Autonomous beam switching

Abstract

Various embodiments include a method for autonomous beam switching by a wireless device. A processor of the wireless device may: measure signal parameters of signals received from a first synchronization signal block (SSB) beam of a base station monitored by the wireless device and other SSB beams of the base station, determine whether a difference in the measured signal parameters of the signals received from the first SSB beam and another SSB beam of the base station satisfies a signal quality difference threshold, and in response to determining that a difference in the measured signal parameters of the signals received from the first SSB beam and the second SSB beam satisfies the signal quality difference threshold, autonomously switch to the second SSB beam as a serving beam. The signal quality difference threshold may be listed in a table in a memory or determined via machine learning.

Description

[0001] Related Applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 016,884, filed on April 28, 2020, entitled “Autonomous Beam Switching,” the entire contents of which are incorporated herein by reference for all purposes. Background Art

[0003] Fifth generation (5G) New Radio (NR) systems can provide high data rate communication services to mobile devices. However, the frequency bands used to provide NR services, such as millimeter wave frequencies, are susceptible to rapid channel variations and suffer from free space path loss and atmospheric absorption. To address these challenges, NR base stations and mobile devices can use highly directional antennas and beamforming to achieve sufficient link budgets in wide area networks.

[0004] In 5G NR systems, the synchronization signal block (SSB) provides information such as the primary synchronization signal (PSS), the secondary synchronization signal (SSS), and the physical broadcast channel demodulation reference signal (PBCH DMRS), which enables wireless devices to synchronize with and receive certain signals of beams from a base station. Typically, a wireless device can measure various aspects of signals received from one or more beams of a base station and provide a report of such measurements to the base station. Based on the report from the wireless device, the base station instructs the wireless device via a medium access control-control element (MAC-CE) message as to which beam to use. Summary of the invention

[0005] Various aspects include systems and methods for autonomous beam switching performed by a processor of a wireless device. Various aspects may include: measuring signal parameters of signals received from a first synchronization signal block (SSB) beam as a service beam of a base station monitored by the wireless device and one or more other SSB beams of the base station; determining whether a measured signal difference of signals received from a first SSB beam and a second SSB beam of the base station meets a signal quality difference threshold; and in response to determining that a measured signal parameter difference of signals received from the first SSB beam and the second SSB beam of the base station meets the signal quality difference threshold, autonomously switching to monitor a second SSB beam of the base station as a service beam.

[0006] In some aspects, determining whether a difference in measured signal parameters of signals received from a first SSB beam and a second SSB beam of a base station satisfies a signal quality difference threshold may include determining whether a difference in measured signal parameters of signals received from the first SSB beam and the second SSB beam of the base station satisfies a signal quality difference threshold before receiving a MAC control element (MAC-CE) indicating that the wireless device switches the serving beam or without receiving the MAC-CE. In some aspects, autonomously switching to monitor the second SSB beam of the base station as a serving beam may include switching to monitor the second SSB beam of the base station before receiving the MAC-CE or without receiving the MAC-CE. Some aspects may include receiving data from a signal received from a first data beam quasi co-located (QCL) with the first SSB beam before autonomously switching to monitor the second SSB beam, and receiving data from a signal received from a second data beam quasi co-located (QCL) with the second SSB beam after autonomously switching to monitor the second SSB beam.

[0007] Some aspects may include obtaining a poor signal quality threshold from a data table stored in a memory using the location of the wireless device as a lookup index. Some aspects may include determining the poor signal quality threshold using a trained neural network. In some aspects, determining the poor signal quality threshold using a trained neural network may include dynamically applying a plurality of parameters including the location of the wireless device to the trained neural network, and receiving the poor signal quality threshold as an output.

[0008] Some aspects may include refining a trained neural network by receiving an instruction from a second base station to switch from monitoring one SSB beam of the second base station to another SSB beam of the second base station, determining a change in link quality (e.g., data throughput) resulting from the switch from monitoring one SSB beam of the second base station to another SSB beam of the second base station, and correlating the change in link quality (e.g., data throughput) resulting from the switch, a location of the wireless device at the time of the switch, and measured signal parameters of signals received from multiple SSB beams of the base station at the time of the switch to refine the trained neural network.

[0009] Some aspects may include training a neural network by determining a link quality (e.g., data throughput) of a signal received from each SSB beam of a base station at a location of the wireless device, and training the neural network using the determined link quality of the signal received from each SSB beam of the base station at the location of the wireless device. Some aspects may include repeatedly moving the wireless device to a new location, determining a link quality (e.g., data throughput) of a signal received from each SSB beam of the base station at each new location, and training the neural network using the determined link quality of the signal received from each SSB beam of the base station at each new location. Some aspects may include measuring one or more parameters other than the location of the wireless device at each new location. In these aspects, training the neural network using the determined link quality of the signal received from each SSB beam of the base station at each new location may include training the neural network using the determined link quality of the signal received from each SSB beam of the base station at each new location and the measured one or more parameters.

[0010] In some aspects, measuring the one or more parameters may include determining or measuring one or more of the following: a serving SSB identifier, a cell identifier of the serving cell, a signal strength of a signal received from each SSB beam, a signal quality of a signal received from each SSB beam, a difference in reference signal received power (RSRP) of a signal received from each SSB beam, a difference in reference signal received quality (RSRQ) of a signal received from each SSB beam, a difference in signal-to-noise ratio (SNR) of a signal received from each SSB beam, an initial block error rate (iBLER) of a signal received from each SSB beam, a residual block error rate (rBLER) of a signal received from each SSB beam, and mobility of the wireless device at the time of measurement, orientation of the wireless device at the time of measurement, number of detectable SSB beams, number of configured SSB beams, beam status reports configured by the base station, number of SSB status reports sent by the wireless device to the base station, number and frequency of autonomous SSB switches performed by the wireless device, number and frequency of SSB beam switches performed in response to instructions from the base station, average duration between transmission of beam status reports and receipt of beam switch instructions from the base station, beam failure detection and recovery statistics, frequency and time tracking loop statistics, the mobile network code (MNC) or mobile country code (MCC) of the communications network associated with the base station, or the infrastructure provider associated with the base station.

[0011] A further aspect may include a wireless device having a processor configured to perform one or more operations of any of the methods outlined above. A further aspect may include a non-transitory processor-readable storage medium having processor-executable instructions stored thereon, the processor-executable instructions configured to cause a processor of the wireless device to perform the operations of any of the methods outlined above. A further aspect includes a wireless device having components for performing the functions of any of the methods outlined above. A further aspect includes a system on a chip for a wireless device including a processor configured to perform one or more operations of any of the methods outlined above.

[0012] Further aspects include systems and methods executed by a processor of a computing device for training a neural network for use by a wireless device in autonomous beam switching. Various aspects may include: determining a data throughput or other measure of link quality of a signal received from each of a plurality of synchronization signal block (SSB) beams of a base station at a location of the computing device; training the neural network using the determined throughput or other measure of link quality of the signal received from each SSB beam of the base station at the location of the wireless device; and providing the trained neural network to the wireless device in a configuration that enables the wireless device to determine a signal quality difference threshold, the signal quality difference threshold being used to determine whether to autonomously switch a monitored SSB beam of the base station.

[0013] Some aspects may include: repeatedly moving the computing device to new locations; determining a data throughput or other measure of link quality of signals received from each SSB beam of a base station at each new location; and training the neural network using the determined throughput of each SSB beam of the base station at each new location. Some aspects may include: measuring one or more parameters other than the location of the wireless device at each new location. In such aspects, training the neural network using the determined throughput or other measure of link quality of signals received from each SSB beam of a base station at each new location may include training the neural network using the determined throughput or other measure of link quality of signals received from each SSB beam of a base station at each new location and the measured one or more parameters.

[0014] In some aspects, measuring the plurality of parameters may include determining or measuring one or more of the following: a serving SSB identifier, a cell identifier of the serving cell, a signal strength of a signal received from each SSB beam, a signal quality of a signal received from each SSB beam, a difference in reference signal received power (RSRP) of a signal received from each SSB beam, a difference in reference signal received quality (RSRQ) of a signal received from each SSB beam, a difference in signal-to-noise ratio (SNR) of a signal received from each SSB beam, an initial block error rate (iBLER) of a signal received from each SSB beam, a difference in reference signal received quality (RSRQ) of a signal received from each SSB beam, a difference in signal-to-noise ratio (SNR) of a signal received from each SSB beam, a difference in initial block error rate (iBLER) of a signal received from each SSB beam, a difference in reference signal received quality (RSRQ) of a signal received from each SSB beam, a difference in reference signal received power (RSRP) of a signal received from each SSB beam, a difference in reference signal received quality (RSRQ ... power (RSRP) of a signal received from each SSB beam, a difference in reference signal received power (RSRP) of a signal received from each SSB beam, a difference in reference signal received power (RSRP) of a signal received from residual block error rate (rBLER) of the received signal, mobility of the device at the measurement time, orientation of the device at the measurement time, number of detectable SSB beams, number of configured SSB beams, beam status reports configured by the base station, number of SSB status reports sent by the wireless device to the base station, average duration between the transmission of a beam status report and the receipt of a beam switching instruction from the base station, beam failure detection and recovery statistics, frequency and time tracking loop statistics, mobile network code (MNC) or mobile country code (MCC) of the communications network associated with the base station, or the infrastructure provider associated with the base station.

[0015] A further aspect may include a computing device having a processor configured to perform one or more operations of any of the methods outlined above. A further aspect may include a non-transitory processor-readable storage medium having processor-executable instructions stored thereon, the processor-executable instructions configured to cause a processor of a computing device to perform operations of any of the methods outlined above. A further aspect includes a computing device having components for performing the functions of any of the methods outlined above. A further aspect includes a system on a chip for a computing device including a processor configured to perform one or more operations of any of the methods outlined above. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated herein and constitute a part of this specification, illustrate exemplary embodiments of the claims and, together with the general description given above and the detailed description given below, serve to explain features of the claims.

[0017] Figure 1 is a system block diagram illustrating an example communication system suitable for implementing any of the various embodiments.

[0018] Figure 2 is a component block diagram illustrating an example computing and wireless modem system suitable for implementing any of the various embodiments.

[0019] Figure 3 is a component block diagram illustrating a software architecture including a wireless protocol stack for user and control planes in wireless communications suitable for implementing any of the various embodiments.

[0020] Figure 4A and 4B is a component block diagram illustrating a system configured for managing information transmission for wireless communications performed by a processor of a base station in accordance with various embodiments.

[0021] Figure 5A is a block diagram illustrating received SSB signal strength received by a wireless device from a base station in accordance with various embodiments.

[0022] Figure 5B is a message flow diagram illustrating a method 550 for autonomous beam switching according to various embodiments.

[0023] Figure 6 is a process flow diagram illustrating a method 600 that may be performed by a processor of a wireless device for autonomous beam switching in accordance with various embodiments.

[0024] Figure 7-Figure 13 is a process flow diagram illustrating operations that may be performed by a processor of a wireless device as part of a method for autonomous beam switching in accordance with some embodiments.

[0025] Fig.14 is a process flow diagram illustrating a method that may be performed by a processor of a computing device for training a neural network for use by a wireless device in autonomous beam switching in accordance with various embodiments.

[0026] Fig.15 and Fig.16 is a process flow diagram illustrating operations that may be performed by a processor of a computing device for training a neural network for use by a wireless device in autonomous beam switching in accordance with some embodiments.

[0027] Fig.17 is a component block diagram of a wireless device suitable for use with some embodiments.

[0028] Fig.18 is a component block diagram of a wireless computing device suitable for use with some embodiments. DETAILED DESCRIPTION

[0029] Various embodiments will be described in detail with reference to the accompanying drawings. Wherever possible, the same reference numerals will be used throughout the drawings to represent the same or similar parts. Reference to specific examples and embodiments is for illustrative purposes and is not intended to limit the scope of the claims.

[0030] Various embodiments include methods for autonomous beam switching by a wireless device. Various embodiments enable a wireless device to determine when to autonomously switch to another SSB beam of a base station (or other network element) based on SSB beam signal parameters determined by the wireless device. A processor of the wireless device may measure signal parameters of a signal received from an SSB beam of a base station, and determine whether the difference in measured signal parameters of signals received from a first SSB beam and a second SSB beam differs by at least a signal quality difference threshold. If this condition is met, the wireless device may autonomously switch to monitor a second SSB beam of the base station. An appropriate signal quality difference threshold for making this determination may depend on the location and / or wireless network. Thus, the signal quality difference threshold used by the wireless device may be dynamically determined based on one or more parameters including the location of the device. In some embodiments, the signal quality difference threshold may be determined using a table lookup process, or the signal quality difference threshold may be determined using a trained neural network, which is generated using machine learning techniques and loaded onto the wireless device.

[0031] The term "wireless device" is used herein to refer to cellular telephones, smart phones, portable computing devices, personal or mobile multimedia players, laptop computers, tablet computers, smartbooks, ultrabooks, handheld computers, wireless telecommunication receivers, multimedia Internet-enabled cellular telephones, medical devices and equipment, biometric sensors / devices, wearable devices (including smart watches, smart clothing, smart glasses, smart wristbands, smart jewelry (e.g., smart rings, smart bracelets, etc.)), entertainment devices (e.g., wireless game controllers, music and video players, satellite radios, etc.), wireless network-enabled Internet of Things (IoT) devices (including smart meters / sensors, industrial manufacturing equipment, large and small machines and appliances for home or business use), wireless communication elements within autonomous and semi-autonomous vehicles, wireless devices fixed to or incorporated into various mobile platforms, and any or all of similar electronic devices that include memory, wireless communication components, and programmable processors.

[0032] The term "system on chip" (SOC) is used herein to refer to a single integrated circuit (IC) chip that contains multiple resources and / or processors integrated on a single substrate. A single SOC may contain circuits for digital, analog, mixed signal, and radio frequency functions. A single SOC may also include any number of general and / or special processors (digital signal processors, modem processors, video processors, etc.), memory blocks (e.g., ROM, RAM, flash memory, etc.), and resources (e.g., timers, voltage regulators, oscillators, etc.). The SOC may also include software for controlling the integrated resources and processors and for controlling peripheral devices.

[0033] The term "system-in-package" (SIP) may be used herein to refer to a single module or package that contains multiple resources, computing units, cores, and / or processors on two or more IC chips, substrates, or SOCs. For example, a SIP may include a single substrate on which multiple IC chips or semiconductor dies are stacked in a vertical configuration. Similarly, a SIP may include one or more multi-chip modules (MCMs) on which multiple ICs or semiconductor dies are packaged into a unified substrate. A SIP may also include multiple independent SOCs that are coupled together via high-speed communication circuits and packaged in close proximity (such as on a single motherboard or in a single wireless device). The proximity of SOCs facilitates high-speed communication and sharing of memory and resources.

[0034] As used herein, "beam" refers to a signal formed at a transmitting device using beamforming or beamsteering techniques, which are applied via a combination of physical equipment and signal processing, which are variously referred to as beamforming functions, mapping functions, or spatial filters. Beam reception by a receiving device may involve configuring the physical equipment and signal processing of the receiving device to receive the signal sent by the transmitting device in a beam. In some cases, beam reception by a receiving device may also involve configuring the physical equipment and signal processing of the receiving device via a beamforming function, a mapping function, or a spatial filter to preferentially receive signals (e.g., with enhanced gain) from a particular direction (e.g., in a direction aligned with the transmitting device).

[0035] As used herein, "monitoring an SSB beam" refers to a wireless device receiving a signal from the monitored SSB beam that serves as a source of information that enables the wireless device to synchronize with and receive certain signals sent in the beam from a base station to facilitate communication with the base station (e.g., as a serving beam). Such information may include a primary synchronization signal (PSS), a secondary synchronization signal (SSS), and a physical broadcast channel demodulation reference signal (PBCH DM-RS). Although the wireless device may perform measurements of other SSB beams to determine one or more parameters of the other SSB beams, "monitoring" an SSB beam refers to performing operations different from measuring parameters of the SSB beam.

[0036] As used herein, a “service beam” refers to a beam of RF signals used to provide data and / or control information to a wireless device, which may include a primary synchronization signal (PSS), a secondary synchronization signal (SSS), and a physical broadcast channel demodulation reference signal (PBCH DM-RS).

[0037] Typically, the base station instructs the wireless device as to which beam to use via a Medium Access Control - Control Element (MAC-CE) message. However, this functionality relies on signaling from the base station. If the base station fails to send a beam instruction, or the wireless device fails to receive the instruction, the wireless device may continue to receive signals from an SSB beam that provides poorer reception by the wireless device than another SSB beam sent by the base station, and as a result, signal quality and / or data throughput is reduced compared to being able to receive signals from a more superior SSB beam of the base station.

[0038] Various embodiments include methods and wireless devices configured to perform methods for autonomous beam switching. In various embodiments, the wireless device may perform autonomous beam switching without a beam switching instruction or beam switching information from a base station (e.g., via a MAC-CE message). In various embodiments, the wireless device may measure signal parameters of signals received from multiple SSB beams of a base station on which the wireless device resides, including signals received from a first SSB beam of a base station that the wireless device is currently monitoring as a serving beam. The wireless device may determine whether a difference in measured signal parameters of signals received from a first SSB beam and a second SSB beam of the base station meets a signal quality difference threshold. In response to determining that a difference in measured signal parameters of signals received from a first SSB beam and a second SSB beam of the base station meets a signal quality difference threshold, the wireless device may autonomously switch to monitoring the second SSB beam of the base station as a serving beam. In some embodiments, the wireless device may receive data in a signal received from a first data beam that is quasi co-located (QCL) with a first SSB beam, and after autonomously switching to monitor a second SSB beam, the wireless device may receive data in a signal received from a second data beam that is QCL with the second SSB beam.

[0039] The poor signal quality threshold is a value related to the SSB beam and network conditions that indicates that the wireless device can benefit (e.g., achieve greater data throughput) by autonomously switching to an SSB beam that provides better signal reception. The poor signal quality threshold can be dynamically determined based on the location of the wireless device and other factors, because the benefit in data throughput from autonomously switching SSB beams varies from location to location and from network to network. In some embodiments, the wireless device can obtain the poor signal quality threshold from a data table stored in a memory using the location of the wireless device as a lookup index. In some embodiments, the wireless device can dynamically determine the poor signal quality threshold using a trained neural network. In such an embodiment, the wireless device can dynamically apply multiple parameters including the location of the wireless device to the trained neural network. In such an embodiment, the wireless device can receive the poor signal quality threshold as an output from the trained neural network.

[0040] In some embodiments using a trained neural network, the wireless device may use additional information received during operation to refine or "tune" the neural network. For example, the wireless device may refine the trained neural network by receiving an instruction from the base station (or a second base station) to switch from monitoring one SSB beam to another SSB beam. The wireless device may determine a change in link quality (e.g., data throughput) resulting from the switch from monitoring one SSB beam to another SSB beam, and correlate the change in link quality (e.g., data throughput) resulting from the switch, the location of the wireless device at the time of the switch, and measured signal parameters of signals received from multiple SSB beams of the base station at the time of the switch to refine the trained neural network.

[0041] In some embodiments, the training process of the neural network may include determining the link quality (e.g., data throughput) of the signal received from each SSB beam of the base station at the location of the wireless device (e.g., by sequentially selecting each or some of the SSB beams and measuring the resulting data throughput or other link quality parameter), and using the determined throughput or other link quality parameter of the signal received from each SSB beam of the base station at the location of the wireless device to train the neural network. Such embodiments may also include repeatedly moving the wireless device to a new location, determining the data throughput or other link quality parameter of the signal received from each or some of the SSB beams of the base station at each new location, and using the determined throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location to train the neural network. Such embodiments may also include measuring one or more parameters other than the location of the wireless device at each new location, and using the determined throughput of the signal received from each SSB beam of the base station and the measured one or more parameters at each new location to train the neural network.

[0042] In various embodiments, the measured one or more parameters applied to the trained neural network and / or the trained neural network may include one or more of the following: a serving SSB identifier, a cell identifier of a serving cell, a signal strength of a signal received from each SSB beam, a signal quality of a signal received from each SSB beam, a difference in reference signal received power (RSRP) of a signal received from each SSB beam, a difference in reference signal received quality (RSRQ) of a signal received from each SSB beam, a difference in signal-to-noise ratio (SNR) of a signal received from each SSB beam, an initial block error rate (iBLER) of a signal received from each SSB beam, a residual block error rate of a signal received from each SSB beam (rBLER), mobility of the wireless device at the measurement time, orientation of the wireless device at the measurement time, number of detectable SSB beams, number of configured SSB beams, beam status reports configured by the base station, number of SSB status reports sent by the wireless device to the base station, number and frequency of autonomous SSB switches performed by the wireless device, number and frequency of SSB beam switches performed in response to instructions from the base station, average duration between the transmission of beam status reports and the receipt of beam switching instructions from the base station, beam failure detection and recovery statistics, frequency and time tracking loop statistics, mobile network code (MNC) or mobile country code (MCC) of the communication network associated with the base station, or the infrastructure provider associated with the base station.

[0043] Further embodiments include methods and computing devices configured to perform methods for training a neural network for use by a wireless device in autonomous beam switching. In such embodiments, the computing device may: access a base station at a given location; determine a data throughput or other link quality parameter of a signal received from each of a plurality of SSB signal beams, the plurality of SSB signal beams being signals received from each base station accessible at the location; and train the neural network using the determined throughput or other link quality parameter of a signal received from each of the SSB beams of the signal received from each of the base stations at the location of the wireless device, using the location and / or other measured parameters as parameters related to the SSB beam throughput or other link quality parameter. Specifically, the computing device may determine a difference in signal quality of the signals received from the respective SSB beams, and determine a resulting change in data throughput or other link quality parameter achieved by changing from one SSB beam to another, and use the result to train the neural network. Training the neural network may include repeatedly moving the wireless device to new locations, determining a data throughput or other link quality parameter of signals received from each SSB beam of the base station at each new location, and training the neural network using the determined throughput or other link quality parameter of signals received from each SSB beam of the base station at each new location. The computing device may provide the trained neural network to the wireless device in a configuration that enables the wireless device to determine a poor signal quality threshold useful for determining whether to autonomously switch a monitored SSB beam of the base station.

[0044] In some embodiments, training the neural network by the computing device may include measuring one or more parameters in addition to the location of the wireless device at each new location, and training the neural network using the determined throughput or other link quality parameter of the signal received from each of the SSB beams of the signal received from each of the base stations at each new location and the measured one or more parameters. In various embodiments, the measured parameters may include one or more of the following: a serving SSB identifier, a cell identifier of the serving cell, a signal strength of the signal received from each SSB beam, a signal quality of the signal received from each SSB beam, a difference in reference signal received power (RSRP) of the signal received from each SSB beam, a difference in reference signal received quality (RSRQ) of the signal received from each SSB beam, a difference in signal-to-noise ratio (SNR) of the signal received from each SSB beam, an initial block error rate (iBLER) of the signal received from each SSB beam, a residual block error rate (rBLER) of the signal received from each SSB beam, a difference in the signal received from the SSB beam at the measurement time, Mobility of the wireless device, orientation of the wireless device at the measurement time, number of detectable SSB beams, number of configured SSB beams, beam status reports configured by the base station, number of SSB status reports sent by the wireless device to the base station, number and frequency of autonomous SSB switches performed by the wireless device, number and frequency of SSB beam switches performed in response to instructions from the base station, average duration between the transmission of beam status reports and the receipt of beam switching instructions from the base station, beam failure detection and recovery statistics, frequency and time tracking loop statistics, the Mobile Network Code (MNC) or Mobile Country Code (MCC) of the communications network associated with the base station, or the infrastructure provider associated with the base station.

[0045] Various embodiments improve the operation and performance of a wireless device by enabling the device to autonomously utilize an SSB beam that will provide improved data throughput performance or other link quality performance in situations where the wireless network fails to instruct the wireless device to shift to a better SSB beam.

[0046] Figure 1 1 is a system block diagram illustrating an example communication system 100 suitable for implementing any of the various embodiments. The communication system 100 may be a 5G New Radio (NR) network, or any other suitable network such as a Long Term Evolution (LTE) network.

[0047] The communication system 100 may include a heterogeneous network architecture including a core network 140 and various wireless devices (in Figure 1The communication system 100 may also include one or more network computing devices 125 capable of communicating with the wireless devices 120a-120e. In some embodiments, the wireless devices 120a-120e may send data to the network computing device 125 for processing as part of a computing task.

[0048] The communication system 100 may also include several base stations (shown as BS110a, BS110b, BS110c and BS110d) and other network entities. A base station is an entity that communicates with a wireless device and may also be referred to as a NodeB, Node B, LTE evolved Node B (eNB), access point (AP), radio head, transmit receive point (TRP), new radio base station (NR BS), 5G Node B (NB), next generation Node B (gNB), etc. Each base station can provide communication coverage for a specific geographic area. In 3GPP, the term "cell" can refer to the coverage area of ​​a base station, a base station subsystem serving the coverage area, or a combination thereof, depending on the context in which the term is used.

[0049] Base stations 110a-110d may provide communication coverage for a macro cell, a pico cell, a femto cell, another type of cell, or a combination thereof. A macro cell may cover a relatively large geographic area (e.g., a radius of several kilometers) and may allow unrestricted access by wireless devices with a service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by wireless devices with a service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by wireless devices associated with the femto cell (e.g., wireless devices in a closed subscriber group (CSG)). A base station for a macro cell may be referred to as a macro BS. A base station for a pico cell may be referred to as a pico BS. A base station for a femto cell may be referred to as a femto BS or a home BS. In Figure 1 In the example shown in , base station 110a can be a macro BS for macro cell 102a, base station 110b can be a pico BS for pico cell 102b, and base station 110c can be a femto BS for femto cell 102c. Base stations 110a-110d can support one or more (e.g., three) cells. The terms "eNB", "base station", "NR BS", "gNB", "TRP", "AP", "Node B", "5G NB" and "cell" may be used interchangeably herein.

[0050] In some examples, the cell may not be fixed, and the geographic area of ​​the cell may move depending on the location of the mobile base station. In some examples, the base stations 110a-110d may be interconnected to each other and to one or more other base stations or network nodes (not shown) in the communication system 100 via various types of backhaul interfaces, such as direct physical connections, virtual networks, or combinations thereof using any suitable transport networks.

[0051] The base stations 110a-110d may communicate with the core network 140 via a wired or wireless communication link 126. The wireless devices 120a-120e may communicate with the base stations 110a-110d via a wireless communication link 122.

[0052] The wired communication link 126 can use various wired networks (e.g., Ethernet, TV cable, telephone, fiber optic, and other forms of physical network connections), which can use one or more wired communication protocols, such as Ethernet, Point-to-Point Protocol, High-Level Data Link Control (HDLC), Advanced Data Communications Control Protocol (ADCCP), and Transmission Control Protocol / Internet Protocol (TCP / IP).

[0053] The communication system 100 may also include a relay station (e.g., relay BS 110d). A relay station is an entity that can receive transmissions of data from an upstream station (e.g., a base station or wireless device) and send the data to a downstream station (e.g., a wireless device or base station). A relay station may also be a wireless device that is capable of relaying transmissions for other wireless devices. Figure 1 In the example shown, a relay station 110d may communicate with the macro base station 110a and the wireless device 120d to facilitate communication between the base station 110a and the wireless device 120d. A relay station may also be referred to as a relay base station, relay base station, relay, etc.

[0054] The communication system 100 may be a heterogeneous network including different types of base stations (e.g., macro base stations, pico base stations, femto base stations, relay base stations, etc.). These different types of base stations may have different transmit power levels, different coverage areas, and different impacts on interference in the communication system 100. For example, a macro base station may have a high transmit power level (e.g., 5 to 40 watts), while a pico base station, a femto base station, and a relay base station may have a lower transmit power level (e.g., 0.1 to 2 watts).

[0055] A network controller 130 may be coupled to a set of base stations and may provide coordination and control for these base stations. The network controller 130 may communicate with the base stations via a backhaul. The base stations may also communicate with each other, for example, directly or indirectly via a wireless or wired backhaul.

[0056] Wireless devices 120a, 120b, 120c may be dispersed throughout wireless network 100, and each wireless device may be fixed or mobile. A wireless device may also be referred to as an access terminal, terminal, mobile station, subscriber unit, station, or the like.

[0057] The macro base station 110a may communicate with the communication network 140 via a wired or wireless communication link 126. The wireless devices 120a, 120b, 120c may communicate with the base stations 110a-110d via a wireless communication link 122.

[0058] The wireless communication links 122, 124 may include multiple carrier signals, frequencies or frequency bands, each of which may include multiple logical channels. The wireless communication links 122 and 124 may utilize one or more radio access technologies (RATs). Examples of RATs that may be used in the wireless communication links include 3GPP LTE, 3G, 4G, 5G (e.g., NR), GSM, Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMAX), Time Division Multiple Access (TDMA), and other mobile phone communication technology cellular RATs. Other examples of RATs that can be used in one or more of the various wireless communication links 122, 124 within the communication system 100 include medium range protocols (such as Wi-Fi, LTE-U, LTE-Direct, LAA, MuLTEfire) and relatively short range RATs (such as ZigBee, Bluetooth, and Bluetooth Low Energy (LE)).

[0059] Some wireless networks (e.g., LTE) utilize orthogonal frequency division multiplexing (OFDM) on the downlink and single carrier frequency division multiplexing (SC-FDM) on the uplink. OFDM and SC-FDM divide the system bandwidth into multiple (K) orthogonal subcarriers, which are also commonly referred to as tones, bins, etc. Each subcarrier can be modulated with data. Typically, modulation symbols are sent in the frequency domain with OFDM and in the time domain with SC-FDM. The spacing between adjacent subcarriers can be fixed, and the total number of subcarriers (K) can depend on the system bandwidth. For example, the spacing of the subcarriers can be 15kHz, and the minimum resource allocation (called a "resource block") can be 12 subcarriers (or 180kHz). Therefore, for system bandwidths of 1.25, 2.5, 5, 10, or 20 megahertz (MHz), the nominal fast file transfer (FFT) size can be equal to 128, 256, 512, 1024, or 2048, respectively. The system bandwidth can also be divided into subbands. For example, a subband may cover 1.08 MHz (ie, 6 resource blocks), and there may be 1, 2, 4, 8 or 16 subbands for system bandwidths of 1.25, 2.5, 5, 10 or 20 MHz, respectively.

[0060] Although the description of some embodiments may use terms and examples associated with LTE technology, various embodiments may be applicable to other wireless communication systems, such as new radio (NR) or 5G networks. NR can utilize OFDM with a cyclic prefix (CP) on the uplink (UL) and downlink (DL), and includes support for half-duplex operation using time division duplex (TDD). A single component carrier bandwidth of 100 MHz can be supported. NR resource blocks can span 12 subcarriers, where the subcarrier bandwidth is 75 kHz over a duration of 0.1 milliseconds (ms). Each radio frame can consist of 50 subframes with a length of 10 ms. Therefore, each subframe can have a length of 0.2 ms. Each subframe can indicate the link direction (ie, DL or UL) for data transmission, and the link direction of each subframe can be switched dynamically. Each subframe can include DL / UL data and DL / UL control data. Beamforming can be supported, and the beam direction can be dynamically configured. Multiple-input multiple-output (MIMO) transmission with precoding can also be supported. MIMO configurations in the DL can support up to eight transmit antennas with multi-layer DL transmissions of up to eight data streams and up to two data streams per wireless device. Multi-layer transmissions with up to 2 streams per wireless device can be supported. Aggregation of multiple cells can be supported with up to eight serving cells. Alternatively, NR can support an air interface different from the OFDM-based air interface.

[0061] Some wireless devices may be considered to be machine type communication (MTC) or evolved or enhanced machine type communication (eMTC) wireless devices. MTC and eMTC wireless devices include, for example, robots, drones, remote devices, sensors, meters, monitors, location tags, etc., which can communicate with a base station, another device (e.g., a remote device), or some other entity. For example, a wireless node may provide a connection to a network (e.g., a wide area network such as the Internet or a cellular network) or provide a connection to a network via a wired or wireless communication link. Some wireless devices may be considered to be Internet of Things (IP) devices or may be implemented as NB-IoT (narrowband Internet of Things) devices. The wireless devices 120a-120e may be included inside a housing that houses components of the wireless device, such as a processor component, a memory component, a similar component, or a combination thereof.

[0062] Generally, any number of communication systems and any number of wireless networks can be deployed in a given geographic area. Each communication system and wireless network can support a specific radio access technology (RAT) and can operate on one or more frequencies. RAT can also be referred to as radio technology, air interface, etc. Frequency can also be referred to as carrier, frequency channel, etc. Each frequency can support a single RAT in a given geographic area to avoid interference between communication systems of different RATs. In some cases, NR or 5G RAT networks can be deployed.

[0063] In some embodiments, two or more wireless devices 120a-120e (e.g., illustrated as wireless device 120a and wireless device 120e) may communicate directly (e.g., without using base station 110a-110d as an intermediary for communicating with each other) using one or more sidelink channels 124. For example, the wireless devices 120a-120e may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocols (which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, or similar protocols), mesh networks, or similar networks, or a combination thereof. In this case, the wireless devices 120a-120e may perform scheduling operations, resource selection operations, and other operations performed by the base station 110a as described elsewhere herein.

[0064] Figure 2 is a component block diagram illustrating an example computing and wireless modem system 200 suitable for implementing any of the various embodiments. The various embodiments may be implemented on a variety of single-processor and multi-processor computer systems, including system-on-chip (SOC) or system-in-package (SIP).

[0065] refer to Figure 1 and Figure 2 , the illustrated example computing system 200 (which may be a SIP in some embodiments) includes two SOCs 202, 204 coupled to a clock 206, a voltage regulator 208, and a wireless transceiver 266, which is configured to send and receive wireless communications to / from a wireless device such as a base station 110a via an antenna (not shown). In some embodiments, the first SOC 202 operates as a central processing unit (CPU) of the wireless device, which executes instructions of a software application by performing arithmetic, logic, control, and input / output (I / O) operations specified by the instructions. In some embodiments, the second SOC 204 can operate as a dedicated processing unit. For example, the second SOC 204 can operate as a dedicated 5G processing unit responsible for managing high-capacity, high-speed (e.g., 5 Gbps, etc.) and / or very high frequency short wavelength (e.g., 28 GHz millimeter wave spectrum, etc.) communications.

[0066] The first SOC 202 may include a digital signal processor (DSP) 210, a modem processor 212, a graphics processor 214, an application processor 216, one or more coprocessors 218 (e.g., vector coprocessors) connected to one or more of the processors, memory 220, custom circuits 222, system components and resources 224, an interconnect / bus module 226, one or more temperature sensors 230, a thermal management unit 232, and a thermal power envelope (TPE) component 234. The second SOC 204 may include a 5G modem processor 252, a power management unit 254, an interconnect / bus module 264, a plurality of millimeter wave transceivers 256, a memory 258, and various additional processors 260, such as an application processor, a packet processor, and the like.

[0067] Each processor 210, 212, 214, 216, 218, 252, 260 may include one or more cores, and each processor / core may perform operations independently of other processors / cores. For example, the first SOC 202 may include a processor that executes a first type of operating system (e.g., FreeBSD, LINUX, OS X, etc.) and a processor that executes a second type of operating system (e.g., MICROSOFT WINDOWS10). In addition, any or all of the processors 210, 212, 214, 216, 218, 252, 260 may be included as part of a processor cluster architecture (e.g., a synchronous processor cluster architecture, an asynchronous or heterogeneous processor cluster architecture, etc.).

[0068] The first SOC 202 and the second SOC 204 may include various system components, resources, and custom circuits for managing sensor data, analog-to-digital conversion, wireless data transmission, and for performing other specialized operations, such as decoding data packets and processing encoded audio and video signals for presentation in a web browser. For example, the system components and resources 224 of the first SOC 202 may include power amplifiers, voltage regulators, oscillators, phase-locked loops, peripheral bridges, data controllers, memory controllers, system controllers, access ports, timers, and other similar components for supporting processors and software clients running on wireless devices. The system components and resources 224 and / or custom circuits 222 may also include circuits for interfacing with peripheral devices such as cameras, electronic displays, wireless communication devices, external memory chips, and the like.

[0069] The first SOC 202 and the second SOC 204 can communicate via an interconnect / bus module 250. The various processors 210, 212, 214, 216, 218 can be interconnected to one or more memory elements 220, system components and resources 224, and custom circuits 222 and thermal management units 232 via an interconnect / bus module 226. Similarly, the processor 252 can be interconnected to a power management unit 254, a millimeter wave transceiver 256, a memory 258, and various additional processors 260 via an interconnect / bus module 264. The interconnect / bus modules 226, 250, 264 can include an array of reconfigurable logic gates and / or implement a bus architecture (e.g., CoreConnect, AMBA, etc.). Communications can be provided by advanced interconnects, such as a high-performance network on chip (NoC).

[0070] The first SOC 202 and the second SOC 204 may also include input / output modules (not shown) for communicating with resources external to the SOC, such as a clock 206 and a voltage regulator 208. Resources external to the SOC (e.g., clock 206, voltage regulator 208) may be shared by two or more internal SOC processors / cores.

[0071] In addition to the example SIP 200 discussed above, various embodiments may be implemented in a wide variety of computing systems that may include a single processor, multiple processors, multi-core processors, or any combination thereof.

[0072] Figure 3 3 is a component block diagram illustrating a software architecture 300 suitable for implementing any of the various embodiments, the software architecture including a radio protocol stack for user and control planes in wireless communications. Figure 1-Figure 3, the wireless device 320 may implement the software architecture 300 to facilitate communication between the wireless device 320 (e.g., the wireless devices 120a-120e, 200) and a base station 350 (e.g., base station 110a) of a communication system (e.g., 100). In various embodiments, the layers in the software architecture 300 may form a logical connection with the corresponding layers in the software of the base station 350. The software architecture 300 may be distributed among one or more processors (e.g., processors 212, 214, 216, 218, 252, 260). Although illustrated with respect to one radio protocol stack, in a multi-SIM (subscriber identity module) wireless device, the software architecture 300 may include multiple protocol stacks, each of which may be associated with a different SIM (e.g., in a dual SIM wireless communication device, two protocol stacks are associated with two SIMs, respectively). Although described below with reference to LTE communication layers, the software architecture 300 may support any of a variety of standards and protocols for wireless communication, and / or may include additional protocol stacks that support any of a variety of standards and protocols for wireless communication.

[0073] The software architecture 300 may include a non-access stratum (NAS) 302 and an access stratum (AS) 304. The NAS 302 may include functions and protocols to support packet filtering, security management, mobility control, session management, and services and signaling between a SIM of a wireless device (e.g., SIM 204) and its core network 140. The AS 304 may include functions and protocols to support communication between entities of a SIM (e.g., SIM 204) and a supported access network (e.g., a base station). In particular, the AS 304 may include at least three layers (layer 1, layer 2, and layer 3), each of which may include multiple sublayers.

[0074] In the user and control planes, layer 1 (L1) of AS 304 may be a physical layer (PHY) 306, which may oversee functions that enable transmission and / or reception over an air interface via a wireless transceiver (e.g., 256). Examples of such physical layer 306 functions may include cyclic redundancy check (CRC) addition, decoding blocks, scrambling and descrambling, modulation and demodulation, signal measurement, MIMO, etc. The physical layer may include various logical channels, including a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH).

[0075] In the user and control planes, Layer 2 (L2) of AS 304 may be responsible for the link between wireless device 320 and base station 350 above physical layer 306. In various embodiments, Layer 2 may include a medium access control (MAC) sublayer 308, a radio link control (RLC) sublayer 310, and a packet data convergence protocol (PDCP) 312 sublayer, each of which forms a logical connection that terminates at base station 350.

[0076] In the control plane, layer 3 (L3) of AS 304 may include a radio resource control (RRC) sublayer 3. Although not shown, software architecture 300 may include additional layer 3 sublayers, as well as various upper layers above layer 3. In various embodiments, RRC sublayer 313 may provide functionality including broadcasting system information, paging, and establishing and releasing RRC signaling connections between wireless devices 320 and base stations 350.

[0077] In various embodiments, the PDCP sublayer 312 may provide uplink functions including multiplexing between different radio bearers and logical channels, sequence number addition, handover data processing, integrity protection, encryption, and header compression. In the downlink, the PDCP sublayer 312 may provide functions including sequential delivery of data packets, duplicate data packet detection, integrity verification, decryption, and header decompression.

[0078] In the uplink, the RLC sublayer 310 may provide segmentation and concatenation of upper layer data packets, retransmission of lost data packets, and automatic repeat request (ARQ). In the downlink, the functions of the RLC sublayer 310 may include reordering of data packets to compensate for out-of-order reception, reassembly of upper layer data packets, and ARQ.

[0079] In the uplink, the MAC sublayer 308 can provide functions including multiplexing between logical and transport channels, random access procedures, logical channel priorities, and hybrid ARQ (HARQ) operations. In the downlink, MAC layer functions may include channel mapping within a cell, demultiplexing, discontinuous reception (DRX), and HARQ operations.

[0080] While the software architecture 300 may provide functionality to send data over a physical medium, the software architecture 300 may also include at least one host layer 314 to provide data transfer services to various applications in the wireless device 320. In some embodiments, the application-specific functionality provided by the at least one host layer 314 may provide an interface between the software architecture and the general purpose processor 206.

[0081] In other embodiments, the software architecture 300 may include one or more higher logical layers (e.g., transport, session, presentation, application, etc.) that provide host layer functionality. For example, in some embodiments, the software architecture 300 may include a network layer (e.g., an Internet Protocol (IP) layer) where the logical connection terminates at a packet data network (PDN) gateway (PGW). In some embodiments, the software architecture 300 may include an application layer where the logical connection terminates at another device (e.g., an end-user device, a server, etc.). In some embodiments, the software architecture 300 may also include a hardware interface 316 between the physical layer 306 and communication hardware (e.g., one or more radio frequency (RF) transceivers) in the AS 304.

[0082] Figure 4A is a block diagram illustrating components of a system 400 configured for processing data using computing resources of a remote network computing device according to various embodiments. Figure 1-4A , system 400 may include wireless device 120 and base station 110. Wireless device 120 and base station 110 may communicate via wireless communication network 424 (which aspects are described in Figure 1 ) for communication.

[0083] refer to Figure 4A , the wireless device 120 may include one or more processors 428 coupled to electronic storage 426 and a wireless transceiver (e.g., 266). The wireless transceiver 266 may be configured to receive messages to be sent in uplink transmissions from the processor 428 and to transmit such messages via an antenna (not shown) to the wireless communication network 424 for relaying to the base station 110. Similarly, the wireless transceiver 266 may be configured to receive messages from the base station 110 in downlink transmissions from the wireless communication network 424 and pass the messages to the one or more processors 428 (e.g., via a modem (e.g., 252) which demodulates the messages).

[0084] The processor 428 may be configured by machine readable instructions 406. The machine readable instructions 406 may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of a signal parameter module 408, a signal quality difference threshold module 410, an autonomous beam switching module 412, a neural network refinement / training module 414, or other instruction modules.

[0085] The signal parameter module 408 may be configured to measure signal parameters of signals received from a first synchronization signal block (SSB) beam of a base station monitored by the wireless device and one or more other SSB beams of the base station.

[0086] The signal quality difference threshold module 410 may be configured to determine whether the difference in measured signal parameters of the signal received from the first SSB beam and the second SSB beam of the base station satisfies the signal quality difference threshold. The signal quality difference threshold module 410 may be configured to obtain the signal quality difference threshold from a data table stored in a memory using the location of the wireless device as a lookup index. The signal quality difference threshold module 410 may be configured to use a trained neural network to dynamically determine the signal quality difference threshold. The signal quality difference threshold module 410 may be configured to dynamically apply multiple parameters including the location of the wireless location to the trained neural network and receive the signal quality difference threshold as an output.

[0087] The autonomous beam switching module 412 may be configured to autonomously switch to the second SSB beam of the monitoring base station in response to determining that a difference in measured signal parameters of signals received from the first and second SSB beams of the base station satisfies a signal quality difference threshold.

[0088] The neural network refinement / training module 414 may be configured to refine the trained neural network by receiving an instruction from the second base station to switch from monitoring one SSB beam of the second base station to another SSB beam of the second base station, determining a change in data throughput or other link quality parameter resulting from the switch from monitoring one SSB beam of the second base station to another SSB beam of the second base station, and correlating the change in data throughput or other link quality parameter resulting from the switch, the location of the wireless device at the time of the switch, and measured signal parameters of signals received from multiple SSB beams of the base station at the time of the switch to refine the trained neural network. The neural network refinement / training module 414 may be configured to train the neural network by determining a data throughput or other link quality parameter of signals received from each SSB beam of the base station at the location of the wireless device, and training the neural network using the determined throughput or other link quality parameter of signals received from each SSB beam of the base station at the location of the wireless device.

[0089] The neural network refinement / training module 414 may be configured to repeatedly move the wireless device to new locations, determine the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location, and train the neural network using the determined link quality or other link quality parameter of the signal received from each SSB beam of the base station at each new location. The neural network refinement / training module 414 may be configured to measure one or more parameters in addition to the location of the wireless device at each new location. The neural network refinement / training module 414 may be configured to train the neural network using the determined throughput or other link quality parameter of the signal received from each SSB beam of the base station and the measured one or more parameters at each new location.

[0090] Electronic storage 426 may include non-transitory storage media that electronically stores information. The electronic storage media of electronic storage 426 may include one or both of system storage provided integrally (i.e., substantially non-removable) with wireless device 120 and / or removable storage such as a SIM card that is removably connected to wireless device 120. Electronic storage 426 may include one or more optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy disk drive, etc.), charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drives, etc.), and / or other electronically readable storage media. Electronic storage 426 may store software algorithms, information determined by processor 428, information received from computing device 110, or other information that enables wireless device 120 to function as described herein.

[0091] The processor 428 may be configured to provide information processing capabilities in the wireless device 120. As such, the processor 428 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information. Although the processor 428 is shown as a single entity, this is for illustrative purposes only, and the processor 428 may include multiple processing units and / or processor cores. The processor 428 may be configured to execute modules 408-414 and / or other modules by: software; hardware; firmware; some combination of software, hardware, and / or firmware; and / or other mechanisms for configuring processing capabilities on the processor 428. As used herein, the term "module" may refer to any component or collection of components that perform the functionality belonging to the module. This may include one or more physical processors during the execution of processor-readable instructions, processor-readable instructions, circuits, hardware, storage media, or any other components.

[0092] The description of the functionality provided by the different modules 408-414 is for illustrative purposes and is not intended to be limiting, as any of the modules 408-414 may provide more or less functionality than described. For example, one or more of the modules 408-414 may be removed, and some or all of their functionality may be provided by other modules 408-414 and modules 436-440. As another example, the processor 428 may be configured to execute one or more additional modules that may perform some or all of the functionality attributed below to one of the modules 408-414.

[0093] Figure 4B 4 is a component block diagram illustrating a system 450 for training a neural network to support autonomous SSB beam steering for a wireless device in accordance with various embodiments. Figure 1-4B , system 400 may include a wireless computing device 402 configured to communicate with wireless device 120 and network base station 110. Wireless computing device 402 may include one or more processors 432 coupled to electronic storage 430 and wireless transceiver 406. Wireless transceiver 406 may be configured to access SSB beams of base stations 110 within wireless communication network 424 and measure various parameters, including characteristics of signals received from SSB beams (e.g., qos), data throughput or other link quality parameters of signals received from one or more SSB beams, location, and other parameters described herein.

[0094] Processor 432 may be configured by machine readable instructions 434. Machine readable instructions 434 may include one or more instruction modules. Instruction modules may include computer program modules. Instruction modules may include one or more of link quality module 436, neural network training module 438, neural network providing module 440, or other instruction modules.

[0095] The link quality module 436 may be configured to determine a data throughput of a signal received from each of a plurality of SSB beams of any base station 110 accessible at the location of the computing device 402. The link quality module 436 may also be configured to measure other parameters or characteristics of the signal received from each SSB beam, such as an initial BLER, a residual BLER, etc.

[0096] The neural network training module 438 may be configured to train the neural network using the determined link quality (e.g., data throughput) of the signal received from each of the SSB beams of the signal received from each base station 110 at each location, and other parameters or characteristics of the signal received from each SSB beam. The computing device may be moved to different locations, and the neural network training module 438 may be configured to repeat the following operations: determining the link quality (e.g., data throughput) and other characteristics of the signal received from each of the SSB beams of the signal received from each accessible base station 110 at each new location, and training the neural network using the collected information.

[0097] The neural network providing module 438 may be configured to provide a trained neural network to the wireless device in a configuration that enables the wireless device to determine a poor signal quality threshold useful for determining whether to autonomously switch a monitored SSB beam of the base station 110 .

[0098] Electronic storage 430 may include non-transitory storage media that electronically stores information. The electronic storage media of electronic storage 430 may include one or both of system storage provided integrally with computing device 402 (i.e., substantially non-removable) and / or removable storage that is removably connected to computing device 402 via, for example, a port (e.g., a universal serial bus (USB) port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage 430 may include one or more optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, a magnetic hard drive, a floppy disk drive, etc.), charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drives, etc.), and / or other electronically readable storage media. Electronic storage 430 may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and / or other virtual storage resources). Electronic storage 430 may store software algorithms, information determined by processor 432, information received by computing device 402, or other information that enables computing device 402 to function as described herein.

[0099] Processor 432 may be configured to provide information processing capabilities in computing device 402. As such, processor 432 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information. Although processor 432 is shown as a single entity, this is for illustrative purposes only. In some embodiments, processor 432 may include multiple processing units and / or processor cores. A processing unit may be physically located within the same device, or processor 432 may represent processing functionality of multiple devices operating in coordination. Processor 432 may be configured to execute modules 436-440 and / or other modules by: software; hardware; firmware; some combination of software, hardware, and / or firmware; and / or other mechanisms for configuring processing capabilities on processor 432.

[0100] The description of the functionality provided by the different modules 436-440 is for purposes of illustration and not limitation, as any module 436-440 may provide more or less functionality than described. For example, one or more of the modules 436-440 may be removed, and some or all of their functionality may be provided by other modules 436-440. As another example, the processor 432 may be configured to execute one or more additional modules capable of performing some or all of the functionality attributed to one of the modules 436-440.

[0101] Figure 5A is a block diagram illustrating received SSB signal strength 500 received by a wireless device 502 (e.g., 120a-120e, 200, 320, 120) from a base station 504 (e.g., 110a-110d, 402) according to various embodiments. Figure 1-5A, the base station 504 may transmit multiple SSB beams (e.g., SSB0 520, SSB1 522, and SSB2 524) in different directions. The wireless device 502 may receive signals transmitted in one or more of the SSB beams 520-524, and may measure one or more parameters of the signals received from the one or more SSB beams. The wireless device 502 may provide the information determined by the wireless device 502 to the base station 504 in a report or message (e.g., a channel state indicator (CSI) report). The parameters of the signal received from each SSB beam measured by the wireless device 502 may vary depending on various conditions. When the wireless device 502 moves relative to the base station 504, the wireless device may receive the signals received from different SSB beams more strongly or more clearly, which will be reflected in the various parameter measurements performed by the wireless device. For example, as the wireless device 502 moves relative to the base station 504, the wireless device may measure a decrease in the signal strength of the signal received from SSB0 520 and may measure an increase in the signal strength of the signal received from SSB2 524. Initially, the reception of the signal received from SSB0 520 is better than the reception of the signal received from SSB2 522, but as the wireless device 502 moves relative to the base station 504, the reception of the signal received from SSB2 524 becomes better than the reception of the signal received from SSB0 520.

[0102] Initially, the base station 504 may instruct the wireless device 502 to monitor SSBO in the beam instruction (e.g., via a MAC-CE message). As the wireless device 502 moves and the reception of the signal received from SSB2 524 becomes better than the reception of the signal received from SSBO 520, the base station 504 may instruct the wireless device 502 to monitor SSB2. However, if the base station 504 fails to send the beam instruction, or if the wireless device 502 fails to receive the beam instruction, the wireless device 502 may remain on SSBO 520 (i.e., experience reception of a signal that is worse than the reception of the signal received from SSB2 522). As a result, the wireless device 502 may experience degraded signal quality and / or data throughput.

[0103] Figure 5B is a message flow diagram illustrating a method 550 for autonomous beam switching according to various embodiments. Figure 1-5B According to various embodiments, the operations of method 550 may be implemented by a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428) of a wireless device 502 (e.g., 120a-120e, 200, 320, 120) and / or a base station 504 (e.g., 110a-110d, 402).

[0104] In some embodiments, the wireless device 502 may receive a signal transmitted in a first SSB beam 552 (e.g., 520). The wireless device 502 may also measure a signal parameter of a signal received from the first SSB beam 552 as a serving beam of the base station 504. The wireless device 502 may also receive data in a signal transmitted in a first data beam 554 (e.g., PDSCH-1) QCLed with the first SSB beam 552. In some embodiments, the wireless device 502 may receive a signal transmitted in one or more other SSB beams 556 (e.g., 522, 524) of the base station 504. The wireless device 502 may measure a signal parameter of a signal received from one or more other SSB beams 556.

[0105] The wireless device may determine 558 whether a difference in measured signal parameters of signals transmitted in the first SSB beam and the second SSB beam of the base station satisfies a poor signal quality threshold. In some embodiments, the wireless device may determine whether a difference in measured signal parameters of signals received from the first SSB beam 552 and the second SSB beam (i.e., one of the other SSB beams 556) satisfies a poor signal quality threshold before or without receiving a MAC-CE from the base station 504.

[0106] For example, conventionally, the wireless device 502 may send one or more reports 566 to the base station 504 including information about the reception of signals received from the first SSB beam 552 and the other SSB beams 556, and the base station 504 may send a MAC-CE 568 to the wireless device 502 including instructions to switch to the second SSB beam. However, when configured to perform autonomous beam switching to the second SSB beam, the wireless device 502 does not need to rely on instructions from the base station that may not be sent by the base station 504 or that may not be received by the wireless device 502 even if sent. In various embodiments, the wireless device may determine whether the difference in measured signal parameters of the signals received from the first SSB beam and the second SSB beam of the base station meets the signal quality difference threshold before receiving the MAC-CE 568 or without receiving the MAC-CE 568.

[0107] In response to determining that the difference in measured signal parameters of signals received from the first SSB beam and the second SSB beam of the base station satisfies the signal quality difference threshold, the wireless device 502 may autonomously switch 560 to monitor the second SSB beam 562 of the base station as the serving beam. In some embodiments, the wireless device 502 may also receive data in a signal received from a second data beam 564 (e.g., PDSCH-2) that is QCL with the second SSB beam 562.

[0108] The wireless device may repeat 570 operations 552-564 from time to time.

[0109] Figure 6 is a process flow diagram illustrating a method 600 that may be performed by a processor of a wireless device for autonomous beam switching according to various embodiments. Figure 1-Figure 6 , the method 600 may be implemented by a processor (eg, 210, 212, 214, 216, 218, 252, 260, 428) of a wireless device (eg, 120a-120e, 200, 320, 120).

[0110] In block 602, the processor may measure signal parameters of a signal received from a first synchronization signal block (SSB) beam of a base station that is a serving beam monitored by a wireless device, and one or more other SSB beams of the base station. For example, the processor may measure signal parameters of a signal received from SSB beams 520-524 ( Figure 5A ) signal parameters of the signal received by the receiving SSB beam. In various embodiments, the processor may determine or measure one or more of the various parameters, including: a serving SSB identifier, a cell identifier of the serving cell, a signal strength of the signal received from each SSB beam, a signal quality of the signal received from each SSB beam, a difference in reference signal received power (RSRP) of the signal received from each SSB beam, a difference in reference signal received quality (RSRQ) of the signal received from each SSB beam, a difference in signal-to-noise ratio (SNR) of the signal received from each SSB beam, an initial block error rate (iBLER) of the signal received from each SSB beam, a residual block error rate (rBLER) of the signal received from each SSB beam, and a signal parameter of the signal received by the receiving SSB beam. mobility of the wireless device over time, orientation of the wireless device at the time of measurement, number of detectable SSB beams, number of configured SSB beams, beam status reports configured by the base station, number of SSB status reports sent by the wireless device to the base station, number and frequency of autonomous SSB switches performed by the wireless device, number and frequency of SSB beam switches performed in response to instructions from the base station, average duration between transmission of beam status reports and receipt of beam switch instructions from the base station, beam failure detection and recovery statistics, frequency and time tracking loop statistics, mobile network code (MNC) or mobile country code (MCC) of the communication network associated with the base station, or infrastructure provider associated with the base station. In some embodiments, the processor may determine trends in one or more of the above parameters and / or historical performance of signals received from SSB beams at the location of the wireless device.

[0111] In some embodiments, the wireless device may receive data in a signal received from a first data beam QCLed with a first SSB beam. Means for performing the functions of the operations in block 602 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432) coupled to a wireless transceiver (e.g., 266).

[0112] In block 604, the processor may determine whether a difference in measured signal parameters of signals received from a first SSB beam and a second SSB beam of a base station satisfies a signal quality difference threshold. In some embodiments, the processor may determine whether a difference in measured signal parameters of signals received from a first SSB beam and a second SSB beam of a base station satisfies a signal quality difference threshold before receiving a MAC control element (MAC-CE) indicating that the wireless device switches a serving beam, or without receiving a MAC-CE from the base station. For example, the processor may determine whether a difference in measured signal parameters of signals received from SSB0 520 and SSB2 524 satisfies a signal quality difference threshold. The means for performing the functions of the operations in block 604 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0113] In block 606, in response to determining that the difference in measured signal parameters of signals received from the first SSB beam and the second SSB beam of the base station satisfies the poor signal quality threshold, the processor may autonomously switch to monitoring signals received from the second SSB beam of the base station as the serving beam. For example, in response to determining that the difference in measured signal parameters of signals received from SSB0 520 and SSB2 524 satisfies the poor signal quality threshold, the processor may autonomously switch from monitoring SSB0 520 to monitoring SSB2 524. Thus, if the wireless device does not receive a MAC-CE from the base station to switch the serving beam when the processor determines that the difference in measured signal parameters of signals received from the first SSB beam and the second SSB beam of the base station satisfies the poor signal quality threshold, the processor autonomously switches to monitor the second SSB beam of the base station as the serving beam. On the other hand, if the wireless device receives a MAC-CE from the base station to switch the service beam before the difference in measured signal parameters of the signals received from the first SSB beam and the second SSB beam of the base station satisfies the signal quality difference threshold, the wireless device will switch the service beam based on the MAC-CE in a traditional manner.

[0114] In some embodiments, after autonomously switching to monitor the second SSB beam, the processor may receive data in a signal received from a second data beam that is QCL with the second SSB beam. Means for performing the functions of the operations in block 606 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432) coupled to a wireless transceiver (e.g., 266).

[0115] The processor may repeat the operations of blocks 602 - 606 from time to time.

[0116] Figure 7-Figure 12 is a process flow diagram illustrating operations 700, 800, 900, 1000, 1100, 1200 that may be performed by a processor of a wireless device as part of a method of processing data using computing resources of a remote network computing device in accordance with various embodiments. Figure 1-Figure 12 , operations 700, 800, 900, 1000, 1100, 1200 may be implemented by a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428) of a wireless device (e.g., 120a-120e, 200, 320, 120).

[0117] refer to Figure 7 Operation 700 shown in the method 600 ( Figure 6 ), in block 702, the processor may use the location of the wireless device as a lookup index to obtain a poor signal quality threshold from a data table stored in the memory. For example, the processor may retrieve a table, index, list, or another suitable data structure from the memory for the poor signal quality threshold. In some embodiments, the processor may use the location of the wireless device as an index to obtain the poor signal quality threshold. In some embodiments, the processor may use global positioning system (GPS) data, a geo-fence location (such as a geo-fence identifier), a network-based approximate location, or other suitable location information as an index to find the poor signal quality threshold. The means for performing the functions of the operations in block 702 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0118] The processor may then execute the described method 600 ( Figure 6 ) operation of box 604.

[0119] refer to Figure 8 Operation 800 shown in method 600 ( Figure 6), in block 802, the processor may use the trained neural network to dynamically determine the poor signal quality threshold. The means for performing the functions of the operations in block 802 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0120] The processor may then execute the described method 600 ( Figure 6 ) operation of box 604.

[0121] refer to Fig. 9 Operation 900 shown in the method 600 ( Figure 6 ), the processor may dynamically apply multiple parameters including the location of the wireless device to the trained neural network in block 902, and receive a poor signal quality threshold as an output. Means for performing the functions of the operations in block 902 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0122] The processor may then execute the described method 600 ( Figure 6 ) operation of box 604.

[0123] refer to Fig.10 In operation 1000, the wireless device may perform operations to refine the trained neural network. In some embodiments, in method 600 ( Figure 6 ), the processor may receive an instruction from the second base station in block 1002 to switch from monitoring one SSB beam of the second base station to another SSB beam of the second base station. Means for performing the functions of the operations in block 1002 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432) coupled to a wireless transceiver (e.g., 266).

[0124] In block 1004, the processor may determine a change in link quality (e.g., data throughput) caused by switching from monitoring one SSB beam of the second base station to another SSB beam of the second base station. For example, the processor may determine an increase or decrease in data throughput, iBLER, rBLER, or other link quality parameter caused by switching to the new SSB beam compared to the link quality or performance of the previous SSB beam. Means for performing the functions of the operations in block 1004 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0125] In block 1006, the processor may correlate the change in link quality (e.g., data throughput) caused by the handover, the location of the wireless device at the time of the handover, and the measured signal parameters of the signals received from the plurality of SSB beams of the base station at the time of the handover to refine the trained neural network. Means for performing the functions of the operations in block 1006 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0126] The processor may then execute the described method 600 ( Figure 6 ) operation of box 602.

[0127] refer to Fig.11 As shown in operation 1100, the wireless device may perform operations for training a neural network. In some embodiments, the processor may determine the link quality (e.g., data throughput) of the signal received from each SSB beam of the base station at the location of the wireless device in block 1102. The means for performing the functions of the operations in block 1102 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432) coupled to a wireless transceiver (e.g., 266).

[0128] In block 1104, the processor may train a neural network using the determined link quality (e.g., data throughput) of the signal received from each SSB beam of the base station at the location of the wireless device. Means for performing the functions of the operations in block 1104 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0129] The processor may then execute the described method 600 ( Figure 6 ) operation of box 602.

[0130] refer to Fig.12 As shown in operation 1200, the wireless device may perform operations to train a neural network. In some embodiments, at block 1102 ( Fig.11 ), the processor may repeatedly move the wireless device to a new location in block 1202. The means for performing the functions of the operations in block 1202 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432)

[0131] In block 1204, the processor may determine a data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location. Means for performing the functions of the operations in block 1202 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432) coupled to a wireless transceiver (e.g., 266).

[0132] In block 1206, the processor may use the determined data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location to neural network. Means for performing the functions of the operations in block 1204 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0133] The processor may then execute the described method 600 ( Figure 6 ) operation of box 602.

[0134] refer to Fig.13 In operation 1300, the wireless device may perform operations for training a neural network. In some embodiments, at block 1202 ( Fig.12 ), the processor may train a neural network using the determined data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location and the measured one or more parameters in block 1302. Means for performing the functions of the operations in block 1302 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0135] The processor may then execute the described method 600 ( Figure 6 ) operation of box 602.

[0136] Fig.14 1 is a process flow diagram illustrating a method 1400 for training a neural network for use by a wireless device in autonomous beam switching, which may be performed by a computing device (eg, 402) in accordance with various embodiments. Figure 1-Figure 14 , method 1400 can be implemented by a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428) of a computing device (e.g., 402), the computing device including a wireless transceiver (e.g., 406) configured to measure signal quality of various SSB beams from a base station.

[0137] In block 1402, a processor may determine a data throughput or other link quality parameter of a signal received from each of a plurality of synchronization signal block (SSB) beams of a base station at a wireless device location. Means for performing the functions of the operations in block 1402 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428) coupled to a wireless transceiver (e.g., 406).

[0138] In block 1404, the processor may train a neural network using the determined data throughput or other link quality parameter of the signals received from each SSB beam of the base station at the location of the computing device. Means for performing the functions of the operations in block 1404 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428) of a computing device (e.g., 402) coupled to a wireless transceiver (e.g., 406).

[0139] In block 1406, the processor may provide the trained neural network to the wireless device in a configuration that enables the wireless device to determine a poor signal quality threshold for determining whether to autonomously switch a monitored SSB beam of a base station. Means for performing the functions of the operations in block 1406 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428) of a computing device (e.g., 402) coupled to a wireless transceiver (e.g., 406).

[0140] Fig.15 and Fig.16 is a process flow diagram illustrating operations 1500 and 1600 that may be performed by a processor of a computing device as part of a method of training a neural network for use by a wireless device in autonomous beam switching, according to various embodiments. Figure 1-Figure 16 , operations 1500 and 1600 may be implemented by a processor (eg, 210 , 212 , 214 , 216 , 218 , 252 , 260 , 428 ) of a computing device (eg, 402 ).

[0141] refer to Fig.15 15. As shown in operation 1500, the computing device may be moved to a new location at block 1502.

[0142] At block 1504, the processor may determine a data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location. Means for performing the functions of the operations in block 1504 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428) of a computing device (e.g., 402) coupled to a wireless transceiver (e.g., 406).

[0143] In block 1506, the processor may train the neural network using the determined data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location. Means for performing the functions of the operations in block 1506 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 432).

[0144] The processor may then execute the described method 1400 ( Fig.14 ) operation of box 1406.

[0145] refer to Fig.16 Operation 1600 shown, at block 1504 ( Fig.15), the processor in block 1602 may also measure one or more parameters in addition to the location of the wireless device at each new location. For example, the processor may measure signal parameters of the signals received from the SSB beams 520-524 (FIG. 5). In some embodiments, the processor may determine or measure one or more of a variety of parameters including: a serving SSB identifier, a cell identifier of a serving cell, a signal strength of the signal received from each SSB beam, a signal quality of the signal received from each SSB beam, a difference in reference signal received power (RSRP) of the signal received from each SSB beam, a difference in reference signal received quality (RSRQ) of the signal received from each SSB beam, a difference in signal-to-noise ratio (SNR) of the signal received from each SSB beam, an initial block error rate (iBLER) of the signal received from each SSB beam, a signal quality (RSRQ) of the signal received from each SSB beam, a signal-to-noise ratio (SNR ...-to-noise ratio (SNR) of the signal received from each SSB beam, a signal-to-noise ratio (SNR) of the signal received from each SSB beam, a signal-to-noise ratio (SNR) of the signal received from each SSB beam, a signal-to-noise ratio (SNR) of the signal received from each SSB beam, a signal-to-noise ratio (SNR) of the signal received from each SSB beam, a signal-to-noise ratio (SNR) of the signal received from each SSB beam, a signal-to-no The residual block error rate (rBLER) of the signal received by the B beam, the mobility of the device at the measurement time, the orientation of the device at the measurement time, the number of detectable SSB beams, the number of configured SSB beams, the beam status reports configured by the base station, the number of SSB status reports sent by the wireless device to the base station, the average duration between the transmission of the beam status report and the receipt of the beam switching instruction from the base station, the beam failure detection and recovery statistics, the frequency and time tracking loop statistics, the mobile network code (MNC) or mobile country code (MCC) of the communication network associated with the base station, or the infrastructure provider associated with the base station. In some embodiments, the processor may determine a trend of one or more of the above parameters and / or the historical performance of the SSB beam at the location of the wireless device. The means for performing the functions of the operations in block 1602 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428) of a computing device (e.g., 402) coupled to a wireless transceiver (e.g., 406).

[0146] In block 1604, the processor may train a neural network using the determined data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location and the measured one or more parameters. Means for performing the functions of the operations in block 1604 may include a processor (e.g., 210, 212, 214, 216, 218, 252, 260, 428).

[0147] Various embodiments including methods and operations 550, 600, 700, 800, 900, 1000, 1100, 1200, 1300 may be performed in various wireless devices (e.g., wireless devices 120a-120e, 200, 320, 120). Fig.17 An example of this is shown in Fig.17is a block diagram of components of a wireless device 1700 suitable for use with various embodiments. Figure 1-Figure 17 , the wireless device 1700 may include a first SOC 202 (e.g., SOC-CPU) coupled to a second SOC 204 (e.g., a 5G-capable SOC). The first SOC 202 and the second SOC 204 may be coupled to internal memory 430, 1716, a display 1712, and a speaker 1714. In addition, the wireless device 1700 may include an antenna 1704 for sending and receiving electromagnetic radiation, which may be connected to a wireless data link and / or cellular telephone transceiver 266 coupled to one or more processors in the first SOC 202 and / or the second SOC 204. The wireless device 1700 may also include a menu selection button or rocker switch 1720 for receiving user input.

[0148] The wireless device 1700 may also include a sound coding / decoding (CODEC) circuit 1710 that digitizes the sound received from the microphone into a data packet suitable for wireless transmission, and decodes the received sound data packet to generate an analog signal, which is provided to the speaker to generate sound. In addition, the first SOC 202 and the second SOC 204, the wireless transceiver 266, and one or more processors in the CODEC 1710 may include a digital signal processor (DSP) circuit (not shown separately).

[0149] This article is based on reference Figure 14-16 The method and apparatus for training a neural network described in the embodiments described in the present invention can be implemented in a variety of computing systems, in particular mobile computing devices equipped with a wireless transceiver, and Fig.18 An example of this is shown in the form of a laptop computer 800. Laptop computer 1800 typically includes a processor 1802 coupled to volatile memory 1812 and a large capacity non-volatile memory such as a compact disk (CD) drive 1813 or flash memory. In addition, laptop computer 1800 may have one or more antennas 1808 for sending and receiving electromagnetic radiation, which may be connected to a wireless data link and / or cellular telephone transceiver 1816 coupled to processor 1802. Laptop computer 1800 may also include a floppy disk drive 1814 and a CD drive 1813 coupled to processor 1812. The laptop computer housing may include a battery 1815, a touch pad touch surface 1818 for use as a computer pointing device, a keyboard 1818, and a display 1819, all of which are coupled to processor 1802. Other configurations of computing devices may include a computer mouse or trackball coupled to a processor (e.g., via a USB input) as is known, which may also be used in conjunction with various embodiments.

[0150] The processors of the wireless device (e.g., 120, 1700) and the computing device may be any programmable microprocessor, microcomputer, or multi-processor chip that can be configured by software instructions (applications) to perform various functions (including the functions of the various embodiments described below). In some wireless devices, multiple processors may be provided, such as one processor dedicated to wireless communication functions within SOC 204 and one processor dedicated to running other applications within SOC 202. Before the processor executable instructions are accessed and loaded into the processor, the software application may be stored in a memory (e.g., 426, 430, 1716, 1812, 1813, 1814). The processor may include internal memory sufficient to store the application software instructions.

[0151] The various embodiments illustrated and described are provided as examples only to illustrate the various features of the claims. However, the features shown and described with respect to any given embodiment are not necessarily limited to the associated embodiments, and may be used or combined with other embodiments shown and described. In addition, the claims are not intended to be limited by any one of the example embodiments. For example, one or more of the methods and operations 550, 600, 700, 800, 900, 1000, 1100, 1200, 1300 may be replaced by one or more operations of the methods and operations 550, 600, 700, 800, 900, 1000, 1100, 1200, 1300, or combined with one or more operations of the methods and operations 550, 600, 700, 800, 900, 1000, 1100, 1200, 1300.

[0152] Implementation examples are described in the following paragraphs. Although some of the following implementation examples are described in terms of example methods, further example implementations may include: the example methods discussed in the following paragraphs are implemented by a base station, the base station includes a processor configured with processor-executable instructions to perform the operations of the following implementation example methods; the example methods discussed in the following paragraphs implemented by the base station include components for performing the functions of the following implementation example methods; and the example methods discussed in the following paragraphs may be implemented as a non-transitory processor-readable storage medium having processor-executable instructions stored thereon, the processor-executable instructions being configured to cause the processor of the base station to perform the operations of the following implementation example methods.

[0153] Example 1. A method for autonomous beam switching performed by a processor of a wireless device, comprising: measuring signal parameters of a signal received from a first synchronization signal block (SSB) beam that is a service beam of a base station monitored by the wireless device and a signal received from one or more other SSB beams of the base station; determining whether the difference in the measured signal parameters of the signal received from the first SSB beam and the second SSB beam of the base station satisfies a signal quality difference threshold; and in response to determining that the difference in the measured signal parameters of the signal received from the first SSB beam and the second SSB beam of the base station satisfies the signal quality difference threshold, autonomously switching to monitor the second SSB beam of the base station as the service beam.

[0154] Example 2. A method as in Example 1, wherein determining whether the difference in measured signal parameters of signals received from a first SSB beam and a second SSB beam of a base station satisfies a signal quality difference threshold includes: determining whether the difference in measured signal parameters of signals received from a first SSB beam and a second SSB beam of a base station satisfies a signal quality difference threshold before receiving a MAC control element (MAC-CE) indicating that the wireless device switches a service beam or without receiving a MAC-CE.

[0155] Example 3. A method as in any of Examples 1 and 2, wherein autonomously switching to monitor the second SSB beam of the base station as a service beam includes: switching to monitor the second SSB beam of the base station before receiving a MAC-CE or without receiving a MAC-CE.

[0156] Example 4. A method as in any of Examples 1-3, comprising: before autonomously switching to monitor a second SSB beam, receiving data from a signal received from a first data beam that is quasi co-located (QCL) with the first SSB beam; and after autonomously switching to monitor the second SSB beam, receiving data from a signal received from a second data beam that is quasi co-located (QCL) with the second SSB beam.

[0157] Example 5. The method of any one of Examples 1-4, further comprising: obtaining a poor signal quality threshold from a data table stored in a memory using a location of the wireless device as a lookup index.

[0158] Example 6. The method of any of Examples 1-5, further comprising: using a trained neural network to determine a poor signal quality threshold.

[0159] Example 7. The method of Example 6, wherein determining the poor signal quality threshold using the trained neural network comprises: dynamically applying a plurality of parameters including the location of the wireless device to the trained neural network, and receiving the poor signal quality threshold as an output.

[0160] Example 8. A method as in any of Examples 6 and 7, further comprising: refining the trained neural network by: receiving an instruction from a second base station to switch from monitoring one SSB beam of the second base station to another SSB beam of the second base station; determining a change in data throughput or other link quality parameters caused by switching from monitoring one SSB beam of the second base station to another SSB beam of the second base station; and correlating the change in data throughput or other link quality parameters caused by the switching, the position of the wireless device at the time of the switching, and the measured signal parameters of the signals received from the multiple SSB beams of the base station at the time of the switching to refine the trained neural network.

[0161] Example 9. A method as in any of Examples 6-8, further comprising: training a neural network by: determining a data throughput or other link quality parameter of a signal received from each SSB beam of a base station at the location of the wireless device; and training the neural network using the determined data throughput or other link quality parameter of a signal received from each SSB beam of a base station at the location of the wireless device.

[0162] Example 10. A method as in any of Examples 6-9, wherein training the neural network using the determined link quality or other link quality parameter of the signal received from each SSB beam of the base station at the location of the wireless device includes: repeatedly moving the wireless device to a new location, determining the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location, and training the neural network using the determined data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location.

[0163] Example 11. The method of Example 10, further comprising: measuring one or more parameters other than the position of the wireless device at each new location, wherein training the neural network using the determined data throughput or other link quality parameters of the signal received from each SSB beam of the base station at each new location comprises: training the neural network using the determined data throughput or other link quality parameters of the signal received from each SSB beam of the base station at each new location and the measured one or more parameters.

[0164] Example 12. A method as in Example 11, wherein measuring one or more parameters includes: determining or measuring one or more of the following: a serving beam identifier; a cell identifier of a serving cell, a signal strength of a signal received from each SSB beam, a signal quality of a signal received from each SSB beam; a difference in reference signal received power (RSRP) of a signal received from each SSB beam; a difference in reference signal received quality (RSRQ) of a signal received from each SSB beam; a difference in signal-to-noise ratio (SNR) of a signal received from each SSB beam; an initial block error rate (iBLER) of a signal received from each SSB beam; a residual block error rate (rBLER) of a signal received from each SSB beam ); mobility of the wireless device at the measurement time; orientation of the wireless device at the measurement time; number of detectable SSB beams; number of configured SSB beams; beam status reports configured by the base station; number of SSB status reports sent by the wireless device to the base station; number and frequency of autonomous SSB switches performed by the wireless device; number and frequency of SSB beam switches performed in response to instructions from the base station; average duration between the transmission of beam status reports and the receipt of beam switching instructions from the base station; beam failure detection and recovery statistics; frequency and time tracking loop statistics; the mobile network code (MNC) or mobile country code (MCC) of the communication network associated with the base station; or the infrastructure provider associated with the base station.

[0165] Example 13. A method performed by a computing device to train a neural network for use by a wireless device in autonomous beam switching, comprising: determining a data throughput or other link quality parameter of a signal received from each of a plurality of synchronization signal block (SSB) beams of a base station at a location of the computing device; training a neural network using the determined data throughput or other link quality parameter of the signal received from each SSB beam of the base station at the location of the computing device; and providing the trained neural network to the wireless device in a configuration that enables the wireless device to determine a signal quality difference threshold, the signal quality difference threshold being used to determine whether to autonomously switch a monitored SSB beam of the base station.

[0166] Example 14. The method of Example 13 further includes: repeatedly moving the computing device to new locations, determining a data throughput or other link quality parameter of a signal received from each SSB beam of a base station at each new location, and training a neural network using the determined data throughput or other link quality parameter of a signal received from each SSB beam of a base station at each new location.

[0167] Example 15. A method as in any of Examples 13 or 14, further comprising: measuring one or more parameters other than the position of the wireless device at each new location, wherein training the neural network using the determined data throughput or other link quality parameters of the signal received from each SSB beam of the base station at each new location comprises: training the neural network using the determined data throughput or other link quality parameters of the signal received from each SSB beam of the base station at each new location, and the measured one or more parameters.

[0168] Example 16. A method as described in any of Examples 13-15, wherein measuring one or more parameters includes: determining or measuring one or more of the following: a serving beam identifier; a cell identifier of a serving cell, a signal strength of a signal received from each SSB beam, a signal quality of a signal received from each SSB beam; a difference in reference signal received power (RSRP) of a signal received from each SSB beam; a difference in reference signal received quality (RSRQ) of a signal received from each SSB beam; a difference in signal-to-noise ratio (SNR) of a signal received from each SSB beam; an initial value of a signal received from each SSB beam; block error rate (iBLER); residual block error rate (rBLER) of the signal received from each SSB beam; mobility of the device calculated at the measurement time; orientation of the device calculated at the measurement time; number of detectable SSB beams; number of configured SSB beams; beam status reports configured by the base station; average duration between the transmission of a beam status report and the receipt of a beam switching instruction from the base station; beam failure detection and recovery statistics; frequency and time tracking loop statistics; mobile network code (MNC) or mobile country code (MCC) of the communication network associated with the base station; or the infrastructure provider associated with the base station.

[0169] As used in this application, the terms "component", "module", "system", etc. are intended to include computer-related entities, such as but not limited to hardware, firmware, a combination of hardware and software, software, or software in execution, which is configured to perform specific operations or functions. For example, a component can be but not limited to a process, a processor, an object, an executable program, an execution thread, a program, and / or a computer running on a processor. As an illustration, both an application running on a wireless device and a wireless device can be referred to as a component. One or more components can reside in a process and / or an execution thread, and a component can be located on a processor or core and / or distributed between two or more processors or cores. In addition, these components can be executed from various non-temporary computer-readable media on which various instructions and / or data structures are stored. Components can communicate through local and / or remote processes, function or procedure calls, electronic signals, data packets, memory read / write, and other known network, computer, processor, and / or process-related communication methods.

[0170] Many different cellular and mobile communication services and standards are available or expected in the future, all of which can be implemented and benefit from various embodiments. Such services and standards include, for example, the Third Generation Partnership Project (3GPP), Long Term Evolution (LTE) system, third generation wireless mobile communication technology (3G), fourth generation wireless mobile communication technology (4G), fifth generation wireless mobile communication technology (5G), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), 3GSM, general packet radio service (GPRS), code division multiple access (CDMA) system (e.g., cdmaOne, CDMA1020TM), enhanced data rates for GSM evolution (EDGE), advanced mobile phone system (AMPS), digital AMPS (IS-136 / TDMA), evolution data optimized (EV-DO), digital enhanced cordless telecommunications (DECT), world wide interoperability for microwave access (WiMAX), wireless local area network (WLAN), Wi-Fi protected access I & II (WPA, WPA2), and integrated digital enhanced network (iDEN). Each of these technologies involves, for example, the transmission and reception of voice, data, signaling and / or content messages. It should be understood that any reference to terminology and / or technical details related to a single telecommunication standard or technology is for illustrative purposes only and is not intended to limit the scope of the claims to a particular communication system or technology unless specifically stated in the claim language.

[0171] The various embodiments illustrated and described are provided as examples only to illustrate the various features of the claims. However, the features shown and described with respect to any given embodiment are not necessarily limited to the associated embodiment, and may be used or combined with other embodiments shown and described. In addition, the claims are not intended to be limited by any one of the example embodiments. For example, one or more operations of the above method may replace one or more operations of the above method or be combined with one or more operations of the above method.

[0172] The foregoing method descriptions and process flow charts are provided only as illustrative examples, and are not intended to require or imply that the operations of the various embodiments must be performed in the order presented. Those skilled in the art will appreciate that the order of operations in the foregoing embodiments may be performed in any order. Words such as "thereafter," "then," "next," etc. are not intended to limit the order of operations; these words are used to guide the reader through the description of the method. In addition, any reference to a claim element in the singular form, such as using the articles "a," "an," or "the," should not be understood as limiting the element to the singular.

[0173] The various illustrative logic blocks, modules, components, circuits, and algorithmic operations described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and operations have been generally described above with respect to their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the entire system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such embodiment decisions should not be interpreted as resulting in a departure from the scope of the claims.

[0174] Hardware for implementing the various illustrative logics, logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed using a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of receiver smart objects, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors and a DSP core, or any other such configuration. Alternatively, some operations or methods may be performed by circuits specific to a given function.

[0175] In one or more embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or codes on a non-transitory computer-readable storage medium or a non-transitory processor-readable storage medium. The operation of the method or algorithm disclosed herein may be implemented in a processor-executable software module or a processor-executable instruction, which may reside on a non-transitory computer-readable or processor-readable storage medium. A non-transitory computer-readable or processor-readable storage medium may be any storage medium that can be accessed by a computer or processor. As an example and not limitation, such a non-transitory computer-readable or processor-readable storage medium may include RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, disk storage or other magnetic storage smart objects, or any other medium that can be used to store the required program code in the form of an instruction or data structure and can be accessed by a computer. The disks and discs used here include compact disks (CDs), laser disks, optical disks, digital versatile disks (DVDs), floppy disks, and blue-ray disks, wherein disks generally reproduce data magnetically, and optical disks reproduce data optically with lasers. The above combinations are also included within the scope of non-transitory computer-readable and processor-readable media. Furthermore, the operations of a method or algorithm may reside as one or any combination or set of codes and / or instructions on a non-transitory processor-readable storage medium and / or a computer-readable storage medium, which may be incorporated into a computer program product.

[0176] The foregoing description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the claims. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the claims. Therefore, the present disclosure is not intended to be limited to the embodiments shown herein, but is to be consistent with the widest scope consistent with the following claims and the principles and novel features disclosed herein.

Claims

1. A method for autonomous beam switching performed by a wireless device, include: measuring signal parameters of a signal received from a first synchronization signal block (SSB) beam that is a serving beam of a base station monitored by the wireless device and signals received from one or more other SSB beams of the base station; determining whether a difference in the measured signal parameters of signals received from the first SSB beam and the second SSB beam of the base station satisfies a signal quality difference threshold associated with a location of the wireless device; as well as In response to determining that the difference in the measured signal parameters of the signals received from the first SSB beam and the second SSB beam of the base station meets the signal quality difference threshold, autonomously switching to monitor the second SSB beam of the base station as the serving beam.

2. The method according to claim 1, in, Determining whether the difference between the measured signal parameters of the signals received from the first SSB beam and the second SSB beam of the base station meets the signal quality difference threshold includes: before receiving the MAC control element MAC-CE instructing the wireless device to switch the service beam or without receiving the MAC-CE, determining whether the difference between the measured signal parameters of the signals received from the first SSB beam and the second SSB beam of the base station meets the signal quality difference threshold.

3. The method according to claim 1, in, Autonomously switching to monitor the second SSB beam of the base station as the service beam includes: switching to monitor the second SSB beam of the base station before receiving a MAC control element MAC-CE or without receiving the MAC-CE.

4. The method according to claim 1, further comprising: include: receiving data on a first data beam quasi-co-located QCL with the first SSB beam before autonomously switching to monitor the second SSB beam; as well as After autonomously switching to monitor the second SSB beam, data is received on a second data beam that is quasi co-located QCL with the second SSB beam.

5. The method of claim 1 , further comprising determining the poor signal quality threshold associated with the location of the wireless device, comprising obtaining the poor signal quality threshold from a data table stored in a memory using the location of the wireless device as a lookup index.

6. The method of claim 1, further comprising determining the poor signal quality threshold associated with the location of the wireless device, comprising determining the poor signal quality threshold using a trained neural network.

7. The method according to claim 6, in, Determining the poor signal quality threshold using the trained neural network includes dynamically applying a plurality of input parameters to the trained neural network to generate an output, the plurality of input parameters including the location of the wireless device, and receiving an indication of the poor signal quality threshold based on the output of the trained neural network.

8. The method according to claim 6, further comprising: include: receiving an instruction from a second base station to switch from monitoring one SSB beam of the second base station to another SSB beam of the second base station; as well as The trained neural network is refined by correlating: The change in data throughput or other link quality parameters resulting from switching from monitoring one SSB beam of the second base station to another SSB beam of the second base station, the position of the wireless device at the time of the switching, and the measured signal parameters of the signals received from multiple SSB beams of the base station at the time of the switching.

9. The method of claim 6, further comprising training the neural network by: The neural network is trained using data throughput or other link quality parameters of signals received from each SSB beam of the base station at the location of the wireless device.

10. The method according to claim 9, in, Training the neural network using the data throughput or other link quality parameter of the signals received from each SSB beam of the base station at the location of the wireless device includes: repeatedly moving the wireless device to a new location, determining the data throughput or other link quality parameter of the signals received from each SSB beam of the base station at each new location, and training the neural network using the data throughput or other link quality parameter of the signals received from each SSB beam of the base station at each new location.

11. The method of claim 10, further comprising measuring one or more parameters other than the position of the wireless device at each new position, in, Training the neural network using the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location includes: training the neural network using the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location and the measured one or more parameters.

12. The method according to claim 11, in, Measuring one or more parameters includes determining or measuring one or more of the following: serving beam identifier; a cell identifier of the serving cell, a signal strength of a signal received from each SSB beam, and a signal quality of a signal received from each SSB beam; The difference in reference signal received power (RSRP) of the signal received from each SSB beam; The difference in reference signal reception quality RSRQ of the signal received from each SSB beam; The difference in the signal-to-noise ratio (SNR) of the signal received from each SSB beam; The initial block error rate iBLER of the signal received from each SSB beam; The residual block error rate rBLER of the signal received from each SSB beam; the mobility of the wireless device at the measurement time; the orientation of the wireless device at the measurement time; The number of detectable SSB beams; The number of configured SSB beams; beam status reports configured by the base station; a number of SSB status reports sent by the wireless device to the base station; a number and frequency of autonomous SSB handoffs performed by the wireless device; the number and frequency of SSB beam switching in response to instructions from the base station; the average duration between the sending of a beam status report and the receipt of a beam switching instruction from the base station; Beam failure detection and restoration statistics; Frequency and time tracking loop statistics; the mobile network code MNC or the mobile country code MCC of the communication network associated with the base station; or An infrastructure provider associated with the base station.

13. A method performed by a computing device to train a neural network for use in autonomous beam switching by a wireless device, include: training the neural network using data throughput or other link quality parameters of signals received from each SSB beam of a base station at the location of the computing device; as well as The trained neural network is provided to the wireless device in a configuration that enables the wireless device to determine a signal quality difference threshold associated with a location of the wireless device and to determine whether to autonomously switch the monitored SSB beam of the base station.

14. The method of claim 13 further comprising repeatedly moving the computing device to new locations, determining the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location, and training the neural network using the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location.

15. The method of claim 13, further comprising measuring one or more parameters other than the position of the wireless device at each new position, in, Training the neural network using the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location includes: training the neural network using the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location and the measured one or more parameters.

16. The method according to claim 13, in, Measuring one or more parameters includes determining or measuring one or more of the following: serving beam identifier; a cell identifier of the serving cell, a signal strength of a signal received from each SSB beam, and a signal quality of a signal received from each SSB beam; The difference in reference signal received power (RSRP) of the signal received from each SSB beam; The difference in reference signal reception quality RSRQ of the signal received from each SSB beam; The difference in the signal-to-noise ratio (SNR) of the signal received from each SSB beam; The initial block error rate iBLER of the signal received from each SSB beam; The residual block error rate rBLER of the signal received from each SSB beam; the mobility of the computing device at the measurement time; the orientation of the computing device at the measurement time; The number of detectable SSB beams; The number of configured SSB beams; beam status reports configured by the base station; the average duration between the sending of a beam status report and the receipt of a beam switching instruction from the base station; Beam failure detection and restoration statistics; Frequency and time tracking loop statistics; the mobile network code MNC or the mobile country code MCC of the communication network associated with the base station; or An infrastructure provider associated with the base station.

17. A wireless device, include: A processor configured with processor-executable instructions to: measuring signal parameters of a signal received from a first synchronization signal block (SSB) beam that is a serving beam of a base station monitored by the wireless device and signals received from one or more other SSB beams of the base station; determining whether a difference in the measured signal parameters of signals received from the first SSB beam and the second SSB beam of the base station satisfies a signal quality difference threshold associated with a location of the wireless device; as well as In response to determining that the difference in the measured signal parameters of the signals received from the first SSB beam and the second SSB beam of the base station meets the signal quality difference threshold, autonomously switching to monitor the second SSB beam of the base station as the serving beam.

18. The wireless device according to claim 17, in, The processor is also configured with processor-executable instructions to determine whether the difference between the measured signal parameters of the signals received from the first SSB beam and the second SSB beam of the base station meets the signal quality difference threshold before receiving a MAC control element MAC-CE instructing the wireless device to switch the service beam or without receiving the MAC-CE.

19. The wireless device according to claim 17, in, The processor is also configured with processor-executable instructions to switch to monitor the second SSB beam of the base station before receiving a MAC control element MAC-CE or without receiving the MAC-CE instructing the wireless device to switch the serving beam.

20. The wireless device according to claim 17, in, The processor is also configured with processor-executable instructions to: receiving data in a signal received from a first data beam of a QCL quasi-co-located with the first SSB beam before autonomously switching to monitor the second SSB beam; as well as After autonomously switching to monitor the second SSB beam, data is received in a signal received from a second data beam of the QCL quasi-co-located with the second SSB beam.

21. The wireless device according to claim 17, in, The processor is also configured with processor-executable instructions to determine the poor signal quality threshold associated with the location of the wireless device by obtaining the poor signal quality threshold from a data table stored in a memory using the location of the wireless device as a lookup index.

22. The wireless device according to claim 17, in, The processor is also configured with processor-executable instructions to determine the poor signal quality threshold associated with the location of the wireless device by using a trained neural network to determine the poor signal quality threshold.

23. The wireless device according to claim 22, in, The processor is also configured with processor-executable instructions to dynamically apply a plurality of input parameters to the trained neural network to generate an output, the plurality of input parameters including the location of the wireless device, and to receive an indication of the poor signal quality threshold based on the output of the trained neural network.

24. The wireless device according to claim 22, in, The processor is also configured with processor-executable instructions to: receiving an instruction from a second base station to switch from monitoring one SSB beam of the second base station to another SSB beam of the second base station; as well as The trained neural network is refined by correlating: The change in data throughput or other link quality parameters resulting from switching from monitoring one SSB beam of the second base station to another SSB beam of the second base station, the position of the wireless device at the time of the switching, and the measured signal parameters of the signals received from multiple SSB beams of the base station at the time of the switching.

25. The wireless device according to claim 22, in, The processor is also configured with processor-executable instructions to: The neural network is trained using data throughput or other link quality parameters of signals received from each SSB beam of the base station at the location of the wireless device.

26. The wireless device according to claim 25, in, The processor is also configured with processor-executable instructions to: repeatedly move the wireless device to new locations, confirm the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location, and train the neural network using the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location.

27. The wireless device according to claim 26, in, The processor is also configured with processor-executable instructions to: measuring, at each new location, one or more parameters other than the location of the wireless device; as well as The neural network is trained using the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location and the measured one or more parameters.

28. A computing device, include: A processor configured with processor-executable instructions to: determining a data throughput or other link quality parameter of a signal received from each of a plurality of synchronization signal block (SSB) beams of a base station at the location of the computing device; training a neural network using the data throughput or other link quality parameter of signals received from each SSB beam of the base station at the location of the computing device; as well as The trained neural network is provided to a wireless device in a configuration that enables the wireless device to determine a poor signal quality threshold associated with a location of the wireless device and to determine whether to autonomously switch the monitored SSB beam of the base station.

29. The computing device of claim 28, in, The processor is also configured with processor-executable instructions to: repeatedly move the computing device to new locations, determine a data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location, and train the neural network using the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location.

30. The computing device of claim 28, in, The processor is also configured with processor-executable instructions to: measuring, at each new location, one or more parameters other than the location of the wireless device; as well as The neural network is trained using the data throughput or other link quality parameter of the signal received from each SSB beam of the base station at each new location and the measured one or more parameters.

31. A wireless device comprising means for performing the method according to any one of claims 1-12.

32. A computing device comprising means for performing the method according to any one of claims 13-16.

33. A non-transitory computer readable medium having program code recorded thereon, in, The program code may be executed by one or more processors of a wireless device to cause the processor to perform a method according to any one of claims 1-12.

34. A non-transitory computer readable medium having program code recorded thereon, in, The program code may be executed by one or more processors of a computing device to cause the processors to perform a method according to any one of claims 13-16.

35. A computer program product comprising a computer readable medium having instructions stored thereon, in, The instructions may be executed by one or more processors of a wireless device to cause the processors to perform a method according to any one of claims 1-12.

36. A computer program product comprising a computer readable medium having instructions stored thereon, in, The instructions are executable by one or more processors of a computing device to cause the processors to perform a method according to any one of claims 13-16.

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