Uplink transmission configuration method and apparatus

Abstract

Embodiments of the present application provide an uplink transmission configuration method and apparatus. The uplink transmission configuration method comprises: a terminal device receives configuration information from a network device, the configuration information being used for configuring an AI / ML functionality / model and / or uplink power control; and, on the basis of the configuration information, the terminal device obtains uplink power control information for uplink transmission.

Description

Uplink transmission configuration method and device Technical Field

[0001] The embodiments of the present application relate to the field of communication technologies. Background Art

[0002] NR Release 18 investigates artificial intelligence / machine learning (AI / ML) over the air interface. AI / ML can be used for the following use cases: channel state information (CSI) feedback enhancement, beam management, and positioning enhancement. CSI feedback enhancement can include CSI prediction and CSI compression; beam management can include spatial beam prediction and temporal beam prediction; and positioning enhancement can include direct positioning and AI / ML-assisted positioning.

[0003] In some sub-use cases, a two-sided model can be used, with the AI / ML model located on both the end device and the network equipment. In other sub-use cases, a one-sided model can be used, with the AI / ML model located on either the end device or the network equipment. For beam management, the AI / ML model can be located on the end device and / or the network equipment.

[0004] It should be noted that the above introduction to the technical background is merely intended to provide a clear and complete description of the technical solutions of this application and facilitate understanding by those skilled in the art. Simply because these solutions are described in the background technology section of this application, it should not be assumed that the above technical solutions are well known to those skilled in the art.

[0005] Summary of the Invention

[0006] The inventors found that terminal devices and / or network devices can use AI / ML functionality / models to predict beams based on beam measurement results, but there is currently no clear solution for how to consider uplink power control.

[0007] To address at least one of the above problems, embodiments of the present application provide an uplink transmission configuration method and apparatus.

[0008] According to one aspect of an embodiment of the present application, a method for configuring uplink transmission is provided, including:

[0009] The terminal device receives configuration information from the network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control;

[0010] The terminal device obtains uplink power control information for uplink transmission based on the configuration information.

[0011] According to another aspect of an embodiment of the present application, an uplink transmission configuration device is provided, including:

[0012] a receiving unit configured to receive configuration information from a network device; the configuration information being used to configure AI / ML functionality / model and / or uplink power control;

[0013] A processing unit is configured to obtain uplink power control information for uplink transmission according to the configuration information.

[0014] According to another aspect of an embodiment of the present application, a method for configuring uplink transmission is provided, including:

[0015] A network device sends configuration information to a terminal device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control; the configuration information is used by the terminal device to obtain uplink power control information for uplink transmission.

[0016] According to another aspect of an embodiment of the present application, an uplink transmission configuration device is provided, including:

[0017] A sending unit that sends configuration information to a terminal device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control; the configuration information is used by the terminal device to obtain uplink power control information for uplink transmission.

[0018] According to another aspect of an embodiment of the present application, a communication system is provided, including:

[0019] A network device that sends configuration information to a terminal device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control;

[0020] A terminal device obtains uplink power control information for uplink transmission based on the configuration information.

[0021] One of the beneficial effects of the embodiments of the present application is that: the terminal device receives configuration information from the network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control, thereby more accurately obtaining uplink power control information including path loss, thereby improving the accuracy and reliability of uplink transmission.

[0022] With reference to the following description and accompanying drawings, specific embodiments of the present application are disclosed in detail, indicating the manner in which the principles of the present application can be employed. It should be understood that the embodiments of the present application are not limited in scope. Within the spirit and scope of the appended claims, the embodiments of the present application include many variations, modifications and equivalents.

[0023] Features described and / or illustrated with respect to one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or substituted for features in other embodiments.

[0024] It should be emphasized that the term "include / comprising" when used herein refers to the presence of features, integers, steps or components, but does not exclude the presence or addition of one or more other features, integers, steps or components. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The elements and features described in one figure or one embodiment of the present application can be combined with the elements and features shown in one or more other figures or embodiments. In addition, in the accompanying drawings, similar reference numerals represent corresponding parts in several figures and can be used to indicate corresponding parts used in more than one embodiment.

[0026] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0027] FIG2 is a schematic diagram of an uplink transmission configuration method according to an embodiment of the present application;

[0028] FIG3 is another schematic diagram of the uplink transmission method according to an embodiment of the present application;

[0029] FIG4 is another schematic diagram of the uplink transmission configuration method according to an embodiment of the present application;

[0030] FIG5 is a schematic diagram of an uplink transmission configuration device according to an embodiment of the present application;

[0031] FIG6 is another schematic diagram of an uplink transmission configuration device according to an embodiment of the present application;

[0032] FIG7 is a schematic diagram of a terminal device according to an embodiment of the present application;

[0033] FIG8 is a schematic diagram of a network device according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] The above and other features of the present application will become apparent through the following description with reference to the accompanying drawings. In the description and the accompanying drawings, specific embodiments of the present application are disclosed in detail, which illustrate some embodiments in which the principles of the present application can be adopted. It should be understood that the present application is not limited to the described embodiments. On the contrary, the present application includes all modifications, variations and equivalents that fall within the scope of the appended claims.

[0035] In the embodiments of the present application, the terms "first", "second", etc. are used to distinguish different elements from the name, but do not indicate the spatial arrangement or temporal order of these elements, and these elements should not be limited by these terms. The term "and / or" includes any one and all combinations of one or more of the associated listed terms. The terms "comprising", "including", "having", etc. refer to the presence of the stated features, elements, components or components, but do not exclude the presence or addition of one or more other features, elements, components or components.

[0036] In the embodiments of this application, the singular forms "a," "the," etc. include plural forms and should be broadly understood to mean "a" or "a type" rather than being limited to "one." Furthermore, the term "said" should be understood to include both singular and plural forms, unless the context clearly indicates otherwise. Furthermore, the term "according to" should be understood to mean "at least in part based on...", and the term "based on" should be understood to mean "at least in part based on...", unless the context clearly indicates otherwise.

[0037] In the embodiments of the present application, the term "communication network" or "wireless communication network" may refer to a network that complies with any of the following communication standards, such as Long Term Evolution (LTE), enhanced Long Term Evolution (LTE-A, LTE-Advanced), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), etc.

[0038] Furthermore, communication between devices in the communication system may be carried out according to communication protocols of any stage, for example, including but not limited to the following communication protocols: 1G (generation), 2G, 2.5G, 2.75G, 3G, 4G, 4.5G and 5G, New Radio (NR), future 6G, etc., and / or other communication protocols currently known or to be developed in the future.

[0039] In the embodiments of the present application, the term "network device" refers to, for example, a device in a communication system that connects a terminal device to the communication network and provides services to the terminal device. Network devices may include, but are not limited to, the following devices: base station (BS), access point (AP), transmission reception point (TRP), broadcast transmitter, mobile management entity (MME), gateway, server, radio network controller (RNC), base station controller (BSC), etc.

[0040] Among them, base stations may include but are not limited to: NodeB (NodeB or NB), evolved NodeB (eNodeB or eNB) and 5G base station (gNB), IAB host, etc., and may also include remote radio head (RRH, Remote Radio Head), remote radio unit (RRU, Remote Radio Unit), relay (relay) or low-power node (such as femeto, pico, etc.). The term "base station" can include some or all of their functions. Each base station can provide communication coverage for a specific geographical area. The term "cell" can refer to a base station and / or its coverage area, depending on the context in which the term is used.

[0041] In the embodiments of the present application, the term "user equipment" (UE) or "terminal equipment" (TE) refers to, for example, a device that accesses a communication network through a network device and receives network services. A terminal device can be fixed or mobile and may also be referred to as a mobile station (MS), a terminal, a subscriber station (SS), an access terminal (AT), a station, and so on.

[0042] Among them, terminal devices may include but are not limited to the following devices: cellular phones, personal digital assistants (PDAs), wireless modems, wireless communication devices, handheld devices, machine-type communication devices, laptop computers, cordless phones, smart phones, smart watches, digital cameras, etc.

[0043] For another example, in scenarios such as the Internet of Things (IoT), the terminal device can also be a machine or device for monitoring or measurement, including but not limited to: machine type communication (MTC) terminal, vehicle-mounted communication terminal, device-to-device (D2D) terminal, machine-to-machine (M2M) terminal, and so on.

[0044] In addition, the term "network side" or "network device side" refers to one side of the network, which can be a base station or one or more network devices as described above. The term "user side" or "terminal side" or "terminal device side" refers to the user or terminal side, which can be a UE or one or more terminal devices as described above. Unless otherwise specified herein, "device" can refer to either network equipment or terminal equipment.

[0045] The following describes the scenarios of the embodiments of the present application through examples, but the present application is not limited thereto.

[0046] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application, schematically illustrating a situation using a terminal device and a network device as an example. As shown in FIG1 , a communication system 100 may include a network device 101 and terminal devices 102 and 103. For simplicity, FIG1 illustrates only two terminal devices and one network device as an example, but the embodiments of the present application are not limited thereto.

[0047] In the embodiment of the present application, existing services or future services can be transmitted between the network device 101 and the terminal devices 102 and 103. For example, these services may include but are not limited to: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low-latency communication (URLLC), etc.

[0048] It is worth noting that FIG1 shows that both terminal devices 102 and 103 are within the coverage range of network device 101, but the present application is not limited thereto. Both terminal devices 102 and 103 may not be within the coverage range of network device 101, or one terminal device 102 may be within the coverage range of network device 101 while the other terminal device 103 is outside the coverage range of network device 101.

[0049] In the embodiments of the present application, the high-layer signaling may be, for example, radio resource control (RRC) signaling; for example, an RRC message, including, for example, an MIB, system information, or a dedicated RRC message; or an RRC information element (RRC IE). The high-layer signaling may also be, for example, MAC (Medium Access Control) signaling; or a MAC control element (MAC CE). However, the present application is not limited thereto.

[0050] For uplink transmissions, including the Physical Uplink Shared Channel (PUSCH), Physical Uplink Control Channel (PUCCH), and Sounding Reference Signal (SRS), uplink power control can be performed. For example, network equipment can configure a path loss reference signal (PRS) for the terminal device, and the terminal device can measure the reference signal received power (RSRP) of the PRS to further calculate or estimate the path loss.

[0051] With the introduction of AI / ML, terminal devices and / or network equipment can leverage AI / ML functionality / models, such as beam prediction based on beam measurement results. However, there is currently no clear solution for how to consider uplink power control in these scenarios.

[0052] For example, in AI / ML for beam management, the first reference signal resource set (set A) used for beam prediction may not be sent to reduce overhead. In this case, it is currently unclear how to configure the path loss reference signal and how to estimate the uplink transmission path loss.

[0053] In embodiments of the present application, one or more AI / ML models may be configured and run in a network device and / or a terminal device. The AI / ML models may be used for various signal processing functions in wireless communications, such as CSI prediction, CSI compression, beam management, positioning management, etc.; however, the present application is not limited thereto.

[0054] Embodiments of the first aspect

[0055] An embodiment of the present application provides an uplink transmission configuration method, which is described from the terminal device side.

[0056] FIG2 is a schematic diagram of an uplink transmission configuration method according to an embodiment of the present application. As shown in FIG2 , the method includes:

[0057] 201. A terminal device receives configuration information from a network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control.

[0058] 202. The terminal device obtains uplink power control information for uplink transmission according to the configuration information.

[0059] It is worth noting that FIG2 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG2 above.

[0060] In some embodiments, functionality refers to an AI / ML feature / feature group enabled by a configuration, where the configuration is supported based on conditions indicated by UE capabilities.

[0061] For example, the AL / ML function may be one or more functions, or one or more logical models, or one or more sub-functions, or one or more features, or one or more feature groups.

[0062] For another example, the function can be to use AI / ML for spatial beam prediction, or to use AI / ML for time beam prediction, or to use AI / ML for CSI prediction, or to use AI / ML for direct positioning, or to use AI / ML for assisted positioning, and so on.

[0063] In some embodiments, the configuration information may configure beam management and may include configuration information of one or more reference signals used for beam management or beam measurement, such as CSI-RS configuration information. Furthermore, the configuration information may configure uplink power control, such as including configuration information of one or more path loss reference signals used for uplink power control. The present application is not limited thereto, and for specific configuration information, reference may be made to related technologies.

[0064] In some embodiments, the terminal device estimates or predicts the uplink power control information based on AI / ML functionality / model. For example, the uplink power control information includes path loss, but the present application is not limited thereto and may also include other information.

[0065] In some embodiments, the AI / ML functionality / model is used to predict the reference signal received power (RSRP) of a path loss reference signal to estimate the path loss corresponding to the path loss reference signal. The present application is not limited thereto, and uplink power control information may also be obtained through non-AI / ML methods.

[0066] For example, one or more reference signals are used for measurement and the measurement results (measured RSRP) are input into the AI / ML functionality / model, and another one or more reference signals are used as the output of the AI / ML functionality / model for inference. For details about the AI / ML functionality / model and specific concepts such as inference, please refer to the relevant art.

[0067] In some embodiments, the AI / ML functionality / model is used to estimate or predict path loss of certain beams in the spatial domain.

[0068] For example, the input of the AI / ML functionality / model is the RSRP measured by one or more reference signals, the AI / ML functionality / model outputs the RSRP of certain beams for spatial prediction, and another one or more reference signals are used at the output of the AI / ML functionality / model for inference.

[0069] In some embodiments, the AI / ML functionality / model is used to estimate or predict the path loss of certain beams in the temporal domain.

[0070] For example, the input of the AI / ML functionality / model is the RSRP measured by one or more reference signals, the output of the AI / ML functionality / model predicts the RSRP of certain beams for multiple time instances, and another one or more reference signals are used at the output of the AI / ML functionality / model for inference.

[0071] In some embodiments, the AI / ML functionality / model used for uplink power control is the same as the AI / ML functionality / model used for beam management. In other embodiments, the AI / ML functionality / model used for uplink power control is different from the AI / ML functionality / model used for beam management.

[0072] FIG3 is another schematic diagram of an uplink transmission method according to an embodiment of the present application, which is illustrated by taking a terminal device configured with AIML as an example. As shown in FIG3 , the method includes:

[0073] 301, the terminal device receives configuration information from the network device;

[0074] For example, the configuration information includes configuration for uplink power control, such as the configuration of the path loss reference signal. In addition, the configuration information can also be used for beam management, for example, it can also include a second reference signal resource set (set B) for beam measurement and / or a first reference signal resource set (set A) for beam prediction;

[0075] 302, the terminal device obtains uplink power control information;

[0076] For example, the terminal device can receive a path loss reference signal from a network device and use AI / ML to estimate or predict RSRP to obtain the path loss. In addition, the terminal device can also perform beam measurement and input the beam measurement results into the AI / ML functionality / model; for example, the measurement results of the reference signal in set B are used as the input of AI / ML, and the reference signal in set A is used for prediction (or inference); and

[0077] 303. The terminal device sends an uplink transmission to the network device.

[0078] For example, the AI / ML function is located on the terminal device side. After the AI / ML function is enabled or activated, the terminal device measures the path loss reference signal from the network side, uses AI / ML to make predictions based on the measurement results to obtain uplink power control information (such as path loss), and sends uplink transmissions to the network device based on the path loss.

[0079] For another example, the AI / ML function is located on the terminal device side. After the AI / ML function is enabled or activated, the terminal device performs beam measurement based on the reference signal from the network side, uses AI / ML to perform beam prediction based on the beam measurement results, and sends the prediction results to the network device.

[0080] It is worth noting that FIG3 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG3 above.

[0081] The above schematically illustrates AI / ML-based beam management. The uplink power control process can be combined with the beam management process in Figure 3 or performed separately, similar to Figure 3. The following schematically illustrates the input and output of the AI / ML functionality / model for uplink power control.

[0082] In some embodiments, the configured path loss reference signal is used as the input of the AI / ML functionality / model for uplink power control, and the RSRP predicted for the current beam or the estimated path loss is used as the output of the AI / ML functionality / model for uplink power control.

[0083] For example, a path loss reference signal can be configured for uplink power control, and this configured path loss reference signal is used as an input to the AI / ML functionality / model for uplink power control. Furthermore, a reference signal can be selected from set A for beam management (inference for AI / ML for beam management) as the current beam, and the predicted RSRP or estimated path loss for this current beam is used as the output of the AI / ML functionality / model for uplink power control.

[0084] In some embodiments, the reference signal in the second reference signal set (set B) for beam management is used as the input of the AI / ML functionality / model for uplink power control, and the RSRP or estimated path loss predicted for the configured path loss reference signal is used as the output of the AI / ML functionality / model for uplink power control.

[0085] For example, a path loss reference signal can be configured for uplink power control, and the configured path loss reference signal (predicted RSRP or estimated path loss) is used as the output (inference or prediction) of the AI / ML functionality / model for uplink power control. In addition, a reference signal can be selected from set B for beam management (input of AI / ML for beam management), and this reference signal is used as the input of the AI / ML functionality / model for uplink power control.

[0086] In some embodiments, the reference signals in the second reference signal set (set B) for beam management are used as inputs to the AI / ML functionality / model for uplink power control, and the reference signals in the first reference signal set (set A) for beam management are used as outputs of the AI / ML functionality / model for uplink power control.

[0087] In some embodiments, the predicted RSRP of the reference signal selected from the first reference signal set (set A) is used to estimate the path loss.

[0088] For example, a reference signal can be selected from set B for beam management (input to the AI / ML for beam management) and used as input to the AI / ML functionality / model for uplink power control. Furthermore, a reference signal can be selected from set A for beam management (inference for the AI / ML for beam management) as the current beam, and the predicted RSRP or estimated path loss for the current beam is used as the output of the AI / ML functionality / model for uplink power control.

[0089] For another example, the AI / ML functionality / model used for beam management is reused for uplink power control. That is, the AI / ML functionality / model used for beam management is also used for RSRP prediction and path loss estimation for uplink power control.

[0090] The above schematically illustrates the AI / ML functionality / model for uplink power control. The following describes the path loss reference signal used for uplink power control. In the following description, uplink power control can be based on AI / ML or non-AI / ML (e.g., using a legacy solution). For example, the AI / ML functionality / model used for uplink power control can be configured or enabled, or not configured or enabled.

[0091] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; one or more reference signals in a second reference signal set (set B) for beam management are configured as path loss reference signals for uplink power control.

[0092] For example, some reference signals may be selected from set B and used as path loss reference signals.

[0093] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; one or more reference signals in a first reference signal set (set A) for beam management are configured as path loss reference signals for uplink power control.

[0094] For example, some reference signals may be selected from set A and used as path loss reference signals.

[0095] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; and one or more reference signals in a reference signal set for performance monitoring are configured as path loss reference signals for uplink power control.

[0096] For example, the reference signal set used for performance monitoring is the same as the first reference signal set (set A) used for beam management, or the reference signal set used for performance monitoring is different from the first reference signal set (set A), or the reference signal set used for performance monitoring at least partially overlaps with the first reference signal set (set A), or the reference signal set used for performance monitoring is a subset of the first reference signal set (set A). Some reference signals can be selected from the reference signal set and used as path loss reference signals.

[0097] In some embodiments, AI / ML functionality / model for beam management is configured or enabled; the reference signal of the current beam is used for performance monitoring and is configured as a path loss reference signal for uplink power control.

[0098] For example, the reference signal of the current beam is used for both performance monitoring and path loss reference signal.

[0099] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; and a demodulation reference signal (DMRS) of a physical downlink control channel (PDCCH) is configured as a path loss reference signal for uplink power control.

[0100] For example, the DMRS of a specific CORESET (eg, the CORESET with the lowest ID) is used as the path loss reference signal. The present application is not limited thereto, and the DMRS may also be a predefined CORESET DMRS or a configured CORESET DMRS.

[0101] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; and a demodulation reference signal (DMRS) of a physical downlink shared channel (PDSCH) is configured as a path loss reference signal for uplink power control.

[0102] In some embodiments, AI / ML functionality / model for beam management is configured or enabled; and an additional offset is configured for uplink power control to compensate for path loss estimation errors.

[0103] In some embodiments, the additional offset is predefined or configured; and the path loss estimation error is caused by inconsistency between a path loss reference signal and an uplink beam.

[0104] For example, for uplink power control, if AI / ML operation for beam management has been configured / enabled, an additional offset can be configured for uplink power control. This additional offset can be predefined or configurable. This additional offset can be used to compensate for path loss estimation errors caused by misalignment between the path loss reference signal and the uplink beam.

[0105] As another example, additional offsets may be configured for uplink power control even if AI / ML operations for beam management are not enabled.

[0106] In the embodiments of the present application, uplink power control may include power control of PUSCH, PUCCH, or SRS, but the present application is not limited thereto. Furthermore, the embodiments of the present application may be applied to a terminal device side model (UE-side model) and / or a network device side model (gNB-side model). The embodiments of the present application may be applied to BM case 1 (spatial beam prediction) and / or BM case 2 (temporal beam prediction).

[0107] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0108] As can be seen from the above embodiments, the terminal device receives configuration information from the network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control, thereby more accurately obtaining uplink power control information including path loss, thereby improving the accuracy and reliability of uplink transmission.

[0109] Embodiments of the second aspect

[0110] The embodiment of the present application provides an uplink transmission configuration method, which is described from the perspective of a network device. The embodiment of the second aspect can be combined with the embodiment of the first aspect, and the same contents as the embodiment of the first aspect will not be repeated.

[0111] FIG4 is another schematic diagram of the uplink transmission configuration method according to an embodiment of the present application. As shown in FIG4 , the method includes:

[0112] 401. A network device sends configuration information to a terminal device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control; the configuration information is used by the terminal device to obtain uplink power control information for uplink transmission.

[0113] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0114] As can be seen from the above embodiments, the terminal device receives configuration information from the network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control, thereby more accurately obtaining uplink power control information including path loss, thereby improving the accuracy and reliability of uplink transmission.

[0115] Embodiments of the third aspect

[0116] The embodiment of the present application provides an uplink transmission configuration device, which may be, for example, a terminal device, or one or more components or assemblies configured in the terminal device, and the contents that are the same as those in the first and second aspects of the embodiment are not repeated here.

[0117] FIG5 is another schematic diagram of an uplink transmission configuration apparatus according to an embodiment of the present application. As shown in FIG5 , an uplink transmission configuration apparatus 500 according to an embodiment of the present application includes:

[0118] A receiving unit 501 receives configuration information from a network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control;

[0119] The processing unit 502 obtains uplink power control information for uplink transmission according to the configuration information.

[0120] In some embodiments, the terminal device estimates or predicts the uplink power control information based on AI / ML functionality / model.

[0121] In some embodiments, the uplink power control information includes path loss;

[0122] The AI / ML functionality / model is used to predict a reference signal received power (RSRP) of a path loss reference signal to estimate a path loss corresponding to the path loss reference signal.

[0123] In some embodiments, one or more reference signals are used for measurement and the measured RSRP is input into the AI / ML functionality / model, and another one or more reference signals are used for the output of the AI / ML functionality / model for inference.

[0124] In some embodiments, the AI / ML functionality / model is used to estimate or predict the path loss of one or more beams in the spatial domain, and / or, the AI / ML functionality / model is used to estimate or predict the path loss of one or more beams in the temporal domain.

[0125] In some embodiments, the AI / ML functionality / model for uplink power control is the same as the AI / ML functionality / model for beam management, or the AI / ML functionality / model for uplink power control is different from the AI / ML functionality / model for beam management.

[0126] In some embodiments, the configured path loss reference signal is used as the input of the AI / ML functionality / model for uplink power control, and the RSRP predicted for the current beam or the estimated path loss is used as the output of the AI / ML functionality / model for uplink power control.

[0127] In some embodiments, the reference signal in the second reference signal set (set B) for beam management is used as the input of the AI / ML functionality / model for uplink power control, and the RSRP or estimated path loss predicted for the configured path loss reference signal is used as the output of the AI / ML functionality / model for uplink power control.

[0128] In some embodiments, the reference signals in the second reference signal set (set B) for beam management are used as inputs to the AI / ML functionality / model for uplink power control, and the reference signals in the first reference signal set (set A) for beam management are used as outputs of the AI / ML functionality / model for uplink power control.

[0129] In some embodiments, the predicted RSRP of the reference signal selected from the first reference signal set (set A) is used to estimate the path loss.

[0130] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; one or more reference signals in a second reference signal set (set B) for beam management are configured as path loss reference signals for uplink power control.

[0131] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; one or more reference signals in a first reference signal set (set A) for beam management are configured as path loss reference signals for uplink power control.

[0132] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; and one or more reference signals in a reference signal set for performance monitoring are configured as path loss reference signals for uplink power control.

[0133] In some embodiments, the reference signal set for performance monitoring is the same as the first reference signal set (set A) for beam management, or the reference signal set for performance monitoring is different from the first reference signal set (set A), or the reference signal set for performance monitoring at least partially overlaps with the first reference signal set (set A), or the reference signal set for performance monitoring is a subset of the first reference signal set (set A).

[0134] In some embodiments, AI / ML functionality / model for beam management is configured or enabled; the reference signal of the current beam is used for performance monitoring and is configured as a path loss reference signal for uplink power control.

[0135] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; and a demodulation reference signal (DMRS) of a physical downlink control channel (PDCCH) is configured as a path loss reference signal for uplink power control.

[0136] In some embodiments, an AI / ML functionality / model for beam management is configured or enabled; and a demodulation reference signal (DMRS) of a physical downlink shared channel (PDSCH) is configured as a path loss reference signal for uplink power control.

[0137] In some embodiments, AI / ML functionality / model for beam management is configured or enabled; and an additional offset is configured for uplink power control to compensate for path loss estimation errors.

[0138] In some embodiments, the additional offset is predefined or configured; and the path loss estimation error is caused by inconsistency between a path loss reference signal and an uplink beam.

[0139] In some embodiments, as shown in FIG5 , the apparatus further comprises:

[0140] The sending unit 503 sends an uplink transmission to the network device.

[0141] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0142] It is worth noting that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The uplink transmission configuration device 500 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0143] In addition, for simplicity, FIG5 only illustrates the connection relationship or signal path between various components or modules. However, those skilled in the art should be aware that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; this application is not limited to this.

[0144] As can be seen from the above embodiments, the terminal device receives configuration information from the network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control, thereby more accurately obtaining uplink power control information including path loss, thereby improving the accuracy and reliability of uplink transmission.

[0145] Embodiments of the fourth aspect

[0146] The embodiment of the present application provides an uplink transmission configuration device, which may be, for example, a network device, or one or more components or assemblies configured in the network device, and the contents identical to those in the first to third aspects of the embodiment will not be repeated.

[0147] FIG6 is another schematic diagram of an uplink transmission configuration apparatus according to an embodiment of the present application. As shown in FIG6 , the uplink transmission configuration apparatus 600 includes:

[0148] A sending unit 601 sends configuration information to a terminal device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control; the terminal device obtains uplink power control information for uplink transmission based on the configuration information.

[0149] In some embodiments, as shown in FIG6 , the uplink transmission configuration apparatus 600 may further include:

[0150] The receiving unit 602 receives the uplink transmission sent by the terminal device.

[0151] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0152] It is worth noting that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The uplink transmission configuration device 600 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0153] In addition, for the sake of simplicity, FIG6 only illustrates the connection relationship or signal direction between various components or modules. However, it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; the implementation of this application is not limited to this.

[0154] As can be seen from the above embodiments, the terminal device receives configuration information from the network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control, thereby more accurately obtaining uplink power control information including path loss, thereby improving the accuracy and reliability of uplink transmission.

[0155] Embodiments of the fifth aspect

[0156] An embodiment of the present application also provides a communication system, and reference may be made to FIG1 . The contents that are the same as those in the first to fourth aspects of the embodiments will not be repeated.

[0157] In some embodiments, the communication system 100 may include at least:

[0158] A network device that sends configuration information to a terminal device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control;

[0159] The terminal device obtains uplink power control information for uplink transmission based on the configuration information.

[0160] The embodiment of the present application also provides a terminal device, but the present application is not limited thereto and may also be other devices.

[0161] Figure 7 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Figure 7 , terminal device 700 may include a processor 710 and a memory 720. Memory 720 stores data and programs and is coupled to processor 710. It should be noted that this diagram is exemplary; other types of structures may be used to supplement or replace this structure to implement telecommunication or other functions.

[0162] For example, the processor 710 may be configured to execute a program to implement the uplink transmission configuration method as described in the embodiment of the first aspect. For example, the processor 710 may be configured to perform the following control: receiving configuration information from a network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control; and obtaining uplink power control information for uplink transmission based on the configuration information.

[0163] As shown in Figure 7 , the terminal device 700 may further include: a communication module 730, an input unit 740, a display 750, and a power supply 760. The functions of these components are similar to those in the prior art and are not described in detail here. It is worth noting that the terminal device 700 does not necessarily include all of the components shown in Figure 7 , and these components are not essential. Furthermore, the terminal device 700 may also include components not shown in Figure 7 , for which reference may be made to the prior art.

[0164] An embodiment of the present application further provides a network device, which may be, for example, a base station, but the present application is not limited thereto and may also be other network devices.

[0165] Figure 8 is a schematic diagram illustrating the structure of a network device according to an embodiment of the present application. As shown in Figure 8 , network device 800 may include a processor 810 (e.g., a central processing unit (CPU)) and a memory 820 ; the memory 820 is coupled to the processor 810 . The memory 820 may store various data and may also store an information processing program 830 , which is executed under the control of the processor 810 .

[0166] For example, the processor 810 may be configured to execute a program to implement the uplink transmission configuration method as described in the embodiment of the second aspect. For example, the processor 810 may be configured to perform the following control: sending configuration information to a terminal device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control; the configuration information is used by the terminal device to obtain uplink power control information for uplink transmission.

[0167] In addition, as shown in FIG8 , network device 800 may further include: a transceiver 840 and an antenna 850, etc.; wherein, the functions of the above components are similar to those in the prior art and are not described in detail here. It is worth noting that network device 800 does not necessarily include all the components shown in FIG8 ; in addition, network device 800 may also include components not shown in FIG8 , and reference may be made to the prior art for details.

[0168] An embodiment of the present application also provides a computer program, wherein when the program is executed in a terminal device, the program enables the terminal device to execute the uplink transmission configuration method described in the embodiment of the first aspect.

[0169] An embodiment of the present application also provides a storage medium storing a computer program, wherein the computer program enables a terminal device to execute the uplink transmission configuration method described in the embodiment of the first aspect.

[0170] An embodiment of the present application also provides a computer program, wherein when the program is executed in a network device, the program enables the network device to execute the uplink transmission configuration method described in the embodiment of the second aspect.

[0171] An embodiment of the present application also provides a storage medium storing a computer program, wherein the computer program enables a network device to execute the uplink transmission configuration method described in the embodiment of the second aspect.

[0172] The above devices and methods of the present application can be implemented by hardware or by a combination of hardware and software. The present application relates to such a computer-readable program that, when executed by a logic component, enables the logic component to implement the devices or components described above, or enables the logic component to implement the various methods or steps described above. The present application also relates to a storage medium for storing the above program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, etc.

[0173] The method / device described in conjunction with the embodiments of the present application can be directly embodied as hardware, a software module executed by a processor, or a combination of the two. For example, one or more of the functional block diagrams shown in the figure and / or one or more combinations of functional block diagrams can correspond to various software modules of the computer program flow or to various hardware modules. These software modules can respectively correspond to the various steps shown in the figure. These hardware modules can be implemented by solidifying these software modules, for example, using a field programmable gate array (FPGA).

[0174] The software module may be located in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. A storage medium may be coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium; or the storage medium may be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The software module may be stored in the memory of the mobile terminal or in a memory card that can be inserted into the mobile terminal. For example, if the device (such as a mobile terminal) uses a large-capacity MEGA-SIM card or a large-capacity flash memory device, the software module may be stored in the MEGA-SIM card or the large-capacity flash memory device.

[0175] One or more of the functional blocks and / or one or more combinations of functional blocks described in the accompanying drawings may be implemented as 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, a discrete gate or transistor logic device, a discrete hardware component, or any appropriate combination thereof for performing the functions described in this application. One or more of the functional blocks and / or one or more combinations of functional blocks described in the accompanying drawings may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0176] The present application has been described above in conjunction with specific embodiments. However, those skilled in the art should understand that these descriptions are merely illustrative and are not intended to limit the scope of protection of the present application. Those skilled in the art may make various modifications and variations to the present application based on the spirit and principles of the present application, and such modifications and variations are also within the scope of the present application.

[0177] Regarding the implementation methods including the above embodiments, the following additional notes are also disclosed:

[0178] 1. A method for configuring uplink transmission, comprising:

[0179] The terminal device receives configuration information from the network device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control;

[0180] The terminal device obtains uplink power control information for uplink transmission based on the configuration information.

[0181] 2. A method for configuring uplink transmission, comprising:

[0182] A network device sends configuration information to a terminal device; the configuration information is used to configure AI / ML functionality / model and / or uplink power control; the configuration information is used by the terminal device to obtain uplink power control information for uplink transmission.

[0183] 3. A terminal device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the uplink transmission configuration method as described in Note 1.

[0184] 4. A network device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the uplink transmission configuration method as described in Note 2.

Claims

1. An uplink transmission configuration device, comprising: a receiving unit that receives configuration information from a network device; the configuration information is used to configure AI / ML functions / models and / or uplink power control; a processing unit that obtains uplink power control information for uplink transmission according to the configuration information.

2. The device according to claim 1, wherein The processing unit estimates or predicts the uplink power control information based on the AI / ML functions / models.

3. The device according to claim 2, wherein, The uplink power control information includes path loss; The AI / ML functions / models are used to predict the reference signal reception power of a path loss reference signal to estimate the path loss corresponding to the path loss reference signal.

4. The apparatus according to claim 2, wherein One or more reference signals are used for measurement and the measured reference signal reception power is input to the AI / ML functions / models, and another or more reference signals are used for the output of the AI / ML functions / models for inference.

5. The apparatus according to claim 2, wherein, The AI / ML functions / models are used to estimate or predict the path loss of one or more beams in the spatial domain, and / or the AI / ML functions / models are used to estimate or predict the path loss of one or more beams in the time domain.

6. The device according to claim 1, wherein the AI / ML function / model for uplink power control is the same as the AI / ML function / model for beam management, or the AI / ML function / model for uplink power control is different from the AI / ML function / model for beam management.

7. The apparatus according to claim 6, wherein, The configured path loss reference signal is used for the input of the AI / ML function / model for uplink power control, and the predicted reference signal reception power or estimated path loss for the current beam is used for the output of the AI / ML function / model for uplink power control.

8. The apparatus according to claim 6, wherein, The reference signals in the second reference signal set for beam management are used for the input of the AI / ML function / model for uplink power control, and the predicted reference signal reception power or estimated path loss for the configured path loss reference signal is used for the output of the AI / ML function / model for uplink power control.

9. The device according to claim 6, wherein, The reference signals in the second reference signal set for beam management are used for the input of the AI / ML function / model for uplink power control, and the reference signals in the first reference signal set for beam management are used for the output of the AI / ML function / model for uplink power control.

10. The apparatus according to claim 9, wherein, The predicted reference signal reception power of the reference signal selected from the first reference signal set is used to estimate the path loss.

11. The device according to claim 1, wherein, The AI / ML function / model for beam management is configured or enabled; One or more reference signals in the second reference signal set for beam management are configured as path loss reference signals for uplink power control.

12. The device according to claim 1, wherein, The AI / ML function / model for beam management is configured or enabled; One or more reference signals in the first reference signal set for beam management are configured as path loss reference signals for uplink power control.

13. The apparatus according to claim 1, wherein, The AI / ML function / model for beam management is configured or enabled; One or more reference signals in the reference signal set for performance monitoring are configured as path loss reference signals for uplink power control.

14. The apparatus according to claim 13, wherein, the set of reference signals for performance monitoring is the same as the first set of reference signals for beam management, or the set of reference signals for performance monitoring is different from the first set of reference signals, or the set of reference signals for performance monitoring at least partially overlaps with the first set of reference signals, or the set of reference signals for performance monitoring is a subset of the first set of reference signals.

15. The device according to claim 1, wherein The AI / ML function / model for beam management is configured or enabled; The reference signal of the current beam is used for performance monitoring and is configured as the path loss reference signal for uplink power control.

16. The device according to claim 1, wherein, The AI / ML function / model for beam management is configured or enabled; The demodulation reference signal of the physical downlink control channel is configured as the path loss reference signal for uplink power control.

17. The device according to claim 1, wherein, The AI / ML function / model for beam management is configured or enabled; The demodulation reference signal of the physical downlink shared channel is configured as the path loss reference signal for uplink power control.

18. The device according to claim 1, wherein, The AI / ML function / model for beam management is configured or enabled; An additional offset for compensating for path loss estimation errors is configured for uplink power control; the additional offset is predefined or configured; the path loss estimation error is caused by the inconsistency between the path loss reference signal and the uplink beam.

19. An uplink transmission configuration apparatus, comprising: a sending unit, which sends configuration information to a terminal device; the configuration information is used to configure the AI / ML function / model and / or uplink power control; the terminal device uses the configuration information to obtain uplink power control information for uplink transmission.

20. A communication system, comprising: a network device, which sends configuration information to a terminal device; the configuration information is used to configure the AI / ML function / model and / or uplink power control; a terminal device, which obtains uplink power control information for uplink transmission according to the configuration information.

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