with the AoA of the AoA estimate maintained during SABST. If the AoA of the global AoA estimate matches the AoA of the maintained AoA estimate, the UE can determine to implement a sensor reliability switch. If the AoA of the global AoA estimate does not match the maintained AoA estimate, the UE can determine to implement a sensor lock switch. In some implementations where the probability of the global AoA estimate is below a threshold, the UE can remain in SABST state without performing a comparison.

[0086] Figure 8 A beam measurement-based AoA probability determination method 800 according to some implementations is shown. Specifically, method 800 shows how the probability of steering direction (such as an AoA estimate) can be determined. Method 800 can be used by a UE (such as UE 102). Figure 1 )) Implement the probability to determine the direction of steering.

[0087] Method 800 may include performing beam measurement 802 on one or more beams. Specifically, the UE may perform beam measurement 802 on beams from a base station (such as base station 106). Figure 1 Beam measurement is performed on one or more received beams. Beam measurement can generate RSRP and / or SINR for each measured beam. The response from beam measurement can be represented as γ(w1).

[0088] In 804, 806, and 808, the probability of AoA as the approximate steering direction can be obtained from RSRP and / or SINR. Specifically, in 804, the UE can determine the probability of AoA as the approximate steering direction w1. Furthermore, in 806, the UE can determine the probability of AoA as the approximate steering direction w2. In 808, the UE can determine the probability of AoA as the approximate steering direction w3. The probability can be determined based on hard or soft decision-making. In hard decision-making, it can be determined through... Determine the probability. In soft decision-making, this can be achieved through P(w) k )∝γ(w k Determine the probability.

[0089] For this method, the probability of the steering direction can be distributed across all mapped grids in 810, 812, 814, and 816. For example, the probability of the steering direction can be obtained through... Confirmed, among which Where P(w) k ) is the probability that AoA is covered by the beam. It is a subset The base number. In some other implementations, the probability of the steering direction can be determined by... To determine this. Specifically, the UE can determine the probability of the steering direction in 810. The probability of turning direction in 812 The probability of turning direction in 814 The probability of turning direction in 816 If the AoA grid is mapped from more than one beam, then the probability of this grid is the sum of the probabilities of all source beams. The probability of this grid can be obtained through... To determine.

[0090] Sensor-based AoA estimation can be stopped after M beam measurement opportunities according to a specific stopping rule. For example, if the UE is still in a sensor-based AoA estimation state (such as sensor-based AoA estimation state 202) after a certain amount of AoA estimation has been performed, the UE can be stopped. Figure 2 If the UE transitions from the sensor-based AoA estimation state, it can then transition to the SENSOR_RELIABLE state and trigger sensor-assisted beam tracking. Specifically, based on a certain amount of AoA estimation already performed while in the sensor-based AoA estimation state, the UE can determine whether to implement a sensor-reliable transition (such as sensor-reliable transition 208). Figure 2 And the UE can switch to sensor-assisted beam tracking state (such as sensor-assisted beam tracking state 204). Figure 2 )).

[0091] In the first case, the stopping criterion could include using the AoA grid with the highest probability in sensor-assisted beam tracking if only one AoA grid has a sufficiently high probability. In the second case, the stopping criterion could include using multiple potential AoA grid points (e.g., non-line-of-sight (NLOS) channels) in sensor-assisted beam tracking if the maximum number of beam measurements performed. Multiple potential AoA grid points can be selected through... Determined, where (φ) m ,θ m ;t) are grid points that satisfy the threshold, and F(P(φ) m ,θ m (), m=1,…,M) is a function that maps the probability set to AoA. For example, if any stopping criterion is met, the UE can use the indicated value to transition to sensor-assisted beam tracking state.

[0092] If multiple potential AoA are observed at the end of the window, they can be processed in various ways. In the first case, for AoA grid points with sufficient reliability, multiple potential AoA can be processed by outputting the average AoA (AoA expectation) for each reliability, which can be defined as... In the second case, multiple potential AoA can be processed by outputting multiple AoA to the beam tracking engine, and the beam tracking engine can apply advanced beam selection.

[0093] Once stopped, the local AoA at the stop time, based on the stop criterion or multiple potential AoA, can be transferred to the GCS and remain constant until the AoA estimation is triggered again (such as in the SENSOR_LOCK state). For example, once stopped, the local AoA estimate determined at the stop time can be transferred to the global AoA estimate and remain constant until the UE implements a sensor lock transition (such as in sensor lock transition 206). Figure 2 )).

[0094] The following provides exemplary algorithms that can be implemented to perform one or more methods described throughout this disclosure.

[0095] In the initialization section of the algorithm, the two AoA grids are located in the LCS and GCS, respectively. Note that the sizes may be different. For example, during initialization, the grid arrangement can be generated in the LCS and the grid arrangement in the GCS. In some implementations, the grid arrangements in the LCS and the GCS can have different sizes. Codebook and beam-to-LCS AoA mapping (1:n). For example, the codebook and beam-to-LCS AoA mapping can be defined in the initialization section. The initialization section may include:

[0096]

[0097] In the second part of the algorithm, the GCS AoA probability at time t can be determined based on beam measurement and LCS-to-GCS rotation. This probability can be determined using either hard or soft decision-making. In hard decision-making... In soft decision-making, The second part of the algorithm may include:

[0098]

[0099]

[0100] In the third part of the algorithm, the probability of GCS AoA during durations 1, 2, ..., t can be determined. The third part of the algorithm may include:

[0101]

[0102] In the fourth part of the algorithm, a termination criterion can be checked. The fourth part of the algorithm may include:

[0103]

[0104] When using codebook-based beamforming, the beams in the codebook can be scanned to measure the beam quality of each beam. Alternatively, a set of probe beams can be applied, and the received signals from the probe beams can be used to estimate the original channel. The channel estimate can be used for beam determination, such as in channel-based beamforming.

[0105] In this scenario, a reference codebook can still be deployed for AoA estimation. In some implementations, the quality of each beam in the codebook can be determined by applying the AoA beam to a channel estimate in the baseband. In other implementations, the quality of each beam in the codebook can be determined by associating a channel-based beam with the AoA beam.

[0106] Considering noise measurements and other practical limitations, some AoA grid points may have unreliable probabilities. For example, due to the physical constraints of human behavior, the variation of LCS AoA estimates over time may be limited and may not change significantly. For instance, LCS AoA estimates may be limited based on variations that are impossible and / or unlikely for human-used UEs. Regularization rules can be applied to remove AoA outliers in the local or global coordinate system.

[0107] One or more optimal GCS AoA estimates can be output to the sensor-assisted beam tracking module to initialize beam tracking. In the case of an NLOS channel, multiple AoA estimates can be output. This enables the soft output utilized by the beam tracking module. Alternatively, the reliability of each AoA grid point can be output, allowing beam tracking to make reliability-based decisions for beam scanning or antenna detection.

[0108] Earlier sensor-assisted beamforming frameworks could indicate two operational phases. The consistency of the AoA could be checked using new beam quality measurements. If the AoA estimation was detected to be unreliable, operation could switch back to AoA estimation (such as sensor-based AoA estimation state 202).

[0109] Alternatively, after an initial AoA estimate has been estimated, joint sensor-assisted beamforming and AoA estimate updates can be applied. Sensor readings and the current AoA estimate can be used to update the beam between beam measurement opportunities. Once the beam quality has been updated under a new measurement opportunity, the AoA estimate can be updated directly without switching operating modes.

[0110] Figure 9A The first part of an exemplary process 900 for identifying a beam used for communication, according to some embodiments, is shown. Figure 9B The second part of an exemplary process 900 for identifying a beam used for communication, according to some embodiments, is shown. Process 900 may be performed by a UE (such as UE 102). Figure 1 The UE-executable procedure 900 determines the base station (such as base station 106) to be used. Figure 1 The beam for communication between the UE and the UE.

[0111] Process 900 may include performing multiple beam measurements at 902. Specifically, the UE may perform multiple beam measurements corresponding to beams received by the UE. The UE may receive beams from the base station. The UE performing multiple beam measurements may generate RSRP and / or SINR for the beams.

[0112] Process 900 may further include 904 generating a grid arrangement in the LCS. Specifically, the UE may generate a grid arrangement in the LCS. The UE may generate the grid arrangement according to any of the methods for generating a grid arrangement described throughout this disclosure. In some embodiments, 904 may be omitted.

[0113] Process 900 may further include 906 generating a mesh arrangement in the GCS. Specifically, the UE may generate a mesh arrangement in the GCS. The UE may generate a mesh arrangement according to any of the methods for generating a mesh arrangement described throughout this disclosure. In some embodiments, 906 may be omitted.

[0114] Process 900 may include determining multiple local AoA estimates at 908. Specifically, the UE may determine multiple local AoA estimates corresponding to beams based on multiple beam measurements. The UE may determine the multiple local AoA estimates according to any of the methods for determining local AoA estimates described throughout this disclosure. In some embodiments, determining the multiple local AoA estimates may include determining the local grid locations of the multiple local AoA estimates based on the multiple beam measurements and the grid arrangement generated in the LCS at 904. In some embodiments, the multiple local AoA estimates may be established in the grid system of the LCS.

[0115] Process 900 may include converting multiple local AoA estimates at 910. Specifically, the UE may convert the multiple local AoA estimates into multiple global AoA estimates. The UE may convert the multiple local AoA estimates into multiple global AoA estimates using any of the methods described throughout this disclosure for converting local AoA estimates into global AoA estimates.

[0116] In some implementations, converting multiple local AoA estimates into multiple global AoA estimates may include determining the global grid location of the multiple global AoA estimates based on the local grid locations of the multiple local AoA estimates.

[0117] In some implementations, converting multiple local AoA estimates into multiple global AoA estimates may include converting multiple AoA estimates from the LCS mesh system to the GCS mesh system. For example, when establishing local AoA estimates in the LCS mesh system, the UE may convert multiple AoA estimates from the LCS mesh system to the GCS mesh system.

[0118] In some implementations, converting multiple local AoA estimates into multiple global AoA estimates may include determining sensor data corresponding to each local AoA estimate of the multiple AoA estimates. Specifically, the UE may obtain sensor data from one or more sensor devices (such as sensor device 104). Figure 1 The UE receives data to determine sensor data. Furthermore, the UE can convert these multiple local AoA estimates into multiple global AoA estimates based on sensor data corresponding to each local AoA estimate.

[0119] Process 900 may include determining the global AoA at 912. Specifically, the UE may determine the global AoA based on multiple global AoA estimates. The UE may determine the global AoA according to any of the methods described herein for determining the global AoA. In some embodiments, the global AoA may be an estimate of the actual global AoA that can be estimated based on multiple global AoA estimates. In some embodiments, the multiple global AoA estimates may have corresponding multiple probabilities of being the actual global AoA.

[0120] In some implementations, determining the global AoA may include converting a first set of local AoA estimates from multiple local AoA estimates into a first set of global AoA estimates. The first set of local AoA estimates may correspond to a first time period. Determining the global AoA may also include converting a second set of local AoA estimates from multiple local AoA estimates into a second set of global AoA estimates. The second set of local AoA estimates may correspond to a second time period. Furthermore, determining the global AoA may include determining, based on the determination that a first global AoA estimate overlaps with a second global AoA estimate, a first global AoA estimate corresponding to the first set of global AoA estimates and a second global AoA estimate corresponding to the second set of global AoA estimates.

[0121] In some implementations, determining the global AoA may include identifying the global AoA estimate with the highest probability based on multiple probabilities corresponding to multiple global AoA estimates. The UE may then set the global AoA to the global AoA estimate with the highest probability.

[0122] Process 900 may include determining at 914 whether reliability has been achieved. Specifically, the UE may determine whether reliability has been achieved for the global AoA based on the probability that the global AoA is the actual global AoA. The UE may determine whether reliability has been achieved according to any of the methods described throughout this disclosure for determining whether reliability has been achieved. In some embodiments, 914 may be omitted.

[0123] Process 900 may further include determining at 916 which state to implement. Specifically, the UE may determine whether to implement a sensor-based AoA estimation state or a sensor-assisted beam tracking state based on whether reliability has been achieved. When the UE determines that reliability has been achieved, the UE may determine to implement the sensor-assisted beam tracking state based on the achieved reliability. When the UE determines that reliability has not been achieved, the UE may determine to implement the sensor-based AoA estimation state based on the unachieved reliability. In some implementations, 916 may be omitted.

[0124] Process 900 may also include converting the global AoA to a local anchor at 918. Specifically, the UE may convert the global AoA to a local anchor for use in sensor-assisted beam tracking state. In some implementations, 918 may be omitted.

[0125] Process 900 may also include maintaining the local AoA at 920. Specifically, the UE may maintain the local AoA based on the local anchor and sensor data from sensors (such as sensor device 104) of the UE when implementing sensor-assisted beam tracking state. In some embodiments, 920 may be omitted. 922 may show the transition from... Figures 9A to 9B The transformation.

[0126] Process 900 may also include identifying the beam using a global AoA at 924. Specifically, the UE may use the global AoA to identify the beam received from the base station. The UE may determine that the beam is used for communication between the UE and the base station. The UE may transmit an indication to the base station that the beam will be used.

[0127] Process 900 may also include performing a second plurality of beam measurements at 926. Specifically, the UE may perform a second plurality of beam measurements corresponding to a beam while implementing sensor-assisted beam tracking. In some embodiments, 926 may be omitted.

[0128] Process 900 may further include determining a second plurality of local AoA estimates at 928. Specifically, the UE may determine a second plurality of local AoA estimates corresponding to a beam based on a second plurality of beam measurements from 926. In some embodiments, 928 may be omitted.

[0129] Process 900 may further include converting a second plurality of local AoA estimates at 930. Specifically, the UE may convert the second plurality of local AoA estimates into a second plurality of global AoA estimates. In some implementations, 930 may be omitted.

[0130] Process 900 may further include determining a second global AoA at 932. Specifically, the UE may determine the second global AoA based on a second plurality of global AoA estimates. In some implementations, 932 may be omitted.

[0131] The process 900 may further include comparing the second global AoA with the local AoA at 934. Specifically, the UE may compare the second global AoA with the local AoA maintained at 920 to determine whether the local AoA has a reliability greater than a predetermined threshold. In some embodiments, 934 may be omitted.

[0132] Figure 10A The first part of another exemplary process 1000 for identifying a beam used for communication, according to some embodiments, is shown. Figure 10BThe second part of an exemplary process 1000 for identifying a beam used for communication, according to some embodiments, is shown. Process 1000 can be performed by a UE (such as UE102). Figure 1 The UE-executable process 1000 determines the base station (such as base station 106) to be used. Figure 1 The beam for communication between the UE and the UE.

[0133] Process 1000 may include performing multiple beam measurements at 1002. Specifically, the UE may perform multiple beam measurements corresponding to beams received by the UE. The UE performing multiple beam measurements may generate RSRP and / or SINR for the beams.

[0134] Process 1000 may include determining the orientation of the UE at 1004. Specifically, the UE may determine the orientation of the UE corresponding to each of a plurality of beam measurements. For example, the UE may determine the orientation from one or more sensors (such as sensor device 104). Figure 1 It receives sensor data and can determine the orientation of the UE based on the sensor data.

[0135] Process 1000 may include determining multiple local AoA estimates at 1006. Specifically, the UE may determine multiple local AoA estimates corresponding to a beam based on multiple beam measurements. The multiple local AoA estimates may be determined based on beam measurements and / or the orientation determined from 1004.

[0136] Process 1000 may include converting multiple local AoA estimates at 1008. Specifically, the UE may convert the multiple local AoA estimates into multiple global AoA estimates based on the UE's orientation corresponding to each of the multiple beam measurements. In some embodiments, converting multiple local AoA estimates into multiple global AoA estimates may include converting multiple local AoA estimates from the LCS to the GCS to produce multiple global AoA estimates.

[0137] Process 1000 may include determining the probability of each local AoA estimate at 1010. Specifically, the UE may determine the probability of each local AoA estimate based on multiple beam measurements. In some embodiments, determining the probability of each local AoA estimate may include determining the probability of each local AoA estimate based on the RSRP and / or SINR of the multiple local AoA estimates from the multiple beam measurements. In some embodiments, 1010 may be omitted.

[0138] Process 1000 may include determining the probability of each global AoA estimate at 1012. Specifically, the UE may determine the probability of each global AoA estimate of multiple global AoA estimates based on the probability of each local AoA estimate of multiple local AoA estimates. In some embodiments, 1012 may be omitted.

[0139] Process 1000 may include determining the global AoA at 1014. Specifically, the UE may determine the global AoA based on the overlap of multiple global AoA estimates. In some embodiments, the global AoA may be an estimate of the actual global AoA that can be estimated based on multiple global AoA estimates. In some embodiments, determining the global AoA may include determining the probability of the global AoA being the maximum probability of the AoA estimates within the GCS based on the probability of each AoA among the multiple global AoA estimates. In some embodiments, determining the global AoA may include determining the location of the maximum number of overlapping global AoA estimates, wherein the global AoA is determined to be located at that location.

[0140] Process 1000 may include determining at 1016 whether the global AoA meets reliability requirements. Specifically, the UE may determine whether the global AoA meets reliability requirements based on the probability that the global AoA is greater than a predetermined threshold. In some implementations, 1016 may be omitted.

[0141] Process 1000 may include determining, at 1018, the state in which the UE will operate. Specifically, the UE may determine whether it operates in a sensor-based AoA estimation state or a sensor-assisted beam tracking state based on whether the global AoA reliability is met. For example, the UE may determine that it operates in a sensor-based AoA estimation state based on the global AoA reliability not being met, and may determine that it operates in a sensor-assisted beam tracking state based on the global AoA reliability being met. In some embodiments, 1018 may be omitted. 1020 may show from Figures 10A to 10B The transformation.

[0142] Process 1000 may include entering a state at 1022. Specifically, the UE may enter a determined state at 1018. For example, the UE may enter a sensor-assisted beam tracking state based on determining that the global AoA meets reliability requirements. Alternatively, the UE may enter a sensor-based AoA estimation state based on determining that the global AoA fails to meet reliability requirements. In some implementations, 1022 may be omitted.

[0143] Process 1000 may include converting the global AoA to a local anchor at 1024. Specifically, the UE may convert the global AoA to a local anchor for use in sensor-assisted beam tracking state. In some implementations, 1024 may be omitted.

[0144] Process 1000 may include maintaining a local AoA at 1026. Specifically, the UE may maintain the local AoA based on a local anchor and sensor data related to the UE's rotation. For example, the UE may receive sensor data from one or more sensors (such as sensor device 104) and may determine the UE's rotation based on the sensor data. The UE may maintain the local AoA in the LCS based on the local anchor from 1024 and data related to the UE's rotation. In some embodiments, 1026 may be omitted.

[0145] Figure 11 An exemplary process 1100 for determining a beam for communication, according to some embodiments, is shown. Process 1100 may be performed by a base station (such as base station 106). Figure 1 The base station executable process 1100 determines the procedures to be used by the base station and the UE (such as UE 102). Figure 1 The beam for communication between ))

[0146] Process 1100 may include transmitting multiple training signals at 1102. Specifically, the base station may transmit multiple training signals that will be used by the UE to determine multiple local AoA estimates in order to determine the beam to be used by the UE. In some embodiments, transmitting multiple training signals includes transmitting multiple training signals via a scanning method or a detection method.

[0147] Process 1100 may include transmitting an indication of the orientation of the GCS at 1104. Specifically, the base station may transmit an indication of the orientation of the GCS to the UE, wherein the UE may use the GCS to determine a beam. For example, the UE may use the indication of the orientation of the GCS to generate the GCS, the grid allocation for generating the GCS, and / or the grid system for generating the GCS. In some embodiments, 1104 may be omitted.

[0148] Process 1100 may include identifying a beam indication at 1106. Specifically, the base station may identify a beam indication received from the UE for use in communicating with the UE. The UE may determine the beam based on multiple local AoA estimates determined from multiple training signals.

[0149] Process 1100 may include transmitting to the UE using a beam. Specifically, the base station may use the beam to communicate with the UE.

[0150] Figure 12 An exemplary beamforming circuit 1200 according to some embodiments is shown. The beamforming circuit 1200 may include a first antenna panel, namely panel 1 1204, and a second antenna panel, namely panel 2 1208. Each antenna panel may include multiple antenna elements. Other embodiments may include other numbers of antenna panels.

[0151] The digital beamforming (BF) component 1228 can be derived from, for example, a baseband processor (e.g., Figure 13 The baseband processor 1304A receives the input baseband (BB) signal. The digital BF component 1228 can rely on complex weights to precode the BB signal and provide beamformed BB signals to the parallel radio frequency (RF) chains 1220 / 1224.

[0152] Each RF chain 1220 / 1224 may include a digital-to-analog converter that converts the BB signal into the analog domain; a mixer that mixes the baseband signal into an RF signal; and a power amplifier that amplifies the RF signal for transmission.

[0153] RF signals can be provided to analog beamforming components 1212 / 1216, which can further apply beamforming by providing a phase shift in the analog domain. The RF signals can then be provided to antenna panels 1204 / 1208 for transmission.

[0154] In some implementations, beamforming may be performed only in the digital domain or only in the analog domain, instead of the hybrid beamforming shown herein.

[0155] In various implementations, control circuitry residing in the baseband processor can provide BF weights to the analog / digital BF components to provide a transmission beam at the corresponding antenna panel. These BF weights can be determined by the control circuitry to provide directional allocation of the serving cell as described herein. In some implementations, the BF components and antenna panels can operate together to provide a dynamic phased array capable of guiding the beam in a desired direction.

[0156] Figure 13 An example of an exemplary UE 1300 according to some implementations is shown. UE 1300 can be any mobile or non-mobile computing device, such as, for example, a mobile phone, computer, tablet, industrial wireless sensors (e.g., microphones, carbon dioxide sensors, pressure sensors, humidity sensors, thermometers, motion sensors, accelerometers, laser scanners, fluid level sensors, stock sensors, voltmeters / ammeters, actuators, etc.), video surveillance / monitoring devices (e.g., cameras, camcorders, etc.), wearable devices (e.g., smartwatches), and loosely coupled IoT devices. In some implementations, UE 1300 can be a RedCap UE or an NR-Light UE.

[0157] UE 1300 may include a processor 1304, RF interface circuitry 1308, memory / storage device 1312, user interface 1316, sensor 1320, drive circuitry 1322, power management integrated circuit (PMIC) 1324, antenna structure 1326, and battery 1328. Components of UE 1300 may be implemented as integrated circuits (ICs), portions of integrated circuits, discrete electronic devices or other modules, logic components, hardware, software, firmware, or combinations thereof. Figure 13 The block diagram is intended to show a high-level view of some of the components of the UE 1300. However, some of the components shown may be omitted, additional components may be present, and different arrangements of the components shown may occur in other specific implementations.

[0158] Components of UE 1300 can be coupled to various other components via one or more interconnects 1332, which can represent any type of interface, input / output, bus (local, system, or extended), transmission line, trace, optical connector, etc., that allows various circuit components (on common or different chips or chipsets) to interact with each other.

[0159] Processor 1304 may include processor circuitry such as baseband processor circuitry (BB) 1304A, central processing unit circuitry (CPU) 1304B, and graphics processing unit circuitry (GPU) 1304C. Processor 1304 may include any type of circuitry or processor circuitry that executes or otherwise operates computer-executable instructions (such as program code, software modules, or functional processes from memory / storage device 1312) to cause UE 1300 to perform the operations described herein.

[0160] In some implementations, the baseband processor circuit 1304A can access the communication protocol stack 1336 in the memory / storage device 1312 to communicate over a 3GPP-compliant network. Generally, the baseband processor circuit 1304A can access the communication protocol stack to perform the following operations: user plane functions at the PHY, MAC, RLC, PDCP, SDAP, and PDU layers; and control plane functions at the PHY, MAC, RLC, PDCP, RRC, and non-access layers. In some implementations, PHY layer operations may additionally / optionally be performed by components of the RF interface circuit 1308.

[0161] The baseband processor circuit 1304A can generate or process baseband signals or waveforms carrying information in a 3GPP-compliant network. In some implementations, the waveforms used for NR can be based on cyclic prefix OFDM (CP-OFDM) in the uplink or downlink, and Discrete Fourier Transform Extended OFDM (DFT-S-OFDM) in the uplink.

[0162] Memory / storage device 1312 may include one or more non-transitory computer-readable media, including instructions (e.g., communication protocol stack 1336) that can be executed by one or more processors in processor 1304 to cause UE 1300 to perform the various operations described herein. Memory / storage device 1312 includes any type of volatile or non-volatile memory that can be distributed throughout UE 1300. In some embodiments, some memory / storage devices in memory / storage device 1312 may be located on processor 1304 itself (e.g., L1 cache and L2 cache), while other memory / storage devices 1312 may be located external to processor 1304 but accessible via a memory interface. Memory / storage device 1312 may include any suitable volatile or non-volatile memory, such as, but not limited to, dynamic random access memory (DRAM), static random access memory (SRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, solid-state memory, or any other type of memory device technology.

[0163] The RF interface circuitry 1308 may include transceiver circuitry and a radio frequency front-end module (RFEM), which allows the UE 1300 to communicate with other devices via a radio access network. The RF interface circuitry 1308 may include various components arranged in the transmit or receive path. These components may include, for example, switches, mixers, amplifiers, filters, synthesizer circuitry, control circuitry, etc.

[0164] In the receiving path, the RFEM can receive the radiated signal from the air interface via antenna structure 1326 and continue to filter and amplify the signal (using a low-noise amplifier). This signal can be provided to the receiver of the transceiver, which downconverts the RF signal into a baseband signal that is provided to the baseband processor of processor 1304.

[0165] In the transmission path, the transceiver's transmitter upconverts the baseband signal received from the baseband processor and provides the RF signal to the RFEM. The RFEM amplifies the RF signal using a power amplifier before it is radiated across the air interface via antenna 1326.

[0166] In various implementations, the RF interface circuit 1308 can be configured to transmit / receive signals in a manner compatible with NR access technology.

[0167] Antenna 1326 may include antenna elements to convert electrical signals into radio waves for propagation through the air and to convert received radio waves back into electrical signals. These antenna elements may be arranged in one or more antenna panels. Antenna 1326 may have omnidirectional, directional, or combinations thereof antenna panels to enable beamforming and multiple-input / multiple-output communication. Antenna 1326 may include microstrip antennas, printed antennas fabricated on the surface of one or more printed circuit boards, patch antennas, phased array antennas, etc. Antenna 1326 may have one or more panels designed for a specific frequency band included in FR1 or FR2.

[0168] In some implementations, UE 1300 may include beamforming circuitry 1200. Figure 12 The beamforming circuit 1200 can be used to communicate with the UE 1300. In some embodiments, components of the UE 1300 and the beamforming circuit can be shared. For example, the UE's antenna 1326 may include panel 1 1204 and panel 2 1208 of the beamforming circuit 1200.

[0169] User interface circuitry 1316 includes various input / output (I / O) devices designed to enable users to interact with UE 1300. User interface circuitry 1316 includes input device circuitry and output device circuitry. Input device circuitry includes any physical or virtual means for accepting input, particularly including one or more physical or virtual buttons (e.g., a reset button), a physical keyboard, a keypad, a mouse, a touchpad, a touchscreen, a microphone, a scanner, a headset, etc. Output device circuitry includes any physical or virtual means for displaying information or otherwise conveying information (such as sensor readings, actuator positions, or other similar information). Output device circuitry may include any number or combination of audio or visual displays, particularly including one or more simple visual outputs / indicators (e.g., binary status indicators, such as light-emitting diodes (LEDs)) and multi-character visual outputs, or more complex outputs, such as display devices or touchscreens (e.g., liquid crystal displays (LCDs), LED displays, quantum dot displays, projectors, etc.), wherein the output of characters, graphics, multimedia objects, etc., is generated or produced by the operation of UE 1300.

[0170] Sensor 1320 may include devices, modules, or subsystems intended to detect events or changes in their environment and transmit information about the detected events (sensor data) to other devices, modules, subsystems, etc. Examples of such sensors include, in particular: inertial measurement units including accelerometers, gyroscopes, or magnetometers; microelectromechanical systems (MEMS) or nanoelectromechanical systems (NEMS) including triaxial accelerometers, triaxial gyroscopes, or magnetometers; level sensors; flow sensors; temperature sensors (e.g., thermistors); pressure sensors; barometric pressure sensors; gravimeters; altimeters; image capture devices (e.g., cameras or lensless aperture sensors); light detection and ranging sensors; proximity sensors (e.g., infrared radiation detectors, etc.); depth sensors; ambient light sensors; ultrasonic transceivers; microphones or other similar audio capture devices; etc.

[0171] The driving circuitry 1322 may include software and hardware elements for controlling specific devices embedded in, attached to, or otherwise communicatively coupled to the UE 1300. The driving circuitry 1322 may include various drivers that allow other components to interact with or control various input / output (I / O) devices that may exist within or be connected to the UE 1300. For example, the driving circuitry 1322 may include: a display driver for controlling and allowing access to a display device; a touchscreen driver for controlling and allowing access to a touchscreen interface; a sensor driver for acquiring sensor readings of the sensor circuitry 1320 and controlling and allowing access to the sensor circuitry 1320; a driver for acquiring actuator positions of electromechanical components or controlling and allowing access to electromechanical components; a camera driver for controlling and allowing access to an embedded image capture device; and an audio driver for controlling and allowing access to one or more audio devices.

[0172] The PMIC 1324 manages the power supplied to various components of the UE 1300. Specifically, relative to the processor 1304, the PMIC 1324 controls power selection, voltage scaling, battery charging, or DC-DC conversion.

[0173] In some implementations, the PMIC 1324 can control or otherwise become part of various power-saving mechanisms of the UE 1300. For example, if the platform UE is in the RRC_Connected state, where it remains connected to the RAN node as it anticipates receiving traffic soon, it can enter a state known as Discontinuous Receive Mode (DRX) after a period of inactivity. During this state, the UE 1300 can power down for short intervals to save power. If there is no data traffic activity over a longer period, the UE 1300 can transition to the RRC_Idle state, in which the UE is disconnected from the network and does not perform operations such as channel quality feedback, handover, etc. The UE 1300 enters a very low-power state and performs paging, in which the platform periodically wakes up again to listen to the network and then power down again. The UE 1300 may not receive data in this state; to receive data, the platform must transition back to the RRC_Connected state. Additional power-saving modes can allow the device to be unable to use the network for longer than the paging interval (ranging from a few seconds to several hours). During this period, the device is completely unable to connect to the network and can be completely powered off. Any data sent during this time will result in significant latency, which is assumed to be acceptable.

[0174] Battery 1328 can power UE 1300, but in some examples, UE 1300 may be mounted in a fixed location and may have a power source coupled to the mains. Battery 1328 may be a lithium-ion battery, a metal-air battery such as a zinc-air battery, an aluminum-air battery, a lithium-air battery, etc. In some specific implementations, such as in vehicle-based applications, battery 1328 may be a typical lead-acid automotive battery.

[0175] Figure 14 An exemplary gNB 1400 according to some embodiments is shown. The gNB 1400 may include a processor 1404, an RF interface circuit 1408, a core network (CN) interface circuit 1412, a memory / storage device circuit 1416, and an antenna structure 1426.

[0176] The gNB 1400 components can be coupled to various other components via one or more interconnects 1428.

[0177] The processor 1404, RF interface circuit 1408, memory / storage device circuit 1416 (including communication protocol stack 1410), antenna structure 1426, and interconnect 1428 can be similar to those described above. Figure 13 Similar named elements are shown and described.

[0178] The CN interface circuit 1412 can provide connectivity to a core network (e.g., a 5GC using a 5G core network (5GC) compatible network interface protocol (such as Carrier Ethernet) or some other suitable protocol). Network connectivity can be provided to / from the gNB 1400 via fiber optic or wireless backhaul. The CN interface circuit 1412 may include one or more dedicated processors or FPGAs for communicating using one or more of the aforementioned protocols. In some implementations, the CN interface circuit 1412 may include multiple controllers for providing connectivity to other networks using the same or different protocols.

[0179] As is widely recognized, the use of personally identifiable information should comply with privacy policies and practices that are generally accepted to meet or exceed industry or governmental requirements for protecting user privacy. Specifically, personally identifiable information data should be managed and processed to minimize the risk of unintentional or unauthorized access or use, and the nature of authorized use should be clearly explained to users.

[0180] For one or more embodiments, at least one of the components shown in one or more of the foregoing figures may be configured to perform one or more operations, techniques, processes, or methods as described in the Examples section below. For example, the baseband circuitry described above in conjunction with one or more of the foregoing figures may be configured to operate according to one or more of the examples below. Similarly, circuitry associated with the UE, base station, network element, etc., described above in conjunction with one or more of the foregoing figures may be configured to operate according to one or more of the examples shown in the Examples section below.

[0181] Example

[0182] Further exemplary implementations are provided in the following sections.

[0183] Example 1 may include a method comprising performing a plurality of beam measurements corresponding to a beam received by a UE, determining a plurality of local angle of arrival (AoA) estimates corresponding to the beam based on the plurality of beam measurements, converting the plurality of local AoA estimates into a plurality of global AoA estimates, determining a global AoA based on the plurality of global AoA estimates, and using the global AoA to identify a beam received from a base station.

[0184] Example 2 may include the method of Example 1, wherein determining the global AoA includes: converting a first set of local AoA estimates of the plurality of local AoA estimates into a first set of global AoA estimates of the plurality of global AoA estimates, the first set of local AoA estimates corresponding to a first time; converting a second set of local AoA estimates of the plurality of local AoA estimates into a second set of global AoA estimates of the plurality of global AoA estimates, the second set of local AoA estimates corresponding to a second time; and determining, based on the determination that the first global AoA estimate overlaps with the second global AoA estimate, the global AoA corresponds to the first global AoA estimate of the first set of global AoA estimates and the second global AoA estimate of the second set of global AoA estimates.

[0185] Example 3 may include the method of Example 1, wherein the plurality of global AoA estimates have a plurality of corresponding probabilities that are the actual global AoA, and wherein determining the global AoA includes determining the global AoA estimate with the highest probability based on the plurality of corresponding probabilities of the plurality of global AoA estimates, and setting the global AoA as the global AoA estimate with the highest probability.

[0186] Example 4 may include the method of Example 1, which further includes determining whether reliability has been achieved for global AoA based on the probability that global AoA is the actual global AoA, and determining which state, sensor-based AoA estimation state or sensor-assisted beam tracking state, should be implemented based on whether such reliability has been achieved.

[0187] Example 5 may include the method of Example 4, wherein the method includes determining that reliability has been achieved, wherein a sensor-assisted beam tracking state is to be implemented based on the achieved reliability, and the method further includes converting the global AoA to a local anchor, and maintaining the local AoA based on the local anchor and sensor data from the UE's sensors when implementing the sensor-assisted beam tracking state.

[0188] Example 6 may include the method of Example 5, wherein the plurality of beam measurements are a first plurality of beam measurements, wherein the plurality of local AoA estimates are a first plurality of local AoA estimates, wherein the plurality of global AoA estimates are a first plurality of global AoA estimates, wherein the global AoA is a first global AoA, and wherein the method further includes: performing a second plurality of beam measurements corresponding to the beam when implementing a sensor-assisted beam tracking state; determining a second plurality of local AoA estimates based on the second plurality of beam measurements; converting the second plurality of local AoA estimates into a second plurality of global AoA estimates; determining a second global AoA based on the second plurality of global AoA estimates; and comparing the second global AoA with the local AoA to determine whether the local AoA has a reliability greater than a predetermined threshold.

[0189] Example 7 may include the method of Example 1, the method further comprising: generating a mesh arrangement in a local coordinate system (LCS), wherein determining the plurality of local AoA estimates includes determining the local mesh positions of the plurality of local AoA estimates based on the plurality of beam measurements and the mesh arrangement in the LCS; and generating a mesh arrangement in a global coordinate system (GCS), wherein converting the plurality of local AoA estimates into the plurality of global AoA estimates includes determining the global mesh positions of the plurality of global AoA estimates based on the local mesh positions of the plurality of local AoA estimates.

[0190] Example 8 may include the method of Example 1, wherein the plurality of local AoA estimates are established in a local coordinate system (LCS) grid system, and wherein converting the plurality of local AoA estimates to the plurality of global AoA estimates includes converting the plurality of local AoA estimates from the LCS grid system to the global coordinate system (GCS) grid system.

[0191] Example 9 may include the method of Example 1, wherein converting the plurality of local AoA estimates into the plurality of global AoA estimates includes determining sensor data corresponding to each of the plurality of local AoA estimates, and converting the plurality of local AoA estimates into the plurality of global AoA estimates based on the sensor data corresponding to each of the plurality of local AoA estimates.

[0192] Example 10 may include a method comprising performing a plurality of beam measurements corresponding to a beam received by a UE, determining the orientation of the UE corresponding to each of the plurality of beam measurements, determining a plurality of local angle of arrival (AoA) estimates corresponding to the beam based on the plurality of beam measurements, converting the plurality of local AoA estimates into a plurality of global AoA estimates based on the orientation of the UE corresponding to each of the plurality of beam measurements, and determining a global AoA based on the overlap of the plurality of global AoA estimates.

[0193] Example 11 may include the method of Example 10, which further includes determining the probability of each local AoA estimate of the plurality of local AoA estimates based on the plurality of beam measurements, and determining the probability of each global AoA estimate of the plurality of global AoA estimates based on the probability of each local AoA estimate of the plurality of local AoA estimates, wherein determining the global AoA includes determining the probability of the global AoA based on the probability of each global AoA estimate among the plurality of global AoA estimates as the maximum probability of the AoA estimate in the global coordinate system (GCS).

[0194] Example 12 may include the method of Example 11, wherein the probability of determining each of the plurality of local AoA estimates includes determining the probability of each of the plurality of local AoA estimates based on the reference signal received power (RSRP) and / or signal-to-interference-plus-noise ratio (SINR) of the plurality of local AoA estimates from the plurality of beam measurements.

[0195] Example 13 may include the method of Example 11, which further includes determining whether the global AoA meets reliability based on the probability that the global AoA is greater than a predetermined threshold, and determining whether the UE is operating in a sensor-based AoA estimation state or a sensor-assisted beam tracking state based on whether the global AoA meets reliability.

[0196] Example 14 may include the method of Example 13, which further includes entering a sensor-assisted beam tracking state based on determining that the global AoA meets reliability, and maintaining the local AoA based on local anchor and sensor data related to the rotation of the UE.

[0197] Example 15 may include the method of Example 13, which further includes entering a sensor-based AoA estimation state based on determining that the global AoA fails to meet reliability requirements.

[0198] Example 16 may include the method of Example 10, wherein determining the global AoA includes determining the location of the maximum number of overlapping global AoA estimates, wherein the global AoA is located at that location.

[0199] Example 17 may include the method of Example 10, wherein converting the plurality of local AoA estimates into the plurality of global AoA estimates includes converting the plurality of local AoA estimates from the local coordinate system (LCS) to the global coordinate system (GCS) to generate the plurality of global AoA estimates.

[0200] Example 18 may include a method for determining the angle of arrival (AoA), the method comprising: transmitting a plurality of training signals by a base station, the plurality of training signals being used by a user equipment (UE) to determine a plurality of local AoA estimates in order to determine a beam to be used by the UE; identifying by the base station an indication of a beam received from the UE for use in communicating with the UE, the beam being determined based on the plurality of local AoA estimates determined from the plurality of training signals; and transmitting by the base station to the UE using the beam.

[0201] Example 19 may include the method of Example 18, which further includes transmitting an orientation indication of a global coordinate system (GCS) from the base station to the UE, which the UE uses to determine a beam.

[0202] Example 20 may include the method of Example 18, wherein transmitting the plurality of training signals includes transmitting the plurality of training signals via a scanning method or a detection method.

[0203] Example 21 may include an apparatus comprising one or more elements for performing the method or any other method or process described herein, as described in or associated with any of Examples 1 to 20.

[0204] Example 22 may include one or more non-transitory computer-readable media, the one or more non-transitory computer-readable media including instructions that, when executed by one or more processors of an electronic device, cause the electronic device to perform one or more elements of the method or any other method or process described herein, as described or associated with any of Examples 1 to 20.

[0205] Example 23 may include an apparatus comprising logic components, modules, or circuitry for performing one or more elements of the method described or associated with any of Examples 1 to 20 or any other method or process described herein.

[0206] Example 24 may include a method, technique, or process, or a part or component thereof, described or associated with any of Examples 1 to 20.

[0207] Example 25 may include an apparatus comprising one or more processors and one or more computer-readable media, the one or more computer-readable media including instructions that, when executed by the one or more processors, cause the one or more processors to perform a method, technique, or process, or a portion thereof, as described or associated with any of Examples 1 to 20.

[0208] Example 26 may include a signal, or a portion thereof, described or associated with any of Examples 1 to 20.

[0209] Example 27 may include a datagram, information element, packet, frame, segment, PDU or message, or a portion or component thereof, as described or associated with any of Examples 1 to 20, or otherwise described in this disclosure.

[0210] Example 28 may include a signal encoded with data according to or associated with any of Examples 1 to 20, or a portion or component thereof, or otherwise described in this disclosure.

[0211] Example 29 may include a signal, or a portion or component thereof, encoded as a datagram, IE, packet, frame, segment, PDU, or message, as described or associated with any of Examples 1 to 20, or otherwise described in this disclosure.

[0212] Example 30 may include an electromagnetic signal carrying computer-readable instructions, wherein execution of the computer-readable instructions by one or more processors will cause one or more processors to perform the methods, techniques, or processes, or portions thereof, described or associated with any of Examples 1 to 20.

[0213] Example 31 may include a computer program comprising instructions, wherein execution of the program by a processing element will cause the processing element to perform, or in part with, the methods, techniques or processes described or associated with any of Examples 1 to 20.

[0214] Example 32 may include signals in a wireless network as shown and described herein.

[0215] Example 33 may include methods for communicating in a wireless network as shown and described herein.

[0216] Example 34 may include a system for providing wireless communication as shown and described herein.

[0217] Example 35 may include a device for providing wireless communication as shown and described herein.

[0218] Unless otherwise expressly stated, any of the examples above may be combined with any other example (or combination of examples). The foregoing description of one or more specific embodiments provides illustration and description, but is not intended to be exhaustive or to limit the scope of the embodiments to the precise form disclosed. In light of the teachings above, modifications and variations are possible, or modifications and variations may be derived from practice of various embodiments.

[0219] Although the above embodiments have been described in considerable detail, many variations and modifications will become apparent to those skilled in the art once the disclosure is fully understood. This disclosure is intended to render the following claims as encompassing all such variations and modifications.

Claims

1. A computer-readable medium storing instructions that, when executed, cause processing circuitry to perform the following operations: Perform multiple beam measurements corresponding to the beams received by the user equipment (UE); Based on the multiple beam measurements, generate multiple local angle of arrival (AoA) estimates corresponding to the beams; The multiple local AoA estimates are converted into multiple global AoA estimates; Global AoA is identified based on the multiple global AoA estimates; The global AoA is used to identify the beam received from the base station; The reliability threshold for the global AoA is determined based on the probability that the global AoA is the actual global AoA; and The UE is placed in either sensor-based AoA estimation state or sensor-assisted beam tracking state based on whether the reliability threshold has been reached.

2. The computer-readable medium of claim 1, wherein identifying the global AoA comprises: The first set of local AoA estimates among the plurality of local AoA estimates is converted into the first set of global AoA estimates among the plurality of global AoA estimates, wherein the first set of local AoA estimates corresponds to the first time. The second set of local AoA estimates from the plurality of local AoA estimates is converted into a second set of global AoA estimates from the plurality of global AoA estimates, wherein the second set of local AoA estimates corresponds to a second time; and Based on the fact that the first global AoA estimate in the first group of global AoA estimates overlaps with the second global AoA estimate in the second group of global AoA estimates, the global AoA is determined to correspond to the first global AoA estimate and the second global AoA estimate.

3. The computer-readable medium according to claim 1 or claim 2, wherein the plurality of global AoA estimates have a plurality of probabilities corresponding to the actual global AoA, and wherein identifying the global AoA comprises: Based on the multiple global AoA estimates, the global AoA estimate with the highest probability is identified from the corresponding multiple probabilities. as well as Set the global AoA to the estimated global AoA with the highest probability.

4. The computer-readable medium of claim 1 or 2, wherein determining whether a reliability threshold has been reached for the global AoA includes determining that the reliability threshold has been reached, wherein the UE is placed in the sensor-assisted beam tracking state based on the reliability threshold being reached, and wherein the instruction, when executed, further causes the processing circuitry to: Convert the global AoA to a local anchor; and When the UE is placed in the sensor-assisted beam tracking state, the local AoA is maintained based on the local anchor and sensor data from the UE's sensors.

5. The computer-readable medium of claim 4, wherein the plurality of beam measurements are a first plurality of beam measurements, wherein the plurality of local AoA estimates are a first plurality of local AoA estimates, wherein the plurality of global AoA estimates are a first plurality of global AoA estimates, wherein the global AoA is a first global AoA, and wherein the instruction, when executed, further causes the processing circuitry to: When the UE is placed in the sensor-assisted beam tracking state, a second plurality of beam measurements corresponding to the beam are performed; Based on the second plurality of beam measurements, identify the second plurality of local AoA estimates corresponding to the beam; The second plurality of local AoA estimates are converted into a second plurality of global AoA estimates; Identify the second global AoA based on the second plurality of global AoA estimates; and The second global AoA is compared with the local AoA to determine whether the local AoA has a reliability greater than a predetermined threshold.

6. The computer-readable medium according to claim 1 or claim 2, wherein the instructions, when executed, further cause the processing circuitry to: Generating a mesh arrangement in a local coordinate system (LCS), wherein generating the plurality of local AoA estimates includes determining the local mesh positions of the plurality of local AoA estimates based on the plurality of beam measurements and the mesh arrangement in the LCS; and Generate a mesh layout in the global coordinate system (GCS), wherein converting the plurality of local AoA estimates into the plurality of global AoA estimates includes determining the global mesh position of the plurality of global AoA estimates based on the local mesh positions of the plurality of local AoA estimates.

7. The computer-readable medium of claim 1 or claim 2, wherein the plurality of local AoA estimates are established in a grid system of a local coordinate system (LCS), and wherein converting the plurality of local AoA estimates to the plurality of global AoA estimates comprises converting the plurality of local AoA estimates from the grid system of the LCS to the grid system of a global coordinate system (GCS).

8. The computer-readable medium of claim 1 or claim 2, wherein converting the plurality of local AoA estimates into the plurality of global AoA estimates comprises: Identify sensor data corresponding to each of the plurality of local AoA estimates; as well as The plurality of local AoA estimates are converted into the plurality of global AoA estimates based on the sensor data corresponding to each of the plurality of local AoA estimates.

9. An electronic device, the electronic device comprising: Sensors used to determine the rotation of the user equipment (UE); and The processing circuit, coupled to the sensor, is used for: Perform multiple beam measurements corresponding to the beams received by the UE; Identify the orientation of the UE corresponding to each of the plurality of beam measurements; Based on the multiple beam measurements, generate multiple local angle of arrival (AoA) estimates corresponding to the beams; Based on the orientation of the UE corresponding to each of the plurality of beam measurements, the plurality of local AoA estimates are converted into a plurality of global AoA estimates; Global AoA is identified based on the overlap of the multiple global AoA estimates; The reliability threshold for the global AoA is determined based on the probability that the global AoA is the actual global AoA. as well as The UE is placed in either sensor-based AoA estimation state or sensor-assisted beam tracking state based on whether the global AoA reaches the reliability threshold.

10. The electronic device of claim 9, wherein the processing circuitry is further configured to: Based on the multiple beam measurements, determine the probability of each local AoA estimate among the multiple local AoA estimates; and The probability of each global AoA estimate among the plurality of local AoA estimates is determined based on the probability of each local AoA estimate among the plurality of local AoA estimates, wherein identifying the global AoA includes determining the probability of the global AoA based on the probability of each global AoA estimate among the plurality of global AoA estimates as the maximum probability of the AoA estimate within the global coordinate system GCS.

11. The electronic device of claim 10, wherein determining the probability of each local AoA estimate among the plurality of local AoA estimates comprises: The probability of each local AoA estimate is determined based on the reference signal received power (RSRP) or signal-to-interference-plus-noise ratio (SINR) of the plurality of local AoA estimates from the plurality of beam measurements.

12. The electronic device of claim 10, wherein the processing circuit is further configured to: Based on the determination that the global AoA has reached the reliability threshold, the sensor-assisted beam tracking state is entered; Convert the global AoA to a local anchor; and The local AoA is maintained based on the local anchor and sensor data related to the rotation of the UE.

13. The electronic device of claim 10, wherein the processing circuitry further enters the sensor-based AoA estimation state based on determining that the global AoA has not reached the reliability threshold.

14. The electronic device according to any one of claims 9 to 13, wherein identifying the global AoA includes determining the location of the plurality of global AoA estimates with the maximum number of overlaps, wherein the global AoA is determined to be located at the location.

15. The electronic device according to any one of claims 9 to 13, wherein converting the plurality of local AoA estimates into the plurality of global AoA estimates comprises converting the plurality of local AoA estimates from a local coordinate system (LCS) to a global coordinate system (GCS) to generate the plurality of global AoA estimates.

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