PATENT DOCUMENT

Publication Number: US-11894909-B2
Application Number: US-202217878863-A
Country: US
Kind Code: B2

Title: Sensor assisted beam tracking

Abstract:
The present application relates to devices and components including apparatus, systems, and methods for determining a beam for communication between a user equipment and a base station. For example, angle of arrival estimates may be utilized for determining the beam for communication.

Claims:
What is claimed is: 
     
       1. One or more non-transitory computer-readable media having instructions that, when executed by one or more processors, cause a user equipment (UE) to:
 perform a plurality of beam measurements corresponding to a beam received by the UE; 
 determine a plurality of local angle of arrival (AoA) estimates corresponding to the beam based on the plurality of beam measurements; 
 convert the plurality of local AoA estimates to a plurality of global AoA estimates; 
 determine a global AoA based on the plurality of global AoA estimates; 
 utilize the global AoA for identification of a beam received from a base station; 
 determine whether a reliability threshold has been achieved for the global AoA based on a probability of the global AoA being an actual global AoA; and 
 determine which of a sensor based AoA estimation state or a sensor assisted beam tracking state is to be implemented based on whether the reliability threshold has been achieved. 
 
     
     
       2. The one or more non-transitory computer-readable media of  claim 1 , wherein to determine the global AoA includes to:
 convert a first set of local AoA estimates of the plurality of local AoA estimates to a first set of global AoA estimates of the plurality of global AoA estimates, the first set of local AoA estimates to correspond to a first time; 
 convert a second set of local AoA estimates of the plurality of local AoA estimates to a second set of global AoA estimates of the plurality of global AoA estimates, the second set of local AoA estimates to correspond to a second time; and 
 determine that the global AoA corresponds to a first global AoA estimate of the first set of global AoA estimates and a second global AoA estimate of the second set of global AoA estimates based on a determination that the first global AoA estimate overlaps with the second global AoA estimate. 
 
     
     
       3. The one or more non-transitory computer-readable media of  claim 1 , wherein the plurality of global AoA estimates have a corresponding plurality of probabilities of being an actual global AoA, and wherein to determine the global AoA includes to:
 determine a global AoA estimate with a greatest probability from the corresponding plurality of probabilities of the plurality of global AoA estimates; and 
 set the global AoA to the global AoA estimate with the greatest probability. 
 
     
     
       4. The one or more non-transitory computer-readable media of  claim 1 , wherein the UE is to determine that the reliability has been achieved, wherein the sensor assisted beam tracking state is to be implemented based on the reliability being achieved, and wherein the instructions, when executed by the one or more processors, are further to:
 convert the global AoA to a local anchor; and 
 maintain a local AoA based on the local anchor and sensor data from a sensor of the UE while the sensor assisted beam tracking state is implemented. 
 
     
     
       5. The one or more non-transitory computer-readable media of  claim 4 , wherein the plurality of beam measurements is 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 instructions, when executed by the one or more processors, are further to cause the UE to:
 perform a second plurality of beam measurements corresponding to the beam while the sensor assisted beam tracking state is implemented; 
 determine a second plurality of local AoA estimates corresponding to the beam based on the second plurality of beam measurements; 
 convert the second plurality of local AoA estimates to a second plurality of global AoA estimates; 
 determine a second global AoA based on the second plurality of global AoA estimates; and 
 compare the second global AoA with the local AoA to determine whether the local AoA has a reliability greater than a predetermined threshold. 
 
     
     
       6. The one or more non-transitory computer-readable media of  claim 1 , wherein the instructions, when executed by the one or more processors, further cause the UE to:
 generate a grid arrangement in a local coordinate system (LCS), wherein to determine the plurality of local AoA estimates includes to determine local grid positions for the plurality of local AoA estimates based on the plurality of beam measurements and the grid arrangement in the LCS; and 
 generate a grid arrangement in a global coordinate system (GCS), wherein to convert the plurality of local AoA estimates to the plurality of global AoA estimates includes to determine global grid positions for the plurality of global AoA estimates based on the local grid positions for the plurality of local AoA estimates. 
 
     
     
       7. The one or more non-transitory computer-readable media of  claim 1 , wherein the plurality of local AoA estimates are established in a grid system for a local coordinate system (LCS), and wherein to convert the plurality of local AoA estimates to the plurality of global AoA estimates includes to convert the plurality of local AoA estimates from the grid system for the LCS to a grid system for a global coordinate system (GCS). 
     
     
       8. The one or more non-transitory computer-readable media of  claim 1 , wherein to convert the plurality of local AoA estimates to the plurality of global AoA estimates includes to:
 determine sensor data corresponding to each local AoA estimate of the plurality of local AoA estimates; and 
 convert the plurality of local AoA estimates to the plurality of global AoA estimates based on the sensor data corresponding to each local AoA estimate of the plurality of local AoA estimates. 
 
     
     
       9. A user equipment (UE) comprising:
 a sensor to determine a rotation of the UE; and 
 processing circuitry coupled to the sensor, the processing circuitry to:
 perform a plurality of beam measurements corresponding to a beam received by the UE; 
 determine orientations of the UE corresponding to each of the plurality of beam measurements; 
 determine a plurality of local angle of arrival (AoA) estimates corresponding to the beam based on the plurality of beam measurements; 
 convert the plurality of local AoA estimates to a plurality of global AoA estimates based on the orientations of the UE corresponding to each of the plurality of beam measurements; 
 determine a global AoA based on overlap of the plurality of global AoA estimates; 
 determine whether the global AoA meets a reliability threshold based on the probability for the global AoA being greater than a predetermined threshold; and 
 determine whether the UE is to operate in a sensor based AoA estimation state or a sensor assisted beam tracking state based on whether the global AoA meets the reliability threshold. 
 
 
     
     
       10. The UE of  claim 9 , wherein the processing circuitry is further to:
 determine probabilities for each local AoA estimate of the plurality of local AoA estimates based on the plurality of beam measurements; and 
 determine probabilities for each global AoA estimate of the plurality of global AoA estimates based on the probabilities for each local AoA estimate of the plurality of local AoA estimates, wherein to determine the global AoA includes to determine that a probability for the global AoA is a largest probability of AoA estimates within a global coordinate system (GCS) based on the probabilities for each global AoA estimate of the plurality of global AoA estimates. 
 
     
     
       11. The UE of  claim 10 , wherein to determine the probabilities for each local AoA estimate of the plurality of local AoA estimates includes to determine the probabilities for each local AoA estimate of the plurality of local AoA estimates based on reference signal received power (RSRP) or signal to interference and noise ratio (SINR) for the plurality of local AoA estimates from the plurality of beam measurements. 
     
     
       12. The UE of  claim 9 , wherein the processing circuitry is further to:
 enter the sensor assisted beam tracking state based on a determination that the global AoA meets the reliability; 
 convert the global AoA to a local anchor; and 
 maintain a local AoA based on the local anchor and sensor data related to the rotation of the UE. 
 
     
     
       13. The UE of  claim 9 , wherein the processing circuitry is further to enter the sensor based AoA estimation state based on a determination that the global AoA fails to meet the reliability. 
     
     
       14. The UE of  claim 9 , wherein to determine the global AoA includes to determine a position of a greatest number of the plurality of global AoA estimates that overlap, wherein the global AoA is determined to be located at the position. 
     
     
       15. The UE of  claim 9 , wherein to convert the plurality of local AoA estimates to the plurality of global AoA estimates includes to convert the plurality of local AoA estimates from a local coordinate system (LCS) to a global coordinate system (GCS) to produce the plurality of global AoA estimates. 
     
     
       16. A method for angle of arrival (AoA) determination, comprising:
 transmitting, by a base station, an indication of an orientation of a global coordinate system (GCS) to a user equipment (UE), the GCS to be utilized by the UE for determining a beam to be utilized for communication with the UE; 
 transmitting, by the base station, a plurality of training signals to be utilized by the UE to determine a plurality of local AoA estimates for determination of a beam to be utilized for communication with the UE; 
 identifying, by the base station received from the UE, an indication of the beam to be utilized for communication with the UE, the beam determined based on the plurality of local AoA estimates determined from the plurality of training signals; and 
 utilizing, by the base station, the beam for transmissions to the UE. 
 
     
     
       17. The method of  claim 16 , wherein transmitting the plurality of training signals includes transmitting the plurality of training signals via a sweeping approach or a probing approach.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority to U.S. Provisional Patent Application No. 63/240,852, entitled “Sensor Assisted Beam Tracking”, filed on Sep. 3, 2021, the disclosure of which is incorporated by reference herein in its entirety for all purposes. 
    
    
     BACKGROUND 
     Third Generation Partnership Project (3GPP) networks provide that base stations may utilize beam forming for transmitting signals on beams to user equipments (UEs). The UEs may determine a direction from which a beam is received from the base stations. In some embodiments, the UEs may utilize the determined direction to properly identify a received beam and/or may not monitor directions from which beams are not determined to be received. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example system arrangement in accordance with some embodiments. 
         FIG.  2    illustrates an example state machine for sensor assisted beamforming framework in accordance with some embodiments. 
         FIG.  3    illustrates an example system arrangement in accordance with some embodiments. 
         FIG.  4    illustrates a block diagram of a beam quality based angle of arrival (AoA) estimate approach in accordance with some embodiments. 
         FIG.  5    illustrates a block diagram of another beam quality based AoA estimate approach in accordance with some embodiments. 
         FIG.  6    illustrates an example of a local AoA estimate conversion to a global AoA estimate approach in accordance with some embodiments. 
         FIG.  7    illustrates an example beam quality based AoA estimate approach in accordance with some embodiments. 
         FIG.  8    illustrates an AoA probability based on beam measurement approach in accordance with some embodiments. 
         FIG.  9 A  illustrates a first portion of an example procedure for identifying a beam for communication in accordance with some embodiments. 
         FIG.  9 B  illustrates a second portion of the example procedure for identifying a beam for communication in accordance with some embodiments. 
         FIG.  10 A  illustrates a first portion of another example procedure for identifying a beam for communication in accordance with some embodiments. 
         FIG.  10 B  illustrates a second portion of the example procedure for identifying a beam for communication in accordance with some embodiments. 
         FIG.  11    illustrates an example procedure for determining a beam for communication in accordance with some embodiments. 
         FIG.  12    illustrates example beamforming circuitry in accordance with some embodiments. 
         FIG.  13    illustrates an example user equipment (UE) in accordance with some embodiments. 
         FIG.  14    illustrates an example next generation nodeB (gNB) in accordance with some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description refers to the accompanying drawings. The same reference numbers may be used in different drawings to identify the same or similar elements. In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular structures, architectures, interfaces, techniques, etc. in order to provide a thorough understanding of the various aspects of various embodiments. However, it will be apparent to those skilled in the art having the benefit of the present disclosure that the various aspects of the various embodiments may be practiced in other examples that depart from these specific details. In certain instances, descriptions of well-known devices, circuits, and methods are omitted so as not to obscure the description of the various embodiments with unnecessary detail. For the purposes of the present document, the phrase “A or B” means (A), (B), or (A and B). 
     The following is a glossary of terms that may be used in this disclosure. 
     The term “circuitry” as used herein refers to, is part of, or includes hardware components such as an electronic circuit, a logic circuit, a processor (shared, dedicated, or group) or memory (shared, dedicated, or group), an application specific integrated circuit (ASIC), a field-programmable device (FPD) (e.g., a field-programmable gate array (FPGA), a programmable logic device (PLD), a complex PLD (CPLD), a high-capacity PLD (HCPLD), a structured ASIC, or a programmable system-on-a-chip (SoC)), digital signal processors (DSPs), etc., that are configured to provide the described functionality. In some embodiments, the circuitry may execute one or more software or firmware programs to provide at least some of the described functionality. The term “circuitry” may also refer to a combination of one or more hardware elements (or a combination of circuits used in an electrical or electronic system) with the program code used to carry out the functionality of that program code. In these embodiments, the combination of hardware elements and program code may be referred to as a particular type of circuitry. 
     The term “processor circuitry” as used herein refers to, is part of, or includes circuitry capable of sequentially and automatically carrying out a sequence of arithmetic or logical operations, or recording, storing, or transferring digital data. The term “processor circuitry” may refer an application processor, baseband processor, a central processing unit (CPU), a graphics processing unit, a single-core processor, a dual-core processor, a triple-core processor, a quad-core processor, or any other device capable of executing or otherwise operating computer-executable instructions, such as program code, software modules, or functional processes. 
     The term “interface circuitry” as used herein refers to, is part of, or includes circuitry that enables the exchange of information between two or more components or devices. The term “interface circuitry” may refer to one or more hardware interfaces, for example, buses, I/O interfaces, peripheral component interfaces, network interface cards, or the like. 
     The term “user equipment” or “UE” as used herein refers to a device with radio communication capabilities and may describe a remote user of network resources in a communications network. The term “user equipment” or “UE” may be considered synonymous to, and may be referred to as, client, mobile, 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. Furthermore, the term “user equipment” or “UE” may include any type of wireless/wired device or any computing device including a wireless communications interface. 
     The term “computer system” as used herein refers to any type interconnected electronic devices, computer devices, or components thereof. Additionally, the term “computer system” or “system” may refer to various components of a computer that are communicatively coupled with one another. Furthermore, the term “computer system” or “system” may refer to multiple computer devices or multiple computing systems that are communicatively coupled with one another and configured to share computing or networking resources. 
     The term “resource” as used herein refers to a physical or virtual device, a physical or virtual component within a computing environment, or a physical or virtual component within a particular device, such as computer devices, mechanical devices, memory space, processor/CPU time, processor/CPU usage, processor and accelerator loads, hardware time or usage, electrical power, input/output operations, ports or network sockets, channel/link allocation, throughput, memory usage, storage, network, database and applications, workload units, or the like. A “hardware resource” may refer to compute, storage, or network resources provided by physical hardware element(s). A “virtualized resource” may refer to compute, storage, or network resources provided by virtualization infrastructure to an application, device, system, etc. The term “network resource” or “communication resource” may refer to resources that are accessible by computer devices/systems via a communications network. The term “system resources” may refer to any kind of shared entities to provide services, and may include computing or network resources. System resources may be considered as a set of coherent functions, network data objects or services, accessible through a server where such system resources reside on a single host or multiple hosts and are clearly identifiable. 
     The term “channel” as used herein refers to any transmission medium, either tangible or intangible, which is used to communicate data or a data stream. The term “channel” may be synonymous with or equivalent to “communications channel,” “data communications channel,” “transmission channel,” “data transmission channel,” “access channel,” “data access channel,” “link,” “data link,” “carrier,” “radio-frequency carrier,” or any other like term denoting a pathway or medium through which data is communicated. Additionally, the term “link” as used herein refers to a connection between two devices for the purpose of transmitting and receiving information. 
     The terms “instantiate,” “instantiation,” and the like as used herein refers to the creation of an instance. An “instance” also refers to a concrete occurrence of an object, which may occur, for example, during execution of program code. 
     The term “connected” may mean that two or more elements, at a common communication protocol layer, have an established signaling relationship with one another over a communication channel, link, interface, or reference point. 
     The term “network element” as used herein 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 to or referred to as a networked computer, networking hardware, network equipment, network node, virtualized network function, or the like. 
     The term “information element” refers to a structural element containing one or more fields. The term “field” refers to individual contents of an information element, or a data element that contains content. An information element may include one or more additional information elements. 
     Centi/millimeter wave (mmWave) communication systems can provide a much larger data rate given the ultra-wide bandwidth as compared to wider bandwidth systems. There has been ever-growing interests and commercial deployments for mmWave networks including third generation partnership project (3GPP) fifth generation (5G) new radio (NR) (Frequency range  2 , or FR 2 ). Analog beamforming has been an essential technique to compensate the short range of coverage for mmWave system. Sensor information may be used to improve the beam determination for mmWave devices to reduce the latency and improve reliability. Approaches described herein may fuse the sensor information with radio beamforming. 
     The angle of arrival (AoA) for an incoming signal can be known to the receiver (such as a UE  1300  ( FIG.  13   )) by super resolution algorithms, which require exhaustive antenna pattern measurement and calibration. AoA may be ambiguous to receiver even when super resolution algorithm is applied. For example, with a linear array instead of two-dimensional (2D) array or other limitations on antenna placement the AoA may be ambiguous. 
     Motion sensor data can provide the rotation information for a device accurately, and hence may assist beam tracking. For example, an alternative beam may be selected once the device has rotated by a certain angle. However, device may lock orientation (AoA) to the initial beam (and maintain the lock through the beam tracking). An issue may be presented with sensor assisted beam tracking to resolve the AoA ambiguity or improve AoA resolution without resorting to AoA estimation algorithm. 
       FIG.  1    illustrates an example system arrangement  100  in accordance with some embodiments. In particular, the system arrangement  100  illustrates an example portion of a radio access network (RAN) that may implement beamforming. In some embodiments, the RAN may be a 5G network. 
     The system arrangement  100  may include a user equipment (UE)  102 , such as the UE  1300  ( FIG.  13   )). A user may utilize the user equipment for accessing the RAN. For example, the UE  102  may communicate with components within the RAN to provide services to the user, such as voice call, text messaging, and/or other data-based service. The UE  102  may include one or more sensor devices  104 . The sensor devices  104  may include motion sensor devices (such as accelerometers) that can determine motion of the UE  102 . One or more processors of the UE  102  may receive data from the sensor devices  104  and determine an orientation of the UE  102  based on the date from the sensor devices  104 . 
     The system arrangement  100  may further include a base station  106  (such as the gNB  1400  ( FIG.  14   )) and a core network (CN)  108  (such as the a 5th Generation Core network (5GC)). The base station  106  and the core network  108  may operate in combination to provide the services to the UE  102 . The base station  106  may be coupled to the CN  108  through a backend that can provide for communication between the base station  106  and the CN  108 . 
     The base station  106  may include an antenna array  110 . The antenna array  110  may include one or more antennas that can allow the base station  106  to communicate wirelessly with UEs, such as the UE  102 . The antennas of the antenna array  110 , or some portion thereof, may emit beams  112  that can carry signals for communication with the UEs. 
     In some instances, the beams  112  may include training signals that can be utilized by the UEs to determine a beam that can provide service to the UE. The beams  112 , or some portion thereof, may comprise pencil beams that have a narrow bandwidth. 
     The UE  102  may detect beams  112  from the antenna array  110  and utilize the beams  112  to communicate with the base station  106 . When the beams  112  are narrow (such as in the case of pencil beams), if the detection by the UE  102  is not properly aligned with a beam providing service, the signal may be significantly degraded. As the UE  102  may not be static, having the UE  102  quickly adapt to the beam based on the motion of the UE  102  may reduce the chances of the signal being significantly degraded. As the UE  102  is moved, the UE  102  may determine different AoAs at which a beam is received and/or determine a different beam to be utilized based on the motion of the UE  102 . The UE  102  may detect training signals received from the base station  106 , and determine which beam to utilize for communication with the base station  106  and an AoA of the beam. For example, the UE  102  may produce an AoA estimate for the AoA o The UE  102  may utilize data from the sensor devices  104  to maintain the AoA in accordance with the approaches described throughout this disclosure. The use of the data from the sensor devices  104  may resolve AoA reliability issues and may improve AoA resolution as compared to non-sensor AoA determination approaches. 
       FIG.  2    illustrates an example state machine  200  for sensor assisted beamforming framework in accordance with some embodiments. The sensor assisted beamforming framework may define operation of a UE (such as the UE  102  ( FIG.  1   ) and/or the UE  1300  ( FIG.  13   )) for determining an AoA for a beam for utilization of the UE. For example, the state machine  200  illustrates states that may implemented by the UE that defines the operation of the UE with respect to determining an AoA for a beam. Sensor assisted beamforming framework may be a state machine with various operation stages and sensor states. 
     The state machine  200  may include two operation stages. For example, the state machine  200  may include a sensor based AoA estimation state  202 . In some embodiments, the UE may initiate to the sensor based AoA estimation state  202 . While in the sensor based AoA estimation state  202 , the UE may monitor for training signals received from a base station (such as the base station  106  ( FIG.  1   ) and/or the gNB  1400  ( FIG.  14   )). The UE may utilize the training signals to determine an AoA for a beam, such as via the approaches for determining AoAs based on training signals described further throughout this disclosure. The UE may produce an AoA estimate for the AoA based on the determination. The sensor based AoA estimation state  202  may include sensor assisted AoA acquisition to initialize the later beam tracking. 
     The state machine  200  may further include a sensor assisted beam tracking state  204 . While in the sensor assisted beam tracking state  204 , the UE may utilize data from sensor devices (such as the sensor devices  104  ( FIG.  1   )) to maintain an AoA for a beam. For example, the UE may have previously determined that an AoA estimate for the AoA of the beam has achieved a reliability. The UE may utilize the data from the sensor devices to maintain the AoA estimate as the UE is rotated. For example, the UE may produce a local anchor that can indicate a local AoA estimate (which may be an AoA estimate relative to the UE) when the reliability of the AoA estimate is determined. As the UE device is rotated, the UE may update the local AoA estimate based on the local anchor and a change in the orientation of the UE that may be determined based on the data from the sensor devices. Accordingly, the UE may maintain an AoA estimate relative to the UE based on the data from the sensor devices. The sensor assisted beam tracking state  204  may include sensor reading being used to assist beam determination. 
     The state machine  200  may include transitions among the sensor based AoA estimation state  202  and the sensor assisted beam tracking state  204  based on a determined reliability of an AoA estimate. In particular, the state machine  200  may include sensor lock transitions  206  and sensor reliable transitions  208 . The UE may determine whether to implement a sensor lock transition  206  or a sensor reliable transition  208  based on a determined reliability of an AoA estimate of the UE at the time. 
     The UE may determine that a sensor lock transition  206  is to be implemented based on an AoA estimate not achieving a reliability. For example, the UE may determine that a reliability of the AoA estimate is less than a threshold and may determine that the sensor lock transition  206  is to be implemented. The UE may determine whether the AoA estimate achieves the reliability while the UE is in the sensor based AoA estimation state  202  and/or the sensor assisted beam tracking state  204 . For example, the UE may determine whether the AoA estimate achieves the reliability when an AoA estimate is produced during the sensor based AoA estimation state  202  and/or when a maintained AoA estimate is validated in the sensor assisted beam tracking state  204  as described further throughout this disclosure. In some embodiments, the time between validations of the maintained AoA estimate in the sensor assisted beam tracking state  204  may be greater than a time between the AoA estimates are produced in the sensor based AoA estimation state  202 . 
     The sensor lock transition  206  may be implemented by the UE when the AoA information is unknown (such as at initialization) or the AoA information is stalled (such as due to AoA change). The sensor lock transition  206  may indicate that the AoA needs to be relocked. Based on the sensor lock transition  206  being implemented, the UE may transition to the sensor based AoA estimation state  202 , where the operations associated with the sensor based AoA estimation state  202  may be executed. For example, when the sensor lock transition  206  is implemented from the sensor based AoA estimation state  202 , the UE may remain in the sensor based AoA estimation state  202 . When the sensor lock transition  206  is implemented from the sensor assisted beam tracking state  204 , the UE may transition to the sensor based AoA estimate state  202 . 
     The UE may determine that a sensor reliable transition  208  is to be implemented based on an AoA estimate achieving reliability. For example, the UE may determine that a reliability of the AoA estimate is greater than or equal to the threshold and may determine that the sensor reliable transition  208  is to be implemented. The UE may determine whether the AoA estimate achieves the reliability while the UE is in the sensor based AoA estimation state  202  and/or the sensor assisted beam tracking state  204 . For example, the UE may determine whether the AoA estimate achieves the reliability when an AoA estimate is produced during the sensor based AoA estimation state  202  and/or when a maintained AoA estimate is validated in the sensor assisted beam tracking state  204 . In some embodiments, the time between validations of the maintained AoA estimate in the sensor assisted beam tracking state  204  may be greater than a time between the AoA estimates are produced in the sensor based AoA estimation state  202 . 
     The sensor reliable transition  208  may be implemented by the UE when the AoA information is reliable, such that the sensor data can be applied to update the beam. Once the sensor reliable transition  208  is achieved (such as during AoA estimation) or maintained (such as during beam tracking), the sensor assisted beam tracking state  204  may be executed. Based on the sensor reliable transition  208  being implemented, the UE may transition to the sensor assisted beam tracking state  204 , where the operations associated with the sensor assisted beam tracking state  204  may be executed. For example, when the sensor reliable transition  208  is implemented from the sensor based AoA estimation state  202 , the UE may transition to the sensor assisted beam tracking state  204 . When the sensor reliable transition  208  is implemented from the sensor assisted beam tracking state  204 , the UE may remain in the sensor assisted beam tracking state  204 . 
       FIG.  3    illustrates an example system arrangement  300  in accordance with some embodiments. The system arrangement  300  illustrates example coordinate system operation for AoA determination. For example, the system arrangement  300  illustrates coordinate systems that may be implemented by a UE  302  for determining an AoA of a beam  304  received from a base station  306 . The UE  302  may include one or more of the features of the UE  102  ( FIG.  1   ). Further, the base station  306  may include one or more of the features of the base station  106  ( FIG.  1   ). 
     The system arrangement  300  may include a global coordinate system (GCS)  308  (which may be referred to as a reference system), as illustrated by the axes. The GCS  308  may have an orientation in reference to the physical world. For example, the GCS  308  may have an orientation with the z-axis of the GCS  308  extending substantially (within 10 degrees) away from the earth at a radii from the center of the earth, and the x-axis and y-axis extending at perpendicular directions from each other and to the z-axis in some embodiments. An AoA is typically constant over a large time scale in the GCS  308 , such as when the AoA is for a user device being used by a pedestrian. This may be true for mmWave communication systems for which the typical use case of the UE  302  is by a pedestrian. The AoA in the GCS  308  may be typically unchanged for a good amount of time for a pedestrian user, even with rotation of the UE  302 . 
     The system arrangement  300  may further include a local coordinate system (LCS)  310  (which may be referred to as a device system), as illustrated by the axes. The LCS  310  may have an orientation that is fixed related to the body of the UE  302 . For example, the LCS  310  may have an orientation with the z-axis of the LCS  310  extending perpendicular to a top of the UE  302 , the x-axis extending perpendicular to a side of the UE  302 , and the y-axis extending perpendicular to both the z-axis and the x-axis (such as extending perpendicular to a screen of the UE  302 ) in some embodiments. An AoA may be more volatile in the LCS  310  than in the GCS  308  due to user behavior (such as rotation of the UE  302 , flipping of the UE  302 , and/or other motion of the UE  302  that may be applied by the user. In particular, AoA in LCS may change due to device rotation. The volatile nature of the LCS  310  may be helpful and may be leveraged to improve global AoA estimation. 
     Radio based beam measurement may provide the beam qualities for a set of pre-determined beams (such as the AoAs), including the beam  304 . When AoA is based on a single observation, the AoA may be either ambiguous (such as for a linear array) or low resolution (such as for a small codebook or other practical limitations). Once the UE  302  is in motion (for example, rotation of the UE  302 ), multiple (independent) beam measurements may be leveraged together with the sensor data during an observation period to improve AoA estimates, such as AoA estimates made by the UE  302 . 
     The AoAs in different coordinate systems can be converted using the orientation rotations. For example, a global AoA in the GCS  308  may be converted to a local AoA in the LCS  310  using the orientation rotations of the UE  302 , and a local AoA in the LCS  310  may be converted to a global AoA in the GCS  308  using the orientation rotations of the UE. The AoAs may be converted between the GCS  308  and the LCS  310  via quaternion, rotation matrix, Euler angles, and/or other approaches for converting points between different coordinate systems. For example, a global AoA having coordinates (ϕθ) GCS  in the GCS  308  (where ϕ is an azimuth angle and θ is an elevation angle in the GCS  308 ) may be converted to a local AoA having coordinates ({circumflex over (ϕ)}, {circumflex over (θ)}, t) LCS  in the LCS  310  (where {circumflex over (ϕ)} is an azimuth angle and {circumflex over (θ)} is an elevation angle in the LCS  310 , and t is a time), and vice versa. 
     One or more sensor devices (such as motion sensors) can provide LCS-GCS orientation rotation information at each time. For example, the sensor input may be utilized to determine coordinate system orientation rotation, such as quaternion q(t). One or more sensor devices (such as the sensor devices  104  ( FIG.  1   )) may provide information related to an orientation of the UE  102 . Based on the information from the sensor devices, the UE  102  may determine an orientation of the LCS  310 . The UE  302  may further determine a rotation difference between the GCS  308  and the LCS  310  based on the orientation of the LCS  310 . Due to possible limitations, the orientation of the LCS  310  determined and the rotation difference determined may be estimates based on the information from the sensor device. 
     An AoA (and then the beam) of the LCS  310  may be tracked across time, anchored at GCS AoA (ϕ, θ),   ({circumflex over (ϕ)}, {circumflex over (θ)}; t). For example, the UE  302  may determine a global AoA in the GCS  308  for a beam and set the global AoA as an anchor. Based on the rotation difference between the GCS  308  and the LCS  310 , the UE  302  may apply the rotation difference to the anchor to determine a local AoA in the LCS  310 . As the UE  102  rotates over time, the UE  102  may continue to determine the rotation difference at a current time, and apply the rotation different to the anchor to update the local AoA in the LCS  310 . 
       FIG.  4    illustrates a block diagram of a beam quality based AoA estimate approach  400  in accordance with some embodiments. For example, the beam quality based AoA estimate approach  400  may be a beam quality based AoA estimate approach for one measurement. 
     The beam quality based AoA estimate approach  400  may include a codebook  402 . For example, a set of directions and/or a codebook  402  may be defined for beams. A set of beams may be defined and/or calibrated with different steering directions for a phase array. The set of beams for which the set of directions and/or codebook  402  is defined may be defined as  ={w 1 , w 2 , . . . , w K }. 
     A beam measurement  404  may be performed for the beams provided via the codebook  402 . For example, a UE (such as the UE  102  ( FIG.  1   )) may perform a beam measurement of the beams to produce reference signal received power (RSRP) and/or signal to interference and noise ratio (SINR) for each of the beams. The UE may be able to determine the strength of each of the beams based on the RSRP and/or the SINR for each of the beams. A response to the quality of each beam may be defined as γ(w 1 ; t), . . . , γ(w K ; t), where w *  is a beam at reference * and t is a time reference. 
     The beams/steering direction may be mapped to one or more AoAs, such as via an AoA grid  406 . For example, the UE may map each of the beams/steering directions to one or more AoAs within the AoA grid  406 . A beam may be mapped to the same azimuth angle but different elevation angles for a linear array. For example, the mapping may be represented as w k     ={({circumflex over (ϕ)} k     1   , {circumflex over (θ)} k     1   ), . . . , ({circumflex over (ϕ)} k     n   , {circumflex over (θ)} k     n   )}, w k  represents the set of beams,    k  represents the set of mappings for a beam at reference k, {circumflex over (ϕ)} k , is the azimuth angle in the AoA grid  406  for a component * of a beam at reference k, and {circumflex over (θ)} k , is an elevation an angle in the AoA grid  406  for a component * of the beam at reference k. Further, the mapped values may be represented as    1 ∪ . . . ∪   K = , where   is the full grid set. The UE may quantize the field of view of this phase array into a two-dimensional (2D) LCS AoA. For example,  ={({circumflex over (ϕ)} 1 , {circumflex over (θ)} 1 ), . . . ({circumflex over (ϕ)} N , {circumflex over (θ)} N )}⊂ FOV, where {circumflex over (ϕ)} *  is a mapped azimuth angle of beam at reference * in the LCS, {circumflex over (θ)} *  is a mapped elevation angle of beam at reference * in the LCS, and FOV is a field of view. 
     The AoA grid  406  may be defined to represent a whole sphere in the LCS. The sphere may be partitioned into subparts over the sphere. Each beam may cover a range of LCS AoA within the AoA grid. For example, subsets of the sphere may be covered by w k . 
     The UE may produce an AoA estimate  408  based on the beam measurements  404  and/or the 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 the beam measurement  404  of the beam has been performed, the UE may determine the how reliable and/or likely the AoA estimate  408  produced based on the AoA grid  406  is. 
     In some embodiments, the UE may determine whether the AoA estimate  408  has achieved a reliability based on a hard decision. For example, the UE may determine which of the beams has the strongest beam measurement and then determine whether the AoA estimate  408  is within the subset of the AoA grid  406  cover by the beam that has the strongest beam measurement. For example, the hard decision may be represented by 
     
       
         
           
             
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     In some embodiments, the UE may determine whether the AoA estimate  408  has achieved a reliability based on a soft decision. For example, the UE may determine a probability of each AoA in the AoA grid  406 . In particular, the UE may determine for each of the subsets within the AoA grid  406  a likelihood that the AoA is within the subset. The UE may then determine whether a subset corresponding to the AoA estimate  408  has a probability of the AoA being located within the subset greater than a threshold value. For example, the soft decision may be represented by P(({circumflex over (ϕ)}, {circumflex over (θ)}, t) ∈   K ) ∝ γ(w k ). 
     The UE may further convert a probability of the AoA grid  406 , which may be in the LCS, into a probability in the GCS. In some embodiments, the UE may perform a quatemion with a local probability in the LCS corresponding the AoA grid  406  to produce a global probability in the GCS. The conversion may be represented by P({circumflex over (ϕ)}, {circumflex over (θ)}, t)  P(ϕ, θ), where P({circumflex over (ϕ)}, {circumflex over (θ)}; t) is a probability that the AoA is in a subset in the LCS and P(ϕ, θ) is a probability that the AoA is in a subset in the GCS. The beam quality based AoA estimate approach  400  may produce a coarse or ambiguous estimate of AoA. 
       FIG.  5    illustrates a block diagram of another beam quality based AoA estimate approach  500  in accordance with some embodiments. The beam quality based AoA estimate approach  500  may be a beam quality based AoA estimate approach for multiple measurements. In the illustrated embodiment, the beam quality based AoA estimate approach  500  is performed for two measurements. 
     The beam quality based AoA estimate approach  500  may include performing beam measurements of beams at multiple different times, such as two different times in the illustrated embodiment. For example, a UE may perform beam measurements of beams at multiple different times. The UE may produce one or more local AoA estimates at each time based on the beam measurements for the time the local AoA estimates may be produced in accordance with the approach for producing local AoA estimates described in relation to the beam quality based AoA estimate approach  400  ( FIG.  4   ). The UE may produce the local AoA estimates based on the positions corresponding to the local AoA estimates achieving a certain reliability (such as a corresponding reliability exceeding a threshold). 
     In the illustrated embodiment, the beam quality based AoA estimate approach  500  includes a first LCS graph  502 . The first LCS graph  502  may show local AoA estimates produced by the UE based on beam measurements performed by the UE at a first time. In the illustrated embodiment, the first LCS graph  502  includes a first local AoA estimate  504  at a first position and a second local AoA estimate  506  at a second position. The first position may correspond to a first subset of a grid arrangement in the LCS and the second position may correspond to a second subset of a grid arrangement in the LCS. 
     Further, the beam quality based AoA estimate approach  500  includes a second LCS graph  508  in the illustrated embodiment. The second LCS graph  508  may show local AoA estimates produced by the UE based on beam measurements performed by the UE at a second time. The second time may be subsequent to the first time in some instances. In the illustrated embodiment, the second LCS graph  508  includes a third local AoA estimate  520  at a first position and a fourth local AoA estimate  510  at a second position. The first position may correspond to a third subset of a grid arrangement in the LCS and the second position may correspond to a fourth subset of a grid arrangement in the LCS. In some instances, the third subset and the fourth subset may be the same subsets as first subset and the second subset, or portion of each may be the same subsets. 
     The beam quality based AoA estimate approach  500  includes a GCS graph  512  in the illustrated embodiment. The GCS graph  512  may show global AoA estimates produced based on the local AoA estimates from the first LCS graph  502  and the second LCS. For example, the UE may convert the local AoA estimates in the LCS into global AoA estimates. The UE rotation from LCS at each opportunity may be provided by sensor devices (for example, q(t)). In particular, sensor devices (such as the sensor devices  104  ( FIG.  1   ) may provide information to the UE and the UE may determine rotation between the LCS and the GCS at each of times when the measurements were made. The UE may use the rotation between the LCS and the GCS at each of the times to convert the local AoA estimates within the first LCS graph  502  and the local AoA estimate within the second LCS graph  508  to global AoA estimates in the GCS. Accordingly, the GCS AoA (which may be referred to as global AoA) may be estimated ambiguously at each time. 
     The GCS graph  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 embodiments. The UE may have produced the first global AoA estimate  514  by converting the first local AoA estimate  504  to the GCS. Further, the UE may have produced the third global AoA estimate  518  by converting the fourth local AoA estimate  510  to the GCS. In the illustrated embodiment, the conversion of the second local AoA estimate  506  and the conversion of the third local AoA estimate  520  may result in global AoA estimates in a same subset. Accordingly, the GCS conversion of the second local AoA estimate  506  and the third local AoA estimate  520  may overlap and produce the second global AoA estimate  516 . For example, the UE may have converted the second local AoA estimate  506  and the third local AoA estimate  520  to the GCS to produce the second global AoA estimate  516 . 
     The UE may attempt to determine the best beam for operation based on the global AoA estimates presented in the GCS graph  512 . The UE may assume that the true GCS AoA has not changed. Therefore, the ambiguity of the GCS estimates can be resolved by selecting the most overlapped GCS across estimates. For example, the UE may determine that the second global AoA estimate  516  is the most overlapped in the illustrated embodiment based on the conversion of both the second local AoA estimate  506  and the third local AoA estimate  520  resulting in the second global AoA estimate  516 . Accordingly, the UE may determine that the second global AoA estimate  516  corresponds to the best beam and the second global AoA estimate  516  is to be utilized as the global AoA estimate based on the overlapping conversions. 
     Once the global AoA estimate has been determined, the UE may determine whether a sensor reliable transition (such as the sensor reliable transition  208  ( FIG.  2   )) or a sensor lock transition (such as the sensor lock transition  206  ( FIG.  2   )) is to be implemented based on the global AoA estimate. In instances where the UE is operating in a sensor based AoA estimate state (such as the sensor based AoA estimate state  202  ( FIG.  2   )) when the global AoA estimate is produced, the UE may determine whether the global AoA estimate has achieved a reliability. For example, a probability that an AoA may be located at the second global AoA estimate  516  may be determined based on probabilities that the AoA is at the second local AoA estimate  506  and that 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 may determine that a sensor reliable transition is to be implemented based on the global AoA estimate. If the probability corresponding to the second global AoA estimate  516  is determined to be less than the threshold, the UE may determine that a sensor lock transition is to be implemented. 
     In instances where the UE is operating in a sensor assisted beam tracking state (such as the sensor assisted beam tracking state  204  ( FIG.  2   )) when the global AoA estimate is produced, the UE may determine whether the global AoA estimate matches an AoA estimate being maintained during the sensor assisted beam tracking state. For example, the UE may compare the AoA of the second global AoA estimate  516  with an AoA of an AoA estimate being maintained during the sensor assisted beam tracking state. If the AoA of the second global AoA estimate  516  matches the AoA of the AoA estimate being maintained, the UE may determine that the sensor reliable transition is to be implemented. If the AoA of the second global AoA estimate  516  does not match the AoA estimate being maintained, the UE may determine that the sensor lock transition is to be implemented. In some embodiments where a probability of the second global AoA estimate  516  is below a threshold, the UE may remain in the sensor assisted beam tracking state without performing the comparison. 
       FIG.  6    illustrates an example of a local AoA estimate conversion to a global AoA estimate approach  600  in accordance with some embodiments. For example, the approach  600  may illustrate a conversion from the local AoA estimate from a first grid arrangement for an LCS to a global AoA estimate within a second grid arrangement for an GCS. 
     A LCS AoA grid may be defined, such that a beam can be mapped to one or more AoA grid points. Computation/calibration complexity reduction, robustness improvement may result from the implementation of the LCS AoA grid. LCS to GCS AoA conversion can be arbitrary with any sensor reading. There may not be common GCS AoA across measurement opportunities (since LCS AoA is quantized). Therefore, a GCS AoA grid is also defined such that a LCS AoA is converted and quantized to a GCS AoA grid point given the sensor reading. 
     For example, the UE may generate a grid arrangement in the LCS, where the grid arrangement may form a sphere in the LCS with multiple subgroups. Further, a grid arrangement may be generated (either by the UE or the base station) in the GCS, where the grid arrangement may form a sphere in the LCS with multiple subgroups. The grid arrangement in the LCS may differ from the grid arrangement in the GCS such that positions of the subgroups in the grid arrangements do not align perfectly. In these embodiments, the conversion of local AoA estimate from a grid arrangement in the LCS to a global AoA estimate in a grid arrangement in the GCS may result in the converted local AoA estimate being quantized to a defined closest subgroup in the grid arrangement of the GCS. 
     In the illustrated embodiment, the approach  600  may include a LCS graph  602  and a GCS graph  604 . The LCS graph  602  may illustrate one or more local AoA estimates. In the illustrated embodiment, a local AoA estimate  606  is referred to for illustration of conversion from a grid arrangement for the LCS to a grid arrangement for the GCS. The local AoA estimate  606  may be located within a center of a first subgroup  608  of a grid arrangement of the LCS. The position of the local AoA estimate  606  may be defined as ({circumflex over (ϕ)} n , {circumflex over (θ)} n ), where {circumflex over (ϕ)} n  is the azimuth angle of the local AoA estimate  606  and {circumflex over (θ)} n  is the elevation angle of the local AoA estimate  606 . 
     The UE may then convert the local AoA estimate  606  to the GCS in accordance with approaches described throughout this disclosure. The conversion of the local AoA estimate  606  may result in a position  610  in the GCS, which may be defined as (ϕ′, θ′), where ϕ′ is the azimuth angle of the position  610  and θ′ is the elevation angle of the position  610 . As can be seen from the GCS graph  604 , the position  610  does not correspond to any of the global AoA estimates (as illustrated by the +symbols). However, the position  610  may be located within a second subgroup  612  of a grid arrangement of the LCS. The UE may determine that the position  610  is to be represented by a closest global AoA estimate, which is global AoA estimate  614  represented by (ϕ m , θ m ) in the illustrated embodiment, where ϕ m  is the azimuth angle of the global AoA estimate  614  and θ m  is the elevation angle of the global AoA estimate  614 . For example, the UE may determine a global AoA estimate within a grid arrangement of a GCS from a local AoA estimate within a grid arrangement of a LCS based on 
     
       
         
           
             
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       FIG.  7    illustrates an example beam quality based AoA estimate approach  700  in accordance with some embodiments. In particular, the beam quality based AoA estimate approach  700  may be implemented in embodiments where two or more measurements have been performed. 
     The beam quality based AoA estimate approach  700  may include performing a beam measurement  702  at a first time and a beam measurement  704  at a second time. For example, a UE (such as the UE  102  ( FIG.  1   )) may perform the beam measurement  702  of a beam received from a base station (such as the base station  106  ( FIG.  1   )) at a first time and may perform the beam measurement  704  of the beam at a second time. The UE may determine one or more responses from the beam measurement  702 , where the responses may be represented by γ(w 1 ; t 1 ), . . . , γ(w K ; t 1 ), where w *  is a beam at reference * and t 1  is the first time. Further, the UE may determine one or more responses from the beam measurement  704 , where the responses may be represented by γ(w 1 ; t 2 ), . . . , γ(w K ; t 2 ), where w *  is a beam at reference * and t 2  is the second time. 
     The beam quality based AoA estimate approach  700  may further include determining probabilities for a first LCS AoA  706  from the results of the beam measurement  702  at the first time and determining probabilities for a second LCS AoA  708  at the second time. For example, beam quality to LCS AoA estimate may be performed. In particular, the UE may utilize the results of the beam measurement  702  to determine probabilities of a true AoA corresponding to each local AoA estimate within an LCS at the first time, which may be represented by P({circumflex over (ϕ)} n , {circumflex over (θ)} n ; t 1 ); n=1, . . . , N, where n is a reference of a local AoA estimate, {circumflex over (ϕ)} n  is an azimuth angle for the local AoA estimate, and {circumflex over (θ)} n  is an elevation angle for the local AoA estimate. The UE may utilize the results of the beam measurement  704  to determine probabilities of a true AoA corresponding to each local AoA estimate within the LCS at the second time, which may be represented by P({circumflex over (ϕ)} n ; {circumflex over (θ)} n ; t 2 ). 
     The beam quality based AoA estimate approach  700  may further include converting the probabilities of the LCS to GCS and combining the probabilities in GCS AoA  712 . For example LCS to GCS AoA mapping may be performed. In particular, the UE may receive information from one or more sensor devices  710  (such as the sensor devices  104  ( FIG.  1   )) regarding an orientation of the UE, and may determine a difference between the LCS and the GCS at the first time and a difference between the LCS and the GCS at the second time. The UE may utilize the difference between the LCS and the GCS at the first time to convert the probabilities from the first LCS AoA  706  to GCS, which may be represented by  → : P(ϕ m , θ m ; t 1 ); m=1, . . . , M, where ϕ m  is the azimuth angle converted to the GCS and θ m  is the elevation angle converted to the GCS. The UE may utilize the difference between the LCS and the GCS at the second time to convert the probabilities from the second LCS AoA  708  to GCS, which may be represented by P(ϕ m , θ m ; t 2 ). 
     The UE may then combine the probabilities for the global AoA estimates from the first LCS AoA  706  with the probabilities for the global AoA estimates from the second LCS AoA  708 . In some embodiments, the probabilities may be combined with additional probabilities for AoA estimates from beam measurements performed at other times, as indicated by input  714 . The additional probabilities may be represented by P(ϕ m , θ m ; t i ), where t i  is time at i. The combined probabilities may provide a long term probability (for example, a joint probability) for GCS AoA (such as global AoA estimates), which may be represented by P(ϕ m , θ m ; t 1 , t 2 , . . . )=C norm ·Π t P(ϕ m , θ m ; t), where P(ϕ m , θ m ; t) is an instantaneous probability for GCS of ϕ m , θ m  at time t and P(ϕ m , θ m ; t 1 , t 2 , . . . ) is the long term probability over multiple measurements for this GCS grid point. The UE may then determine the global AoA estimate to be utilized is to be the global AoA estimate with the largest probability, which may be represented by ϕ * , 
                 θ   *     =         arg   ⁢        max     m     ⁢          P   (       ϕ   m     ,       θ   m     ;     t   1       ,     t   2     ,       …          )         ,         
where ϕ *  is the azimuth angle of the global AoA estimate to be utilized, and θ *  is the elevation angle of the global AoA estimate to be utilized.
 
     Once the global AoA estimate has been determined, the UE may determine whether a sensor reliable transition (such as the sensor reliable transition  208  ( FIG.  2   )) or a sensor lock transition (such as the sensor lock transition  206  ( FIG.  2   )) is to be implemented based on the global AoA estimate. In instances where the UE is operating in a sensor based AoA estimate state (such as the sensor based AoA estimate state  202  ( FIG.  2   )) when the global AoA estimate is produced, the UE may determine whether the global AoA estimate has achieved a reliability. If the probability corresponding to the global AoA estimate is determined to be greater than or equal to a threshold, the UE may determine that a sensor reliable transition is to be implemented based on the global AoA estimate. If the probability corresponding to the global AoA estimate is determined to be less than the threshold, the UE may determine that a sensor lock transition is to be implemented. 
     In instances where the UE is operating in a sensor assisted beam tracking state (such as the sensor assisted beam tracking state  204  ( FIG.  2   )) when the global AoA estimate is produced, the UE may determine whether the global AoA estimate matches an AoA estimate being maintained during the sensor assisted beam tracking state. For example, the UE may compare the AoA of the global AoA estimate with an AoA of an AoA estimate being maintained during the sensor assisted beam tracking state. If the AoA of the global AoA estimate matches the AoA of the AoA estimate being maintained, the UE may determine that the sensor reliable transition is to be implemented. If the AoA of the global AoA estimate does not match the AoA estimate being maintained, the UE may determine that the sensor lock transition is to be implemented. In some embodiments where a probability of the global AoA estimate is below a threshold, the UE may remain in the sensor assisted beam tracking state without performing the comparison. 
       FIG.  8    illustrates an AoA probability based on beam measurement approach  800  in accordance with some embodiments. In particular, the approach  800  illustrates how probabilities may be determined for steering directions (such as AoA estimates). The approach  800  may be implemented by a UE (such as the UE  102  ( FIG.  1   )) to determine the probabilities for steering directions. 
     The approach  800  may include performing beam measurements  802  for one or more beams. In particular, the UE may perform beam measurements of one or more beams received from a base station (such as the base station  106  ( FIG.  1   )). The beam measurements may produce RSRP and/or SINR for each of the beams measured. The response from the beam measurements may be represented as γ(w 1 ). 
     A probability of AoA as approximately a steering direction can be obtained from the RSRP and/or the SINR in  804 ,  806 , and  808 . In particular, the UE may determine a probability of the AoA as being approximately the steering direction w 1  in  804 . Further, the UE may determine a probability of the AoA as being approximately the steering direction w 2  in  806 . The UE may determine a probability of the AoA as being approximately the steering direction w 3  in  808 . The probability may be determined based on a hard decision or a soft decision. In the hard decision, the probability may be determined by 
     
       
         
           
             
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     In the soft decision, the probability may be determined by P(w k ) ∝ γ(w k ). 
     For the approach, a probability of a steering direction may be spread across all mapped grids in  810 ,  812 ,  814 , and  816 . For example, the probability of a steering direction may be determined by 
               P   ⁡   (       (       ϕ   ˆ     ,     θ   ˆ       )     =     (         ϕ   ˆ     n     ,       θ   ˆ     n       )       )     =       P   ⁡   (     w   k     )             
where ({circumflex over (ϕ)} n , {circumflex over (θ)} n ) ∈   K , where P(w k ) is the probability that AoA is covered by the beam, |   k | is the cardinality of the subset S k . In some other embodiments, the probability of a steering direction may be determined by P(({circumflex over (ϕ)}, {circumflex over (θ)})=({circumflex over (ϕ)} n , {circumflex over (θ)} n ))=P(w k ). In particular, the UE may determine the probability of the steering direction ({circumflex over (ϕ)} 1 , {circumflex over (θ)} 1 ) in  810 , the probability of the steering direction ({circumflex over (ϕ)} 2 , {circumflex over (θ)} 2 ) in  812 , the probability of the steering direction ({circumflex over (ϕ)} 3 , {circumflex over (θ)} 3 ) in  814 , and the probability of the steering direction ({circumflex over (ϕ)} 4 , {circumflex over (θ)} 4 ) in  816 . If an AoA grid is mapped from more than 1 beam, then the probability of this grid is the sum probability over all source beams. The probability of the grid may be determined by
 
     
       
         
           
             
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     The sensor based AoA estimation can stop after M beam measurement opportunities with certain stop rules. For example, when if the UE remains in the sensor based AoA estimation state (such as the sensor based AoA estimation state  202  ( FIG.  2   )) after a certain amount of AoA estimates have been performed, the UE may transition from the sensor based AoA estimation state. The UE may transit to SENSOR_RELIABLE state and trigger sensor assisted beam tracking. In particular, based on the certain amount of AoA estimates having been performed while in the sensor based AoA estimation state, the UE may determine that a sensor reliable transition (such as the sensor reliable transition  208  ( FIG.  2   )) is to be implemented and the UE may transition to a sensor assisted beam tracking state (such as the sensor assisted beam tracking state  204  ( FIG.  2   )). 
     Stop criterion may include in a first instance if only 1 AoA grid has a sufficiently high probability then the highest probability AoA grids is utilized in the sensor assisted beam tracking state. In a second instance, the stop criterion may include if maximum number of beam measurements are performed, then multiple potential AoA grid points (for example, non-line-of-sign (NLOS channels)) is utilized in the sensor assisted beam tracking state. The multiple potential AoA grid points may be determined by 
               (     ϕ   ,     θ   ;   t       )     =     {             (       ϕ   m     ,       θ   m     ;   t       )               if   ⁢          P   ⁡   (     (       ϕ   m     ,     θ   m       )     )       &gt;     P   0       ,     t   &lt;     T   max                   F   ⁡   (       P   ⁡   (       ϕ   m     ,     θ   m       )     ,     m   =   1     ,        …        ,   M     )             if   ⁢         t     ≥     T   max             ,             
where (ϕ m , θ m ; t) is a grid point that satisfies a threshold, and F (P (ϕ m , θ m ), m=1, . . . , M) is a function to map the set of the probability to an AoA. For example, if any of the stop criterion are met, the UE may transition to the sensor assisted beam tracking state with the value indicated.
 
     If multiple potential AoAs have been observed in the end of window, it can be processed in various ways. In a first instance, the multiple potential AoAs can be processed by output of an average AoA per reliability (AoA expectation) for the AoA grid points with sufficient reliability, which may be defined by F(P(ϕ m , θ m ); 
                 1   ≤   m   ≤   M     )     =           Σ     m   :       P   m     &gt;     P   1           ⁢       P   ⁡   (       ϕ   m     ,     θ   m       )     ·     (       ϕ   m     ,     θ   m       )             Σ     m   :       P   m     &gt;     P   1           ⁢     P   ⁡   (       ϕ   m     ,     θ   m       )         .           
In a second instance, the multiple potential AoAs can be processed by output of multiple AoAs to the beam tracking engines and beam tracking engine can apply advanced beam selections.
 
     Once stopped, the local AoA at the stopped time based on the stop criterion or the multiple potential AoAs may be converted to GCS and may remain constant until the AoA estimation is triggered again (such as SENSOR_LOCK state). For example, once the stopped, the local AoA estimate determined at the stop time may be converted to a global AoA estimate and may remain constant until the UE implements a sensor lock transition (such as the sensor lock transition  206  ( FIG.  2   )). 
     Below is provided an example algorithm that may be implemented for performance of one or more of the approaches described throughout the disclosure. 
     In an initialization portion of the algorithm, 2 AoA grid in LCS and GCS respectively. Note the size may not be the same. For example, a grid arrangement may be generated in the LCS and a grid arrangement may be generated in the GCS during the initialization. The grid arrangement in the LCS and the grid arrangement in the GCS may be different sizes in some embodiments. Codebook and beam-to-LCS AoA mapping ( 1 :n). For example, codebook and beam-to-LCS AoA mapping may be defined in the initialization portion. The initialization portion may include: 
     Initialization: 
     Codebook W=[w 1 , w 2 , . . . , w K ] 
     LCS AoA grid  ={({circumflex over (ϕ)} 1 , {circumflex over (θ)} 1 ), ({circumflex over (ϕ)} 2 , {circumflex over (θ)} 2 ), . . . , ({circumflex over (ϕ)} N , {circumflex over (θ)} N )} 
     CB/AoA Mapping f k : W K     ={({circumflex over (ϕ)} k     1   , {circumflex over (θ)} k     1   ), ({circumflex over (ϕ)} k     2   , {circumflex over (θ)} k     2   ), . . . } 
     GCS AoA grid  ={(ϕ 1 , θ 1 ), (ϕ 2 , θ 2 ), . . . , (ϕ m , θ m )} 
     LOCK=False for t=1 . . . T do 
     Input measure beam quality for each beam, γ(w K ; t) 
     Input sensor reading of LCS /GCS orientation rotation, q (t)    
     Initialize instantaneous GCS AoA Prob {tilde over (P)} m =0 for m=1, . . . , M 
     In a second portion of the algorithm, a probability of an GCS AoA based on beam measurement and LCS-to-GCS rotation at time t may be determined. The probability may be determined based on a hard decision or a soft decision. In the hard decision, 
                 g   k     (       γ   ⁡   (         w   i     ;          ,   t     )     ,     i   =   1     ,   …         ,   K     )     =     {           1           0         ⁢               if   ⁢           γ   ⁡   (       w   k     ;   t     )       =     max   ⁢     γ   ⁡   (       w   i     ;   t     )                 otherwise         .               
In the soft decision,
 
     
       
         
           
             
               
                 
                   
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                   Calculate instantaneous prob of LCS AoA,  
               
               
                   
               
               
                 
                   
                     
                       
                         
                           
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                   Rotate LCS AoA quaternion to GCS, p(ϕ′, θ′) = q (t) p({circumflex over (ϕ)} n , {circumflex over (θ)} n )q (t)−1   
               
               
                   
               
               
                   
         Quantize   ⁢         to   ⁢         GCS   ⁢         grid     ,       (       ϕ   m     ,     θ   m       )     =       argmax     1   ≤   i   ≤   M       ⁢     dist   ⁡   (       (       ϕ   ′     ,     θ   ′       )     ,     (       ϕ   i     ,     θ   i       )       )             
 
               
               
                   
               
               
                   Update instantaneous GCS AoA probability, {tilde over (P)} m  ← {tilde over (P)} m  + {circumflex over (P)} n   
               
               
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     In a third portion of the algorithm, a probability of a GCS AoA over a time duration 1, 2, . . . , t may be determined. The third portion of the algorithm may include: 
     
       
         
           
               
             
               
                   
               
             
            
               
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         Initialize   /   update   ⁢         long   ⁢         term   ⁢         GCS   ⁢         AoA   ⁢         Prob     ,       P   m     (   t   )       =     {             P   ~     m             if   ⁢         t     =   1                 P   m     (     t   -   1     )       ·       P   ~     m               if   ⁢         t     &gt;   1                   
 
               
               
                   
               
               
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         Normalize   ⁢         long   ⁢         term   ⁢         GCS   ⁢         AoA   ⁢         Prob     ,       P   m     (   t   )       ≤       P   m     (   t   )           ∑   m       P   m     (   t   )                 
 
               
               
                   
               
               
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     In a fourth portion of the algorithm, a termination criterion may be checked. The fourth portion of the algorithm may include: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                  if max P m   (t)  ≥ P reliable   
               
               
                   
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     When codebook base beamforming is used, the beams in the codebook may be swept to measure the beam quality of each beam. Alternatively, a set of probing beams can be applied and the raw channel can be estimated utilizing the received signals of the probing beams. The channel estimates can be used for beam determination, such as channel based beamforming. 
     In such a case, a reference codebook can still be deployed for AoA estimation, where the quality of each beam in the codebook can be determined by applying the AoA beam on the channel estimates in baseband in some embodiments. In other embodiments, the quality of each beam in the codebook can be determined by correlating the channel based beam with the AoA beam. 
     Given the noisy measurement and other practical limitations, some AoA grid points may have unreliable probabilities. Example: Since human behavior has physical limitation, LCS AoA estimate change across time may have limitations and may not change dramatically. For example, LCS AoA estimations may be limited based on changes that would be impossible and/or unlikely for a UE being used by a human. Regularization rules can be applied to remove AoA outliers, either in local coordinate system or global coordinate system. 
     One or more optimal GCS AoA estimates can be output to sensor assisted beam tracking module to initialize the beam tracking. Multiple AoA estimates can be output in case of NLOS channel. This motivates the soft output for beam tracking module to leverage. Alternatively, the reliabilities of each AoA grid points can be output such that beam tracking can make reliability based decisions for beam sweeping or antenna probing. 
     The earlier sensor assisted beamforming framework may indicate two operating stages. AoA may be checked with new beam quality measurement for consistence. If an AoA estimation is detected to be unreliable, operation may switch back to AoA estimation (such as the sensor based AoA estimation state  202 ). 
     Alternatively, the joint sensor assisted beamforming and AoA estimate update can be applied after the initial AoA estimate has been estimated. Sensor reading and the current AoA estimate may be used to update beam between the beam measurement opportunities. Once beam qualities have been updated at a new measurement opportunity, AoA estimate can be updated directly without switching operation mode. 
       FIG.  9 A  illustrates a first portion of an example procedure  900  for identifying a beam for communication in accordance with some embodiments.  FIG.  9 B  illustrates a second portion of the example procedure  900  for identifying a beam for communication in accordance with some embodiments. The procedure  900  may be performed by a UE, such as the UE  102  ( FIG.  1   ). The UE may perform the procedure  900  to determine a beam to be utilized for communication between a base station (such as the base station  106  ( FIG.  1   )) and the UE. 
     The procedure  900  may include performing a plurality of beam measurements in  902 . In particular, the UE may perform a plurality of beam measurements corresponding to a beam received by the UE. The UE may receive the beam from the base station. The UE performing the plurality of beam measurements may produce RSRP and/or SINR for the beam. 
     The procedure  900  may further include generating a grid arrangement in an LCS in  904 . In particular, the UE may generate a grid arrangement in an LCS. The UE may generate the grid arrangement in accordance with any of the approaches for generating a grid arrangement described throughout this disclosure. In some embodiments,  904  may be omitted. 
     The procedure  900  may further include generating a grid arrangement in a GCS in  906 . In particular, the UE may generate a grid arrangement in a GCS. The UE may generate the gird arrangement in accordance with any of the approaches for generating a grid arrangement described throughout this disclosure. In some embodiments,  906  may be omitted. 
     The procedure  900  may include determining a plurality of local AoA estimates in  908 . In particular, the UE may determine a plurality of local AoA estimates corresponding to the beam based on the plurality of beam measurements. The UE may determine the plurality of local AoA estimates in accordance with any of the approaches for determining a local AoA estimate described throughout this disclosure. In some embodiments, determining the plurality of local AoA estimates may include determining local grid positions for the plurality of local AoA estimates based on the plurality of beam measurements and the grid arrangement in the LCS as generated in  904 . In some embodiments, some embodiments, the plurality of local AoA estimates may be established in a grid system for a LCS. 
     The procedure  900  may include converting the plurality of local AoA estimates in  910 . In particular, the UE may convert the plurality of local AoA estimates to a plurality of global AoA estimates. The UE may convert the plurality of local AoA estimates to the plurality of global AoA estimates in accordance with any of the approaches for converting a local AoA estimate to a global AoA estimate described throughout this disclosure. 
     In some embodiments, converting the plurality of local AoA estimates to the plurality of global AoA estimates may include determining global grid positions for a plurality of global AoA estimates based on the local grid positions for the plurality of local AoA estimates. 
     In some embodiments, converting the plurality of local AoA estimates to the plurality of global AoA estimates may include converting the plurality of AoA estimates from a grid system for the LCS to a grid system for a GCS. For example, the UE may convert the plurality of AoA estimates from the grid system for the LCS to the grid system for the GCS when the local AoA estimates are established in the grid system for the LCS. 
     In some embodiments, converting the plurality of local AoA estimates to the plurality of global AoA estimates may include determining sensor data corresponding to each local AoA estimate of the plurality of AoA estimates. In particular, the UE may receive data from one or more sensor devices (such as the sensor devices  104  ( FIG.  1   )) to determine the sensor data. Further, the UE may convert the plurality of local AoA estimates to the plurality of global AoA estimates based on the sensor data corresponding to each local AoA estimate of the plurality of local AoA estimates. 
     The procedure  900  may include determining a global AoA in  912 . In particular, the UE may determine a global AoA based on the plurality of global AoA estimates. The UE may determine the global AoA in accordance with any of the approaches of determining a global AoA described herein. In some embodiments, the global AoA may be an estimate of an actual global AoA that may be estimated based on the plurality of global AoA estimates. In some embodiments, the plurality of global AoA estimates may have a corresponding plurality of probabilities of being an actual global AoA. 
     In some embodiments, determining the global AoA may include converting a first set of local AoA estimates of the plurality of local AoA estimates to a first set of global AoA estimates of the plurality of global AoA estimates. The first set of local AoA estimates may correspond to a first time. Determining the global AoA may further include converting a second set of local AoA estimates of the plurality of local AoA estimates to a second set of global AoA estimates of the plurality of global AoA estimates. The second set of local AoA estimates may correspond to a second time. Further, determining the global AoA may include determining that the global AoA corresponds to a first global AoA estimate of the first set of global AoA estimates and a second global AoA estimate of the second set of global AoA estimates based on a determination that the first global AoA estimate overlaps with the second global AoA estimate. 
     In some embodiments, determining the global AoA may include determining a global AoA estimate with a greatest probability from the corresponding plurality of probabilities of the plurality of global AoA estimates. The UE may set the global AoA to the global AoA estimate with the greatest probability. 
     The procedure  900  may include determining whether a reliability has been achieved in  914 . In particular, the UE may determine whether a reliability have been achieved for the global AoA based on a probability of the global AoA being an actual global AoA. The UE may determine whether the reliability has been achieved in accordance with any of the approaches for determining whether a reliability has been achieved described throughout this disclosure. In some embodiments,  914  may be omitted. 
     The procedure  900  may further include determining which state is to be implemented in  916 . In particular, the UE may determine which of a sensor based AoA estimation state or a sensor assisted beam tracking state is to be implemented based on whether the reliability has been achieved. When the UE determines that the reliability is achieved, the UE may determine that the sensor assisted beam tracking state is to be implemented based on the reliability being achieved. When the UE determines that the reliability is not achieved, the UE may determine that the sensor based AoA estimate state is to be implemented based on the reliability not being achieved. In some embodiments,  916  may be omitted. 
     The procedure  900  may further include converting the global AoA to a local anchor in  918 . In particular, the UE may convert the global AoA to a local anchor for use in the sensor assisted beam tracking state. In some embodiments,  918  may be omitted. 
     The procedure  900  may further include maintaining a local AoA in  920 . In particular, the UE may maintain a local AoA based on the local anchor and sensor data from a sensor (such as the sensor devices  104 ) of the UE while the sensor assisted beam tracking state is implemented. In some embodiments,  920  may be omitted.  922  may illustrate the transition from  FIG.  9 A  to  FIG.  9 B . 
     The procedure  900  may further include utilizing a global AoA for identification of a beam in  924 . In particular, the UE may utilize the global AoA for identification of a beam received from the base station. The UE may determine that the beam is to utilized for communication between the UE and the base station. The UE may transmit an indication that the beam is to be utilized to the base station. 
     The procedure  900  may further include performing a second plurality of beam measurements in  926 . In particular, the UE may perform a second plurality of beam measurements corresponding to the beam while the sensor assisted beam tracking state is implemented. In some embodiments,  926  may be omitted. 
     The procedure  900  may further include determining a second plurality of local AoA estimates in  928 . In particular, the UE may determine a second plurality of local AoA estimates corresponding to the beam based on the second plurality of beam measurements from  926 . In some embodiments,  928  may be omitted. 
     The procedure  900  may further include converting the second plurality of local AoA estimates in  930 . In particular, the UE may convert the second plurality of local AoA estimates to a second plurality of global AoA estimates. In some embodiments,  930  may be omitted. 
     The procedure  900  may further include determining a second global AoA in  932 . In particular, the UE may determine a second global AoA based on the second plurality of global AoA estimates. In some embodiments,  932  may be omitted. 
     The procedure  900  may further include comparing the second global with the local AoA in  934 . In particular, the UE may compare the second global AoA with the local AoA being maintained in  920  to determine whether the local AoA has a reliability greater than a predetermined threshold. In some embodiments,  934  may be omitted. 
       FIG.  10 A  illustrates a first portion of another example procedure  1000  for identifying a beam for communication in accordance with some embodiments.  FIG.  10 B  illustrates a second portion of the example procedure  1000  for identifying a beam for communication in accordance with some embodiments. The procedure  1000  may be performed by a UE, such as the UE  102  ( FIG.  1   ). The UE may perform the procedure  1000  to determine a beam to be utilized for communication between a base station (such as the base station  106  ( FIG.  1   )) and the UE. 
     The procedure  1000  may include performing a plurality of beam measurements in  1002 . In particular, the UE may perform a plurality of beam measurements corresponding to a beam received by the UE. The UE performing the plurality of beam measurements may produce RSRP and/or SINR for the beam. 
     The procedure  1000  may include determining orientations of the UE in  1004 . In particular, the UE may determine orientations of the UE corresponding to each of the plurality of beam measurements. For example, the UE may receive sensor data from one or more sensors (such as the sensor devices  104  ( FIG.  1   )) and may determine orientations of the UE based on the sensor data. 
     The procedure  1000  may include determining a plurality of local AOA estimates in  1006 . In particular, the UE may determine a plurality of local AOA estimates corresponding to the beam based on the plurality of beam measurements. The plurality of the local AoA estimates may be determined based on the beam measurements and/or the determined orientations from  1004 . 
     The procedure  1000  may include converting the plurality of local AoA estimates in  1008 . In particular, the UE may convert the plurality of local AoA estimates to a plurality of global AoA estimates based on the orientations of the UE corresponding to each of the plurality of beam measurements. In some embodiments, converting the plurality of local AoA estimates to the plurality of global AoA estimates may include converting the plurality of local AoA estimates from a LCS to a GCS to produce the plurality of global AoA estimates. 
     The procedure  1000  may include determining probabilities for each local AoA estimate in  1010 . In particular, the UE may determine probabilities for each local AoA estimate of the plurality of local AoA estimates based on the plurality of beam measurements. In some embodiments, determining the probabilities for each local AoA estimate may include determining the probabilities for each local AoA estimate of the plurality of local AoA estimates based on RSRP and/or SINR for the plurality of local AoA estimates from the plurality of beam measurements. In some embodiments,  1010  may be omitted. 
     The procedure  1000  may include determining probabilities for each global AoA estimate in  1012 . In particular, the UE may determine probabilities for each global AoA estimate of the plurality of global AoA estimates based on the probabilities for each local AoA estimate of the plurality of local AoA estimates. In some embodiments,  1012  may be omitted. 
     The procedure  1000  may include determining a global AoA in  1014 . In particular, the UE may determine a global AoA based on overlap of the plurality of global AoA estimates. In some embodiments, the global AoA may be an estimate of an actual global AoA that may be estimated based on the plurality of global AoA estimates. In some embodiments, determining the global AoA may include determining that a probability for the global AoA is a largest probability of AoA estimates within a GCS based on the probabilities for each AoA of the plurality of global AoA estimates. In some embodiments, determining the global AoA may include determining a location of a greatest number of the plurality of global AoA estimates that overlap, where the global AoA is determined to be located at the location. 
     The procedure  1000  may include determining whether the global AoA meets a reliability in  1016 . In particular, the UE may determine whether the global AoA meets a reliability based on the probability for the global AoA being greater than a predetermined threshold. In some embodiments,  1016  may be omitted. 
     The procedure  1000  may include determining a state in which the UE is to operate in  1018 . In particular, the UE may determine whether the UE is to operate in a sensor based AoA estimation state or a sensor assisted beam tracking state based on whether the global AoA meets the reliability. For example, the UE may determine that the UE is to operate in the sensor based AoA estimation state based on the global AoA not meeting the reliability, and may determine that the UE is to operate in the sensor assisted beam tracking state based on the global AoA meeting the reliability. In some embodiments,  1018  may be omitted.  1020  may illustrate the transition from  FIG.  10 A  to  FIG.  10 B . 
     The procedure  1000  may include entering the state in  1022 . In particular, the UE may enter the state determined in  1018 . For example, the UE may enter the sensor assisted beam tracking state based on a determination that the global AoA meets the reliability. Further, the UE may enter the sensor based AoA estimation state based on a determination that the global AoA fails to meet the reliability. In some embodiments,  1022  may be omitted. 
     The procedure  1000  may include converting the global AoA to a local anchor in  1024 . In particular, the UE may convert the global AoA to a local anchor for utilization in the sensor assisted beam tracking state. In some embodiments,  1024  may be omitted. 
     The procedure  1000  may include maintaining a local AoA in  1026 . In particular, the UE may maintain a local AoA based on the local anchor and sensor data related to a rotation of the UE. For example, the UE may receive sensor data from one or more sensors (such as the sensor device  104 ) and determine a rotation of the UE based on the sensor data. The UE may maintain the local AoA in a LCS based on the local anchor from  1024  and the data related to the rotation of the UE. In some embodiments,  1026  may be omitted. 
       FIG.  11    illustrates an example procedure  1100  for determining a beam for communication in accordance with some embodiments. The procedure  1100  may be performed by a base station, such as the base station  106  ( FIG.  1   ). The base station may perform the procedure  1100  to determine a beam to be utilized for communication between the base station and a UE (such as the UE  102  ( FIG.  1   )). 
     The procedure  1100  may include transmitting a plurality of training signals in  1102 . In particular, the base station may transmit a plurality of training signal to be utilized by the UE to determine a plurality of local AoA estimates for determination of a beam to be utilized for the UE. In some embodiments, transmitting the plurality of training signals includes transmitting the plurality of training signals via a sweeping approach or a probing approach. 
     The procedure  1100  may include transmitting an indication of an orientation of a GCS in  1104 . In particular, the base station may transmit an indication of an orientation of a GCS to the UE, where the GCS may be utilized by the UE for determining the beam. For example, the UE may utilize the indication of the orientation of the GCS for generating the GCS, generating a grid assignment for the GCS, and/or generating a grid system for the GCS. In some embodiments,  1104  may be omitted. 
     The procedure  1100  may include identifying an indication of a beam in  1106 . In particular, the base station may identify an indication, received from the UE, of the beam to be utilized for communication with the UE. The UE may have determined the beam based on a plurality of local AoA estimates determined from the plurality of training signals. 
     The procedure  1100  may include utilizing the beam for transmissions to the UE. In particular, the base station may utilize the beam for communicating with the UE. 
       FIG.  12    illustrates example beamforming circuitry  1200  in accordance with some embodiments. The beamforming circuitry  1200  may include a first antenna panel, panel  1   1204 , and a second antenna panel, panel  2   1208 . Each antenna panel may include a number of antenna elements. Other embodiments may include other numbers of antenna panels. 
     Digital beamforming (BF) components  1228  may receive an input baseband (BB) signal from, for example, a baseband processor such as, for example, baseband processor  1304 A of  FIG.  13   . The digital BF components  1228  may rely on complex weights to pre-code the BB signal and provide a beamformed BB signal to parallel radio frequency (RF) chains  1220 / 1224 . 
     Each RF chain  1220 / 1224  may include a digital-to-analog converter to convert the BB signal into the analog domain; a mixer to mix the baseband signal to an RF signal; and a power amplifier to amplify the RF signal for transmission. 
     The RF signal may be provided to analog BF components  1212 / 1216 , which may apply additionally beamforming by providing phase shifts in the analog domain. The RF signals may then be provided to antenna panels  1204 / 1208  for transmission. 
     In some embodiments, instead of the hybrid beamforming shown here, the beamforming may be done solely in the digital domain or solely in the analog domain. 
     In various embodiments, control circuitry, which may reside in a baseband processor, may provide BF weights to the analog/digital BF components to provide a transmit beam at respective antenna panels. These BF weights may be determined by the control circuitry to provide the directional provisioning of the serving cells as described herein. In some embodiments, the BF components and antenna panels may operate together to provide a dynamic phased-array that is capable of directing the beams in the desired direction. 
       FIG.  13    illustrates an example UE  1300  in accordance with some embodiments. The UE  1300  may be any mobile or non-mobile computing device, such as, for example, mobile phones, computers, tablets, industrial wireless sensors (for example, microphones, carbon dioxide sensors, pressure sensors, humidity sensors, thermometers, motion sensors, accelerometers, laser scanners, fluid level sensors, inventory sensors, electric voltage/current meters, actuators, etc.), video surveillance/monitoring devices (for example, cameras, video cameras, etc.), wearable devices (for example, a smart watch), relaxed-IoT devices. In some embodiments, the UE  1300  may be a RedCap UE or NR-Light UE. 
     The UE  1300  may include processors  1304 , RF interface circuitry  1308 , memory/storage  1312 , user interface  1316 , sensors  1320 , driver circuitry  1322 , power management integrated circuit (PMIC)  1324 , antenna structure  1326 , and battery  1328 . The components of the UE  1300  may be implemented as integrated circuits (ICs), portions thereof, discrete electronic devices, or other modules, logic, hardware, software, firmware, or a combination thereof. The block diagram of  FIG.  13    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 arrangement of the components shown may occur in other implementations. 
     The components of the UE  1300  may be coupled with various other components over one or more interconnects  1332 , which may represent any type of interface, input/output, bus (local, system, or expansion), transmission line, trace, optical connection, etc. that allows various circuit components (on common or different chips or chipsets) to interact with one another. 
     The processors  1304  may include processor circuitry such as, for example, baseband processor circuitry (BB)  1304 A, central processor unit circuitry (CPU)  1304 B, and graphics processor unit circuitry (GPU)  1304 C. The processors  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  1312  to cause the UE  1300  to perform operations as described herein. 
     In some embodiments, the baseband processor circuitry  1304 A may access a communication protocol stack  1336  in the memory/storage  1312  to communicate over a 3GPP compatible network. In general, the baseband processor circuitry  1304 A may access the communication protocol stack to: perform user plane functions at a PHY layer, MAC layer, RLC layer, PDCP layer, SDAP layer, and PDU layer; and perform control plane functions at a PHY layer, MAC layer, RLC layer, PDCP layer, RRC layer, and a non-access stratum layer. In some embodiments, the PHY layer operations may additionally/alternatively be performed by the components of the RF interface circuitry  1308 . 
     The baseband processor circuitry  1304 A may generate or process baseband signals or waveforms that carry information in 3GPP-compatible networks. In some embodiments, the waveforms for NR may be based cyclic prefix OFDM (CP-OFDM) in the uplink or downlink, and discrete Fourier transform spread OFDM (DFT-S-OFDM) in the uplink. 
     The memory/storage  1312  may include one or more non-transitory, computer-readable media that includes instructions (for example, communication protocol stack  1336 ) that may be executed by one or more of the processors  1304  to cause the UE  1300  to perform various operations described herein. The memory/storage  1312  include any type of volatile or non-volatile memory that may be distributed throughout the UE  1300 . In some embodiments, some of the memory/storage  1312  may be located on the processors  1304  themselves (for example, L1 and L2 cache), while other memory/storage  1312  is external to the processors  1304  but accessible thereto via a memory interface. The memory/storage  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), eraseable programmable read only memory (EPROM), electrically eraseable programmable read only memory (EEPROM), Flash memory, solid-state memory, or any other type of memory device technology. 
     The RF interface circuitry  1308  may include transceiver circuitry and radio frequency front module (RFEM) that allows the UE  1300  to communicate with other devices over a radio access network. The RF interface circuitry  1308  may include various elements arranged in transmit or receive paths. These elements may include, for example, switches, mixers, amplifiers, filters, synthesizer circuitry, control circuitry, etc. 
     In the receive path, the RFEM may receive a radiated signal from an air interface via antenna structure  1326  and proceed to filter and amplify (with a low-noise amplifier) the signal. The signal may be provided to a receiver of the transceiver that down-converts the RF signal into a baseband signal that is provided to the baseband processor of the processors  1304 . 
     In the transmit path, the transmitter of the transceiver up-converts the baseband signal received from the baseband processor and provides the RF signal to the RFEM. The RFEM may amplify the RF signal through a power amplifier prior to the signal being radiated across the air interface via the antenna  1326 . 
     In various embodiments, the RF interface circuitry  1308  may be configured to transmit/receive signals in a manner compatible with NR access technologies. 
     The antenna  1326  may include antenna elements to convert electrical signals into radio waves to travel through the air and to convert received radio waves into electrical signals. The antenna elements may be arranged into one or more antenna panels. The antenna  1326  may have antenna panels that are omnidirectional, directional, or a combination thereof to enable beamforming and multiple input, multiple output communications. The 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. The antenna  1326  may have one or more panels designed for specific frequency bands including bands in FR 1  or FR 2 . 
     In some embodiments, the UE  1300  may include the beamforming circuitry  1200  ( FIG.  12   ), where the beamforming circuitry  1200  may be utilized for communication with the UE  1300 . In some embodiments, components of the UE  1300  and the beamforming circuitry may be shared. For example, the antennas  1326  of the UE may include the panel  1   1204  and the panel  2   1208  of the beamforming circuitry  1200 . 
     The user interface circuitry  1316  includes various input/output (I/O) devices designed to enable user interaction with the UE  1300 . The user interface  1316  includes input device circuitry and output device circuitry. Input device circuitry includes any physical or virtual means for accepting an input including, inter alia, one or more physical or virtual buttons (for example, a reset button), a physical keyboard, keypad, mouse, touchpad, touchscreen, microphones, scanner, headset, or the like. The output device circuitry includes any physical or virtual means for showing information or otherwise conveying information, such as sensor readings, actuator position(s), or other like information. Output device circuitry may include any number or combinations of audio or visual display, including, inter alia, one or more simple visual outputs/indicators (for example, binary status indicators such as light emitting diodes “LEDs” and multi-character visual outputs, or more complex outputs such as display devices or touchscreens (for example, liquid crystal displays (LCDs), LED displays, quantum dot displays, projectors, etc.), with the output of characters, graphics, multimedia objects, and the like being generated or produced from the operation of the UE  1300 . 
     The sensors  1320  may include devices, modules, or subsystems whose purpose is to detect events or changes in its environment and send the information (sensor data) about the detected events to some other device, module, subsystem, etc. Examples of such sensors include, inter alia, inertia measurement units comprising accelerometers, gyroscopes, or magnetometers; microelectromechanical systems or nanoelectromechanical systems comprising 3-axis accelerometers, 3-axis gyroscopes, or magnetometers; level sensors; flow sensors; temperature sensors (for example, thermistors); pressure sensors; 
     barometric pressure sensors; gravimeters; altimeters; image capture devices (for example, cameras or lensless apertures); light detection and ranging sensors; proximity sensors (for example, infrared radiation detector and the like); depth sensors; ambient light sensors; ultrasonic transceivers; microphones or other like audio capture devices; etc. 
     The driver circuitry  1322  may include software and hardware elements that operate to control particular devices that are embedded in the UE  1300 , attached to the UE  1300 , or otherwise communicatively coupled with the UE  1300 . The driver circuitry  1322  may include individual drivers allowing other components to interact with or control various input/output (I/O) devices that may be present within, or connected to, the UE  1300 . For example, driver circuitry  1322  may include a display driver to control and allow access to a display device, a touchscreen driver to control and allow access to a touchscreen interface, sensor drivers to obtain sensor readings of sensor circuitry  1320  and control and allow access to sensor circuitry  1320 , drivers to obtain actuator positions of electro-mechanic components or control and allow access to the electro-mechanic components, a camera driver to control and allow access to an embedded image capture device, audio drivers to control and allow access to one or more audio devices. 
     The PMIC  1324  may manage power provided to various components of the UE  1300 . In particular, with respect to the processors  1304 , the PMIC  1324  may control power-source selection, voltage scaling, battery charging, or DC-to-DC conversion. 
     In some embodiments, the PMIC  1324  may control, or otherwise be part of, various power saving mechanisms of the UE  1300 . For example, if the platform UE is in an RRC_Connected state, where it is still connected to the RAN node as it expects to receive traffic shortly, then it may enter a state known as Discontinuous Reception Mode (DRX) after a period of inactivity. During this state, the UE  1300  may power down for brief intervals of time and thus save power. If there is no data traffic activity for an extended period of time, then the UE  1300  may transition off to an RRC_Idle state, where it disconnects from the network and does not perform operations such as channel quality feedback, handover, etc. The UE  1300  goes into a very low power state and it performs paging where again it periodically wakes up to listen to the network and then powers down again. The UE  1300  may not receive data in this state; in order to receive data, it must transition back to RRC_Connected state. An additional power saving mode may allow a device to be unavailable to the network for periods longer than a paging interval (ranging from seconds to a few hours). During this time, the device is totally unreachable to the network and may power down completely. Any data sent during this time incurs a large delay and it is assumed the delay is acceptable. 
     A battery  1328  may power the UE  1300 , although in some examples the UE  1300  may be mounted deployed in a fixed location, and may have a power supply coupled to an electrical grid. The 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, and the like. In some implementations, such as in vehicle-based applications, the battery  1328  may be a typical lead-acid automotive battery. 
       FIG.  14    illustrates an example gNB  1400  in accordance with some embodiments. The gNB  1400  may include processors  1404 , RF interface circuitry  1408 , core network (CN) interface circuitry  1412 , memory/storage circuitry  1416 , and antenna structure  1426 . 
     The components of the gNB  1400  may be coupled with various other components over one or more interconnects  1428 . 
     The processors  1404 , RF interface circuitry  1408 , memory/storage circuitry  1416  (including communication protocol stack  1410 ), antenna structure  1426 , and interconnects  1428  may be similar to like-named elements shown and described with respect to  FIG.  13   . 
     The CN interface circuitry  1412  may provide connectivity to a core network, for example, a 5th Generation Core network (5GC) using a 5GC-compatible network interface protocol such as carrier Ethernet protocols, or some other suitable protocol. Network connectivity may be provided to/from the gNB  1400  via a fiber optic or wireless backhaul. The CN interface circuitry  1412  may include one or more dedicated processors or FPGAs to communicate using one or more of the aforementioned protocols. In some implementations, the CN interface circuitry  1412  may include multiple controllers to provide connectivity to other networks using the same or different protocols. 
     It is well understood that the use of personally identifiable information should follow privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. In particular, personally identifiable information data should be managed and handled so as to minimize risks of unintentional or unauthorized access or use, and the nature of authorized use should be clearly indicated to users. 
     For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, or methods as set forth in the example section below. For example, the baseband circuitry as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below. For another example, circuitry associated with a UE, base station, network element, etc. as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below in the example section. 
     EXAMPLES 
     In the following sections, further exemplary embodiments are provided. 
     Example 1 may include a method, comprising performing a plurality of beam measurements corresponding to a beam received by the 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 to a plurality of global AoA estimates, determining a global AoA based on the plurality of global AoA estimates, and utilizing the global AoA for identification of a beam received from a base station. 
     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 to a first set of global AoA estimates of the plurality of global AoA estimates, the first set of local AoA estimates to correspond to a first time, converting a second set of local AoA estimates of the plurality of local AoA estimates to a second set of global AoA estimates of the plurality of global AoA estimates, the second set of local AoA estimates to correspond to a second time, and determining that the global AoA corresponds to a first global AoA estimate of the first set of global AoA estimates and a second global AoA estimate of the second set of global AoA estimates based on a determination that the first global AoA estimate overlaps with the second global AoA estimate. 
     Example 3 may include the method of example 1, wherein the plurality of global AoA estimates have a corresponding plurality of probabilities of being an actual global AoA, and wherein determining the global AoA includes determining a global AoA estimate with a greatest probability from the corresponding plurality of probabilities of the plurality of global AoA estimates, and setting the global AoA to the global AoA estimate with the greatest probability. 
     Example 4 may include the method of example 1, further comprising determining whether a reliability has been achieved for the global AoA based on a probability of the global AoA being an actual global AoA, and determining which of a sensor based AoA estimation state or a sensor assisted beam tracking state is to be implemented based on whether the reliability has been achieved. 
     Example 5 may include the method of example 4, wherein the method includes determining that the reliability has been achieved, wherein the sensor assisted beam tracking state is to be implemented based on the reliability being achieved, and the method further includes converting the global AoA to a local anchor, and maintaining a local AoA based on the local anchor and sensor data from a sensor of the UE while the sensor assisted beam tracking state is implemented. 
     Example 6 may include the method of example 5, wherein the plurality of beam measurements is 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 comprises performing a second plurality of beam measurements corresponding to the beam while the sensor assisted beam tracking state is implemented, determining a second plurality of local AoA estimates corresponding to the beam based on the second plurality of beam measurements, converting the second plurality of local AoA estimates to 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. 
     Example 7 may include the method of example 1, further comprising generating a grid arrangement in a local coordinate system (LCS), wherein determining the plurality of local AoA estimates includes determining local grid positions for the plurality of local AoA estimates based on the plurality of beam measurements and the grid arrangement in the LCS, and generating a grid arrangement in a global coordinate system (GCS), wherein converting the plurality of local AoA estimates to the plurality of global AoA estimates includes determining global grid positions for the plurality of global AoA estimates based on the local grid positions for the plurality of local AoA estimates. 
     Example 8 may include the method of example 1, wherein the plurality of local AoA estimates are established in a grid system for a local coordinate system (LCS), 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 grid system for the LCS to a grid system for a global coordinate system (GCS). 
     Example 9 may include the method of example 1, wherein converting the plurality of local AoA estimates to the plurality of global AoA estimates includes determining sensor data corresponding to each local AoA estimate of the plurality of local AoA estimates, and converting the plurality of local AoA estimates to the plurality of global AoA estimates based on the sensor data corresponding to each local AoA estimate of the plurality of local AoA estimates. 
     Example 10 may include a method, comprising performing a plurality of beam measurements corresponding to a beam received by a UE, determining orientations 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 to a plurality of global AoA estimates based on the orientations of the UE corresponding to each of the plurality of beam measurements, and determining a global AoA based on overlap of the plurality of global AoA estimates. 
     Example 11 may include the method of example 10, further comprising determining probabilities for each local AoA estimate of the plurality of local AoA estimates based on the plurality of beam measurements, and determining probabilities for each global AoA estimate of the plurality of global AoA estimates based on the probabilities for each local AoA estimate of the plurality of local AoA estimates, wherein determining the global AoA includes determining that a probability for the global AoA is a largest probability of AoA estimates within a global coordinate system (GCS) based on the probabilities for each global AoA estimate of the plurality of global AoA estimates. 
     Example 12 may include the method example 11, wherein determining the probabilities for each local AoA estimate of the plurality of local AoA estimates includes determining the probabilities for each local AoA estimate of the plurality of local AoA estimates based on reference signal received power (RSRP) or signal to interference and noise ratio (SINR) for the plurality of local AoA estimates from the plurality of beam measurements. 
     Example 13 may include the method of example 11, further comprising determining whether the global AoA meets a reliability based on the probability for the global AoA being greater than a predetermined threshold, and determining whether the UE is to operate in a sensor based AoA estimation state or a sensor assisted beam tracking state based on whether the global AoA meets the reliability. 
     Example 14 may include the method of example 13, further comprising entering the sensor assisted beam tracking state based on a determination that the global AoA meets the reliability, converting the global AoA to a local anchor, and maintaining a local AoA based on the local anchor and sensor data related to the rotation of the UE. 
     Example 15 may include the method of example 13, further comprising entering the sensor based AoA estimation state based on a determination that the global AoA fails to meet the reliability. 
     Example 16 may include the method of example 10, wherein determining the global AoA includes determining a position of a greatest number of the plurality of global AoA estimates that overlap, wherein the global AoA is determined to be located at the position. 
     Example 17 may include the method example 10, wherein converting the plurality of local AoA estimates to the plurality of global AoA estimates includes converting the plurality of local AoA estimates from a local coordinate system (LCS) to a global coordinate system (GCS) to produce the plurality of global AoA estimates. 
     Example 18 may include a method for angle of arrival (AoA) determination, comprising transmitting, by a base station, a plurality of training signals to be utilized by a user equipment (UE) to determine a plurality of local AoA estimates for determination of a beam to be utilized for the UE, identifying, by the base station received from the UE, an indication of the beam to be utilized for communication with the UE, the beam determined based on the plurality of local AoA estimates determined from the plurality of training signals, and utilizing, by the base station, the beam for transmissions to the UE. 
     Example 19 may include the method of example 18, further comprising transmitting, by the base station, an indication of an orientation of a global coordinate system (GCS) to the UE, the GCS to be utilized by the UE for determining the beam. 
     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 sweeping approach or a probing approach. 
     Example 21 may include an apparatus comprising means to perform one or more elements of a method described in or related to any of examples 1-20, or any other method or process described herein. 
     Example 22 may include one or more non-transitory computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform one or more elements of a method described in or related to any of examples 1-20, or any other method or process described herein. 
     Example 23 may include an apparatus comprising logic, modules, or circuitry to perform one or more elements of a method described in or related to any of examples 1-20, or any other method or process described herein. 
     Example 24 may include a method, technique, or process as described in or related to any of examples 1-20, or portions or parts thereof. 
     Example 25 may include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform the method, techniques, or process as described in or related to any of examples 1-20, or portions thereof. 
     Example 26 may include a signal as described in or related to any of examples 1-20, or portions or parts thereof. 
     Example 27 may include a datagram, information element, packet, frame, segment, PDU, or message as described in or related to any of examples 1-20, or portions or parts thereof, or otherwise described in the present disclosure. 
     Example 28 may include a signal encoded with data as described in or related to any of examples 1-20, or portions or parts thereof, or otherwise described in the present disclosure. 
     Example 29 may include a signal encoded with a datagram, IE, packet, frame, segment, PDU, or message as described in or related to any of examples 1-20, or portions or parts thereof, or otherwise described in the present disclosure. 
     Example 30 may include an electromagnetic signal carrying computer-readable instructions, wherein execution of the computer-readable instructions by one or more processors is to cause the one or more processors to perform the method, techniques, or process as described in or related to any of examples 1-20, or portions thereof. 
     Example 31 may include a computer program comprising instructions, wherein execution of the program by a processing element is to cause the processing element to carry out the method, techniques, or process as described in or related to any of examples 1-20, or portions thereof. 
     Example 32 may include a signal in a wireless network as shown and described herein. 
     Example 33 may include a method of communicating in a wireless network as shown and described herein. 
     Example 34 may include a system for providing wireless communication as shown and described herein. 
     Example 35 may include a device for providing wireless communication as shown and described herein. 
     Any of the above-described examples may be combined with any other example (or combination of examples), unless explicitly stated otherwise. The foregoing description of one or more implementations provides illustration and description, but is not intended to be exhaustive or to limit the scope of embodiments to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of various embodiments. 
     Although the embodiments above have been described in considerable detail, numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

Metadata:
Filing Date: 20220801
Publication Date: 20240206
Grant Date: 20240206
Priority Date: 20210903
Inventors: SUN, YAKUN
NABAR, ROHIT U.
DOGAN, MITHAT C.
SUBRAHMANYA, PARVATHANATHAN
YAN, Han
JALLOUL, LOUAY
Assignee: APPLE INC
CPC Classifications: [{"code": "H04B7/086", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04B7/043", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B7/0639", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B7/0695", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B7/0695", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04B7/086", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04B7/088", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B7/086", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01S3/04", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W64/006", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04B7/0695", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B7/0639", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B7/043", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 85349462