Beam determination method, node, and storage medium

Abstract

The present application discloses a beam determination method, a node, and a storage medium, the method including receiving beam configuration parameters and determining a first type of beam based on the beam configuration parameters.

Description

Technical Field

[0001] The present application relates to the technical field of wireless communication, and in particular to a beam determination method, a node and a storage medium.

Background Art

[0002] High frequency has abundant spectrum resources and is an effective means to improve the performance of wireless systems. However, due to the high carrier frequency and large path loss of high frequency, it is necessary to adjust the transmission direction to users based on beamforming technology, and concentrate energy transmission information to overcome performance attenuation caused by excessive path loss. A common method for obtaining the direction of a transmit or receive beam is to perform beam scanning in each direction, select a beam direction with good performance from among them, and use it as the beam direction for transmitting information. When performing beam scanning, each beam corresponds to one reference signal resource. If the beam is narrow, there are a great many beam directions that need to be scanned, and correspondingly, the overhead of reference signal resources becomes large. As shown in FIG. 1, when a regular beam such as a single beam based on Discrete Fourier Transform (DFT) is used, many beams in different directions need to be scanned when performing beam scanning, which requires large pilot resource overhead. As shown in FIG. 2, when an irregular beam resource such as a beam including a superposition of a plurality of beam directions is used, beams in a plurality of directions can be simulated at one time, and the number of beam scanning times can be reduced, but the beam direction, beam width, beam gain and the like of the irregular beam are all uncertain. At present, Artificial Intelligence (AI) can reduce the number of reference signal resources used for beam scanning to a certain extent, but in the parameter training stage, because the beams are narrow and too many samples are required for training, the cost may become too high.

Summary of Invention

Problems to be Solved by Invention

[0003] The primary objective of the embodiments of this application is to provide a beam fixing method, node, and storage medium that overcome the drawbacks of related technologies, such as the need for significant pilot resource overhead or high costs to fix a beam. [Means for solving the problem]

[0004] The embodiments of this application are as follows: Receiving beam configuration parameters, This includes determining a first-class beam based on beam configuration parameters. This provides a method for determining the beam.

[0005] The embodiments of this application are as follows: A beam determination method comprising determining beam configuration parameters and transmitting beam configuration parameters, Beam configuration parameters are used to determine a type 1 beam. This provides a method for determining the beam.

[0006] The embodiments of this application are as follows: A receiving module for receiving beam configuration parameters, It includes a processing module for determining a first type beam based on beam configuration parameters, We provide a beam alignment device.

[0007] The embodiments of this application are as follows: A beam determination device comprising a determination module for determining beam configuration parameters and a transmission module for transmitting beam configuration parameters, Beam configuration parameters are used to determine a type 1 beam. We provide a beam alignment device.

[0008] The embodiments of this application are as follows: A processor is provided, and when the processor executes a computer program, the beam determination method described in any embodiment of the present application is realized. Provides a communication node.

[0009] The embodiments of this application are as follows: Once a computer program is stored and executed by the processor, the beam determination method described in any embodiment of the present application is realized. Provides a read / write storage medium. [Effects of the Invention]

[0010] Embodiments of the present invention provide a beam determination method, a node, and a storage medium, the method comprising receiving beam configuration parameters and determining a first type beam based on the beam configuration parameters. The method makes it possible to determine a first type beam, determine parameter values ​​for an artificial intelligence network based on the determined first type beam, or determine a beam for transmission by performing a beam scan, thereby reducing the number of training iterations of the artificial intelligence network or the number of beam scans, and further overcomes the drawbacks of requiring large pilot resource overhead or high costs to determine beams in related technologies. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic diagram showing beam scanning based on a regular beam in related technologies. [Figure 2] This is a schematic diagram showing beam scanning based on an irregular beam in related technologies. [Figure 3] A flowchart of the beam determination method according to an embodiment of the present invention. [Figure 4] A flowchart of another beam determination method according to an embodiment of the present invention. [Figure 5] This is a schematic diagram of the beam determination device according to an embodiment of the present invention. [Figure 6] This is a schematic diagram of the structure of another beam determination device according to an embodiment of the present application. [Figure 7] This is a schematic diagram of the structure of a further beam determination device according to an embodiment of the present invention. [Figure 8] It is a schematic structural diagram of another beam determining apparatus according to an embodiment of the present application. [Figure 9] It is a schematic structural diagram of a communication node according to an embodiment of the present application.

Mode for Carrying Out the Invention

[0012] In order to further clarify the objectives, technical solutions and advantages of the present application, embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that, provided that there is no contradiction, the embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other.

[0013] In addition, in the embodiments of the present application, terms such as "preferably" or "exemplarily" are used to represent an example, proof or description. Any embodiment or design solution described as "preferably" or "exemplarily" in the embodiments of the present application should not be construed as being more preferred or more advantageous than other embodiments or design solutions. Specifically, the use of terms such as "preferably" or "exemplarily" is intended to present related concepts in a specific manner.

[0014] In the embodiments of the present application, a terminal may be a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device with a wireless communication function, a computing device or another processing device connected to a wireless modem, an in-vehicle device, a wearable device, or a terminal device in a 5G network or a future network beyond 5G. A base station may be an evolved base station in Long Term Evolution (LTE) or Long Term Evolution Advanced (LTE-A) ( EvolvedNode B, eNB or eNodeB), or a 5G base station device represented by New Radio (NR) access technology, or a base station in a future communication system. Further, the base station may comprise various network side devices such as various macro base stations, micro base stations, home base stations, remote radio units, routers, or a primary cell and a secondary cell, which is not limited in this embodiment.

[0015] Further, for a clearer and easier understanding of the solution according to the embodiments of the present application, related concepts according to the embodiments of the present application are described herein, which are specifically as follows.

[0016] Signaling includes, but is not limited to, Radio Resource Control (RRC) and Media Access Control control element (MAC CE).

[0017] The beam information may include at least one of the following: angle of arrival (AOA), angle of departure (AOD), zenith angle of departure (ZOD), zenith angle of arrival (ZOA), a vector or vector index constructed of at least one of the angles AOA, AOD, ZOD, and ZOA, a Discrete Fourier Transform (DFT) vector, a codeword in a codebook, a transmit beam, a receive beam, a transmit beam group, a receive beam group, a transmit beam index, a receive beam index, a transmit beam group index, and a receive beam group index. In some embodiments, a beam may mean a spatial domain filter or a spatial receive / transmit parameter, and the spatial domain filter may be at least one of a DFT vector, a precoding vector, a DFT matrix, a precoding matrix, or a vector constructed of a linear combination of multiple DFTs, or a vector constructed of a linear combination of multiple precoding vectors. The beam combination may be a linear or nonlinear combination of multiple vectors (including, but not limited to, DFT vectors, precoding vectors, and vectors composed of DFTs).

[0018] In the embodiments of this application, the concepts of index and indicator are interchangeable, and the concepts of vector and vector quantity are interchangeable.

[0019] Based on the above concept, embodiments of the present application provide a flowchart of a beam determination method that can be applied to a terminal and, as shown in Figure 3, may include, but is not limited to, the following steps.

[0020] In S301, beam configuration parameters are received.

[0021] In the embodiments of the present application, beam configuration parameters may be transmitted from a node communicating with a terminal, for example, from a base station to a terminal. Preferably, the beam configuration parameters may be determined by a first artificial intelligence network, preferably determined by the base station in a preset manner, or calculated and determined according to the channel scene, and the embodiments of the present application are not limited to these methods for determining beam configuration parameters.

[0022] Exemplary beam configuration parameters include at least one of the following: beam width, number of beam directions, array element phase, array element power, beam gain, beam index, beam combination, and random beam initial index.

[0023] Furthermore, the beam width within the beam configuration parameters can be used to determine a broad beam; the beam configuration parameters, including at least one of the number of beam directions, array element phase, and array element power, can be used to determine a random or multidirectional beam; at least one of the beam index, beam width, etc. included in the beam configuration parameters can be used to determine a random beam; and at least one of the beam width, array element power, array element phase, etc. included in the beam configuration parameters can be used to determine a non-deterministic modulus beam.

[0024] In S302, the first type of beam is determined based on the beam configuration parameters.

[0025] In the embodiments of the present invention, the first beam can be used to train the parameters of an artificial intelligence network, for example, to obtain the parameter values ​​of a second artificial intelligence network, or the first beam can be used for beam scanning to determine the beam for transmission (e.g., the second beam). This makes it possible to reduce the number of training iterations of the second artificial intelligence network or the number of beam scans based on the determined first beam, thereby overcoming the drawbacks of requiring significant pilot resource overhead or high costs to determine the beam in related technologies.

[0026] In one example, the first type of beam described above may include at least one of a broad beam, a disordered beam, a multidirectional beam, a random beam, or an atypical modulus beam.

[0027] Furthermore, the terminal can determine a Type 2 beam based on the Type 1 beam and the parameters of the second artificial intelligence network, and this Type 2 beam can be used for signaling or data transmission between the terminal and the base station. For example, the terminal can train the artificial intelligence network using the Type 1 beam, obtain the parameter values ​​of the second artificial intelligence network, select the optimal Type 2 beam index suitable for information transmission at the terminal based on the determined parameter values ​​of the second artificial intelligence network, and transmit data using the beam corresponding to the optimal Type 2 beam index.

[0028] Furthermore, the terminal can determine a second type of beam based on the parameters of a second artificial intelligence network, and these parameters can be obtained based on training of a first type of beam.

[0029] For example, in the embodiment of the present application, assuming that the beam is divided into a first type beam and a second type beam according to its beam width, the first type beam can be divided into a wide beam, and correspondingly, the second type beam can be divided into a narrow beam. Here, a wide beam can be understood as a beam whose beam width at 1 / K power is greater than a first threshold, and a narrow beam can be understood as a beam whose beam width at 1 / K power is smaller than a second threshold. Both the first and second thresholds are numbers greater than 0, and the second threshold is smaller than the first threshold, and K is an integer greater than 0, for example, K values ​​are 2, 5, and 10.

[0030] Preferably, the first type of beam may be divided into a disordered beam, and the second type of beam may be divided into a regular beam; that is, the beam is divided into two types depending on whether it is regular or not. Here, a disordered beam can be understood as a beam with an irregular power emission shape, for example, having multiple different peaks. A regular beam can be understood as a beam with a regular power emission shape, for example, having only one peak, or a beam structured with a DFT vector.

[0031] Preferably, the beam may be further divided into a multidirectional beam and a unidirectional beam, for example, by dividing the first type of beam into a multidirectional beam and the second type of beam into a unidirectional beam. Here, the multidirectional beam may be a beam linearly combined with multiple DFT vectors, or a beam constructed from a linear combination of multiple DFTs, and the unidirectional beam may be a beam composed of one DFT vector, or a beam constructed from one DFT.

[0032] The DFT vectors that make up the beam described above may include, but are not limited to, vectors constructed by the Kronecker product using two or more DFT vectors.

[0033] Preferably, in the embodiments of the present application, the first type of beam may be divided into random beams, and the second type of beam may be divided into non-random beams. Here, a random beam means a beam whose beam direction is generated in a random manner, and includes, for example, the broad beam, narrow beam, regular beam, irregular beam, multidirectional beam, and unidirectional beam, and these beams generate a random direction of direction each time they transmit information. A non-random beam means a beam whose beam direction is determined based on channel state information, and includes, for example, at least one of the narrow beam, regular beam, and unidirectional beam.

[0034] In one example, the beam may be divided into two types of beams: a non-deterministic modulus beam and a constant modulus beam. For example, the first type of beam may be divided into a non-deterministic modulus beam, and the second type of beam may be divided into a constant modulus beam. Here, a non-deterministic modulus beam means that there is at least one difference in the absolute value of each element in the vector corresponding to the beam, while a constant modulus beam means that the absolute values ​​of each element in the vector corresponding to the beam are all the same, for example, a DFT vector.

[0035] Furthermore, the above transmission may include receiving or transmitting, for example, transmitting or receiving information. Here, information includes, but is not limited to, signaling or data, and signaling includes uplink control information or downlink control information or radio resources for bearing each control information such as physical uplink control channels and physical downlink control channels. Data includes uplink data or downlink data, for example, physical downlink sharing channels and physical uplink sharing channels.

[0036] In one example, a mapping relationship exists between a first-type beam and a second-type beam. For example, a terminal can determine the mapping relationship between a first-type beam and a second-type beam based on at least one of the following methods: For example, the terminal determines the mapping relationship between a first-type beam and a second-type beam using a third artificial intelligence network; the terminal determines the mapping relationship between a first-type beam and a second-type beam using a predetermined method; or the terminal determines the mapping relationship between a first-type beam and a second-type beam by receiving configuration signaling.

[0037] Furthermore, the mapping relationship between the first and second type beams can be understood as a related relationship between the first and second type beams. Since the first and second type beams, which have a related relationship, also have a certain similarity in their transmission functions, the second type, trained on the first type beam, artificial intelligence The network parameters can be generalized to select a Type 2 beam, and furthermore, after replacing the beam selected when scanning with a Type 1 beam with a related Type 2 beam, the Type 2 beam can be used to transmit information (including signaling or data). For example, a terminal determines the optimal Type 1 beam index for transmitting information by performing a beam scan using a Type 1 beam, determines the optimal Type 2 beam index corresponding to the optimal Type 1 beam index based on the relationship between Type 1 and Type 2 beams, and transmits data using the beam corresponding to the optimal Type 2 beam index.

[0038] Preferably, the two beams having the above-described relationship may have at least one of the following characteristics: having the same quasi co-location (QCL) parameter value, having the same spatial Rx parameter value, having the same transmission configuration indicator (TCI) value, having the same QCL type D value, and having a certain overlap in spatial coverage, such as the first beam covering the coverage area of ​​the second beam, the transmission direction of the first beam including the transmission direction of the second beam, and the indicator direction of the first beam including the indicator direction of the second beam.

[0039] In one example, the terminal can also be used to receive the gain difference between the first type beam and the second type beam, and this gain difference can be used to determine the transmission power of the second type beam.

[0040] For example, two related beams may differ in beam gain; for instance, a broad beam has less gain than a narrow beam, a multidirectional beam has less gain than a unidirectional beam, a disordered beam has less gain than a regular beam, and an avaricious modulus beam has less gain than a constant modulus beam. A base station can obtain the gain difference between a related first-type beam and a second-type beam, transmit this gain difference to a terminal, and the terminal can avoid performance loss due to the difference between the two by adjusting the power for transmitting information based on the received gain difference. For example, when transmitting data or a signal, the transmit power of the second-type beam may be adjusted, or when receiving a signal or data, the receive power of the second-type beam may be adjusted, or the receive power value may be adjusted to calculate channel quality based on the gain difference between the first-type beam and the second-type beam, for example, the signal-to-interference noise ratio (SIM) -i Interference -plus-noiseThe power used to calculate the signal portion in the ratio (SINR) is adjusted, and the received power value in the Reference signal received power (RSRP) is adjusted.

[0041] In one example, prior to step S301 described above, the embodiment of the present application further provides an implementation which includes the terminal transmitting terminal beam capability to the base station.

[0042] Exemplary, terminal beam capability may include at least one of the following: the ability to support a type I beam, the beam type that supports a type I beam, and the processing capability of a type I beam.

[0043] For example, a terminal can report its terminal beam capability to a base station, and after receiving the capability reported by the terminal, the base station can determine whether or not to set beam configuration parameters for the terminal based on the terminal beam capability reported by the terminal.

[0044] In the embodiments of this application, the first artificial intelligence network, the second artificial intelligence network, and the third artificial intelligence network may all be referred to as artificial intelligence, and said artificial intelligence may include machine learning, deep learning, reinforcement learning, transfer learning, deep reinforcement learning, etc. Exemplarily, in some embodiments, artificial intelligence can be realized by a neural network, and further, the neural network may include at least an input layer, an output layer, and at least one hiding layer, and the neural network of each of these layers may include, but is not limited to, the use of at least one of the following: fully connected layers, dense layers, convolutional layers, transposed convolutional layers, directly connected layers, activation functions, normalization layers, pooling layers, etc. Preferably, the neural network of each of the above layers may further include one sub-neural network such as a residual network, a dense network, a circular network, etc.

[0045] The following provides a detailed explanation of the related functions of each artificial intelligence network, specifically as follows:

[0046] The first artificial intelligence network is primarily used to generate first-type beams suitable for beam scanning or training the second artificial intelligence network. For example, it takes input such as array number, array phase information, amplitude information, and received energy or RSRP tag information to train the beam configuration parameters of N first-type beams used for beam scanning or beam training.

[0047] The second artificial intelligence network is primarily used to select at least one beam from a beamset for transmitting and receiving information, where the information includes, but is not limited to, signaling or data. For example, there are M transmit beams and N receive beams, where M and N are positive integers, and each transmit beam corresponds to at least one channel state information reference signal (CSI-RS) resource or CSI-RS resource set. The received CSI-RS resource or the reference signal received power (RSRP) corresponding to the CSI-RS resource is input to the second artificial intelligence network, which maps the input at least one RSRP to one optimal beam index, i.e., a beam index or beam pair index for transmitting information, uses the corresponding received beam for its own information transmission, and feeds the transmit beam index back to the base station for the base station's information transmission. Preferably, the RSRP may be replaced with the signal-to-noise and interference ratio (SINR) corresponding to the CSI-RS resource to select a beam index or beam pair index. Preferably, the M transmit beams may be one subset of all selectable beams, or the N beams may be one subset of all selectable beams, so that the second artificial intelligence network can predict all beams in the beamset with one subbeamset, i.e., it performs the effect of one spatial interpolation and reduces the resource overhead during beam scanning.

[0048] For example, assuming there are 128 transmit beams and 32 receive beams, M=16 transmit beams and N=4 receive beams are uniformly selected from them and used for beam training. These 16 transmit beams and 4 receive beams that receive RSRPs and input them into a second artificial intelligence network to predict the optimal beam for the case of 128 transmit beams and 32 receive beams. In other words, the second artificial intelligence network is mainly used to select the second type of beam, but when training the parameters of the second artificial network, the first type of beam can be used to reduce the number of beam training iterations or overhead.

[0049] The third artificial intelligence network is primarily used to associate or map Type 1 and Type 2 beams. For example, it takes a Type 1 beam and the corresponding received RSRP or SINR as input and outputs the corresponding Type 2 beam or beam index of the Type 2 beam.

[0050] In the embodiment of the present invention, in order to reduce the number of training cycles for the second artificial intelligence network and reduce resource overhead, the terminal receives beam configuration parameters transmitted from the base station, generates beams for transmitting information based on the beam configuration parameters set by the base station, and generates a transmit beam for transmitting information on the uplink, for example. Based on the M transmit beams generated, the terminal transmits information using M sounding reference signal resources (SRS resources), and the base station receives the transmitted reference pilot signals using N receive beams, for example, an SRS resource for beam training, and at least one of the received information corresponding to the M transmit beams received by the N receive beams, or an RSRP or SINR corresponding to the received information, is used as one sample. The optimal transmit beam index and receive beam index corresponding to the sample can be a tag, and the parameters of the second artificial intelligence network are updated by collecting samples and tags from multiple terminals at at least one time point and using them as input to the second artificial intelligence network.

[0051] Preferably, in order to reduce the number of beam scans and reduce resource overhead, the terminal receives beam configuration parameters transmitted from the base station and generates beams for transmitting information based on the beam configuration parameters set by the base station, for example, generating transmit beams for transmitting information on the uplink. Based on the M transmit beams generated, the terminal transmits information using M sounding reference signal resources (SRS resources), and the base station receives a reference pilot signal transmitted using N receive beams, for example, an SRS resource for beam training, and uses received information corresponding to the M transmit beams received by the N receive beams, or at least one of the RSRP or SINR corresponding to the received information, as the basis for selecting beams, for example, the transmit beam and receive beam corresponding to the largest value of RSRP or SINR corresponding to the M*N pair of beams are selected as the beams for transmitting information.

[0052] Figure 4 is a flowchart of a beam determination method according to an embodiment of the present invention, which can be applied to a base station, and as shown in Figure 4, the method may include, but is not limited to, the following steps.

[0053] In S401, the beam configuration parameters are determined.

[0054] In the embodiment of the present invention, the base station can determine the beam configuration parameters using a first artificial intelligence network. For example, the base station can input the input information necessary for training into the first artificial intelligence network, and the first artificial intelligence network can train the beam configuration parameters of a first type of beam used for beam scanning or beam training.

[0055] The beam configuration parameters may include at least one of the following: beam width, number of beam directions, array element phase, array element power, beam gain, beam index, beam combination, and random beam initial index.

[0056] Furthermore, the beam width within the beam configuration parameters can be used to determine a broad beam; at least one of the beam direction number, array element phase, and array element power included in the beam configuration parameters can be used to determine a random or multidirectional beam; at least one of the beam index, beam width, etc. included in the beam configuration parameters can be used to determine a random beam; and at least one of the beam width, array element power, array element phase, etc. included in the beam configuration parameters can be used to determine a non-deterministic modulus beam.

[0057] In S402, beam configuration parameters are transmitted.

[0058] The beam configuration parameters can be used to determine a first type beam, which can then be used to train parameters of an artificial intelligence network, for example, to obtain parameter values ​​for a second artificial intelligence network, or the first type beam can be used for beam scanning to determine a transmission beam (e.g., a second type beam). This allows for a reduction in the number of training iterations of the second artificial intelligence network or the number of beam scans based on the determined first type beam, overcoming the drawbacks of requiring significant pilot resource overhead or high costs to determine beams in related technologies.

[0059] For example, the first type of beam described above may include at least one of a broad beam, a disordered beam, a multidirectional beam, a random beam, or an atypical modulus beam.

[0060] Preferably, the parameters of the first type beam and the second artificial intelligence network can be used to determine the second type beam that transmits information. For example, the terminal trains the artificial intelligence network using the first type beam, obtains the values ​​of the parameters of the second artificial intelligence network, selects the optimal second type beam index suitable for information transmission at the terminal based on the determined values ​​of the second artificial intelligence network parameters, and transmits information using the beam corresponding to the optimal second type beam index.

[0061] Preferably, in the embodiment of the present application, beam splitting can be performed in the following manner to divide the beam into two types: a first type beam and a second type beam. For example, if the beam is divided into a first type beam and a second type beam according to the beam width, the first type beam can be divided into a wide beam, and the second type beam can be divided into a narrow beam. Here, a wide beam can be understood as a beam whose beam width at 1 / K power is greater than a first threshold, and a narrow beam can be understood as a beam whose beam width at 1 / K power is smaller than a second threshold. Both the first and second thresholds are numbers greater than 0, and the second threshold is smaller than the first threshold, and K is an integer greater than 0, for example, the K values ​​are 2, 5, and 10.

[0062] Alternatively, the first type of beam may be divided into a disordered beam, and the second type of beam into a regular beam; that is, the beam is divided into two types based on whether or not it is regular. Here, a disordered beam can be understood as a beam with an irregular power emission shape, for example, a beam with multiple different peaks. A regular beam can be understood as a beam with a regular power emission shape, for example, a beam with only one peak, or a beam structured with a DFT vector.

[0063] Preferably, the beam may be further divided into a multidirectional beam and a unidirectional beam. For example, the first type of beam may be divided into a multidirectional beam, and the second type of beam may be divided into a unidirectional beam. Here, the multidirectional beam may be a beam linearly combined with multiple DFT vectors, or a beam constructed from a linear combination of multiple DFTs. The unidirectional beam may be a beam composed of one DFT vector, or a beam constructed from one DFT. The DFT vectors that make up the beam may include, but are not limited to, vectors constructed by the Kronecker product using two or more DFT vectors.

[0064] Alternatively, the first type of beam may be divided into random beams, and the second type of beam may be divided into non-random beams. Here, a random beam means a beam whose beam direction is generated in a random manner, such as the broad beam, narrow beam, regular beam, irregular beam, multidirectional beam, and unidirectional beam described above, and each time these beams transmit information, their direction of direction is randomly generated. A non-random beam means a beam whose beam direction is determined based on channel state information, such as at least one of the narrow beam, regular beam, and unidirectional beam.

[0065] In one example, the beam may be divided into two types of beams: a non-deterministic modulus beam and a constant modulus beam. For example, the first type of beam may be divided into a non-deterministic modulus beam, and the second type of beam may be divided into a constant modulus beam. Here, a non-deterministic modulus beam means that there is at least one difference in the absolute value of each element in the vector corresponding to the beam, while a constant modulus beam means that the absolute values ​​of each element in the vector corresponding to the beam are all the same, for example, a DFT vector.

[0066] Furthermore, the second type of beam transmission information described above may include transmitted or received information. Here, information includes, but is not limited to, signaling or data. Signaling includes uplink control information or downlink control information, or radio resources for bearing each control information such as physical uplink control channels and physical downlink control channels. Data includes uplink data or downlink data, for example, physical downlink sharing channels and physical uplink sharing channels.

[0067] In one example, a mapping relationship exists between the first type of beam and the second type of beam.

[0068] Furthermore, the base station can determine the mapping relationship between the first type beam and the second type beam by at least one of the following methods: for example, by determining the mapping relationship between the first type beam and the second type beam using a third artificial intelligence network, by determining the mapping relationship between the first type beam and the second type beam using a predetermined method, and by determining the mapping relationship between the first type beam and the second type beam by transmitting a configuration signaling.

[0069] Furthermore, the mapping relationship between the first and second type beams can be understood as a related relationship between the first and second type beams. Since the first and second type beams, which have a related relationship, also have a certain similarity in their transmission functions, the second type, trained on the first type beam, artificial intelligence The network parameters can be generalized to select a Type 2 beam, and furthermore, after replacing the beam selected when scanning with a Type 1 beam with a related Type 2 beam, the Type 2 beam can be used to transmit information (including signaling or data). For example, a terminal determines the optimal Type 1 beam index for transmitting information by performing a beam scan using a Type 1 beam, determines the optimal Type 2 beam index corresponding to the optimal Type 1 beam index based on the relationship between Type 1 and Type 2 beams, and transmits data using the beam corresponding to the optimal Type 2 beam index.

[0070] Preferably, the two beams having the above-described relationship may have at least one of the following characteristics: having the same quasi co-location (QCL) parameter value, having the same spatial Rx parameter value, having the same transmission configuration indicator (TCI) value, having the same QCL type D value, and having a certain overlap in spatial coverage, such as the first beam covering the coverage area of ​​the second beam, the transmission direction of the first beam including the transmission direction of the second beam, and the indicator direction of the first beam including the indicator direction of the second beam.

[0071] Preferably, the base station may also transmit the gain difference between the first type beam and the second type beam to the terminal, and this gain difference is used to determine the transmission power of the second type beam.

[0072] Two related beams have different beam gains; for example, the gain of a broad beam is smaller than that of a narrow beam, the gain of a multidirectional beam is smaller than that of a unidirectional beam, the gain of a disordered beam is smaller than that of a regular beam, and the gain of an undefined modulus beam is smaller than that of a constant modulus beam. As a result, a base station can obtain the gain difference between a related first-type beam and a second-type beam, transmit this gain difference to a terminal, and the terminal can avoid performance loss due to the difference between the two by adjusting the power for transmitting information based on the received gain difference.

[0073] Preferably, prior to step S401, the base station may also receive terminal beam capability reported from the terminal, which may include at least one of the ability to support a first type beam, the beam type that supports a first type beam, and the processing capability of a first type beam.

[0074] The terminal reports its terminal beam capability to the base station, and after receiving the capability reported by the terminal, the base station can determine whether or not to set beam configuration parameters for the terminal based on the capability reported by the terminal.

[0075] In the embodiment of the present invention, the first artificial intelligence network is mainly used to generate a first type beam suitable for beam scanning or training the second artificial intelligence network. For example, it takes input such as array number, array phase information, amplitude information, and received energy or RSRP tag information to train the beam configuration parameters of N first type beams used for beam scanning or beam training.

[0076] The second artificial intelligence network is primarily used to select at least one beam from a beamset for transmitting and receiving information, where the information includes, but is not limited to, signaling or data. For example, there are M transmit beams and N receive beams, where M and N are positive integers, and each transmit beam corresponds to at least one channel state information reference signal (CSI-RS) resource or CSI-RS resource set. The received CSI-RS resource or the reference signal received power (RSRP) corresponding to the CSI-RS resource is input to the second artificial intelligence network, which maps the input at least one RSRP to one optimal beam index, i.e., a beam index or beam pair index for transmitting information, uses the corresponding received beam for its own information transmission, and feeds the transmit beam index back to the base station for the base station's information transmission. Preferably, the RSRP may be replaced with the signal-to-noise and interference ratio (SINR) corresponding to the CSI-RS resource to select a beam index or beam pair index. Preferably, the M transmit beams may be one subset of all selectable beams, or the N beams may be one subset of all selectable beams, so that the second artificial intelligence network can predict all beams in the beamset with one subbeamset, i.e., it performs the effect of one spatial interpolation and reduces the resource overhead during beam scanning.

[0077] For example, assuming there are 128 transmitting beams and 32 receiving beams, N = 16 transmitting beams and MFour receiving beams are uniformly selected and used for beam training. The 16 transmitting beams and 4 receiving beams respectively receive RSRP and input it into the second artificial intelligence network to predict the optimal beam in the case of 128 transmitting beams and 32 receiving beams. That is, the second artificial intelligence network is mainly used to select the second type of beam, but when training the parameters of the second artificial network, the first type of beam can be used to reduce the number of beam training iterations or overhead.

[0078] The third artificial intelligence network is primarily used to associate or map Type 1 and Type 2 beams. For example, it takes a Type 1 beam and the corresponding received RSRP or SINR as input and outputs the corresponding Type 2 beam or beam index.

[0079] For the base station, in order to reduce the number of training cycles for the second artificial intelligence network and reduce resource overhead, the base station can set beam configuration parameters for the terminal based on the acquired terminal beam capability and transmit the set beam configuration parameters to the terminal. After receiving the beam configuration parameters, the terminal can generate beams for transmitting information based on the beam configuration parameters. For example, in a downlink, it generates a receive beam for receiving information. Based on the M received beams it has generated, the terminal receives each of the reference pilot signals transmitted by the base station using each of the N transmit beams, for example, a CSI-RS resource for beam training, and N received by each of the M received beams. send A sample is defined as at least one of the following: received information corresponding to a beam, or RSRP or SINR corresponding to the received information. The optimal transmit beam index and receive beam index corresponding to this sample are used as tags, and samples and tags from multiple terminals at at least one time point are collected and used as input to the second artificial intelligence network to update the parameters of the second artificial intelligence network.

[0080] Preferably, in order to reduce the number of beam scans and reduce resource overhead, the base station may set beam configuration parameters for the terminal based on the acquired terminal beam capability and transmit the set beam configuration parameters to the terminal. The terminal receives the beam configuration parameters transmitted from the base station and determines the beam for transmitting information based on the received beam configuration parameters, for example, generating a receive beam for receiving information in the downlink. Based on the M received beams generated, the terminal receives each of the reference pilot signals transmitted by the base station using each of the N transmit beams, for example, a CSI-RS resource for beam training and N received by each of the M received beams. send The basis for selecting a beam is to use received information corresponding to the beam, or at least one of the RSRP or SINR corresponding to the received information. For example, the transmit beam and receive beam corresponding to the largest RSRP or SINR for an M*N pair of beams are used as the beams for transmitting information.

[0081] Figure 5 is a schematic diagram of the structure of a beam determination device according to an embodiment of the present invention. As shown in Figure 5, the device may include a receiving module 501 and a processing module 502, where the receiving module is used to receive beam configuration parameters, and the processing module is used to determine a first type beam based on the beam configuration parameters.

[0082] Preferably, the beam configuration parameters can be determined by the first artificial intelligence network.

[0083] Exemplary beam configuration parameters include at least one of the following: beam width, beam directionality, array element phase, array element power, beam gain, beam index, beam combination, and random beam initial index.

[0084] A Type 1 beam includes at least one of the following: a broad beam, a disordered beam, a multidirectional beam, a random beam, or a non-deterministic modulus beam.

[0085] Furthermore, the first type of beam is used to obtain parameter values ​​for the second artificial intelligence network.

[0086] In one example, the processing module is further used to determine a second beam based on the parameters of a first beam and a second artificial intelligence network, and the second beam is used for signaling or data transmission.

[0087] Preferably, a mapping relationship exists between the first type of beam and the second type of beam.

[0088] In one example, the processing module may be used to determine the mapping relationship between a first type beam and a second type beam by a third artificial intelligence network, or the processing module may be used to determine the mapping relationship between a first type beam and a second type beam in a predetermined manner, and the receiving module may be used to receive configuration signaling, and accordingly, the processing module can determine the mapping relationship between a first type beam and a second type beam by setting the signaling.

[0089] In one example, the receiving module may also receive the gain difference between the first type beam and the second type beam, and this gain difference is used to determine the transmission power of the second type beam.

[0090] As shown in Figure 6, the apparatus may further include a transmitting module 503, which is used to transmit the terminal beam capability of the apparatus itself, and the terminal beam capability includes at least one of the ability to support a first type beam, the beam type that supports the first type beam, and the processing capability of the first type beam.

[0091] The beam determination device according to this embodiment is used to realize the beam determination method of the embodiment shown in Figure 3, and its implementation principle and technical effects are similar, so a detailed explanation is omitted here.

[0092] Figure 7 is a schematic diagram of the structure of a beam determination device according to an embodiment of the present invention. As shown in Figure 7, the device may include a determination module 701 and a transmission module 702. The determination module is used to determine the beam configuration parameters, and the transmission module is used to transmit the beam configuration parameters.

[0093] Here, beam configuration parameters are used to determine the type 1 beam.

[0094] Preferably, the beam configuration parameters can be determined by the first artificial intelligence network.

[0095] Exemplary beam configuration parameters include at least one of the following: beam width, beam directionality, array element phase, array element power, beam gain, beam index, beam combination, and random beam initial index.

[0096] A Type 1 beam includes at least one of the following: a broad beam, a disordered beam, a multidirectional beam, a random beam, or a non-deterministic modulus beam.

[0097] Furthermore, the first type of beam is used to obtain parameter values ​​for the second artificial intelligence network.

[0098] In one example, the parameters of the first type beam and the second artificial intelligence network can be used to determine the second type beam, which is then used for signaling or data transmission.

[0099] Preferably, a mapping relationship exists between the first type of beam and the second type of beam.

[0100] As shown in Figure 8, in one example, the apparatus may further include a processing module 703 and a receiving module 704, the processing module being used to determine the mapping relationship between the first type beam and the second type beam by a third artificial intelligence network, and the processing module may determine the mapping relationship between the first type beam and the second type beam in a predetermined manner.

[0101] Preferably, the transmitting module may transmit a configuration signaling, and in response, the processing module may determine the mapping relationship between the first type beam and the second type beam based on the configuration signaling.

[0102] In one example, the transmitting module may be used to transmit the gain difference between a first type beam and a second type beam, and this gain difference is used to determine the transmission power of the second type beam.

[0103] The receiving module can be used to receive terminal beam capability, which is, It includes at least one of the following: the ability to support a type 1 beam, the beam type that supports a type 1 beam, and the processing capacity for a type 1 beam.

[0104] The beam determination device according to this embodiment is used to realize the beam determination method of the embodiment shown in Figure 4, and its implementation principle and technical effects are similar, so a detailed explanation is omitted here.

[0105] Figure 9 is a schematic diagram of the structure of a communication node according to one embodiment. As shown in Figure 9, the node comprises a processor 901 and a memory 902. The number of processors 901 in the node may be one or multiple. In Figure 9, one processor 901 is used as an example. The processors 901 and memory 902 in the node can be connected by a bus or other means. In Figure 9, connection via a bus is used as an example.

[0106] Memory 902 can be used as a computer-readable storage medium to store software programs, computer-executable programs, and modules, such as program commands / modules corresponding to the beam determination method in the embodiments of Figures 3 and 4 of this application (for example, each module in the beam determination apparatus according to the embodiments of Figures 5 to 8). The processor 901 implements the beam determination method by executing the software programs, commands, and modules stored in memory 902.

[0107] The memory 902 may primarily comprise a program storage area and a data storage area, where the program storage area can store an operating system and application programs necessary for at least one function, and the data storage area can store data created based on the use of the set-top box, etc. The memory 902 may also include high-speed random-access memory and may further include non-temporary memory such as at least one magnetic disk storage device, flash memory, or other non-temporary solid-state storage device.

[0108] In one example, if possible, the processor in the node may implement the beam determination method using its internal hardware circuits, such as logic circuits and gate circuits.

[0109] Embodiments of the present invention further provide a read-write storage medium used for computer storage, wherein one or more programs are stored in the storage medium, and one or more programs can be executed by one or more processors to perform the beam determination method in the above embodiment.

[0110] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the equipment, can be implemented as software, firmware, hardware, or a suitable combination thereof.

[0111] In hardware embodiments, the distinctions between functional modules / units referred to above do not necessarily correspond to distinctions between physical components. For example, one physical component may have multiple functions, or one function or step may be performed collaboratively by multiple physical components. Some or all physical components may be implemented as software executed by a processor such as a central processor, a digital signal processor, or a microprocessor, as hardware, or as an integrated circuit such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-temporary media) and communication media (or temporary media). As is well known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (e.g., computer-readable instructions, data structures, program modules, or other data). Computer storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc memory, magnetic cartridges, magnetic tapes, magnetic disk memory or other magnetic storage devices, or any other media that are used to store desired information and are accessible by a computer.Furthermore, as is well known to those skilled in the art, the communication medium typically includes computer-readable instructions, data structures, program modules, or other data within the modulated data signal such as a carrier or other transmission mechanism, and may also include any information transmission medium.

Claims

1. Receiving beam configuration parameters, A beam determination method comprising determining a first type of beam based on the beam configuration parameters, A mapping relationship exists between the first type of beam and the second type of beam. The second type of beam described above is used for signaling or data transmission. Each transmit beam in the first type of beam corresponds to at least one channel state information reference signal (CSI-RS) resource or CSI-RS resource set, The method further includes receiving the gain difference between the first type of beam and the second type of beam, The gain difference is used to determine the transmission power of the second type of beam. Method for determining the beam.

2. The aforementioned beam configuration parameters are: The beam width includes at least one of the beam directionality, beam element phase, array element power, beam gain, beam index, beam combination, and random beam initial index. The method according to claim 1.

3. The aforementioned first type of beam is Including at least one of a broad beam, an irregular beam, a multidirectional beam, a random beam, or an atypical modulus beam, The method according to claim 1.

4. The third artificial intelligence network determines the mapping relationship between the first type of beam and the second type of beam, To determine the mapping relationship between the first type beam and the second type beam in a predetermined manner, The method further includes at least one of the following: determining the mapping relationship between the first type of beam and the second type of beam by receiving configuration signaling. The method according to claim 1.

5. This further includes transmitting terminal beam capability before receiving beam configuration parameters, The terminal beam capability includes at least one of the ability to support the first type of beam, the beam type that supports the first type of beam, and the processing capability of the first type of beam. The method according to claim 1.

6. To determine the beam configuration parameters, A beam determination method comprising transmitting the beam configuration parameters, The aforementioned beam configuration parameters are used to determine a first type of beam. A mapping relationship exists between the first type of beam and the second type of beam. The second type of beam described above is used for signaling or data transmission. Each transmit beam in the first type of beam corresponds to at least one channel state information reference signal (CSI-RS) resource or CSI-RS resource set, The method further includes transmitting the gain difference between the first type of beam and the second type of beam, The gain difference is used to determine the transmission power of the second type of beam. Method for determining the beam.

7. The aforementioned beam configuration parameters are: The beam width includes at least one of the beam directionality, beam element phase, array element power, beam gain, beam index, beam combination, and random beam initial index. The aforementioned first type of beam is Including at least one of a broad beam, an irregular beam, a multidirectional beam, a random beam, or an atypical modulus beam, The method according to claim 6.

8. The third artificial intelligence network determines the mapping relationship between the first type of beam and the second type of beam, To determine the mapping relationship between the first type beam and the second type beam in a predetermined manner, The method further includes at least one of the following: determining the mapping relationship between the first type of beam and the second type of beam by transmitting a configuration signaling. The method according to claim 6.

9. This further includes receiving terminal beam capability before transmitting beam configuration parameters, The terminal beam capability includes at least one of the ability to support the first type of beam, the beam type that supports the first type of beam, and the processing capability of the first type of beam. The method according to claim 6.

10. Equipped with a processor, When the processor executes the computer program, it realizes the beam determination method according to any one of claims 1 to 5 or the beam determination method according to any one of claims 6 to 9. Communication node.

11. Computer programs are stored, When the computer program is executed by the processor, the beam determination method described in any one of claims 1 to 5 or the beam determination method described in any one of claims 6 to 9 is realized. A read-and-write storage medium.

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