Electronic equipment and methods for wireless communication

Abstract

Electronic equipment and methods for wireless communication, and computer-readable storage media. The electronic equipment for wireless communication includes a processing circuit configured to dynamically or semi-statically determine categories of input information for a prediction model for beam prediction of a beam used for communication between the electronic equipment and the user equipment, and to provide beam prediction configuration information, including the determined categories of input information, to the user equipment so that the user equipment collects input information according to the beam prediction configuration information.

Description

[Technical Field]

[0001] This application claims priority to a Chinese patent application filed with the China National Intellectual Property Office on May 6, 2023, with application number 202310508570.0, and titled "Electronic device and method for wireless communication, and computer-readable storage medium," and incorporates its entire contents into this application by reference.

[0002] This disclosure relates to the field of wireless communication technology, and more specifically to electronic equipment and methods for wireless communication, as well as computer-readable storage media. More specifically, it relates to electronic equipment and methods for wireless communication that improve beam prediction performance, as well as computer-readable storage media. [Background technology]

[0003] Beamforming is a technique that uses a large antenna array to form an energy-focused, directional beam to counteract high path loss. Figure 1 shows an example of beamforming. As shown in Figure 1, a large antenna array forms a directional beam for communication between the base station and the user equipment (UE). To implement beamforming, a beam management process is required, i.e., to obtain and track the optimal beam pair for communication. Figure 2 shows an example of performing beam management to obtain the optimal beam pair. As shown in Figure 2, by performing beam management, the optimal (best) beam pair for communication between the base station and the UE can be obtained.

[0004] Beam management methods can generally be divided into three types. The first beam management method is beam scanning-based beam management, which scans all possible beam pairs and selects the beam pair with the highest received power as the optimal beam pair. Because this method covers all possible beam pairs, it has a large beam training overhead and is susceptible to noise, i.e., it is noise-sensitive, and high noise levels directly affect the determination of the optimal beam. The second beam management method is channel model-based beam management, which makes prior assumptions about the channel model and models the problem of finding the optimal beam as an angle estimation problem, selecting the optimal beam pair based on the estimated value. This method is highly dependent on the prior assumptions of the channel model and has a limited scope of application. The third beam management method is deep learning-based beam management, which uses the feature extraction capabilities of deep learning to support beam management. Conventional deep learning-based beam management methods cannot be adapted to users with different motion velocities or channel environments, for example.

[0005] How to achieve effective beam prediction (e.g., optimal beam prediction) is a hot topic in current research. [Overview of the project] [Means for solving the problem]

[0006] The following provides a brief overview of the present invention to offer a basic understanding of certain aspects of it. It should be understood that this overview is not exhaustive. It is not intended to identify any essential or important parts of the invention, nor to intentionally limit its scope. Its purpose is to provide a simplified conceptual framework to precede the more detailed technical discussions that follow.

[0007] According to one aspect of the present disclosure, the present disclosure provides an electronic device for wireless communication, which includes a processing circuit configured to dynamically or semi-statically determine categories of input information for a prediction model for beam prediction of a beam used for communication between the electronic device and the user device, and to provide the user device with beam prediction configuration information, including the determined categories of input information, so that the user device collects input information according to the beam prediction configuration information. According to embodiments of the present disclosure, beam prediction performance can be improved.

[0008] According to one aspect of the present disclosure, the present electronic device includes an electronic device for wireless communication, which includes a processing circuit configured to receive beam prediction configuration information from a network-side device that provides services to the electronic device, the information including a category of input information for a prediction model for performing beam prediction for a beam used for communication between the electronic device and the network-side device, which is dynamically or semistatically determined by the network-side device to the electronic device, and to collect input information according to the beam prediction configuration information. According to embodiments of the present disclosure, beam prediction performance can be improved.

[0009] In one aspect of the present disclosure, a method for wireless communication is provided, which includes dynamically or semi-statically determining categories of input information for a prediction model to perform beam prediction for a beam used for communication between the electronic device and the user device, and providing beam prediction configuration information, including the determined categories of input information, to the user device so that the user device collects input information according to the beam prediction configuration information.

[0010] According to one aspect of the present disclosure, there is provided a method for wireless communication, including receiving beam prediction configuration information including a category of input information of a prediction model for performing beam prediction on a beam used for communication between an electronic device and a network-side device, which is dynamically or semi-statically determined by the network-side device for the electronic device, from a network-side device providing a service to the electronic device, and collecting the input information according to the beam prediction configuration information.

[0011] According to another aspect of the present disclosure, there are further provided computer program code for implementing the above method for wireless communication, a computer program product, and a computer-readable storage medium on which the computer program code for implementing the above method for wireless communication is recorded.

Brief Description of the Drawings

[0012] In order to further illustrate the above and other advantages and features of the present invention, the following will further describe specific embodiments of the present invention in more detail in conjunction with the drawings. The drawings are included in this specification together with the following detailed description and form a part of this specification. Elements having the same function and configuration are denoted by the same reference numerals. It should be noted that these drawings illustrate only typical examples of the present invention and should not be regarded as a limitation to the scope of the present invention. In the drawings,

[0013] [Figure 1] FIG. 1 shows an example of beamforming technology. [Figure 2] FIG. 2 shows an example of performing beam management to obtain an optimal beam pair. [Figure 3] FIG. 3 shows a functional module block diagram of an electronic device for wireless communication according to an embodiment of the present disclosure. [Figure 4] FIG. 4 shows an example of beam prediction according to an embodiment of the present disclosure. [Figure 5] FIG. 5 shows examples of an accelerometer, a gyroscope, and a compass of a user equipment. [Figure 6]Figure 6 shows an example of wireless resource control signaling according to an embodiment of the present disclosure. [Figure 7] Figure 7 shows an example of the schematic configuration of a prediction model according to an embodiment of the present disclosure. [Figure 8] Figure 8 shows an example configuration of a reconstructed network model according to an embodiment of the present disclosure. [Figure 9] Figure 9 shows an example configuration of a multimodal fusion prediction model according to an embodiment of this disclosure. [Figure 10] Figure 10 shows an example of the configuration of a predictive model according to the embodiment of this disclosure. [Figure 11] Figure 11 shows an example of a process in which an electronic device according to an embodiment of this disclosure performs beam prediction for a single user device. [Figure 12] Figure 12 shows an example of performing beam prediction for a group of users according to an embodiment of this disclosure. [Figure 13] Figure 13 shows an example of how, according to an embodiment of the present disclosure, user devices that have the same optimal beam over a predetermined period of time are grouped into the same group. [Figure 14] Figure 14 shows an example of grouping group members into a new group according to an embodiment of the present disclosure. [Figure 15] Figure 15 shows an example of selecting a user subset according to an embodiment of the present disclosure. [Figure 16] Figure 16 shows an example of a process for performing beam prediction for user equipment, where electronic devices are grouped together, according to an embodiment of this disclosure. [Figure 17] Figure 17 shows the change in normalized beam gain with respect to prediction time for different approaches. [Figure 18] Figure 18 shows a functional module block diagram of an electronic device for wireless communication according to another embodiment of the present disclosure. [Figure 19] Figure 19 shows a flowchart of a method for wireless communication according to one embodiment of the present disclosure. [Figure 20]Figure 20 shows a flowchart of a method for wireless communication according to another embodiment of the present disclosure. [Figure 21] Figure 21 is a block diagram showing a first example of a schematic configuration of an eNB or gNB to which the technology described herein can be applied. [Figure 22] Figure 22 is a block diagram showing a second example of a schematic configuration of an eNB or gNB to which the technology described herein can be applied. [Figure 23] Figure 23 is a block diagram showing an example of a schematic configuration of a smartphone to which the technology described herein can be applied. [Figure 24] Figure 24 is a block diagram showing an example of a schematic configuration of a car navigation system to which the technology described herein can be applied. [Figure 25] Figure 25 is a block diagram showing a schematic configuration of a general-purpose personal computer capable of implementing the methods and / or apparatus and / or systems according to the embodiments of this disclosure. [Modes for carrying out the invention]

[0014] The following describes exemplary embodiments of the present invention, accompanied by drawings. For clarity and brevity, not all features of actual embodiments are described in the specification. It should be understood that, for example, decisions to specify such actual embodiments must be made during the development process to achieve the developer's specific goals, such as those relating to system and business limitations, which may vary depending on the embodiment. It should also be understood that while development work can be very complex and time-consuming, such development work is routine for those skilled in the art who would benefit from this disclosure.

[0015] It should also be noted that, in order to avoid obscuring the present disclosure with unnecessary details, the drawings show only the apparatus configuration and / or processing steps closely related to the solution of the present invention, and omit other details that are not significantly related to the present invention.

[0016] Figure 3 shows a functional module block diagram of an electronic device 300 for wireless communication according to one embodiment of the present disclosure.

[0017] As shown in Figure 3, the electronic device 300 includes a determination unit 301 which can be configured to dynamically or semi-statically determine the category of input information for a prediction model (beam prediction model) for performing beam prediction for a beam used for communication between the electronic device 300 and user equipment within the service range of the electronic device 300, and a providing unit 303 which can be configured to provide beam prediction configuration information, including the determined category of input information, to the user equipment so that the user equipment collects input information according to the beam prediction configuration information.

[0018] Furthermore, the determination unit 301 and the provision unit 303 may be implemented by one or more processing circuits, and such processing circuits may be implemented, for example, as a chip or processor. In addition, each functional unit in the electronic device shown in Figure 3 is merely a logic module partitioned based on the specific function it implements, and should be understood as not limiting the specific implementation form.

[0019] The electronic device 300 can be a network-side device in a wireless communication system, and specifically, for example, it may be installed on the base station side or connected to the base station in a communicative manner. Here, the electronic device 300 may be implemented at the chip level or at the device level. For example, the electronic device 300 may operate as the base station itself and may further include external devices such as memory and transceivers (not shown). The memory is used to store programs executed by the electronic device 300 to realize various functions, and related data information. The transceiver may include one or more communication interfaces to support communication between different devices (e.g., UE, base station, etc.), but the implementation form of the transceiver is not specifically limited here.

[0020] For example, a base station may be, for instance, an eNB or a gNB.

[0021] The wireless communication system described herein may be a 5G NR (New Radio) communication system. Furthermore, the wireless communication system described herein may include a non-terrestrial network (NTN). Optionally, the wireless communication system described herein may also include a terrestrial network (TN). Those skilled in the art will also understand that the wireless communication system described herein may be a 4G or 3G communication system.

[0022] Dynamically determining the categories of input information for a predictive model can mean determining the categories of input information for the predictive model in real time and aperiodically. For example, a specific event trigger could determine the categories of input information for the predictive model.

[0023] Semi-static lies between dynamic and periodic. Determining the categories of input information for a predictive model semi-statically can mean periodically determining the categories of input information for the predictive model over a predetermined period of time. Those skilled in the art can predetermine this predetermined time depending on the application scenario or experience.

[0024] Hereinafter, the categories of input information can be called modal (modal can be defined as a category of information), beam prediction modal, and modal information, the set of input information categories determined by the electronic device 300 for the user device can be called the beam prediction modal set, and the user device can also be called the user.

[0025] For example, the electronic device 300 determines a specific beam prediction modal set for each user device, and the user device collects corresponding input information according to this modal set.

[0026] Electronic equipment 300 assigns a number to each beam prediction modal, and the beam prediction modal set includes the modal index (ID). Beam prediction modal sets from different user equipment may contain modals with different numbers (e.g., integers greater than or equal to 1) or different indices (e.g., IDs 1, 2, 3, or 1, 2, 4).

[0027] The predictive model could be, for example, a deep learning model.

[0028] Conventional beam prediction methods typically use fixed modal information as input to the beam prediction model, but this is inadequate for users with different user capabilities and / or located in different channel environments, resulting in reduced beam prediction performance. For example, for users located in complex channel environments (such as line-of-sight channel environments), channel prediction is difficult, and using only one modal information can lead to reduced beam prediction performance.

[0029] The electronic device 300 according to the embodiments of this disclosure dynamically or semi-statically determines (updates) the categories of input information for the prediction model for each user device (dynamically or semi-statically determines beam prediction configuration information), that is, supports the dynamic or semi-static selection of a beam prediction modal set, so that modal information corresponding to all modals in this set can be used as input to the prediction model, thereby improving beam prediction performance (e.g., improving the accuracy of beam prediction). In other words, the electronic device 300 improves beam prediction performance by flexibly determining the information categories to be used for beam prediction for each user device and fusing input information from multiple sources to perform beam prediction.

[0030] For example, the determination unit 301 may be configured to determine the category of input information according to the user capabilities of the user equipment and / or the channel environment in which the user equipment is located, and the determination unit 301 may be configured to receive user capabilities reported by the user equipment via radio resource control (RRC) signaling.

[0031] The electronic device 300 can acquire the channel environment based on channel measurement and obtain information about user capabilities through user capability queries.

[0032] The user capabilities of the user equipment and / or the channel environment in which the user equipment is located will differ, and the modal information required for corresponding beam prediction will also differ.

[0033] For example, in a complex and unstable channel, the electronic device 300 needs to use a lot of modal information to predict the beam, but in a simple and stable channel, the electronic device 300 can achieve accurate predictions using less modal information. Some user equipment (e.g., user equipment with high user capability) can provide a lot of modal information, while other user equipment (e.g., user equipment with low user capability) can only provide a little modal information.

[0034] Figure 4 shows an example of beam prediction according to an embodiment of the present disclosure. As shown in Figure 4, the prediction model can predict the beam used for communication between the electronic equipment 300 and the UE by performing beam prediction based on input information collected by the UE. This beam prediction is beneficial for improving the noise robustness of beam management. For example, in a real-world scenario, the stability of the user's movement ensures the predictability of beam changes. Thus, conventional channel measurement results (e.g., channel measurement results in Figure 4(a)) can be used to extract user motion characteristics, and then the prediction model can be used to achieve optimal beam prediction for the future (e.g., beam prediction according to user motion, as shown in Figure 4(b)).

[0035] As an example, a person skilled in the art may conceive that, in addition to RRC signaling, electronic device 300 may receive user capabilities reported by user equipment via other signaling, but this will not be discussed in detail here.

[0036] For example, the user capability information included in RRC signaling (information that user equipment uses to notify electronic device 300 of the details of its capabilities) includes user assistance sub-information, and the parameters of the user assistance sub-information include categories of assistance information to reflect user capabilities.

[0037] Conventional user capability information only includes information such as the user's basic communication capabilities and does not support notifying the base station of categories of user-aided information that the user can provide. In embodiments of the present invention, extended user capability information including user-aided sub-information is proposed, which notifies the electronic device 300 of categories of user-aided information that the user can provide so that the electronic device 300 can determine the beam prediction modal set. Extended user capability information can be used not only for beam prediction but also for other communication scenarios such as radio positioning and channel prediction.

[0038] As an example, the category of auxiliary information may include at least one of the following: information on the motion characteristics of the user device, information on the location of the user device, sensing information of the user device, communication capabilities of the user device, whether the user device supports the use of a predictive model, and information on the beam.

[0039] For example, the motion characteristic information of the user's device may be information that reflects the speed of the user's device.

[0040] For example, the location information of a user device may be information indicating the location of the user device.

[0041] For example, sensing information from a user device is information acquired based on the software and / or hardware of the user device, and may include motion-related parameters of the user device.

[0042] As an example, the sensing information of the user device may include at least one of the following: the accelerometer information of the user device and the gyroscope information of the user device.

[0043] For example, the sensing information of the user device may include the compass information of the user device.

[0044] Figure 5 shows an example of an accelerometer, gyroscope, and compass in a user device. For example, accelerometer information, gyroscope information, and compass information of the user device can be obtained via the accelerometer, gyroscope, and compass shown in Figure 5.

[0045] In conventional technologies, capability reporting only includes reporting of basic communication capabilities. However, in the embodiments of this disclosure, additional communication capability reporting is added to support beam prediction based on predictive models. For example, to support the inputs of a predictive model, this includes the maximum number of input information items supported in a single report, or the quantization granularity of the parameters of the input information.

[0046] Whether a user's equipment supports the use of a predictive model can be used to indicate whether beam prediction can be performed using the predictive model on the user's equipment.

[0047] For example, beam information can represent information about the beam that the electronic device 300 communicates with the user device, and beam information may include, for example, the shape of the beam and the angle of the beam.

[0048] For example, the parameters of the user accessibility sub-information may further include the transmission period and / or the number of bits transmitted for the accessibility information.

[0049] Figure 6 shows an example of wireless resource control signaling according to an embodiment of the present disclosure. In Figure 6, UE-NR-Capability represents user capability information, and SEQUENCE represents a sequence. "to be defined" means to be defined. accessStratumRelease is part of conventional RRC signaling and represents the access version. UE-Auxiliary-Information represents user auxiliary sub-information. UE-Auxiliary-Parameters represents the parameters of user auxiliary sub-information and includes AuxiliarySet, which includes categories of auxiliary information, i.e., category indexes of auxiliary information that the user can support (e.g., {1,2,3}, where 1 is the user position index, 2 is the user velocity index, and 3 is the user accelerometer information index). For example, in the RRC signaling shown in Figure 5, AuxiliaryPeriod represents the transmission period of auxiliary information, and AuxiliaryBits represents the number of bits transmitted for auxiliary information. For each category index, information such as the corresponding supported transmission period (for example, for user location, the supported transmission periods are {10ms, 20ms, 40ms}) and the number of transmission bits (for example, for user location, the supported number of transmission bits is {4bit, 8bit}) can also be provided.

[0050] For example, the transmission period of auxiliary information is determined by the measurement period of the user equipment and / or the validity period of the auxiliary information. That is, for each category index, the supported transmission period is determined by factors such as the measurement period of the user equipment and / or the validity period of the auxiliary information.

[0051] For example, the number of bits that can be transmitted for auxiliary information is determined by the measurement accuracy of the user's equipment and / or the memory capacity of the user's equipment. That is, for each category index, the number of supported transmission bits is determined by factors such as the measurement accuracy of the user's equipment and / or the user's memory capacity.

[0052] As an example, the determination unit 301 can be configured to determine the channel environment based on channel measurement information, and the channel measurement information includes one or more of the following: the intensity, phase, and power of the sounding reference signal (SRS) received through the channel between the electronic device 300 and the user device; the intensity, phase, and power of the channel status information reference signal (CSI-RS); and channel information in other frequency bands other than the frequency band in which the channel between the electronic device 300 and the user device is located (hereinafter sometimes also referred to as other frequency band channels).

[0053] The received power of the reference signal may simply be called RSRP.

[0054] The results of channel measurements can reflect the nature of the channel environment. For example, channel measurement results (including, but not limited to, channel status information (CSI), channel impulse response (CIR), received signal RSRP, and other frequency band channel information mentioned above) can be used to distinguish between LOS (direct or line-of-sight) scenarios and NLOS (non-direct or beyond line-of-sight) scenarios. Specifically, for example, electronic equipment 300 can distinguish between LOS and NLOS scenarios by calculating the kurtosis value of the CIR. If the kurtosis value is higher than a predetermined threshold, it is determined to be an LOS scenario, and if the kurtosis value is below the predetermined threshold, it is determined to be an NLOS scenario. Generally, LOS scenarios have simpler channels, making beam prediction easier, while NLOS scenarios are more complex, making beam prediction more difficult. As an example, a person skilled in the art can predetermine a predetermined threshold depending on the application scenario or experience.

[0055] As an example, the input information categories include at least one of the following: channel measurement information, user device motion characteristics information, user device location information, user device sensing information, user device communication capability, beam information, electronic device 300 sensing information, and communication sensing integrated information. The user device sensing information is information obtained based on the user device's software and / or hardware and includes motion-related parameters of the user device.

[0056] As an example, the channel measurement information as input information may include one or more of the following: SRS intensity, phase, and power received via the channel between the electronic device 300 and the user device; CSI-RS intensity, phase, and power; and channel information in a frequency band other than the frequency band in which the channel between the electronic device 300 and the user device is located.

[0057] For example, the motion characteristic information of the user's device as input information may be information that reflects the speed of the user's device.

[0058] For example, the location information of the user's device as input information may be information indicating the location of the user's device.

[0059] For example, the sensing information of the user device as input information is information acquired based on the software and / or hardware of the user device and may include motion-related parameters of the user device. For example, the sensing information of the user device may include at least one of the accelerometer information and the gyroscope information of the user device. For example, the sensing information of the user device may include the compass information of the user device.

[0060] For example, the communication capabilities of the user's equipment as input information may include the basic communication capabilities of the user's equipment, the maximum number of input information items supported in a single report, and the quantization granularity of the parameters related to the input information.

[0061] For example, the beam information as input information may refer to beam information that the electronic device 300 communicates with the user device, and the beam information may include, for example, the shape of the beam and the angle of the beam.

[0062] As an example, the sensing information of the electronic device 300 refers to information obtained by the electronic device 300 using its wireless sensing capability, and may include, for example, positioning information used by the electronic device 300 to position a user device.

[0063] In a communication-sensing integrated system, communication and sensing are fused together. The communication-sensing integrated information includes radar target detection information and one or more of the intensity, phase, and power of the radar beam reference signal.

[0064] As an example, the determination unit 301 can be configured such that the number of input information categories for the prediction model determined for user devices whose user capabilities do not meet predetermined user capability conditions is less than the number of input information categories for the prediction model determined for user devices whose user capabilities meet predetermined user capability conditions.

[0065] For example, a person skilled in the art can predetermine certain user capability requirements based on application scenarios or experience.

[0066] For example, the specified user capability conditions may include the ability of the user's equipment to acquire modal information exceeding a predetermined amount.

[0067] For example, a predetermined user capability condition may include the fact that the operating speed of the user's equipment is equal to or greater than a predetermined speed threshold. A person skilled in the art may come up with other examples of predetermined capability conditions, but these will not be repeated here.

[0068] As an example, the determination unit 301 can be configured such that the number of input information categories for the prediction model determined for user devices whose motion speed is less than a predetermined speed threshold is less than the number of input information categories for the prediction model determined for user devices whose motion speed is equal to or greater than the predetermined speed threshold.

[0069] For example, a person skilled in the art can predetermine a predetermined speed threshold depending on the application scenario or experience.

[0070] As an example, the determination unit 301 can be configured such that the number of input information categories for the prediction model determined for user devices whose channel environment satisfies predetermined environmental conditions is less than the number of input information categories for the prediction model determined for user devices whose channel environment does not satisfy predetermined environmental conditions.

[0071] For example, a person skilled in the art can predetermine certain environmental conditions depending on the application scenario or experience.

[0072] For example, in a simple and stable channel (where the channel environment satisfies predetermined environmental conditions), beam prediction can be made using less modal information. However, in a complex and unstable channel (where the channel environment does not satisfy predetermined environmental conditions), it is necessary to use more modal information to predict the beam. Generally, because channels in LOS scenarios are simpler, beam prediction can be made using less modal information. However, because NLOS scenarios are complex, it is necessary to use more modal information to predict the beam.

[0073] As an example, the determination unit 301 can be configured to provide beam prediction configuration information to user equipment via radio resource control (RRC) signaling or downlink control information (DCI).

[0074] As an example, the beam prediction configuration information may further include resource configuration information (transmission configuration information) corresponding to the determined input information category, the resource configuration information may include the transmission period and / or number of transmission bits for the determined input information category, and the determination unit 301 may be configured to perform beam prediction using a prediction model based on the input information reported by the user equipment according to the resource configuration information.

[0075] The electronic device 300 first selects a beam prediction modal set for the user, determines the transmission configuration for the modals in the modal set, and the user device feeds back the information collected according to the beam prediction modal set to the electronic device 300 according to the transmission configuration. For example, when determining the transmission period of various modal information, the electronic device 300 sets the transmission periods of various modals to the same value as much as possible, that is, it takes values ​​from the common set of supported transmission periods. If the common set is empty, it sets the periods of most modals to the same value as much as possible. For modals that do not support a transmission period, the electronic device 300 samples or interpolates the received modal information so that the format of all modal data is the same.

[0076] For example, the beam prediction modal set includes three information categories: SRS received signal, Sub-6GHz frequency band channel information, and user velocity, and the transmission configuration must have a total period of 40ms. The user feeds back the three types of modal information—SRS received signal, Sub-6GHz frequency band channel information, and user velocity parameters—to the electronic device 300 as three types of modal information inputs to the beam prediction model at a 40ms period.

[0077] For example, the beam prediction modal set may include sensing information from the user equipment. As described above, the sensing information from the user equipment may include at least one of the user equipment's accelerometer information and the user equipment's gyroscope information. The feeding back of information collected by the user equipment in accordance with the beam prediction modal set to the electronic equipment 300 according to the transmission configuration belongs to Measurement reporting in the TS38.331 standard and is a type of RRC information. The TS38.331 standard is appropriately modified. 1) The Sensor-NameList in the standard includes only uncompensated Barometeric pressure measurement, UE Speed ​​measurement, and UE orientation information, but in the embodiments of this disclosure, other sensing information such as accelerometer information and / or Gyroscope information is also supplemented. 2) In embodiments of the present invention, the sensing information can be flexibly transmitted and configured, that is, the electronic equipment 300 flexibly selects the transmission period, the number of transmission bits, etc. of the sensing information, and the user equipment reports the sensing information according to its configuration.

[0078] For example, the determination unit 301 can be configured to modify beam prediction configuration information corresponding to the user equipment using an event trigger method and provide the modified beam prediction configuration information to the user equipment. This supports dynamic adjustment of the beam prediction modal set and ensures beam prediction performance.

[0079] For example, an event may include the detection of a change in the channel environment where the user equipment is located that satisfies predetermined environmental change conditions. When the channel environment where the user equipment is located satisfies predetermined environmental change conditions, the beam prediction modal set can be dynamically updated, thereby ensuring beam prediction performance. In addition, the transmission configuration information can be updated accordingly.

[0080] For example, a person skilled in the art can predetermine certain environmental change conditions depending on the application scenario or experience.

[0081] For example, a predetermined environmental change condition may include a change in the user's equipment's motion speed being greater than or equal to a predetermined speed threshold. Alternatively, a predetermined environmental change condition may include a change in the user's equipment's position being greater than or equal to a predetermined position threshold. Another predetermined environmental change condition may include the user's equipment moving from a simple channel environment to a complex channel environment. Those skilled in the art may conceive of other examples of predetermined environmental change conditions, but these are not described again here.

[0082] As an example, the determination unit 301 can be configured to directly modify the beam prediction configuration information corresponding to the user equipment without notifying the user equipment of a re-report of user capability. For example, when the electronic equipment 300 detects that the channel environment in which the user equipment is located has changed, it directly modifies the beam prediction modal set and transmission configuration information of the user equipment and notifies the user equipment. This method is an update led by the electronic equipment 300. This method has low signaling overhead because the user equipment does not need to re-report user capability.

[0083] For example, the determination unit 301 can be configured to notify the user equipment of a re-report of user capability and to modify the beam prediction configuration information corresponding to the user equipment according to the re-received user capability. For example, when the electronic equipment 300 detects that the channel environment in which the user equipment is located has changed, it re-queries the user capability, modifies the beam prediction modal set and transmission configuration information of this user equipment according to the feedback from the user equipment, and transmits it to the user equipment. For example, if the user equipment moves from a simple channel environment (e.g., an LOS channel environment) to a complex channel environment (e.g., an NLOS channel environment), the electronic equipment 300 requests the user equipment to provide more modal information, and if the user equipment agrees, the electronic equipment 300 expands the beam prediction modal set and notifies the user equipment. This method is an interactive update between the electronic equipment 300 and the user equipment and can maintain beam prediction performance as much as possible even under complex channel conditions.

[0084] For example, the same prediction model can be used for beam prediction across different categories of input information that have been determined.

[0085] Because different beam prediction modal sets correspond to different types and numbers of modals, the corresponding input formats differ when using modal information as input. Building N different prediction models for N (where N is a positive integer greater than 1) different beam prediction modal sets results in significant model memory overhead. In the embodiments of this disclosure, predictions can be made for different beam prediction modal sets using a unified model, effectively reducing memory overhead.

[0086] Hereinafter, the prediction model will be referred to as a deep multimodal learning model, and the learning process in beam prediction using the deep multimodal learning model will be referred to as deep multimodal learning (DML). In deep multimodal learning, modal is defined as information of one category. Therefore, multimodal also refers to information of multiple categories. In the embodiments described herein, unified beam prediction based on deep multimodal learning is employed.

[0087] For example, a predictive model can reconstruct missing input information corresponding to categories missing from a predetermined number of predetermined input information categories, depending on the existing input information, so as to complement different input information categories into a predetermined number of predetermined input information categories. For example, a person skilled in the art can predetermine a predetermined number of predetermined input information categories depending on the application scenario or experience.

[0088] For example, the input format (input data format) can be made the same by reconstructing missing modal information based on existing reliable modal information and supplementing modal information corresponding to different beam prediction modal sets.

[0089] Figure 7 shows a schematic configuration example of a prediction model according to an embodiment of the present disclosure. As shown in Figure 7, the deep multimodal learning model improves the model's prediction performance by extracting features from multiple categories of information (e.g., modal 1, modal 2, modal 3) through feature extraction, discovering hidden connections between different categories of information, extracting complementary information between different categories of information (i.e., performing fusion between different information), making predictions, and finally outputting beam prediction results.

[0090] For example, a deep multimodal learning model can include a multimodal fusion prediction model and a reconstruction network model. The multimodal fusion prediction model is used to fuse different types of modal information to make beam predictions, while the reconstruction network model can support the input of modal information corresponding to different beam prediction modal sets to the prediction model by reconstructing missing modal information based on existing reliable modal information. In other words, the reconstruction network model can solve the problem of inconsistent combinations of modal information categories for beam prediction.

[0091] For example, a predictive model might include a variational autoencoder (VAE) for reconstruction, which extracts hidden features from existing input information and estimates missing input information based on those hidden features. A VAE is an example of a reconstruction network model. A VAE learns internal hidden features from the input and uses those hidden features to generate new modal information. Because there are internal connections between different modal information, internal hidden features can be extracted from existing, reliable modal information, and those hidden features can be used to generate missing modal information.

[0092] As an example, a VAE includes an encoding layer, a sampling layer, and a decoding layer. The encoding layer maps existing input information to probability distribution parameters, the sampling layer samples based on the probability distribution parameters to obtain an intermediate hidden layer, i.e., hidden features, and the decoding layer retrieves the input information missing by the intermediate hidden layer. In other words, at the encoding layer stage, the probability distribution of internal hidden features is extracted and obtained from the existing modal information. The sampling layer samples and obtains internal hidden features based on the probability distribution. The decoding layer generates the missing modal information based on the internal hidden features.

[0093] Figure 8 shows an example configuration of a reconstructed network model according to an embodiment of the present disclosure. For example, the received signal of wide beam training is first modal x (1) The user's position is in the second modal x (2) Therefore, the user speed is the third modal x (3) The index in the beam prediction modal set for some users is {1,2,3}, and the index in the beam prediction modal set for some users is {1}.

[0094] For users whose beam prediction modal set is {1}, the reconstructed network model uses the information from the first modal to approximately estimate the corresponding information from the second and third modals, thereby complementing all modal information. A detailed explanation of the reconstructed network model shown in Figure 8 is as follows.

[0095] 1) Input: First modal information x (1) The feature vector z obtained after passing through the convolutional layer (1) That is the case. 2) Encoding layer: The mean and variance (μ, σ) of the intermediate hidden layer are obtained through two fully connected layers. 3) Sampling layer: The intermediate hidden layer ω is sampled and obtained according to the mean and variance obtained from the encoding layer, and is shown as ω~N(μ, σ). 4) Decode layer: Through one fully connected layer, the intermediate hidden layer ω is converted to the feature vectors of the second and third modal information.

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[0096] Therefore, the categories of different input information of all user devices can be processed by a unified prediction model to perform beam prediction.

[0097] As an example, the prediction model further includes a convolutional neural network (CNN) and a long short-term memory neural network (LSTM) for performing beam prediction. For example, CNN and LSTM constitute the aforementioned multimodal fusion prediction model. CNN is a deep learning model that performs feature extraction using convolution and is suitable for extracting feature vectors from large-scale input data. LSTM is a deep learning model that can extract time-series features and is suitable for modeling the beam change process. For example, LSTM obtains the state of the current time based on the state of the previous time and the input of the current time, and thus predicts the output of the current time.

[0098] FIG. 9 shows a configuration example of a multimodal fusion prediction model according to an embodiment of the present disclosure. The multimodal fusion prediction model shown in FIG. 9 will be specifically described as follows.

[0099] 1) Input: The input is the time series x of three modalities over a certain period (m) =[x1 (m) , x2 (m) , …, x n-1 (m) , where m ∈ {1, 2, 3}. Each element x i (m) (1 ≤ i ≤ n - 1) of this series corresponds to the time t i . 2) First fusion layer: Assume that the second modality and the third modality have a similar data format and represent the same type of information. The second modality and the third modality perform pre-data fusion and splice the data at each time directly. 3) Convolutional Layers: Three convolutional blocks are used to extract features from the first modal information and the second and third modal fusion information. Each convolutional block includes one convolutional layer, one BN (batch-norm) layer, and a ReLU activation layer. The ReLU activation layer is

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[0100] For optimization, the Adam optimizer, a gradient backpropagation algorithm, can be used.

[0101] Figure 10 shows an example configuration of a prediction model according to an embodiment of the present disclosure. In Figure 10, the multimodal fusion prediction model shown in Figure 9 and the reconstruction network model shown in Figure 8 are combined to obtain a unified beam prediction model based on deep multimodal learning. As shown in Figure 10, if the user's beam prediction modal set is {1,2,3}, first, feature extraction is performed for each modal using a convolutional layer. Assuming that the second and third modals have similar data formats and represent the same type of information, data fusion is performed in the first fusion layer for the second and third modals to splice the data. Next, the information is fused through the second fusion layer, and finally, the index of the predicted optimal beam is output through the LSTM layer and the fully connected layer. If the user's beam prediction modal set is {1}, first, feature extraction is performed on the first modal using a convolutional layer, then the feature vectors of the second and third modals are estimated through a reconstructed network model, the information is fused through a second fusion layer, and finally, the index of the predicted optimal beam is output through an LSTM layer and a fully connected layer.

[0102] Referring to the explanations in Figures 8-10, for example, the input to the prediction model is a time series in a predetermined number of historical time slots of different input information, and the output of the prediction model is the predicted optimal beam. In other words, the index of the optimal beam is predicted by the deep multimodal learning model, the input to deep multimodal learning is information corresponding to a beam prediction modal set over a certain period in the past, and the output is the index of the predicted optimal beam. The deep multimodal learning model can effectively fuse complementary information between different modals and improve the accuracy of predictions.

[0103] As an example, a person skilled in the art can predetermine the above-mentioned predetermined number depending on the application scenario or experience.

[0104] For example, all modals are input in the form of a sequence. The input is a time series of M modals (where M is a positive integer greater than 1) over a certain period of time.

[0105] x (m) =[x1 (m) , x2 (m) ,…,x n-1 (m) ], m∈{1,2,…,M}

[0106] Each element x of this sequence i (m) (1≦i≦n-1) is at time t i It corresponds to.

[0107] The output is time t n This is the optimal beam index q(tn) corresponding to f. (1) ,f (2) ,…,f (Q) represents all possible beams, and q(t n )∈{1,2,…,Q}.

[0108] Deep multimodal learning models are used to fit the prediction function g(·) as follows:

[0109] q(t n )=g((x1 (1) , x2 (1) ,…,x n-1 (1) ),…,(x1 (M) , x2 (M) ,…,x n-1 (M) ))

[0110] Figure 11 shows an example of an electronic device 300 performing beam prediction for a single user device according to an embodiment of the present disclosure. In the specific steps of Figure 11, the electronic device 300 is referred to as the base station and the user device as the user. The steps include the following, as shown in Figure 11.

[0111] Step 1: The base station queries the user's capabilities.

[0112] Step 2: Perform initial channel measurements.

[0113] Step 3: The base station determines the beam prediction modal set and transmission configuration information.

[0114] Step 4: The base station sends a beam prediction notification to the user.

[0115] Step 5: The base station notifies the user of the beam prediction modal set and transmission configuration information.

[0116] Step 6: Perform channel measurements for beam prediction.

[0117] Step 7: The user collects the corresponding modal information according to the beam prediction modal set.

[0118] Step 8: The user feeds back the collected information to the base station according to the transmission configuration.

[0119] Step 9: The base station integrates all modal information corresponding to the beam prediction modal set and performs beam prediction.

[0120] Steps 6 through 9 are executed periodically.

[0121] Step 10: When the base station detects a change in the channel environment where the user is located, it updates the beam prediction modal set and transmission configuration information and notifies the user. Step 10 is performed when the channel environment changes. Step 10 allows for dynamic adjustment of the beam prediction modal set and transmission configuration information.

[0122] Unless otherwise specified, the signaling for each step in Figure 11 can be implemented by RRC and / or DCI.

[0123] As an example, user equipment is a group member within a group obtained by electronic equipment 300 grouping multiple user equipment within the service range of electronic equipment 300, and the determination unit 301 can be configured to dynamically or semi-statically group at least one user equipment whose location in the channel environment satisfies predetermined similar environment conditions into one group, and the optimal beam corresponding to a group member within the same group satisfies predetermined similar beam conditions. That is, users located in similar channel environments are grouped into one group, and the optimal beams of users within the same group are the same or similar. The grouping method supports dynamic adjustment. By grouping users, beam prediction results can be shared among users within the same group, for example, users in the same room can share beam prediction results.

[0124] For example, a person skilled in the art can pre-determine certain similar beam conditions depending on the application scenario or experience.

[0125] For example, input information collected by group members within the same group based on beam prediction configuration information is fused as input to the prediction model. Within a single group of users, beam prediction modal information from different users can be fused (extracting complementary information from different information categories of users within the same group) and simultaneously used as input to a deep multimodal learning model. Beam prediction for group users (group user beam prediction) can improve beam prediction accuracy.

[0126] As an example, the decision unit 301 can be configured to select at least some group members from a selected group to form a user subset, based on the user capabilities of the group members within the group and / or the channel environment in which the group members are located. For example, among the users of one group, the electronic equipment 300 may select some users as representatives of the users in that group to form a user subset, and the selection criteria include, but are not limited to, the channel environment and / or user capabilities. The beam prediction results of this user subset can be used as a basis for the beam prediction results for the users of the entire group, thereby reducing prediction overhead. For example, by selecting a user subset, other users do not need to frequently provide feedback on beam prediction modal information, and the beam prediction results of the user subset can be used directly, thus reducing signaling overhead.

[0127] Figure 12 shows an example of performing beam prediction for a group of users according to an embodiment of the present disclosure. As shown in Figure 12(a), beam prediction accuracy can be improved by fusing the beam prediction modal information of group members. As shown in Figure 12(b), prediction overhead can be reduced by performing beam prediction for a user subset and using the beam prediction results of the user subset as a baseline for the beam prediction results of the entire group of users.

[0128] As an example, the determination unit 301 can be configured to obtain beam prediction results for a user subset by performing beam prediction based on input information reported by subset members within the user subset, according to the beam prediction configuration information. All group members within the selected group then communicate with the electronic equipment 300 using the beam prediction results for their user subset. For example, only users within a user subset participate in the beam prediction process, periodically transmitting modal information corresponding to their beam prediction modal set, while other users in the group do not participate in the beam prediction process and directly use the beam prediction results of their user subset. In other words, selected users within a group constitute a user subset, and the beam prediction results of this user subset are used for the beam prediction results of all users in the group.

[0129] As an example, the determination unit 301 can be configured to obtain beam prediction results for a user subset by performing beam prediction based on input information reported by subset members within the user subset at a shorter period than a predetermined period, according to the beam prediction configuration information, and the subset members within the user subset communicate with the electronic equipment 300 using the beam prediction results for the user subset. Alternatively, the determination unit 301 can be configured to obtain beam prediction results for other group members by performing beam prediction based on input information reported by other group members other than the user subset within the selected group at a period longer than a predetermined period, according to the beam prediction configuration information, and the other group members obtain adjusted beam prediction results by adjusting the beam prediction results for the user subset based on the beam prediction results for the other group members, and the other group members communicate with the electronic equipment 300 using the adjusted beam prediction results.

[0130] For example, all users in a group participate in the beam prediction process. Users within a user subset send modal information corresponding to their beam prediction modal set at shorter intervals, other users send modal information corresponding to their beam prediction modal set at longer intervals, and other users adjust the beam prediction results of the user subset according to their own beam prediction results.

[0131] For example, a person skilled in the art can determine a predetermined period depending on the application scenario or experience.

[0132] For example, the electronic device 300 can initially group users based on the initial channel measurement results.

[0133] As an example, the determination unit 301 can be configured to group user devices into the same group if the correlation between channel status information (CSI) is greater than a predetermined correlation threshold within a predetermined time period. In other words, user devices with highly correlated CSIs can be grouped into one group within a predetermined time period.

[0134] For example, a person skilled in the art can predetermine the aforementioned predetermined time depending on the application scenario or experience.

[0135] For example, a person skilled in the art can predetermine a predetermined correlation threshold depending on the application scenario or experience.

[0136] For example, the determination unit 301 can be configured to group user devices that have the same optimal beam within a predetermined time period into the same group.

[0137] Figure 13 shows an example of grouping user devices that have the same optimal beam for a given time period into the same group, according to an embodiment of the present disclosure. As shown in Figure 13, when three users move, the optimal beams corresponding to them change. For example, when the three users are at position 1, the optimal beam corresponding to the three users is beam 1; when the three users are at position 2, the optimal beam corresponding to the three users is beam 2; and when the three users are at position 3, the optimal beam corresponding to the three users is beam 3. However, since the optimal beam corresponding to the three users remains the same, the three users are grouped into the same group.

[0138] As an example, the determination unit 301 may be configured to group members into a new group if the reference signal received power (RSRP) measured by the group members based on the beam prediction results for a selected subset of users within the group is less than a predetermined power threshold. As an example, a person skilled in the art can predetermine a predetermined power threshold depending on the application scenario or experience. For example, the electronic equipment 300 may configure an RSRP threshold r0. All users in the group measure and predict the RSRP of the optimal beam (for example, here the predicted optimal beam is the beam prediction result for the subset of users within this group). If the RSRP is less than threshold r0, the corresponding users are grouped into a new group (i.e., intragroup splitting). If the RSRP values ​​measured by some users are less than threshold r0, it means that these some users have obtained low received power using the beam prediction results within the group. Therefore, these some users are grouped into a new group (i.e., intergroup merge), a new subset of users is selected, and beam prediction is performed again.

[0139] Figure 14 shows an example of grouping group members into a new group according to an embodiment of the present disclosure. As shown in Figure 14(a), there is one group, and beam prediction is performed for that group. As shown in Figure 14(b), the one group shown in Figure 14(a) is grouped into two groups, and beam prediction is performed for each of the two groups.

[0140] As an example, the decision unit 301 can be configured to merge different groups into a new group if the beam prediction results for those groups are the same within a predetermined time period. In other words, if the predicted optimal beams for some user groups are the same within a certain time period, these user groups are merged into a new group. When the predicted optimal beams for some user groups are the same within a certain time period, it indicates that their channel environments are similar, and merging them into a new group allows beam prediction to be performed together, thereby reducing signaling overhead.

[0141] Initial user grouping results may not always be ideal, user states may change, and the initial user grouping results may no longer be applicable. Embodiments of this disclosure support dynamic adjustment of user grouping to achieve real-time validity of grouping results and improve beam prediction performance for grouped users.

[0142] As an example, the determination unit 301 can be configured to group group members with the same user capabilities into the same category for a selected group, determine which categories need to be included in the user subset, and select at least some group members from the group members corresponding to the determined categories to form a user subset. Different user groups are located in different channel environments, and the channel environment in which a user group is located can change at any time. A fixed user subset may degrade the beam prediction performance of group users. In the embodiments of this disclosure, beam prediction accuracy can be improved by flexibly selecting user subsets and integrating modal information collected by the user subsets.

[0143] Figure 15 shows an example of selecting a user subset according to an embodiment of the present disclosure. Within a group of users, users are classified based on their capabilities, and users who support the same combination of capabilities are classified into one category. For example, a user who can only support channel measurement is classified into category 1, a user who supports channel measurement and user location provision is classified into category 2, a user who supports channel measurement and user speed provision is classified into category 3, and a user who supports channel measurement and user location and speed provision is classified into category 4, and so on (categories 1 to 3 are shown in Figure 15). The electronic device 300 determines the categories that need to be included in the user subset and selects users from the corresponding categories.

[0144] As the channel environment of users in this group changes, the electronic equipment 300 can flexibly modify the user subset. For example, in a simple channel, the user subset may include only Category 1 users, while in a complex channel, the user subset must include users in more powerful categories (e.g., Category 1 users plus Category 2 and Category 3 users) to perform information fusion on the modal information of users in these categories. For example, if a group of users moves from a simple channel to a complex channel, the electronic equipment 300 expands the user subset to include users from more categories, thereby fusing more modal information and improving beam prediction accuracy. Conversely, if a group of users moves from a complex channel to a simple channel, the electronic equipment 300 shrinks the user subset to reduce the overhead of beam prediction.

[0145] Figure 16 shows an example in which an electronic device 300 according to an embodiment of the present disclosure performs beam prediction for a group of user devices. In the specific steps of Figure 16, the electronic device 300 is referred to as the base station and the user devices are referred to as users. In this example step, the overhead of beam prediction can be reduced and beam prediction accuracy can be maintained by dynamically adjusting the user grouping results and the selection of user subsets. As shown in Figure 16, the steps include the following:

[0146] Step 1: The base station queries the user's capabilities.

[0147] Step 2: Perform initial channel measurements. The results of the initial channel measurements may be used by the user for initial grouping and for determining the user's beam prediction modal set and transmission configuration information.

[0148] Step 3: Perform initial user grouping.

[0149] Step 4: The base station selects a user subset.

[0150] Step 5: The base station determines the beam prediction modal set and transmission configuration information.

[0151] Step 6: The base station sends a beam prediction notification to the user group. There are two methods for the base station to send beam prediction notifications to the user group. Method 1: The base station sends a beam prediction notification to a subset of users, which is suitable when only users within that subset participate in the beam prediction process and helps reduce signaling overhead. Method 2: The base station sends a beam prediction notification to all users in the user group, which is suitable when all users in the group participate in the beam prediction process and helps improve prediction accuracy.

[0152] Step 7: The base station notifies the user of the beam prediction modal set and transmission configuration information.

[0153] Step 8: Perform channel measurements for beam prediction.

[0154] Step 9: The user collects the corresponding modal information according to the beam prediction modal set.

[0155] Step 10: The user feeds back the collected information to the base station according to the transmission configuration.

[0156] Step 11: The base station integrates all modal information corresponding to the beam prediction modal set and performs beam prediction.

[0157] Steps 7 through 11 are executed periodically.

[0158] Step 12: When the base station detects that the channel environment in which the user is located has changed, it updates the beam prediction modal set and transmission configuration information and transmits it to the user.

[0159] Step 13: The base station configures the RSRP threshold r0.

[0160] Step 14: All users in the group measure the RSRP of the predicted optimal beam.

[0161] Step 15: Perform intra-group splitting or inter-group merging.

[0162] Steps 13 through 15 concern the dynamic adjustment of user groups.

[0163] Step 16: The base station selects a new user subset for the new user group.

[0164] Step 16 is executed when a new user group is created.

[0165] Unless otherwise specified, the signaling for each step in Figure 16 may be implemented by RRC and / or DCI.

[0166] As an example, the decision unit 301 groups the selected group members into high-capacity users whose user capabilities meet predetermined capability conditions and low-capacity users whose user capabilities do not meet predetermined capability conditions, according to their user capabilities. By notifying the group members within the group of information about the group and the grouping results regarding high-capacity users and / or low-capacity users, the group members within the group can be configured to perform beam prediction based on the prediction model. In this case, the beam prediction model is located on the user equipment side.

[0167] For example, a person skilled in the art can predetermine certain competency requirements based on application scenarios or experience.

[0168] For example, a predetermined capability requirement may include the ability of user equipment to perform beam prediction using a prediction model. User equipment capable of performing beam prediction using a prediction model may be grouped as high-capacity users, and user equipment not capable of performing beam prediction using a prediction model may be grouped as low-capacity users. For example, the beam prediction model may be assigned to high-capacity users.

[0169] For example, a predetermined capability condition may include the ability of the user's equipment to acquire modal information exceeding a predetermined amount. Those skilled in the art may come up with other examples of predetermined capability conditions, which will not be repeated here.

[0170] As an example, the decision unit 301 may be configured to notify group members of information about the group and the grouping results regarding high-capacity users and / or low-capacity users via RRC signaling or DCI.

[0171] As an example, highly capable users within a selected group can obtain beam prediction results by using a predictive model based on collected input information, and share these results with less capable users. Information from users within the same group can be shared directly via D2D (Device-to-Device) communication. For instance, highly capable users within a group can integrate their modal information, input it into the predictive model, obtain the predicted optimal beam, and share these prediction results with less capable users within the group.

[0172] For example, a highly capable user within a selected group will share at least some of the collected input information with a less capable user in response to a request received from that user. For instance, a highly capable user within a group can share their modal information with a less capable user via D2D communication. For example, if a less capable user within a group needs modal information that they cannot provide, they can request it from a highly capable user within the group, who will then share the requested modal information.

[0173] For example, D2D communication is used between members of a fleet in a vehicle network. Key members within the fleet can handle the high-capacity users described above, while other members can handle the low-capacity users described above.

[0174] Within the same group of users, some modal information can be shared, while other modal information cannot. Because the channel environments of users within the same group are similar, channel measurement information (such as SRS received signals and / or RSRP, CSI-RS received signals and / or RSRP, and other frequency band channel information mentioned above) and integrated communication sensing information (such as radar target detection information, radar beam received signals and / or RSRP) can be shared. Furthermore, user motion information such as user position, user speed, and user accelerometer information, which falls under the category of auxiliary information reflecting user capabilities, does not differ significantly among different users within the same group and can therefore be shared. On the other hand, detailed information that differs significantly among different users within the same group, such as user gyroscope information and user compass information, does not support information sharing.

[0175] The following outlines an example of the process for sharing D2D scene modal information. In the following specific steps, electronic device 300 will be referred to as the base station, and user equipment will be referred to as the user.

[0176] In S1, the base station queries the user's capabilities.

[0177] In S2, perform the initial channel measurement.

[0178] Perform initial user grouping in S3.

[0179] In S4, the base station classifies users according to their capability level (high-capacity user or low-capacity user).

[0180] In S5, the base station notifies the user of the grouping results and capability classification results.

[0181] In S8, high-performance users collect modal information and perform beam prediction.

[0182] In S9, high-skill users share their prediction results with low-skill users.

[0183] In S10, when a low-skill user within a group makes a request to a high-skill user within the group, the high-skill user within the group shares the requested modal information.

[0184] The flow for sharing D2D scene modal information may include at least one of S9 and S10.

[0185] Unless otherwise specified, signaling related to steps S1 to S10 may be implemented by RRC and / or DCI.

[0186] We consider a millimeter-wave downlink transmission scenario. The user's maximum motion speed is 30 m / s, the maximum acceleration is 0.2 times the velocity, and the direction of motion is randomly generated within the range [0, 2π]. Channel data is generated using a conventional Deep MIMO model (see Deep MIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications, A. Alkhateeb, et al., in Proc. ITA, Feb. 2019, pp. 1-8). The simulation parameters are shown in Table 1.

[0187] [Table 1]

[0188] We consider three beam prediction modals. The received signal trained with wide beam is used in the first modal x (1) The user's position is in the second modal x (2)Therefore, the user speed is the third modal x (3) The signal is transmitted through the channel, and electronic device 300 receives noisy beam prediction modal information. The noise parameters are as shown in Table 2.

[0189] [Table 2]

[0190] The following three cases are considered for beam prediction modal sets: Case 1: The beam prediction modal set for all users is {1,2,3}, and is referred to below as "fully modal". Case 2: The beam prediction modal set for 90% of users is {1,2,3}, and the beam prediction modal set for 10% of users is {1}, and is referred to below as "10% missing modal". Case 3: The beam prediction modal set for 80% of users is {1,2,3}, and the beam prediction modal set for 20% of users is {1}, and is referred to below as "20% missing modal".

[0191] As evaluation metrics, prediction accuracy and normalized beam gain are used. Assuming that the total number of samples used in the evaluation is N1, and the number of samples where the beam obtained by beam prediction is the actual optimal beam is N2, the prediction accuracy is shown as follows.

[0192]

number

[0193] The average received power obtained using the predicted optimal beam is

number

[0194]

number

[0195] We consider setting up a prediction model that uses only the received signal trained with a wide beam as input as Baseline 1, and a prediction model that uses only the user's position and velocity information as input as Baseline 2. For the beam prediction scheme proposed in this disclosure (hereinafter referred to as the "Proposed Scheme"), simulations are performed for fully modal, 10% missing modal, and 20% missing modal cases, respectively.

[0196] The prediction accuracy of the different approaches is shown in Table 3. The proposed approach shows a significant improvement in prediction accuracy compared to baseline 1 and baseline 2. Furthermore, the proposed approach maintains high prediction accuracy even when modals are missing.

[0197] [Table 3]

[0198] Figure 17 shows the change in normalized beam gain with respect to prediction time (beam prediction time) for different schemes. It can be seen that the proposed scheme can provide a higher normalized beam gain compared to baseline 1 and baseline 2. Furthermore, the proposed scheme can maintain a high normalized beam gain even when modal is missing.

[0199] This disclosure further provides electronic equipment for wireless communication according to another embodiment. Figure 18 shows a functional module block diagram of electronic equipment 1900 for wireless communication according to another embodiment of the present invention.

[0200] As shown in Figure 18, the electronic device 1900 receives beam prediction configuration information from a network-side device that provides services to the electronic device 1900. The beam prediction configuration information includes categories of input information for a prediction model dynamically or semi-statically determined by the network-side device for the electronic device 1900. The prediction model comprises a communication unit 1901 that can be configured to perform beam prediction for a beam used for communication between the electronic device 1900 and the network-side device, and a collection unit 1903 that can be configured to collect input information according to the beam prediction configuration information. It should be understood that each functional unit in the electronic device shown in Figure 18 is merely a logical module partitioned based on the specific function to be implemented, and does not limit the specific implementation form.

[0201] The communication unit 1901 and the data collection unit 1903 may be implemented by one or more processing circuits, which may be implemented, for example, as a chip.

[0202] The electronic device 1900 may, for example, be installed on the user equipment side or connected to the user equipment in a communicative manner. Here, the electronic device 1900 may be implemented at the chip level or at the device level. For example, the electronic device 1900 may operate as the user equipment itself, and may further include external devices such as memory and transceivers (not shown). The memory is used to store programs executed by the base station to perform various functions, and related data information. The transceiver may include one or more communication interfaces to support communication between different devices (e.g., base station, other user equipment, etc.), but the implementation form of the transceiver is not specifically limited here.

[0203] The wireless communication system described herein may be a 5G NR (New Radio) communication system. Furthermore, the wireless communication system described herein may include a non-terrestrial network (NTN). Optionally, the wireless communication system described herein may also include a terrestrial network (TN). Those skilled in the art will also understand that the wireless communication system described herein may be a 4G or 3G communication system.

[0204] For example, the network-side device in the embodiment of electronic device 1900 may be the electronic device 300 described above. Also, for example, electronic device 1900 may be a user device according to the embodiment of electronic device 300 described above.

[0205] According to embodiments of this disclosure, the electronic device 1900 can collect input information for beam prediction according to the category of input information for the prediction model (dynamically or semi-statically determined beam prediction configuration information) which is dynamically or semi-statically determined (updated) by the network-side device, thereby improving beam prediction performance (for example, improving the accuracy of beam prediction).

[0206] For example, the category of input information may be determined according to the user capabilities of the electronic device 1900 and / or the channel environment in which the electronic device 1900 is located, and the communication unit 1901 may be configured to report the user capabilities to network-side equipment via radio resource control (RRC) signaling.

[0207] The user capabilities of electronic device 1900 and / or the channel environment in which electronic device 1900 is located differ, and the modal information required for corresponding beam prediction also differs.

[0208] As an example, the user capability information included in the RRC signaling described above includes user auxiliary sub-information, and the parameters of the user auxiliary sub-information include categories of auxiliary information to reflect the user capability of the electronic device 1900. The RRC signaling will not be explained again here, as it can be found in the description of the embodiment of the electronic device 300 described in conjunction with Figure 6.

[0209] In embodiments of this disclosure, extended user capability information including user auxiliary sub-information is proposed, and the network-side equipment determines the beam prediction modal set by notifying the network-side equipment of the categories of auxiliary information that the electronic device 1900 can provide.

[0210] As an example, the category of auxiliary information includes at least one of the following: motion characteristics information of the electronic device 1900, location information of the electronic device 1900, sensing information of the electronic device 1900, communication capabilities of the electronic device 1900, whether the electronic device 1900 supports the use of a predictive model, and information about the beam.

[0211] For example, sensing information from an electronic device is information acquired based on the software and / or hardware of the electronic device 1900, and includes motion-related parameters of the electronic device 1900.

[0212] Information regarding the motion characteristics of the electronic device, the position information of the electronic device 1900, the sensing information of the electronic device 1900, the communication capabilities of the electronic device 1900, whether the electronic device 1900 supports the use of a predictive model, and information regarding the beam can be found in the description of the embodiment of the electronic device 300 described above, and will not be repeated here.

[0213] For example, the parameters of the user accessibility sub-information may further include the transmission period and / or the number of bits transmitted for the accessibility information.

[0214] For example, the transmission period of the auxiliary information is determined by the measurement period of the electronic device 1900 and / or the effective period of the auxiliary information.

[0215] For example, the number of bits transmitted for auxiliary information is determined by the measurement accuracy of the electronic device 1900 and / or the memory capacity of the electronic device 1900.

[0216] For example, the channel environment is obtained based on channel measurement information, which includes one or more of the following: the intensity, phase, and power of the sounding reference signal (SRS) received through the channel between the electronic device 1900 and the network-side device; the intensity, phase, and power of the channel status information reference signal (CSI-RS); and channel information for frequency bands other than the frequency band in which the channel is located.

[0217] The channel measurement information can be found in the description of the embodiment of the electronic device 300 described above, so it will not be explained again here.

[0218] As an example, the input information category includes at least one of the following: channel measurement information, motion characteristic information of the electronic device 1900, location information of the electronic device 1900, sensing information of the electronic device 1900, sensing information of network-side equipment, and communication sensing integrated information. The sensing information of the electronic device 1900 is information obtained based on the software and / or hardware of the electronic device 1900 and includes motion-related parameters of the electronic device 1900.

[0219] For example, integrated communication sensing information includes radar target detection information and one or more of the following: intensity, phase, and power of the radar beam reference signal.

[0220] The channel measurement information, motion characteristic information of the electronic device 1900, position information of the electronic device 1900, sensing information of the electronic device 1900, and sensing information of the network-side device, as input information, can be found in the description of the embodiment of the electronic device 300 described above, and will not be explained again here.

[0221] For example, the number of input information categories for a predictive model determined for an electronic device whose user capabilities do not meet the predetermined user capability conditions is less than the number of input information categories for a predictive model determined for an electronic device whose user capabilities meet the predetermined user capability conditions.

[0222] Examples of specified user capability conditions can be found in the description of the embodiment of the electronic device 300 described above, and will not be repeated here.

[0223] For example, the number of input information categories for a predictive model determined for electronic devices whose motion speed is below a predetermined speed threshold is less than the number of input information categories for a predictive model determined for electronic devices whose motion speed is above the predetermined speed threshold.

[0224] For example, the number of input information categories for a predictive model determined for electronic devices whose channel environment satisfies predetermined environmental conditions is less than the number of input information categories for a predictive model determined for electronic devices whose channel environment does not satisfy predetermined environmental conditions.

[0225] Examples of specified environmental conditions can be found in the description of the embodiment of the electronic device 300 above, so they will not be repeated here.

[0226] As an example, the communication unit 1901 may be configured to receive beam prediction configuration information via RRC signaling or downlink control information (DCI).

[0227] As an example, the beam prediction configuration information may further include resource configuration information corresponding to a category of input information determined by the network-side equipment, the resource configuration information including the transmission period and / or number of transmission bits for the determined category of input information, and the communication unit 1901 may be configured to report input information based on the resource configuration information so that the network-side equipment performs beam prediction using a prediction model.

[0228] Examples of beam prediction configuration information, including resource configuration information, can be found in the description of the embodiment of the electronic device 300 described above, and therefore will not be repeated here.

[0229] As an example, the communications unit 1901 may be configured to receive beam prediction configuration information corresponding to the electronic equipment 1900 modified in an event-triggered manner.

[0230] For example, an event may include the detection that a change in the channel environment in which the electronic device 1900 is located satisfies predetermined environmental change conditions. For example, the electronic device may not be notified to re-report user capability, and the beam prediction configuration information corresponding to the electronic device 1900 may be directly modified by the network-side equipment. For example, the communication unit 1901 may be configured to receive a notification to re-report user capability and to re-report the user capability, and the beam prediction configuration information corresponding to the electronic device 1900 may be modified by the network-side equipment according to the re-reported user capability.

[0231] Examples of predetermined environmental change conditions can be found in the description of the embodiment of the electronic device 300 described above, so they will not be explained again here.

[0232] An example of electronic device 1900 receiving beam prediction configuration information corresponding to the modified electronic device 1900 in an event-triggered manner can be found in the description of the embodiment of electronic device 300 above, and will not be described again here.

[0233] The same prediction model is used to perform beam prediction for different categories of input information that have been determined.

[0234] As an example, a predictive model can complement a predetermined number of predetermined input information categories by reconstructing missing input information corresponding to categories that are missing from a predetermined number of predetermined input information categories, depending on the existing input information.

[0235] As an example, the prediction model includes a variational autoencoder (VAE) for reconstruction. The VAE extracts hidden features from existing input information and estimates the missing input information based on the hidden features.

[0236] As an example, the VAE includes an encoder, a sampler, and a decoder. The encoder maps the existing input information to probability distribution parameters, the sampler samples based on the probability distribution parameters to obtain an intermediate hidden layer, that is, a hidden feature, and the decoder obtains the input information missing by the intermediate hidden layer.

[0237] As an example, the prediction model further includes a convolutional neural network and a long short-term memory neural network to perform beam prediction.

[0238] As an example, the input of the prediction model is a time series in a predetermined number of history time slots of different input information, and the output of the prediction model is the predicted optimal beam.

[0239] For examples of the prediction model, reference can be made to the description of the embodiments of the electronic device 300 described above, and thus it will not be repeatedly described here.

[0240] As an example, the electronic device 1900 is a group member within a group obtained by a network-side device grouping a plurality of electronic devices within its service range. At least one electronic device whose located channel environment satisfies predetermined similar environmental conditions is grouped into the group dynamically or semi-statically, and the optimal beams corresponding to the group members within the same group satisfy predetermined similar beam conditions.

[0241] As an example, the input information collected by the group members within the same group according to the beam prediction configuration information is fused as the input of the prediction model.

[0242] As an example, for a selected group, at least some group members are selected from the group to form a user subset, depending on the user capabilities of the group members within the group and / or the channel environment in which the group members are located.

[0243] For example, beam prediction results for a user subset are obtained by performing beam prediction based on input information reported by subset members within the user subset, according to beam prediction configuration information. All group members within the selected group use the beam prediction results for the user subset to communicate with network-side equipment.

[0244] As an example, beam prediction results for a user subset are obtained by performing beam prediction based on input information reported by subset members within the user subset at a shorter period than a predetermined period, according to the beam prediction configuration information. Subset members within the user subset use the beam prediction results for the user subset to communicate with network-side equipment. Beam prediction results for other group members are obtained by performing beam prediction based on input information reported by other group members (excluding the user subset) within the selected group at a period longer than a predetermined period, according to the beam prediction configuration information. Other group members adjust the beam prediction results for the user subset based on the beam prediction results for other group members to obtain adjusted beam prediction results, and other group members use the adjusted beam prediction results to communicate with network-side equipment.

[0245] For example, electronic devices whose channel status information (CSI) correlations exceed a predetermined correlation threshold over a given period of time are grouped into the same group.

[0246] For example, electronic devices that have the same optimal beam for a given period of time are grouped together.

[0247] For example, if, for a selected group, the reference signal received power measured by the group members based on beam prediction results for a selected subset of users within the group is below a predetermined power threshold, the group members are grouped into a new group.

[0248] For example, if the beam prediction results for different groups are the same within a given time period, the different groups are fused into a new group.

[0249] As an example, for a selected group, group members with the same user capabilities are grouped into the same category, the categories that need to be included in the user subset are determined, and at least some group members are selected from the group members corresponding to the determined categories to form the user subset.

[0250] For example, for a selected group, the group members are grouped according to their user capabilities into high-capacity users whose user capabilities meet predetermined capability conditions and low-capacity users whose user capabilities do not meet predetermined capability conditions. The group members within the group receive information about the group and the grouping results regarding high-capacity users and / or low-capacity users from the network-side equipment, and the group members within the group perform beam prediction based on the prediction model.

[0251] For example, group members receive information about the group and grouping results regarding high-performing and / or low-performing users via RRC signaling or DCI.

[0252] As an example, high-skilled users within a selected group obtain beam prediction results by using a predictive model to perform beam prediction based on the collected input information, and then share these beam prediction results with low-skilled users.

[0253] For example, highly capable users within a selected group can respond to requests received from less capable users by sharing at least some of the collected input information with them, and the less capable users can obtain beam prediction results by using a predictive model to perform beam prediction.

[0254] Examples of grouping, selection of user subsets, and selection of high- and low-skill users can be found in the description of the embodiment of the electronic device 300 above, and will not be repeated here.

[0255] In describing the electronic devices for wireless communication in the embodiments described above, several processes or methods have obviously been disclosed. The following outlines these methods without repeating some of the details already discussed in the preamble. While these methods were disclosed in the process of describing the electronic devices for wireless communication, they do not necessarily utilize or be implemented by the components described. For example, embodiments of electronic devices for wireless communication may be implemented partially or entirely by hardware and / or firmware, while the following methods for wireless communication may be implemented entirely by computer-executable programs. Of course, these methods may also utilize the hardware and / or firmware of the electronic devices for wireless communication.

[0256] Figure 19 shows a flowchart of Method S2000 for wireless communication according to one embodiment of the present disclosure. Method S2000 begins in step S2002. In step S2004, categories of input information for a prediction model to perform beam prediction for the beam used for communication between the electronic equipment and the user equipment are dynamically or semistatically determined for user equipment within the service range of the electronic equipment. In step S2006, beam prediction configuration information, including the determined categories of input information, is provided to the user equipment so that the user equipment collects input information according to the beam prediction configuration information. Method S2000 ends in step S2008.

[0257] This method may be executed, for example, by the above-described electronic device 300. For specific details, reference can be made to the description of the related processing of the electronic device 300, and thus it will not be repeatedly described here.

[0258] FIG. 20 shows a flowchart of a wireless communication method S2100 according to another embodiment of the present disclosure. The method S2100 starts from step S2102. In step S2104, beam prediction configuration information including a category of input information of a prediction model for performing beam prediction on a beam used for communication between an electronic device and a network-side device, which is dynamically or semi-statically determined by the network-side device for the electronic device, is received from the network-side device that provides a service to the electronic device. In step S2106, input information is collected according to the beam prediction configuration information. The method S2100 ends at step S2108.

[0259] This method may be executed, for example, by the above-described electronic device 1900. For specific details, reference can be made to the description of the related processing of the above-described electronic device 1900, and thus it will not be repeatedly described here.

[0260] The technology of the present disclosure can be applied to various products.

[0261] Electronic equipment 300 may be implemented as various network-side devices. Network-side devices may be installed on the base station side or connected to the base station. The base station may be implemented as any type of eNB (evolved Node B) or gNB (5G base station). eNBs include, for example, macro eNBs and small eNBs. Small eNBs may be eNBs that cover cells smaller than macrocells, such as pico eNBs, micro eNBs, and home (femto) eNBs. The same may be true for gNBs. Alternatively, the base station may be implemented as any other type of base station, such as a Node B or a base station transceiver (BTS). The base station may include a principal (also called a base station device) configured to control radio communication and one or more remote radio heads (RRHs) located separately from the principal. Also, various types of user equipment can operate as a base station by temporarily or semi-permanently performing base station functions.

[0262] The electronic device 1900 may be implemented as various user devices. These user devices may be implemented as mobile terminals (e.g., smartphones, tablet personal computers (PCs), notebook PCs, portable game consoles, portable / dongle mobile routers, and digital imaging devices) or in-vehicle terminals (e.g., car navigation systems). Furthermore, these user devices may be implemented as terminals that perform machine-to-machine (M2M) communication (also called machine-type communication (MTC) terminals). Alternatively, the user device may be a wireless communication module (e.g., an integrated circuit module including a single chip) mounted on each of these terminals.

[0263] [Application examples for base stations] (First application example) Figure 21 is a block diagram showing a first example of a schematic configuration of an eNB or gNB to which the technology described herein can be applied. The following description uses an eNB as an example, but is similarly applicable to a gNB. The eNB 800 has one or more antennas 810 and a base station device 820. The base station device 820 and each antenna 810 may be connected to each other via an RF cable.

[0264] Each of the antennas 810 includes one or more antenna elements (for example, multiple antenna elements included in a MIMO antenna) and is used for transmitting and receiving radio signals by the base station equipment 820. The eNB800 may include multiple antennas 810, as shown in Figure 21. Multiple antennas 810 may be compatible with multiple frequency bands used by the eNB800, for example. Although Figure 21 shows an example in which the eNB800 includes multiple antennas 810, the eNB800 may also include a single antenna 810.

[0265] The base station device 820 includes a controller 821, a memory 822, a network interface 823, and a wireless communication interface 825.

[0266] The controller 821 may be, for example, a CPU or a DSP, and operates various functions of the upper layer of the base station equipment 820. For example, the controller 821 generates data packets from data in signals processed by the wireless communication interface 825 and forwards the generated packets via the network interface 823. The controller 821 can generate bundle packets by bundling data from multiple baseband processors and forward the generated bundle packets. The controller 821 may also have logical functions to perform controls such as radio resource control, radio bearer control, mobility management, admission control, or scheduling. Furthermore, these controls can be performed in cooperation with surrounding eNBs or core network nodes. The memory 822 includes RAM and ROM and stores programs executed by the controller 821, as well as various control data (e.g., terminal list, transmit power data, and scheduling data).

[0267] Network interface 823 is a communication interface for connecting base station equipment 820 to core network 824. Controller 821 can communicate with core network nodes or other eNBs via network interface 823. In this case, eNB 800 and the core network nodes or other eNBs are connected to each other by logical interfaces (e.g., S1 interface and X2 interface). Network interface 823 may be a wired communication interface or a wireless communication interface for a wireless backhaul line. If network interface 823 is a wireless communication interface, it can use a higher frequency band for wireless communication than the frequency band used by wireless communication interface 825.

[0268] The wireless communication interface 825 supports any cellular communication scheme (e.g., Long Term Evolution (LTE) and LTE-Advanced) and provides wireless connectivity to terminals located in the cells of the eNB800 via the antenna 810. The wireless communication interface 825 may typically include, for example, a baseband (BB) processor 826 and RF circuitry 827. The BB processor 826 can perform, for example, coding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and can perform signal processing of various layers (e.g., Layer 1, Media Access Control (MAC), Radio Link Control (RLC), Packet Data Aggregation Protocol (PDCP)). The BB processor 826 may have some or all of the logical functions described above in place of the controller 821. The BB processor 826 may be a memory that stores a communication control program, or it may be a module that includes a processor and associated circuitry configured to execute the program. Program updates can change the functionality of the BB processor 826. This module may be a card or blade inserted into a slot in the base station equipment 820. Alternatively, this module may be a chip mounted on a card or blade. At the same time, the RF circuit 827 may include, for example, a mixer, a filter, and an amplifier, and may transmit and receive radio signals via the antenna 810.

[0269] As shown in Figure 21, the wireless communication interface 825 may include multiple BB processors 826. For example, multiple BB processors 826 may be compatible with multiple frequency bands used by the eNB800. As shown in Figure 21, the wireless communication interface 825 may include multiple RF circuits 827. For example, multiple RF circuits 827 may be compatible with multiple antenna elements. Although Figure 21 shows an example in which the wireless communication interface 825 includes multiple BB processors 826 and multiple RF circuits 827, the wireless communication interface 825 may include a single BB processor 826 or a single RF circuit 827.

[0270] In the eNB800 shown in Figure 21, if the electronic equipment 300 is implemented as a base station, its transceiver may be implemented by a wireless communication interface 825. At least part of the function may be implemented by a controller 821. For example, the controller 821 can improve beam prediction performance by performing some of the functions of the electronic equipment 300.

[0271] (Second application example) Figure 22 is a block diagram showing a second example of a schematic configuration of an eNB or gNB to which the technology described herein can be applied. Similarly, although the following description uses an eNB as an example, it is also applicable to a gNB. The eNB 830 includes one or more antennas 840, a base station device 850, and an RRH 860. The RRH 860 and each antenna 840 may be connected to each other via an RF cable. The base station device 850 and the RRH 860 may also be connected to each other via a high-speed line such as an optical fiber cable.

[0272] Each of the antennas 840 includes one or more antenna elements (for example, multiple antenna elements included in a MIMO antenna) and is used for transmitting and receiving radio signals by the RRH860. The eNB830 may include multiple antennas 840, as shown in Figure 22. Multiple antennas 840 may be compatible with multiple frequency bands used by the eNB830, for example. Although Figure 22 shows an example in which the eNB830 includes multiple antennas 840, the eNB830 may also include a single antenna 840.

[0273] The base station device 850 includes a controller 851, a memory 852, a network interface 853, a wireless communication interface 855, and a connection interface 857. The controller 851, memory 852, and network interface 853 are the same as the controller 821, memory 822, and network interface 823 described with reference to Figure 21.

[0274] The wireless communication interface 855 supports any cellular communication method (e.g., LTE and LTE-Advanced) and provides wireless connectivity to terminals located in the sector corresponding to the RRH860 via the RRH860 and antenna 840. The wireless communication interface 855 may typically include, for example, a BB processor 856. The BB processor 856 is similar to the BB processor 826 described with reference to Figure 21, except that it is connected to the RF circuit 864 of the RRH860 via connection interface 857. The wireless communication interface 855 may include multiple BB processors 856, as shown in Figure 22. Multiple BB processors 856 may be compatible with multiple frequency bands used by, for example, the eNB830. Although Figure 22 shows an example in which the wireless communication interface 855 includes multiple BB processors 856, the wireless communication interface 855 may include a single BB processor 856.

[0275] The connection interface 857 is an interface for connecting the base station device 850 (wireless communication interface 855) to the RRH860. The connection interface 857 may also be a communication module for communication on the high-speed line described above for connecting the base station device 850 (wireless communication interface 855) to the RRH860.

[0276] The RRH860 includes a connection interface 861 and a wireless communication interface 863.

[0277] The connection interface 861 is an interface for connecting the RRH860 (wireless communication interface 863) to the base station device 850. The connection interface 861 may also be a communication module for communication on the high-speed line described above.

[0278] The wireless communication interface 863 transmits and receives radio signals via the antenna 840. The wireless communication interface 863 may typically include, for example, an RF circuit 864. The RF circuit 864 may include, for example, a mixer, a filter, and an amplifier, and may transmit and receive radio signals via the antenna 840. The wireless communication interface 863 may include multiple RF circuits 864, as shown in Figure 22. Multiple RF circuits 864 can support multiple antenna elements. Although Figure 22 shows an example in which the wireless communication interface 863 includes multiple RF circuits 864, the wireless communication interface 863 may also include a single RF circuit 864.

[0279] In the eNB830 shown in Figure 22, if the electronic equipment 300 is implemented as a base station, its transceiver may be implemented by a wireless communication interface 855. At least part of the function may be implemented by a controller 851. For example, the controller 851 can improve beam prediction performance by performing some of the functions in the electronic equipment 300.

[0280] [Application examples for user devices] (First application example) Figure 23 is a block diagram showing an example of a schematic configuration of a smartphone 900 to which the technology described herein can be applied. The smartphone 900 includes a processor 901, memory 902, storage device 903, external connection interface 904, imaging device 906, sensor 907, microphone 908, input device 909, display device 910, speaker 911, wireless communication interface 912, one or more antenna switches 915, one or more antennas 916, bus 917, battery 918, and auxiliary controller 919.

[0281] The processor 901 is, for example, a CPU or a system-on-a-chip (SoC) and can control the functions of the application layer and other layers of the smartphone 900. The memory 902 includes RAM and ROM and stores data and programs executed by the processor 901. The storage device 903 can include, for example, semiconductor memory and storage media such as a hard disk. The external connection interface 904 is an interface for connecting external devices (e.g., memory cards and Universal Serial Bus (USB) devices) to the smartphone 900.

[0282] The imaging device 906 includes an image sensor (e.g., a charge-coupled device (CCD) and a complementary metal-oxide-semiconductor (CMOS)) and generates an image. Sensor 907 may include a set of sensors such as a measuring sensor, a gyroscope, a geomagnetic sensor, and an accelerometer. Microphone 908 converts sound input to the smartphone 900 into an audio signal. Input device 909 includes, for example, a touch sensor, a keypad, a keyboard, a button, or a switch configured to detect touches on the screen of the display device 910 and receives operations or information input from the user. Display device 910 includes a screen (e.g., a liquid crystal display (LCD), an organic light-emitting diode (OLED) display) and displays the output image from the smartphone 900. Speaker 911 converts the audio signal output from the smartphone 900 into sound.

[0283] The wireless communication interface 912 supports any cellular communication method (e.g., LTE and LTE-Advanced) and performs wireless communication. The wireless communication interface 912 typically includes, for example, a broadband processor 913 and an RF circuit 914. The broadband processor 913 can perform various types of signal processing for wireless communication, such as encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing. Simultaneously, the RF circuit 914 includes, for example, a mixer, filter, and amplifier, and can transmit and receive wireless signals via the antenna 916. Note that the figure shows a case where one RF link is connected to one antenna, but this is merely an example; a single RF link can also be connected to multiple antennas via multiple phase shifters. The wireless communication interface 912 can be a single chip module with the broadband processor 913 and RF circuit 914 integrated on it. As shown in Figure 23, the wireless communication interface 912 can include multiple broadband processors 913 and multiple RF circuits 914. Figure 23 shows an example in which the wireless communication interface 912 includes multiple BB processors 913 and multiple RF circuits 914, but the wireless communication interface 912 may also include a single BB processor 913 or a single RF circuit 914.

[0284] In addition to cellular communication, the wireless communication interface 912 can support other types of wireless communication, such as short-range wireless communication, proximity communication, and wireless local network (LAN) communication. In this case, the wireless communication interface 912 may include a BB processor 913 and an RF circuit 914 for various wireless communication methods.

[0285] Each of the antenna switches 915 switches the destination of the antenna 916 among multiple circuits included in the wireless communication interface 912 (for example, circuits used for different wireless communication methods).

[0286] Each of the antennas 916 includes one or more antenna elements (for example, multiple antenna elements included in a MIMO antenna) and is used to transmit and receive radio signals via the wireless communication interface 912. As shown in Figure 23, the smartphone 900 may include multiple antennas 916. Although Figure 23 shows an example in which the smartphone 900 includes multiple antennas 916, the smartphone 900 may also include a single antenna 916.

[0287] The smartphone 900 may include an antenna 916 for various wireless communication methods. In this case, the antenna switch 915 can be omitted from the configuration of the smartphone 900.

[0288] Bus 917 connects the processor 901, memory 902, storage device 903, external connection interface 904, imaging device 906, sensor 907, microphone 908, input device 909, display device 910, speaker 911, wireless communication interface 912, and auxiliary controller 919 to each other. Battery 918 supplies power to each block of the smartphone 900 shown in Figure 23 via power lines, which are represented as partially dotted lines in the drawing. The auxiliary controller 919 operates the minimum necessary functions of the smartphone 900, for example, in sleep mode.

[0289] In the smartphone 900 shown in Figure 23, if the electronic device 1900 is implemented, for example, as a smartphone on the user device side, the transceiver of the electronic device 1900 may be implemented by a wireless communication interface 912. At least part of the functions may be implemented by a processor 901 or an auxiliary controller 919. For example, the processor 901 or the auxiliary controller 919 can improve beam prediction performance by performing the functions of the electronic device 1900 described above.

[0290] (Second application example) Figure 24 is a block diagram showing an example of a schematic configuration of a car navigation device 920 to which the technology described herein can be applied. The car navigation device 920 includes a processor 921, memory 922, global positioning system (GPS) module 924, sensor 925, data interface 926, content player 927, storage medium interface 928, input device 929, display device 930, speaker 931, wireless communication interface 933, one or more antenna switches 936, one or more antennas 937, and a battery 938.

[0291] The processor 921 is, for example, a CPU or SoC, and can control the navigation and other functions of the car navigation device 920. The memory 922 includes RAM and ROM and stores data and programs executed by the processor 921.

[0292] The GPS module 924 measures the position (e.g., latitude, longitude, altitude) of the car navigation device 920 using GPS signals received from GPS satellites. The sensor 925 may include a set of sensors, such as a gyro sensor, a geomagnetic sensor, and a barometric pressure sensor. The data interface 926 connects to an in-vehicle network 941, for example, via a terminal (not shown), to acquire data generated by the vehicle (e.g., vehicle speed data).

[0293] The content player 927 plays content stored on a storage medium (e.g., CD and DVD) inserted into the storage medium interface 928. The input device 929 includes, for example, a touch sensor, button, or switch configured to detect touches on the screen of the display device 930, and receives operations or information input from the user. The display device 930 includes, for example, an LCD or OLED display screen, and displays images of the navigation function or the played content. The speaker 931 outputs sounds of the navigation function or the played content.

[0294] The wireless communication interface 933 supports any cellular communication scheme (e.g., LTE and LTE-Advanced) and performs wireless communication. The wireless communication interface 933 typically includes, for example, a broadband processor 934 and an RF circuit 935. The broadband processor 934 can perform various types of signal processing for wireless communication, such as coding / decoding, modulation / demodulation, and multiplexing / demultiplexing. Simultaneously, the RF circuit 935 includes, for example, a mixer, filter, and amplifier, and can transmit and receive wireless signals via the antenna 937. The wireless communication interface 933 can also be a single chip module with the broadband processor 934 and RF circuit 935 integrated on it. As shown in Figure 24, the wireless communication interface 933 can include multiple broadband processors 934 and multiple RF circuits 935. While Figure 24 shows an example where the wireless communication interface 933 includes multiple broadband processors 934 and multiple RF circuits 935, the wireless communication interface 933 may include a single broadband processor 934 or a single RF circuit 935.

[0295] In addition to cellular communication, the wireless communication interface 933 can support other types of wireless communication, such as short-range wireless communication, proximity communication, and wireless LAN. In this case, the wireless communication interface 933 can include a BB processor 934 and an RF circuit 935 for each type of wireless communication.

[0296] Each of the antenna switches 936 switches the destination of the antenna 937 among multiple circuits included in the wireless communication interface 933 (for example, circuits used for different wireless communication methods).

[0297] Each of the antennas 937 includes one or more antenna elements (for example, multiple antenna elements included in a MIMO antenna) and is used to transmit and receive radio signals via the wireless communication interface 933. As shown in Figure 24, the car navigation device 920 may include multiple antennas 937. Although Figure 24 shows an example in which the car navigation device 920 includes multiple antennas 937, the car navigation device 920 may also include a single antenna 937.

[0298] The car navigation system 920 may include an antenna 937 for various wireless communication methods. In this case, the antenna switch 936 can be omitted from the configuration of the car navigation system 920.

[0299] Battery 938 supplies power to each block of the car navigation system 920 shown in Figure 24 via power lines, which are partially represented as dotted lines in the drawing. Battery 938 stores power supplied from the vehicle.

[0300] In the car navigation device 920 shown in Figure 24, if the electronic device 1900 is implemented, for example, as a car navigation device on the user's device side, the transceiver of the electronic device 1900 may be implemented by a wireless communication interface 933. At least part of the function may be implemented by a processor 921. For example, the processor 921 can improve beam prediction performance by performing the functions of the electronic device 1900.

[0301] The technology described herein may be implemented as an in-vehicle system (or vehicle) 940 including one or more blocks of a car navigation device 920, an in-vehicle network 941, and a vehicle module 942. The vehicle module 942 generates vehicle data (e.g., vehicle speed, engine speed, fault information) and outputs the generated data to the in-vehicle network 941.

[0302] The above describes the basic principles of the present invention by combining specific embodiments. However, those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computer device (including processors, storage media, etc.) or network of computer devices by hardware, firmware, software, or a combination thereof, and that this can be implemented by those skilled in the art by reading the description of the present invention and using their basic circuit design knowledge or basic programming skills.

[0303] Furthermore, the present invention provides a program product that stores machine-readable instruction codes. When the instruction codes are read and executed by a machine, they perform the methods according to the embodiments of the present invention described above.

[0304] Accordingly, the disclosure of the present invention also includes a storage medium for storing program products containing the above-mentioned machine-readable instruction codes. The storage medium includes, but is not limited to, flexible disks, optical disks, magneto-optical disks, memory cards, memory sticks, and the like.

[0305] When the present invention is implemented using software or firmware, the programs constituting the software are installed from a storage medium or network to a computer having a dedicated hardware configuration (for example, the general-purpose computer 2600 shown in Figure 25), and once various programs are installed, the computer can perform various functions.

[0306] In Figure 25, the central processing unit (CPU) 2601 executes various processes based on programs stored in the read-only memory (ROM) 2602, or programs loaded from the memory unit 2608 into the random access memory (RAM) 2603. The RAM 2603 stores data necessary for the CPU 2601 to execute various processes as needed. The CPU 2601, ROM 2602, and RAM 2603 are connected to each other via the bus 2604. The input / output interface 2605 is also connected to the bus 2604.

[0307] The input section 2606 (including keyboard, mouse, etc.), output section 2607 (including displays such as cathode ray tubes (CRTs), liquid crystal displays (LCDs), etc., and speakers, etc.), storage section 2608 (including hard disks, etc.), and communication section 2609 (including network interface cards such as LAN cards and modulators / demodulators) are connected to the input / output interface 2605. The communication section 2609 performs communication processing over a network, such as the Internet. If necessary, the drive 2610 may be connected to the input / output interface 2605. Removable media 2611, such as magnetic disks, optical disks, magneto-optical disks, and semiconductor memory, are mounted on the drive 2610 if necessary, so that computer programs read from them are installed in the storage section 2608 if necessary.

[0308] When the above series of processes are implemented using software, the programs that make up the software are installed from a network such as the internet, or from a storage medium such as removable media 2611.

[0309] Those skilled in the art should understand that such a storage medium is not limited to the removable media 2611 shown in Figure 25, which stores the program and provides the program to the user by being distributed separately from the device. Examples of removable media 2611 include magnetic disks (including Flexible Disks®), optical disks (including Optical Disk Read-Only Memory (CD-ROM) and Digital General Purpose Disks (DVD)), magneto-optical disks (including MiniDisc (MD)®), and semiconductor memory. Alternatively, the storage medium may be a ROM 2602, a hard disk included in the storage section 2608, etc., which stores the program and is distributed to the user together with the device containing them.

[0310] In the apparatus, method, and system of the present invention, each component or step can be disassembled and / or reassembled. These disassembly and / or reassembly should also be considered equivalent solutions of the present invention. The execution steps of the above series of processes can be performed in the order described and in chronological order, but are not necessarily required to be performed in chronological order. Some steps may be performed in parallel or independently of each other.

[0311] Finally, the terms “include,” “incorporate,” or any other variation thereof are intended to include non-exclusive inclusion, thereby including not only those elements but also other elements not explicitly listed, or the inherent elements of such process, method, item, or device. Also, unless specifically limited, the element limited by the phrase “include one…” does not preclude the presence of other identical elements in the process, method, item, or device that includes the element.

[0312] Although embodiments of the present invention have been described in detail above with reference to the drawings, it should be understood that the embodiments described above are for illustrative purposes only and do not limit the present invention. Those skilled in the art will know that various modifications and changes can be made to the above embodiments without departing from the substance and scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims and their equivalents.

[0313] This technology can also be implemented as follows: Plan 1 Electronic equipment for wireless communication, For user equipment within the service range of the electronic device, the categories of input information for a prediction model to perform beam prediction for the beam used for communication between the electronic device and the user equipment are determined dynamically or semi-statically. Electronic equipment including a processing circuit configured to provide beam prediction configuration information, which includes the determined category of input information, to the user equipment so that the user equipment collects the input information according to the beam prediction configuration information. Plan 2 The processing circuit is configured to determine the category of the input information according to the user capabilities of the user device and / or the channel environment in which the user device is located. The electronic device according to Plan 1, wherein the processing circuit is configured to receive user capabilities reported by the user device via wireless resource control RRC signaling. Plan 3 The electronic device according to Plan 2, wherein the user capability information included in the RRC signaling includes user auxiliary sub-information, and the parameters of the user auxiliary sub-information include categories of auxiliary information to reflect the user capability. Plan 4 The electronic device according to Plan 3, wherein the category of auxiliary information includes at least one of the following: motion characteristic information of the user device, location information of the user device, sensing information of the user device, communication capability of the user device, whether the user device supports the use of the predictive model, and beam information. Plan 5 The sensing information of the user device is information acquired based on the software and / or hardware of the user device, and includes motion-related parameters of the user device, as described in Plan 4. Plan 6 The electronic device according to any one of the solutions 3 to 5, wherein the parameters of the user assistance sub-information further include the transmission period and / or the number of transmission bits of the assistance information. Plan 7 The electronic device according to Plan 6, wherein the transmission period of the auxiliary information is determined by the measurement period of the user device and / or the effective time of the auxiliary information. Plan 8 The electronic device according to plan 6 or 7, wherein the number of bits transmitted for the auxiliary information is determined by the measurement accuracy of the user device and / or the memory capacity of the user device. Plan 9 The processing circuit is configured to determine the channel environment based on channel measurement information. The electronic device according to any one of the solutions 2 to 8, wherein the channel measurement information includes one or more of the following: the intensity, phase, and power of a sounding reference signal SRS received through a channel between the electronic device and the user device; the intensity, phase, and power of a channel state information reference signal CSI-RS; and channel information in a frequency band other than the frequency band in which the channel is located. Plan 10 The categories of input information include at least one of the following: channel measurement information, motion characteristic information of the user device, location information of the user device, sensing information of the user device, communication capability of the user device, beam information, sensing information of the electronic device, and integrated communication sensing information. The sensing information of the user device is information acquired based on the software and / or hardware of the user device, and includes motion-related parameters of the user device, as described in any one of the solutions 1 to 9. Plan 11 The electronic device according to plan 10, wherein the integrated communication sensing information includes radar target detection information and one or more of the intensity, phase, and power of the radar beam reference signal. Plan 12 The electronic device according to any one of the solutions 2 to 11, wherein the processing circuit is configured such that the number of input information categories for the prediction model determined for a user device whose user capability does not meet predetermined user capability conditions is less than the number of input information categories for the prediction model determined for a user device whose user capability meets the predetermined user capability conditions. Plan 13 The electronic device according to plan 12, wherein the processing circuit is configured such that the number of input information categories for the prediction model determined for user devices whose motion speed is less than a predetermined speed threshold is less than the number of input information categories for the prediction model determined for user devices whose motion speed is equal to or greater than the predetermined speed threshold. Plan 14 The electronic device according to any one of the solutions 2 to 13, wherein the processing circuit is configured such that the number of categories of input information for the prediction model determined for a user device whose channel environment satisfies predetermined environmental conditions is less than the number of categories of input information for the prediction model determined for a user device whose channel environment does not satisfy the predetermined environmental conditions. Plan 15 The electronic device according to any one of the solutions 1 to 14, wherein the processing circuit is configured to modify beam prediction configuration information corresponding to the user device in an event-triggered manner and provide the modified beam prediction configuration information to the user device. Plan 16 The electronic device according to plan 15, wherein the event includes the detection that a change in the channel environment in which the user device is located satisfies predetermined environmental change conditions. Plan 17 The processing circuit is configured to provide the beam prediction configuration information to the user equipment via wireless resource control (RRC) signaling or downlink control information (DCI). An electronic device as described in one of the solutions 1 to 16. Plan 18 For the determined different categories of input information, the same prediction model is used for beam prediction, as described in one of the solutions 1 to 17. Plan 19 The beam prediction configuration information further includes resource configuration information corresponding to the determined input information category, and the resource configuration information includes the transmission period and / or number of transmission bits of the determined input information category. The electronic device according to any one of the solutions 1 to 18, wherein the processing circuit is configured to perform the beam prediction using the prediction model based on input information reported by the user device according to the resource configuration information. Plan 20 Electronic equipment for wireless communication, The network-side equipment providing services to the electronic device receives beam prediction configuration information, which includes categories of input information for a prediction model for performing beam prediction for the beam used for communication between the electronic device and the network-side equipment, which is dynamically or semi-statically determined by the network-side equipment for the electronic device. An electronic device including a processing circuit configured to collect the input information according to the beam prediction configuration information. Plan 21 The category of the input information is determined according to the user capabilities of the electronic device and / or the channel environment in which the electronic device is located. The electronic device according to plan 20, wherein the processing circuit is configured to report user capabilities to the network-side device via wireless resource control RRC signaling. Plan 22 The electronic device according to plan 21, wherein the user capability information included in the RRC signaling includes user auxiliary sub-information, and the parameters of the user auxiliary sub-information include categories of auxiliary information to reflect the user capability. Plan 23 The electronic device according to plan 22, wherein the category of auxiliary information includes at least one of the following: motion characteristic information of the electronic device, location information of the electronic device, sensing information of the electronic device, communication capability of the electronic device, whether the electronic device supports the use of the predictive model, and beam information. Plan 24 The electronic device according to plan 23, wherein the sensing information of the electronic device is information obtained based on the software and / or hardware of the electronic device, and includes motion-related parameters of the electronic device. Plan 25 The electronic device according to any one of the solutions 22 to 24, wherein the parameters of the user assistance sub-information further include the transmission period and / or the number of transmission bits of the assistance information. Plan 26 The electronic device according to plan 25, wherein the transmission period of the auxiliary information is determined by the measurement period of the electronic device and / or the effective time of the auxiliary information. Plan 27 The electronic device according to plan 25 or 26, wherein the number of bits transmitted for the auxiliary information is determined by the measurement accuracy and / or the memory capacity of the electronic device. Plan 28 The channel environment is obtained based on channel measurement information, The electronic device according to any one of the solutions 21 to 27, wherein the channel measurement information includes one or more of the following: the intensity, phase, and power of a sounding reference signal SRS received via a channel between the electronic device and the network-side device; the intensity, phase, and power of a channel state information reference signal CSI-RS; and channel information in a frequency band other than the frequency band in which the channel is located. Plan 29 The categories of input information include at least one of the following: channel measurement information, motion characteristic information of the electronic device, location information of the electronic device, sensing information of the electronic device, sensing information of the network-side device, and integrated communication sensing information. The sensing information of the electronic device is information obtained based on the software and / or hardware of the electronic device, and includes motion-related parameters of the electronic device, as described in any one of the solutions 20 to 28. Plan 30 The electronic device according to plan 29, wherein the integrated communication sensing information includes radar target detection information and one or more of the intensity, phase, and power of the radar beam reference signal. Plan 31 An electronic device according to any one of the solutions 20 to 30, wherein the number of input information categories for the prediction model determined for an electronic device whose user capabilities do not meet the predetermined user capability conditions is less than the number of input information categories for the prediction model determined for an electronic device whose user capabilities meet the predetermined user capability conditions. Plan 32 The electronic device according to solution 31, wherein the number of input information categories for the prediction model determined for an electronic device whose motion speed is less than a predetermined speed threshold is less than the number of input information categories for the prediction model determined for an electronic device whose motion speed is equal to or greater than the predetermined speed threshold. Plan 33 The electronic device according to any one of the solutions 21 to 32, wherein the number of categories of input information for the predictive model determined for an electronic device whose channel environment satisfies predetermined environmental conditions is less than the number of categories of input information for the predictive model determined for an electronic device whose channel environment does not satisfy the predetermined environmental conditions. Plan 34 The electronic device according to any one of the 20 to 33 schemes, wherein the processing circuit is configured to receive beam prediction configuration information corresponding to the electronic device, which has been modified in an event-triggered manner. Plan 35 The electronic device according to plan 34, wherein the event includes the detection that a change in the channel environment in which the electronic device is located satisfies predetermined environmental change conditions. Plan 36 The electronic device according to any one of the solutions 20 to 35, wherein the processing circuit is configured to receive the beam prediction configuration information via wireless resource control RRC signaling or downlink control information DCI. Plan 37 For the determined categories of different input information, the same prediction model is used for beam prediction, according to one of the electronic devices described in any one of the schemes 20 to 36. Plan 38 The beam prediction configuration information further includes resource configuration information corresponding to the category of input information determined by the network-side equipment, and the resource configuration information includes the transmission period and / or number of transmission bits of the determined category of input information. The electronic device according to any one of the solutions 20 to 37, wherein the processing circuit is configured to report the input information based on the resource configuration information so that the network-side device performs the beam prediction using the prediction model. Plan 39 A method for wireless communication, For user equipment within the service scope of the electronic equipment, the categories of input information for a prediction model used to perform beam prediction for the beam used for communication between the electronic equipment and the user equipment are to be determined dynamically or semi-statically, A method comprising providing the user equipment with beam prediction configuration information, including the determined categories of input information, so that the user equipment collects the input information according to the beam prediction configuration information. Plan 40 A method for wireless communication, Receiving beam prediction configuration information from a network-side device that provides services to an electronic device, which includes categories of input information for a prediction model for performing beam prediction for the beam used for communication between the electronic device and the network-side device, which is dynamically or semi-statically determined by the network-side device for the electronic device. A method comprising collecting the input information in accordance with the beam prediction configuration information. Plan 41 A computer-readable storage medium having stored computer-executable instructions that, when executed, cause a computer to perform the method for wireless communication described in claim 39 or 40.

Claims

1. Electronic equipment for wireless communication, For user equipment within the service range of the electronic equipment, the categories of input information for a prediction model to perform beam prediction for the beam used for communication between the electronic equipment and the user equipment are determined dynamically or semi-statically. Electronic equipment including a processing circuit configured to provide beam prediction configuration information, which includes the determined category of input information, to the user equipment so that the user equipment collects the input information according to the beam prediction configuration information.

2. The processing circuit is configured to determine the category of the input information according to the user capabilities of the user device and / or the channel environment in which the user device is located. The electronic device according to claim 1, wherein the processing circuit is configured to receive user capabilities reported by the user device via wireless resource control RRC signaling.

3. The electronic device according to claim 2, wherein the user capability information included in the RRC signaling includes user auxiliary sub-information, and the parameters of the user auxiliary sub-information include categories of auxiliary information for reflecting the user capability.

4. The electronic device according to claim 3, wherein the category of auxiliary information includes at least one of the following: motion characteristic information of the user device, location information of the user device, sensing information of the user device, communication capability of the user device, whether the user device supports the use of the predictive model, and beam information.

5. The electronic device according to claim 4, wherein the sensing information of the user device is information obtained based on the software and / or hardware of the user device, and includes motion-related parameters of the user device.

6. The electronic device according to any one of claims 3 to 5, wherein the parameters of the user assistance sub-information further include the transmission period and / or the number of transmission bits of the assistance information.

7. The electronic device according to claim 6, wherein the transmission period of the auxiliary information is determined by the measurement period of the user device and / or the effective time of the auxiliary information.

8. The electronic device according to claim 6 or 7, wherein the number of bits transmitted for the auxiliary information is determined by the measurement accuracy of the user device and / or the memory capacity of the user device.

9. The processing circuit is configured to determine the channel environment based on channel measurement information. The electronic device according to any one of claims 2 to 8, wherein the channel measurement information includes one or more of the following: the intensity, phase, and power of a sounding reference signal SRS received through a channel between the electronic device and the user device; the intensity, phase, and power of a channel state information reference signal CSI-RS; and channel information in a frequency band other than the frequency band in which the channel is located.

10. The categories of input information include at least one of the following: channel measurement information, motion characteristic information of the user device, location information of the user device, sensing information of the user device, communication capability of the user device, beam information, sensing information of the electronic device, and integrated communication sensing information. The electronic device according to any one of claims 1 to 9, wherein the sensing information of the user device is information acquired based on the software and / or hardware of the user device, and includes motion-related parameters of the user device.

11. The electronic device according to claim 10, wherein the integrated communication sensing information includes radar target detection information and one or more of the intensity, phase, and power of the radar beam reference signal.

12. The electronic device according to any one of claims 2 to 11, wherein the processing circuit is configured such that the number of input information categories for the prediction model determined for a user device whose user capabilities do not meet predetermined user capability conditions is less than the number of input information categories for the prediction model determined for a user device whose user capabilities meet predetermined user capability conditions.

13. The electronic device according to claim 12, wherein the processing circuit is configured such that the number of categories of input information for the prediction model determined for user devices whose motion speed is less than a predetermined speed threshold is less than the number of categories of input information for the prediction model determined for user devices whose motion speed is equal to or greater than the predetermined speed threshold.

14. The electronic device according to any one of claims 2 to 13, wherein the processing circuit is configured such that the number of categories of input information for the prediction model determined for a user device whose channel environment satisfies predetermined environmental conditions is less than the number of categories of input information for the prediction model determined for a user device whose channel environment does not satisfy predetermined environmental conditions.

15. The electronic device according to any one of claims 1 to 14, wherein the processing circuit is configured to modify beam prediction configuration information corresponding to the user device in an event-triggered manner and provide the modified beam prediction configuration information to the user device.

16. The electronic device according to claim 15, wherein the event includes detecting that a change in the channel environment in which the user device is located satisfies predetermined environmental change conditions.

17. The electronic device according to any one of claims 1 to 16, wherein the processing circuit is configured to provide the beam prediction configuration information to the user device via wireless resource control RRC signaling or downlink control information DCI.

18. The electronic device according to any one of claims 1 to 17, wherein the same prediction model is used for beam prediction for different categories of input information determined.

19. The beam prediction configuration information further includes resource configuration information corresponding to the category of input information determined, and the resource configuration information includes the transmission period and / or number of transmission bits of the category of input information determined. The electronic device according to any one of claims 1 to 18, wherein the processing circuit is configured to perform beam prediction using the prediction model based on input information reported by the user device according to the resource configuration information.

20. Electronic equipment for wireless communication, The network-side equipment providing services to the electronic device receives beam prediction configuration information, which includes categories of input information for a prediction model for performing beam prediction for the beam used for communication between the electronic device and the network-side equipment, which is dynamically or semi-statically determined by the network-side equipment for the electronic device. An electronic device including a processing circuit configured to collect the input information according to the beam prediction configuration information.

21. The category of the input information is determined according to the user capabilities of the electronic device and / or the channel environment in which the electronic device is located. The electronic device according to claim 20, wherein the processing circuit is configured to report user capabilities to the network-side device via wireless resource control RRC signaling.

22. The electronic device according to claim 21, wherein the user capability information included in the RRC signaling includes user auxiliary sub-information, and the parameters of the user auxiliary sub-information include categories of auxiliary information for reflecting the user capability.

23. The electronic device according to claim 22, wherein the category of auxiliary information includes at least one of the following: motion characteristic information of the electronic device, location information of the electronic device, sensing information of the electronic device, communication capability of the electronic device, whether the electronic device supports the use of the predictive model, and beam information.

24. The electronic device according to claim 23, wherein the sensing information of the electronic device is information obtained based on the software and / or hardware of the electronic device, and includes motion-related parameters of the electronic device.

25. The electronic device according to any one of claims 22 to 24, wherein the parameters of the user assistance sub-information further include the transmission period and / or the number of transmission bits of the assistance information.

26. The electronic device according to claim 25, wherein the transmission period of the auxiliary information is determined by the measurement period of the electronic device and / or the effective time of the auxiliary information.

27. The electronic device according to claim 25 or 26, wherein the number of bits transmitted for the auxiliary information is determined by the measurement accuracy and / or the memory capacity of the electronic device.

28. The channel environment is obtained based on channel measurement information. The electronic device according to any one of claims 21 to 27, wherein the channel measurement information includes one or more of the following: the intensity, phase, and power of a sounding reference signal SRS received via a channel between the electronic device and the network-side device; the intensity, phase, and power of a channel state information reference signal CSI-RS; and channel information in a frequency band other than the frequency band in which the channel is located.

29. The categories of input information include at least one of the following: channel measurement information, motion characteristic information of the electronic device, location information of the electronic device, sensing information of the electronic device, sensing information of the network-side device, and integrated communication sensing information. The electronic device according to any one of claims 20 to 28, wherein the sensing information of the electronic device is information acquired based on the software and / or hardware of the electronic device, and includes motion-related parameters of the electronic device.

30. The electronic device according to claim 29, wherein the integrated communication sensing information includes radar target detection information and one or more of the intensity, phase, and power of the radar beam reference signal.

31. The electronic device according to any one of claims 20 to 30, wherein the number of input information categories for the prediction model determined for an electronic device whose user capabilities do not meet predetermined user capability conditions is less than the number of input information categories for the prediction model determined for an electronic device whose user capabilities meet the predetermined user capability conditions.

32. The electronic device according to claim 31, wherein the number of categories of input information for the prediction model determined for an electronic device whose motion speed is less than a predetermined speed threshold is less than the number of categories of input information for the prediction model determined for an electronic device whose motion speed is equal to or greater than the predetermined speed threshold.

33. The electronic device according to any one of claims 21 to 32, wherein the number of categories of input information for the prediction model determined for an electronic device whose channel environment satisfies predetermined environmental conditions is less than the number of categories of input information for the prediction model determined for an electronic device whose channel environment does not satisfy the predetermined environmental conditions.

34. The electronic device according to any one of claims 20 to 33, wherein the processing circuit is configured to receive beam prediction configuration information corresponding to the electronic device, which has been modified in an event-triggered manner.

35. The electronic device according to claim 34, wherein the event includes detecting that a change in the channel environment in which the electronic device is located satisfies predetermined environmental change conditions.

36. The electronic device according to any one of claims 20 to 35, wherein the processing circuit is configured to receive the beam prediction configuration information via wireless resource control RRC signaling or downlink control information DCI.

37. The electronic device according to any one of claims 20 to 36, wherein the same prediction model is used for beam prediction for different categories of input information determined.

38. The beam prediction configuration information further includes resource configuration information corresponding to the category of input information determined by the network-side equipment, and the resource configuration information includes the transmission period and / or number of transmission bits of the determined category of input information. The electronic device according to any one of claims 20 to 37, wherein the processing circuit is configured to report the input information based on the resource configuration information so that the network-side device performs the beam prediction using the prediction model.

39. A method for wireless communication, For user equipment within the service scope of the electronic equipment, the categories of input information for a prediction model used to perform beam prediction for the beam used for communication between the electronic equipment and the user equipment are to be determined dynamically or semi-statically, A method comprising providing the user equipment with beam prediction configuration information, including the determined categories of input information, so that the user equipment collects the input information according to the beam prediction configuration information.

40. A method for wireless communication, Receiving beam prediction configuration information from a network-side device that provides services to an electronic device, which includes categories of input information for a prediction model for performing beam prediction for the beam used for communication between the electronic device and the network-side device, which is dynamically or semi-statically determined by the network-side device for the electronic device. A method comprising collecting the input information in accordance with the beam prediction configuration information.

41. A computer-readable storage medium having stored computer-executable instructions that, when executed, cause a computer to perform the method for wireless communication described in claim 39 or 40.

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