Electronic device and method for wireless communication, and computer-readable storage medium

Abstract

An electronic device and a method for wireless communication, and a computer-readable storage medium. The electronic device for wireless communication comprises a processing circuit, wherein the processing circuit is configured to: dynamically or semi-statically determine the category of input information of a prediction model for a user equipment which is within a service range of the electronic device, wherein the prediction model is used for performing beam prediction on a beam to be used for communication between the electronic device and the user equipment; and provide the user equipment with beam prediction configuration information which comprises the determined category of the input information, so that the user equipment collects the input information according to the beam prediction configuration information.

Description

Electronic device and method for wireless communication and computer-readable storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on May 6, 2023, with application number 202310508570.0 and invention name “Electronic device and method for wireless communication and computer-readable storage medium”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present disclosure relates to the field of wireless communication technology, and more particularly to an electronic device and method for wireless communication, and a computer-readable storage medium. More particularly, the present disclosure relates to an electronic device and method for wireless communication, and a computer-readable storage medium for improving beam prediction performance. Background Art

[0003] Beamforming technology uses large-scale antenna arrays to form directional beams with concentrated energy to combat high path loss. Figure 1 shows an example of beamforming technology. As shown in Figure 1, a large-scale antenna array forms a directional beam for communication between a base station and a user equipment (UE). To implement beamforming technology, it is necessary to perform a beam management process, that is, to obtain and track the optimal beam pair for communication. Figure 2 shows an example of obtaining the optimal beam pair through beam management. 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 be roughly divided into three types: First, a beam management method based on beam scanning. This method scans all possible beam pairs and selects a pair of beams with the maximum received power as the optimal beam pair. This method traverses all possible beam pairs, has high beam training overhead, and is easily affected by noise, that is, it is sensitive to noise, and when the noise is large, it directly affects the optimal beam judgment; Second, a beam management method based on a channel model. This method makes a priori assumptions on the channel model, models the problem of finding the optimal beam as an angle estimation problem, and selects the optimal beam pair based on the estimated value. It is highly dependent on the prior assumptions of the channel model and has a limited scope of use; Third, a beam management method based on deep learning. This method uses the feature extraction capabilities of deep learning to assist beam management. Existing beam management methods based on deep learning cannot adapt to users with different movement speeds and in different channel environments, for example.

[0005] How to achieve effective prediction of beams (eg, optimal beam prediction) is a current research hotspot.

[0006] Summary of the Invention

[0007] A brief overview of the present invention is provided below to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important aspects of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is simply to present certain concepts in a simplified form as a prelude to the more detailed description discussed later.

[0008] According to one aspect of the present disclosure, an electronic device for wireless communication is provided, comprising a processing circuit, the processing circuit being configured to: dynamically or semi-statically determine a category of input information of a prediction model for a user device within a service range of the electronic device, wherein the prediction model is used to perform beam prediction on a beam to be used by the electronic device for communication with the user device; and provide beam prediction configuration information including the determined category of input information to the user device, so that the user device collects input information based on the beam prediction configuration information. According to embodiments of the present disclosure, beam prediction performance can be improved.

[0009] According to one aspect of the present disclosure, an electronic device for wireless communication is provided, comprising a processing circuit configured to: receive beam prediction configuration information from a network device providing a service for the electronic device, wherein the beam prediction configuration information includes a category of input information of a prediction model dynamically or semi-statically determined for the electronic device by the network device, and the prediction model is used to perform beam prediction on a beam to be used by the electronic device for communication with the network device; and collect input information based on the beam prediction configuration information. According to embodiments of the present disclosure, beam prediction performance can be improved.

[0010] According to one aspect of the present disclosure, a method for wireless communication is provided, comprising: dynamically or semi-statically determining a category of input information of a prediction model for a user device within a service range of the electronic device, wherein the prediction model is used to perform beam prediction on a beam to be used for communication between the electronic device and the user device, and providing beam prediction configuration information including the determined category of input information to the user device, so that the user device collects input information based on the beam prediction configuration information.

[0011] According to one aspect of the present disclosure, a method for wireless communication is provided, including: receiving beam prediction configuration information from a network-side device that provides services for an electronic device, wherein the beam prediction configuration information includes a category of input information of a prediction model dynamically or semi-statically determined by the network-side device for the electronic device, and the prediction model is used to perform beam prediction on a beam to be used by the electronic device to communicate with the network-side device; and collecting input information according to the beam prediction configuration information.

[0012] According to other aspects of the present invention, a computer program code and a computer program product for implementing the above-mentioned method for wireless communication, as well as a computer-readable storage medium having the computer program code for implementing the above-mentioned method for wireless communication recorded thereon are also provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to further illustrate the above and other advantages and features of the present invention, the following is a further detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings. The accompanying drawings, together with the detailed description below, are included in this specification and form a part of this specification. Elements with the same function and structure are represented by the same reference numerals. It should be understood that these drawings only depict typical examples of the present invention and should not be regarded as limiting the scope of the present invention. In the drawings:

[0014] Figure 1 shows an example of beamforming technology;

[0015] FIG2 shows an example of obtaining an optimal beam pair by performing beam management;

[0016] FIG3 shows a functional module block diagram of an electronic device for wireless communication according to an embodiment of the present disclosure;

[0017] FIG4 shows an example of beam prediction according to an embodiment of the present disclosure;

[0018] Figure 5 shows an example of an accelerometer, gyroscope, and compass of a user device;

[0019] FIG6 shows an example of radio resource control signaling according to an embodiment of the present disclosure;

[0020] FIG7 shows an example of a simplified structure of a prediction model according to an embodiment of the present disclosure;

[0021] FIG8 shows an example of a structure of a reconstructed network model according to an embodiment of the present disclosure;

[0022] FIG9 shows an example structure of a multimodal fusion prediction model according to an embodiment of the present disclosure;

[0023] FIG10 shows an example of a structure of a prediction model according to an embodiment of the present disclosure;

[0024] FIG11 shows an example of a process of an electronic device performing beam prediction for a single user equipment according to an embodiment of the present disclosure;

[0025] FIG12 shows an example of performing beam prediction for grouped users according to an embodiment of the present disclosure;

[0026] FIG13 shows an example in which user equipments having the same optimal beam as their corresponding ones are divided into the same group during a predetermined time period according to an embodiment of the present disclosure;

[0027] FIG14 shows an example of dividing group members into new groups according to an embodiment of the present disclosure;

[0028] FIG15 shows an example of selecting a user subset according to an embodiment of the present disclosure;

[0029] FIG16 shows an example of a process of an electronic device performing beam prediction for grouped user equipment according to an embodiment of the present disclosure;

[0030] FIG17 shows the variation of normalized beam gain with prediction time according to different schemes;

[0031] FIG18 shows a functional module block diagram of an electronic device for wireless communication according to another embodiment of the present disclosure;

[0032] FIG19 shows a flowchart of a method for wireless communication according to one embodiment of the present disclosure;

[0033] FIG20 shows a flowchart of a method for wireless communication according to another embodiment of the present disclosure;

[0034] FIG21 is a block diagram showing a first example of a schematic configuration of an eNB or gNB to which the technology of the present disclosure may be applied;

[0035] FIG22 is a block diagram illustrating a second example of a schematic configuration of an eNB or gNB to which the technology of the present disclosure may be applied;

[0036] FIG23 is a block diagram showing an example of a schematic configuration of a smartphone to which the technology of the present disclosure can be applied;

[0037] FIG24 is a block diagram showing an example of a schematic configuration of a car navigation device to which the technology of the present disclosure can be applied; and

[0038] 25 is a block diagram of an exemplary structure of a general-purpose personal computer in which methods and / or apparatuses and / or systems according to embodiments of the present invention may be implemented. DETAILED DESCRIPTION

[0039] Exemplary embodiments of the present invention are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of an actual implementation are described in this specification. However, it should be understood that in the process of developing any such actual implementation, many implementation-specific decisions must be made in order to achieve the developer's specific goals, such as meeting system and business-related constraints, which may vary from implementation to implementation. Furthermore, it should be understood that while development work may be complex and time-consuming, it will be a routine task for those skilled in the art who benefit from this disclosure.

[0040] It is also necessary to explain here that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show the device structure and / or processing steps that are closely related to the solution according to the present invention, while other details that are not closely related to the present invention are omitted.

[0041] FIG3 shows a functional module block diagram of an electronic device 300 for wireless communication according to an embodiment of the present disclosure.

[0042] 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 of a prediction model (beam prediction model) for a user device within the service range of the electronic device 300, wherein the prediction model is used to perform beam prediction on the beam to be used by the electronic device 300 to communicate with the user device; and a providing unit 303, which can be configured to provide the user device with beam prediction configuration information including the category of the determined input information, so that the user device collects input information based on the beam prediction configuration information.

[0043] The determining unit 301 and the providing unit 303 may be implemented by one or more processing circuits, which may be implemented as chips or processors, for example. Furthermore, it should be understood that the various functional units in the electronic device 300 shown in FIG3 are merely logical modules divided according to the specific functions they implement, and are not intended to limit specific implementation methods.

[0044] The electronic device 300 can serve as a network side device in a wireless communication system, and specifically, for example, can be set on the base station side or communicatively connected to the base station. Here, it should also be noted that the electronic device 300 can be implemented at the chip level, or it can also be implemented at the device level. For example, the electronic device 300 can work as the base station itself, and can also include external devices such as memory and transceiver (not shown). The memory can be used to store programs and related data information that need to be executed by the electronic device 300 to implement various functions. The transceiver may include one or more communication interfaces to support communication with different devices (e.g., UE, base station, etc.), and the implementation form of the transceiver is not specifically limited here.

[0045] As an example, the base station may be, for example, an eNB or a gNB.

[0046] The wireless communication system according to the present disclosure may be a 5G NR (New Radio) communication system. Furthermore, the wireless communication system according to the present disclosure may include a non-terrestrial network (NTN). Optionally, the wireless communication system according to the present disclosure may also include a terrestrial network (TN). In addition, those skilled in the art will appreciate that the wireless communication system according to the present disclosure may also be a 4G or 3G communication system.

[0047] Dynamically determining the category of the input information of the prediction model may mean determining the category of the input information of the prediction model in real time and non-periodically, for example, by triggering the determination of the category of the input information of the prediction model through a specific event.

[0048] Semi-static determination of the category of the input information of the prediction model is between dynamic and periodic. Semi-static determination of the category of the input information of the prediction model may mean periodically determining the category of the input information of the prediction model within a predetermined time period. Those skilled in the art may predetermine the predetermined time period based on application scenarios or experience.

[0049] In the following, the category of input information can be referred to as modality (modality can be defined as a category of information), beam prediction modality, modal information, and the set of categories of input information determined by the electronic device 300 for the user device can also be referred to as a beam prediction modality set, and the user device can also be referred to as a user.

[0050] For example, the electronic device 300 determines a specific beam prediction modality set for each user equipment, and the user equipment collects corresponding input information according to the modality set.

[0051] Electronic device 300 numbers all beam prediction modalities, and a beam prediction modality set includes modality indexes (IDs). Beam prediction modality sets for different user devices may include different numbers (e.g., an integer greater than or equal to 1) or different indexes (e.g., IDs 1, 2, 3 or 1, 2, 4) of modalities.

[0052] The prediction model can be, for example, a deep learning model.

[0053] Existing beam prediction solutions typically use a fixed modality as input to the beam prediction model. This makes them incapable of adapting to users with varying capabilities and / or in varying channel environments, resulting in reduced beam prediction performance. For example, for users in complex channel environments (e.g., non-line-of-sight channels), channel prediction is challenging, and using only a single modality can result in degraded beam prediction performance.

[0054] According to an embodiment of the present disclosure, the electronic device 300 dynamically or semi-statically determines (updates) the category of input information of the prediction model for each user device (dynamically or semi-statically determines beam prediction configuration information), that is, supports dynamic or semi-static selection of a beam prediction modality set, so that the modal information corresponding to all modalities in the set can be used as prediction model input, thereby improving the performance of beam prediction (for example, improving beam prediction accuracy). In other words, the electronic device 300 flexibly determines the information category used for beam prediction for each user device, integrates input information from multiple sources for beam prediction, and thus improves the performance of beam prediction.

[0055] As an example, the determination unit 301 can be configured to determine the category of the input information based on the user capabilities of the user equipment and / or the channel environment in which the user equipment is located, and the determination unit 301 can be configured to receive the user capabilities reported by the user equipment through radio resource control (RRC) signaling.

[0056] The electronic device 300 may obtain a channel environment based on channel measurement and obtain information about user capabilities through user capability query.

[0057] Different user capabilities of user equipment and / or different channel environments in which the user equipment is located may result in different modal information required for beam prediction.

[0058] For example, in complex, unstable channels, electronic device 300 needs to use more modal information to predict the beam; whereas in simple, stable channels, electronic device 300 can achieve accurate prediction using less modal information. Some user devices (e.g., those with higher capabilities) can provide more modal information, while other user devices (e.g., those with lower capabilities) can only provide less modal information.

[0059] 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 perform beam prediction based on the input information collected by the UE, thereby predicting the beam to be used by the electronic device 300 to communicate with the UE. This beam prediction is conducive to improving the noise robustness of beam management. For example, in actual scenarios, the stability of user movement ensures the predictability of beam changes. Therefore, previous channel measurement results (such as the channel measurement results in Figure 4 (a)) can be used to extract user motion features, and then the prediction model can be used to achieve future optimal beam prediction (such as beam prediction as the user moves as shown in Figure 4 (b)).

[0060] As an example, in addition to RRC signaling, those skilled in the art may also conceive that the electronic device 300 may receive the user capabilities reported by the user equipment through other signaling, which will not be repeated here.

[0061] As an example, the user capability information included in the RRC signaling (which is information in which the user equipment notifies the electronic device 300 of its capability details) contains user assistance sub-information, and the parameters of the user assistance sub-information include the category of auxiliary information used to reflect the user capability.

[0062] Existing user capability information only includes information such as the user's basic communication capabilities and does not support users notifying the base station of the types of user-assisted information they can provide. In an embodiment of the present disclosure, extended user capability information, including user-assisted sub-information, is proposed. This information notifies the electronic device 300 of the types of auxiliary information that the user can provide, allowing the electronic device 300 to determine a beam prediction modality set. Extended user capability information can be used not only for beam prediction but also for other communication scenarios such as wireless positioning and channel prediction.

[0063] As an example, the category of auxiliary information may include at least one of motion feature information of the user equipment, location information of the user equipment, sensing information of the user equipment, communication capability of the user equipment, whether the user equipment supports the use of a prediction model, and information about beams.

[0064] As an example, the motion characteristic information of the user equipment may be information reflecting the speed of the user equipment.

[0065] As an example, the location information of the user equipment may be information indicating the location of the user equipment.

[0066] As an example, the sensing information of the user equipment may be information acquired based on software and / or hardware of the user equipment, which includes motion-related parameters of the user equipment.

[0067] As an example, the sensing information of the user equipment may include at least one of accelerometer information of the user equipment and gyroscope information of the user equipment.

[0068] As an example, the sensory information of the user device may include compass information of the user device.

[0069] Figure 5 shows an example of an accelerometer, a gyroscope, and a compass of a user device. For example, via the accelerometer, the gyroscope, and the compass shown in Figure 5, the accelerometer information of the user device, the gyroscope information of the user device, and the compass information can be obtained respectively.

[0070] Conventional capability reporting only includes reporting of basic communication capabilities. However, in the embodiments of the present disclosure, new communication capability reporting is added to support beam prediction based on prediction models. For example, to support prediction model input, the maximum number of inputs supported in a single report, or the quantization granularity of parameters related to the input information, are specified.

[0071] Whether the user equipment supports the use of the prediction model may be used to indicate whether the prediction model can be used at the user equipment to perform beam prediction.

[0072] As an example, the information about the beam may represent information about the beam used by the electronic device 300 to communicate with the user equipment. The information about the beam may include, for example, the shape of the beam, the angle of the beam, and the like.

[0073] As an example, the parameters of the user assistance sub-information further include a transmission period and / or a number of transmission bits of the assistance information.

[0074] FIG6 shows an example of radio resource control signaling according to an embodiment of the present disclosure. In FIG6, “UE-NR-Capability” represents user capability information; “SEQUENCE” represents sequence; “to be "defined" indicates to be defined; accessStratumRelease is part of the existing RRC signaling and indicates the access version; UE-Auxiliary-Information indicates user auxiliary sub-information; UE-Auxiliary-Parameters indicates parameters of the user auxiliary sub-information, which includes AuxiliarySet, where AuxiliarySet includes the category of auxiliary information, that is, the category index of the auxiliary information that the user can support (for example, {1, 2, 3}, 1 is the user position index, 2 is the user speed index, and 3 is the user accelerometer information index). For example, in the RRC signaling shown in Figure 5, AuxiliaryPeriod indicates the transmission period of the auxiliary information, and AuxiliaryBits indicates the number of transmission bits of the auxiliary information. For each category index, the corresponding supported transmission period (for example, for user position, the supported transmission period is {10ms, 20ms, 40ms}) and the number of transmission bits (for example, for user position, the supported number of transmission bits is {4bit, 8bit}) and other information can also be given.

[0075] As an example, the transmission period of the auxiliary information is determined by the user equipment's measurement period and / or the effective duration of the auxiliary information. That is, for each category index, the supportable transmission period is determined by factors such as the user equipment's measurement period and / or the effective duration of the auxiliary information.

[0076] As an example, the number of transmission bits of the auxiliary information is determined by the measurement accuracy and / or storage capacity of the user equipment. That is, for each category index, the supported number of transmission bits is determined by factors such as user measurement accuracy and / or user storage capacity.

[0077] 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 strength, phase and power of the sounding reference signal (SRS) received through the channel between the electronic device 300 and the user equipment, the strength, phase and power of the channel state information reference signal (CSI-RS), and channel information of other frequency bands other than the frequency band in which the channel between the electronic device 300 and the user equipment is located (hereinafter, sometimes also referred to as other frequency band channels).

[0078] The received reference signal power may be referred to as RSRP.

[0079] The results of the channel measurement can reflect the nature of the channel environment. For example, the LOS (direct or line-of-sight) and NLOS (non-direct or non-line-of-sight) scenarios can be distinguished by the channel measurement results (including but not limited to channel state information (CSI), channel impulse response (CIR), received signal RSRP, the above-mentioned other frequency band channel information, etc.). Specifically, for example, the electronic device 300 can distinguish between LOS and NLOS scenarios by calculating the kurtosis value of the CIR: a kurtosis value higher than a predetermined threshold is judged as an LOS scenario, and a kurtosis value equal to or lower than a predetermined threshold is judged as an NLOS scenario. Generally, the LOS scenario channel is simpler, so it is easier to perform beam prediction; on the contrary, the NLOS scenario is more complex and more difficult to perform beam prediction. As an example, those skilled in the art can predetermine the predetermined threshold based on the application scenario or experience.

[0080] As an example, the categories of input information include channel measurement information, motion characteristic information of the user device, location information of the user device, sensing information of the user device, communication capabilities of the user device, information about beams, perception information of the electronic device 300, and at least one of communication-perception integrated information, wherein the sensing information of the user device is information obtained based on the software and / or hardware of the user device, which includes motion-related parameters of the user device.

[0081] As an example, the channel measurement information as input information may include one or more of the following: the strength, phase and power of the SRS received through the channel between the electronic device 300 and the user equipment, the strength, phase and power of the CSI-RS, and channel information of other frequency bands other than the frequency band in which the channel between the electronic device 300 and the user equipment is located.

[0082] As an example, the motion feature information of the user equipment as the input information may be information reflecting the speed of the user equipment.

[0083] As an example, the location information of the user equipment as the input information may be information indicating the location of the user equipment.

[0084] As an example, the sensory information of the user device as input information may be information acquired based on the software and / or hardware of the user device, including motion-related parameters of the user device. As an example, the sensory information of the user device may include at least one of accelerometer information of the user device and gyroscope information of the user device. As an example, the sensory information of the user device may include compass information of the user device.

[0085] As an example, the communication capability of the user equipment as input information may include the basic communication capability of the user equipment, the maximum number of input information supported in one report, the quantization granularity of parameters related to the input information, and the like.

[0086] As an example, the information about the beam as input information may represent information about the beam used by the electronic device 300 to communicate with the user equipment. The information about the beam may include, for example, the shape of the beam, the angle of the beam, and the like.

[0087] As an example, the perception information of the electronic device 300 represents information obtained by the electronic device 300 through perception using wireless perception capabilities, which may include, for example, positioning information of the user equipment positioned by the electronic device 300.

[0088] In the communication and perception integration system, communication and perception are integrated. The communication and perception integration information may include one or more of the following: radar target detection information, and the strength, phase, and power of the radar beam reference signal.

[0089] As an example, the determination unit 301 can be configured to determine that the number of categories of input information of the prediction model determined for a user device whose user capability does not meet the predetermined user capability condition is less than the number of categories of input information of the prediction model determined for a user device whose user capability meets the predetermined user capability condition.

[0090] As an example, those skilled in the art may predetermine the predetermined user capability condition based on application scenarios or experience.

[0091] For example, the predetermined user capability condition may include the user device being able to obtain more than a predetermined amount of modality information.

[0092] For example, the predetermined user capability condition may include that the movement speed of the user device is greater than or equal to a predetermined speed threshold. Those skilled in the art may also conceive of other examples of the predetermined capability condition, which will not be repeated here.

[0093] As an example, the determination unit 301 can be configured to determine that the number of categories of input information of the prediction model determined for a user device whose movement speed is less than a predetermined speed threshold is less than the number of categories of input information of the prediction model determined for a user device whose movement speed is greater than or equal to the predetermined speed threshold.

[0094] As an example, those skilled in the art may predetermine the predetermined speed threshold based on application scenarios or experience.

[0095] As an example, the determination unit 301 can be configured to determine that the number of categories of input information of the prediction model determined for a user device whose channel environment meets the predetermined environmental conditions is less than the number of categories of input information of the prediction model determined for a user device whose channel environment does not meet the predetermined environmental conditions.

[0096] As an example, those skilled in the art may predetermine the predetermined environmental conditions based on application scenarios or experience.

[0097] For example, in a simple, stationary channel (where the channel environment meets the predetermined environmental conditions), less modal information can be used to predict the beam; whereas in a complex, unstable channel (where the channel environment does not meet the predetermined environmental conditions), more modal information is required to predict the beam. Generally, LOS scenarios have simpler channels and can use less modal information to predict the beam; conversely, NLOS scenarios are more complex and require more modal information to predict the beam.

[0098] As an example, the determining unit 301 may be configured to provide the beam prediction configuration information to the user equipment through radio resource control (RRC) signaling or downlink control information (DCI).

[0099] As an example, the beam prediction configuration information also includes resource configuration information (transmission configuration information) corresponding to the category of the determined input information, wherein the resource configuration information includes the transmission period and / or number of transmission bits of the category of the determined input information, and the determination unit 301 can be configured to perform beam prediction based on the input information reported by the user equipment according to the resource configuration information using a prediction model.

[0100] The electronic device 300 first selects a beam prediction modal set for the user. For the modalities in the modal set, the electronic device 300 determines its transmission configuration. The user device feeds back the information collected based on 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 period of various modalities to the same value as much as possible, that is, it takes the value from the intersection of its supported transmission periods. If the intersection is empty, it ensures that the periods of most modalities are set to the same value as much as possible. For modalities that do not support the transmission period, the electronic device 300 will sample or interpolate the received modal information so that all modal data formats are the same.

[0101] For example, the beam prediction modal set includes three types of information: SRS received signals, sub-6 GHz frequency band channel information, and user speed. The transmission configuration requires a 40 ms period. The user then feeds back three types of modal information (i.e., SRS received signals, sub-6 GHz frequency band channel information, and user speed parameters) to the electronic device 300 based on a 40 ms period, which serve as the three modal information inputs to the beam prediction model.

[0102] For example, the beam prediction modality set may include sensor information of the user equipment. As described above, the sensor information of the user equipment may include at least one of the accelerometer information of the user equipment and the gyroscope information of the user equipment. The user equipment feeds back the information collected according to the beam prediction modality set to the electronic device 300 according to the transmission configuration, which belongs to Measurement reporting in the TS38.331 standard and is a type of RRC information. Appropriate modifications are made to the TS38.331 standard: 1) The Sensor-NameList in the standard only includes uncompensated Barometeric pressure measurement, UE Speed ​​measurement, and UE orientation information, while other sensor information such as accelerometer information and / or Gyroscope information are also added in the embodiment of the present disclosure; 2) In the embodiment of the present disclosure, the sensor information can be flexibly transmitted and configured, that is, the electronic device 300 flexibly selects the transmission period, number of transmission bits, etc. of the sensor information, and the user equipment reports the sensor information according to the configuration.

[0103] As an example, the determining unit 301 may be configured to modify the beam prediction configuration information corresponding to the user equipment in an event-triggered manner and provide the modified beam prediction configuration information to the user equipment. This supports dynamic adjustment of the beam prediction mode set and ensures beam prediction performance.

[0104] As an example, an event may include detecting that a change in the channel environment of the user equipment satisfies a predetermined environmental change condition. If the change in the channel environment of the user equipment satisfies the predetermined environmental change condition, the beam prediction modality set may be dynamically updated to ensure beam prediction performance. Furthermore, transmission configuration information may be updated accordingly.

[0105] As an example, those skilled in the art may predetermine the predetermined environmental change condition based on application scenarios or experience.

[0106] For example, the predetermined environmental change condition may include a change in the speed of the user equipment being greater than or equal to a predetermined speed threshold; a change in the location of the user equipment being greater than or equal to a predetermined location threshold; or a change in the location of the user equipment moving from a simple channel environment to a complex channel environment. Other examples of predetermined environmental change conditions may be conceivable to those skilled in the art, which are not detailed here.

[0107] As an example, determination unit 301 can be configured to directly modify the beam prediction configuration information corresponding to the user equipment without notifying the user equipment to re-report its capabilities. For example, when electronic device 300 detects a change in the channel environment of the user equipment, electronic device 300 directly modifies the beam prediction mode set and transmission configuration information of the user equipment and notifies the user equipment of the change. This method is an update initiated by electronic device 300. Because this method does not require the user equipment to re-report its capabilities, it reduces signaling overhead.

[0108] As an example, the determination unit 301 can be configured to notify the user equipment to re-report the user capabilities, and to modify the beam prediction configuration information corresponding to the user equipment based on the re-received user capabilities. For example, when the electronic device 300 detects that the channel environment in which the user equipment is located has changed, the electronic device 300 re-inquires the user capabilities, modifies the beam prediction modal set and transmission configuration information of the user equipment based on the feedback from the user equipment, and sends it to the user equipment. For example, when the user equipment moves from a simple channel environment (such as a LOS channel environment) to a complex channel environment (such as an NLOS channel environment), the electronic device 300 requests the user equipment to provide more modal information. After the user equipment agrees, the electronic device 300 expands the beam prediction modal set and notifies the user equipment. This method is an interactive update of the electronic device 300 and the user equipment, which can maintain the performance of beam prediction as much as possible under complex channel conditions.

[0109] As an example, for different determined categories of input information, the same prediction model is used to perform beam prediction.

[0110] Because different beam prediction modality sets correspond to different modality types and numbers, their modality information, when used as input information, corresponds to different input formats. If N different prediction models are established for N different beam prediction modality sets (N is a positive integer greater than 1), the model storage overhead is high. In the embodiments of the present disclosure, a unified model can be used for prediction across different beam prediction modality sets, effectively reducing storage overhead.

[0111] Hereinafter, the prediction model will sometimes be referred to as a deep multimodal learning model, and the learning used in beam prediction using the deep multimodal learning model will be referred to as deep multimodal learning (DML). In deep multimodal learning, modality is defined as a category of information. Therefore, multimodality also refers to multiple categories of information. In the embodiments of the present disclosure, unified beam prediction based on deep multimodal learning is adopted.

[0112] As an example, the prediction model can reconstruct missing input information corresponding to different input information categories that are missing relative to a predetermined number of predetermined input information categories based on existing input information, so as to complete the different input information categories into the predetermined number of predetermined input information categories. As an example, those skilled in the art can predetermine the predetermined number of predetermined input information categories based on application scenarios or experience.

[0113] For example, the missing modal information can be reconstructed based on the existing reliable modal information, and the modal information corresponding to different beam prediction modal sets can be completed so that their input formats (input data formats) are the same.

[0114] Figure 7 shows an example of a simplified structure of a prediction model according to an embodiment of the present disclosure. As shown in Figure 7, the deep multimodal learning model extracts features from multiple categories of information (e.g., modality 1, modality 2, and modality 3) through feature extraction, finds hidden connections between different information categories, and then extracts complementary information from different information categories (i.e., fuses different information). It then performs predictions and ultimately outputs beam prediction results, thereby improving the model's prediction performance.

[0115] As an 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 and perform beam prediction. The reconstruction network can reconstruct missing modal information based on existing reliable modal information, thereby supporting the use of modal information corresponding to different beam prediction modal sets as input to the prediction model. In other words, the reconstruction network can address the issue of inconsistent combinations of modal information categories used for beam prediction.

[0116] As an example, the prediction model includes a variational autoencoder (VAE) for reconstruction, where the VAE abstracts hidden features from existing input information and estimates missing input information based on the hidden features. For example, a VAE is an example of a reconstruction network model. In a VAE, internal hidden features are learned from the input and then used to generate new modal information. Due to the internal connections between different modal information, internal hidden features can be abstracted from existing reliable modal information and then used to generate missing modal information.

[0117] As an example, a VAE includes an encoding layer, a sampling layer, and a decoding layer. The encoding layer maps existing input information into 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 obtains missing input information through the intermediate hidden layer. That is, at the encoding layer stage, the probability distribution of internal hidden features is extracted from the existing modal information. The sampling layer samples the internal hidden features based on the probability distribution. The decoding layer generates the missing modal information based on the internal hidden features.

[0118] FIG8 shows an example of a structure of a reconstruction network model according to an embodiment of the present disclosure. For example, the received signal of the wide beam training is the first mode x (1) , the user position is the second modal x (2) , the user speed is the third mode x (3) The indexes in the beam prediction modality set of some users are {1, 2, 3}, and the indexes in the beam prediction modality set of some users are {1}.

[0119] For users whose beam prediction modality set is {1}, the reconstruction network model can use the information of the first modality to approximately estimate the corresponding information of the second and third modalities, thereby completing all modal information. The reconstruction network model shown in Figure 8 is described in detail as follows.

[0120] 1) Input: First modal information x (1) The feature vector z obtained after the convolution layer (1) .

[0121] 2) Encoding layer: The mean and variance (μ, σ) of the middle hidden layer are obtained through two fully connected layers.

[0122] 3) Sampling layer: According to the mean and variance obtained from the encoding layer, the intermediate hidden layer ω is sampled and expressed as

[0123] 4) Decoding layer: Through a fully connected layer, the middle hidden layer ω is transformed into the feature vector of the second and third modal information

[0124] 5) Output: Feature vectors of the second and third modal information Feature vectors based on the second and third modal information The second modal information x can be obtained (2) and the third modal information x (3) .

[0125] Thus, different categories of input information of all user equipments can be processed by a unified prediction model to perform beam prediction.

[0126] As an example, the prediction model also includes a convolutional neural network (CNN) and a long short-term memory neural network (LSTM) for beam prediction. For example, CNN and LSTM constitute the aforementioned multimodal fusion prediction model. CNN is a deep learning model that uses convolution for feature extraction 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 beam change processes. For example, LSTM derives the current state based on the previous state and the current input, and then predicts the current output.

[0127] Figure 9 shows an example of the structure of a multimodal fusion prediction model according to an embodiment of the present disclosure. The multimodal fusion prediction model shown in Figure 9 is described in detail as follows.

[0128] 1) Input: The input is a time series of three modes over a period of time m∈{1,2,3}. Each element in this sequence 1≤i≤n-1 and time t i correspond.

[0129] 2) First fusion layer: Assuming that the second and third modalities are similar in data format and express the same type of information, the second and third modalities are fused in advance and the data at each moment is directly spliced ​​together.

[0130] 3) Convolutional layer: A three-layer convolutional block is used to extract the features of the first modal information and the second and third modal fusion information. Each convolutional block includes a convolutional layer, a batch-norm (BN) layer, and a ReLU activation layer. The ReLU activation layer can be expressed as

[0131] There is a pooling layer after the last convolution layer to downsample the features extracted by the convolution layer.

[0132] 4) Second fusion layer: concatenate the feature vector of the first modal information with the feature vectors of the second and third modal fusion information.

[0133] 5) LSTM layer: The input of LSTM consists of two parts: 1. Feature vector input at a certain moment; 2. LSTM output and cell state at the previous moment.

[0134] 6) Fully connected layer: transforms the output of the LSTM layer into a specified size Q×1, where Q is the number of all beams.

[0135] The fully connected layer is represented as

[0136] y(t n )=Wx(t n )+b

[0137] where x(t n ) is the LSTM output, y(t n ) is the output of the fully connected layer. W and b are the linear weight and bias of the fully connected layer respectively. The Softmax activation function converts the output of the last fully connected layer into a probability vector.

[0138] in is the probability that the qth (1≤q≤Q)th beam is the optimal beam. The subscript with the largest probability corresponds to the predicted optimal beam index.

[0139] 7) Output: Predict the optimal beam index

[0140] When training a deep multimodal learning model, the cross entropy function can be used as the loss function. The cross entropy loss function can be expressed as:

[0141] Among them, when the qth beam is the actual optimal beam, o q (t n )=1; otherwise o q (t n )=0.

[0142] The Adam optimizer in the gradient back propagation algorithm can be used for optimization.

[0143] Figure 10 shows an example of the structure 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, when the user's beam prediction modality set is {1, 2, 3}, each modality first undergoes feature extraction through a convolutional layer; assuming that the second modality and the third modality are similar in data form and express the same type of information, the second modality and the third modality are fused in advance on the data in the first fusion layer and the data is spliced; then the information is fused through the second fusion layer; finally, the predicted optimal beam index is output through the LSTM layer and the fully connected layer. When the user's beam prediction modality set is {1}, the first modality first undergoes feature extraction through a convolutional layer; then the feature vectors of the second and third modalities are estimated through the reconstruction network model; then the information is fused through the second fusion layer; finally, the predicted optimal beam index is output through the LSTM layer and the fully connected layer.

[0144] As shown in Figures 8 to 10 , for example, the prediction model input is a time series of different input information over a predetermined number of historical time slots, and the prediction model output is the predicted optimal beam. Specifically, the optimal beam index is predicted by a deep multimodal learning model. The input of deep multimodal learning is information corresponding to a set of beam prediction modalities over a period of time, and the output is the predicted optimal beam index. The deep multimodal learning model can effectively integrate complementary information between different modalities, improving prediction accuracy.

[0145] As an example, those skilled in the art may predetermine the above-mentioned predetermined number based on application scenarios or experience.

[0146] For example, all modalities are input in the form of sequences. The input is a time series of M (M is a positive integer greater than 1) modalities over a period of time:

[0147] m∈{1,2,…,M}

[0148] Each element in the sequence 1≤i≤n-1 and time t i correspond.

[0149] The output is at time t n The corresponding optimal beam index q(t n ). f (1) , f (2) ,...,f (Q) represents all possible beams, then q(t n )∈{1,2,…,Q}。

[0150] The deep multimodal learning model is used to fit the prediction function g(·) so that

[0151] FIG11 shows an example of a process for performing beam prediction for a single user equipment by an electronic device 300 according to an embodiment of the present disclosure. In the specific steps of FIG11 , the electronic device 300 is referred to as a base station, and the user equipment is referred to as a user. As shown in FIG11 , the process includes the following steps:

[0152] Step 1: The base station inquires about the user's capabilities.

[0153] Step 2: Perform initial channel measurement.

[0154] Step 3: The base station determines the beam prediction mode set and transmission configuration information.

[0155] Step 4: The base station sends a beam prediction notification to the user.

[0156] Step 5: The base station notifies the user of the beam prediction mode set and transmission configuration information.

[0157] Step 6: Perform channel measurements for beam prediction.

[0158] In step 7, the user collects corresponding modal information based on the beam prediction modal set.

[0159] Step 8: The user feeds back the collected information to the base station according to the transmission configuration.

[0160] In step 9, the base station integrates all modal information corresponding to the beam prediction modal set and performs beam prediction.

[0161] Among them, steps 6 to 9 are executed periodically.

[0162] In step 10, when the base station detects a change in the user's channel environment, it updates the beam prediction mode set and transmission configuration information and notifies the user. Step 10 is executed when the channel environment changes. Step 10 allows for dynamic adjustment of the beam prediction mode set and transmission configuration information.

[0163] Unless explicitly stated, the signaling involved in each step of FIG11 may be implemented through RRC and / or DCI.

[0164] As an example, a user device is a group member in a group obtained by the electronic device 300 grouping multiple user devices within the service range of the electronic device 300, and the determination unit 301 can be configured to dynamically or semi-statically divide at least one user device whose channel environment meets a predetermined similar environment condition into a group, wherein the optimal beam corresponding to the group members in the same group meets the predetermined similar beam condition. That is, users in similar channel environments are divided into a group, and the optimal beams of users in the same group are the same or similar. The grouping method supports dynamic adjustment. Through user grouping, the results of beam prediction of users in the same group can be shared. For example, users in the same room can share the results of beam prediction.

[0165] As an example, those skilled in the art may predetermine the predetermined similar beam condition based on application scenarios or experience.

[0166] For example, the input information collected by group members based on the beam prediction configuration information is fused as input to the prediction model. Within a group of users, beam prediction modal information from different users can be fused (extracting complementary information from different information categories within a group of users) and simultaneously used as input to a deep multimodal learning model. Beam prediction for grouped users (grouped user beam prediction) can improve beam prediction accuracy.

[0167] As an example, the determination unit 301 may be configured to, for the selected group, select at least a portion of the group members from the group to form a user subset based on the user capabilities of the group members in the group and / or the channel environment in which the group members are located. For example, in a group of users, the electronic device 300 selects some users as representatives of the group of users to form a user subset, and the selection basis includes but is not limited to the channel environment and / or user capabilities. The beam prediction results of the user subset can be used as a benchmark for the beam prediction results of the entire group of users, thereby reducing the prediction overhead. For example, the selection of the user subset eliminates the need for other users to frequently feedback beam prediction modal information, and can directly use the beam prediction results of the user subset, thereby reducing signaling overhead.

[0168] Figure 12 illustrates an example of beam prediction for grouped users according to an embodiment of the present disclosure. As shown in Figure 12(a), beam prediction modal information for group members can be fused, thereby improving beam prediction accuracy. As shown in Figure 12(b), beam prediction is performed for a subset of users. The beam prediction results for the subset can be used as a benchmark for the beam prediction results for the entire group of users, thereby reducing prediction overhead.

[0169] As an example, the determination unit 301 may be configured to perform beam prediction based on input information reported by subset members in the user subset according to beam prediction configuration information to obtain a beam prediction result for the user subset, wherein all group members in the selected group use the beam prediction result for the user subset to communicate with the electronic device 300. For example, only users in the user subset participate in the beam prediction process and periodically transmit modal information corresponding to their beam prediction modality set; other users in the group do not participate in the beam prediction process and directly use the beam prediction result of the user subset. That is, the selected users in the group constitute the user subset, and the beam prediction result of the user subset will be used for the beam prediction result of the entire group of users.

[0170] As an example, the determination unit 301 can be configured to perform beam prediction based on the input information reported by the subset members in the user subset according to the beam prediction configuration information at a period less than a predetermined period to obtain a beam prediction result for the user subset, wherein the subset members in the user subset use the beam prediction result for the user subset to communicate with the electronic device 300, and perform beam prediction based on the input information reported by other group members other than the user subset in the selected group according to the beam prediction configuration information at a period greater than or equal to the predetermined period to obtain beam prediction results for other group members, wherein the other group members adjust the beam prediction result for the user subset based on the beam prediction result for the other group members to obtain the adjusted beam prediction result, and the other group members use the adjusted beam prediction result to communicate with the electronic device 300.

[0171] For example, all users in the group participate in the beam prediction process. Users in the user subset send modal information corresponding to their beam prediction modal set at a shorter period; other users send modal information corresponding to their beam prediction modal set at a longer period, and other users adjust the beam prediction results of the user subset based on their own beam prediction results.

[0172] As an example, those skilled in the art may predetermine the predetermined period based on application scenarios or experience.

[0173] For example, the electronic device 300 may initially group the users based on the initial channel measurement result.

[0174] As an example, the determining unit 301 may be configured to group user equipments whose channel state information (CSI) correlation during a predetermined time period is greater than a predetermined correlation threshold into the same group. That is, user equipments with highly correlated CSI within a predetermined time period may be grouped together.

[0175] As an example, those skilled in the art may predetermine the above-mentioned predetermined time period based on application scenarios or experience.

[0176] As an example, those skilled in the art may predetermine the predetermined correlation threshold based on application scenarios or experience.

[0177] As an example, the determining unit 301 may be configured to divide user equipments having the same corresponding optimal beam during a predetermined time period into the same group.

[0178] Figure 13 illustrates an example of how user devices with the same optimal beam are grouped together during a predetermined time period according to an embodiment of the present disclosure. As shown in Figure 13 , as the three users move, their corresponding optimal beams change. For example, when the three users are at position 1, their corresponding optimal beams are beam 1; when they are at position 2, their corresponding optimal beams are beam 2; and when they are at position 3, their corresponding optimal beams are beam 3. However, the optimal beams for these three users remain the same, and therefore, they are grouped together.

[0179] As an example, the determination unit 301 can be configured to, for the selected group, divide the group members into new groups when the reference signal received power (RSRP) measured by the group members based on the beam prediction results for the user subset selected in the group is less than a predetermined power threshold. As an example, a person skilled in the art can predetermine the predetermined power threshold based on the application scenario or experience. For example, the electronic device 300 can be configured with an RSRP threshold r0. All users in the group measure and predict the RSRP of the optimal beam (for example, the predicted optimal beam here is the beam prediction result of the user subset within the group). When the RSRP is less than the threshold r0, the corresponding users will be divided into a new group (i.e., intra-group division). If the RSRP values ​​measured by some users are less than the threshold r0, it means that the received power obtained by these users using the beam prediction results within the group is low. Therefore, these users should be divided into a new group (i.e., inter-group merging), a new user subset is selected, and beam prediction is performed again.

[0180] Figure 14 illustrates an example of dividing group members into new groups according to an embodiment of the present disclosure. As shown in Figure 14(a), there is one group, for which beam prediction is performed. As shown in Figure 14(b), the group shown in Figure 14(a) is divided into two groups, and beam prediction is performed on each of the two groups.

[0181] As an example, the determination unit 301 may be configured to merge different groups into a new group if the beam prediction results for different groups are the same during a predetermined period. That is, if the predicted optimal beams for some user groups remain the same over a period of time, these user groups will be merged into a new group. If the predicted optimal beams for some user groups remain the same over a period of time, it indicates that their channel environments are similar and they can be merged into a new group for beam prediction, reducing signaling overhead.

[0182] The initial user grouping results may not be ideal; the user status may change at any time, and the initial user grouping results may no longer be applicable. In the embodiments according to the present disclosure, dynamic adjustment of user grouping is supported to achieve real-time validity of the grouping results and improve the beam prediction performance of grouped users.

[0183] As an example, the determination unit 301 can be configured to, for the selected group, divide group members with the same user capabilities into the same category, determine the category that needs to be included in the user subset, and select at least a portion of the group members from the group members corresponding to the determined category to form the user subset. Different user groups are located in different channel environments; the channel environment in which the user groups are located may change at any time. A fixed user subset may cause the group user beam prediction performance to degrade. In an embodiment according to the present disclosure, user subsets can be flexibly selected, and the modal information collected by the user subsets can be integrated to improve beam prediction accuracy.

[0184] Figure 15 shows an example of selecting a user subset according to an embodiment of the present disclosure. In a group of users, users are classified based on capabilities, and users who support the same combination of capabilities are classified into one category. For example, users who can only support channel measurement are classified into category 1, users who support channel measurement and provide user location are classified into category 2, users who support channel measurement and provide user speed are classified into category 3, users who support channel measurement and provide user location and speed are classified into category 4, and so on (in Figure 15, categories 1-3 are illustrated). The electronic device 300 determines the categories that need to be included in the user subset and selects users in the corresponding categories.

[0185] As the channel environment of the group of users changes, the electronic device 300 can flexibly change the user subset. For example, in a simple channel, the user subset may only include users in category 1; in a complex channel, the user subset needs to include users of more powerful categories (for example, in addition to users in category 1, also users in categories 2 and 3) to fuse the modal information of users in these categories. For example, when a group of users moves from a simple channel to a complex channel, the electronic device 300 expands the user subset and includes more categories of users in the user subset to fuse information from more modalities and improve beam prediction accuracy; conversely, when a group of users moves from a complex channel to a simple channel, the electronic device 300 shrinks the user subset to reduce beam prediction overhead.

[0186] Figure 16 illustrates an example process for electronic device 300 to perform beam prediction for grouped user devices, according to an embodiment of the present disclosure. In the specific steps of Figure 16 , electronic device 300 is referred to as a base station, and the user devices are referred to as users. In this example process, dynamic adjustment of user grouping results and user subset selection can reduce beam prediction overhead and maintain beam prediction accuracy. As shown in Figure 16 , the process includes the following steps:

[0187] Step 1: The base station inquires about the user's capabilities.

[0188] Step 2: Perform initial channel measurement. The results of the initial channel measurement can be used for initial user grouping and for determining user beam prediction mode sets and transmission configuration information.

[0189] Step 3: Initial grouping of users.

[0190] Step 4: The base station selects a user subset.

[0191] Step 5: The base station determines the beam prediction mode set and transmission configuration information.

[0192] In step 6, the base station sends a beam prediction notification to the user group. There are two ways the base station can send a beam prediction notification to the user group. Method 1: The base station sends a beam prediction notification to a subset of users. This method only involves users in the subset, reducing signaling overhead. Method 2: The base station sends a beam prediction notification to all users in the user group. This method involves all users in the group participating in the beam prediction process, improving prediction accuracy.

[0193] Step 7: The base station notifies the user of the beam prediction mode set and transmission configuration information.

[0194] Step 8: Perform channel measurement for beam prediction.

[0195] In step 9, the user collects corresponding modal information based on the beam prediction modal set.

[0196] Step 10: The user feeds back the collected information to the base station according to the transmission configuration.

[0197] In step 11, the base station integrates all modal information corresponding to the beam prediction modal set and performs beam prediction.

[0198] Among them, steps 7 to 11 are performed periodically.

[0199] Step 12: When the base station detects that the channel environment in which the user is located has changed, it updates the beam prediction mode set and transmission configuration information and sends them to the user.

[0200] Step 13: The base station configures the RSRP threshold r0.

[0201] Step 14: All users in the group measure and predict the RSRP of the optimal beam.

[0202] Step 15: Intra-group splitting or inter-group merging.

[0203] Among them, steps 13 to 15 involve dynamic adjustment of user groups.

[0204] Step 16: The base station selects a new user subset for the new user group.

[0205] Wherein, step 16 is executed when a new user group is generated.

[0206] Unless explicitly stated, the signaling involved in each step of FIG16 may be implemented through RRC and / or DCI.

[0207] As an example, the determination unit 301 may be configured to, for the selected group, divide the group members into high-capability users whose user capabilities meet a predetermined capability condition and low-capability users whose user capabilities do not meet the predetermined capability condition based on user capabilities, and notify the group members of the group of information about the group and the division results of the high-capability users and / or low-capability users, so that the group members in the group can perform beam prediction based on the prediction model. In this case, the beam prediction model is deployed on the user equipment side.

[0208] As an example, those skilled in the art may predetermine the predetermined capability condition based on application scenarios or experience.

[0209] For example, the predetermined capability condition may include the user equipment being able to use the prediction model for beam prediction. User equipment that can use the prediction model for beam prediction may be classified as high-capability users, while user equipment that cannot use the prediction model for beam prediction may be classified as low-capability. For example, the beam prediction model may be deployed at high-capability users.

[0210] For example, the predetermined capability condition may include that the user equipment is capable of obtaining more than a predetermined amount of modal information. Those skilled in the art may also conceive of other examples of the predetermined capability condition, which will not be repeated here.

[0211] As an example, the determining unit 301 may be configured to notify the group members of the information about the group and the division result of the high-capability users and / or the low-capability users through RRC signaling or DCI.

[0212] For example, high-capability users in the selected group use the prediction model to perform beam prediction based on their collected input information to obtain a beam prediction result, and then share the beam prediction result with low-capability users. Information of users in the same group can be directly shared via D2D (device-to-device) communication. For example, a high-capability user in the group integrates its own modal information and inputs it into the prediction model to obtain a predicted optimal beam, and then shares the prediction result with the low-capability users in the group.

[0213] As an example, a high-capability user in a selected group, in response to a request received from a low-capability user, shares at least a portion of its collected input information with the low-capability user. For example, a high-capability user in a group can share its modal information with a low-capability user via D2D communication. For example, when a low-capability user in a group requires modal information that it cannot provide, it makes a request to a high-capability user in the group, and the high-capability user in the group shares the requested modal information.

[0214] For example, D2D communication is used between members of a fleet in a vehicle network, wherein the primary member of the fleet may correspond to the high-capability user, and the other members of the fleet may correspond to the low-capability users.

[0215] Among users in the same group, some modal information can be shared, while some modal information cannot be shared. Since the channel environments of users in the same group are similar, channel measurement information (SRS received signal and / or RSRP, CSI-RS received signal and / or RSRP, the above-mentioned other frequency band channel information, etc.) and communication perception integration information (radar target detection information, radar beam received signal and / or RSRP, etc.) can be shared. In addition, user motion information such as user position, user speed, and user accelerometer information in the category of auxiliary information reflecting user capabilities does not differ much between different users in the same group and can be shared. On the other hand, detailed information such as user gyroscope information and user compass information may vary greatly between different users in the same group, and information sharing is not supported.

[0216] The following briefly describes an example of a process for sharing D2D scene modality information. In the following specific steps, the electronic device 300 is referred to as a base station, and the user equipment is referred to as a user.

[0217] S1, the base station inquires about the user's capabilities.

[0218] S2, perform initial channel measurement.

[0219] S3, initial grouping of users.

[0220] S4: The base station classifies users according to their capability levels (high-capability users or low-capability users).

[0221] S5: The base station notifies the user of the grouping result and capability division result.

[0222] S8, the high-capability user collects modal information and performs beam prediction.

[0223] S9, the high-ability user shares its prediction results with the low-ability user.

[0224] S10: When a low-ability user in the group makes a request to a high-ability user in the group, the high-ability user in the group shares the requested modality information.

[0225] The process of sharing D2D scene modality information may include at least one of S9 and S10.

[0226] Unless explicitly stated, the signaling involved in steps S1 to S10 may be implemented through RRC and / or DCI.

[0227] Consider a millimeter wave downlink transmission scenario. The maximum user velocity is 30 m / s, the maximum acceleration is 0.2 times the velocity, and the motion direction is randomly generated in the range [0, 2π]. Channel data is generated using the existing 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, February 2019, pp. 1-8). The simulation parameters are shown in Table 1.

[0228] Table 1 Simulation parameters

[0229] Consider three beam prediction modes: the received signal of wide beam training is the first mode x (1) , the user position is the second modal x (2) , the user speed is the third mode x (3) After channel transmission, the electronic device 300 receives the noisy beam prediction modal information, and the noise parameters are shown in Table 2.

[0230] Table 2 Noise parameters

[0231] Three beam prediction modal set cases are considered: Case 1, the beam prediction modal set of all users is {1, 2, 3}, hereinafter referred to as "complete modality"; Case 2, the beam prediction modal set of 90% of users is {1, 2, 3}, and the beam prediction modal set of 10% of users is {1}, hereinafter referred to as "10% missing modality"; Case 3, the beam prediction modal set of 80% of users is {1, 2, 3}, and the beam prediction modal set of 20% of users is {1}, hereinafter referred to as "20% missing modality".

[0232] The prediction accuracy and normalized beam gain are used as evaluation indicators. Assuming that the total number of samples used for evaluation is N1, and the number of samples where the beam predicted is the actual optimal beam is N2, the prediction accuracy is expressed as:

[0233] Assume that the average received power obtained by using the predicted optimal beam is The average received power P obtained by using the actual predicted optimal beam a , the normalized beam gain is expressed as:

[0234] Consider a prediction model that uses only the received signal trained with wide beams as input, referred to as Baseline 1, and a prediction model that uses only user position and velocity information as input, referred to as Baseline 2. Simulations were performed for the proposed beam prediction scheme (hereinafter referred to as the proposed scheme) in the present disclosure under the conditions of complete mode, 10% missing mode, and 20% missing mode.

[0235] The prediction accuracy of different schemes is shown in Table 3. It can be seen that the proposed scheme has a significant improvement in prediction accuracy compared to Baseline 1 and Baseline 2. Furthermore, even in the presence of missing modal data, the proposed scheme can still maintain a high prediction accuracy.

[0236] Table 3 Prediction accuracy simulation results

[0237] Figure 17 shows the variation of normalized beam gain over prediction time (beam prediction moment) for different schemes. It can be seen that the proposed scheme provides higher normalized beam gain compared to Baselines 1 and 2. Furthermore, the proposed scheme maintains high normalized beam gain even in the presence of missing modes.

[0238] The present disclosure further provides an electronic device for wireless communication according to another embodiment. FIG18 shows a functional module block diagram of an electronic device 1900 for wireless communication according to another embodiment of the present disclosure.

[0239] As shown in FIG18 , electronic device 1900 includes: a communication unit 1901, which can be configured to receive beam prediction configuration information from a network-side device providing a service for electronic device 1900, wherein the beam prediction configuration information includes the category of input information of a prediction model dynamically or semi-statically determined by the network-side device for electronic device 1900, and the prediction model is used to perform beam prediction on the beam to be used by electronic device 1900 to communicate with the network-side device; and a collection unit 1903, which can be configured to collect input information based on the beam prediction configuration information. Furthermore, it should be understood that the various functional units in the electronic device shown in FIG18 are merely logical modules divided according to the specific functions they implement, and are not intended to limit specific implementation methods.

[0240] The communication unit 1901 and the collection unit 1903 may be implemented by one or more processing circuits, which may be implemented as a chip, for example.

[0241] The electronic device 1900 can, for example, be arranged on the user equipment side or be communicatively connected to the user equipment. Here, it should also be noted that the electronic device 1900 can be implemented at the chip level, or it can also be implemented at the device level. For example, the electronic device 1900 can work as the user equipment itself, and can also include external devices such as memory, transceiver (not shown in the figure), etc. The memory can be used to store programs and related data information that need to be executed by the user equipment to implement various functions. The transceiver may include one or more communication interfaces to support communication with different devices (for example, base stations, other user equipment, etc.), and the implementation form of the transceiver is not specifically limited here.

[0242] The wireless communication system according to the present disclosure may be a 5G NR (New Radio) communication system. Furthermore, the wireless communication system according to the present disclosure may include a non-terrestrial network (NTN). Optionally, the wireless communication system according to the present disclosure may also include a terrestrial network (TN). In addition, those skilled in the art will appreciate that the wireless communication system according to the present disclosure may also be a 4G or 3G communication system.

[0243] As an example, the network side device in the embodiment of the electronic device 1900 may be the electronic device 300 mentioned above. As an example, the electronic device 1900 may be the user equipment involved in the embodiment of the electronic device 300 mentioned above.

[0244] According to an embodiment of the present disclosure, the electronic device 1900 is capable of collecting input information for beam prediction based on the category of input information of the prediction model dynamically or semi-statically determined (updated) by the network side device (beam prediction configuration information determined dynamically or semi-statically), thereby improving the performance of beam prediction (for example, improving the accuracy of beam prediction).

[0245] As an example, the category of the input information is determined based on 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 can be configured to report the user capabilities to the network side device via radio resource control (RRC) signaling.

[0246] The user capabilities of the electronic device 1900 and / or the channel environment in which the electronic device 1900 is located are different, and the corresponding modal information required for beam prediction is different.

[0247] As an example, the user capability information included in the above RRC signaling includes user assistance sub-information, and the parameters of the user assistance sub-information include the category of auxiliary information used to reflect the user capability of the electronic device 1900. For relevant RRC signaling, please refer to the description of the electronic device 300 embodiment in conjunction with Figure 6, and will not be repeated here.

[0248] In an embodiment according to the present disclosure, extended user capability information including user assistance sub-information is proposed, which notifies the network side device of the category of assistance information that the electronic device 1900 can provide, so that the network side device determines the beam prediction modality set.

[0249] As an example, the categories of auxiliary information include at least one of motion characteristic information of the electronic device 1900, location information of the electronic device 1900, sensor information of the electronic device 1900, communication capabilities of the electronic device 1900, whether the electronic device 1900 supports the use of a prediction model, and information about a beam.

[0250] As an example, the sensing information of the electronic device is information obtained based on the software and / or hardware of the electronic device 1900 , which includes motion-related parameters of the electronic device 1900 .

[0251] For information about the motion characteristics of the electronic device, the location information of the electronic device 1900, the sensor information of the electronic device 1900, the communication capability of the electronic device 1900, whether the electronic device 1900 supports the use of the prediction model, and information about the beam, please refer to the description in the above embodiment of the electronic device 300, which will not be repeated here.

[0252] As an example, the parameters of the user assistance sub-information further include a transmission period and / or a number of transmission bits of the assistance information.

[0253] As an example, the transmission period of the assistance information is determined by a measurement period of the electronic device 1900 and / or a valid duration of the assistance information.

[0254] As an example, the number of transmission bits of the auxiliary information is determined by the measurement accuracy of the electronic device 1900 and / or the storage capacity of the electronic device 1900 .

[0255] As an example, the channel environment is obtained based on channel measurement information, and the channel measurement information includes one or more of the following: the strength, phase and power of the sounding reference signal (SRS) received through the channel between the electronic device 1900 and the network side device, the strength, phase and power of the channel state information reference signal (CSI-RS), and channel information of other frequency bands other than the frequency bands in which the above-mentioned channels are located.

[0256] For the channel measurement information, please refer to the description in the embodiment of the electronic device 300 above, which will not be repeated here.

[0257] As an example, the categories of input information include at least one of channel measurement information, motion characteristic information of the electronic device 1900, location information of the electronic device 1900, sensor information of the electronic device 1900, perception information of the network side device, and communication perception integrated information, wherein the sensor information of the electronic device 1900 is information obtained based on the software and / or hardware of the electronic device 1900, which includes motion-related parameters of the electronic device 1900.

[0258] As an example, the communication awareness integration information includes one or more of the following: radar target detection information, and the strength, phase, and power of a radar beam reference signal.

[0259] For information on the channel measurement information, motion feature information of the electronic device 1900, location information of the electronic device 1900, sensor information of the electronic device 1900, and perception information of the network side device as input information, please refer to the description in the above embodiment of the electronic device 300, which will not be repeated here.

[0260] As an example, the number of categories of input information of 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 categories of input information of the prediction model determined for an electronic device whose user capabilities meet the predetermined user capability conditions.

[0261] For examples of predetermined user capability conditions, please refer to the description in the above embodiment of the electronic device 300, which will not be repeated here.

[0262] As an example, the number of categories of input information of the prediction model determined for an electronic device whose movement speed is less than a predetermined speed threshold is less than the number of categories of input information of the prediction model determined for an electronic device whose movement speed is greater than or equal to the predetermined speed threshold.

[0263] As an example, the number of categories of input information of the prediction model determined for an electronic device whose channel environment meets the predetermined environmental conditions is less than the number of categories of input information of the prediction model determined for an electronic device whose channel environment does not meet the predetermined environmental conditions.

[0264] For examples of predetermined environmental conditions, please refer to the description in the above embodiment of the electronic device 300, which will not be repeated here.

[0265] As an example, the communication unit 1901 may be configured to receive beam prediction configuration information through RRC signaling or downlink control information (DCI).

[0266] As an example, the beam prediction configuration information also includes resource configuration information corresponding to the category of input information determined by the network side device, wherein the resource configuration information includes the transmission period and / or number of transmission bits of the determined category of input information, and the communication unit 1901 can be configured to report the input information based on the resource configuration information for the network side device to perform beam prediction using a prediction model.

[0267] For an example of beam prediction configuration information including resource configuration information, please refer to the description in the embodiment of the electronic device 300 above, which will not be repeated here.

[0268] As an example, the communication unit 1901 may be configured to receive beam prediction configuration information corresponding to the electronic device 1900 that is modified in an event-triggered manner.

[0269] As an example, the event includes detecting that a change in the channel environment in which electronic device 1900 is located satisfies a predetermined environment change condition. As an example, the electronic device is not notified to re-report user capabilities, and the beam prediction configuration information corresponding to electronic device 1900 is directly modified by the network-side device. As an example, communication unit 1901 can be configured to receive a notification of re-reporting user capabilities and re-report the user capabilities, wherein the beam prediction configuration information corresponding to electronic device 1900 is modified by the network-side device based on the re-reported user capabilities.

[0270] For examples of predetermined environmental change conditions, please refer to the description in the above embodiment of the electronic device 300, which will not be repeated here.

[0271] For an example of the electronic device 1900 receiving beam prediction configuration information corresponding to the electronic device 1900 that is modified in an event-triggered manner, please refer to the description in the embodiment of the electronic device 300 above, which will not be repeated here.

[0272] For different determined categories of input information, the same prediction model is used to perform beam prediction.

[0273] As an example, the prediction model can reconstruct the missing input information corresponding to the missing categories of different input information categories relative to a predetermined number of predetermined input information categories based on the existing input information, so as to complete the different input information categories into a predetermined number of predetermined input information categories.

[0274] As an example, the prediction model includes a variational autoencoder (VAE) for reconstruction, wherein the VAE abstracts hidden features from existing input information and estimates missing input information based on the hidden features.

[0275] As an example, VAE includes an encoder, a sampler, and a decoder, wherein the encoder maps existing input information into probability distribution parameters, the sampler samples based on the probability distribution parameters to obtain an intermediate hidden layer, i.e., hidden features, and the decoder obtains missing input information through the intermediate hidden layer.

[0276] As an example, the prediction model also includes a convolutional neural network and a long short-term memory neural network for beam prediction.

[0277] As an example, the input of the prediction model is a time series of different input information in a predetermined number of historical time slots, and the output of the prediction model is the predicted optimal beam.

[0278] For examples of the prediction model, please refer to the description in the embodiment of the electronic device 300 above, which will not be repeated here.

[0279] As an example, electronic device 1900 is a group member in a group obtained by a network side device grouping multiple electronic devices within its service range, wherein at least one electronic device whose channel environment satisfies a predetermined similar environment condition is dynamically or semi-statically divided into a group, and the optimal beam corresponding to the group members in the same group satisfies a predetermined similar beam condition.

[0280] As an example, input information collected by group members in the same group according to beam prediction configuration information is fused as input to the prediction model.

[0281] As an example, for the selected group, at least a portion of the group members are selected from the group to form a user subset based on the user capabilities of the group members in the group and / or the channel environment in which the group members are located.

[0282] As an example, the beam prediction result for the user subset is obtained by performing beam prediction based on the input information reported by the subset members in the user subset according to the beam prediction configuration information, and all group members in the selected group use the beam prediction result for the user subset to communicate with the network side device.

[0283] As an example, the beam prediction result for the user subset is obtained by performing beam prediction based on input information reported by subset members in the user subset according to beam prediction configuration information at a period less than a predetermined period, wherein the subset members in the user subset use the beam prediction result for the user subset to communicate with the network side device, and the beam prediction result for other group members is obtained by performing beam prediction based on input information reported by other group members in the selected group other than the user subset according to the beam prediction configuration information at a period greater than or equal to the predetermined period, wherein the other group members adjust the beam prediction result for the user subset based on the beam prediction result for the other group members to obtain the adjusted beam prediction result, and the other group members use the adjusted beam prediction result to communicate with the network side device.

[0284] As an example, electronic devices whose channel state information (CSI) correlations during a predetermined time period are greater than a predetermined correlation threshold are divided into the same group.

[0285] As an example, electronic devices having the same optimal beam as their corresponding ones during a predetermined time period are divided into the same group.

[0286] As an example, for the selected group, if the reference signal received power measured by the group members based on the beam prediction results for the selected user subset in the group is less than a predetermined power threshold, the group members are divided into a new group.

[0287] As an example, in a case where beam prediction results for different groups are the same during a predetermined period, the different groups are merged into a new group.

[0288] As an example, for the selected group, group members with the same user capabilities are divided into the same category, wherein the category that needs to be included in the user subset is determined, and at least a part of the group members are selected from the group members corresponding to the determined category to form the user subset.

[0289] As an example, for the selected group, the group members are divided into high-ability users whose user capabilities meet predetermined capability conditions and low-ability users whose user capabilities do not meet predetermined capability conditions according to user capabilities. The group members in the group receive information about the group and the division results of high-ability users and / or low-ability users from the network side device so that the group members in the group can perform beam prediction based on the prediction model.

[0290] As an example, the group members receive information about the group and the division results about the high-capability users and / or low-capability users through RRC signaling or DCI.

[0291] As an example, the high-capability users in the selected group perform beam prediction based on their collected input information and the prediction model to obtain beam prediction results, and share the beam prediction results with the low-capability users.

[0292] As an example, the high-ability users in the selected group share at least a portion of the input information they collected with the low-ability users in response to the request received from the low-ability users, so that the low-ability users can use the prediction model to perform beam prediction to obtain beam prediction results.

[0293] For examples of grouping, selection of user subsets, high-capability users and low-capability users, etc., please refer to the description in the embodiment of the electronic device 300 above, which will not be repeated here.

[0294] In the process of describing the electronic device for wireless communication in the above embodiments, it is obvious that some processes or methods are also disclosed. Below, an overview of these methods is given without repeating some of the details discussed above, but it should be noted that although these methods are disclosed in the process of describing the electronic device for wireless communication, these methods do not necessarily use the components described or are not necessarily performed by those components. For example, the embodiments of the electronic device for wireless communication can be partially or completely implemented using hardware and / or firmware, and the methods for wireless communication discussed below can be completely implemented by computer-executable programs, although these methods can also use the hardware and / or firmware of the electronic device for wireless communication.

[0295] FIG19 illustrates a flowchart of a method S2000 for wireless communication according to an embodiment of the present disclosure. Method S2000 begins at step S2002. At step S2004, a category of input information for a prediction model is dynamically or semi-statically determined for a user device within the service range of the electronic device, wherein the prediction model is used to perform beam prediction on a beam to be used by the electronic device for communication with the user device. At step S2006, beam prediction configuration information including the determined category of input information is provided to the user device, so that the user device collects input information based on the beam prediction configuration information. Method S2000 concludes at step S2008.

[0296] The method may be executed, for example, by the electronic device 300 described above. For specific details, please refer to the description of the related processing of the electronic device 300, which will not be repeated here.

[0297] Figure 20 shows a flowchart of a method S2100 for wireless communication according to another embodiment of the present disclosure. Method S2100 begins at step S2102. At step S2104, beam prediction configuration information is received from a network device providing a service to the electronic device. The beam prediction configuration information includes the category of input information for a prediction model dynamically or semi-statically determined by the network device for the electronic device, and the prediction model used to perform beam prediction on the beam to be used by the electronic device for communication with the network device. At step S2106, input information is collected based on the beam prediction configuration information. Method S2100 ends at step S2108.

[0298] The method may be executed, for example, by the electronic device 1900 described above. For specific details, please refer to the description of the related processing of the electronic device 1900, which will not be repeated here.

[0299] The technology of the present disclosure can be applied to various products.

[0300] The electronic device 300 can be implemented as various network-side devices. The network-side device can be set on the base station side or connected to the base station. The base station can be implemented as any type of evolved Node B (eNB) or gNB (5G base station). eNB includes, for example, macro eNB and small eNB. Small eNB can be an eNB that covers a cell smaller than a macro cell, such as a pico eNB, micro eNB, and home (femto) eNB. Similar situations can also be encountered for gNB. Alternatively, the base station can be implemented as any other type of base station, such as a NodeB and a base transceiver station (BTS). The base station may include: a main body (also called a base station device) configured to control wireless communications; and one or more remote radio heads (RRHs) set at a location different from the main body. In addition, various types of electronic devices can work as a base station by temporarily or semi-permanently performing base station functions.

[0301] The electronic device 1900 can be implemented as various user devices. The user device can be implemented as a mobile terminal (such as a smartphone, a tablet personal computer (PC), a notebook PC, a portable game terminal, a portable / dongle-type mobile router, and a digital camera) or an in-vehicle terminal (such as a car navigation device). The user device can also be implemented as a terminal that performs machine-to-machine (M2M) communication (also known as a machine type communication (MTC) terminal). In addition, the user device can be a wireless communication module (such as an integrated circuit module including a single chip) installed on each of the above terminals.

[0302] [Application examples for base stations]

[0303] (First application example)

[0304] FIG21 is a block diagram illustrating a first example of a schematic configuration of an eNB or gNB to which the techniques of this disclosure can be applied. Note that the following description uses an eNB as an example, but is equally applicable to a gNB. An eNB 800 includes one or more antennas 810 and a base station device 820. The base station device 820 and each antenna 810 can be connected to each other via an RF cable.

[0305] Each of the antennas 810 includes a single or multiple antenna elements (such as multiple antenna elements included in a multiple-input multiple-output (MIMO) antenna) and is used for base station device 820 to transmit and receive wireless signals. As shown in FIG21 , eNB 800 may include multiple antennas 810. For example, multiple antennas 810 may be compatible with multiple frequency bands used by eNB 800. Although FIG21 shows an example in which eNB 800 includes multiple antennas 810, eNB 800 may also include a single antenna 810.

[0306] The base station device 820 includes a controller 821 , a memory 822 , a network interface 823 , and a wireless communication interface 825 .

[0307] The controller 821 may be, for example, a CPU or a DSP, and operates various functions of the higher layers of the base station device 820. For example, the controller 821 generates data packets based on the data in the signal processed by the wireless communication interface 825, and transmits the generated packets via the network interface 823. The controller 821 may bundle data from multiple baseband processors to generate bundled packets, and transmit the generated bundled packets. The controller 821 may have logic functions for performing the following controls: the control may be radio resource control, radio bearer control, mobility management, admission control, and scheduling. The control may be performed in conjunction with a nearby eNB or core network node. The memory 822 includes RAM and ROM, and stores programs executed by the controller 821 and various types of control data (such as a terminal list, transmission power data, and scheduling data).

[0308] The network interface 823 is a communication interface for connecting the base station device 820 to the core network 824. The controller 821 can communicate with the core network node or another eNB via the network interface 823. In this case, the eNB 800 and the core network node or other eNBs can be connected to each other through a logical interface (such as an S1 interface and an X2 interface). The network interface 823 can also be a wired communication interface or a wireless communication interface for a wireless backhaul line. If the network interface 823 is a wireless communication interface, the network interface 823 can use a higher frequency band for wireless communication than the frequency band used by the wireless communication interface 825.

[0309] The wireless communication interface 825 supports any cellular communication scheme, such as Long Term Evolution (LTE) and LTE-Advanced, and provides wireless connectivity to terminals located in the cell of the eNB 800 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 various signal processing functions, such as encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and performs various types of signal processing for layers such as Layer 1, Medium Access Control (MAC), Radio Link Control (RLC), and Packet Data Convergence Protocol (PDCP). In place of the controller 821, the BB processor 826 may have some or all of the aforementioned logical functions. The BB processor 826 may be a memory that stores communication control programs, or a module including a processor configured to execute programs and associated circuitry. Program updates can modify the functionality of the BB processor 826. This module may be a card or blade inserted into a slot in the base station device 820. Alternatively, the module may be a chip mounted on the card or blade. Meanwhile, the RF circuit 827 may include, for example, a mixer, a filter, and an amplifier, and transmit and receive wireless signals via the antenna 810 .

[0310] As shown in FIG21 , 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 eNB 800. As shown in FIG21 , 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 FIG21 illustrates 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 also include a single BB processor 826 or a single RF circuit 827.

[0311] In the eNB 800 shown in FIG21 , when the electronic device 300 is implemented as a base station, its transceiver may be implemented by the wireless communication interface 825. At least a portion of the functions may also be implemented by the controller 821. For example, the controller 821 may improve beam prediction performance by executing the functions of the units in the electronic device 300.

[0312] (Second application example)

[0313] FIG22 is a block diagram illustrating a second example of a schematic configuration of an eNB or gNB to which the techniques of this disclosure can be applied. Note that similarly, the following description uses an eNB as an example, but is equally applicable to a gNB. An eNB 830 includes one or more antennas 840, a base station device 850, and an RRH 860. The RRH 860 and each antenna 840 can be connected to each other via an RF cable. The base station device 850 and the RRH 860 can be connected to each other via a high-speed line such as an optical fiber cable.

[0314] Each of the antennas 840 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for RRH 860 to transmit and receive wireless signals. As shown in FIG22 , eNB 830 may include multiple antennas 840. For example, multiple antennas 840 may be compatible with multiple frequency bands used by eNB 830. Although FIG22 shows an example in which eNB 830 includes multiple antennas 840, eNB 830 may also include a single antenna 840.

[0315] 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, the memory 852, and the network interface 853 are the same as the controller 821, the memory 822, and the network interface 823 described with reference to FIG.

[0316] The wireless communication interface 855 supports any cellular communication scheme (such as LTE and LTE-Advanced) and provides wireless communication to terminals located in the sector corresponding to the RRH 860 via the RRH 860 and the antenna 840. The wireless communication interface 855 may generally include, for example, a BB processor 856. The BB processor 856 is the same as the BB processor 826 described with reference to FIG. 21, except that the BB processor 856 is connected to the RF circuit 864 of the RRH 860 via the connection interface 857. As shown in FIG. 22, the wireless communication interface 855 may include multiple BB processors 856. For example, the multiple BB processors 856 may be compatible with multiple frequency bands used by the eNB 830. Although FIG. 22 shows an example in which the wireless communication interface 855 includes multiple BB processors 856, the wireless communication interface 855 may also include a single BB processor 856.

[0317] The connection interface 857 is an interface for connecting the base station device 850 (wireless communication interface 855) to the RRH 860. The connection interface 857 may also be a communication module for connecting the base station device 850 (wireless communication interface 855) to the RRH 860 for communication in the high-speed line.

[0318] The RRH 860 includes a connection interface 861 and a wireless communication interface 863 .

[0319] The connection interface 861 is an interface for connecting the RRH 860 (wireless communication interface 863) to the base station device 850. The connection interface 861 may also be a communication module for communication in the above-mentioned high-speed line.

[0320] The wireless communication interface 863 transmits and receives wireless signals via the antenna 840. The wireless communication interface 863 may generally include, for example, an RF circuit 864. The RF circuit 864 may include, for example, a mixer, a filter, and an amplifier, and transmits and receives wireless signals via the antenna 840. As shown in FIG22 , the wireless communication interface 863 may include multiple RF circuits 864. For example, multiple RF circuits 864 may support multiple antenna elements. Although FIG22 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.

[0321] In the eNB 830 shown in FIG22 , when the electronic device 300 is implemented as a base station, its transceiver may be implemented by the wireless communication interface 855. At least a portion of the functions may also be implemented by the controller 851. For example, the controller 851 may improve beam prediction performance by executing the functions of the units in the electronic device 300.

[0322] [Application examples on user devices]

[0323] (First application example)

[0324] 23 is a block diagram showing an example of a schematic configuration of a smartphone 900 to which the technology of the present disclosure can be applied. The smartphone 900 includes a processor 901, a memory 902, a storage device 903, an external connection interface 904, a camera 906, a sensor 907, a microphone 908, an input device 909, a display device 910, a speaker 911, a wireless communication interface 912, one or more antenna switches 915, one or more antennas 916, a bus 917, a battery 918, and an auxiliary controller 919.

[0325] The processor 901 may be, for example, a CPU or a system on a chip (SoC), and controls 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 may include storage media such as semiconductor memories and hard disks. The external connection interface 904 is an interface for connecting external devices (such as memory cards and universal serial bus (USB) devices) to the smartphone 900.

[0326] The camera 906 includes an image sensor such as a charge coupled device (CCD) and a complementary metal oxide semiconductor (CMOS) and generates a captured image. The sensor 907 may include a group of sensors such as a measurement sensor, a gyroscope sensor, a geomagnetic sensor, and an acceleration sensor. The microphone 908 converts the sound input to the smartphone 900 into an audio signal. The input device 909 includes, for example, a touch sensor, a keypad, a keyboard, a button, or a switch configured to detect a touch on the screen of the display device 910, and receives an operation or information input from the user. The display device 910 includes a screen such as a liquid crystal display (LCD) and an organic light emitting diode (OLED) display and displays an output image of the smartphone 900. The speaker 911 converts the audio signal output from the smartphone 900 into sound.

[0327] The wireless communication interface 912 supports any cellular communication scheme (such as LTE and LTE-Advanced) and performs wireless communications. The wireless communication interface 912 may typically include, for example, a BB processor 913 and an RF circuit 914. The BB processor 913 may perform, for example, encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and may also perform various types of signal processing for wireless communications. Meanwhile, the RF circuit 914 may include, for example, mixers, filters, and amplifiers, and transmit and receive wireless signals via an antenna 916. Note that while the figure shows a scenario where one RF link is connected to one antenna, this is merely illustrative, and also encompasses scenarios where one RF link is connected to multiple antennas via multiple phase shifters. The wireless communication interface 912 may be a chip module on which the BB processor 913 and RF circuit 914 are integrated. As shown in FIG23 , the wireless communication interface 912 may include multiple BB processors 913 and multiple RF circuits 914. While FIG23 illustrates an example in which the wireless communication interface 912 includes multiple BB processors 913 and multiple RF circuits 914, the wireless communication interface 912 may also include a single BB processor 913 or a single RF circuit 914.

[0328] In addition, in addition to the cellular communication scheme, the wireless communication interface 912 can support other types of wireless communication schemes, such as a short-range wireless communication scheme, a near-field communication scheme, and a wireless local area network (LAN) scheme. In this case, the wireless communication interface 912 may include a BB processor 913 and an RF circuit 914 for each wireless communication scheme.

[0329] Each of the antenna switches 915 switches a connection destination of the antenna 916 between a plurality of circuits (eg, circuits for different wireless communication schemes) included in the wireless communication interface 912 .

[0330] Each of the antennas 916 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for transmitting and receiving wireless signals via the wireless communication interface 912. As shown in FIG23, the smartphone 900 may include multiple antennas 916. Although FIG23 shows an example in which the smartphone 900 includes multiple antennas 916, the smartphone 900 may also include a single antenna 916.

[0331] In addition, the smartphone 900 may include an antenna 916 for each wireless communication scheme. In this case, the antenna switch 915 may be omitted from the configuration of the smartphone 900.

[0332] The bus 917 connects the processor 901, the memory 902, the storage device 903, the external connection interface 904, the camera 906, the sensor 907, the microphone 908, the input device 909, the display device 910, the speaker 911, the wireless communication interface 912, and the auxiliary controller 919. The battery 918 supplies power to the various blocks of the smartphone 900 shown in FIG. 23 via feeders, which are partially shown as dashed lines in the figure. The auxiliary controller 919 operates the minimum necessary functions of the smartphone 900, for example, in sleep mode.

[0333] In the smartphone 900 shown in FIG23 , when the electronic device 1900 is implemented as a smartphone serving as a user device, for example, the transceiver of the electronic device 1900 may be implemented by the wireless communication interface 912. At least a portion of the functionality may also be implemented by the processor 901 or the auxiliary controller 919. For example, the processor 901 or the auxiliary controller 919 may improve beam prediction performance by executing the functionality of the aforementioned units in the electronic device 1900.

[0334] (Second application example)

[0335] 24 is a block diagram showing an example of a schematic configuration of a car navigation device 920 to which the technology of the present disclosure can be applied. The car navigation device 920 includes a processor 921, a memory 922, a global positioning system (GPS) module 924, a sensor 925, a data interface 926, a content player 927, a storage medium interface 928, an input device 929, a display device 930, a speaker 931, a wireless communication interface 933, one or more antenna switches 936, one or more antennas 937, and a battery 938.

[0336] The processor 921 may be, for example, a CPU or an SoC, and controls a navigation function and other functions of the car navigation apparatus 920. The memory 922 includes a RAM and a ROM, and stores data and programs executed by the processor 921.

[0337] The GPS module 924 measures the position (such as latitude, longitude, and altitude) of the car navigation device 920 using GPS signals received from GPS satellites. The sensor 925 may include a group of sensors such as a gyroscope sensor, a geomagnetic sensor, and an air pressure sensor. The data interface 926 is connected to, for example, the in-vehicle network 941 via an unillustrated terminal and acquires data generated by the vehicle (such as vehicle speed data).

[0338] The content player 927 reproduces content stored in a storage medium (such as a CD or DVD) inserted into the storage medium interface 928. The input device 929 includes, for example, a touch sensor, button, or switch configured to detect a touch on the screen of the display device 930, and receives an operation or information input from the user. The display device 930 includes a screen such as an LCD or OLED display and displays an image of a navigation function or reproduced content. The speaker 931 outputs the sound of the navigation function or the reproduced content.

[0339] The wireless communication interface 933 supports any cellular communication scheme (such as LTE and LTE-Advanced) and performs wireless communication. The wireless communication interface 933 may generally include, for example, a BB processor 934 and an RF circuit 935. The BB processor 934 may perform, for example, encoding / decoding, modulation / demodulation, and multiplexing / demultiplexing, and perform various types of signal processing for wireless communication. Meanwhile, the RF circuit 935 may include, for example, a mixer, a filter, and an amplifier, and transmit and receive wireless signals via an antenna 937. The wireless communication interface 933 may also be a chip module on which the BB processor 934 and the RF circuit 935 are integrated. As shown in Figure 24, the wireless communication interface 933 may include multiple BB processors 934 and multiple RF circuits 935. Although Figure 24 shows an example in which the wireless communication interface 933 includes multiple BB processors 934 and multiple RF circuits 935, the wireless communication interface 933 may also include a single BB processor 934 or a single RF circuit 935.

[0340] In addition, in addition to the cellular communication scheme, the wireless communication interface 933 can support other types of wireless communication schemes, such as a short-range wireless communication scheme, a near field communication scheme, and a wireless LAN scheme. In this case, for each wireless communication scheme, the wireless communication interface 933 can include a BB processor 934 and an RF circuit 935.

[0341] Each of the antenna switches 936 switches a connection destination of the antenna 937 between a plurality of circuits included in the wireless communication interface 933 , such as circuits for different wireless communication schemes.

[0342] Each of the antennas 937 includes a single or multiple antenna elements (such as multiple antenna elements included in a MIMO antenna) and is used for transmitting and receiving wireless signals via the wireless communication interface 933. As shown in FIG24, the car navigation device 920 may include multiple antennas 937. Although FIG24 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.

[0343] Furthermore, the car navigation device 920 may include an antenna 937 for each wireless communication scheme. In this case, the antenna switch 936 may be omitted from the configuration of the car navigation device 920.

[0344] The battery 938 supplies power to the respective blocks of the car navigation apparatus 920 shown in Fig. 24 via a feeder line, which is partially shown as a dotted line in the figure. The battery 938 accumulates the power supplied from the vehicle.

[0345] In the car navigation device 920 shown in FIG24 , when the electronic device 1900 is implemented as a car navigation device serving as a user device, for example, the transceiver of the electronic device 1900 may be implemented by the wireless communication interface 933. At least a portion of the functionality may also be implemented by the processor 921. For example, the processor 921 may improve beam prediction performance by executing the functions of the units in the electronic device 1900 described above.

[0346] The technology of the present disclosure can also be implemented as an in-vehicle system (or vehicle) 940 including a car navigation device 920, an in-vehicle network 941, and one or more blocks of a vehicle module 942. The vehicle module 942 generates vehicle data (such as vehicle speed, engine speed, and fault information) and outputs the generated data to the in-vehicle network 941.

[0347] The basic principles of the present invention are described above in conjunction with specific embodiments. However, it should be pointed out that those skilled in the art will understand that all or any steps or components of the methods and devices of the present invention can be implemented in any computing device (including a processor, storage medium, etc.) or a network of computing devices in the form of hardware, firmware, software, or a combination thereof. This can be achieved by those skilled in the art using their basic circuit design knowledge or basic programming skills after reading the description of the present invention.

[0348] Furthermore, the present invention also provides a program product storing machine-readable instruction codes. When the instruction codes are read and executed by a machine, the method according to the embodiment of the present invention can be executed.

[0349] Accordingly, the storage medium for carrying the program product storing the machine-readable instruction code is also included in the disclosure of the present invention. The storage medium includes but is not limited to a floppy disk, an optical disk, a magneto-optical disk, a memory card, a memory stick, and the like.

[0350] When the present invention is implemented through software or firmware, the programs constituting the software are installed from a storage medium or a network to a computer with a dedicated hardware structure (such as the general-purpose computer 2600 shown in Figure 25). When various programs are installed on the computer, it can perform various functions, etc.

[0351] 25 , a central processing unit (CPU) 2601 executes various processes according to a program stored in a read-only memory (ROM) 2602 or a program loaded from a storage section 2608 to a random access memory (RAM) 2603. In the RAM 2603, data required when the CPU 2601 executes various processes, etc., is also stored as needed. The CPU 2601, the ROM 2602, and the RAM 2603 are connected to each other via a bus 2604. An input / output interface 2605 is also connected to the bus 2604.

[0352] The following components are connected to the input / output interface 2605: an input section 2606 (including a keyboard, a mouse, etc.), an output section 2607 (including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and speakers, etc.), a storage section 2608 (including a hard disk, etc.), and a communication section 2609 (including a network interface card such as a LAN card, a modem, etc.). The communication section 2609 performs communication processing via a network such as the Internet. A drive 2610 may also be connected to the input / output interface 2605 as needed. A removable medium 2611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is installed in the drive 2610 as needed, so that a computer program read therefrom is installed in the storage section 2608 as needed.

[0353] In the case where the above-described series of processing is realized by software, a program constituting the software is installed from a network such as the Internet or a storage medium such as the removable medium 2611 .

[0354] It should be understood by those skilled in the art that such storage media is not limited to the removable medium 2611 shown in FIG. 25 , which stores the program and is distributed separately from the device to provide the program to the user. Examples of the removable medium 2611 include magnetic disks (including floppy disks (registered trademark)), optical disks (including compact disk read-only memories (CD-ROMs) and digital versatile disks (DVDs)), magneto-optical disks (including minidiscs (MDs) (registered trademark)), and semiconductor memories. Alternatively, the storage medium may be a ROM 2602, a hard disk included in the storage portion 2608, or the like, in which the program is stored and distributed to the user together with the device containing them.

[0355] It should also be noted that in the apparatus, method, and system of the present invention, each component or step can be decomposed and / or recombined. Such decomposition and / or recombination should be considered equivalent solutions of the present invention. Furthermore, the steps of performing the above series of processes can naturally be performed in chronological order according to the order described, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0356] Finally, it should be noted that the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. Furthermore, in the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0357] Although the embodiments of the present invention have been described in detail above with reference to the accompanying drawings, it should be understood that the embodiments described above are merely illustrative of the present invention and are not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations may be made to the embodiments described above without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention is limited solely by the appended claims and their equivalents.

[0358] The present technology can also be implemented as follows.

[0359] Solution 1. An electronic device for wireless communication, comprising:

[0360] The processing circuit is configured to:

[0361] dynamically or semi-statically determining a category of input information of a prediction model for a user equipment within a service range of the electronic device, wherein the prediction model is used to perform beam prediction on a beam to be used by the electronic device to communicate with the user equipment, and

[0362] Beam prediction configuration information including the determined category of the input information is provided to the user equipment, so that the user equipment collects the input information according to the beam prediction configuration information.

[0363] Solution 2. The electronic device according to Solution 1, wherein:

[0364] The processing circuit is configured to determine the category of the input information according to the user capability of the user equipment and / or the channel environment in which the user equipment is located, and

[0365] The processing circuit is configured to receive user capabilities reported by the user equipment through radio resource control (RRC) signaling.

[0366] Solution 3. An electronic device according to Solution 2, wherein the user capability information included in the RRC signaling includes user assistance sub-information, and the parameters of the user assistance sub-information include a category of auxiliary information used to reflect the user capability.

[0367] Solution 4. An electronic device according to Solution 3, wherein the category of the auxiliary information includes motion feature information of the user device, location information of the user device, sensor information of the user device, communication capability of the user device, whether the user device supports the use of the prediction model, and at least one of information about the beam.

[0368] Solution 5. An electronic device according to Solution 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.

[0369] Solution 6. The electronic device according to any one of Solutions 3 to 5, wherein the parameters of the user assistance sub-information further include a transmission period and / or a number of transmission bits of the assistance information.

[0370] Solution 7. The electronic device according to Solution 6, wherein the transmission period of the auxiliary information is determined by the measurement period of the user equipment and / or the validity period of the auxiliary information.

[0371] Solution 8. The electronic device according to Solution 6 or 7, wherein the number of transmission bits of the auxiliary information is determined by the measurement accuracy of the user equipment and / or the storage capacity of the user equipment.

[0372] Solution 9. The electronic device according to any one of Solution 2 to Solution 8, wherein:

[0373] The processing circuit is configured to determine the channel environment based on channel measurement information, and

[0374] The channel measurement information includes one or more of the following: the strength, phase and power of the sounding reference signal SRS received through the channel between the electronic device and the user equipment, the strength, phase and power of the channel state information reference signal CSI-RS, and channel information of other frequency bands except the frequency band where the channel is located.

[0375] Solution 10. The electronic device according to any one of solutions 1 to 9, wherein:

[0376] The category of the input information includes at least one of channel measurement information, motion characteristic information of the user equipment, location information of the user equipment, sensing information of the user equipment, communication capability of the user equipment, information about beams, perception information of the electronic device, and communication perception integrated information.

[0377] The sensing information of the user equipment is information acquired based on the software and / or hardware of the user equipment, and includes motion-related parameters of the user equipment.

[0378] Solution 11. An electronic device according to Solution 10, wherein the communication perception integration information includes one or more of the following: radar target detection information, and the strength, phase and power of the radar beam reference signal.

[0379] Option 12. An electronic device according to any one of Options 2 to 11, wherein the processing circuit is configured to determine a number of categories of input information of the prediction model for a user device whose user capabilities do not meet a predetermined user capability condition, which is less than the number of categories of input information of the prediction model determined for a user device whose user capabilities meet the predetermined user capability condition.

[0380] Option 13. An electronic device according to Option 12, wherein the processing circuit is configured to determine the number of categories of input information of the prediction model for a user device whose movement speed is less than a predetermined speed threshold, which is less than the number of categories of input information of the prediction model determined for a user device whose movement speed is greater than or equal to the predetermined speed threshold.

[0381] Scheme 14. An electronic device according to any one of Schemes 2 to 13, wherein the processing circuit is configured to determine a number of categories of input information of the prediction model for a user device in which the channel environment satisfies a predetermined environmental condition, which is less than the number of categories of input information of the prediction model determined for a user device in which the channel environment does not satisfy the predetermined environmental condition.

[0382] Scheme 15. An electronic device according to any one of Schemes 1 to 14, wherein the processing circuit is configured to modify the beam prediction configuration information corresponding to the user equipment in an event-triggered manner, and provide the modified beam prediction configuration information to the user equipment.

[0383] Solution 16. The electronic device according to Solution 15, wherein the event includes detecting that a change in the channel environment in which the user equipment is located satisfies a predetermined environment change condition.

[0384] Solution 17. An electronic device according to any one of Solutions 1 to 16, wherein the processing circuit is configured to provide the beam prediction configuration information to the user equipment through radio resource control RRC signaling or downlink control information DCI.

[0385] Solution 18. The electronic device according to any one of Solutions 1 to 17, wherein the same prediction model is used to perform the beam prediction for different determined categories of input information.

[0386] Solution 19. The electronic device according to any one of Solutions 1 to 18, wherein:

[0387] The beam prediction configuration information further includes resource configuration information corresponding to the determined category of the input information, wherein the resource configuration information includes a transmission period and / or a number of transmission bits of the determined category of the input information, and

[0388] The processing circuit is configured to perform the beam prediction using the prediction model based on input information reported by the user equipment according to the resource configuration information.

[0389] Solution 20. An electronic device for wireless communication, comprising:

[0390] The processing circuit is configured to:

[0391] receiving beam prediction configuration information from a network-side device providing a service for the electronic device, wherein the beam prediction configuration information includes a category of input information of a prediction model dynamically or semi-statically determined by the network-side device for the electronic device, and the prediction model is used to perform beam prediction on a beam to be used by the electronic device for communicating with the network-side device; and

[0392] The input information is collected according to the beam prediction configuration information.

[0393] Solution 21. The electronic device according to Solution 20, wherein:

[0394] The category of the input information is determined based on the user capability of the electronic device and / or the channel environment in which the electronic device is located, and

[0395] The processing circuit is configured to report user capabilities to the network side device through radio resource control RRC signaling.

[0396] Solution 22. An electronic device according to Solution 21, wherein the user capability information included in the RRC signaling includes user assistance sub-information, and the parameters of the user assistance sub-information include a category of auxiliary information used to reflect the user capability.

[0397] Option 23. An electronic device according to Option 22, wherein the category of the auxiliary information includes motion feature information of the electronic device, location information of the electronic device, sensor information of the electronic device, communication capability of the electronic device, whether the electronic device supports the use of the prediction model, and at least one of information about the beam.

[0398] Solution 24. An electronic device according to Solution 23, wherein the sensing information of the electronic device is information obtained based on the software and / or hardware of the electronic device, which includes motion-related parameters of the electronic device.

[0399] Solution 25. The electronic device according to any one of Solutions 22 to 24, wherein the parameters of the user assistance sub-information further include a transmission period and / or a number of transmission bits of the assistance information.

[0400] Solution 26. The electronic device according to Solution 25, wherein a transmission period of the auxiliary information is determined by a measurement period of the electronic device and / or a valid duration of the auxiliary information.

[0401] Solution 27. The electronic device according to Solution 25 or 26, wherein the number of transmission bits of the auxiliary information is determined by the measurement accuracy of the electronic device and / or the storage capacity of the electronic device.

[0402] Solution 28. The electronic device according to any one of Solution 21 to Solution 27, wherein:

[0403] The channel environment is obtained based on channel measurement information, and

[0404] The channel measurement information includes one or more of the following: the strength, phase and power of the sounding reference signal SRS received through the channel between the electronic device and the network side device, the strength, phase and power of the channel state information reference signal CSI-RS, and channel information of other frequency bands except the frequency band where the channel is located.

[0405] Solution 29. The electronic device according to any one of Solution 20 to Solution 28, wherein:

[0406] The category of the input information includes at least one of channel measurement information, motion characteristic information of the electronic device, location information of the electronic device, sensor information of the electronic device, perception information of the network side device, and communication perception integrated information.

[0407] 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.

[0408] Option 30. An electronic device according to Option 29, wherein the communication perception integration information includes one or more of the following: radar target detection information, and the strength, phase and power of the radar beam reference signal.

[0409] Scheme 31. An electronic device according to any one of Schemes 20 to 30, wherein the number of categories of input information of 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 categories of input information of the prediction model determined for an electronic device whose user capabilities meet the predetermined user capability conditions.

[0410] Option 32. An electronic device according to Option 31, wherein the number of categories of input information of the prediction model determined for an electronic device whose movement speed is less than a predetermined speed threshold is less than the number of categories of input information of the prediction model determined for an electronic device whose movement speed is greater than or equal to the predetermined speed threshold.

[0411] Scheme 33. An electronic device according to any one of Schemes 21 to 32, wherein the number of categories of input information of the prediction model determined for the electronic device whose channel environment satisfies predetermined environmental conditions is less than the number of categories of input information of the prediction model determined for the electronic device whose channel environment does not satisfy the predetermined environmental conditions.

[0412] Solution 34. An electronic device according to any one of Solutions 20 to 33, wherein the processing circuit is configured to receive beam prediction configuration information corresponding to the electronic device that is modified in an event-triggered manner.

[0413] Solution 35. The electronic device according to Solution 34, wherein the event includes detecting that a change in a channel environment in which the electronic device is located satisfies a predetermined environment change condition.

[0414] Solution 36. An electronic device according to any one of Solutions 20 to 35, wherein the processing circuit is configured to receive the beam prediction configuration information through radio resource control RRC signaling or downlink control information DCI.

[0415] Solution 37. An electronic device according to any one of Solutions 20 to 36, wherein the same prediction model is used to perform the beam prediction for different determined categories of input information.

[0416] Solution 38. The electronic device according to any one of Solutions 20 to 37, wherein:

[0417] The beam prediction configuration information further includes resource configuration information corresponding to the category of input information determined by the network side device, wherein the resource configuration information includes a transmission period and / or a number of transmission bits of the determined category of input information, and

[0418] The processing circuit is configured to report the input information based on the resource configuration information, so that the network-side device can perform the beam prediction using the prediction model.

[0419] Solution 39. A method for wireless communication, comprising:

[0420] Dynamically or semi-statically determining a category of input information of a prediction model for a user equipment within a service range of an electronic device, wherein the prediction model is used to perform beam prediction on a beam to be used by the electronic device to communicate with the user equipment, and

[0421] Beam prediction configuration information including the determined category of the input information is provided to the user equipment, so that the user equipment collects the input information according to the beam prediction configuration information.

[0422] Solution 40. A method for wireless communication, comprising:

[0423] receiving beam prediction configuration information from a network-side device providing a service for an electronic device, wherein the beam prediction configuration information includes a category of input information of a prediction model dynamically or semi-statically determined by the network-side device for the electronic device, and the prediction model is used to perform beam prediction on a beam to be used by the electronic device for communicating with the network-side device; and

[0424] The input information is collected according to the beam prediction configuration information.

[0425] Solution 41. A computer-readable storage medium having computer-executable instructions stored thereon, which, when the computer-executable instructions are executed, performs the method for wireless communication according to Solution 39 or 40.

Claims

1. An electronic device for wireless communication, comprising: a processing circuit configured to: dynamically or semi-statically determine a category of input information for a prediction model for a user equipment within the service range of the electronic device, wherein the prediction model is used for beam prediction of a beam to be used for communication between the electronic device and the user equipment, and provide the user equipment with beam prediction configuration information including the determined category of the input information for the user equipment to collect the input information according to the beam prediction configuration information.

2. The electronic device according to claim 1, wherein the processing circuit is configured to determine the category of the input information according to the user capabilities of the user equipment and / or the channel environment where the user equipment is located, and the processing circuit is configured to receive the user capabilities reported by the user equipment through radio 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 parameters of the user auxiliary sub-information include categories of auxiliary information for reflecting the user capabilities.

4. The electronic device according to claim 3, wherein, The categories of the auxiliary information include at least one of motion characteristic information of the user equipment, location information of the user equipment, sensing information of the user equipment, communication capabilities of the user equipment, whether the user equipment supports using the prediction model, and information about beams.

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

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

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

9. The electronic device according to any one of claims 2 to 8, wherein the processing circuit is 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 a sounding reference signal (SRS) received through a channel between the electronic device and the user equipment, the intensity, phase, and power of a channel state information reference signal (CSI-RS), and channel information of other frequency bands except the frequency band where the channel is located.

10. The electronic device according to any one of claims 1 to 9, wherein the category of the input information includes at least one of channel measurement information, motion characteristic information of the user equipment, location information of the user equipment, sensing information of the user equipment, communication capabilities of the user equipment, information about beams, sensing information of the electronic device, and communication sensing integration information, wherein the sensing information of the user equipment is information obtained based on the software and / or hardware of the user equipment, and includes motion-related parameters of the user equipment.

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

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

13. The electronic device according to claim 12, wherein, The processing circuit is configured such that the number of categories of input information of the prediction model determined for a user equipment whose moving speed is less than a predetermined speed threshold is less than the number of categories of input information of the prediction model determined for a user equipment whose moving speed is greater than or equal to 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 of the prediction model determined for a user equipment whose channel environment meets a predetermined environment condition is less than the number of categories of input information of the prediction model determined for a user equipment whose channel environment does not meet the predetermined environment condition.

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

16. The electronic device according to claim 15, wherein, The event includes detecting that a change in the channel environment where the user equipment is located meets a predetermined environment change condition.

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 equipment via radio resource control (RRC) signaling or downlink control information (DCI).

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

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

20. An electronic device for wireless communication, comprising: A processing circuit configured to: Receive beam prediction configuration information from a network-side device serving the electronic device, where the beam prediction configuration information includes the categories of input information of a prediction model dynamically or semi-statically determined by the network-side device for the electronic device, and the prediction model is used to perform beam prediction on the beam to be used for communication between the electronic device and the network-side device, and Collect the input information according to the beam prediction configuration information.

21. The electronic device according to claim 20, wherein, The categories of input information are determined based on the user capabilities of the electronic device and / or the channel environment where the electronic device is located, and The processing circuit is configured to report the user capabilities to the network-side device via radio resource control (RRC) signaling.

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

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

24. The electronic device according to claim 23, wherein, The sensing information of the electronic device is the information obtained based on the software and / or hardware of the electronic device, and it includes the 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 auxiliary sub-information further include the transmission period and / or the number of transmission bits of the auxiliary 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 duration of the auxiliary information.

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

28. The electronic device according to any one of claims 21 to 27, wherein, the channel environment is obtained based on the 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 and the network-side device, the intensity, phase, and power of the channel state information reference signal CSI-RS, and the channel information of other frequency bands except the frequency band where the channel is located.

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

30. The electronic device according to claim 29, wherein, The communication sensing integrated information includes one or more of the following: radar target detection information, and 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 categories of the input information of the prediction model determined for an electronic device whose user capability does not meet the predetermined user capability condition is less than the number of categories of the input information of the prediction model determined for an electronic device whose user capability meets the predetermined user capability condition.

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

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

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 is 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 where the electronic device is located satisfies a predetermined environment change condition.

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 through radio resource control (RRC) signaling or downlink control information (DCI).

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

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

39. A method for wireless communication, comprising: dynamically or semi-statically determining, for a user equipment within the service range of an electronic device, a category of input information of a prediction model, where the prediction model is used to perform beam prediction on a beam to be used for communication between the electronic device and the user equipment, and providing the user equipment with beam prediction configuration information including the determined category of input information for the user equipment to collect the input information according to the beam prediction configuration information.

40. A method for wireless communication, comprising: receiving, from a network-side device serving an electronic device, beam prediction configuration information, where the beam prediction configuration information includes a category of input information of a prediction model dynamically or semi-statically determined by the network-side device for the electronic device, and the prediction model is used to perform beam prediction on a beam to be used for communication between the electronic device and the network-side device, and collecting the input information according to the beam prediction configuration information.

41. A computer-readable storage medium, on which computer-executable instructions are stored, and when the computer-executable instructions are executed, the method for wireless communication according to claim 39 or 40 is performed.

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