CN117674915A - Determination method, apparatus and readable storage medium - Google Patents
Determination method, apparatus and readable storage medium Download PDFInfo
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Abstract
The application discloses a determining method, a device and a readable storage medium, which belong to the field of communication, and the determining method of the embodiment of the application comprises the following steps: the method comprises the steps that first equipment obtains first beam information, and the first equipment determines beam information of a target beam according to the first beam information; the first device determines expected information according to beam information of a target beam; the desired information is used to influence the output information of the artificial intelligence AI model; wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination.
Description
Technical Field
The application belongs to the technical field of communication, and particularly relates to a determining method, device and readable storage medium.
Background
At present, the network side device can predict a wave beam through an artificial intelligence (Artificial Intelligence, AI) model, increase expected information on the input side of the AI model, and circulate and change the expected information through the AI model, so that the network side device can acquire output results of the AI model corresponding to a plurality of expected information obtained through multiple circulation, and then the network side device can determine an optimal output result of the AI model from the output results of the AI models.
However, if the desired information is added to the input side of the AI model, all the beam information may be exposed, and if the desired information is not added to the input side of the AI model, the generalization performance of the AI model may be affected, resulting in a decrease in the generalization performance of the AI model. Thus, there is a need for a method to ensure the generalization performance of AI models while minimizing the exposure of beam information.
Disclosure of Invention
The embodiment of the application provides a determination method, determination equipment and a readable storage medium, which ensure the generalization performance of an AI model.
In a first aspect, a determination method is provided, the method comprising: the method comprises the steps that first equipment obtains first beam information, and the first equipment determines beam information of a target beam according to the first beam information; the first device determines expected information according to beam information of a target beam; the desired information is used to influence the output information of the artificial intelligence AI model; wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination.
In a second aspect, there is provided a determining apparatus comprising: an acquisition module and a determination module; and the acquisition module is used for acquiring the first beam information. The determining module is used for determining the beam information of the target beam according to the first beam information acquired by the acquiring module and determining the expected information according to the beam information of the target beam; the desired information is used to influence the output information of the artificial intelligence AI model. Wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination.
In a third aspect, there is provided an apparatus comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, performs the steps of the method according to the first aspect.
In a fourth aspect, an apparatus is provided, including a processor and a communication interface, where the processor is configured to obtain first beam information, determine beam information of a target beam according to the first beam information, and determine desired information according to the beam information of the target beam; the desired information is used to influence the output information of the AI model; wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination.
In a fifth aspect, there is provided a readable storage medium having stored thereon a program or instructions which when executed by a processor realizes the steps of the method according to the first aspect.
In a sixth aspect, there is provided a chip comprising a processor and a communication interface coupled to the processor for running a program or instructions to implement the method of the first aspect.
In a seventh aspect, there is provided a computer program/program product stored in a storage medium, the computer program/program product being executed by at least one processor to carry out the steps of the method according to the first aspect.
In the embodiment of the application, a first device acquires first beam information, and the first device determines beam information of a target beam according to the first beam information; the first device determines expected information according to beam information of a target beam; the desired information is used to influence the output information of the artificial intelligence AI model; wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination. Since the first device may acquire the first beam information, and then determine the beam information of the target beam, that is, determine the transmission beam of the second device, or determine the reception beam of the second device, according to the first beam information, by a direct or indirect manner, and determine the desired information for influencing the output information of the artificial intelligence AI model according to the determined beam information of the target beam, the determination manner includes not only the direct manner but also the indirect manner, so that implicit interaction information may be enabled, and since the desired information is determined, the desired information may be used to influence the output information of the AI model, so that generalization performance of the AI model may be ensured; thus, the generalization performance of the AI model is ensured on the premise of reducing the exposure of the beam information as much as possible.
Drawings
Fig. 1 is a schematic architecture diagram of a communication system according to an embodiment of the present application;
fig. 2 is a schematic diagram of a composition of an AI neural network according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a neuron according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a feedback report structure according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a feedback report structure of a group-based beam report according to an embodiment of the present application;
FIG. 6 is a schematic diagram of beam prediction using AI method according to an embodiment of the disclosure;
FIG. 7 is a schematic diagram of enhancing beam prediction performance using an AI method provided by an embodiment of the application;
FIG. 8 is a schematic diagram of an embodiment of the present application for improving enhanced beam prediction performance using the AI method;
FIG. 9 is a flow chart of a determination method provided by an embodiment of the present application;
FIG. 10 is an interaction diagram of a determination method provided by an embodiment of the present application;
fig. 11 is a schematic structural view of a determining device according to an embodiment of the present application;
fig. 12 is a schematic hardware structure of a communication device according to an embodiment of the present application;
fig. 13 is a schematic hardware structure of a UE according to an embodiment of the present application;
Fig. 14 is a schematic hardware structure of a network side device according to an embodiment of the present application.
Detailed Description
Technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of the protection of the present application.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the terms "first" and "second" are generally intended to be used in a generic sense and not to limit the number of objects, for example, the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/" generally means a relationship in which the associated object is an "or" before and after.
It is noted that the techniques described in embodiments of the present application are not limited to long term evolution (Long Term Evolution, LTE)/LTE evolution (LTE-Advanced, LTE-a) systems, but may also be used in other wireless communication systems, such as code division multiple access (Code Division Multiple Access, CDMA), time division multiple access (Time Division Multiple Access, TDMA), frequency division multiple access (Frequency Division Multiple Access, FDMA), orthogonal frequency division multiple access (Orthogonal Frequency Division Multiple Access, OFDMA), single carrier frequency division multiple access (Single-carrier Frequency Division Multiple Access, SC-FDMA), and other systems. The terms "system" and "network" in embodiments of the present application are often used interchangeably, and the techniques described are applicable to bothThe above mentioned systems and radio technologies may also be used for other systems and radio technologies. The following description describes a New air interface (NR) system for purposes of example and uses NR terminology in much of the description that follows, but these techniques are also applicable to applications other than NR system applications, such as generation 6 (6) th Generation, 6G) communication system.
Fig. 1 shows a block diagram of a wireless communication system to which embodiments of the present application are applicable. The wireless communication system includes a terminal 11 and a network device 12. The terminal 11 may be a mobile phone, a tablet (Tablet Personal Computer), a Laptop (Laptop Computer) or a terminal-side Device called a notebook, a personal digital assistant (Personal Digital Assistant, PDA), a palm top, a netbook, an ultra-mobile personal Computer (ultra-mobile personal Computer, UMPC), a mobile internet appliance (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/Virtual Reality (VR) Device, a robot, a Wearable Device (weather Device), a vehicle-mounted Device (VUE), a pedestrian terminal (PUE), a smart home (home Device with a wireless communication function, such as a refrigerator, a television, a washing machine, or a furniture), a game machine, a personal Computer (personal Computer, PC), a teller machine, or a self-service machine, and the Wearable Device includes: intelligent wrist-watch, intelligent bracelet, intelligent earphone, intelligent glasses, intelligent ornament (intelligent bracelet, intelligent ring, intelligent necklace, intelligent anklet, intelligent foot chain etc.), intelligent wrist strap, intelligent clothing etc.. Note that, the specific type of the terminal 11 is not limited in the embodiment of the present application. The network-side device 12 may comprise an access network device or a core network device, wherein the access network device 12 may also be referred to as a radio access network device, a radio access network (Radio Access Network, RAN), a radio access network function or a radio access network element. Access network device 12 may include a base station, a WLAN access point, a WiFi node, or the like, which may be referred to as a node B, an evolved node B (eNB), an access point, a base transceiver station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service set (Basic Service Set, BSS), an extended service set (Extended Service Set, ESS), a home node B, a home evolved node B, a transmission and reception point (Transmitting Receiving Point, TRP), or some other suitable terminology in the art, and the base station is not limited to a particular technical vocabulary so long as the same technical effect is achieved, and it should be noted that in the embodiments of the present application, only a base station in an NR system is described as an example, and the specific type of the base station is not limited.
Currently, AI is widely used in a variety of fields. AI networks have a variety of implementations, such as: neural networks, decision trees, support vector machines, bayesian classifiers, etc.
Fig. 2 shows a schematic diagram of the composition of an AI neural network, which is composed of neurons as shown in fig. 2.
FIG. 3 shows a schematic diagram of a neuron, as shown in FIG. 3, a 1 ，a 2 ，…a K For input, w is a weight (multiplicative coefficient), b is a bias (additive coefficient), and σ () is an activation function. Common activation functions include Sigmoid, tanh, modified linear units (Rectified Linear Unit, reLU), etc.
The parameters of the neural network may be optimized by an optimization algorithm. An optimization algorithm is a class of algorithms that can assist a developer or user in minimizing or maximizing an objective function (also referred to as a loss function). Whereas the objective function is often a mathematical combination of model parameters and data. For example: given the data X and its corresponding label Y, a developer can construct a neural network model f (), from which the predicted output f (X) can be derived from the input X, and the gap (f (X) -Y), i.e. the loss function, between the predicted value and the true value can be calculated. The purpose of the developer is to find a suitable W, b, so that the value of the loss function can reach the minimum, and the smaller the loss value, the closer the model is to the real situation.
The optimization algorithm in embodiments of the present application may be an error back propagation (Error Back Propagation, BP) based algorithm. The basic idea of the BP algorithm is that the learning process consists of two processes, forward propagation of the signal and backward propagation of the error. In forward propagation, an input sample is transmitted from an input layer, is processed layer by each hidden layer, and is transmitted to an output layer. If the actual output of the output layer does not match the desired output, the back propagation phase of the error is shifted. The error back transmission is to make the output error pass through hidden layer to input layer in a certain form and to distribute the error to all units of each layer, so as to obtain the error signal of each layer unit, which is used as the basis for correcting the weight of each unit. The process of adjusting the weights of the layers of forward propagation and error back propagation of the signal is performed repeatedly. The constant weight adjustment process is the learning training process of the network. This process is continued until the error in the network output is reduced to an acceptable level or until a preset number of learnings is performed.
Common optimization algorithms are Gradient Descent (GD), random Gradient Descent (Stochastic Gradient Descent, SGD), small-batch Gradient Descent (Mini-Batch Gradient Descent), momentum method (Momentum), random Gradient Descent with Momentum (Nesterov), adaptive Gradient Descent (ADAptive GRADient Descent, adagard), adadelta, root mean square error Descent (Root Mean Square Prop, RMSprop), adaptive Momentum estimation (Adaptive Moment Estimation, adam), and the like.
When the errors are counter-propagated, the optimization algorithms are all errors/losses obtained according to a loss function, the derivative/partial derivative of the current neuron is obtained, influence of learning rate, previous gradient/derivative/partial derivative and the like is added, and therefore the gradient can be obtained and is transmitted to the upper layer.
Some concepts and/or terms related to the determination method provided in the embodiments of the present application are explained below.
Beam Indication (Beam Indication) mechanism
After beam measurement and beam report, the network side device can perform beam indication on the downlink and uplink channels or reference signals, and the beam indication is used for establishing a beam link between the network side device and the UE so as to realize the transmission of the channels or the reference signals.
For beam indication of the physical downlink control channel (Physical Downlink Control Channel, PDCCH), the network configures K transmission configuration indicators (Transmission Configuration Indication, TCI) states for each core set (CORESET) using radio resource control (Radio Resource Control, RRC) signaling; when K >1, 1 TCI state is indicated or activated by the medium access layer control unit (Media Access Control Layer, MAC CE), when k=1, no additional MAC CE commands are needed. When the UE listens to the PDCCH, the same Quasi co-location (QCL), i.e., the same TCI state, is used for all search spaces in the CORESET to listen to the PDCCH. The Reference Signal (Reference Signal) (e.g., periodic CSI-RS resource, semi-persistent CSI-RS resource, SS block, etc.) and UE-specific PDCCH demodulation Reference Signal (DemodulationReference Sgnal, DMRS) ports in this TCI state are spatial QCL. The UE can learn which reception beam to use to receive the PDCCH according to the TCI state.
For beam indication of PDSCH, the network side equipment configures M TCI states through RRC signaling, and then activates 2 by using MAC CE command N The TCI state is then notified by the N-bit TCI field of the DCI, the Reference Signal in this TCI state being QCL with the DMRS port of the PDSCH to be scheduled. The UE can know which reception beam to use to receive PDSCH according to the TCI state.
For beam indication of the CSI-RS, when the CSI-RS type is periodic CSI-RS, the network side device configures QCL information for CSI-RS resources (resource) through RRC signaling. When the CSI-RS type is semi-persistent CSI-RS, the network side equipment activates one CSI-RS resource from the CSI-RS resource set configured by RRC through a MAC CE command, and indicates QCL information of the CSI-RS resource. When the CSI-RS type is aperiodic CSI-RS, the network side equipment configures QCL for the CSI-RS resource through RRC signaling, and uses DCI to trigger the CSI-RS.
For beam indication of physical uplink control channel (Physical Uplink Control Channel, PUCCH), the network side device configures spatial relationship information (Spatial Relation Information) for each PUCCH resource through PUCCH-spatial correlation info parameter using RRC signaling, and when Spatial Relation Information configured for PUCCH resource contains a plurality, one spatial relation information is indicated or activated using MAC-CE. When spatial relation information configured for PUCCH resource contains only 1, no additional MAC CE command is required.
For beam indication of PUSCH, the spatial correlation information of PUSCH is that when downlink control channel information (Downlink Control Information, DCI) carried by PDCCH schedules a physical uplink shared channel (Physical Uplink Shared Channel, PUSCH), each SRI code point (code point) of an uplink scheduling request indication information field (Schduling Request Indication field, SRI field) in DCI indicates one SRI, which is used to indicate Spatial Relation Information of PUSCH.
For beam indication of SRS, when the SRS type is periodic SRS, the network configures Spatial Relation Information for SRS resource through RRC signaling. When the SRS type is semi-persistent SRS, the network activates one from the set Spatial Relation Information of RRC configurations through a MAC CE command. When the SRS type is an aperiodic SRS, the network configures Spatial Relation Information for SRS resource through RRC signaling.
The beam indication may be achieved by a unified transmission configuration indication state (unified TCI indication), i.e., indicating subsequent reference signals and beam information of a plurality of channels through a TCI field in one DCI.
The beam information, spatial Relation information, spatial Filter Spatial Domain Transmission Filter information, spatial Filter information, TCI State information, QCL parameters, spatial Relation information, beam association Relation, and the like are expressed in the same or similar meaning. The downlink beam information may be generally represented by TCI state information and QCL information. Upstream beam information may generally be represented using Spatial relationship information.
Demodulation sensitivity calculation method
The reception sensitivity can be achieved by a demodulation formula, wherein the demodulation formula is: s (dBm) =thermal noise (dBm) +10log (BW) +nf (dB) +demodulation threshold, thermal noise-174 dBm/Hz.
Ignoring the demodulation threshold, taking 30ghz,120 khscs as an example,
base noise = -174+10 log10 (120 x 10 x 3) +10 = -174+50.8+10 = -113.2dBm on one subcarrier.
The energy of the background noise is relatively large for high frequency large subcarrier spacing.
Beam measurement and reporting (Beam Measurement And Beam Reporting)
Because the analog beam forming is full-bandwidth transmitting, and each polarization direction array element on the panel of each high-frequency antenna array can only transmit the analog beam in a time division multiplexing mode, the forming weight of the analog beam is realized by adjusting parameters of devices such as a radio frequency front-end phase shifter and the like.
In the related art, the training of the analog beamforming vector can be performed by using a polling mode, that is, the array elements of each polarization direction of each antenna panel sequentially transmit training signals (i.e., candidate beamforming vectors) in a time division multiplexing mode at a preset time, and the terminal feeds back a beam report after measurement, so that the network side can adopt the training signals to realize analog beam transmission when transmitting the service next time. The content of the beam report typically includes an optimal number of transmit beam identities and measured received power for each transmit beam.
In making beam measurements, the network side device configures a reference signal resource set (RS resource set) comprising at least one reference signal resource, such as SSB resource or CSI-RS resource. The UE measures the L1-RSRP/L1-SINR of each RS resource, and reports at least one optimal measurement result to the network side equipment, wherein the report content comprises SSBRI or CRI and L1-RSRP/L1-SINR. The report content reflects at least one optimal beam and its quality for the network side device to determine the beam to use to transmit a channel or signal to the UE.
When the UE feedback report contains only one L1-RSRP, the quantization step is 1dB and the quantization range is-140 dBm to-44 dBm by using a 7bit quantization method. When the UE is instructed to include multiple L1-RSRP in the feedback report or group-based beam report Group Based Beam Report is enabled, the strongest RSRP quantization uses 7bit quantization, the rest of the RSRP quantization uses a 4bit differential quantization method, the quantization step is 2dB.
Fig. 4 shows a schematic diagram of a feedback report structure.
Fig. 5 shows a schematic diagram of a feedback report structure for group-based beam reporting.
The number of feedback reports is determined by parameters configured to the UE by the network side device, and by the RRC configuration parameters, and by configuring the number of RSs and RSRPs that should be included in the feedback report of the UE, the number configuration takes values of 1,2,3,4, and a default value of 1, and in addition, the number limitation is based on the UE capability, the UE will report the maximum number that can be supported first.
Beam prediction using AI method:
fig. 6 shows a schematic diagram of beam prediction using AI method. As shown in fig. 6, the RSRP of a partial beam pair may be used as input, and the output of the AI model is the RSRP result of all beam pairs. Wherein the beam pairs are composed of a transmit beam and a receive beam, and the input number of the AI model is the number of selected partial beam pairs, and the output number is the number of all beam pairs.
Fig. 7 shows a schematic diagram of enhancing beam prediction performance using AI method. As shown in fig. 7, association information, which is generally angle-related information, beam ID information, etc. corresponding to a beam pair selected for input, may be added to the input side. The number of inputs to such a model is therefore also related to the number of partial beam pairs that are picked up, and the number of outputs is also equal to the number of all beam pairs.
Fig. 8 shows a schematic diagram of an improvement in enhanced beam prediction performance using AI methods. As shown in fig. 8, this method mainly affects the output of the AI model by changing desired information by the AI model.
Wherein the input type of the AI model includes at least one of:
beam quality related information;
Association information about the beam;
the A end sends the association information related to the wave beam;
the B-side receives the association information related to the wave beam;
the association information of the beam expected by the B end;
the expected B-end receives the association information related to the wave beam;
the A terminal expected by the B terminal sends the association information related to the wave beam;
time-related information related to beam quality-related information;
expected predicted time related information.
The beam-related association information refers to beam information corresponding to the beam, and the beam information includes, but is not limited to, at least one of the following:
beam ID information;
beam angle information;
beam gain information;
beam width information, etc.
Wherein the beam ID information is information for characterizing the identity of the beam, including but not limited to at least one of:
transmitting a beam ID;
receiving a beam ID;
a beam ID;
a reference signal set ID corresponding to the beam;
a reference signal resource ID corresponding to the wave beam;
a uniquely identified random ID;
the encoded value after the additional AI network processing;
beam angle information, etc.
The beam angle information is used for representing angle information corresponding to the beam, and includes but is not limited to at least one of the following:
angle information;
Transmitting angle information;
angle information is received.
Wherein the angle information is information for characterizing the angle, such as angle, radian, index code value, code value processed by additional AI network, etc
However, since the training position, the inference position, of the AI model may be uncertain, the training position and the inference position may both be in one location, e.g., both in the UE, the base station, or the central node, etc., or the model training position and the inference position are in two locations, e.g., the training position is in the base station, the inference position is in the UE.
Therefore, the performance of the AI model may be degraded due to uncertainty in the training position and the reasoning position of the AI model; in addition, because the feasible schemes of the implementation of the AI model are more, the situation that the AI model is not matched possibly occurs, and in addition, the implementation of the AI model scheme needs some auxiliary information interaction, so that the normal use of the AI model can be ensured, therefore, a method for ensuring the normal use of the AI model is needed, and the performance of the AI model is improved.
The determining method provided by the embodiment of the application is described in detail below by some embodiments and application scenes thereof with reference to the accompanying drawings.
Example 1
The embodiment of the application provides a determining method, and fig. 9 shows a flowchart of the determining method provided by the embodiment of the application. As shown in fig. 9, the determining method provided in the embodiment of the present application may include the following steps 201 to 203.
Step 201, a first device acquires first beam information.
Step 202, the first device determines beam information of the target beam according to the first beam information.
Step 203, the first device determines the desired information according to the beam information of the target beam.
In the embodiment of the application, the expected information is used for influencing the output information of the artificial intelligence AI model.
Wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination.
The embodiment of the application provides a determining method, wherein first equipment acquires first beam information, and the first equipment determines the beam information of a target beam according to the first beam information; the first device determines expected information according to beam information of a target beam; the desired information is used to influence the output information of the artificial intelligence AI model; wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination. Since the first device may acquire the first beam information, then determine the beam information of the target beam, that is, determine the transmission beam of the second device, or determine the reception beam of the second device, according to the first beam information, by a direct or indirect manner, and determine the desired information for affecting the output information of the artificial intelligence AI model according to the determined beam information of the target beam, the determination manner includes not only the direct manner but also the indirect manner, so that implicit interaction information may be enabled, and since the desired information is determined, the desired information may be used to affect the output information of the AI model, and the performance of the AI model on beam prediction may be increased; therefore, the generalization performance of the AI model can be ensured; thus, the generalization performance of the AI model is ensured on the premise of reducing the exposure of the beam information as much as possible.
Optionally, in an embodiment of the present application, the first device may be any one of the following:
network side equipment and UE.
Optionally, in an embodiment of the present application, the second device may be any one of the following:
network side equipment and UE.
Optionally, in an embodiment of the present application, the first beam information includes: first information and second information; the first information is the received beam information of the UE; the second information is the sending beam information of the network side equipment.
Optionally, in the embodiment of the present application, when the first device is a network side device and the second device is a UE, the network side device interacts the first information with the UE; and under the condition that the first equipment is the UE and the second equipment is the network side equipment, the UE interacts second information with the network side equipment.
Optionally, in the embodiment of the present application, the first device is a network side device, and the second device is a user equipment UE, and the step 201 may be implemented by the following steps 201a1 and 201a 2.
Step 201a1, the network device receives the request information sent by the UE.
Step 201a2, the network side device obtains the first beam information according to the request information.
Optionally, in the embodiment of the present application, the first device is a network side device, and the second device is a user equipment UE, and the step 201 may be implemented by the following steps 201b1 and 201b 2.
Step 201b1, the network side device receives the transmitted capability information of the UE.
Step 201b2, the network side device obtains the first beam information according to the capability information.
Optionally, in the embodiment of the present application, the first device is a UE, the second device is a network side device, and the step 201 may be implemented by the following steps 201c1 and 201c 2.
In step 201c1, the UE receives configuration information of a network side device.
Step 201c2, the UE acquires the first beam information according to the configuration information.
Optionally, in the embodiment of the present application, after the step 202c2, the determining method provided in the embodiment of the present application further includes the following steps 203 to 205.
Step 203, the UE receives a first resource sent by the network device.
In this embodiment of the present application, the first resource is a reference resource for beam training.
Step 204, the UE determines resource information included in the input of the AI model according to the first beam information and the first resource.
Step 205, the UE determines, according to the first target information, resource information included in the output of the AI model.
Alternatively, in the embodiment of the present application, the above step 201 may be implemented by the following step 201 d.
Step 201d, the first device obtains the first beam information according to the information agreed by the protocol.
Optionally, in an embodiment of the present application, the beam information of the target beam includes at least one of:
beam horizontal angle information of the target beam;
beam vertical angle information of the target beam;
beam identity ID information of the target beam;
beam level ID information of the target beam;
beam vertical ID information of the target beam;
beam index information of the target beam;
beam horizontal angle index information of the target beam;
beam vertical angle index information of the target beam;
reference signal resource ID information for target beam scanning;
reference signal resource index information of target beam scanning;
panel identity identification information of the target beam;
the transmission of the target beam receives the point identification information.
Optionally, in an embodiment of the present application, the determining manner of the beam information of the target beam includes indirect determination, and the first beam information includes at least one of the following:
beam angle start value information of the target beam;
beam radian start value information of the target beam;
angular interval information of the target beam;
radian interval information of the target beam;
angle interval number information of the target beam;
radian interval number information of the target beam;
Angle end value information of the target beam;
radian end value information of the target beam;
ID start value information of the target beam;
ID interval information of the target beam;
ID interval number information of the target beam;
ID end value information of the target beam;
index start value information of the target beam;
index interval information of the target beam;
index interval number information of the target beam;
index end value information of the target beam.
In the embodiment of the present application, the first device may determine the beam information of the target beam in an indirect manner, so as to reduce exposure of the beam information possibly caused by adding the desired information as much as possible, so that information interaction between the first device and the second device may be implicit, and thus the AI model may be used normally.
Optionally, in an embodiment of the present application, the determining manner of the beam information of the target beam includes direct determination, and the first beam information includes at least one of the following:
horizontal angle information of the target beam;
vertical angle information of target beam
Horizontal radian information of the target beam;
vertical radian information of the target beam;
beam ID information of the target beam;
Beam level ID information of the target beam;
beam vertical ID information of the target beam;
reference signal resource ID information for target beam scanning;
beam index information of the target beam;
beam level index information of the target beam;
beam vertical index information of the target beam;
reference signal resource index information for target beam scanning.
In the embodiment of the present application, when the first device determines the beam information of the target beam in a direct manner, the first device may determine the expected information, and since the expected information may be used to influence the output information of the AI model, the AI model may not only be used normally, but also may increase the performance of the AI model on beam prediction.
Optionally, in the embodiment of the present application, the beam angle information, the beam ID information, or the beam index information is processing value information processed by the second device and/or the third device.
The third device may be a core network device, for example.
If the first information includes or is associated with a value 60, it does not represent that the angle of the beam is 60 degrees, or that the index information of the beam is 60, and the ue may input 60 to the AI model after obtaining the processed value information (value 60).
Optionally, in an embodiment of the present application, the expected information includes beam information of a transmission beam expected by the first device and/or beam information of a reception beam expected by the first device.
Optionally, the determining method provided in the embodiment of the present application further includes the following step 301.
Step 301, the first device determines target beam information according to the first beam information.
In the embodiment of the application, the target beam information includes a subset of a beam information set corresponding to the first beam information; alternatively, the target beam information includes beam information within a range to which the first beam information corresponds.
In this embodiment of the present application, the target beam information is beam information included in or associated with a beam measurement feedback report of the UE.
Optionally, the beam information included or associated in the beam measurement feedback report has a correspondence with the beam quality information included or associated in the beam measurement feedback report, where the correspondence includes: one-to-one correspondence and/or one-to-many correspondence.
Illustratively, each beam information corresponds to one beam quality information.
Also illustratively, each beam information corresponds to a plurality of beam quality information.
Optionally, in an embodiment of the present application, the first beam information includes a number of horizontal beam information and/or a number of vertical beam information of the second device.
It should be noted that, the first beam information includes or associates the number of the horizontal beam information of the second device in a display manner and/or an implicit manner; and/or the first beam information includes or associates, by display means and/or implicit means, the number of vertical beam information of the second device; the display mode is to directly indicate the number of the horizontal beam information and/or the number of the vertical beam information; the implicit way is to distinguish the amount of horizontal beam information and/or the amount of vertical beam information by different signaling positions.
For example, two signaling positions are included in RRC signaling, wherein the first signaling position corresponds to the amount of horizontal beam information and the second signaling position corresponds to the amount of vertical beam information.
In this embodiment of the present application, after acquiring the first beam information, the first device may determine, according to the first beam information, part of information in the first beam information as target beam information; or, the beam information in the range corresponding to the first beam information is determined to be the target beam information, so that the flexibility of the first device in determining the target beam information is improved.
Optionally, the determining method provided in the embodiment of the present application further includes the following step 401.
Step 401, the first device sends a first amount of information.
In the embodiment of the application, the first quantity information is used for indicating the quantity of target expected information input by the AI model.
Wherein the target desired information includes any one of:
the first device expects to transmit beam information;
the first device expects the received beam information;
the first device expects transmit beam information and the first device expects receive beam information.
Optionally, in an embodiment of the present application, the first amount of information includes first desired information and second desired information.
Optionally, in the embodiment of the present application, in a case where the first device is a UE, the UE sends first expected information, where the first expected information is used to indicate an amount of expected information that is used once in the AI model
Optionally, in the embodiment of the present application, the network side device sends second desired information, where the second desired information is used to determine the number of intervals in the indirect determination method or determine the angle/radian/ID interval.
In this embodiment of the present application, the first device may send the amount of the target desired information used to indicate the AI model to inform the second device of the amount of the desired information, so that the second device may configure the first device according to the amount of the desired information of the first device, and thus, the first device may receive the required or desired amount of the information.
Optionally, the determining method provided in the embodiment of the present application further includes the following step 501.
Step 501, a first device obtains target quantity information.
Wherein the target quantity information includes at least one of: beam number information and second number information of the target beam;
the beam number information is used for indicating the number of transmission beams of the second device; alternatively, the beam number information is used to indicate the number of reception beams of the second device;
the beam number information and/or the second number information of the target beam are used for determining first target information, and the first target information is at least one of the following:
angle interval number information of the target beam;
radian interval number information of the target beam;
angular interval information of the target beam;
radian interval information of the target beam;
ID interval information of the target beam;
the ID interval number information of the target beam.
In this embodiment of the present application, the first device may acquire target number information, where the target number information may include beam number information of a target beam and second number information, where the beam number information is used to indicate the number of transmission beams of the second device; alternatively, the beam number information is used to indicate the number of reception beams of the second device; the beam quantity information and/or the second quantity information of the target beams are used for determining first target information, and angle information, interval information, quantity information and the like of the target beams of the first target information; therefore, the first device can determine the information of the parameters required by the AI model according to the information, and the normal use of the AI model can be ensured.
Example two
The embodiment of the application provides a determining method, which may include the following step 11.
Step 11, the network side equipment interacts the first information to the UE.
Wherein the first information is used to determine beam information of a transmission beam.
In this embodiment of the present application, the above determination method includes: direct determination and/or indirect determination.
Optionally, in an embodiment of the present application, the beam information includes at least one of:
beam horizontal angle information;
beam vertical angle information;
beam identity ID information;
beam level ID information;
beam vertical ID information;
beam index information;
beam horizontal angle index information;
beam vertical angle index information;
reference signal resource ID information;
reference signal resource index information;
panel identity identification information;
and sending the identification information of the receiving point.
Optionally, in an embodiment of the present application, where the determining manner includes indirect determination, the first information includes or is associated with at least one of the following of the transmission beam:
beam angle start value information;
beam radian start value information;
angle interval information;
radian interval information;
angle interval number information;
Radian interval number information;
angle end value information;
radian end value information;
ID start value information;
ID interval information;
ID interval number information;
ID end value information;
indexing the start value information;
index interval information;
index interval number information;
index end value information.
Optionally, in an embodiment of the present application, where the determining manner includes direct determination, the first information includes or is associated with at least one of the following of the transmission beam:
horizontal angle information;
vertical angle information
Horizontal radian information;
vertical radian information;
beam ID information;
beam level ID information;
beam vertical ID information;
reference signal resource ID information;
beam index information;
beam level index information;
beam vertical index information;
reference signal resource index information.
Alternatively, in the embodiments of the present application,
optionally, in the embodiment of the present application, the beam angle information, the beam ID information, or the beam index information is processing value information processed by the UE and/or the core network device.
Optionally, in the embodiment of the present application, beam information of the target beam is used to determine the desired information; the desired information includes beam information of a transmission beam desired by the network-side device.
Alternatively, in embodiments of the present application, the desired information is used to influence the output information of the artificial intelligence AI model.
Optionally, the determining method provided in the embodiment of the present application further includes the following step 12.
And step 12, the network side equipment determines target beam information according to the first information.
In the embodiment of the application, the target beam information includes a subset of a beam information set corresponding to the first information; alternatively, the target beam information includes beam information within a range corresponding to the first information.
In this embodiment of the present application, the target beam information is beam information included in or associated with a beam measurement feedback report of the UE.
Optionally, in an embodiment of the present application, the first information includes a number of horizontal beam information and/or a number of vertical beam information of the UE.
Optionally, the determining method provided in the embodiment of the present application further includes the following step 13.
And step 13, the network side equipment transmits second desired information.
In the embodiment of the application, the second desired information is used for indicating the amount of target desired information input by the AI model.
Wherein the target desired information includes any one of:
the network side equipment expects to send beam information;
the network side equipment expects receiving beam information;
The information of the sending beam expected by the network side equipment and the information of the receiving beam expected by the network side equipment.
Optionally, the network side device interacts the number information of the transmission beams, where the number information of the transmission beams is used to indicate the number of the transmission beams of the network side device, and the number information of the transmission beams is used to determine the number of intervals in the indirect determination method and/or determine the angle/radian/ID interval.
Optionally, the determining method provided in the embodiment of the present application further includes the following step 14.
And 14, the network side equipment acquires the target quantity information.
Wherein the target quantity information includes at least one of: beam number information and second number information of the target beam;
the beam number information is used for indicating the number of received beams of the UE;
the beam number information and/or the second number information of the target beam is used for determining first target information, wherein the first target information is at least one of the following of the transmission beam:
angle interval number information;
radian interval number information;
angle interval information;
radian interval information;
ID interval information;
ID interval number information.
Illustratively, FIG. 10 shows an interaction diagram of a determination method provided by an embodiment of the present application; as shown in fig. 10, the determining method provided in the embodiment of the present application includes the following steps a to e.
Step a, the UE sends first request information to network side equipment (for example, a base station).
And b, the base station sends first information to the UE according to the first request information.
The first information may directly include: horizontal direction: 0, 10, 20, 30, 40, 50, 60, 70, 80, 90; vertical direction: 0, 30, 60, 90; i.e. the first information comprises: 10 beams in the horizontal direction and 4 beams in the vertical direction; a total of 32 beams are included.
Alternatively, the first information indirectly indicates: horizontal direction: start position 0, end position 90, interval 10; vertical direction: start position 0, end position 90, interval 30, i.e. the first information comprises: the 10 beams in the horizontal direction and the 4 beams in the vertical direction include 32 beams in total.
And c, the base station transmits reference resources for beam training to the UE.
And d, measuring by the UE to obtain the RSRP.
Step e, the UE uses a specified number of measured RSRP and possibly corresponding transmit and receive beam angles as input to the AI model, and the UE may use one of the 32 beams as the desired Tx information.
In the embodiment of the present application, the AI model is thereby caused to output the RSRP under the one desired Tx beam information.
Alternatively, in the embodiment of the present application, the RSRP under the other beam expected information may be obtained by changing the expected Tx information.
For example, if the number of pieces of desired information input by the AI model is plural, for example, 2, if the UE uses 2 beams out of the 32 beams as desired Tx information, the output of the AI model is RSRP of the 2 desired Tx beam information, and if the UE continues to loop 15 times, RSRP corresponding to the 32 desired beams can be obtained.
Example III
The embodiment of the application provides a determining method, which may include the following step 15.
And step 15, the UE interacts second information to the network side equipment.
Wherein the second information is used to determine beam information of the reception beam.
In this embodiment of the present application, the above determination method includes: direct determination and/or indirect determination.
Optionally, in an embodiment of the present application, the beam information includes at least one of:
beam horizontal angle information;
beam vertical angle information;
beam identity ID information;
beam level ID information;
beam vertical ID information;
beam index information;
beam horizontal angle index information;
beam vertical angle index information;
Reference signal resource ID information;
reference signal resource index information;
panel identity identification information;
and sending the identification information of the receiving point.
Optionally, in an embodiment of the present application, where the determining manner includes indirect determination, the second information includes or is associated with at least one of the following of the transmission beam:
beam angle start value information;
beam radian start value information;
angle interval information;
radian interval information;
angle interval number information;
radian interval number information;
angle end value information;
radian end value information;
ID start value information;
ID interval information;
ID interval number information;
ID end value information;
indexing the start value information;
index interval information;
index interval number information;
index end value information.
Optionally, in an embodiment of the present application, where the determining manner includes direct determination, the second information includes or is associated with at least one of the following of the transmission beam:
horizontal angle information;
vertical angle information
Horizontal radian information;
vertical radian information;
beam ID information;
beam level ID information;
beam vertical ID information;
reference signal resource ID information;
beam index information;
beam level index information;
Beam vertical index information;
reference signal resource index information.
Alternatively, in the embodiments of the present application,
optionally, in the embodiment of the present application, the beam angle information, the beam ID information, or the beam index information is processing value information processed by the network side device and/or the core network device.
Optionally, in the embodiment of the present application, beam information of the target beam is used to determine the desired information; the desired information includes beam information of a transmission beam desired by the network-side device.
Alternatively, in embodiments of the present application, the desired information is used to influence the output information of the artificial intelligence AI model.
Optionally, the determining method provided in the embodiment of the present application further includes the following step 16.
And step 16, the UE determines target beam information according to the second information.
In the embodiment of the present application, the target beam information includes a subset of the beam information set corresponding to the second information; alternatively, the target beam information includes beam information in a range corresponding to the second information.
In this embodiment of the present application, the target beam information is beam information included in or associated with a beam measurement feedback report of the UE.
Optionally, in an embodiment of the present application, the second information includes a number of horizontal beam information and/or a number of vertical beam information of the UE.
Optionally, the determining method provided in the embodiment of the present application further includes the following step 17.
Step 17, the UE transmits the first desired information.
In the embodiment of the application, the first expected information is used for indicating the quantity of target expected information input by the AI model.
Optionally, in the embodiment of the present application, the UE interacts first expected information, and the amount of the first expected information is used to indicate the amount of expected information that is used once in the AI model.
Wherein the target desired information includes any one of:
the UE expects sending beam information;
the UE expects receiving beam information;
UE desired transmit beam information and UE desired receive beam information.
Optionally, the determining method provided in the embodiment of the present application further includes the following step 18.
And step 18, the UE acquires the target quantity information.
Wherein the target quantity information includes at least one of: beam number information and second number information of the target beam;
the beam number information is used for indicating the number of received beams of the UE;
the beam number information and/or the second number information of the target beam is used for determining first target information, wherein the first target information is at least one of the following of the transmission beam:
angle interval number information;
Radian interval number information;
angle interval information;
radian interval information;
ID interval information;
ID interval number information.
According to the determining method provided by the embodiment of the application, the execution body can be the determining device. In the embodiment of the present application, an example of a determining method performed by a determining device is described as the determining device provided in the embodiment of the present application.
Fig. 11 shows a schematic diagram of one possible configuration of the determination device involved in the embodiment of the present application. As shown in fig. 11, the determining means 40 may include: an acquisition module 41 and a determination module 42.
Wherein, the obtaining module 41 is configured to obtain the first beam information. A determining module 42, configured to determine beam information of the target beam according to the first beam information, and determine desired information according to the beam information of the target beam; the expected information is used for influencing the output information of the artificial intelligence AI model; wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination.
The embodiment of the application provides a determining device, because a first device can acquire first beam information, then according to the first beam information, determine beam information of a target beam, namely determine a transmitting beam of a second device, or determine a receiving beam of the second device, and determine expected information for influencing output information of an artificial intelligence AI model according to the determined beam information of the target beam, the determining method not only comprises a direct method but also comprises an indirect method, so that implicit interaction information can be caused, and because the expected information is determined, the expected information can be used for influencing the output information of the AI model, and performance of the AI model on beam prediction can be increased; therefore, the generalization performance of the AI model can be ensured; thus, the generalization performance of the AI model is ensured on the premise of reducing the exposure of the beam information as much as possible.
In one possible implementation, the beam information of the target beam includes at least one of:
beam horizontal angle information of the target beam;
beam vertical angle information of the target beam;
beam identity ID information of the target beam;
beam level ID information of the target beam;
beam vertical ID information of the target beam;
beam index information of the target beam;
beam horizontal angle index information of the target beam;
beam vertical angle index information of the target beam;
reference signal resource ID information for target beam scanning;
reference signal resource index information of target beam scanning;
panel identity identification information of the target beam;
the transmission of the target beam receives the point identification information.
In the embodiment of the present application, the beam information of the target beam may be determined between the first device and the second device according to the first beam information, and the beam information may include association information such as angle information, index information, ID information, etc. of the target beam, where the association information is associated with the input beam of the AI model, and after the association information is obtained, performance of the AI model on beam prediction may be further increased, so that generalization performance of the AI model may be ensured.
In one possible implementation, the determining of the beam information of the target beam includes an indirect determination, and the first beam information includes at least one of:
beam angle start value information of the target beam;
beam radian start value information of the target beam;
angular interval information of the target beam;
radian interval information of the target beam;
angle interval number information of the target beam;
radian interval number information of the target beam;
angle end value information of the target beam;
radian end value information of the target beam;
ID start value information of the target beam;
ID interval information of the target beam;
ID interval number information of the target beam;
ID end value information of the target beam;
index start value information of the target beam;
index interval information of the target beam;
index interval number information of the target beam;
index end value information of the target beam.
In one possible implementation, the determining of the beam information of the target beam includes directly determining, the first beam information including at least one of:
horizontal angle information of the target beam;
vertical angle information of target beam
Horizontal radian information of the target beam;
Vertical radian information of the target beam;
beam ID information of the target beam;
beam level ID information of the target beam;
beam vertical ID information of the target beam;
reference signal resource ID information for target beam scanning;
beam index information of the target beam;
beam level index information of the target beam;
beam vertical index information of the target beam;
reference signal resource index information for target beam scanning.
In one possible implementation, the beam angle information, the beam ID information, or the beam index information is processing value information processed by the second device and/or the third device.
In one possible implementation, beam information of the target beam is used to determine desired information;
the desired information includes beam information of a transmit beam desired by the first device and/or beam information of a receive beam desired by the first device.
In one possible implementation, the apparatus 40 further includes: a determining module; the determining module is used for determining target beam information according to the first beam information;
the target beam information comprises a subset of a beam information set corresponding to the first beam information;
or the target beam information comprises beam information in a range corresponding to the first beam information;
The target beam information is beam information contained in or associated with a beam measurement feedback report of the user equipment UE.
In one possible implementation, the first beam information includes a number of horizontal beam information and/or a number of vertical beam information of the second device.
In one possible implementation manner, the determining device 40 further includes: a transmitting module; the transmission module is used for transmitting first quantity information, wherein the first quantity information is used for indicating the quantity of target expected information input by the AI model;
wherein the target desired information includes any one of:
the first device expects to transmit beam information;
the first device expects the received beam information;
the first device expects transmit beam information and the first device expects receive beam information.
In one possible implementation, the obtaining module 41 is further configured to obtain the target quantity information;
wherein the target quantity information includes at least one of: beam number information and second number information of the target beam;
the beam number information is used for indicating the number of transmission beams of the second device; alternatively, the beam number information is used to indicate the number of reception beams of the second device;
the beam number information and/or the second number information of the target beam are used for determining first target information, and the first target information is at least one of the following:
Angle interval number information of the target beam;
radian interval number information of the target beam;
angular interval information of the target beam;
radian interval information of the target beam;
ID interval information of the target beam;
the ID interval number information of the target beam.
In one possible implementation manner, the first device is a network side device, and the second device is a user equipment UE; an obtaining module 41, specifically configured to receive the request information sent by the UE; and acquiring first beam information according to the request information.
In one possible implementation manner, the first device is a network side device, and the second device is a UE; an obtaining module 41, specifically configured to receive the sent capability information of the UE; and acquiring first beam information according to the capability information.
In one possible implementation manner, the first device is UE, and the second device is a network side device; the obtaining module 41 is specifically configured to receive configuration information of the network side device; and acquiring first beam information according to the configuration information.
In one possible implementation, the apparatus further includes: a receiving module and a determining module; the receiving module is configured to receive a first resource sent by the network side device after the obtaining module 41 obtains the first beam information, where the first resource is a reference resource used for beam training; a determining module 42, configured to determine resource information included in the input of the AI model according to the first beam information and the first resource received by the receiving module; and first target information, determining resource information contained in the output of the AI model.
In one possible implementation, the acquiring module 41 is specifically configured to acquire the first beam information according to the information agreed by the protocol.
The determining device in the embodiment of the present application may be an electronic device, for example, an electronic device with an operating system, or may be a component in an electronic device, for example, an integrated circuit or a chip. The electronic device may be a terminal, or may be other devices than a terminal. By way of example, terminals may include, but are not limited to, the types of terminals 11 listed above, other devices may be servers, network attached storage (Network Attached Storage, NAS), etc., and embodiments of the application are not specifically limited.
The determining device provided in the embodiment of the present application can implement each process implemented by the foregoing method embodiment, and achieve the same technical effects, so that repetition is avoided, and details are not repeated here.
Optionally, as shown in fig. 12, the embodiment of the present application further provides a communication device 600, including a processor 601 and a memory 602, where the memory 602 stores a program or instructions that can be executed on the processor 601, for example, when the communication device 600 is a terminal, the program or instructions implement the steps of the above-mentioned determining method embodiment when executed by the processor 601, and achieve the same technical effects. When the communication device 600 is a network side device, the program or the instruction, when executed by the processor 601, implements the steps of the above determining method embodiment, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.
The embodiment of the application also provides the UE, which comprises a processor and a communication interface and is used for realizing the determination method when the first equipment is the UE. The UE embodiment corresponds to the method embodiment when the first device is a UE, and each implementation process and implementation manner of the method embodiment are applicable to the UE embodiment, and the same technical effects can be achieved. Specifically, fig. 13 is a schematic hardware structure of a UE implementing an embodiment of the present application.
The UE100 (or terminal 100) includes, but is not limited to: at least some of the components of the radio frequency unit 101, the network module 102, the audio output unit 103, the input unit 104, the sensor 105, the display unit 106, the user input unit 107, the interface unit 108, the memory 109, and the processor 110, etc.
Those skilled in the art will appreciate that the terminal 100 may further include a power source (e.g., a battery) for powering the various components, and the power source may be logically coupled to the processor 110 by a power management system to perform functions such as managing charging, discharging, and power consumption by the power management system. The terminal structure shown in fig. 13 does not constitute a limitation of the terminal, and the terminal may include more or less components than shown, or may combine certain components, or may be arranged in different components, which will not be described in detail herein.
It should be appreciated that in embodiments of the present application, the input unit 104 may include a graphics processing unit (Graphics Processing Unit, GPU) 1041 and a microphone 1042, with the graphics processor 1041 processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The display unit 106 may include a display panel 1061, and the display panel 1061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 107 includes at least one of a touch panel 1071 and other input devices 1072. The touch panel 1071 is also referred to as a touch screen. The touch panel 1071 may include two parts of a touch detection device and a touch controller. Other input devices 1072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein.
In this embodiment, after receiving downlink data from the network side device, the radio frequency unit 101 may transmit the downlink data to the processor 110 for processing; in addition, the radio frequency unit 101 may send uplink data to the network side device. Typically, the radio frequency unit 101 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
Memory 109 may be used to store software programs or instructions and various data. The memory 109 may mainly include a first memory area storing programs or instructions and a second memory area storing data, wherein the first memory area may store an operating system, application programs or instructions (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like. Further, the memory 109 may include volatile memory or nonvolatile memory, or the memory 109 may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM), static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (ddr SDRAM), enhanced SDRAM (Enhanced SDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRRAM). Memory 109 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.
Processor 110 may include one or more processing units; optionally, the processor 110 integrates an application processor that primarily processes operations involving an operating system, user interface, application programs, etc., and a modem processor that primarily processes wireless communication signals, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into the processor 110.
The processor 110 is configured to acquire first beam information, determine beam information of a target beam according to the first beam information, and determine desired information according to the beam information of the target beam; the desired information is used to influence the output information of the AI model; wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination.
The embodiment of the application provides a UE, because a first device may acquire first beam information, and then determine, according to the first beam information, beam information of a target beam, that is, determine a transmitting beam of a second device, or determine a receiving beam of the second device, and determine, according to the determined beam information of the target beam, desired information for affecting output information of an artificial intelligence AI model, where the determining manner includes not only a direct manner but also an indirect manner, so that implicit interaction information may be caused, and because desired information is determined, the desired information may be used to affect output information of the AI model, and performance of the AI model on beam prediction may be increased; therefore, the generalization performance of the AI model can be ensured; thus, the generalization performance of the AI model is ensured on the premise of reducing the exposure of the beam information as much as possible.
Optionally, the processor 110 is configured to determine target beam information according to the first beam information; the target beam information comprises a subset of a beam information set corresponding to the first beam information; or the target beam information comprises beam information in a range corresponding to the first beam information; the target beam information is beam information contained in or associated with a beam measurement feedback report of the user equipment UE.
Optionally, the radio frequency unit 101 is configured to send first quantity information, where the first quantity information is used to indicate a quantity of target expected information input by the AI model;
wherein the target desired information includes any one of:
the first device expects to transmit beam information;
the first device expects the received beam information;
the first device expects transmit beam information and the first device expects receive beam information.
Optionally, the processor 110 is further configured to obtain the target quantity information;
wherein the target quantity information includes at least one of: beam number information and second number information of the target beam;
the beam number information is used for indicating the number of transmission beams of the second device; alternatively, the beam number information is used to indicate the number of reception beams of the second device;
The beam number information and/or the second number information of the target beam are used for determining first target information, and the first target information is at least one of the following:
angle interval number information of the target beam;
radian interval number information of the target beam;
angular interval information of the target beam;
radian interval information of the target beam;
ID interval information of the target beam;
the ID interval number information of the target beam.
Optionally, the first device is a network side device, and the second device is a user equipment UE; a processor 110, specifically configured to receive the request information sent by the UE; and acquiring first beam information according to the request information.
Optionally, the first device is a network side device, and the second device is a UE; a processor 110, specifically configured to receive the transmitted capability information of the UE; and acquiring first beam information according to the capability information.
Optionally, the first device is UE, and the second device is a network side device; the processor 110 is specifically configured to receive configuration information of the network side device; and acquiring first beam information according to the configuration information.
Optionally, the radio frequency unit 101 is configured to receive, after acquiring the first beam information, a first resource sent by the network side device, where the first resource is a reference resource used for beam training; the processor 110 is further configured to determine resource information included in the input of the AI model according to the first beam information and the first resource received by the receiving module; and first target information, determining resource information contained in the output of the AI model.
Optionally, the processor 110 is specifically configured to obtain the first beam information according to the information agreed by the protocol.
The embodiment of the application also provides network side equipment, which comprises a processor and a communication interface, and is used for realizing the determination method when the first equipment is the network side equipment. The network side device embodiment corresponds to the method embodiment when the first device is the network side device, and each implementation process and implementation manner of the method embodiment are applicable to the network side device embodiment and can achieve the same technical effects.
Specifically, fig. 14 is a schematic hardware structure of a network side device for implementing an embodiment of the present application. As shown in fig. 14, the network side device 700 includes: an antenna 71, a radio frequency device 72, a baseband device 73, a processor 74 and a memory 75. The antenna 71 is connected to a radio frequency device 72. In the uplink direction, the radio frequency device 72 receives information via the antenna 71, and transmits the received information to the baseband device 73 for processing. In the downlink direction, the baseband device 73 processes information to be transmitted, and transmits the processed information to the radio frequency device 72, and the radio frequency device 72 processes the received information and transmits the processed information through the antenna 71.
Wherein the processor 74 is configured to obtain first beam information, determine beam information of the target beam according to the first beam information, and determine desired information according to the beam information of the target beam; the desired information is used to influence the output information of the AI model; wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of the second device, and the second target beam is a transmitting beam of the second device; the manner of determining the beam information of the target beam includes indirect determination and/or direct determination.
The embodiment of the application provides a network side device, because a first device can acquire first beam information, then according to the first beam information, determine beam information of a target beam, namely determine a transmitting beam of a second device, or determine a receiving beam of the second device, and determine expected information for influencing output information of an artificial intelligent AI model according to the determined beam information of the target beam, the determining mode not only comprises a direct mode but also comprises an indirect mode, so that implicit interaction information can be caused, and because the expected information is determined, the expected information can be used for influencing the output information of the AI model, and the performance of the AI model on beam prediction can be increased; therefore, the generalization performance of the AI model can be ensured; thus, the generalization performance of the AI model is ensured on the premise of reducing the exposure of the beam information as much as possible.
Optionally, the processor 74 is further configured to determine target beam information from the first beam information; the target beam information comprises a subset of a beam information set corresponding to the first beam information; or the target beam information comprises beam information in a range corresponding to the first beam information; the target beam information is beam information contained in or associated with a beam measurement feedback report of the user equipment UE.
Optionally, the radio frequency device 72 is configured to send a first amount of information, where the first amount of information is used to indicate an amount of target desired information input by the AI model;
wherein the target desired information includes any one of:
the first device expects to transmit beam information;
the first device expects the received beam information;
the first device expects transmit beam information and the first device expects receive beam information.
Optionally, the processor 74 is further configured to obtain target quantity information;
wherein the target quantity information includes at least one of: beam number information and second number information of the target beam;
the beam number information is used for indicating the number of transmission beams of the second device; alternatively, the beam number information is used to indicate the number of reception beams of the second device;
The beam number information and/or the second number information of the target beam are used for determining first target information, and the first target information is at least one of the following:
angle interval number information of the target beam;
radian interval number information of the target beam;
angular interval information of the target beam;
radian interval information of the target beam;
ID interval information of the target beam;
the ID interval number information of the target beam.
Optionally, the first device is a network side device, and the second device is a user equipment UE;74, specifically for receiving the UE's transmitted request information; and acquiring first beam information according to the request information.
Optionally, the first device is a network side device, and the second device is a UE; a processor 74, in particular for receiving the UE's transmitted capability information; and acquiring first beam information according to the capability information.
Optionally, the first device is UE, and the second device is a network side device; a processor 74, specifically configured to receive configuration information of the network side device; and acquiring first beam information according to the configuration information.
Optionally, the radio frequency unit 72 is configured to receive, after acquiring the first beam information, a first resource sent by the network side device, where the first resource is a reference resource used for beam training; the processor 74 is further configured to determine resource information included in the input of the AI model according to the first beam information and the first resource received by the receiving module; and first target information, determining resource information contained in the output of the AI model.
Optionally, the processor 74 is specifically configured to obtain the first beam information according to the information agreed by the protocol.
The method performed by the network side device in the above embodiment may be implemented in the baseband apparatus 73, where the baseband apparatus 73 includes a baseband processor.
The baseband device 73 may, for example, comprise at least one baseband board, on which a plurality of chips are disposed, as shown in fig. 14, where one chip, for example, a baseband processor, is connected to the memory 75 through a bus interface, so as to call a program in the memory 75 to perform the network device operation shown in the above method embodiment.
The network side device may also include a network interface 76, such as a common public radio interface (common public radio interface, CPRI).
Specifically, the network side device 700 of the embodiment of the present invention further includes: instructions or programs stored in the memory 75 and executable on the processor 74, the processor 74 invokes the instructions or programs in the memory 75 to perform the methods performed by the modules shown in fig. 14 and achieve the same technical effects, and are not repeated here.
The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the above determining method embodiment, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.
Wherein the processor is a processor in the terminal described in the above embodiment. The readable storage medium includes computer readable storage medium such as computer readable memory ROM, random access memory RAM, magnetic or optical disk, etc.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, or the like.
The embodiments of the present application further provide a computer program/program product, where the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement each process of the above-mentioned determining method embodiment, and the same technical effects can be achieved, so that repetition is avoided, and details are not repeated herein.
The embodiment of the application also provides a determining system, which comprises: the UE and the network side equipment can be used for executing the steps of the determining method, and the network side equipment can be used for executing the steps of the determining method.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solutions of the present application may be embodied essentially or in a part contributing to the prior art in the form of a computer software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are also within the protection of the present application.
Claims (32)
1. A method of determining, the method comprising:
the first device acquires first beam information,
the first device determines beam information of a target beam according to the first beam information;
the first device determines expected information according to the beam information of the target beam; the expected information is used for influencing the output information of the artificial intelligence AI model;
wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of a second device, and the second target beam is a transmitting beam of the second device; the determination mode of the beam information of the target beam comprises indirect determination and/or direct determination.
2. The method of claim 1, wherein the beam information of the target beam comprises at least one of:
beam horizontal angle information of the target beam;
beam vertical angle information of the target beam;
beam Identity (ID) information of the target beam;
beam level ID information of the target beam;
beam vertical ID information of the target beam;
beam index information of the target beam;
Beam horizontal angle index information of the target beam;
beam vertical angle index information of the target beam;
the reference signal resource ID information of the target beam scanning;
the reference signal resource index information of the target beam scanning;
the panel identity information of the target beam;
and transmitting and receiving the point identity identification information by the target beam.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the determining mode of the beam information of the target beam comprises indirect determination, and the first beam information comprises at least one of the following:
beam angle start value information of the target beam;
beam radian start value information of the target beam;
angle interval information of the target beam;
radian interval information of the target beam;
angle interval number information of the target beam;
radian interval number information of the target beam;
angle end value information of the target beam;
radian end value information of the target beam;
ID start value information of the target beam;
ID interval information of the target beam;
ID interval number information of the target beam;
ID end value information of the target beam;
Index start value information of the target beam;
index interval information of the target beam;
index interval number information of the target beam;
and index end value information of the target beam.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the determining mode of the beam information of the target beam comprises direct determination, and the first beam information comprises at least one of the following:
horizontal angle information of the target beam;
vertical angle information of the target beam
Horizontal radian information of the target beam;
vertical radian information of the target beam;
beam ID information of the target beam;
beam level ID information of the target beam;
beam vertical ID information of the target beam;
the reference signal resource ID information of the target beam scanning;
beam index information of the target beam;
beam level index information of the target beam;
beam vertical index information of the target beam;
and the reference signal resource index information of the target beam scanning.
5. The method according to any one of claims 2 to 4, wherein the beam angle information, the beam ID information or the beam index information is processed value information processed by the second device and/or third device.
6. The method according to claim 1, wherein the desired information comprises beam information of a transmit beam desired by the first device and/or beam information of a receive beam desired by the first device.
7. The method according to claim 1 or 2, characterized in that the method further comprises:
the first device determines target beam information according to the first beam information;
the target beam information comprises a subset of a beam information set corresponding to the first beam information;
or the target beam information comprises beam information in a range corresponding to the first beam information;
the target beam information is beam information contained in or associated with a beam measurement feedback report of the user equipment UE.
8. The method according to claim 1, wherein the first beam information comprises a number of horizontal beam information and/or a number of vertical beam information of the second device.
9. The method according to claim 1, wherein the method further comprises:
the first device sends first quantity information, wherein the first quantity information is used for indicating the quantity of target expected information input by an AI model;
Wherein the target desired information includes any one of:
the first device expects sending beam information;
the first device expects receiving beam information;
the first device expects transmit beam information and the first device expects receive beam information.
10. The method according to claim 1, wherein the method further comprises:
the first equipment acquires target quantity information;
wherein the target quantity information includes at least one of: beam quantity information and second quantity information of the target beam;
the beam number information is used for indicating the number of transmission beams of the second device; or, the beam number information is used to indicate the number of the reception beams of the second device;
the beam quantity information and/or the second quantity information of the target beam are used for determining first target information, and the first target information is at least one of the following:
angle interval number information of the target beam;
radian interval number information of the target beam;
angle interval information of the target beam;
radian interval information of the target beam;
ID interval information of the target beam;
The ID interval number information of the target beam.
11. The method of claim 1, wherein the first device is a network-side device and the second device is a user equipment UE;
the first device obtaining first beam information includes:
the network side equipment receives the request information sent by the UE;
and the network side equipment acquires the first beam information according to the request information.
12. The method of claim 1, wherein the first device is a network-side device and the second device is a UE;
the first device obtaining first beam information includes:
the network side equipment receives the sent capability information of the UE;
and the network side equipment acquires the first beam information according to the capability information.
13. The method of claim 1, wherein the first device is a UE and the second device is a network-side device;
the first device obtaining first beam information includes:
the UE receives configuration information of the network side equipment;
and the UE acquires the first beam information according to the configuration information.
14. The method of claim 13, wherein the step of determining the position of the probe is performed,
After the acquiring the first beam information, the method further includes:
the UE receives a first resource sent by the network side equipment, wherein the first resource is a reference resource for beam training;
the UE determines resource information contained in the input of an AI model according to the first beam information and the first resource;
and the UE determines resource information contained in the output of the AI model according to the first target information.
15. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the first device obtaining first beam information includes:
and the first device acquires the first beam information according to the information agreed by the protocol.
16. A determining apparatus, the apparatus comprising: an acquisition module and a determination module;
the acquisition module is used for acquiring first beam information,
the determining module is used for determining beam information of a target beam according to the first beam information acquired by the acquiring module and determining expected information according to the beam information of the target beam; the expected information is used for influencing the output information of the artificial intelligence AI model;
wherein the target beam comprises a first target beam and/or a second target beam; the first target beam is a receiving beam of a second device, and the second target beam is a transmitting beam of the second device; the determination mode of the beam information of the target beam comprises indirect determination and/or direct determination.
17. The apparatus of claim 16, wherein the beam information of the target beam comprises at least one of:
beam horizontal angle information of the target beam;
beam vertical angle information of the target beam;
beam Identity (ID) information of the target beam;
beam level ID information of the target beam;
beam vertical ID information of the target beam;
beam index information of the target beam;
beam horizontal angle index information of the target beam;
beam vertical angle index information of the target beam;
the reference signal resource ID information of the target beam scanning;
the reference signal resource index information of the target beam scanning;
the panel identity information of the target beam;
and transmitting and receiving the point identity identification information by the target beam.
18. The apparatus of claim 16, wherein the device comprises a plurality of sensors,
the determining mode of the beam information of the target beam comprises indirect determination, and the first beam information comprises at least one of the following:
beam angle start value information of the target beam;
beam radian start value information of the target beam;
angle interval information of the target beam;
Radian interval information of the target beam;
angle interval number information of the target beam;
radian interval number information of the target beam;
angle end value information of the target beam;
radian end value information of the target beam;
ID start value information of the target beam;
ID interval information of the target beam;
ID interval number information of the target beam;
ID end value information of the target beam;
index start value information of the target beam;
index interval information of the target beam;
index interval number information of the target beam;
and index end value information of the target beam.
19. The apparatus of claim 16, wherein the device comprises a plurality of sensors,
the determining mode of the beam information of the target beam comprises direct determination, and the first beam information comprises at least one of the following:
horizontal angle information of the target beam;
vertical angle information of the target beam
Horizontal radian information of the target beam;
vertical radian information of the target beam;
beam ID information of the target beam;
beam level ID information of the target beam;
beam vertical ID information of the target beam;
The reference signal resource ID information of the target beam scanning;
beam index information of the target beam;
beam level index information of the target beam;
beam vertical index information of the target beam;
and the reference signal resource index information of the target beam scanning.
20. The apparatus according to any one of claims 16 to 19, wherein the beam angle information, the beam ID information or the beam index information is processed value information processed by the second device and/or third device.
21. The apparatus of claim 16, wherein the desired information comprises beam information for a transmit beam desired by the first device and/or beam information for a receive beam desired by the first device.
22. The apparatus according to claim 16 or 17, characterized in that the apparatus further comprises: the determining module is used for determining target beam information according to the first beam information;
the target beam information comprises a subset of a beam information set corresponding to the first beam information;
or the target beam information comprises beam information in a range corresponding to the first beam information;
The target beam information is beam information contained in or associated with a beam measurement feedback report of the user equipment UE.
23. The apparatus of claim 16, wherein the first beam information comprises a number of horizontal beam information and/or a number of vertical beam information of the second device.
24. The apparatus of claim 16, wherein the apparatus further comprises: a transmitting module;
the sending module is used for sending first quantity information, wherein the first quantity information is used for indicating the quantity of target expected information input by the AI model;
wherein the target desired information includes any one of:
the first device expects sending beam information;
the first device expects receiving beam information;
the first device expects transmit beam information and the first device expects receive beam information.
25. The apparatus of claim 16, wherein the device comprises a plurality of sensors,
the acquisition module is also used for acquiring the target quantity information;
wherein the target quantity information includes at least one of: beam quantity information and second quantity information of the target beam;
the beam number information is used for indicating the number of transmission beams of the second device; or, the beam number information is used to indicate the number of the reception beams of the second device;
The beam quantity information and/or the second quantity information of the target beam are used for determining first target information, and the first target information is at least one of the following:
angle interval number information of the target beam;
radian interval number information of the target beam;
angle interval information of the target beam;
radian interval information of the target beam;
ID interval information of the target beam;
the ID interval number information of the target beam.
26. The apparatus of claim 16, wherein the device comprises a plurality of sensors,
the first device is a network side device, and the second device is a User Equipment (UE);
the acquisition module is specifically configured to receive the request information sent by the UE; and acquiring the first beam information according to the request information.
27. The apparatus of claim 16, wherein the first device is a network-side device and the second device is a UE;
the acquisition module is specifically configured to receive the sent capability information of the UE; and acquiring the first beam information according to the capability information.
28. The apparatus of claim 16, wherein the first device is a UE and the second device is a network-side device;
The acquisition module is specifically configured to receive configuration information of the network side device; and acquiring the first beam information according to the configuration information.
29. The apparatus of claim 28, wherein the apparatus further comprises: a receiving module;
the receiving module is configured to receive a first resource sent by the network side device after the acquiring module acquires the first beam information, where the first resource is a reference resource used for beam training;
the determining module is further configured to determine resource information included in the input of the AI model according to the first beam information and the first resource received by the receiving module; and the first target information determines resource information contained in the output of the AI model.
30. The apparatus of claim 16, wherein the device comprises a plurality of sensors,
the acquisition module is specifically configured to acquire the first beam information according to information agreed by a protocol.
31. An apparatus comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, performs the steps of the determination method of any one of claims 1 to 15.
32. A readable storage medium, characterized in that it stores thereon a program or instructions which, when executed by a processor, implement the steps of the determination method according to any one of claims 1 to 15.
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