WO2024175951A1 - Adaptation de périodicité de suivi de faisceau - Google Patents

Adaptation de périodicité de suivi de faisceau Download PDF

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Publication number
WO2024175951A1
WO2024175951A1 PCT/IB2023/051583 IB2023051583W WO2024175951A1 WO 2024175951 A1 WO2024175951 A1 WO 2024175951A1 IB 2023051583 W IB2023051583 W IB 2023051583W WO 2024175951 A1 WO2024175951 A1 WO 2024175951A1
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WIPO (PCT)
Prior art keywords
tracking measurement
wireless device
beam tracking
periodicity
network node
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PCT/IB2023/051583
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English (en)
Inventor
Per FRYKING
Akram Bin Sediq
Rupesh KUMAR THAKUR
Anders Berkeman
Carl DROUGGE
Gustav FAHLÉN
Original Assignee
Telefonaktiebolaget Lm Ericsson (Publ)
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by Telefonaktiebolaget Lm Ericsson (Publ) filed Critical Telefonaktiebolaget Lm Ericsson (Publ)
Priority to PCT/IB2023/051583 priority Critical patent/WO2024175951A1/fr
Publication of WO2024175951A1 publication Critical patent/WO2024175951A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H04B7/06952—Selecting one or more beams from a plurality of beams, e.g. beam training, management or sweeping

Definitions

  • the present disclosure relates to wireless communications, and in particular, to adapting the triggering of beam tracking measurement.
  • the Third Generation Partnership Project (3 GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems. Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WD), as well as communication between network nodes and between WDs.
  • 4G Fourth Generation
  • 5G Fifth Generation
  • NR New Radio
  • Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WD), as well as communication between network nodes and between WDs.
  • the 3GPP is also developing standards for Sixth Generation (6G) wireless communication networks.
  • the 5th generation standard, NR supports the use of higher frequency bands in the frequency range 24.250 GHz - 52.600 GHz, usually referred to as frequency range 2 (FR2) or millimeter wave (as specified in, e.g., 3GPP Technical Specification (T.S.) 38.101-1.
  • FR2 frequency range 2
  • T.S. 3GPP Technical Specification
  • NR 5th generation standard
  • FR2 frequency range 2
  • millimeter wave as specified in, e.g., 3GPP Technical Specification (T.S.) 38.101-1.
  • FR2 frequency range 2
  • T.S. 38.101-1 millimeter wave
  • FR2 frequency range 2
  • T.S. 38.101-1 millimeter wave
  • AMF Analog Beam Forming
  • Directional links may require fine alignment of the transmitter and receiver side beams to compensate for wireless device movements, rotations, and environment changes. This is achieved by continuously adjusting wide/narrow beams, collectively known as Beam Tracking.
  • One way to do beam tracking and adjust the network node beam is to periodically or aperiodically transmit multiple reference signals from the network node to the wireless devices, each with different beam, and request the wireless device to measure these reference signals and report the measurement to the network node. Based on the reported signal strength measurements from the different beams, the network node may switch the beam used to communicate with the wireless device, e.g., if a new beam is found to have higher signal strength compared to the previous beam.
  • a common way to perform such adjustment is to periodically or aperiodically transmitting multiple reference signals from the network node to the wireless devices, all are transmitted by the same network node beam.
  • the wireless device use different reception beams to measure the multiple reference signals and chose the beam that results in the strongest received signal strength.
  • beam tracking is usually triggered periodically with a preconfigured beam-tracking period. For instance, a common value used in practice is 40 msec for wireless devices with active data transmission and 80 msec for wireless devices without active data transmission. In general, lower peridocity provides better robustness and better tracking of beam changes; however, it comes at the cost of increased signaling overhead as the network node is required to transmit downlink (DL) reference signals and the wireless device is required to transmit the measurement report in the uplink (UL), both of which takes away radio resources that could otherwise be used for DL data transmission and UL data transmission, respectively, not to mention the wasted energy consumed to make such transmission.
  • DL downlink
  • UL uplink
  • Some embodiments advantageously provide methods, systems, and apparatuses for adapting the triggering of beam tracking measurement.
  • Beam- tracking periodicity controller An integrator controller is used to continuously adjust the peridocity for each wireless device to achieve a targetHitProbability, defined as the target probability that beam measurement has resulted in beam change. This is achieved by increasing the peridocity when a measurement report resulted in no beam-change and decreasing the periodicity otherwise. The amount of increase and decrease are calculated a priory based on the desired targetHitProbability and a stepsize parameter.
  • RL- based aperiodic beam- tracking an RL- based agent is devised to adapt the triggering of beam tracking measurements. Specifically, at each time step for each wireless device, the agent receives a state (or a context) as an input and use it to derive a binary action, where the action is either to request a beam-tracking measurement or not. The agent also receives two feedbacks associated with the action that was taken: new state and a reward. The agent uses these feedbacks to adjust its policy of mapping states to actions.
  • two example algorithms that may be used include: o Linear upper confidence bound (LinUCB) o Tabular Q-learning
  • a network node in communication with a wireless device includes processing circuitry configured to determine whether to trigger a beam tracking measurement of at least one beam of a plurality of candidate beams for communication with the wireless device. The determination is based on at least one of: a periodicity for triggering the beam tracking measurement, the periodicity being adaptable based on whether a previous beam tracking measurement resulted in a beam selection change; and a machine-learning procedure for controlling the triggering of beam tracking measurements.
  • the network node is configured to select a beam from the plurality of candidate beams. The selection is based on a result of the determination.
  • the network node is configured to communicate with the wireless device using the selected beam.
  • the periodicity is decreased if the previous beam tracking measurement resulted in a beam selection change, and the periodicity is increased if the previous beam tracking measurement did not result in a beam selection change.
  • the periodicity is adapted based on a target probability that the beam measurement results in a beam selection change, targetHitProbability, and an integrator controller parameter, ki, corresponding to a pre-determined value.
  • the periodicity is decreased by a first amount, where the first amount is equal to ki * (1 - targetHitProbability) based on the previous beam tracking measurement resulting in a beam selection change.
  • the periodicity is increased by a second amount, where the second amount is equal to ki * targetHitProbability based on the previous beam tracking measurement not resulting in a beam selection change.
  • the determination based on the machine learning procedure includes using the machine learning procedure to analyze a state associated with the wireless device according to at least one policy to determine whether the beam tracking measurement is to be triggered.
  • the machine learning procedure adjusts at least one policy based on feedback associated with the beam tracking measurement.
  • the feedback indicates a new state associated with the wireless device and a reward associated with a result of the beam tracking measurement.
  • the reward is based on at least one of: whether the wireless device is in a data active state; whether the beam tracking measurement blocked another wireless device; whether a previous beam tracking measurement resulted in a beam selection change; and whether a hybrid automatic repeat request (HARQ) failure has occurred.
  • HARQ hybrid automatic repeat request
  • the machine learning procedure is configured to use at least one of Linear Upper Confidence Bound, Linucb, and Tabular Q-Learning.
  • a wireless device in communication with a network node includes processing circuitry.
  • the processing circuitry is configured to receive an indication to perform a beam tracking measurement of at least one beam of a plurality of candidate beams for communication with the network node.
  • a triggering of the indication is based on at least one of: a periodicity for performing the beam tracking measurement, the periodicity being adaptable based on whether a previous beam tracking measurement resulted in a beam selection change; and a machine-learning procedure for controlling the performance of beam tracking measurements.
  • the wireless device is configured to receive an indication of a selected beam from the plurality of candidate beams. The selection is based on performance of the beam tracking measurement.
  • the wireless device is configured to communicate with the network node using the selected beam.
  • the periodicity is decreased if the previous beam tracking measurement resulted in a beam selection change, and the periodicity is increased if the previous beam tracking measurement did not result in a beam selection change.
  • the periodicity is adapted based on a target probability that the beam measurement results in a beam selection change, targetHitProbability, and an integrator controller parameter, ki, corresponding to a pre-determined value.
  • the periodicity is decreased by a first amount, where the first amount is equal to ki * (1 - targetHitProbability) based on the previous beam tracking measurement resulting in a beam selection change.
  • the periodicity is increased by a second amount, where the second amount is equal to ki * targetHitProbability based on the previous beam tracking measurement not resulting in a beam selection change.
  • the triggering being based on the machine learning procedure includes using the machine learning procedure to analyze a state associated with the wireless device according to at least one policy to determine whether the beam tracking measurement is to be performed.
  • the machine learning procedure adjusts at least one policy based on feedback associated with the beam tracking measurement.
  • the feedback indicates a new state associated with the wireless device and a reward associated with a result of the beam tracking measurement.
  • the reward is based on at least one of: whether the wireless device (22) is in a data active state; whether the beam tracking measurement blocked another wireless device (22); whether a previous beam tracking measurement resulted in a beam selection change; and whether a hybrid automatic repeat request, HARQ, failure has occurred.
  • the machine learning procedure is configured to use at least one of Linear Upper Confidence Bound, Linucb, and Tabular Q-Learning.
  • a method performed in a network node in communication with a wireless device includes determining whether to trigger a beam tracking measurement of at least one beam of a plurality of candidate beams for communication with the wireless device. The determination is based on at least one of: a periodicity for triggering the beam tracking measurement, the periodicity being adaptable based on whether a previous beam tracking measurement resulted in a beam selection change; and a machinelearning procedure for controlling the triggering of beam tracking measurements.
  • the method includes selecting a beam from the plurality of candidate beams, the selection being based on a result of the determination.
  • the method includes communicating with the wireless device using the selected beam.
  • the periodicity is decreased if the previous beam tracking measurement resulted in a beam selection change, and the periodicity is increased if the previous beam tracking measurement did not result in a beam selection change.
  • the periodicity is adapted based on a target probability that the beam measurement results in a beam selection change, targetHitProbability, and an integrator controller parameter, ki, corresponding to a pre-determined value.
  • the periodicity is decreased by a first amount, where the first amount is equal to ki * (1 - targetHitProbability) based on the previous beam tracking measurement resulting in a beam selection change.
  • the periodicity is increased by a second amount, where the second amount is equal to ki * targetHitProbability based on the previous beam tracking measurement not resulting in a beam selection change.
  • the determination based on the machine learning procedure includes using the machine learning procedure to analyze a state associated with the wireless device according to at least one policy to determine whether the beam tracking measurement is to be triggered.
  • the machine learning procedure adjusts at least one policy based on feedback associated with the beam tracking measurement.
  • the feedback indicates a new state associated with the wireless device and a reward associated with a result of the beam tracking measurement.
  • the reward is based on at least one of: whether the wireless device (22) is in a data active state; whether the beam tracking measurement blocked another wireless device (22); whether a previous beam tracking measurement resulted in a beam selection change; and whether a hybrid automatic repeat request, HARQ, failure has occurred.
  • the machine learning procedure is configured to use at least one of Linear Upper Confidence Bound, Linucb, and Tabular Q-Learning.
  • a method performed in a wireless device in communication with a network node includes receiving an indication to perform a beam tracking measurement of at least one beam of a plurality of candidate beams for communication with the network node.
  • a triggering of the indication being is on at least one of: a periodicity for performing the beam tracking measurement, the periodicity being adaptable based on whether a previous beam tracking measurement resulted in a beam selection change; and a machine-learning procedure for controlling the performance of beam tracking measurements.
  • the method includes receiving an indication of a selected beam from the plurality of candidate beams, the selection being based on performance of the beam tracking measurement.
  • the method includes communicating with the network node using the selected beam.
  • the periodicity is decreased if the previous beam tracking measurement resulted in a beam selection change, and the periodicity is increased if the previous beam tracking measurement did not result in a beam selection change.
  • the periodicity is adapted based on a target probability that the beam measurement results in a beam selection change, targetHitProbability, and an integrator controller parameter, ki, corresponding to a pre-determined value.
  • the periodicity is decreased by a first amount, where the first amount is equal to ki * (1 - targetHitProbability) based on the previous beam tracking measurement resulting in a beam selection change.
  • the periodicity is increased by a second amount, where the second amount is equal to ki * targetHitProbability based on the previous beam tracking measurement not resulting in a beam selection change.
  • the triggering being based on the machine learning procedure includes using the machine learning procedure to analyze a state associated with the wireless device according to at least one policy to determine whether the beam tracking measurement is to be triggered.
  • the machine learning procedure adjusts at least one policy based on feedback associated with the beam tracking measurement.
  • the feedback indicates a new state associated with the wireless device and a reward associated with a result of the beam tracking measurement.
  • the reward is based on at least one of: whether the wireless device is in a data active state; whether the beam tracking measurement blocked another wireless device; whether a previous beam tracking measurement resulted in a beam selection change; and whether a hybrid automatic repeat request, HARQ, failure has occurred.
  • the machine learning procedure is configured to use at least one of Linear Upper Confidence Bound, Linucb, and Tabular Q-Learning.
  • FIG. 1 is a schematic diagram of an example network architecture illustrating a communication system connected via an intermediate network to a host computer according to the principles in the present disclosure
  • FIG. 2 is a block diagram of a host computer communicating via a network node with a wireless device over an at least partially wireless connection according to some embodiments of the present disclosure
  • FIG. 3 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure
  • FIG. 4 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a wireless device according to some embodiments of the present disclosure
  • FIG. 5 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data from the wireless device at a host computer according to some embodiments of the present disclosure
  • FIG. 6 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure
  • FIG. 7 is a flowchart of an example process in a network node according to some embodiments of the present disclosure.
  • FIG. 8 is a flowchart of an example process in a wireless device for according to some embodiments of the present disclosure.
  • FIG. 9 is a flowchart of an example process according to some embodiments of the present disclosure.
  • FIG. 10 is a graph of a first example scenario according to some embodiments of the present disclosure.
  • FIG. 11 is a graph of a second example scenario according to some embodiments of the present disclosure.
  • FIG. 12 is a graph of a third example scenario according to some embodiments of the present disclosure.
  • FIG. 13 is a graph of a fourth example scenario according to some embodiments of the present disclosure.
  • FIG. 14 is a graph of a fifth example scenario according to some embodiments of the present disclosure.
  • FIG. 15 is a graph of a sixth example scenario according to some embodiments of the present disclosure.
  • FIG. 16 is a graph of a seventh example scenario according to some embodiments of the present disclosure
  • FIG. 17 is a graph of an eighth example scenario according to some embodiments of the present disclosure
  • FIG. 18 is a graph of a ninth example scenario according to some embodiments of the present disclosure.
  • FIG. 19 is a graph of a tenth example scenario according to some embodiments of the present disclosure.
  • FIG. 20 is a graph of an eleventh example scenario according to some embodiments of the present disclosure.
  • FIG. 21 is a graph of a twelfth example scenario according to some embodiments of the present disclosure.
  • FIG. 22 is a graph of a thirteenth example scenario according to some embodiments of the present disclosure.
  • FIG. 23 is a graph of a fourteenth example scenario according to some embodiments of the present disclosure.
  • FIG. 24 is a graph of a fifteenth example scenario according to some embodiments of the present disclosure.
  • FIG. 25 is a graph of a sixteenth example scenario according to some embodiments of the present disclosure.
  • FIG. 26 is a graph of a seventeenth example scenario according to some embodiments of the present disclosure.
  • relational terms such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements.
  • the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein.
  • the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.
  • the joining term, “in communication with” and the like may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • electrical or data communication may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • Coupled may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.
  • network node can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multi- standard radio (MSR) radio node such as MSR BS, multi-cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (
  • BS base station
  • wireless device or a user equipment (UE) are used interchangeably.
  • the WD herein can be any type of wireless device capable of communicating with a network node or another WD over radio signals, such as wireless device (WD).
  • the WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.
  • D2D device to device
  • M2M machine to machine communication
  • M2M machine to machine communication
  • Tablet mobile terminals
  • smart phone laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles
  • CPE Customer Premises Equipment
  • LME Customer Premises Equipment
  • NB-IOT Narrowband loT
  • radio network node can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell/multicast Coordination Entity (MCE), IAB node, relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).
  • RNC evolved Node B
  • MCE Multi-cell/multicast Coordination Entity
  • IAB node IAB node
  • relay node access point
  • radio access point radio access point
  • RRU Remote Radio Unit
  • RRH Remote Radio Head
  • WCDMA Wide Band Code Division Multiple Access
  • WiMax Worldwide Interoperability for Microwave Access
  • UMB Ultra Mobile Broadband
  • GSM Global System for Mobile Communications
  • the general description elements in the form of “one of A and B” corresponds to A or B.
  • at least one of A and B corresponds to A, B or AB, or to one or more of A and B, or one or both of A and B .
  • at least one of A, B and C corresponds to one or more of A, B and C, and/or A, B, C or a combination thereof.
  • functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.
  • Some embodiments provide for adapting the triggering of beam tracking measurement.
  • FIG. 1 a schematic diagram of a communication system 10, according to an embodiment, such as a 3GPP-type cellular network that may support standards such as LTE and/or NR (5G), which comprises an access network 12, such as a radio access network, and a core network 14.
  • the access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18).
  • Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20.
  • a first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a.
  • a second WD 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16.
  • a WD 22 can be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16.
  • a WD 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR.
  • WD 22 can be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.
  • the communication system 10 may itself be connected to a host computer 24, which may be embodied in the hardware and/or software of a standalone server, a cloud-implemented server, a distributed server or as processing resources in a server farm.
  • the host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider.
  • the connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30.
  • the intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network.
  • the intermediate network 30, if any, may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may comprise two or more sub-networks (not shown).
  • the communication system of FIG. 1 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24.
  • the connectivity may be described as an over-the-top (OTT) connection.
  • the host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries.
  • the OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications.
  • a network node 16 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 24 to be forwarded (e.g., handed over) to a connected WD 22a. Similarly, the network node 16 need not be aware of the future routing of an outgoing uplink communication originating from the WD 22a towards the host computer 24.
  • a network node 16 is configured to include a configuration unit 32 which is configured to perform one or more network node 16 functions described herein, including functions related to adapting the triggering of beam tracking measurement.
  • a wireless device 22 is configured to include an implementation unit 34 which is configured to perform one or more wireless device 22 functions described herein, including functions related to adapting the triggering of beam tracking measurement.
  • a host computer 24 comprises hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10.
  • the host computer 24 further comprises processing circuitry 42, which may have storage and/or processing capabilities.
  • the processing circuitry 42 may include a processor 44 and memory 46.
  • the processing circuitry 42 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • processors and/or processor cores and/or FPGAs Field Programmable Gate Array
  • ASICs Application Specific Integrated Circuitry
  • the processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read- Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 46 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read- Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24.
  • Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein.
  • the host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24.
  • the instructions may be software associated with the host computer 24.
  • the software 48 may be executable by the processing circuitry 42.
  • the software 48 includes a host application 50.
  • the host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the host application 50 may provide user data which is transmitted using the OTT connection 52.
  • the “user data” may be data and information described herein as implementing the described functionality.
  • the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider.
  • the processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and or the wireless device 22.
  • the processing circuitry 42 of the host computer 24 may include a control unit 54 configured to enable the service provider to observe/monitor/ control/transmit to/receive from the network node 16 and/or the wireless device 22.
  • the communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22.
  • the hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16.
  • the radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the communication interface 60 may be configured to facilitate a connection 66 to the host computer 24.
  • the connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.
  • the hardware 58 of the network node 16 further includes processing circuitry 68.
  • the processing circuitry 68 may include a processor 70 and a memory 72.
  • the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • FPGAs Field Programmable Gate Array
  • ASICs Application Specific Integrated Circuitry
  • the processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the memory 72 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection.
  • the software 74 may be executable by the processing circuitry 68.
  • the processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16.
  • Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein.
  • the memory 72 is configured to store data, programmatic software code and/or other information described herein.
  • the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16.
  • processing circuitry 68 of the network node 16 may include configuration unit 32 configured to perform one or more network node 16 functions described herein, including functions related to adapting the triggering of beam tracking measurement.
  • the communication system 10 further includes the WD 22 already referred to.
  • the WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located.
  • the radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the hardware 80 of the WD 22 further includes processing circuitry 84.
  • the processing circuitry 84 may include a processor 86 and memory 88.
  • the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • the processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 88 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22.
  • the software 90 may be executable by the processing circuitry 84.
  • the software 90 may include a client application 92.
  • the client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24.
  • an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the client application 92 may receive request data from the host application 50 and provide user data in response to the request data.
  • the OTT connection 52 may transfer both the request data and the user data.
  • the client application 92 may interact with the user to generate the user data that it provides.
  • the processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22.
  • the processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein.
  • the WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22.
  • the processing circuitry 84 of the wireless device 22 may include an implementation unit 34 configured to perform one or more wireless device 22 functions described herein, including functions related to adapting the triggering of beam tracking measurement.
  • the inner workings of the network node 16, WD 22, and host computer 24 may be as shown in FIG. 2 and independently, the surrounding network topology may be that of FIG. 1.
  • the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).
  • the wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure.
  • One or more of the various embodiments improve the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc.
  • a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve.
  • the measurement procedure and/or the network functionality for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host computer 24 or in the software 90 of the WD 22, or both.
  • sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 52 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 48, 90 may compute or estimate the monitored quantities.
  • the reconfiguring of the OTT connection 52 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functionalities may be known and practiced in the art.
  • measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like.
  • the measurements may be implemented in that the software 48, 90 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 52 while it monitors propagation times, errors, etc.
  • the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22.
  • the cellular network also includes the network node 16 with a radio interface 62.
  • the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the WD 22, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the WD 22.
  • the host computer 24 includes processing circuitry 42 and a communication interface 40 that is configured to a communication interface 40 configured to receive user data originating from a transmission from a WD 22 to a network node 16.
  • the WD 22 is configured to, and/or comprises a radio interface 82 and/or processing circuitry 84 configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the network node 16, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the network node 16.
  • FIGS. 1 and 2 show various “units” such as configuration unit 32, and implementation unit 34 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.
  • FIG. 3 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIGS. 1 and 2, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIG. 2.
  • the host computer 24 provides user data (Block S100).
  • the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block S102).
  • the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block S104).
  • the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block S106).
  • the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block s 108).
  • FIG. 4 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2.
  • the host computer 24 provides user data (Block SI 10).
  • the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50.
  • the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block S 112).
  • the transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure.
  • the WD 22 receives the user data carried in the transmission (Block S 114).
  • FIG. 5 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2.
  • the WD 22 receives input data provided by the host computer 24 (Block S 116).
  • the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block SI 18).
  • the WD 22 provides user data (Block S120).
  • the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122).
  • client application 92 may further consider user input received from the user.
  • the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block S124).
  • the host computer 24 receives the user data transmitted from the WD 22, in accordance with the teachings of the embodiments described throughout this disclosure (Block S126).
  • FIG. 6 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2.
  • the network node 16 receives user data from the WD 22 (Block S128).
  • the network node 16 initiates transmission of the received user data to the host computer 24 (Block S130).
  • the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block S132).
  • FIG. 7 is a flowchart of an example process in a network node 16 according to some embodiments of the present disclosure.
  • One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the configuration unit 32), processor 70, radio interface 62 and/or communication interface 60.
  • Network node 16 is configured to determine (Block S134) whether to trigger a beam tracking measurement of at least one beam of a plurality of candidate beams for communication with the wireless device 22, the determination being based on at least one of: a periodicity for triggering the beam tracking measurement, the periodicity being adaptable based on whether a previous beam tracking measurement resulted in a beam selection change; and a machine-learning procedure for controlling the triggering of beam tracking measurements.
  • Network node 16 is configured to select (Block S136) a beam from the plurality of candidate beams, the selection being based on a result of the determination.
  • Network node 16 is configured to communicate (Block S138) with
  • the periodicity is decreased if the previous beam tracking measurement resulted in a beam selection change, and the periodicity is increased if the previous beam tracking measurement did not result in a beam selection change.
  • the periodicity is adapted based on a target probability that the beam measurement results in a beam selection change, targetHitProbability, and an integrator controller parameter, ki, corresponding to a pre-determined value.
  • the periodicity is decreased by a first amount, where the first amount is equal to ki * (1 - targetHitProbability) based on the previous beam tracking measurement resulting in a beam selection change.
  • the periodicity is increased by a second amount, where the second amount is equal to ki * targetHitProbability based on the previous beam tracking measurement not resulting in a beam selection change.
  • the determination based on the machine learning procedure includes using the machine learning procedure to analyze a state associated with the wireless device according to at least one policy to determine whether the beam tracking measurement is to be triggered.
  • the machine learning procedure adjusts at least one policy based on feedback associated with the beam tracking measurement.
  • the feedback indicates a new state associated with the wireless device and a reward associated with a result of the beam tracking measurement.
  • the reward is based on at least one of: whether the wireless device 22 is in a data active state; whether the beam tracking measurement blocked another wireless device 22; whether a previous beam tracking measurement resulted in a beam selection change; and whether a hybrid automatic repeat request, HARQ, failure has occurred.
  • the machine learning procedure is configured to use at least one of Linear Upper Confidence Bound, Linucb, and Tabular Q-Leaming.
  • FIG. 8 is a flowchart of an example process in a wireless device 22 according to some embodiments of the present disclosure.
  • One or more blocks described herein may be performed by one or more elements of wireless device 22 such as by one or more of processing circuitry 84 (including the implementation unit 34), processor 86, radio interface 82 and/or communication interface 60.
  • Wireless device 22 is configured to receive (Block S140) an indication to perform a beam tracking measurement of at least one beam of a plurality of candidate beams for communication with the network node 16, a triggering of the indication being based on at least one of: a periodicity for performing the beam tracking measurement, the periodicity being adaptable based on whether a previous beam tracking measurement resulted in a beam selection change; and a machine-learning procedure for controlling the performance of beam tracking measurements.
  • Wireless device 22 is configured to receive (Block S142) an indication of a selected beam from the plurality of candidate beams, the selection being based on performance of the beam tracking measurement.
  • Wireless device 22 is configured to communicate (Block S144) with the network node 16 using the selected beam.
  • the periodicity is decreased if the previous beam tracking measurement resulted in a beam selection change, and the periodicity is increased if the previous beam tracking measurement did not result in a beam selection change.
  • the periodicity is adapted based on a target probability that the beam measurement results in a beam selection change, targetHitProbability, and an integrator controller parameter, ki, corresponding to a pre-determined value.
  • the periodicity is decreased by a first amount, where the first amount is equal to ki * (1 - targetHitProbability) based on the previous beam tracking measurement resulting in a beam selection change.
  • the periodicity is increased by a second amount, where the second amount is equal to ki * targetHitProbability based on the previous beam tracking measurement not resulting in a beam selection change.
  • the triggering being based on the machine learning procedure includes using the machine learning procedure to analyze a state associated with the wireless device according to at least one policy to determine whether the beam tracking measurement is to be triggered.
  • the machine learning procedure adjusts at least one policy based on feedback associated with the beam tracking measurement.
  • the feedback indicates a new state associated with the wireless device and a reward associated with a result of the beam tracking measurement.
  • the reward is based on at least one of: whether the wireless device is in a data active state; whether the beam tracking measurement blocked another wireless device; whether a previous beam tracking measurement resulted in a beam selection change; and whether a hybrid automatic repeat request, HARQ, failure has occurred.
  • the machine learning procedure is configured to use at least one of Linear Upper Confidence Bound, Linucb, and Tabular Q-Leaming.
  • One or more wireless device 22 functions described below may be performed by one or more of processing circuitry 84, processor 86, implementation unit 34, etc.
  • One or more network node 16 functions described below may be performed by one or more of processing circuitry 68, processor 70, configuration unit 32, etc.
  • Periodicity-based beam tracking peridocity may be inversely proportional to the beam-change rate.
  • One way to adjust the periodicity in a reactive manner is to implement a controller that adjusts the on the observed beam-change rate. Concretely:
  • targetHitProbability G [0,1] is a parameter representing the target probability that beam measurement has resulted in beam change
  • Ki is an integrator controller parameter with default value of 0.5. Ki is a constant for the integral controller that can be tuned to control convergence time and overshoot, using simulations before deploying the invention in a live network, or it can be tuned during the operation of the invention in a live network. At least one embodiment uses a value of 0.5 to achieving a fast convergence without excessive overshoot, however other values may be selected, e.g., based on other tuning methods.
  • the periodicity is updated as follows:
  • CSI-RS channel state information reference signal
  • Various embodiments may facilitate efficiency in an online fashion.
  • KPIs weighted key performance indicators
  • objectives may be chosen that can translate into (almost) immediate rewards of actions taken as follows:
  • the objective was chosen such that it can be optimized efficiently in an online fashion.
  • the chosen objective is that may require tuning of the weight of different individual objectives.
  • the default weights were tuned based on multi-dimensional search to achieve good DL and UL File Transfer Protocol (FTP) throughput for different number of wireless devices 22 and different speeds. — In practice, only one of the weights might be tuned according to the desired impact (e.g., to improve UL data resource utilization, can be increased, etc).
  • FTP File Transfer Protocol
  • SSB synchronization signal block
  • the RL agent is executed for every wireless device every time_step and decides whether to request measurement or not for a given state/context.
  • the reward is received in the next time_step after the action is executed.
  • step_time is set to 40 msec.
  • the maximum periodicity is set 320 msec (configurable). That is, the RL agent’s action will be overridden to request measurement if that last measurement is outdated by 320 msec or more.
  • NrofConsecutiveNoTriggers state: number of consecutive triggers where the RL agent decides not to request beam- tracking measurement 6 ⁇ 0,1, ... ,8 ⁇ . It is reset when the RL agent decides to request beam-tracking measurement (3 bits per UE)
  • CQI Quantized channel quality indicator
  • deltaTimeBetweenBeamS witches G ⁇ 0, . .,64 ⁇ (6 bits per wireless device 22) deltaTimeBeamDidntChange
  • dRsrpBestBeam (1 — a) dRsrpBestBeam + a(new_RSRP_best_beam — old_RSRP_best_beam)
  • dRsrpTop2Nbs (1 — a) dRsrpTop2Nbs + a RSRP_best_beam — RSRP_2nd_best_beam)
  • Reward may be collected over step_time and it reflects the objective described herein:
  • the current formulation has the input as a mixture of a state (nrofConsecutiveNoTriggers, impacted by the action) and contexts (not impacted by the action).
  • CMAB Contextual-multi-arm bandit
  • Algorithms may be narrowed down to the ones that are online in nature, i.e., the ones that do not require storing the data. This allows implementing the learning efficiently in Ericsson Many-Core Architecture (EMCA)
  • MDP Markov decision process
  • Random speed cell area is divided into four regions (quarters), where each region has different wireless device 22 speeds.
  • a wireless device 22 is dropped uniformly and its speed is determined randomly in the range [0, 3 km/h], [3 km/h, 10 km/h], [10 km/h, 50 km/h], or [50 km/h, 100 km/h], if it is dropped in region 1, 2,3, or 4, respectively.
  • the first five seconds is not logged. All wireless devices 22 in the first five seconds are dropped, and new wireless devices 22 are generated. This is to examine the performance of RL algorithms after they have converged.
  • Baseline periodicity 40 msec. It doubles if the wireless device 22 is data-inactive for 8-msec. It double gradually when the number of wireless devices 22 is greater than 50.
  • total reward is plotted against the number of wireless devices 22 for different algorithms.
  • LinUCB and Q-Learning algorithms achieve substantially higher total rewards compared to baseline and periodicity-controller schemes, especially for large number of wireless devices 22.
  • gain in average DL and UL FTP throughputs are plotted against the number of wireless devices 22 for different algorithms.
  • gain is measured with respect to the baseline algorithm.
  • Example Scenario 2 100 km/h
  • total reward is plotted against the number of wireless devices 22 for different algorithms. Both LinUCB and Q-Learning algorithms achieve substantially higher total rewards compared to baseline and periodicity-controller schemes, especially for large number of wireless devices 22.
  • gain in average DL and UL FTP throughputs are plotted against the number of wireless devices 22 for different algorithms.
  • gain is measured with respect to the baseline algorithm.
  • LinUCB result in large loss in UL FTP for 75 and 100 wireless devices 22.
  • total reward is plotted against the number of wireless devices 22 for different algorithms.
  • Both LinUCB and Q-Learning algorithms achieve substantially higher total rewards compared to baseline and periodicity-controller schemes, especially for large number of wireless devices 22.
  • Adding beamStability feature improves the reward for both LinUCB and Q-Leaming algorithms.
  • FIG. 21 and FIG. 22 the CDF of the time between consecutive measurements is plotted for different algorithms.
  • LinUCB and Q-Leaming algorithms converge to quite different policies for large number of wireless devices 22. Both algorithms result in wider range of inter-measurement trigger- times.
  • gain in average DL and UL FTP throughputs are plotted against the number of wireless devices 22 for different algorithms.
  • gain is measured with respect to the baseline algorithm.
  • Example 1 A system and method for adapting the periodicity for beam tracking comprising a controller that increases the periodicity by an upStep when a beam measurement does not result in a beam change and decreases the periodicity by a downStep when a beam measurement results in a beam change.
  • Example 2 The system and method in Example 1, where the following parameters are set by the operator: a. targetHitProbability: the target probability that beam measurement has resulted in beam change b. ki: integrator controller parameter
  • Example 4 A system and method for adapting the trigger of beam tracking by using a reinforcement learning agent that at each time for each wireless device 22, it receives a state (or a context) as an input and use it to derive a binary action, where the action is either to request a beam-tracking measurement or not.
  • Example 5 The system and method in Example 4 where the agent receives two feedbacks associated with the action that was taken: new state and a reward, and it uses these feedbacks to adjust its policy of mapping states to actions.
  • Example 6 The system and method of any one of Examples 4-5, where the definitions of state and reward are as described herein.
  • Example 7 The system and method of any one of Examples 4-6, where the operator can adjust the weight of different components of the reward as described herein.
  • the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware.
  • the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer.
  • Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.
  • These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++.
  • the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer.
  • the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.

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Abstract

Un procédé, un système et un appareil sont divulgués. Dans au moins un mode de réalisation, un nœud de réseau est en communication avec un dispositif sans fil. Le nœud de réseau est configuré pour déterminer s'il faut déclencher une mesure de suivi de faisceau d'au moins un faisceau d'une pluralité de faisceaux candidats aux fins d'une communication avec le dispositif sans fil sur la base d'au moins l'une parmi : une périodicité pour déclencher la mesure de suivi de faisceau, la périodicité pouvant être adaptée sur la base du fait qu'une mesure de suivi de faisceau précédente a entraîné un changement de sélection de faisceau ; et une procédure d'apprentissage machine pour commander le déclenchement de mesures de suivi de faisceau. Le nœud de réseau est configuré pour sélectionner un faisceau parmi la pluralité de faisceaux candidats, la sélection étant basée sur un résultat de la détermination. Le nœud de réseau est configuré pour communiquer avec le dispositif sans fil à l'aide du faisceau sélectionné.
PCT/IB2023/051583 2023-02-21 2023-02-21 Adaptation de périodicité de suivi de faisceau WO2024175951A1 (fr)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170127400A1 (en) * 2014-09-26 2017-05-04 Mediatek Inc. Beam Misalignment Detection for Wireless Communication System with Beamforming
WO2021077372A1 (fr) * 2019-10-24 2021-04-29 Telefonaktiebolaget Lm Ericsson (Publ) Procédé et nœud de réseau d'accès pour gestion de faisceaux

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170127400A1 (en) * 2014-09-26 2017-05-04 Mediatek Inc. Beam Misalignment Detection for Wireless Communication System with Beamforming
WO2021077372A1 (fr) * 2019-10-24 2021-04-29 Telefonaktiebolaget Lm Ericsson (Publ) Procédé et nœud de réseau d'accès pour gestion de faisceaux

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