Information processing method and apparatus, communication device, and storage medium

Abstract

This disclosure provides an information processing method, apparatus, communication device, and storage medium; the information processing method is executed by a UE and may include: performing channel estimation using the AI ​​model corresponding to the DMRS pattern based on the number of AI models corresponding to the DMRS pattern.

Description

Technical Field

[0001] This disclosure relates to, but is not limited to, the field of communication technology, and in particular to an information processing method, apparatus, communication device, and storage medium. Background Technology

[0002] Given the complex and diverse application scenarios and richer service types faced by New Radio (NR) systems, the design of the Demodulation Reference Signal (DMRS) needs to fully consider the flexibility of configuring various system parameters. Therefore, there will be several different types of DMRS in NR systems.

[0003] In fifth-generation (B5G) or sixth-generation (6G) mobile communication systems, the increasing number of antennas at the transceiver end and the extensive use of multiple-input multiple-output (MIMO) significantly complicate channel estimation. Many studies have considered introducing artificial intelligence (AI) methods for channel estimation. However, in scenarios where channel environments and user equipment (UE) mobility vary greatly, using a single AI model for channel estimation is clearly insufficient. Summary of the Invention

[0004] This disclosure provides an information processing method, apparatus, communication device, and storage medium.

[0005] According to a first aspect of this disclosure, an information processing method is provided, executed by a UE, comprising:

[0006] Based on the number of AI models corresponding to the DMRS pattern, channel estimation is performed using the AI ​​model corresponding to the DMRS pattern.

[0007] According to a second aspect of this disclosure, an information processing method is provided, executed by a base station, comprising:

[0008] Send configuration information indicating the number of AI models corresponding to the DMRS pattern, wherein the number of AI models corresponding to the DMRS pattern is used to instruct the UE to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern.

[0009] According to a third aspect of this disclosure, an information processing method is provided, executed by a base station, comprising:

[0010] Receive first suggestion information; wherein the first suggestion information indicates the DMRS pattern to be used by the UE, or the first suggestion information indicates the DMRS pattern to be used by the UE and indicates the AI ​​model required by the UE; wherein the AI ​​model is used by the UE to perform channel estimation;

[0011] Based on the first recommendation information, determine the model information of the AI ​​model corresponding to the DMRS pattern required by the UE;

[0012] Send model information.

[0013] According to a fourth aspect of this disclosure, an information processing apparatus is provided, applied to a UE for execution, comprising:

[0014] The first processing module is configured to perform channel estimation using the AI ​​model corresponding to the DMRS pattern, based on the number of AI models corresponding to the DMRS pattern.

[0015] According to a fifth aspect of the present disclosure, an information processing apparatus is provided, applied to a base station, comprising:

[0016] The second transmitting module is configured to transmit configuration information indicating the number of AI models corresponding to the DMRS pattern, wherein the number of AI models corresponding to the DMRS pattern is used by the UE to instruct the UE to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern.

[0017] According to a sixth aspect of this disclosure, an information processing apparatus is provided, applied to a base station, comprising:

[0018] The third receiving module is configured to receive first suggestion information; wherein the first suggestion information indicates the DMRS pattern used by the UE, or the first suggestion information indicates the DMRS pattern used by the UE and indicates the AI ​​model required by the UE; wherein the AI ​​model is used by the UE to perform channel estimation.

[0019] The third processing module is configured to determine the model information of the AI ​​model corresponding to the DMRS pattern required by the UE based on the first suggestion information.

[0020] The third sending module is configured to send model information.

[0021] According to a seventh aspect of this disclosure, a communication device is provided, wherein the communication device includes:

[0022] processor;

[0023] Memory used to store processor-executable instructions;

[0024] The processor is configured to implement the information processing method of any embodiment of this disclosure when running executable instructions.

[0025] According to an eighth aspect of this disclosure, a computer storage medium is provided, wherein the computer storage medium stores a computer executable program, which, when executed by a processor, implements an information processing method according to any embodiment of this disclosure.

[0026] The technical solutions provided in this disclosure may have the following beneficial effects:

[0027] In this embodiment, the UE performs channel estimation using the AI ​​model corresponding to the DMRS pattern based on the number of AI models corresponding to the DMRS pattern. This approach offers several advantages: firstly, it eliminates the need to use the same AI model for all DMRS patterns, allowing for the selection of appropriate AI models for different DMRS patterns; this improves the applicability of the AI ​​method in channel estimation and better adapts to various channel environments. Secondly, the number of AI models corresponding to the DMRS pattern accurately determines the AI ​​model used for channel estimation, thereby improving the accuracy of AI model selection and ultimately enhancing the efficiency and performance of channel estimation.

[0028] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the embodiments of this disclosure. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the structure of a wireless communication system.

[0030] Figure 2 This is a flowchart illustrating an information processing method according to an exemplary embodiment of the present disclosure.

[0031] Figure 3 This is a flowchart illustrating an information processing method according to an exemplary embodiment of the present disclosure.

[0032] Figure 4 This is a flowchart illustrating an information processing method according to an exemplary embodiment of the present disclosure.

[0033] Figure 5 This is a flowchart illustrating an information processing method according to an exemplary embodiment of the present disclosure.

[0034] Figure 6 This is a flowchart illustrating an information processing method according to an exemplary embodiment of the present disclosure.

[0035] Figure 7 This is a flowchart illustrating an information processing method according to an exemplary embodiment of the present disclosure.

[0036] Figure 8 This is a flowchart illustrating an information processing method according to an exemplary embodiment of the present disclosure.

[0037] Figure 9 This is a flowchart illustrating an information processing method according to an exemplary embodiment of the present disclosure.

[0038] Figure 10 This is a flowchart illustrating an information processing method according to an exemplary embodiment of the present disclosure.

[0039] Figure 11 This is a block diagram illustrating an information processing apparatus according to an exemplary embodiment of the present disclosure.

[0040] Figure 12 This is a block diagram illustrating an information processing apparatus according to an exemplary embodiment of the present disclosure.

[0041] Figure 13 This is a block diagram illustrating an information processing apparatus according to an exemplary embodiment of the present disclosure.

[0042] Figure 14 This is a block diagram illustrating a UE according to an exemplary embodiment.

[0043] Figure 15 This is a block diagram illustrating a base station according to an exemplary embodiment. Detailed Implementation

[0044] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this disclosure as detailed in the appended claims.

[0045] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0046] It should be understood that although the terms first, second, third, etc., may be used to describe various information in embodiments of this disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of embodiments of this disclosure, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0047] Please refer to Figure 1 This illustration shows a schematic diagram of the structure of a wireless communication system provided in an embodiment of this disclosure. Figure 1 As shown, the wireless communication system is a communication system based on cellular mobile communication technology. The wireless communication system may include: several user equipment 110 and several base stations 120.

[0048] User equipment 110 can be a device that provides voice and / or data connectivity to a user. User equipment 110 can communicate with one or more core networks via a Radio Access Network (RAN). User equipment 110 can be an Internet of Things (IoT) user equipment, such as sensor devices, mobile phones (or "cellular" phones), and computers with IoT user equipment capabilities. For example, it can be a fixed, portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted device. Examples include a station (STA), subscriber unit, subscriber station, mobile station, mobile station, remote station, access point, remote terminal, access terminal, user terminal, user agent, user device, or user equipment. Alternatively, user equipment 110 can also be a device from an unmanned aerial vehicle (UAV). Alternatively, user equipment 110 can also be a vehicle-mounted device, such as a vehicle computer with wireless communication capabilities, or a wireless user equipment connected to an external vehicle computer. Alternatively, user equipment 110 can also be a roadside device, such as a street light, traffic light, or other roadside device with wireless communication capabilities.

[0049] Base station 120 can be a network-side device in a wireless communication system. This wireless communication system can be a 4G system (also known as Long Term Evolution, LTE); or it can be a 5G system (also known as a New Radio, 5G NR, or 5G NR system). Alternatively, it can be the next generation after 5G. In this case, the access network in the 5G system can be called a New Generation-Radio Access Network (NG-RAN).

[0050] The base station 120 can be an evolved NB (eNB) used in a 4G system. Alternatively, the base station 120 can also be a gNB (gNB) using a centralized-distributed architecture in a 5G system. When the base station 120 adopts a centralized-distributed architecture, it typically includes a central unit (CU) and at least two distributed units (DUs). The central unit is equipped with a protocol stack of the Packet Data Convergence Protocol (PDCP) layer, the Radio Link Control (RLC) layer, and the Medium Access Control (MAC) layer; the distributed units are equipped with a physical (PHY) layer protocol stack. This disclosure does not limit the specific implementation of the base station 120.

[0051] Base station 120 and user equipment 110 can establish a wireless connection via a wireless air interface. In different implementations, the wireless air interface is a wireless air interface based on the fourth-generation mobile communication network technology (4G) standard; or, the wireless air interface is a wireless air interface based on the fifth-generation mobile communication network technology (5G) standard, such as a new air interface; or, the wireless air interface can also be a wireless air interface based on a next-generation mobile communication network technology standard based on 5G.

[0052] In some embodiments, user equipment 110 can also establish E2E (End to End) connections. Examples include vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, and vehicle-to-pedestrian (V2P) communication in vehicle-to-everything (V2X) communication.

[0053] Here, the user equipment mentioned above can be considered as the terminal equipment in the following embodiments.

[0054] In some embodiments, the wireless communication system described above may further include a network management device 130.

[0055] Several base stations 120 are connected to network management device 130. Network management device 130 can be a core network device in a wireless communication system, such as a Mobility Management Entity (MME) in an Evolved Packet Core (EPC). Alternatively, it can be other core network devices, such as a Serving Gateway (SGW), a Public Data Network Gateway (PGW), a Policy and Charging Rules Function (PCRF), or a Home Subscriber Server (HSS). The implementation of network management device 130 is not limited in this embodiment.

[0056] To facilitate understanding by those skilled in the art, this disclosure provides multiple embodiments to clearly illustrate the technical solutions of the embodiments of this disclosure. Of course, those skilled in the art will understand that the multiple embodiments provided in this disclosure can be executed individually, or in combination with the methods of other embodiments in this disclosure, or individually or in combination with some methods in other related technologies; this disclosure does not limit these aspects.

[0057] This disclosure provides an information processing method, executed by a UE, including:

[0058] Step S11: Determine whether to use an AI model for channel estimation.

[0059] In some possible implementations, the scheme may include: determining whether to use an AI model for channel estimation based on AI indication information received from network devices.

[0060] Here, UE can be various terminals. For example, UE can be, but is not limited to, mobile phones, computers, servers, wearable devices, game control platforms, or multimedia devices.

[0061] Here, network equipment includes access network equipment or core network equipment. Access network equipment can be base stations, etc.; the base station can be of various types, such as 2G base stations, 3G base stations, 4G base stations, 5G base stations, or other evolved base stations. Core network equipment can be various physical or logical entities; for example, it can be various network functions (NFs), such as the Access and Mobility Management Function (AMF).

[0062] In some potential use cases, the input and output dimensions of the AI ​​model used for channel estimation will differ depending on the DMRS pattern and additional DMRS configuration of the base station. Furthermore, the required AI model complexity varies for different channel environments. Therefore, the decision to enable the AI ​​method can be made based on specific circumstances. In some possible implementations, the UE can determine whether to use the AI ​​model for channel estimation based on indication information received from the network side. For example, the indication information may include an AI_indicator to indicate whether the AI ​​method is enabled; for instance, AI_indicator = 0 indicates that the AI ​​method is not enabled for channel estimation, while AI_indicator = 1 indicates that the AI ​​method is enabled for channel estimation.

[0063] In some potential use cases, the UE can determine which AI model to use for channel estimation based on indication information received from the network side. For example, the indication information may include a Model_indictor to indicate the selected channel estimation model.

[0064] In this way, it is possible to accurately determine whether to perform channel estimation based on an AI model based on AI indication information.

[0065] like Figure 2 As shown, this disclosure provides an information processing method, executed by a UE, which may include:

[0066] Step S21: Based on the number of AI models corresponding to the DMRS pattern, use the AI ​​model corresponding to the DMRS pattern to perform channel estimation.

[0067] Here, UE can be various terminals. For example, UE can be, but is not limited to, mobile phones, computers, servers, wearable devices, game control platforms, or multimedia devices.

[0068] Here, a DMRS pattern includes the time-frequency mapping resources of DMRS within a resource block (RB) or resource element (RE). For example, a DMRS pattern includes DMRS carried on the 4th and 8th symbols of an RB. Another example is a DMRS pattern including DMRS carried on the 4th and 8th symbols of the 1st and 2nd subcarriers of an RB.

[0069] Here, the number of AI models corresponding to a DRMS ​​pattern can be one or more.

[0070] In one embodiment, the UE may determine one or more AI models corresponding to the DMRS pattern based on the protocol agreement.

[0071] In another embodiment, the UE can determine that the AI ​​model corresponding to the DMRS pattern is one or more based on the UE's model deployment information. For example, the UE receives model deployment information from the network device to the UE; the model deployment information includes: a correspondence between at least one DMRS pattern and an AI model; the correspondence includes: a correspondence between one DMRS pattern and one AI model, and / or a correspondence between one DMRS pattern and multiple AI models.

[0072] Here, network equipment includes access network equipment or core network equipment. Access network equipment can be base stations, etc.; the base station can be various types of base stations, such as 2G base stations, 3G base stations, 4G base stations, 5G base stations, or other evolved base stations. Core network equipment can be various physical or logical entities; for example, it can be various network elements (NFs), such as an AMF (Active Network Frame).

[0073] If the UE receives information sent by the core network equipment, it can be that the UE receives information sent by the core network equipment and forwarded by the base station.

[0074] In another embodiment, the UE can determine whether the AI ​​model corresponding to the DMRS pattern is one or more based on the configuration information sent by the base station. For example, the UE receives configuration information sent by the base station indicating the number of AI models corresponding to the DMRS pattern; if the UE determines that the configuration information indicates that the DMRS pattern corresponds to one AI model, the UE determines that the DMRS pattern corresponds to one AI model, or if the UE determines that the configuration information indicates that the DMRS pattern corresponds to multiple AI models, the UE determines that the DMRS pattern corresponds to multiple AI models.

[0075] In this way, it is possible to determine whether there is one or more AI models corresponding to the DMRS pattern based on various methods such as protocol agreement, model deployment information in the UE, or base station configuration.

[0076] In this embodiment, the UE performs channel estimation using the AI ​​model corresponding to the DMRS pattern based on the number of AI models corresponding to the DMRS pattern. This approach offers several advantages: firstly, it eliminates the need to use the same AI model for all DMRS patterns, allowing for the selection of appropriate AI models for different DMRS patterns; this improves the applicability of the AI ​​method in channel estimation and better adapts to various channel environments. Secondly, the number of AI models corresponding to the DMRS pattern accurately determines the AI ​​model used for channel estimation, thereby improving the accuracy of AI model selection and ultimately enhancing the efficiency and performance of channel estimation.

[0077] Step S21 may include one of the following:

[0078] Since there is only one AI model corresponding to the DMRS pattern, channel estimation is performed using the AI ​​model corresponding to the DMRS pattern.

[0079] Since there are multiple AI models corresponding to the DMRS pattern, one AI model corresponding to the DMRS pattern is selected from the multiple AI models for channel estimation.

[0080] The response is that there are multiple AI models corresponding to the DMRS pattern. One AI model corresponding to the DMRS pattern is selected from these multiple AI models for channel estimation, including one of the following:

[0081] Since there are multiple AI models corresponding to the DMRS pattern, an AI model is selected from the multiple AI models based on the AI ​​model indication information for channel estimation.

[0082] Since there are multiple AI models corresponding to the DMRS pattern, channel estimation is performed by randomly selecting one AI model from these multiple AI models.

[0083] There are multiple AI models corresponding to the DMRS pattern. One AI model is selected from these multiple AI models to match one of the following: the UE's moving speed, channel quality, UE's computing power, and UE's storage capacity.

[0084] An information processing method provided in this disclosure, executed by a UE, may include:

[0085] Since there is only one AI model corresponding to the DMRS pattern, channel estimation is performed using the AI ​​model corresponding to the DMRS pattern.

[0086] or,

[0087] Since there are multiple AI models corresponding to the DMRS pattern, one AI model is selected from the multiple AI models based on the AI ​​model indication information for channel estimation.

[0088] The AI ​​model indication information can be sent by the base station. For example, the UE receives configuration information sent by the base station, which includes: AI model indication information, instructing the UE to determine the AI ​​model for channel estimation from the AI ​​models corresponding to the DMRS pattern; the UE obtains the AI ​​model indication information based on the configuration information.

[0089] For example, if the UE determines that there is only one AI model corresponding to the DMRS pattern, then it determines to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0090] For example, if the UE determines that there are multiple AI models corresponding to the DMRS pattern, the UE performs channel estimation based on one of the multiple AI models corresponding to the DMRS pattern indicated by the AI ​​model indication information. For example, the multiple AI models corresponding to the DMRS pattern are AI model 1, AI model 2, and AI model 3; if the AI ​​model indication information received by the UE indicates AI model 3, then the UE performs channel estimation based on AI model 3.

[0091] Thus, in this embodiment of the disclosure, the UE can also receive AI model indication information configured by the base station, so as to accurately indicate the AI ​​model that the UE needs to use when there are multiple AI models corresponding to the DMRS pattern; in this way, the UE can determine the appropriate AI model for channel estimation. Alternatively, if there is only one AI model corresponding to the DMRS pattern, the AI ​​model can be indicated without AI model indication information, so that the UE can directly perform channel estimation based on the AI ​​model corresponding to the DMRS pattern.

[0092] An information processing method provided in this disclosure, executed by a UE, may include: in response to the presence of multiple AI models corresponding to a DMRS pattern, determining any one AI model from the multiple AI models for channel estimation.

[0093] Therefore, in this embodiment of the disclosure, if there are multiple AI models corresponding to a DMRS pattern, one AI model can be arbitrarily selected from the multiple AI models for channel estimation. In this way, a suitable AI model can be selected for channel estimation for different DMRS patterns, thereby improving the applicability of the AI ​​method in channel estimation and better adapting to various channel estimation methods; thus, to a certain extent, improving the efficiency and performance of channel estimation.

[0094] An information processing method provided in this disclosure, executed by a UE, may include: in response to the presence of multiple AI models corresponding to a DMRS pattern, selecting an AI model from the multiple AI models that matches one of the UE's moving speed, channel quality, computing power, and storage capacity.

[0095] For example, if there are multiple AI models corresponding to the DMRS pattern, the UE's moving speed is positively correlated with the size of the input dimension of the AI ​​model selected by the UE. For instance, if the UE moves relatively faster, the UE can select an AI model with a relatively larger input dimension for channel estimation from among the multiple AI models; if the UE moves relatively slower, the UE can select an AI model with a relatively smaller input dimension for channel estimation from among the multiple AI models.

[0096] For example, if there are multiple AI models corresponding to the DMRS pattern, the quality of the channel environment indicated by the channel quality indicator is inversely correlated with the size of the input dimension of the AI ​​model selected by the UE. For instance, if the channel environment of the UE is relatively worse, the UE can choose an AI model with a relatively larger input dimension for channel estimation from among the multiple AI models; if the channel environment of the UE is relatively better, the UE can choose an AI model with a relatively smaller input dimension for channel estimation from among the multiple AI models.

[0097] For example, if there are multiple AI models corresponding to the DMRS pattern, the UE's computing power is positively correlated with the size of the input dimension of the AI ​​model selected by the UE. For instance, if the UE has relatively stronger computing power, it can select an AI model with a relatively larger input dimension for channel estimation from among the multiple AI models; if the UE has relatively weaker computing power, it can select an AI model with a relatively smaller input dimension for channel estimation from among the multiple AI models.

[0098] For example, if there are multiple AI models corresponding to the DMRS pattern, the UE's computing power is positively correlated with the size of the input dimension of the AI ​​model selected by the UE. For instance, if the UE's computing power is relatively strong, the UE can select an AI model with a relatively large input dimension for channel estimation from among the multiple AI models; if the UE's computing power is relatively weak, the UE can select an AI model with a relatively small input dimension for channel estimation from among the multiple AI models.

[0099] For example, if there are multiple AI models corresponding to the DMRS pattern, the UE's storage capacity is positively correlated with the size of the input dimension of the AI ​​model selected by the UE. For instance, if the UE has a relatively larger storage capacity, it can select an AI model with a relatively larger input dimension for channel estimation from among the multiple AI models; conversely, if the UE has a relatively smaller computing capacity, it can select an AI model with a relatively smaller input dimension for channel estimation from among the multiple AI models.

[0100] Therefore, in this embodiment of the disclosure, if the UE does not obtain AI model indication information, it can accurately determine an AI model from multiple AI models corresponding to the DMRS pattern for channel estimation based on at least one of the following: the UE's moving speed, the quality of the channel in which the UE is located, the strength of the UE's computing power, and the size of the UE's storage capacity. This allows for the selection of an AI model that matches the UE's capabilities, and also improves the efficiency and performance of channel estimation.

[0101] It should be noted that those skilled in the art will understand that the methods provided in the embodiments of this disclosure can be executed alone or together with some methods in the embodiments of this disclosure or some methods in related technologies.

[0102] In some embodiments, prior to step S21, the method further includes:

[0103] Determine the DMRS pattern;

[0104] And / or,

[0105] Determine whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0106] like Figure 3 As shown, this disclosure provides an information processing method, executed by a UE, including:

[0107] Step S31: Determine whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0108] In some embodiments of this disclosure, the DMRS pattern is the DMRS pattern in step S21.

[0109] In some possible implementations, the scheme may include: determining whether to use an AI model for channel estimation based on indication information received from the network device.

[0110] In some potential use cases, the input and output dimensions of the AI ​​model used for channel estimation will differ depending on the DMRS pattern and additional DMRS configuration of the base station. Furthermore, the required AI model complexity varies for different channel environments. Therefore, the decision to enable the AI ​​method can be made based on specific circumstances. In some possible implementations, the UE can determine whether to use the AI ​​model for channel estimation based on indication information received from the network side. For example, the indication information may include an AI_indicator to indicate whether the AI ​​method is enabled; for instance, AI_indicator = 0 indicates that the AI ​​method is not enabled for channel estimation, while AI_indicator = 1 indicates that the AI ​​method is enabled for channel estimation.

[0111] In some potential use cases, the UE can determine which AI model to use for channel estimation based on AI indication information received from the network side. For example, the indication information may include a Model_indictor to indicate the selected channel estimation model.

[0112] This disclosure provides an information processing method, executed by a UE, which may include: determining a DMRS pattern.

[0113] The determination of the DMRS pattern may be, but is not limited to, the following: the UE receives configuration information indicating the DMRS pattern sent by the base station, and / or the UE determines the DMRS pattern.

[0114] Step S31 can be: determining to use the AI ​​model corresponding to the DMRS pattern for channel estimation; or determining not to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0115] In this embodiment, the UE can determine the DMRS pattern and whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation. This eliminates the need to select the same AI model for all DMRS patterns, allowing for the use of appropriate AI models for different DMRS patterns. This improves the applicability of AI methods in channel estimation, enabling better adaptation to various channel environments and thus enhancing the efficiency and performance of channel estimation.

[0116] It should be noted that those skilled in the art will understand that the methods provided in the embodiments of this disclosure can be executed alone or together with some methods in the embodiments of this disclosure or some methods in related technologies.

[0117] The determination of the DMRS pattern can be achieved by receiving configuration information indicating the DMRS pattern.

[0118] like Figure 4As shown, this disclosure provides an information processing method, executed by a UE, which may include:

[0119] Step S41: Receive configuration information indicating the DMRS pattern.

[0120] Step S41 can be: receiving configuration information indicating the DMRS pattern sent by the base station.

[0121] This configuration information includes: AI instruction information, indicating whether to start the AI ​​model for channel estimation.

[0122] For example, the UE receives configuration information indicating the DMRS pattern sent by the base station; based on the DMRS pattern, the UE uses an AI model corresponding to the DMRS pattern to perform channel estimation.

[0123] For example, the UE receives configuration information of the DMRS pattern sent by the base station, wherein the configuration information also includes AI indication information; the UE instructs the start of the AI ​​model to perform channel estimation based on the AI ​​indication information, and uses the AI ​​model corresponding to the DMRS pattern to perform channel estimation.

[0124] For example, the UE receives configuration information indicating the DMRS pattern sent by the base station, wherein the configuration information also includes AI indication information; the UE instructs not to start the AI ​​model for channel estimation based on the AI ​​indication information, and determines that the AI ​​model is not used for channel estimation.

[0125] Thus, in this embodiment, the DMRS pattern required by the UE can be determined by the configuration information received by the UE from the base station, providing a method for determining the DMRS pattern based on base station configuration. Furthermore, in this embodiment, it can also be determined whether the UE can perform channel estimation based on the AI ​​model corresponding to the DMRS pattern based on the AI ​​indication information configured by the base station. Therefore, the configuration method for flexibly selecting the DMRS pattern and determining whether to activate the AI ​​model for channel estimation can be tailored to different network environments and requirements.

[0126] This configuration information includes: AI model indication information, which instructs the UE to determine the AI ​​model for channel estimation from the AI ​​model corresponding to the DMRS pattern.

[0127] Step S31 can be: if there is only one AI model corresponding to the DMRS pattern, then use the AI ​​model corresponding to the DMRS pattern for channel estimation; or...

[0128] If there are multiple AI models corresponding to the DMRS pattern, one AI model is selected from the multiple AI models for channel estimation based on the AI ​​model indication information.

[0129] For example, the DMRS pattern determined by the UE corresponds to multiple AI models. The UE receives configuration information indicating the DMRS pattern sent by the base station, wherein the configuration information includes: AI model indication information; the UE performs channel estimation based on one of the multiple AI models corresponding to the DMRS pattern indicated by the AI ​​model indication information. For example, the multiple AI models corresponding to the DMRS pattern are: AI model 1, AI model 2, and AI model 3; if the AI ​​model indication information received by the UE indicates AI model 3, then the UE performs channel estimation based on AI model 3.

[0130] For example, the DMRS pattern determined by the UE corresponds to multiple AI models. The UE receives configuration information indicating the DMRS pattern sent by the base station, wherein the configuration information includes: AI indication information and AI model indication information; if the UE determines that the AI ​​indication information indicates the initiation of AI model for channel estimation, then the UE performs channel estimation based on one of the multiple AI models indicated by the AI ​​model indication information.

[0131] Thus, in this embodiment of the disclosure, the UE can also receive AI model indication information configured by the base station, so as to accurately indicate the AI ​​model that the UE needs to use when there are multiple AI models corresponding to the DMRS pattern; in this way, the UE can determine the appropriate AI model for channel estimation. Alternatively, if there is only one AI model corresponding to the DMRS pattern, the AI ​​model can be indicated without AI model indication information, so that the UE can directly perform channel estimation based on the AI ​​model corresponding to the DMRS pattern.

[0132] It should be noted that those skilled in the art will understand that the methods provided in the embodiments of this disclosure can be executed alone or together with some methods in the embodiments of this disclosure or some methods in related technologies.

[0133] like Figure 5 As shown, this disclosure provides an information processing method, executed by a UE, which may include:

[0134] Step S51: Report the second recommendation information, wherein the second recommendation information indicates the DMRS pattern recommended by the UE for the base station to determine the configuration information.

[0135] In some embodiments of this disclosure, the configuration information may be the configuration information described in the above embodiments.

[0136] The second recommendation information is used by the base station to determine configuration information including indications of the DMRS pattern. For example, if the UE reports the second recommendation information to the base station, the base station determines the DMRS pattern that the UE needs to use based on the DMRS pattern recommended in the second recommendation information. Thus, the second recommendation information is used by the base station to determine configuration information including indications of the DMRS pattern.

[0137] The second recommendation information indicates that the recommended DMRS pattern may be the same as or different from the DMRS pattern indicated in the configuration information.

[0138] The second recommendation information is used by the base station to determine configuration information including AI indication information. For example, if the UE reports the second recommendation information to the base station, the base station determines whether the UE should initiate an AI model for channel estimation based on the DMRS pattern suggested in the second recommendation information. In this case, the second recommendation information is used by the base station to determine configuration information including AI indication information.

[0139] In the example above, if the base station determines that the UE initiates the AI ​​model to perform channel estimation, the second suggestion information is used to help the base station determine configuration information including the indication DMRS pattern and AI indication information.

[0140] The second recommendation information is used by the base station to determine configuration information including AI model indication information. For example, if the UE reports the second recommendation information to the base station, the base station determines, based on the second recommendation information indicating the recommended DMRS pattern to be used, that the DMRS pattern used by the UE corresponds to multiple AI models. Then, the second recommendation information is used by the base station to determine configuration information including AI model indication information, or configuration information including both indication DMRS pattern and AI model indication information.

[0141] Thus, in this embodiment of the disclosure, the UE can report second suggestion information indicating the DMRS pattern that the UE recommends to use. This allows the base station to determine, based on the reported information, whether the UE should initiate channel estimation using an AI model, and / or the DMRS pattern that the UE needs to use. Furthermore, when the base station determines that the DMRS pattern that the UE needs to use corresponds to multiple AI models, it can also determine configuration information including AI model indication information.

[0142] This disclosure provides an information processing method executed by a UE, which may include: determining a DMRS pattern recommended for use by the UE based on at least one of the UE's mobility information, channel quality information, computing power information, and storage power information.

[0143] Here, UE mobility information indicates the UE's movement speed and is positively correlated with the density of the DMRS pattern. That is, the faster the UE moves, the denser the determined DMRS pattern; the slower the UE moves, the sparser the determined DMRS pattern.

[0144] Here, the density of the DMRS pattern refers to the number of resource units carrying DMRS within a resource block. For example, the more resource units carrying DMRS within a resource block, the denser the DMRS pattern; conversely, the fewer resource units carrying DMRS within a resource, the sparser the DMRS pattern.

[0145] Here, channel quality information indicates the quality of the channel environment and is inversely correlated with the density of the DMRS pattern. That is, the worse the channel environment, the denser the determined DMRS pattern; the better the channel environment, the sparser the determined DMRS pattern.

[0146] Here, the computing power indicated by the UE's computing power information is positively correlated with the density of the DMRS pattern. That is, the stronger the UE's computing power, the denser the determined DMRS pattern; the weaker the UE's computing power, the sparser the determined DMRS pattern. In one embodiment, the strength of computing power can be the magnitude of computing power.

[0147] Here, the storage capacity indicated by the UE's storage capacity information is positively correlated with the density of the DMRS pattern. That is, the larger the UE's storage capacity, the denser the determined DMRS pattern; the smaller the UE's storage capacity, the sparser the determined DMRS pattern.

[0148] Thus, in this embodiment of the disclosure, a suitable density of DMRS patterns can be determined based on at least one of the following: the UE's mobility information indicating its moving speed, the channel quality information indicating the channel environment's quality, the UE's computing power information indicating its computing power's strength, and the UE's storage capacity information indicating its storage capacity's size. This allows for the accurate determination of suitable recommended DMRS patterns. When the UE reports the recommended DMRS pattern to the base station, it facilitates the base station in determining suitable DMRS patterns for the UE based on the recommended DMRS pattern.

[0149] It should be noted that those skilled in the art will understand that the methods provided in the embodiments of this disclosure can be executed alone or together with some methods in the embodiments of this disclosure or some methods in related technologies.

[0150] The determination of the DMRS pattern can be based on at least one of the UE's mobility information, channel quality information, computing power information, and storage power information.

[0151] like Figure 6 As shown, this disclosure provides an information processing method, executed by a UE, which may include:

[0152] Step S61: Determine the DMRS pattern based on at least one of the UE's mobility information, channel quality information, computing power information, and storage power information.

[0153] In this embodiment of the disclosure, the determination of the DMRS pattern in step S51 based on at least one of the UE's mobility information, channel quality information, computing power information, and storage capacity information is similar to the method of determining the DMRS pattern recommended by the UE based on at least one of the UE's mobility information, channel quality information, computing power information, and storage capacity information in the above embodiment, and will not be repeated here.

[0154] Thus, in this embodiment of the disclosure, the UE can receive one of the UE's mobility information, channel quality information, computing power information, and storage power information to determine the DMRS pattern to be used; thus, a flexible configuration method for the DMRS pattern can be realized based on the UE's selection of the DMRS pattern.

[0155] It should be noted that those skilled in the art will understand that the methods provided in the embodiments of this disclosure can be executed alone or together with some methods in the embodiments of this disclosure or some methods in related technologies.

[0156] like Figure 7 As shown, this disclosure provides an information processing method, executed by a UE, which may include:

[0157] Step S71: Receive model information of at least one AI model corresponding to the DRMS ​​pattern; or, according to the protocol, determine the model information of at least one AI model corresponding to the DRMS ​​pattern.

[0158] The step S71, receiving model information of at least one AI model corresponding to the DMRS pattern, may include: receiving model information of at least one AI model corresponding to the DMRS pattern sent by the base station. Here, it can be determined whether the UE has model information of at least one AI model corresponding to the DMRS pattern based on the UE's model deployment information stored by the base station; if there is no AI model information corresponding to the DMRS pattern, then the model information of at least one AI model corresponding to the DMRS pattern is directly sent to the UE.

[0159] The step S71, which receives model information for at least one AI model corresponding to the DMRS pattern, may include:

[0160] In response to the absence of an AI model corresponding to the DMRS pattern in the UE, model information of at least one AI model corresponding to the DMRS image is received.

[0161] The absence of an AI model corresponding to the DMRS pattern in the UE can be: the UE does not have an AI model corresponding to the DMRS pattern; or, the UE has at least one AI model corresponding to the DMRS pattern, but none of the at least one AI model is the AI ​​model indicated by the AI ​​model indication information.

[0162] This disclosure provides an information processing method executed by a UE, which may include: in response to the absence of an AI model corresponding to a DMRS pattern in the UE, receiving model information of at least one AI model corresponding to a DMRS image.

[0163] This disclosure provides an information processing method executed by a UE, which may include: reporting first suggestion information; wherein the first suggestion information indicates the DMRS pattern used by the UE, or the first suggestion information indicates the DMRS pattern used by the UE and indicates the AI ​​model required by the UE; wherein the first suggestion information is used for the base station to determine model information.

[0164] One scenario in which the UE reports the first recommendation information is: the UE does not care whether there is an AI model in the UE that corresponds to the DMRS pattern, and reports the first recommendation information regardless.

[0165] Another scenario in which the UE reports the first suggestion information is: the UE determines that there is no AI model in the UE corresponding to the DMRS pattern and reports the first suggestion information.

[0166] Here, if the UE sends the first suggestion information of the DMRS pattern to the base station, the base station can determine at least one AI model corresponding to the DMRS pattern based on the DMRS pattern; then the base station can send information including at least one AI model corresponding to the DMRS pattern to the UE.

[0167] Here, if the UE sends first suggestion information indicating the AI ​​model required by the UE to the base station, the base station can send information including the AI ​​model required by the UE to the UE. Here, the AI ​​model required by the UE is determined by the UE based on the DMRS pattern.

[0168] Thus, in this embodiment of the disclosure, if the UE does not have the AI ​​model required by the UE (i.e., the AI ​​model corresponding to the DMRA pattern to be used), the UE can report the first suggestion information to the base station so that the UE can obtain the AI ​​model required by the UE.

[0169] Alternatively, the UE can disregard whether an AI model corresponding to the DMRS pattern exists in the UE and directly report the first recommendation information to obtain an AI model corresponding to the DMRS pattern required by the UE.

[0170] Here, the UE only obtains the AI ​​model corresponding to the DMRS pattern that the UE needs to use, sent by the base station, etc., when it determines that there is no AI model corresponding to the DMRS pattern that the UE needs to use. Since the model information of the AI ​​model is only received when the UE does not have the required AI model, the signaling overhead can be further reduced.

[0171] In step S61, according to the protocol, one or more AI models can be specified for each DMRS pattern. This protocol can be set in the wireless communication protocol or agreed upon by the UE and the network-side equipment.

[0172] The UE can determine at least one AI model corresponding to the DMRS pattern from the network deployment information stored in the UE through a protocol agreement.

[0173] Thus, in this embodiment of the disclosure, the UE can determine the AI ​​model corresponding to the DMRS pattern through protocol agreement, so that the UE can obtain the AI ​​model required by the UE. Moreover, this embodiment of the disclosure can determine the AI ​​model corresponding to the DMRS pattern from the network deployment information in the UE, without needing to receive model information including the AI ​​model from network devices such as base stations, and can also save signaling overhead.

[0174] Step S31 determines whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation, including one of the following:

[0175] In response to the existence of an AI model corresponding to the DRMS ​​pattern in the UE, it is determined that the AI ​​model corresponding to the DMRS pattern will be used for channel estimation.

[0176] If no AI model corresponding to the DMRS pattern exists in the UE, it is determined that the AI ​​model corresponding to the DMRS pattern will not be used for channel estimation.

[0177] This disclosure provides an information processing method executed by a UE, which may include: in response to the existence of an AI model corresponding to a DRMS ​​pattern in the UE, determining to use the AI ​​model corresponding to the DRMS ​​pattern for channel estimation.

[0178] For example, in response to the UE determining that there is an AI model corresponding to the DMRS pattern, the UE determines to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0179] For example, in response to the UE determining that there are multiple AI models corresponding to the DMRS pattern, an AI model is selected from the multiple AI models for channel estimation based on the AI ​​model indication information.

[0180] This disclosure provides an information processing method executed by a UE, which may include: if there is no AI model corresponding to the DMRS pattern in the UE, determining that the AI ​​model corresponding to the DMRS pattern will not be used for channel estimation.

[0181] The decision not to use the AI ​​model corresponding to the DMRS pattern for channel estimation can be: using the AI ​​model corresponding to the DMRS pattern for channel estimation, but using other AI models besides the AI ​​model corresponding to the DMRS pattern for channel estimation; or, not using any AI model for channel estimation.

[0182] Thus, in this embodiment of the disclosure, if the UE has an AI model corresponding to the DMRS pattern, channel estimation can be performed based on the AI ​​model; if the UE does not have an AI model corresponding to the DMRS pattern, then the AI ​​model corresponding to the DMRS model is not used for channel estimation. In this way, the UE can accurately determine whether to activate the AI ​​model for channel estimation, and / or, based on which desired AI model for channel estimation.

[0183] This disclosure provides an information processing method, executed by a UE, which may include:

[0184] Send a first suggestion message; wherein the first suggestion message indicates the DMRS pattern used by the UE, or the first suggestion message indicates the DMRS pattern used by the UE and indicates the AI ​​model required by the UE;

[0185] If model information for at least one AI model corresponding to the DRMS ​​pattern is received based on the first suggestion information, it is determined that the AI ​​model corresponding to the DRMS ​​pattern will be used for channel estimation; or,

[0186] If no model information including at least one AI model corresponding to the DMRS pattern is received, it is determined that the AI ​​model corresponding to the DMRS model will not be used for channel estimation.

[0187] Thus, in this embodiment of the disclosure, the UE can perform channel estimation by actively acquiring the AI ​​model required by the UE when there is no AI model for channel estimation in the UE, thereby adapting to more scenarios that utilize AI models for channel estimation.

[0188] It should be noted that those skilled in the art will understand that the methods provided in the embodiments of this disclosure can be executed alone or together with some methods in the embodiments of this disclosure or some methods in related technologies.

[0189] The following information processing method is executed by the base station and is similar to the information processing method executed by the UE described above. For technical details not disclosed in the embodiments of the information processing method executed by the base station, please refer to the description of the example of the information processing method executed by the UE, which will not be described in detail here.

[0190] like Figure 8 As shown, this disclosure provides an information processing method, executed by a base station, including:

[0191] Step S81: Send configuration information indicating the number of AI models corresponding to the DMRS pattern, wherein the number of AI models corresponding to the DMRS pattern is used to instruct the UE to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern.

[0192] Step S81 can be: sending configuration information to the UE to indicate the number of AI models corresponding to the DMRS pattern.

[0193] In one embodiment, the number of AI models corresponding to the DMRS pattern in the configuration information is used to instruct the UE to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern, including one of the following:

[0194] In response to the fact that there is only one AI model corresponding to the DMRS pattern, the configuration information is used to instruct the UE to use the AI ​​model corresponding to the DMRS pattern for channel estimation;

[0195] In response to the fact that there are multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to determine one AI model from multiple AI models for channel estimation based on the AI ​​model indication information.

[0196] In response to the fact that there are multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to select any one of the multiple AI models for channel estimation;

[0197] In response to the fact that there are multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to select one of the multiple AI models that matches the UE's mobile speed, channel quality, computing power, and storage capacity.

[0198] In one embodiment, configuration information is used to indicate whether the UE uses an AI model corresponding to the DMRS pattern for channel estimation.

[0199] In another embodiment, the configuration information may further include: a DMRS pattern; wherein the DMRS pattern is used by the UE to determine whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation before determining whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0200] This disclosure provides an information processing method executed by a base station, which may include: sending configuration information indicating a DMRS pattern before sending configuration information indicating the number of AI models corresponding to the DMRS pattern; wherein the DMRS pattern is used by the UE to determine whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0201] like Figure 9 As shown, this disclosure provides an information processing method, executed by a base station, which may include:

[0202] Step S91: Send configuration information indicating the DMRS pattern, wherein the DMRS pattern is used by the UE to determine whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0203] In some embodiments of this disclosure, the configuration information in step S91 may be the configuration information in the above embodiments.

[0204] This configuration information may include: AI indication information, indicating whether to start the AI ​​model for channel estimation; wherein, when the AI ​​indication information indicates that the AI ​​model is started for channel estimation, the UE uses the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0205] This configuration information may include: AI model indication information, which instructs the UE to select an AI model for channel estimation from the AI ​​models corresponding to the DMRS pattern.

[0206] This step S91 can be: sending configuration information indicating the DMRS pattern to the UE.

[0207] This disclosure provides an information processing method executed by a base station, which may include: sending model deployment information of a UE; wherein the model deployment information includes: a correspondence between at least one DMRS pattern and an AI model; the correspondence includes: a correspondence between one DMRS pattern and one AI model, and / or a correspondence between one DMRS pattern and multiple AI models.

[0208] The base station can pre-deploy DMRS patterns and corresponding AI models for the UE. For example, the base station can determine the number of DMRS patterns used by the UE based on at least one of the UE's mobility information, channel quality information, computing power information, and storage power information.

[0209] For example, there are 10 selectable DMRS patterns in the network system, and UEs commonly use 3 of them. For UEs with relatively high mobility, relatively poor channel environment, relatively strong computing power, and / or relatively large storage capacity, all or most of the 10 DMRS patterns can be deployed for them; for UEs with relatively low mobility, relatively good channel environment, relatively weak computing power, and / or relatively small storage capacity, all or part of the 3 commonly used DMRS patterns, or a small part of the 10 DMRS patterns can be deployed for them.

[0210] In the above example, if one DMRS pattern corresponds to multiple AI models, then for UEs with relatively high mobility, relatively poor channel environment, relatively strong computing power, and / or relatively large storage capacity, all or most of the multiple AI models can be deployed for them; for UEs with relatively low mobility, relatively good channel environment, relatively weak computing power, and / or relatively small storage capacity, one or a small part of the multiple AI models can be deployed for them.

[0211] In this way, it is possible to adapt to changes in DMRS patterns and promptly obtain the number of DMRS patterns and / or AI model data that match the UE's current mobility, channel environment, computing power and / or computing capabilities.

[0212] The base station can also deploy DMRS patterns and corresponding AI models to the UE based on the UE's needs. For example, the base station can send model information of the AI ​​model corresponding to the DMRS pattern to the UE based on the first suggestion information carrying the indication DMRS pattern reported by the UE.

[0213] Thus, in this embodiment of the disclosure, a suitable AI model can be selected and deployed for the UE in a targeted manner, thereby saving the communication overhead, storage and computing overhead required for the UE's model deployment.

[0214] This disclosure provides an information processing method executed by a base station, which may include storing model deployment information for each UE. This facilitates the base station in determining whether an AI model corresponding to a DMRS pattern is stored in the UE.

[0215] This disclosure provides an information processing method executed by a base station, which may include: in response to determining that there is no AI model corresponding to the DMRS pattern in the UE, sending model information of at least one AI model corresponding to the DMRS pattern.

[0216] For example, the base station can determine whether there is an AI model in the UE corresponding to the DMRS pattern based on the stored model deployment information of the UE; if the base station determines that there is no AI model in the UE corresponding to the DMRS pattern, it sends model information of at least one AI model corresponding to the DMRS pattern to the UE.

[0217] This disclosure provides an information processing method, executed by a base station, which may include: receiving second suggestion information; determining configuration information based on the second suggestion information indicating the DMRS pattern recommended by the UE.

[0218] The base station receives the second suggestion information sent by the UE, and can instruct the UE to use the DMRS pattern based on the second suggestion information, determine whether the UE enables the AI ​​model for channel estimation, and / or determine the DMRS pattern used by the UE; the base station sends the configuration information indicating the DMRS pattern and / or including the AI ​​model indication information to the UE.

[0219] If the base station determines that the DMRS pattern used by the UE corresponds to multiple AI models, it can also send configuration information, including AI model indication information, to the UE.

[0220] like Figure 10 The present disclosure provides an information processing method, executed by a base station, comprising:

[0221] Step S101: Receive first suggestion information; wherein the first suggestion information indicates the DMRS pattern used by the UE, or the first suggestion information indicates the DMRS pattern used by the UE and indicates the AI ​​model required by the UE; wherein the AI ​​model is used by the UE to perform channel estimation;

[0222] Step S102: Based on the first recommendation information, determine the model information of the AI ​​model corresponding to the DMRS pattern required by the UE;

[0223] Step S103: Send model information.

[0224] Here, the base station determines the model information of the AI ​​model required by the UE, which can be: determining the model information of one AI model corresponding to the DMRS pattern, or determining the model information of multiple AI models corresponding to the DMRS pattern.

[0225] The above implementation methods can be referred to the description on the UE side for details, and will not be repeated here.

[0226] It should be noted that those skilled in the art will understand that the methods provided in the embodiments of this disclosure can be executed alone or together with some methods in the embodiments of this disclosure or some methods in related technologies.

[0227] To further explain any embodiment of this disclosure, a specific embodiment is provided below.

[0228] This disclosure provides an information processing method, which may include the following steps:

[0229] Step S111: The base station determines and stores the model deployment information of each UE; wherein, the model deployment information includes: the correspondence between a DMRS pattern and an AI model, and / or, the correspondence between a DMRS pattern and multiple AI models; and sends the model deployment information of the UE to the UE;

[0230] Scenario 1: For a DMRS pattern corresponding to one AI model, the following AI channel estimation parameter configuration methods are included:

[0231] Method A1: Configuration method based on base station configuration of AI channel estimation parameters:

[0232] Step S1121: The base station determines the DMRS pattern and determines whether the UE should start the AI ​​model for channel estimation;

[0233] Step S1122: The base station sends configuration information, including AI indication information and indication DMRS pattern, to the UE; wherein, the AI ​​indication information indicates whether to start the AI ​​model for channel estimation;

[0234] Step S1123: If the base station determines that the UE has started the AI ​​model for channel estimation, it queries the stored model deployment information of the UE; if there is no AI model corresponding to the DMRS pattern in the model deployment information, it sends the model information of the AI ​​model corresponding to the DMRS pattern to the UE.

[0235] Method B1: A configuration method based on UE-reported suggestion information, where the base station configures AI channel parameter estimation based on the reported suggestion information.

[0236] Step S1131: The UE determines the DMRS pattern to be used by the UE based on at least one of the UE's mobility information, channel quality information, computing power information, and storage capacity information.

[0237] Step S1132: The UE sends a second recommendation message to the UE, indicating the DMRS pattern to be used;

[0238] Step S1133: Based on the second suggestion information indicating the DMRS pattern recommended by the UE, determine whether the UE should start the AI ​​model for channel estimation, and determine the DMRS pattern required by the UE;

[0239] Step S1134: The base station sends configuration information, including AI indication information and indication DMRS pattern, to the UE; wherein, the AI ​​indication information indicates whether to start the AI ​​model for channel estimation;

[0240] Step S1135: If the base station determines that the UE has started the AI ​​model for channel estimation, it queries the stored model deployment information of the UE; if there is no AI model corresponding to the DMRS pattern in the model deployment information, it sends the model information of the AI ​​model corresponding to the DMRS pattern to the UE.

[0241] Method C1: Configuration method for determining AI channel estimation parameters based on UE:

[0242] Step S1141: The UE determines the DMRS pattern based on at least one of the UE's mobility information, channel quality information, computing power information, and storage capacity information.

[0243] Step S1142a: The UE queries the stored UE model deployment information. If there is an AI model corresponding to the DMRS pattern in the model deployment information, it determines to start the AI ​​model corresponding to the DMRS pattern for channel estimation; or, if there is no AI model corresponding to the DMRS pattern in the model deployment information, it determines not to start the AI ​​model for channel estimation.

[0244] or,

[0245] Step S1142b: The UE queries the stored UE model deployment information. If there is an AI model corresponding to the DMRS pattern in the model deployment information, it determines to start the AI ​​model corresponding to the DMRS pattern for channel estimation; or, if there is no AI model corresponding to the DMRS pattern in the model deployment information, it waits for the base station to send the model information of the AI ​​model.

[0246] Step S1143: The UE sends first suggestion information to the base station, wherein the first suggestion information indicates the DMRS pattern used by the UE and / or indicates the AI ​​model required by the UE;

[0247] Step S1144: The base station queries the stored model deployment information of the UE. If it is determined that there is no AI model corresponding to the DMRS pattern in the model deployment information, the base station determines the model information of the AI ​​model corresponding to the DMRS pattern required by the UE based on the first suggestion information, and sends the model information of the AI ​​model corresponding to the DMRS pattern to the UE.

[0248] Scenario 2: For a single DMRS pattern corresponding to multiple AI models, including the following AI channel estimation parameter configuration methods:

[0249] Method A2: Configuration method based on base station configuration of AI channel estimation parameters:

[0250] Step S1151: The base station determines the DMRS pattern and determines whether the UE starts the AI ​​model for channel estimation; if the base station determines that the UE starts the AI ​​model for channel estimation, it selects an AI model from multiple AI models corresponding to the DMRS pattern.

[0251] Step S1152: The base station sends configuration information including AI indication information, AI model indication information and DMRS pattern indication to the UE; wherein, the AI ​​indication information indicates whether to start the AI ​​model for channel estimation; the AI ​​model indication information instructs the UE to determine the AI ​​model for channel estimation from the AI ​​models corresponding to the DMRS pattern.

[0252] Step S1153: If the base station determines that the UE has started the AI ​​model to perform channel estimation, it queries the stored model deployment information of the UE; if there is no AI model corresponding to the DMRS pattern in the model deployment information, it sends the AI ​​model corresponding to the DMRS pattern to the UE.

[0253] Method B2: A configuration method based on the suggestion information reported by the UE, and the base station configuring AI channel parameter estimation based on the reported suggestion information:

[0254] Step S1161: The UE determines the DMRS pattern to be used by the UE based on at least one of the UE's mobility information, channel quality information, computing power information, and storage capacity information.

[0255] Step S1162: The UE sends a second recommendation message to the UE, indicating the DMRS pattern to be used;

[0256] Step S1163: Based on the second suggestion information indicating the DMRS pattern suggested by the UE, determine whether the UE should start the AI ​​model for channel estimation, determine the DMRS pattern to be used by the UE, and determine to select an AI model from multiple AI models corresponding to the DMRS pattern;

[0257] Step S1164: The base station sends configuration information including AI indication information, AI model indication information and DMRS pattern indication to the UE; wherein, the AI ​​indication information indicates whether to start the AI ​​model for channel estimation; the AI ​​model indication information instructs the UE to determine the AI ​​model for channel estimation from the AI ​​models corresponding to the DMRS pattern.

[0258] Step S1165: If the base station determines that the UE has started the AI ​​model for channel estimation, it queries the stored model deployment information of the UE; if there is no AI model corresponding to the DMRS pattern in the model deployment information, it sends the model information of the AI ​​model corresponding to the DMRS pattern to the UE.

[0259] Method C2: Configuration method for determining AI channel estimation parameters based on UE:

[0260] Step S1171: The UE determines the DMRS pattern based on at least one of the UE's mobility information, channel quality information, computing power information, and storage capacity information.

[0261] Step S1172a: The UE queries the stored UE model deployment information. If there is at least one AI model corresponding to the DMRS pattern in the model deployment information, then select an AI model that meets the predetermined conditions from the at least one AI model for channel estimation; or, if there is no AI model corresponding to the DMRS pattern in the model deployment information, then determine not to start the AI ​​model for channel estimation.

[0262] Here, selecting an AI model that meets the predetermined conditions from at least one AI model may be, but is not limited to, one of the following: selecting any AI model from at least one AI model; selecting an AI model from at least one AI model that matches one of the following: the UE's moving speed, the quality of the channel environment, the UE's computing power, and the UE's storage capacity.

[0263] or,

[0264] Step S1172b: The UE queries the stored UE model deployment information. If there is at least one AI model corresponding to the DMRS pattern in the model deployment information, then select an AI model that meets the predetermined conditions from the at least one AI model for channel estimation; or, if there is no AI model corresponding to the DMRS pattern in the model deployment information, then wait for the base station to send the model information of the AI ​​model.

[0265] Step S1173: The UE sends first suggestion information to the base station, wherein the first suggestion information indicates the DMRS pattern used by the UE and / or indicates the AI ​​model required by the UE;

[0266] Step S1174: The base station queries the stored model deployment information of the UE. If it is determined that there is no AI model corresponding to the DMRS pattern in the model deployment information, the base station determines the model information of the AI ​​model corresponding to the DMRS pattern required by the UE based on the first suggestion information, and sends the model information of the AI ​​model corresponding to the DMRS pattern to the UE.

[0267] It should be noted that those skilled in the art will understand that the methods provided in the embodiments of this disclosure can be executed alone or together with some methods in the embodiments of this disclosure or some methods in related technologies.

[0268] like Figure 11 As shown, this disclosure provides an information processing apparatus applied to a UE, comprising:

[0269] The first processing module 51 is configured to perform channel estimation using the AI ​​model corresponding to the DMRS pattern based on the number of AI models corresponding to the DMRS pattern.

[0270] This disclosure provides an information processing apparatus for a UE, which may include: a first processing module 51 configured to perform channel estimation using the AI ​​model corresponding to the DMRS pattern in response to the AI ​​model being one.

[0271] This disclosure provides an information processing apparatus for a UE, which may include: a first processing module 51 configured to select one AI model from the plurality of AI models for channel estimation based on AI model indication information in response to the presence of a plurality of AI models corresponding to the DMRS pattern.

[0272] This disclosure provides an information processing apparatus for a UE, which may include: a first processing module 51 configured to determine any one AI model from the plurality of AI models to perform channel estimation in response to the presence of a plurality of AI models corresponding to the DMRS pattern.

[0273] This disclosure provides an information processing apparatus for a UE, which may include: a first processing module 51 configured to select an AI model that matches one of the following factors in response to a plurality of AI models corresponding to the DMRS pattern: the UE's moving speed, channel environment quality, UE's computing power, and UE's storage capacity.

[0274] This disclosure provides an information processing apparatus applied to a UE, which may include: a determination module configured to determine a DMRS pattern.

[0275] This disclosure provides an information processing apparatus for a UE, which may include a first processing module 51 configured to determine whether to use an AI model for channel estimation.

[0276] This disclosure provides an information processing apparatus for a UE, which may include a first processing module 51 configured to determine whether to use an AI model corresponding to a DMRS pattern for channel estimation.

[0277] This disclosure provides an information processing apparatus for a UE, which may include a determination module configured to receive configuration information indicating a DMRS pattern.

[0278] This disclosure provides an information processing apparatus applied to a UE, which may include: a determination module configured to determine a DMRS pattern based on at least one of the UE's mobility information, channel quality information, computing power information, and storage power information.

[0279] This disclosure provides an information processing apparatus applied to a UE, which may include: a determination module configured to receive configuration information indicating a DMRS pattern; wherein the configuration information further includes: AI indication information indicating whether to start an AI model for channel estimation.

[0280] This disclosure provides an information processing apparatus applied to a UE, which may include: a determination module configured to receive configuration information indicating a DMRS pattern; wherein the configuration information further includes: AI model indication information, instructing the UE to select an AI model for channel estimation from the AI ​​models corresponding to the DMRS pattern.

[0281] This disclosure provides an information processing apparatus for a UE, which may include: a first receiving module configured to receive model information of at least one AI model corresponding to a DRMS ​​pattern.

[0282] This disclosure provides an information processing apparatus applied to a UE, which may include: a determination module configured to determine model information of at least one AI model corresponding to a DRMS ​​pattern according to a protocol agreement.

[0283] This disclosure provides an information processing apparatus applied to a UE, which may include: a first receiving module configured to receive model information of at least one AI model corresponding to a DMRS image in response to the absence of an AI model corresponding to a DMRS image in the UE.

[0284] This disclosure provides an information processing apparatus applied to a UE, which may include: a first transmitting module configured to report first suggestion information, wherein the first suggestion information indicates the DMRS pattern used by the UE and / or indicates the AI ​​model required by the UE; wherein the first suggestion information is used for the base station to determine model information.

[0285] This disclosure provides an information processing apparatus for a UE, which may include: a first processing module 51 configured to perform channel estimation using the AI ​​model corresponding to the DMRS pattern if there is only one AI model corresponding to the DMRS pattern.

[0286] This disclosure provides an information processing apparatus for a UE, which may include: a first processing module 51 configured to select one AI model from multiple AI models for channel estimation based on AI model indication information if there are multiple AI models corresponding to the DMRS pattern.

[0287] This disclosure provides an information processing apparatus applied to a UE, which may include: a first processing module 51 configured to determine, in response to the existence of an AI model corresponding to a DRMS ​​pattern in the UE, to use the AI ​​model corresponding to the DRMS ​​pattern for channel estimation.

[0288] This disclosure provides an information processing apparatus applied to a UE, which may include: a first processing module 51 configured to determine that if there is no AI model corresponding to the DMRS pattern in the UE, the AI ​​model corresponding to the DMRS pattern will not be used for channel estimation.

[0289] This disclosure provides an information processing apparatus applied to a UE, which may include: a first transmitting module configured to report second recommendation information, wherein the second recommendation information indicates a DMRS pattern recommended by the UE for use by a base station to determine configuration information.

[0290] This disclosure provides an information processing apparatus applied to a UE, which may include: a determination module configured to determine a DMRS pattern recommended for use by the UE based on at least one of the UE's mobility information, channel quality information, computing power information, and storage power information.

[0291] like Figure 12 As shown, this disclosure provides an information processing apparatus applied to a base station, comprising:

[0292] The second transmitting module 61 is configured to transmit configuration information indicating the number of AI models corresponding to the DMRS pattern, wherein the number of AI models corresponding to the DMRS pattern is used to instruct the UE to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern.

[0293] In some embodiments, the number of AI models corresponding to the DMRS pattern in the configuration information is used to instruct the UE to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern, including one of the following:

[0294] In response to the fact that there is only one AI model corresponding to the DMRS pattern, the configuration information is used to instruct the UE to use the AI ​​model corresponding to the DMRS pattern for channel estimation;

[0295] In response to the fact that there are multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to determine one AI model from multiple AI models for channel estimation based on the AI ​​model indication information.

[0296] In response to the fact that there are multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to select any one of the multiple AI models for channel estimation;

[0297] In response to the fact that there are multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to select one of the multiple AI models that matches the UE's mobile speed, channel quality, computing power, and storage capacity.

[0298] In some embodiments, the configuration information includes: a DMRS pattern; the configuration information is used to indicate whether the UE uses an AI model corresponding to the DMRS pattern for channel estimation.

[0299] In other embodiments, the configuration information includes: a DMRS pattern; wherein the DMRS pattern is used by the UE to determine whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation before determining whether to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

[0300] This disclosure provides an information processing apparatus applied to a base station, including: a second transmitting module 61, configured to transmit configuration information indicating the number of AI models corresponding to a DMRS pattern before transmitting configuration information indicating the number of AI models corresponding to the DMRS pattern.

[0301] This disclosure provides an information processing apparatus applied to a base station, including: a second transmitting module 61 configured to transmit configuration information indicating a DMRS pattern, wherein the DMRS pattern is used by the UE to determine whether to use an AI model corresponding to the DMRS pattern for channel estimation.

[0302] In some embodiments, the configuration information further includes: AI indication information, indicating whether to start an AI model for channel estimation; wherein, in response to the AI ​​indication information indicating to start an AI model for channel estimation, the UE uses an AI model corresponding to the DMRS pattern for channel estimation.

[0303] In some embodiments, the configuration information further includes: AI model indication information, which instructs the UE to select an AI model for channel estimation from the AI ​​models corresponding to the DMRS pattern.

[0304] This disclosure provides an information processing apparatus applied to a UE, which may include: a second sending module 61 configured to send model information of at least one AI model corresponding to a DMRS pattern in response to determining that no AI model corresponding to a DMRS pattern exists in the UE.

[0305] This disclosure provides an information processing apparatus, applied to a UE, which may include:

[0306] The second receiving module is configured to receive the second suggestion information;

[0307] The second processing module is configured to determine configuration information based on the second recommendation information indicating the DMRS pattern recommended by the UE.

[0308] like Figure 13 As shown, this disclosure provides an information processing apparatus applied to a base station, comprising:

[0309] The third receiving module 71 is configured to receive first suggestion information; wherein the first suggestion information indicates the DMRS pattern used by the UE, or the first suggestion information indicates the DMRS pattern used by the UE and indicates the AI ​​model required by the UE; wherein the AI ​​model is used by the UE to perform channel estimation.

[0310] The third processing module 72 is configured to determine the model information of the AI ​​model corresponding to the DMRS pattern required by the UE based on the first suggestion information.

[0311] The third sending module 73 is configured to send model information.

[0312] It should be noted that those skilled in the art will understand that the apparatus provided in the embodiments of this disclosure can be executed alone or together with some apparatus in the embodiments of this disclosure or some apparatus in related technologies.

[0313] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0314] This disclosure provides a communication device, including:

[0315] processor;

[0316] Memory used to store processor-executable instructions;

[0317] The processor is configured to implement the information processing method of any embodiment of this disclosure when running executable instructions.

[0318] In one embodiment, the communication device may be a base station or a UE.

[0319] The processor may include various types of storage media, which are non-transitory computer storage media that can continue to store information after the user equipment loses power.

[0320] The processor can connect to memory via a bus or similar means to read executable programs stored in memory, for example... Figures 2 to 10 At least one of the methods shown.

[0321] This disclosure also provides a computer storage medium storing a computer-executable program, which, when executed by a processor, implements the information processing method of any embodiment of this disclosure. For example, such as... Figures 2 to 10 At least one of the methods shown.

[0322] Regarding the apparatus or storage medium in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0323] Figure 14 This is a block diagram illustrating a user equipment 800 according to an exemplary embodiment. For example, user equipment 800 may be a mobile phone, computer, digital broadcast user equipment, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0324] Reference Figure 14 User equipment 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0325] Processing component 802 typically controls the overall operation of user equipment 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0326] Memory 804 is configured to store various types of data to support the operation of user equipment 800. Examples of this data include instructions for any application or method operating on user equipment 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0327] Power supply component 806 provides power to various components of user equipment 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to user equipment 800.

[0328] Multimedia component 808 includes a screen that provides an output interface between the user equipment 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the user equipment 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0329] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when user equipment 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0330] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0331] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of user equipment 800. For example, sensor assembly 814 may detect the on / off state of user equipment 800, the relative positioning of components such as the display and keypad of user equipment 800, changes in position of user equipment 800 or a component of user equipment 800, the presence or absence of user contact with user equipment 800, orientation or acceleration / deceleration of user equipment 800, and temperature changes of user equipment 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0332] Communication component 816 is configured to facilitate wired or wireless communication between user equipment 800 and other devices. User equipment 800 can access wireless networks based on communication standards, such as WiFi, 4G, or 5G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0333] In an exemplary embodiment, the user equipment 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0334] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of a user device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0335] like Figure 15As shown, one embodiment of this disclosure illustrates the structure of a base station. For example, base station 900 can be provided as a network-side device. (Refer to...) Figure 15 The base station 900 includes a processing component 922, which further includes one or more processors, and memory resources represented by a memory 932 for storing instructions executable by the processing component 922, such as application programs. The application programs stored in the memory 932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 922 is configured to execute instructions to perform any of the methods described above applied to the base station, such as... Figures 2 to 10 The method shown.

[0336] Base station 900 may also include a power supply component 926 configured to perform power management of base station 900, a wired or wireless network interface 950 configured to connect base station 900 to a network, and an input / output (I / O) interface 958. Base station 900 can operate on an operating system stored in memory 932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0337] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0338] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. An information processing method, wherein, Performed by the user equipment (UE), including: Based on the number of artificial intelligence (AI) models corresponding to the demodulated reference signal (DMRS) pattern, channel estimation is performed using the AI ​​model corresponding to the DMRS pattern. The step of using the AI ​​model corresponding to the DMRS pattern to perform channel estimation based on the number of AI models corresponding to the demodulated reference signal (DMRS) pattern includes one of the following: In response to the fact that there is only one AI model corresponding to the DMRS pattern, channel estimation is performed using the AI ​​model corresponding to the DMRS pattern; In response to the presence of multiple AI models corresponding to the DMRS pattern, one AI model is determined from the multiple AI models for channel estimation based on AI model indication information; In response to the presence of multiple AI models corresponding to the DMRS pattern, channel estimation is performed by selecting any one of the multiple AI models; In response to the presence of multiple AI models corresponding to the DMRS pattern, one AI model is selected from the multiple AI models to match one of the following: the UE's mobile speed, channel quality, UE's computing power, and UE's storage capacity.

2. The method according to claim 1, wherein, The method further includes: Determine whether to use the AI ​​model for channel estimation.

3. The method according to claim 2, wherein, The method further includes: Receive configuration information indicating the DMRS pattern; or, The DMRS pattern is determined based on at least one of the UE's mobility information, channel quality information, computing power information, and storage power information.

4. The method according to claim 3, wherein, The configuration information also includes: AI instruction information, indicating whether to start the AI ​​model for channel estimation.

5. The method according to claim 3 or 4, wherein, The configuration information also includes: AI model indication information, which instructs the UE to determine the AI ​​model for channel estimation from the AI ​​model corresponding to the DMRS pattern.

6. The method according to any one of claims 1 to 4, wherein, The method further includes: Receive model information of at least one AI model corresponding to the DMRS pattern; or, According to the agreement, model information of at least one AI model corresponding to the DMRS pattern is determined.

7. The method according to claim 6, wherein, The step of receiving model information of at least one AI model corresponding to the DMRS pattern includes: In response to the absence of an AI model corresponding to the DMRS pattern in the UE, model information of at least one AI model corresponding to the DMRS image is received.

8. The method according to claim 6, wherein, The method further includes: The first suggestion information is reported, wherein the first suggestion information indicates the DMRS pattern used by the UE, or the first suggestion information indicates the DMRS pattern used by the UE and indicates the AI ​​model required by the UE; wherein the first suggestion information is used for the base station to determine the model information.

9. The method according to any one of claims 2 to 4, wherein, The determination of whether to use the AI ​​model for channel estimation includes one of the following: In response to the existence of the AI ​​model corresponding to the DMRS pattern in the UE, it is determined that the AI ​​model corresponding to the DMRS pattern will be used for channel estimation; In response to the absence of an AI model corresponding to the DMRS pattern in the UE, it is determined that the AI ​​model corresponding to the DMRS pattern will not be used for channel estimation.

10. The method according to claim 3, wherein, The method further includes: The second recommendation information is reported, wherein the second recommendation information indicates the DMRS pattern that the UE recommends to use, for the base station to determine the configuration information.

11. The method according to claim 10, wherein, The method includes: Based on at least one of the UE's mobility information, channel quality information, computing power information, and storage capacity information, the DMRS pattern recommended for use by the UE is determined.

12. An information processing method, wherein, Performed by the base station, including: Send configuration information for indicating the number of artificial intelligence (AI) models corresponding to the demodulation reference signal (DMRS) pattern, wherein the number of AI models corresponding to the DMRS pattern is used to instruct the user equipment (UE) to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern; The number of AI models corresponding to the DMRS pattern in the configuration information is used to instruct the user equipment (UE) to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern, including one of the following: In response to the fact that there is one AI model corresponding to the DMRS pattern, the configuration information is used to instruct the UE to use the AI ​​model corresponding to the DMRS pattern for channel estimation; In response to the presence of multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to determine one AI model from the multiple AI models for channel estimation based on the AI ​​model indication information; In response to the presence of multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to determine any one AI model from the multiple AI models for channel estimation; In response to the presence of multiple AI models corresponding to the DMRS pattern, the configuration information instructs the UE to select an AI model from the multiple AI models that matches one of the UE's mobile speed, channel quality, computing power, and storage capacity.

13. The method according to claim 12, wherein, The configuration information also includes: the DMRS pattern; The configuration information is used to indicate whether the UE uses the AI ​​model corresponding to the DMRS pattern for channel estimation.

14. The method according to claim 13, wherein, The configuration information also includes: AI instruction information, indicating whether to start the AI ​​model for channel estimation; When the AI ​​indication information indicates that the AI ​​model should be started for channel estimation, the UE uses the AI ​​model corresponding to the DMRS pattern to perform channel estimation.

15. The method according to claim 13 or 14, wherein, The configuration information also includes: AI model indication information instructs the UE to select the AI ​​model for channel estimation from the AI ​​models corresponding to the DMRS pattern.

16. The method according to claim 12 or 13, wherein, The method further includes: In response to determining that there is no AI model corresponding to the DMRS pattern in the UE, model information of at least one AI model corresponding to the DMRS pattern is sent.

17. The method according to claim 12 or 13, wherein, The method includes: Receive the second suggestion message; The configuration information is determined based on the DMRS pattern recommended by the UE according to the second recommendation information.

18. An information processing method, wherein, Performed by the base station, including: Receive first suggestion information; wherein the first suggestion information indicates the demodulation reference signal (DMRS) pattern used by the user equipment (UE), or the first suggestion information indicates the DMRS pattern used by the UE and indicates the artificial intelligence (AI) model required by the UE; wherein the AI ​​model is used by the UE to perform channel estimation. Based on the first suggestion information, determine the model information of the AI ​​model required by the UE corresponding to the DMRS pattern; Send the model information.

19. An information processing apparatus, wherein, Applied to User Equipment (UE), including: The first processing module is configured to perform channel estimation using the AI ​​model corresponding to the DMRS pattern based on the number of AI models corresponding to the demodulated reference signal (DMRS) pattern. The first processing module is configured as one of the following: In response to the fact that there is only one AI model corresponding to the DMRS pattern, channel estimation is performed using the AI ​​model corresponding to the DMRS pattern; In response to the presence of multiple AI models corresponding to the DMRS pattern, an AI model is selected from the multiple AI models for channel estimation based on AI model indication information; In response to the presence of multiple AI models corresponding to the DMRS pattern, channel estimation is performed by selecting any one of the multiple AI models; In response to the presence of multiple AI models corresponding to the DMRS pattern, one AI model is selected from the multiple AI models to match one of the following: the UE's mobile speed, channel quality, UE's computing power, and UE's storage capacity.

20. The apparatus according to claim 19, wherein, The first processing module is configured to determine whether to use an AI model for channel estimation.

21. The apparatus according to claim 20, wherein, The determining module is configured to receive configuration information indicating the DMRS pattern; or, The determining module is configured to determine the DMRS pattern based on at least one of the UE's mobility information, channel quality information, computing power information, and storage power information.

22. The apparatus according to claim 21, wherein, The configuration information also includes: AI instruction information, indicating whether to start the AI ​​model for channel estimation.

23. The apparatus according to claim 21 or 22, wherein, The configuration information also includes: AI model indication information, which instructs the UE to select the AI ​​model for channel estimation from the AI ​​model corresponding to the DMRS pattern.

24. The apparatus according to any one of claims 19 to 22, wherein, The device further includes: a first receiving module configured to receive model information of at least one AI model corresponding to the DMRS pattern; or, The determining module is configured to determine, according to the protocol, the model information of at least one AI model corresponding to the DMRS pattern.

25. The apparatus according to claim 24, wherein, The first receiving module is configured to receive model information of at least one AI model corresponding to the DMRS image in response to the absence of an AI model corresponding to the DMRS pattern in the UE.

26. The apparatus according to claim 24, wherein, The device further includes: A first sending module is configured to report first suggestion information, wherein the first suggestion information indicates the DMRS pattern used by the UE, or the first suggestion information indicates the DMRS pattern used by the UE and indicates the AI ​​model required by the UE; wherein the first suggestion information is used for the base station to determine the model information.

27. The apparatus according to any one of claims 20 to 22, wherein, The first processing module is configured to determine, in response to the existence of an AI model in the UE corresponding to the DMRS pattern, to use the AI ​​model corresponding to the DMRS pattern for channel estimation; or, The first processing module is configured to determine, in response to the absence of an AI model corresponding to the DMRS pattern in the UE, not to use the AI ​​model corresponding to the DMRS pattern for channel estimation.

28. The apparatus according to claim 21, wherein, The device further includes: The first transmitting module is configured to report second recommendation information, wherein the second recommendation information indicates the DMRS pattern recommended by the UE for the base station to determine the configuration information.

29. The apparatus according to claim 28, wherein, The determining module is configured to determine the DMRS pattern recommended by the UE based on at least one of the UE's mobility information, channel quality information, computing power information, and storage power information.

30. An information processing apparatus, wherein, Applied to base stations, including: The second transmitting module is configured to transmit configuration information for indicating the number of artificial intelligence (AI) models corresponding to the demodulation reference signal (DMRS) pattern, wherein the number of AI models corresponding to the DMRS pattern is used to instruct the user equipment (UE) to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern. The number of AI models corresponding to the DMRS pattern in the configuration information is used to instruct the user equipment (UE) to determine the AI ​​model for channel estimation based on the number of AI models corresponding to the DMRS pattern, including one of the following: In response to the fact that there is one AI model corresponding to the DMRS pattern, the configuration information is used to instruct the UE to use the AI ​​model corresponding to the DMRS pattern for channel estimation; In response to the presence of multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to determine one AI model from the multiple AI models for channel estimation based on the AI ​​model indication information; In response to the presence of multiple AI models corresponding to the DMRS pattern, the configuration information is used to instruct the UE to determine any one AI model from the multiple AI models for channel estimation; In response to the presence of multiple AI models corresponding to the DMRS pattern, the configuration information instructs the UE to select an AI model from the multiple AI models that matches one of the UE's mobile speed, channel quality, computing power, and storage capacity.

31. The apparatus according to claim 30, wherein, The configuration information also includes: DMRS pattern; The configuration information is used to indicate whether the UE uses the AI ​​model corresponding to the DMRS pattern for channel estimation.

32. The apparatus according to claim 31, wherein, The configuration information also includes: AI instruction information indicates whether to activate the AI ​​model for channel estimation; When the AI ​​indication information indicates that the AI ​​model should be started for channel estimation, the UE uses the AI ​​model corresponding to the DMRS pattern to perform channel estimation.

33. The apparatus according to claim 31 or 32, wherein, The configuration information also includes: AI model indication information instructs the UE to select the AI ​​model for channel estimation from the AI ​​models corresponding to the DMRS pattern.

34. The apparatus according to claim 30 or 31, wherein, The second sending module is configured to, in response to determining that there is no AI model in the UE corresponding to the DMRS pattern, send model information of at least one AI model corresponding to the DMRS pattern.

35. The apparatus according to claim 30 or 31, wherein, The device includes: The second receiving module is configured to receive the second suggestion information; The second processing module is configured to determine the configuration information based on the DMRS pattern recommended by the UE according to the second recommendation information.

36. An information processing apparatus, wherein, Applied to base stations, including: The third receiving module is configured to receive first suggestion information; wherein the first suggestion information indicates the demodulation reference signal (DMRS) pattern used by the user equipment (UE), or the first suggestion information indicates the DMRS pattern used by the UE and indicates the artificial intelligence (AI) model required by the UE; wherein the AI ​​model is used by the UE to perform channel estimation. The third processing module is configured to determine, based on the first suggestion information, the model information of the AI ​​model required by the UE corresponding to the DMRS pattern; The third sending module is configured to send the model information.

37. A communication device, wherein, The communication device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to implement the information processing method according to any one of claims 1 to 11, 12 to 17, or 18 when executing the executable instructions.

38. A computer storage medium, wherein, The computer storage medium stores a computer-executable program, which, when executed by a processor, implements the information processing method according to any one of claims 1 to 11, 12 to 17, or 18.

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