Communication method and related apparatus

Abstract

The present application is applied to the field of communications. Provided are a communication method and a related apparatus. The method comprises: a base station acquiring channel measurement information of a terminal, wherein the measurement information is associated with location information of the terminal; furthermore, the base station being able to obtain channel information of the terminal on the basis of the channel measurement information and a first model, wherein the channel information is used for a target task, and the first model is obtained by means of performing training on the basis of a training dataset, the training data set comprising sample channel measurement information of at least one terminal and corresponding sample channel information. By means of such a method, the disclosure of location information of a terminal can be avoided, thus improving the privacy of the terminal.

Description

Communication methods and related devices

[0001] This application claims priority to Chinese Patent Application No. 202510099637.9, filed on January 21, 2025, with the China National Intellectual Property Administration, entitled "Communication Method and Related Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of communication technology, and in particular to a communication method and related apparatus. Background Technology

[0003] In the field of communications, artificial intelligence (AI) technology is used to train machine learning models with massive amounts of data to construct a channel map (RF-MAP). This channel map is used to model the base station environment, expressing the mapping relationship between terminal location and channel state information. The base station can use the terminal location information as input to the channel map to obtain channel-related parameters between the terminal and the base station. Subsequently, the base station and the terminal can perform related tasks such as beam selection, channel prediction, precoding, and scheduling based on these channel-related parameters.

[0004] It is evident that base stations typically need to obtain terminal location information when using channel maps, which raises privacy concerns. Therefore, how to mitigate privacy risks when using channel maps is an unresolved issue. Summary of the Invention

[0005] This application provides a communication method and related apparatus that can avoid leaking the terminal's location information and improve terminal privacy.

[0006] In a first aspect, this application provides a communication method, which is applied to a network-side device or a module (e.g., a chip or chip system) in a network-side device. Taking the application to a base station as an example, the method includes: the base station acquiring channel measurement information of a terminal; the channel measurement information being associated with the location information of the terminal; the base station also obtaining channel information of the terminal based on the channel measurement information and a first model, the channel information being used for a target task; wherein the first model is obtained based on a training dataset, the training dataset including sample channel measurement information of at least one terminal and corresponding sample channel information.

[0007] Based on the method described in the first aspect, since the first model is trained using channel measurement information associated with location information, the first model is a location-independent model. Therefore, when using the first model to predict the channel information of the terminal, it is necessary to obtain the location-associated channel measurement information as the input of the first model in order to predict the channel information of the terminal. This can avoid using the terminal location information as the input of the channel map, thereby avoiding the leakage of the terminal's location information and improving the terminal's privacy.

[0008] In one possible implementation, the base station acquires the channel measurement information of the terminal, including: the base station sending first configuration information to the terminal, the first configuration information being used to configure a first measurement resource; the base station receiving the channel measurement information obtained by the terminal on the first measurement resource; or, the base station determining the channel measurement information of the terminal based on a reference signal sent by the terminal on the first measurement resource.

[0009] In this embodiment, the base station uses the configured first measurement resources to obtain channel measurement information associated with the location information as input to the model, rather than obtaining the terminal's location information as input to the model, thereby avoiding the leakage of the terminal's location.

[0010] In one possible implementation, the base station acquires channel measurement information of the terminal, including: the base station sending second configuration information to the terminal, the second configuration information being used to configure second measurement resources; and the base station sending third configuration information to the terminal, the third configuration information being used to configure third measurement resources.

[0011] In this approach, the base station can obtain channel measurements from the terminal in the following three ways, including but not limited to:

[0012] In one approach, the base station receives channel measurement information obtained by the terminal from second and third measurement resources. This channel measurement information includes first measurement information from the second measurement resource and second measurement information from the third measurement resource, or it includes information fused by the terminal from the first and second measurement information. Therefore, this embodiment reasonably obtains the terminal's channel measurement information through two downlink channel measurements. Furthermore, in the two channel measurements, the terminal can report two sets of measurement information separately, and the base station can then obtain the channel measurement information based on the first and second measurement information; alternatively, the terminal can obtain the channel measurement information based on the first and second measurement information and then report it. In summary, one scenario involves the terminal not understanding how to determine the channel measurement information, while the other involves the terminal understanding how to determine the channel measurement information; thus, the reporting method achieves the effect of terminal awareness and terminal non-awareness.

[0013] Alternatively, the base station can determine the terminal's channel measurement information based on the reference signal transmitted by the terminal on the second measurement resource and the reference signal transmitted by the terminal on the third measurement resource. Therefore, this embodiment can reasonably obtain the terminal's channel measurement information through two uplink channel measurement methods.

[0014] In another approach, the base station can determine the terminal's channel measurement information based on the measurement information obtained by the terminal on the second measurement resource and the reference signal sent by the terminal on the third measurement resource. Therefore, this embodiment can reasonably obtain the terminal's channel measurement information through one downlink channel measurement and one uplink channel measurement.

[0015] In this embodiment, the base station is configured with multiple measurement resources, enabling multi-level and multi-dimensional measurements, effectively improving the accuracy of channel measurement information. Furthermore, since the channel measurement information serves as input to the first model for predicting channel information, high-precision channel measurement information can improve the prediction accuracy of the first model and thus enhance the overall prediction accuracy of the channel information.

[0016] Secondly, this application provides a communication method, which is applied to a terminal or a module (such as a chip or chip system) in the terminal. Taking the application to a terminal as an example, the method includes: the terminal receiving first configuration information; the first configuration information being used to configure first measurement resources; the terminal sending channel measurement information obtained based on the first measurement resources; or, the terminal sending a reference signal on the first measurement resources, the reference information being used by a base station to measure and obtain the channel measurement information of the terminal.

[0017] Based on the method described in the second aspect, the terminal receives configuration information sent by the base station, enabling the base station and the terminal to perform a channel measurement and obtain the terminal's channel measurement information. This is beneficial for the base station to use the channel measurement information to predict the terminal's channel information.

[0018] Thirdly, this application provides a communication method, which is applied to a terminal or a module (such as a chip or chip system) in the terminal. Taking the application to a terminal as an example, the method includes: the terminal receiving second configuration information sent by a base station, the second configuration information being used to configure second measurement resources; the terminal may also receive third configuration information sent by the base station, the third configuration information being used to configure third measurement resources.

[0019] In this method, the terminal's transmission to the base station can include, but is not limited to, the following three scenarios:

[0020] In one scenario, the terminal sends channel measurement information obtained from the second and third measurement resources to the base station. This channel measurement information includes first measurement information from the second measurement resource and second measurement information from the third measurement resource; alternatively, the channel measurement information includes information fused from the first and second measurement information by the terminal. It is evident that this embodiment obtains the terminal's channel measurement information reasonably through two downlink channel measurements. Furthermore, in the two channel measurements, the terminal can report two sets of measurement information separately, or the terminal can obtain the channel measurement information based on the first and second measurement information and then report it. In summary, one scenario involves the terminal not understanding how to determine the channel measurement information, while the other involves the terminal understanding how to determine the channel measurement information; thus, the reporting method achieves the effect of terminal awareness and terminal non-awareness.

[0021] In another scenario, the terminal can send reference signals on the second measurement resource and the third measurement resource to the base station. As can be seen, this embodiment involves the terminal sending reference signals corresponding to the uplink channel measurement methods twice, thus allowing the terminal's channel measurement information to be reasonably obtained through these two uplink channel measurement methods.

[0022] In another scenario, the terminal can send measurement information obtained from the second measurement resource and a reference signal from the third measurement resource to the base station. Thus, in this embodiment, by sending measurement information and a reference signal to the base station, the terminal can reasonably obtain its channel measurement information through one downlink channel measurement and one uplink channel measurement.

[0023] Based on the method described in the third aspect, the terminal can receive configuration information sent multiple times by the base station, enabling multiple channel measurements between the base station and the terminal, and obtaining measurement information from multiple channel measurements. This facilitates the subsequent combination of multiple measurement information to obtain the terminal's channel measurement information. Therefore, in this embodiment, the terminal's channel measurement information is obtained from multiple measurement information, which improves the accuracy of the channel measurement information compared to using the measurement information from a single channel measurement.

[0024] Fourthly, this application provides a model training method, which is applied to a network-side device or a module (e.g., a chip or chip system) in a network-side device. Taking its application to a base station as an example, the method includes: the base station acquiring a training dataset, which includes sample channel measurement information of at least one terminal and corresponding sample channel information, wherein the sample channel measurement information is associated with the location information of the terminal, and the sample channel information is used for the target task; the base station can acquire a first model based on the training dataset, wherein the first model is used to represent the mapping relationship between the channel measurement information and the channel information of the terminal.

[0025] Based on the method described in the fourth aspect, the base station does not use direct location information for model training, but instead obtains sample channel measurement information associated with the location information and performs model training, thereby obtaining a location-independent first model.

[0026] In one possible implementation, the base station can acquire the training dataset in the following ways: The base station can receive measurement reports reported by each terminal in at least one terminal. The measurement reports include the terminal's location information and sample channel measurement information associated with the location information. For each terminal, the base station inputs the terminal's location information into a second model to obtain the sample channel information corresponding to the location information. The second model is used to represent the mapping relationship between the terminal's location information and the sample channel information of the location information. Further, the base station can construct a training dataset based on the sample channel measurement information associated with the location information of each terminal and the sample channel information corresponding to the location information of each terminal.

[0027] In this implementation, a terminal's measurement report includes the terminal's location information and the corresponding sample channel measurement information. Since a location-based second model is deployed at the base station, the second model can be used to predict the sample channel information corresponding to the location information, thus achieving a rational acquisition of the sample channel information. In addition, the training dataset is also constructed based on the sample channel measurement information in the measurement report and the predicted sample channel information, ensuring the reliability of the training dataset.

[0028] In one possible implementation, the base station can obtain the training dataset in the following way: the base station receives measurement reports reported by each terminal in at least one terminal. The measurement reports include the terminal's location information, sample channel measurement information associated with the location information, and sample channel information corresponding to the location information. Then, the base station can construct the training dataset based on the received measurement reports.

[0029] In this implementation, the measurement report directly includes the sample channel information corresponding to the location information, eliminating the need for a second model to predict the sample channel information based on the location information. Compared to using a second model to predict the sample channel information, this reduces the complexity of constructing the training dataset.

[0030] In one possible implementation, the method further includes: the base station receiving an activation request for the MDT minimized drive test procedure; the base station sending configuration information for the MDT procedure to each terminal, the configuration information being used to instruct the terminal to report a measurement report.

[0031] In this implementation, the base station can obtain a measurement report for building the training dataset through the MDT process, thereby improving the rationality of the training dataset construction.

[0032] In one possible implementation, after the base station obtains the first model based on the training dataset, the method further includes: the base station receiving a deactivation request from the MDT procedure.

[0033] In this implementation, after the base station obtains the first model, the base station can promptly stop executing the MDT process to reduce network load.

[0034] In one possible implementation, the method further includes: the base station sending a request to acquire the second model; the base station receiving and deploying the second model.

[0035] In this implementation, the base station deploys the original model to be trained so that after obtaining the training dataset, the model can be trained directly based on the training dataset, thereby ensuring the smooth execution of training.

[0036] In one possible implementation, the second model is stored in a model storage area, and the method further includes: the base station sending a request to the OAM node to obtain the second model;

[0037] In this approach, the base station can receive and deploy the second model in the following ways, including but not limited to:

[0038] One approach involves deploying a model storage area on the OAM node. The base station receives a second model from the OAM node, which retrieves it from the model storage area, and then deploys the second model. As can be seen, when the model storage area is deployed on the OAM node, the OAM node directly retrieves the second model from this area, making the acquisition of the second model simple.

[0039] Another method involves the model storage area not being deployed on the OAM node. The base station receives the second model sent by the model storage area and deploys it. Therefore, when the model storage area is not deployed on the OAM node, after receiving a request, the OAM node needs to send the request to the model storage area so that the model storage area can respond and send the second model to the base station for deployment. This series of operations ensures that the base station can successfully obtain the second model.

[0040] In one possible implementation, the model storage area is not deployed on the OAM node, and the method further includes: the base station sending a request to the model storage area to acquire a second model; the base station receiving and deploying the second model; the base station sending a model training request to the OAM node; and the base station receiving an activation request for the MDT process sent by the OAM node.

[0041] In this implementation, the storage pressure on the OAM node is reduced by not deploying the model storage area on the OAM node. In addition, the base station also sends a model training request to the OAM node. Since the activation of the MDT process requires the OAM node to complete, the base station sends a model training request to the OAM node in addition to initiating the model acquisition from the model storage area. This informs the OAM node which base station needs to activate the MDT process, thereby triggering the OAM node to send an activation request for the MDT process to the base station to ensure the smooth execution of the MDT process.

[0042] Fifthly, this application provides a model training method, which is applied to a terminal or a module in the terminal (such as a chip or chip system). Taking the application to a terminal as an example, the method includes: the terminal receiving configuration information for the MDT process sent by the base station; the terminal performing measurements based on the configuration information, obtaining a measurement report, and sending the measurement report to the base station.

[0043] Based on the method described in the fifth aspect, the terminal performs measurements using the configuration information sent by the base station and reports the measurement report to the base station so that the base station can obtain the training dataset based on the measurement report.

[0044] Sixthly, this application provides a model training method, which is applied to a control node or a module (e.g., a chip or chip system) in a control node. Taking the application to a control node as an example, the method includes: the control node receiving a second model acquisition request sent by a base station, the second model being used to represent the mapping relationship between the location information of a terminal and the channel information of the location information; the control node responding to the acquisition request to send the second model to the base station; the control node also sending an activation request for an MDT procedure to the base station; the activation request being used to trigger the base station to send configuration information of the MDT procedure to each terminal in at least one terminal, the configuration information being used to instruct the terminal to report a measurement report.

[0045] Based on the method described in the sixth aspect, the control node can send the model to be trained to the base station, so that the base station can train on the model to obtain the first model. The control node can also trigger the activation of the MDT procedure, so that the base station can obtain the training dataset based on the MDT procedure.

[0046] In one possible implementation, the control node is an OAM node, and a model storage area storing the second model is deployed on the OAM node. The method further includes: the OAM node receiving a request to obtain the second model sent by the base station, the OAM node obtaining the second model from the model storage area, and sending the second model to the base station.

[0047] In this implementation, by deploying the model storage area on the OAM node, when a request to obtain a second model is sent to the OAM node, the OAM node can directly obtain it from this area, thus achieving a simple model acquisition method.

[0048] In one possible implementation, the control node is an OAM node, and the model storage area storing the second model is not deployed on the OAM node. The method further includes: the OAM node receiving a second model acquisition request sent by the base station, and the OAM node sending the second model acquisition request to the model storage area, so that the model storage area responds to the acquisition request and sends the second model to the base station.

[0049] In this implementation, when the model storage area is not deployed on the OAM node, after receiving the acquisition request, the OAM node needs to send the acquisition request to the model storage area so that the second model can be obtained from the model storage area. Through a series of operations, it is ensured that the base station can successfully obtain the second model.

[0050] In one possible implementation, the control node is an OAM node, and the model storage area storing the second model is not deployed on the OAM node. The method further includes: the OAM node receiving a model training request sent by the base station; and the OAM node sending an activation request for the MDT process to the base station.

[0051] In this implementation, the base station sends a request to the model storage area to obtain the second model. The model storage area responds to the request by sending the second model to the base station, so that the base station receives and deploys the second model. Since the MDT process requires the activation of the OAM node, the base station also needs to send a request to the OAM node to trigger the OAM node to send an activation request for the MDT process to the base station, thereby ensuring that the OAM node activates the MDT process at the base station.

[0052] In one possible implementation, the method further includes: the control node sending a deactivation request for the MDT procedure to the base station.

[0053] In this implementation, the MDT process can be stopped in a timely manner, reducing network load.

[0054] In one possible implementation, the method further includes: a control node receiving a measurement report sent by a base station and determining a training dataset based on the measurement report; the control node also obtaining a first model based on the training dataset; the first model is used to represent the mapping relationship between the terminal's measurement information and channel information; and then the control node can send the first model to the base station.

[0055] In this implementation, compared to training the model at the base station, training the model at other nodes can reduce the data processing pressure on the base station and avoid generating large computational overhead at the base station.

[0056] In one possible implementation, the method further includes: a control node receiving a training dataset sent by a base station; the control node obtaining a first model based on the training dataset; the first model representing the mapping relationship between the terminal's measurement information and channel information; and then the control node sending the first model to the base station.

[0057] In this implementation, compared to training the model at the base station, training the model at other nodes reduces the data processing pressure on the base station and avoids significant computational overhead at the base station. Additionally, the base station can send the training dataset obtained from measurement reports to the control node, further reducing the computational burden on the control node.

[0058] Seventhly, this application provides a communication device, which can be a network-side device, a device within a network-side device, or a device compatible with a network-side device. The communication device can also be a chip system. The communication device can execute the methods described in the first or fourth aspect. The functions of the communication device can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more units or modules corresponding to the above functions. The unit or module can be software and / or hardware. The operations performed by the communication device and its beneficial effects can be found in the methods described in the first or fourth aspect and their beneficial effects.

[0059] Eighthly, this application provides a communication device, which can be a terminal, a device within a terminal, or a device compatible with a terminal. The communication device can also be a chip system. The communication device can execute the methods described in the second, third, or fifth aspects. The functions of the communication device can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more units or modules corresponding to the above functions. The unit or module can be software and / or hardware. The operations performed by the communication device and its beneficial effects can be found in the methods described in the second, third, or fifth aspects above, as well as their beneficial effects.

[0060] Ninthly, this application provides a communication device, which can be a control node, a device within a control node, or a device compatible with a control node. The communication device can also be a chip system. The communication device can perform the method described in the sixth aspect. The functions of the communication device can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more units or modules corresponding to the above functions. The unit or module can be software and / or hardware. The operations performed by the communication device and its beneficial effects can be found in the method described in the sixth aspect above.

[0061] In a tenth aspect, this application provides a communication device including a processor and an interface circuit. The interface circuit is configured to receive signals from other communication devices outside the communication device and transmit them to the processor, or to send signals from the processor to other communication devices outside the communication device. The processor is configured to implement the method described in any one of the first to sixth aspects via logic circuits or execution code instructions.

[0062] Eleventhly, this application provides a communication device including a processor connected to a memory for calling a program stored in the memory to execute the method described in any one of the first to sixth aspects. The memory may be located within a network-side device, terminal, or control node, or it may be located outside of the network-side device, terminal, or control node. Furthermore, the processor may include one or more processors.

[0063] In a twelfth aspect, this application provides a computer-readable storage medium storing a computer program or instructions that, when executed by a communication device, implement the method described in any one of the first to sixth aspects.

[0064] In a thirteenth aspect, this application provides a computer program product including instructions that, when read and executed by a communication device, cause the communication device to perform the method described in any one of the first to sixth aspects.

[0065] In a fourteenth aspect, this application provides a communication system including communication means for performing the methods described in the first to sixth aspects above. Attached Figure Description

[0066] Figure 1a is a schematic diagram of the architecture of a communication system provided in an embodiment of this application;

[0067] Figure 1b is another schematic diagram of a wireless communication system applicable to an embodiment of this application;

[0068] Figure 1c is a schematic diagram of the application scenario of the channel map provided in the embodiment of this application;

[0069] Figure 1d is a schematic diagram of the application scenario of the first model provided in the embodiment of this application;

[0070] Figure 2 is a flowchart illustrating a communication method provided in an embodiment of this application;

[0071] Figure 3 is a schematic flowchart of a channel measurement method provided in an embodiment of this application;

[0072] Figure 4 is a flowchart illustrating another channel measurement method provided in an embodiment of this application;

[0073] Figure 5 is a flowchart illustrating another channel measurement method provided in an embodiment of this application;

[0074] Figure 6 is a flowchart illustrating another channel measurement method provided in an embodiment of this application;

[0075] Figure 7 is a flowchart illustrating another channel measurement method provided in an embodiment of this application;

[0076] Figure 8 is a flowchart illustrating another channel measurement method provided in an embodiment of this application;

[0077] Figure 9 is a flowchart illustrating another channel measurement method provided in an embodiment of this application;

[0078] Figure 10 is a flowchart illustrating a model training method provided in an embodiment of this application;

[0079] Figure 11 is a flowchart illustrating another model training method provided in an embodiment of this application;

[0080] Figure 12 is a flowchart illustrating another model training method provided in an embodiment of this application;

[0081] Figure 13 is a flowchart illustrating another model training method provided in an embodiment of this application;

[0082] Figure 14 is a flowchart illustrating another communication method provided in an embodiment of this application;

[0083] Figure 15 is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0084] Figure 16 is a schematic diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0085] To facilitate understanding of the embodiments of this application, the system architecture involved in the embodiments of this application will be introduced below.

[0086] Figure 1a is a schematic diagram of the architecture of the communication system 1000 used in an embodiment of this application. As shown in Figure 1a, the communication system includes a radio access network (RAN) 100 and a core network (CN) 200. Optionally, the communication system 1000 may also include an Internet 300. The RAN 100 includes at least one RAN node (110a and 110b in Figure 1a, collectively referred to as 110), and may also include at least one terminal (120a-120j in Figure 1a, collectively referred to as 120). The RAN 100 may also include other RAN nodes, such as wireless relay devices and / or wireless backhaul devices (not shown in Figure 1a). The terminal 120 is wirelessly connected to the RAN node 110, and the RAN node 110 is wirelessly or wiredly connected to the core network 200. The core network equipment in the core network 200 and the RAN node 110 in the RAN 100 can be independent and different physical devices, or they can be the same physical device integrating the logical functions of the core network equipment and the logical functions of the RAN node. Terminals can be connected to each other, as can RAN nodes, via wired or wireless means.

[0087] RAN 100 can be an evolved universal terrestrial radio access (E-UTRA) system, a new radio (NR) system, or a future radio access system as defined in the 3rd generation partnership project (3GPP). RAN 100 can also include two or more of the above-mentioned different radio access systems. RAN 100 can also be an open RAN (O-RAN).

[0088] RAN nodes, also known as radio access network devices, RAN entities, or access nodes (hereinafter referred to as network devices), are used to help terminals access communication systems wirelessly. In one application scenario, an RAN node can be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next-generation NodeB (gNB) in a 5G mobile communication system, a next-generation base station in a 6G mobile communication system, or a base station in a future mobile communication system. RAN nodes can be macro base stations (such as 110a in Figure 1a), micro base stations or indoor stations (such as 110b in Figure 1a), relay nodes, or donor nodes.

[0089] In another application scenario, multiple RAN nodes can collaborate to help terminals achieve wireless access, with different RAN nodes implementing different functions of the base station. For example, RAN nodes can be central units (CUs), distributed units (DUs), or radio units (RUs). Here, the CU performs the functions of the base station's radio resource control protocol and packet data convergence protocol (PDCP), and can also perform the functions of the service data adaptation protocol (SDAP). The DU performs the functions of the base station's radio link control layer and medium access control (MAC) layer, and can also perform some or all of the physical layer functions. For specific descriptions of these protocol layers, refer to the relevant 3GPP technical specifications. The RU can be used to implement radio frequency signal transmission and reception. The CU and DU can be two independent RAN nodes or integrated into the same RAN node, such as within a baseband unit (BBU). The RU can be included in radio frequency equipment, such as in a remote radio unit (RRU) or an active antenna unit (AAU). The CU can be further divided into two types of RAN nodes: CU-control plane and CU-user plane.

[0090] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an open access network (open RAN, O-RAN, or ORAN) system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0091] In different systems, RAN nodes may have different names. For example, in an O-RAN system, a CU can be called an open CU (O-CU), a DU can be called an open DU (O-DU), and a RU can be called an open RU (O-RU).

[0092] The correspondence between network elements and their achievable protocol layer functions in the ORAN system can be found in Table 1 below.

[0093] Table 1

[0094] RAN nodes can support one or more types of fronthaul interfaces. Different fronthaul interfaces correspond to DUs and RUs with different functions. If the fronthaul interface between the DU and RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and RU is an enhanced common public radio interface (eCPRI), compared to CPRI, some downlink and / or uplink baseband functions are moved from the DU to the RU. Different splitting methods between DUs and RUs correspond to different types (category, Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0095] Taking eCPRI Cat A as an example, for downlink transmission, the DU is configured to implement one or more functions before and after layer mapping (i.e., coding, rate matching, scrambling, modulation, and layer mapping), while other functions after layer mapping (e.g., resource element (RE) mapping, digital beamforming (BF), or one or more functions of inverse fast Fourier transform (IFFT) / adding cyclic prefix (CP)) are moved to the RU. For uplink transmission, the DU is configured to implement one or more functions before and after de-mapping (i.e., decoding, rate matching de-mapping, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and de-RE mapping), while other functions after de-mapping (e.g., digital BF or one or more functions of fast Fourier transform (FFT) / removing CP) are moved to the RU. It is understandable that the functional descriptions of the DU and RU corresponding to various types of eCPRI can be found in the eCPRI protocol, and will not be elaborated here.

[0096] In one possible design, the processing unit in the BBU used to implement baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU / AAU / RRH used to implement baseband functions is called the baseband low (BBL) unit.

[0097] The RAN node in the embodiments of this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules. For example, the RAN node can be a server loaded with the corresponding software module. The embodiments of this application do not limit the specific technology or device form used in the RAN node. For ease of description, a base station is used as an example of a RAN node in the following description.

[0098] A base station is a device deployed in an access network to provide wireless communication functions for terminals. For example, it can be a device in the access network that communicates with terminals via one or more cells over the air interface, or it can be a transmitting and receiving point (TRP), transmitting point (TP), mobile switching center, or a device that performs base station functions in device-to-device (D2D), vehicle-to-everything (V2X), and machine-to-machine (M2M) communications. Base stations can include various forms of macro base stations, micro base stations (also called small cells), relay stations, access points, etc. In systems employing different wireless access technologies, the names of devices with base station functions may vary. For example, a base station may include an evolved NodeB (or eNB, e-NodeB) in a Long Term Evolution (LTE) system or Long Term Evolution-Advanced (LTE-A) system; it may also include a next-generation Node B (gNB) in an evolved packet core (EPC), the 5th generation (5G), or a new radio (NR) system (also simply referred to as the NR system); or it may include a centralized unit (CU) and a distributed unit (DU) in a cloud radio access network (Cloud RAN) system, as well as satellites, drones, balloons, or airplanes. For ease of description, in the embodiments of this application, the device providing wireless communication functions to the terminal is collectively referred to as a base station.

[0099] A terminal is a device with wireless transceiver capabilities, capable of sending signals to or receiving signals from a base station. Terminals may include devices providing voice access to users, devices providing data connectivity to users, devices providing both voice and data connectivity to users, handheld devices with wireless connectivity, or processing devices connected to a wireless modem. The terminal can be user equipment (UE), wireless terminal equipment, mobile terminal equipment, device-to-device (D2D) terminal equipment, vehicle-to-everything (V2X) terminal equipment, machine-to-machine / machine-type communications (M2M / MTC) terminal equipment, Internet of Things (IoT) terminal equipment, subscriber unit, subscriber station, mobile station, remote station, access point (AP), remote terminal equipment, access terminal equipment, user terminal equipment, user agent, or user device, satellite, drone, balloon, or aircraft, etc. For example, it can include mobile phones (or "cellular" phones), computers with mobile terminal equipment, portable, pocket-sized, handheld, or computer-embedded mobile devices, etc. Examples include personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), and other similar devices. It also includes limited devices, such as those with low power consumption, limited storage capacity, or limited computing power. Examples include information sensing devices such as barcode scanners, radio frequency identification (RFID), sensors, global positioning systems (GPS), and laser scanners. By way of example and not limitation, in this embodiment, the terminal can also be a wearable device.Wearable devices, also known as wearable smart devices or smart wearable devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables. The various terminals described above, if located in a vehicle (e.g., placed inside or installed in a vehicle), can be considered in-vehicle terminal devices, also known as on-board units (OBUs). Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), the Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, and smart cities. Terminals can be mobile phones, tablets, computers with wireless transceiver capabilities, wearable devices, vehicles, airplanes, ships, robots, robotic arms, smart home devices, etc. The embodiments of this application do not limit the specific technology or device form used in the terminals.

[0100] Base stations and terminals can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can be deployed on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of the base stations and terminals.

[0101] The roles of base stations and terminals can be relative. For example, the helicopter or drone 120i in Figure 1a can be configured as a mobile base station. For terminals 120j that access the wireless access network 100 through 120i, terminal 120i is a base station; however, for base station 110a, 120i is a terminal, meaning that 110a and 120i communicate via a wireless air interface protocol. Of course, 110a and 120i can also communicate via a base station-to-base station interface protocol. In this case, relative to 110a, 120i is also a base station. Therefore, both base stations and terminals can be collectively referred to as communication devices. 110a and 110b in Figure 1a can be called communication devices with base station functions, and 120a-120j in Figure 1a can be called communication devices with terminal functions.

[0102] Communication between base stations and terminals, between base stations, and between terminals can be conducted using licensed spectrum, unlicensed spectrum, or both simultaneously. Communication can be conducted using spectrum below 6 GHz, spectrum above 6 GHz, or both simultaneously. The embodiments of this application do not limit the spectrum resources used for wireless communication.

[0103] In the embodiments of this application, the functions of the base station can be executed by modules (such as chips) within the base station, or by a control subsystem that includes base station functions. This control subsystem, including base station functions, can be a control center in the aforementioned application scenarios such as smart grids, industrial control, intelligent transportation, and smart cities. Similarly, the functions of the terminal can be executed by modules (such as chips or modems) within the terminal, or by a device that includes terminal functions.

[0104] Please refer to Figure 1b, which is another schematic diagram of a wireless communication system applicable to embodiments of this application.

[0105] As shown in Figure 1b, the wireless communication system includes a RAN intelligent controller (RIC). As an example, the RIC can be used to implement artificial intelligence (AI) related functions. As an example, the RIC includes near-real-time (near-RT) RICs and non-real-time (non-RT) RICs. Non-real-time RICs primarily process non-real-time information, such as data that is not sensitive to latency, with latency in the order of seconds. Real-time RICs primarily process near-real-time information, such as data that is relatively sensitive to latency, with latency in the order of tens of milliseconds.

[0106] The near real-time RIC is used for model training and inference. For example, it can be used to train an AI model and then use that AI model for inference. The near real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data. Optionally, the near real-time RIC can deliver inference results to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the near real-time RIC delivers the inference result to the DU, and the DU sends it to the RU.

[0107] The non-real-time RIC is also used for model training and inference. For example, it can be used to train an AI model and then use that model for inference. The non-real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to the RAN nodes and / or terminals. Optionally, inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the non-real-time RIC delivers the inference results to the DU, which then forwards them to the RU.

[0108] The near-real-time RIC and non-real-time RIC can also be set up separately as network elements. For example, this network element can be called an AI network element. That is, an AI network element can also be an independently set up network element in a wireless communication system. An AI network element can also be called an AI node, AI device, AI entity, AI module, AI model, or AI unit, etc. Optionally, the near-real-time RIC and non-real-time RIC can also be part of other devices, or the AI ​​network element can be built into the network elements of the wireless communication system. For example, the near-real-time RIC is set in the RAN node (e.g., CU, DU), while the non-real-time RIC is set in the network management (OAM) system, cloud server, core network equipment, or other network equipment. Core network equipment includes, for example, the Mobility Management Entity (MME), Home Subscriber Server (HSS), Serving Gateway (S-GW), Policy and Charging Rules Function (PCRF), and Public Data Network Gateway (PDN Gateway, P-GW) in 4G networks; and access and mobility management functions (AMF), user plane functions (UPF), or session management functions (SMF) in 5G networks. The OAM can be the network management unit for core network equipment and / or for access network equipment. Optionally, AI network elements can also be built into the terminal, such as the terminal itself or its built-in chip, to implement AI-related functions.

[0109] In practical applications, this wireless communication system can include multiple network-side devices (also known as access network devices) and multiple terminals simultaneously, without limitation. One network-side device can serve one or more terminals simultaneously. A terminal can also access one or more network-side devices simultaneously. The embodiments of this application do not limit the number of terminals and network-side devices included in the wireless communication system.

[0110] To facilitate understanding of the relevant content of the embodiments of this application, some terms involved in the embodiments of this application will be explained below. This part is for the purpose of understanding and should not be regarded as a disclosure or specific limitation of the technical solution of this application.

[0111] 1. Radio Frequency Map (RF-MAP)

[0112] A channel map is a database or AI model used to model the base station environment. This allows the base station to obtain channel-related parameters between the terminal and the base station based on the terminal's location information. These parameters can also be applied to downstream tasks such as beam selection, channel prediction, precoding, and scheduling. In general, a channel map reflects the mapping relationship between terminal location information and channel information. Since a channel map requires location information for prediction, it can also be called a location-based RF map (L-RF-MAP) or a location-dependent channel map, etc.

[0113] An AI model is a concrete implementation of AI functionality, representing the mapping relationship between the model's input and output. AI models can be neural networks, deep neural networks, linear regression models, decision tree models, support vector machines (SVMs), Bayesian networks, Q-learning models, or other machine learning (ML) models.

[0114] The terminal's location information is used to represent the terminal's physical location. For example, this location information can be obtained using Global Navigation Satellite System (GNSS) technology, Location Measuring Function (LMF) technology, or terminal (UE) measurement technology.

[0115] 2. Channel measurement information

[0116] Channel measurement information is information obtained through channel measurement methods. Channel measurement information does not include the terminal's location information, but is information associated with the terminal's location information. For example, channel measurement information includes one or more of the following: channel fingerprint, multi-site multi-beam reference signal received power (RSRP), and channel state information (CSI).

[0117] Channel measurement methods include downlink channel measurement and uplink channel measurement. Downlink channel measurement refers to the terminal measuring reference signals transmitted by the base station to obtain relevant information about the downlink channel. These reference signals may include Channel State Information-Reference Signals (CSI-RS), etc. Uplink channel measurement refers to the base station measuring reference signals transmitted by the terminal to obtain relevant information about the uplink channel. These reference signals may include Sounding Reference Signals (SRS), Positioning Reference Signals (PRS), and Sounding Reference Signal-Position (SRS-Position), etc. These are just some examples of reference signals; this application does not limit their application. The reference signal can also be referred to as a pilot signal.

[0118] Channel fingerprinting is a technique that uses the characteristics of wireless signals for identification and localization. In 5G networks, channel fingerprinting can be achieved by extracting multi-beam synchronization signal blocks (SSBs) from the 5G downlink signal, and then obtaining secondary synchronization signals (SSSs) and demodulation reference signals (DM-RSs) based on these SSBs. Next, the received power and quality of these signals are calculated, and they are stacked to form a multi-beam channel fingerprint.

[0119] 3. Channel Information

[0120] With the help of channel maps, channel information can contain richer and more detailed information than channel measurement information. Channel information may include one or more of the following: the channel at the terminal's location, channel multipath components, RSRP, signal-to-interference-plus-noise ratio (SINR), interference information, and signal-to-noise ratio (SNR).

[0121] 4. Minimization Drive Test (MDT) Process

[0122] The MDT (Multi-Targeting Test) process is an automated drive test technique introduced by 3GPP in the LTE system. It involves collecting and reporting measurement data from ordinary user / commercial terminals through network configuration. As long as the user terminal has GPS enabled and supports MDT, it can automatically report MDT data containing user location information to the base station. In summary, through the MDT process, the base station can collect various data, such as the terminal's location information and the corresponding channel measurement information required in this application.

[0123] In practical applications, channel maps are used to model the base station environment, representing the mapping relationship between terminal location information and channel information. The base station can use the terminal's location information, physical map, and auxiliary information as input to the channel map to obtain the channel information between the terminal and the base station, as shown in Figure 1c. Furthermore, the base station or terminal can perform tasks such as beam selection, channel prediction, precoding, and scheduling based on these channel-related parameters. However, when using a channel map, the base station needs to obtain the terminal's location information. Obtaining location information is challenging, and directly obtaining it exposes the terminal's location, raising privacy concerns.

[0124] This application provides a communication method that uses channel measurement information to perform model inference to obtain channel information between a terminal and a base station, as shown in Figure 1d. The first model is trained on a training dataset, which includes sample channel measurement information of at least one terminal and corresponding sample channel information. The input to the first model is the terminal's channel measurement information. Since this channel measurement information is associated with the terminal's location information but does not directly contain it, it is also called location-related information. Accordingly, after model inference using the first model, the channel information between the terminal and the base station is output.

[0125] It is evident that when using the model to predict channel information, it is necessary to obtain channel measurement information associated with the terminal's location information, rather than direct location information. In this case, the acquisition of the terminal's location information can be avoided. At the same time, since the terminal's location information is not used, the terminal's location can be avoided, thereby improving the terminal's location privacy.

[0126] Since the use of the first model here does not involve location information, the first model can also be called a location-free RF map (LF-RF-MAP), or other names.

[0127] The communication method provided in this application will be further described below with reference to the accompanying drawings.

[0128] Please refer to Figure 2, which is a flowchart illustrating a communication method provided in an embodiment of this application. As shown in Figure 2, this embodiment mainly describes the interaction process between the base station and the terminal, focusing on the actual use of the first model. As shown in Figure 2, the method includes, but is not limited to, the following steps:

[0129] S201. The base station obtains the channel measurement information of the terminal, and the channel measurement information is associated with the location information of the terminal.

[0130] In one possible implementation, the base station can use channel measurement to determine the terminal's channel measurement information, which can be understood as information obtained by performing channel measurement on the channel between the base station and the terminal.

[0131] In one embodiment, the base station can perform a channel measurement to obtain the channel measurement information of the terminal. Specifically, the base station can configure first configuration information, which is used to configure the first measurement resource for performing the channel measurement. Since there are downlink channel measurement methods and uplink channel measurement methods, the base station obtaining the terminal's channel measurement information can include the following two cases: the first case is the case of using downlink channel measurement: the base station receives the channel measurement information obtained by the terminal on the first measurement resource; the second case is the case of using uplink channel measurement: the base station determines the terminal's channel measurement information based on the reference signal sent by the terminal on the first measurement resource.

[0132] In one embodiment, a base station can perform multiple channel measurements to obtain channel measurement information from the terminal. In each channel measurement, the base station sends configuration information to the terminal once. Therefore, with multiple channel measurements, the base station can send multiple configuration information messages to the terminal to complete multiple channel measurements, obtaining multiple measurement information messages, and then using these multiple measurement information messages to determine the terminal's channel measurement information. The number of channel measurements can be greater than or equal to two. For example, with two channel measurements, the base station can send two configuration information messages to the terminal to complete two channel measurements, obtaining two measurement information messages, and then determining the terminal's channel measurement information based on these two messages. Therefore, this application can configure multiple configuration information messages to achieve multi-level, multi-dimensional measurements, and improve the accuracy of channel measurement information by combining multiple measurement information messages.

[0133] S202. The base station obtains the terminal's channel information based on channel measurement information and the first model. The channel information is used for the target task.

[0134] The first model is obtained by training a training dataset, which includes sample channel measurement information and corresponding sample channel information of at least one terminal. The training process of the first model can be found in the relevant embodiments below, and will not be repeated here. The first model is obtained by training a second model using the training dataset; that is, the trained second model is the first model. The second model can be a location-based channel map or a novel AI model with inference capabilities.

[0135] The target task can be beam selection, channel prediction, precoding, scheduling, etc., and there are no restrictions on this.

[0136] In one implementation, step S202 includes: the base station inputting channel measurement information into a first model to process the channel measurement information and infer the terminal's channel information using the first model. During the inference process of the first model, the first model can provide relevant prior information based on the input channel measurement information, so that the output channel information contains more information relevant to subsequent target tasks compared to the channel measurement information. That is, the inference process of the first model allows for the acquisition of more detailed channel information using sparse channel measurement information.

[0137] The prior information is obtained during model training. It's important to understand that, since the first model is trained using a location-based channel map, and the location-based channel map training is also obtained through model training, the prior information here is obtained during the training of both the first and second models.

[0138] The first model learns continuously during training. Through this learning process, it extracts key features from the input sample channel measurement information. These features may include channel-related information such as multipath effects and noise levels. The model analyzes these key features to generate prior information. During the inference phase, when new channel measurement information is input into the first model, it utilizes the prior information to conduct a more in-depth analysis, potentially identifying or predicting hidden information within the channel measurement information. In other words, through the inference of the first model, it can output richer and more detailed channel information than the channel measurement information itself.

[0139] In summary, in the actual use of the first model, the main task is to obtain the input data corresponding to the first model, which is the channel measurement information. This channel measurement information can be obtained using channel measurement methods. Then, the base station uses the obtained channel measurement information as the input of the first model, and the output of the first model is the terminal's channel information.

[0140] Optionally, the communication method shown in Figure 2 may further include the following steps:

[0141] S203. The base station uses channel information to perform target tasks.

[0142] Optionally, the communication method shown in Figure 2 may further include the following steps:

[0143] S204. The base station sends channel information to the terminal.

[0144] S205. The terminal uses channel information to perform the target task.

[0145] In this embodiment of the application, when using the model to predict channel information, it is necessary to obtain channel measurement information associated with the terminal's location information, rather than direct location information. In this case, the acquisition of the terminal's location information can be avoided. At the same time, since the terminal's location information is not used, the terminal's location can be avoided, thereby improving the terminal's location privacy.

[0146] The following, with reference to Figures 3-9, explains the implementation of obtaining the terminal's channel measurement information using channel measurement in step S201.

[0147] The base station can be configured to obtain the terminal's channel measurement information through a single channel measurement, or through multiple channel measurements. Figures 3 and 4 describe the process of obtaining the terminal's channel measurement information through a single channel measurement, while Figures 5-9 describe the process of obtaining the terminal's channel measurement information through two channel measurements. Furthermore, channel measurement includes downlink and uplink channel measurement methods. The following description, in conjunction with the accompanying figures, illustrates various implementations of obtaining the terminal's channel measurement information using channel measurement.

[0148] Figure 3 illustrates the process of obtaining channel measurement information from the terminal through a single downlink channel measurement; where,

[0149] S301. The base station sends first configuration information to the terminal, the first configuration information being used to configure the first measurement resource.

[0150] In downlink channel measurement, the first configuration information is used to configure the terminal to perform channel measurement. The first measurement resource has a first reference signal for performing channel measurement, and the first reference signal is a reference signal applicable to downlink channel measurement. For example, the first reference signal may be CSI-RS, etc.

[0151] S302. The base station sends a first reference signal to the terminal based on the first measurement resource.

[0152] S303. The terminal performs channel measurement based on the first reference signal, determines the terminal's channel measurement information, and reports the channel measurement information to the base station.

[0153] Accordingly, the base station receives channel measurement information.

[0154] Figure 4 illustrates the process of obtaining channel measurement information from the terminal through a single uplink channel measurement.

[0155] S401. The base station sends first configuration information to the terminal, the first configuration information being used to configure the first measurement resource.

[0156] In uplink channel measurement, the first configuration information is used to configure the first measurement resource for the terminal to transmit a reference signal. The first measurement resource has a first reference signal for performing channel measurement, and the first reference signal is a reference signal applicable to uplink channel measurement. For example, the first reference signal can be SRS, PRS, SRS-Position, etc.

[0157] S402. The terminal sends a first reference signal to the base station based on the first measurement resource.

[0158] Accordingly, the base station receives the first reference signal.

[0159] S403. The base station performs channel measurement based on the first reference signal to obtain the channel measurement information of the terminal.

[0160] As shown in the flowcharts of Figures 3 and 4, during channel measurement, the first reference signal on the first measurement resource is divided based on different channel measurement methods. That is, the configuration of different signal types of the first reference signal on the first measurement resource is determined based on different channel measurement methods. For example, the first reference signal on the first measurement resource includes the reference signal corresponding to the uplink channel measurement method and the reference signal corresponding to the downlink channel measurement method.

[0161] Optionally, the first reference signal on the first measurement resource can be divided based on whether the terminal perceives it or not, to achieve the effect of terminal perception and non-perception. That is, the configuration of different signal types of the first reference signal on the first measurement resource is determined based on whether the terminal perceives it. For example, reference signals such as CSI-RS and SRS can be understood as ordinary data transmission signals, so the terminal cannot perceive the behavior of the base station, and these reference signals are for signals that the terminal does not perceive. As another example, PRS and SRS-Position can be used for high-precision channel measurement, especially in location services. When using these reference signals, the terminal can perceive that the base station is acquiring its location association information, but cannot accurately determine its location. Therefore, the terminal perceives the behavior of the base station, and these reference signals are for signals that the terminal perceives. The specific implementation of channel measurement using reference signals on the first measurement resource can be referred to the above description, and will not be repeated here.

[0162] In summary, the first reference signal on the first measurement resource can be configured arbitrarily to obtain the terminal's channel measurement information using either the uplink channel measurement method or the downlink channel measurement method. In addition, by configuring different types of reference signals, terminal awareness or non-awareness can also be achieved.

[0163] The following explanation uses two channel measurements as an example to illustrate how to obtain the terminal's channel measurement information. Step S201, which uses channel measurement to obtain the terminal's channel measurement information, includes: the base station sending second configuration information to the terminal; this second configuration information is used to configure second measurement resources; the base station sending third configuration information to the terminal; this third configuration information is used to configure third measurement resources. The second reference signal on the second measurement resource and the third reference signal on the third measurement resource are different reference signals, so that different channel measurements can be performed based on different reference signals, thereby achieving multi-level, multi-dimensional measurement effects.

[0164] Considering that channel measurement can include both uplink and downlink channel measurement, the information returned by the terminal to the base station after the base station sends configuration information to the terminal twice may differ, or in other words, the way the base station determines the channel measurement information may differ. Various possible implementations are shown in Figures 5-9 below:

[0165] Figure 5 illustrates the process of obtaining channel measurement information from a terminal through secondary downlink channel measurements. In this process, the base station receives channel measurement information obtained by the terminal on second and third measurement resources. This channel measurement information may include first measurement information on the second measurement resource and second measurement information on the third measurement resource. The base station determines the terminal's channel measurement information based on the measurement information obtained from the two channel measurements.

[0166] S501. The base station sends second configuration information to the terminal, which is used to configure the second measurement resources.

[0167] In the first downlink channel measurement, the second configuration information is a second measurement resource for configuring the terminal to perform channel measurement. The second measurement resource has a second reference signal for performing channel measurement, and the second reference signal is a reference signal applicable to the downlink channel measurement.

[0168] S502. The base station sends a second reference signal to the terminal based on the second measurement resources.

[0169] Accordingly, the terminal receives the second reference signal.

[0170] S503. The terminal performs channel measurement based on the second reference signal, determines the first measurement information, and reports the first measurement information to the base station.

[0171] S504. The base station sends third configuration information to the terminal, which is used to configure third measurement resources.

[0172] In the second downlink channel measurement, the third configuration information is a third measurement resource used to configure the terminal to perform channel measurement. This third measurement resource has a third reference signal for performing channel measurement, and this third reference signal is a reference signal applicable to downlink channel measurement.

[0173] S505. The base station sends a third reference signal to the terminal based on the third measurement resources.

[0174] Accordingly, the terminal receives the third reference signal.

[0175] S506. The terminal performs channel measurement based on the third reference signal, determines the second measurement information, and reports the second measurement information to the base station.

[0176] S507. The base station determines the terminal's channel measurement information based on the first measurement information and the second measurement information.

[0177] In one possible implementation, the base station performs a fusion process on the first measurement information and the second measurement information to use the fusion result as the terminal's channel measurement information. This fusion process can be a combining process, where the combined result of the first and second measurement information is used as the terminal's channel measurement information; alternatively, this fusion process can be implemented using an AI model with fusion capabilities, without specific limitations on the AI ​​model here; or, the fusion process can also be implemented in other ways, without limitation.

[0178] Figure 6 illustrates the process of obtaining channel measurement information from a terminal through secondary downlink channel measurements. In this process, the base station receives channel measurement information obtained by the terminal on the second and third measurement resources. The terminal determines its own channel measurement information based on the information obtained from the two channel measurements and reports it.

[0179] S601. The base station sends second configuration information to the terminal, which is used to configure the second measurement resources.

[0180] S602. The base station sends a second reference signal to the terminal based on the second measurement resources.

[0181] Accordingly, the terminal receives the second reference signal.

[0182] S603. The terminal performs channel measurement based on the second reference signal to determine the first measurement information.

[0183] S604. The base station sends third configuration information to the terminal. The third configuration information is used to configure the third measurement resources.

[0184] S605. The base station sends a third reference signal to the terminal based on the third measurement resources.

[0185] Accordingly, the terminal receives the third reference signal.

[0186] S606. The terminal performs channel measurement based on the third reference signal to determine the second measurement information.

[0187] S607. The terminal determines its channel measurement information based on the first measurement information and the second measurement information, and reports the channel measurement information to the base station.

[0188] The principle by which the terminal determines the channel measurement information based on two measurement information is similar to the principle by which the base station determines the channel measurement information based on two measurement information in step S507, and will not be repeated here. It can be seen that the terminal's channel measurement information includes the information obtained by fusing the first and second measurement information.

[0189] Figure 7 illustrates the process of obtaining channel measurement information of the terminal through channel measurements of the second uplink. In this process, the base station determines the terminal's channel measurement information based on the reference signal transmitted by the terminal on the second measurement resource and the reference signal transmitted by the terminal on the third measurement resource. That is, the base station determines the terminal's channel measurement information based on the measurement information obtained from the two channel measurements.

[0190] S701. The base station sends second configuration information to the terminal, which is used to configure the second measurement resources.

[0191] S702. The terminal sends a second reference signal to the base station based on the second measurement resources.

[0192] Accordingly, the base station receives the second reference signal.

[0193] S703. The base station performs channel measurements based on the second reference signal to determine the first measurement information.

[0194] S704. The base station sends third configuration information to the terminal, which is used to configure third measurement resources.

[0195] S705. The terminal sends a third reference signal to the base station based on the third measurement resources.

[0196] Accordingly, the base station receives a third reference signal.

[0197] S706. The base station performs channel measurements based on the third reference signal to determine the second measurement information.

[0198] S707. The base station determines the channel measurement information of the terminal based on the first measurement information and the second measurement information.

[0199] The specific implementation of this step can be found in the description of step S507, and will not be repeated here.

[0200] Figure 8 illustrates the process of obtaining channel measurement information of the terminal through one uplink channel measurement and one uplink channel measurement. In this process, the base station determines the terminal's channel measurement information based on the reference signal transmitted by the terminal on the second measurement resource and the measurement information obtained by the terminal on the third measurement resource.

[0201] S801. The base station sends second configuration information to the terminal, which is used to configure the second measurement resources.

[0202] S802. The terminal sends a second reference signal to the base station based on the second measurement resources.

[0203] Accordingly, the base station receives the second reference signal.

[0204] S803. The base station performs channel measurements based on the second reference signal to determine the first measurement information.

[0205] S804. The base station sends third configuration information to the terminal. The third configuration information is used to configure the third measurement resources.

[0206] S805. The base station sends a third reference signal to the terminal based on the third measurement resources.

[0207] S806. The terminal performs channel measurement based on the third reference signal, determines the second measurement information, and reports the second measurement information to the base station.

[0208] Accordingly, the base station receives the second measurement information.

[0209] S807. The base station determines the terminal's channel measurement information based on the first measurement information and the second measurement information.

[0210] The specific implementation of this step can be found in the description of step S507, and will not be repeated here.

[0211] Figure 9 illustrates the process of obtaining channel measurement information for a terminal through one downlink channel measurement and one uplink channel measurement. In this process, the base station can determine the terminal's channel measurement information based on the measurement information obtained by the terminal on the second measurement resource and the reference signal transmitted by the terminal on the third measurement resource; wherein,

[0212] S901. The base station sends second configuration information to the terminal, which is used to configure the second measurement resources.

[0213] S902. The base station sends a second reference signal to the terminal based on the second measurement resources.

[0214] Accordingly, the terminal receives the second reference signal.

[0215] S903. The terminal performs channel measurement based on the second reference signal, determines the first measurement information, and reports the first measurement information to the base station.

[0216] S904. The base station sends third configuration information to the terminal, which is used to configure third measurement resources.

[0217] S905. The terminal sends a third reference signal to the base station based on the third measurement resources.

[0218] Accordingly, the base station receives a third reference signal.

[0219] S906. The base station performs channel measurements based on the third reference signal to determine the second measurement information.

[0220] S907. The base station determines the channel measurement information of the terminal based on the first measurement information and the second measurement information.

[0221] The specific implementation of this step can be found in the description of step S507, and will not be repeated here.

[0222] In summary, this application can configure multiple measurement resources to achieve multi-level and multi-dimensional measurements. For example, with two measurement resources, the first resource can be used for coarse-grained channel measurement, and the second resource for fine-grained channel measurement. Alternatively, the first resource could be used for angle measurement, and the second resource for delay measurement, etc. This multi-level and multi-dimensional measurement effectively improves the accuracy of channel measurement information. Since the channel measurement information is used as input to the first model for channel information prediction, high-precision channel measurement information also improves the model's prediction accuracy.

[0223] In one possible implementation, when the base station sends configuration information to the terminal twice, after sending the second configuration information, it can either send the third configuration information without any restrictions, or it can have corresponding restrictions to determine whether to send the third configuration information. For example, the restriction could be that the base station determines whether to send the third configuration information to the terminal based on the first measurement information obtained under the second configuration information. For instance, the base station could determine whether to send the third configuration information by determining whether the first measurement information meets a preset requirement. If the preset requirement is met, the third configuration information can be omitted, i.e., the first measurement information can be directly used as channel measurement information. If the preset requirement is not met, the third configuration information can be sent again to complete the second channel measurement, so that channel measurement information is obtained through two channel measurements. For example, the preset requirement could be that the first measurement information exceeds a preset quality, i.e., when the quality of the obtained measurement information is high enough, only one channel measurement is needed, thus reducing computational overhead.

[0224] Please refer to Figure 10, which is a flowchart illustrating a model training method provided in an embodiment of this application. As shown in Figure 10, this embodiment mainly describes the interaction process between the base station and the terminal. As shown in Figure 10, the method includes, but is not limited to, the following steps:

[0225] S1001. Obtain the training dataset, which includes sample channel measurement information of at least one terminal and the corresponding sample channel information. The sample channel measurement information is associated with the location information of the terminal, and the sample channel information is used for the target task.

[0226] At least one terminal can be a terminal within a single cell or a terminal in different cells; there is no limitation on this.

[0227] In one possible implementation, step S1001 includes: the base station receiving measurement reports reported by each terminal in at least one terminal, so as to obtain a training dataset based on the measurement reports corresponding to each terminal. Here, one terminal corresponds to one measurement report.

[0228] In one embodiment, for a single terminal, the measurement report reported by the terminal includes the terminal's location information and channel measurement information associated with the location information. For ease of description, this channel measurement information can be referred to as sample channel measurement information. Sample channel measurement information is understood in the same way as channel measurement information, and may include one or more of the following: channel fingerprint, multi-site multi-beam RSRP, channel state information, etc. Sample channel measurement information does not include the terminal's location information.

[0229] In this scenario, the base station determines the training dataset based on the location information of each terminal and the associated sample channel measurement information. Each measurement report may also include a mapping relationship between location information and sample channel measurement information to clearly distinguish the corresponding information for each terminal and avoid confusion. Considering that the principle of constructing the training dataset using information from each terminal's measurement report is similar, the following explanation uses the measurement report of a single terminal as an example.

[0230] For a measurement report from a terminal, the base station can input the terminal's location information from the report into a second model to obtain the channel information corresponding to the location information. This channel information is referred to as sample channel information. The second model can be the location-based channel map described above.

[0231] Through the above method, the base station can obtain the sample channel information corresponding to the location information of each terminal. After obtaining this information, the base station can construct a training dataset based on the sample channel measurement information associated with the location of each terminal and the sample channel information corresponding to that location information. Each terminal corresponds to sample channel measurement information associated with its location and sample channel information corresponding to that location. The base station can then construct a training sample pair from the sample channel measurement information and sample channel information of a terminal. This training sample pair can be represented as (x, y), where x represents the sample value and y represents the sample label. In this application, x is the sample channel measurement information associated with the terminal's location, and y is the sample channel information corresponding to the location information. For each terminal, there can be one training sample pair, and the training sample pair corresponding to each terminal constitutes the training dataset.

[0232] As can be seen, in this application, the base station can construct specific datasets for each terminal to complete the mapping from location information to sample channel measurement information. This mapping allows the input of the second model to be replaced from location information with sample channel measurement information, thereby constructing a location-independent first model. In practical applications, the input of the location-independent first model may only contain channel measurement information, and the output may be the channel information corresponding to the terminal.

[0233] In one embodiment, for a single terminal, the measurement report reported by the terminal may include the terminal's location information, sample channel measurement information associated with the location information, and sample channel information corresponding to the location information. That is, the sample channel information corresponding to the location information already exists in the measurement report, eliminating the need to use a second model and predict the sample channel information based on the location information. In other words, the measurement report contains high-precision channel information, and the location-independent channel map can be trained from scratch. In this case, the base station can construct a training dataset based on the received measurement report. Therefore, the base station can directly construct a training sample pair from the sample channel measurement information and sample channel information in a terminal's measurement report, as described above, and will not be repeated here.

[0234] In one possible implementation, the training dataset may also include one or more of the map and auxiliary information of the cell where each terminal is located. That is, the sample value x in the training sample pair corresponding to a terminal may include not only the sample channel measurement information of the terminal, but also one or more of the map and auxiliary information of the cell where the terminal is located. In one embodiment, assuming that each terminal is in a fixed cell, since the map and auxiliary information are fixed, these two pieces of information can also be removed.

[0235] S1002. Obtain the first model based on the training dataset. The first model is used to represent the mapping relationship between the terminal's channel measurement information and channel information.

[0236] In one possible implementation, the base station uses sample channel measurement information from the training dataset as sample values ​​input to the second model, and sample channel information corresponding to the sample channel measurement information as sample labels output by the second model, to train the model and obtain the trained second model, which is the first model.

[0237] The second model is either a location-based channel map or an AI model with inference capabilities; the network structure of this model is not limited. If the second model is not needed to predict sample channel information during the construction of the training dataset, then the second model can be either a location-based channel map or an AI model; if the second model is needed to predict sample channel information during the construction of the training dataset, then the second model can be a location-based channel map.

[0238] The above-mentioned model training implementation includes: the base station inputs sample channel measurement information from the training dataset into the second model, so that the second model processes the sample channel measurement information to obtain prediction information corresponding to the sample channel measurement information. This prediction information is the channel information predicted using the second model. The base station then trains the second model based on the sample channel information and prediction information corresponding to the sample channel measurement information to obtain the first model. For example, the base station can use a loss function and calculate the model loss value of the second model based on the sample channel information and prediction information corresponding to the sample channel measurement information, and then train the second model based on the model loss value to obtain the first model. Alternatively, the model parameters of the second model can be optimized in the direction of reducing the model loss value to obtain the first model. The loss function can be the cross-entropy loss function or other functions, and there is no limitation on this.

[0239] In summary, this application can construct a training dataset through data collection, and then fine-tune the second model to further change the input of the second model to obtain the first model.

[0240] In this embodiment, a corresponding training dataset can be constructed, and a location-independent first model can be trained using this dataset. The sample values ​​in the training dataset are sample channel measurement information of the terminal. This channel measurement information does not include location information but is associated with it. Therefore, when training the model using this sample channel measurement information, a location-independent first model is obtained. Thus, this application also provides a reasonable way to obtain the first model. Furthermore, when applying the first model to a real-world scenario, obtaining channel measurement information associated with the terminal's location is sufficient, rather than directly obtaining the terminal's location information. This effectively avoids exposing the terminal's location and improves privacy.

[0241] The following further explains how the base station obtains the measurement report in step S1001. As can be seen from the above, the base station obtains the training dataset based on the measurement report reported by the terminal. In order for the terminal to report the measurement report to the base station, the base station's MDT process needs to be activated first. By activating the MDT process, the terminal can obtain and report the measurement report.

[0242] Based on this, it can be seen that the base station can receive activation requests for the MDT process; for example, the control node sends an activation request for the MDT process to the base station, and the base station receives the activation request sent by the control node. After receiving the activation request for the MDT process, the base station sends configuration information for the MDT process to each terminal. This configuration information is used to instruct each terminal to report measurement reports, and it also informs the terminal which data needs to be measured.

[0243] The triggering condition for the control node to send an activation request for the MDT procedure to the base station can be: the control node receives a request from the base station. After the base station sends a request to the control node, the control node can know which base station to activate the MDT procedure to.

[0244] The purpose of this application is to obtain a first model, which requires training the original model to obtain the first model. The original model can be a second model. For example, the process of a base station obtaining a second model includes: the base station sending a request to the control node to obtain the second model; the control node responding to the request and sending the second model to the base station; and the base station receiving and deploying the second model.

[0245] In summary, to successfully train the model, it is necessary to ensure the MDT process that can activate base stations and the availability of the second model.

[0246] The control node can be an OAM node, meaning the node that activates the MDT (Multi-Level Design) process of the base station is the OAM node. The second model can be pre-stored in a model storage area, which can be retrieved from the first model when needed. This model storage area can be called an RF-MAP repository. The model storage area can be deployed on the OAM node or independently on other nodes, such as CN (Network Address Translation) or OTT (Over-The-Top) nodes. OTT refers to providing content or services via the Internet, built on top of basic telecommunications services, requiring no additional support from network operators, and supported by servers of third-party service providers other than network operators. Therefore, to ensure the base station has the second model deployed, the base station can send a retrieval request for the second model to the OAM node or the model storage area; additionally, it is necessary to ensure that the OAM node receives the request. However, in one scenario, the request to obtain the second model is sent to the OAM node. Since the OAM node has already determined which base station needs to activate the MDT process, the base station does not need to send a request to the OAM node again. In another scenario, the request to obtain the second model is sent to the model storage area. Since the OAM node does not know which base station needs to activate the MDT process, the base station still needs to send a request to the OAM node.

[0247] As can be seen, if the base station sends the second model acquisition request to different objects, the implementation of the base station obtaining the measurement report during model training will also be different. To better understand the model training process under different circumstances, the following will further explain it in conjunction with Figures 11-12.

[0248] Figure 11 is a flowchart illustrating a model training method provided in an embodiment of this application. This embodiment describes the implementation of the model training method after the base station sends a request to the OAM node to obtain a second model, and the model storage area is independent of the OAM node. As shown in Figure 11, the model training method involves the OAM node, the model storage area, the base station, and various terminals. This embodiment mainly describes the interaction process between the OAM node, the model storage area, the base station, and various terminals. As shown in Figure 11, the method includes, but is not limited to, the following steps:

[0249] S1101. The base station sends a request to the OAM node to obtain the second model.

[0250] The S1102.OAM node forwards the request to obtain the second model to the model storage area.

[0251] The model storage area can be deployed on OAM nodes, CN nodes, OTT nodes, etc.

[0252] S1103. The model storage area sends the second model to the base station.

[0253] In one embodiment, if the model storage area is deployed on the OAM node, the implementation of steps S1102-S1103 can be replaced by the OAM node obtaining the second model from the model storage area and sending the second model to the base station.

[0254] S1104. The base station receives and deploys the second model.

[0255] The S1105.OAM node sends an activation request for the MDT process to the base station.

[0256] S1106. The base station sends configuration information for the MDT process to each terminal.

[0257] S1107. Each terminal reports a measurement report to the base station.

[0258] S1108. The base station obtains the training dataset based on the measurement report and trains the second model based on the training dataset to obtain the first model.

[0259] The specific implementation of steps S1101-S1108 can be found in the above description, and will not be repeated here.

[0260] S1109, the OAM node sends a deactivation request for the MDT process to the base station.

[0261] In one possible implementation, after the base station obtains the first model, the base station sends a request to the OAM node, which instructs the OAM node to send a deactivation request for the MDT procedure. The OAM node then sends the deactivation request for the MDT procedure to the base station according to the instruction. The base station responds to the deactivation request and stops executing the MDT procedure.

[0262] As previously stated, in addition to sending a second model acquisition request to the OAM node to trigger the OAM node to initiate the MDT process activation request and sending the second model through the model storage area, the base station can also directly send a second model acquisition request to the model storage area. Since this application requires the use of the MDT process to obtain measurement reports, and the activation of the MDT process requires the OAM node to trigger it, the base station needs to send a request to the OAM node to trigger the OAM node to activate the base station's MDT process. For example, this request could be a model training request. In this case, the flowchart of the model training method can be shown in Figure 12. The main difference between Figure 12 and Figure 11 is that Figure 12 describes the implementation of the model training method after the base station sends a second model acquisition request to the model storage area. As shown in Figure 12, the model training method involves the OAM node, the model storage area, the base station, and various terminals. This embodiment mainly describes the interaction process between the OAM node, the model storage area, the base station, and various terminals. This method includes, but is not limited to, the following steps:

[0263] S1201. The base station sends a request to the model storage area to obtain the second model.

[0264] The model storage area can be deployed on CN nodes or OTT nodes, etc.

[0265] S1202. The model storage area sends the second model to the base station.

[0266] S1203. The base station receives and deploys the second model.

[0267] S1204. The base station sends a model training request to the OAM node.

[0268] The S1205.OAM node sends an activation request for the MDT process to the base station.

[0269] S1206. The base station sends configuration information for the MDT process to each terminal.

[0270] S1207. Each terminal reports a measurement report to the base station.

[0271] S1208. The base station obtains the training dataset based on the measurement report and trains the second model based on the training dataset to obtain the first model.

[0272] The S1209.OAM node sends a deactivation request for the MDT procedure to the base station.

[0273] The specific implementation of steps S1201-S1209 can be found in the above description, and will not be repeated here.

[0274] In one possible implementation, regarding the model training operation in step S1002, one approach is to perform the model training operation at the base station; another approach is to perform the model training operation at other nodes, such as the aforementioned control node or other nodes. Based on this, step S1002 can be implemented as follows: the base station sends the training dataset to the control node; the control node trains the second model based on the training dataset to obtain the first model; the control node sends the first model to the base station; and the base station receives and deploys the first model.

[0275] Optionally, in addition to sending the training dataset to the control node, the base station can also directly send the measurement reports reported by the terminals to the control node. After receiving the measurement reports from each terminal, the control node obtains the training dataset based on the measurement reports of each terminal, and uses the training dataset to train the second model to obtain the first model. The specific implementation of the control node obtaining the training dataset and the first model is similar to the principle of the base station obtaining the training dataset and the first model, and will not be elaborated here.

[0276] For example, Figure 13 is a flowchart illustrating a model training method proposed in an embodiment of this application. In this flowchart, the execution node for the model training operation is described as an OAM node or a model storage area. As shown in Figure 13, the model training method may involve an OAM node, a model storage area, a base station, and various terminals. This embodiment mainly describes the interaction process between the OAM node, the model storage area, the base station, and various terminals. This method includes, but is not limited to, the following steps:

[0277] S1301. The base station sends a request to the OAM node to obtain the second model.

[0278] The S1302.OAM node forwards the request to obtain the second model to the model storage area.

[0279] S1303. The model storage area sends the second model to the base station.

[0280] S1304. The base station receives and deploys the second model.

[0281] The S1305.OAM node sends an activation request for the MDT process to the base station.

[0282] S1306. The base station sends configuration information for the MDT process to each terminal.

[0283] S1307. Each terminal reports a measurement report to the base station.

[0284] S1308. The base station sends a measurement report to the OAM node or model storage area.

[0285] The S1309.OAM node or model storage area obtains the training dataset based on the measurement report and trains the second model based on the training dataset to obtain the first model.

[0286] Since the second model is stored in the model storage area, when the OAM node trains the second model, the OAM node can first send a request to the model storage area to obtain the second model. The model storage area can respond to the request and send the second model to the OAM node. The OAM node can receive and deploy the second model so that it can be trained to obtain the first model later.

[0287] S1310. The OAM node or model storage area sends the first model to the base station.

[0288] S1311. The base station receives and deploys the first model.

[0289] As mentioned above, step S1304 indicates that a second model has been deployed at the base station. Therefore, after the base station receives the first model, in order to avoid confusion in the use of models in subsequent applications, the deployed second model can be deleted.

[0290] The S1312.OAM node sends a deactivation request for the MDT procedure to the base station.

[0291] The specific implementation of steps S1301-S1312 can be found in the above description, and will not be repeated here.

[0292] It should be noted that in this model training method, in addition to the base station sending a request to the OAM node to obtain the second model as shown in Figure 13, it is also possible to directly send a request to obtain the second model to the model storage area. In this case, the base station needs to send a model training request to the OAM node so that the OAM node can trigger the activation request of the MDT process.

[0293] For example, referring to Figure 13, since the model training operation is not performed by the base station, there is no need to deploy the second model on the base station. In this case, the corresponding model training method is shown in Figure 14. As shown in Figure 14, the model training method involves OAM nodes, model storage areas, base stations, and various terminals. This embodiment mainly describes the interaction process between OAM nodes, model storage areas, base stations, and various terminals. The main difference between the embodiment shown in Figure 14 and the embodiment shown in Figure 13 is that in the process of Figure 13, the base station requests to obtain and deploy the second model, while in the process of Figure 14, the OAM node requests to obtain and deploy the second model. This method includes, but is not limited to, the following steps:

[0294] S1401. The base station sends a model training request to the OAM node.

[0295] It is important to understand that the base station no longer needs to send a second model acquisition request to the OAM node, but in order to trigger the generation of the activation request for the MDT process, the base station can send a model training request to the OAM node.

[0296] The S1402.OAM node sends an activation request for the MDT process to the base station.

[0297] S1403. The base station sends configuration information for the MDT process to each terminal.

[0298] S1404. Each terminal reports a measurement report to the base station.

[0299] S1405. The base station sends a measurement report to the OAM node or model storage area.

[0300] The S1406.OAM node or model storage area obtains the training dataset based on the measurement report and trains the second model based on the training dataset to obtain the first model.

[0301] S1407. The OAM node or model storage area sends the first model to the base station.

[0302] If model training is performed by the OAM node, the OAM node can first send a request to the model storage area to retrieve the second model. The model storage area receives the request and sends the second model to the OAM node. The OAM node can receive and deploy the second model so that it can train the second model to obtain the first model. If model training is performed by the model storage area, there is no model retrieval operation.

[0303] S1408. The base station receives and deploys the first model.

[0304] The S1409.OAM node sends a deactivation request for the MDT procedure to the base station.

[0305] Understandably, model training requires significant computational overhead, such as hardware costs, and the larger the model, the greater the computational resources required and the higher the hardware costs. Therefore, compared to training the model at the base station, training it at other nodes can reduce the data processing pressure on the base station and avoid incurring significant computational overhead at the base station.

[0306] It is understood that, in order to achieve the aforementioned functions, the device includes corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0307] This application embodiment can divide network-side devices, terminals, or control nodes into functional modules based on the above method examples. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and represents a logical functional division; in actual implementation, there may be other division methods.

[0308] Please refer to Figure 15, which shows a schematic diagram of the structure of a communication device 1500 according to an embodiment of this application. The communication device shown in Figure 15 can be a network-side device, a device within a network-side device, or a device compatible with a network-side device. The communication device shown in Figure 15 may include a communication unit 1501 and a processing unit 1502; the communication device shown in Figure 15 can be a terminal, a device within a terminal, or a device compatible with a terminal. The communication device shown in Figure 15 may include a communication unit 1501 and a processing unit 1502. The communication device shown in Figure 15 can be a control node, a device within a control node, or a device compatible with a control node. The communication device shown in Figure 15 may include a communication unit 1501 and a processing unit 1502. Specifically, the processing unit 1502 is used to process data, which may be data received by the communication unit 1501, and the processed data may also be sent by the communication unit 1501. The communication unit 1501 can be understood as a transceiver unit, including a receiving module and / or a sending module. The receiving module is used to perform the receiving action of the device (i.e., network-side device, terminal, or control node) in any embodiment of Figures 2 to 14, and the sending module is used to perform the sending action of the device (i.e., network-side device, terminal, or control node) in any embodiment of Figures 2 to 14.

[0309] In one embodiment, taking an application to a base station as an example, when the communication device 1500 is a base station, a device in a base station (e.g., a chip or chip system in the base station), or a device that can be used in conjunction with a base station, wherein:

[0310] The communication unit 1501 is used to acquire channel measurement information of the terminal, the channel measurement information being associated with the location information of the terminal; the processing unit 1502 is used to obtain channel information of the terminal based on the channel measurement information and a first model, the channel information being used for a target task; wherein, the first model is obtained by training based on a training dataset, the training dataset including sample channel measurement information of at least one terminal and corresponding sample channel information.

[0311] In one possible implementation, the communication unit 1501 is specifically used to send first configuration information to the terminal, the first configuration information being used to configure a first measurement resource; receive channel measurement information obtained by the terminal on the first measurement resource; or determine the terminal's channel measurement information based on a reference signal sent by the terminal on the first measurement resource.

[0312] In one possible implementation, the communication unit 1501 is specifically configured to send second configuration information to the terminal, the second configuration information being used to configure second measurement resources; send third configuration information to the terminal, the third configuration information being used to configure third measurement resources; receive channel measurement information obtained by the terminal on the second and third measurement resources, the channel measurement information including first measurement information on the second measurement resources and second measurement information on the third measurement resources, or, the channel measurement information including information fused by the terminal from the first and second measurement information; or, determine the terminal's channel measurement information based on reference signals sent by the terminal on the second and third measurement resources; or, determine the terminal's channel measurement information based on measurement information obtained by the terminal on the second and third measurement resources and reference signals sent by the terminal on the third measurement resources.

[0313] In one embodiment, taking an application to a base station as an example, when the communication device 1500 is a base station, a device in a base station (e.g., a chip or chip system in the base station), or a device that can be used in conjunction with a base station, wherein:

[0314] The communication unit 1501 is used to acquire a training dataset, which includes sample channel measurement information of at least one terminal and corresponding sample channel information. The sample channel measurement information is associated with the location information of the terminal and the sample channel information is used for the target task. The processing unit 1502 is used to acquire a first model based on the training dataset. The first model is used to represent the mapping relationship between the channel measurement information and the channel information of the terminal.

[0315] In one possible implementation, the communication unit 1501 is specifically configured to receive measurement reports reported by each terminal in at least one terminal, the measurement reports including the location information of the terminal and sample channel measurement information associated with the location information; the processing unit 1502 is specifically configured to, for each terminal, input the location information of the terminal into a second model to obtain sample channel information corresponding to the location information, the second model being used to represent the mapping relationship between the location information of the terminal and the sample channel information of the location information; and construct a training dataset based on the sample channel measurement information associated with the location information of each terminal and the sample channel information corresponding to the location information of each terminal.

[0316] In one possible implementation, the communication unit 1501 is specifically used to receive measurement reports reported by each terminal in at least one terminal, the measurement reports including the location information of the terminal, sample channel measurement information associated with the location information, and sample channel information corresponding to the location information; the processing unit 1502 is specifically used to construct a training dataset based on the received measurement reports.

[0317] In one possible implementation, the communication unit 1501 is further configured to receive an activation request for the MDT minimized drive test process; and send configuration information for the MDT process to each terminal, the configuration information being used to instruct the terminal to report a measurement report.

[0318] In one possible implementation, the communication unit 1501 is also used to receive deactivation requests from the MDT process.

[0319] In one possible implementation, the communication unit 1501 is also used to send a request to acquire the second model; receive and deploy the second model.

[0320] For a more detailed description of the communication unit 1501 and the processing unit 1502, please refer to the relevant description of the base station in the method embodiments shown in Figures 2 to 14.

[0321] In one embodiment, when the communication device 1500 is a terminal, a device within a terminal, or a device compatible with a terminal, wherein:

[0322] Communication unit 1501 is used to receive second configuration information sent by base station, the second configuration information being used to configure second measurement resources;

[0323] The terminal receives third configuration information sent by a base station, the third configuration information being used to configure third measurement resources; it sends measurement information obtained on second and third measurement resources to the base station, the measurement information including first measurement information on the second measurement resource and second measurement information on the third measurement resource, or the measurement information including information fused by the terminal from the first and second measurement information; or it sends reference signals on the second and third measurement resources to the base station; or it sends measurement information obtained on the second and third measurement resources and reference signals on the third measurement resource to the base station.

[0324] For a more detailed description of the communication unit 1501 and the processing unit 1502, please refer to the relevant description of the network device in the method embodiments shown in Figures 2 to 14.

[0325] In one embodiment, when the communication device 1500 is a control node, a device within the control node (e.g., a chip or chip system within the control node), or a device compatible with the control node, wherein:

[0326] The communication unit 1501 is configured to receive a request for obtaining a second model sent by a base station, wherein the second model represents the mapping relationship between the location information of the terminal and the channel information of the location information; send the second model to the base station; send an activation request for an MDT procedure to the base station; wherein the activation request is configured to trigger the base station to send configuration information of the MDT procedure to each of at least one terminal, wherein the configuration information is configured to instruct the terminal to report a measurement report.

[0327] In one possible implementation, the communication unit 1501 is also configured to send a deactivation request for the MDT procedure to the base station.

[0328] In one possible implementation, the communication unit 1501 is further configured to receive a measurement report sent by the base station, and determine a training dataset based on the measurement report; obtain a first model according to the training dataset; the first model is used to represent the mapping relationship between the terminal's measurement information and channel information; and send the first model to the base station.

[0329] For a more detailed description of the communication unit 1501 and the processing unit 1502, please refer to the relevant description of the network device in the method embodiments shown in Figures 2 to 14.

[0330] In one possible implementation, when the communication device 1500 is a chip, the communication unit 1501 can be a communication interface, pins, or circuits. The communication interface can be used to input data to be processed to the processor and can output the processor's processing results. In a specific implementation, the communication interface can be a general purpose input / output (GPIO) interface, which can be connected to multiple peripheral devices (such as displays (LCDs), cameras, radio frequency (RF) modules, antennas, etc.). The communication interface is connected to the processor via a bus.

[0331] The processing unit 1502 may be a processor, which can execute computer execution instructions stored in the storage module to cause the chip to execute the methods involved in any of the embodiments shown in Figures 2 to 14. Further, the processor may include a controller, an arithmetic logic unit (ALU), and registers. For example, the controller is mainly responsible for instruction decoding and issuing control signals for the operations corresponding to the instructions. The ALU is mainly responsible for performing fixed-point or floating-point arithmetic operations, shift operations, and logical operations, and can also perform address operations and conversions. The registers are mainly responsible for storing register operands and intermediate operation results temporarily stored during instruction execution. In specific implementations, the processor's hardware architecture may be an application-specific integrated circuit (ASIC) architecture, a microprocessor without interlocked piped stages architecture (MIPS) architecture, an advanced reduced instruction set machine (RISC) machine (ARM) architecture, or a network processor (NP) architecture, etc. The processor may be single-core or multi-core. The storage module may be an internal storage module of the chip, such as registers or caches. Storage modules can also be external to the chip, such as read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM), etc.

[0332] It should be noted that the functions of the processor and interface can be implemented through hardware design, software design, or a combination of both; no restrictions are imposed here.

[0333] Figure 16 is a schematic diagram of another communication device provided in an embodiment of this application. It is understood that the communication device 1600 includes necessary means such as modules, units, elements, circuits, or interfaces, appropriately configured together to execute this solution. The communication device 1600 may be the aforementioned network-side device, terminal, or control node, or it may be a component (e.g., a chip) within these devices, used to implement the methods described in the above method embodiments.

[0334] In one possible design, as shown in Figure 16, the communication device 1600 includes a processor 1610 and an interface circuit 1620. The processor 1610 and the interface circuit 1620 are coupled to each other.

[0335] Optionally, the communication device 1600 may include one or more processors 1610. The processor 1610 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control the communication device (e.g., a terminal, network-side device, or chip), execute software programs, and process data from the software programs.

[0336] It is understood that the interface circuit 1620 can be a transceiver or an input / output interface. When the communication device 1600 is a network-side device, terminal, or control node, the interface circuit 1620 is a transceiver, including a transmitter and / or a receiver. The transmitter can be referred to as a transmitting unit, transmitter, or transmitting circuit, etc., and is used to implement the transmitting function. The receiver can be referred to as a receiving unit, receiver, or receiving circuit, etc., and is used to implement the receiving function. When the communication device 1600 is a chip in a network-side device, terminal, or control node, the interface circuit 1620 is the input / output interface of that chip. Optionally, the communication device 1600 may also include an antenna (not shown in the figure). The interface circuit 1620 may sometimes be referred to as a transceiver unit, transceiver, transceiver circuit, or transceiver, etc., and is used to realize the transmitting and receiving functions of the communication device through the antenna.

[0337] Optionally, the communication device 1600 may further include a memory 1630 for storing instructions executed by the processor 1610, or storing input data required by the processor 1610 to execute instructions, or storing data generated after the processor 1610 executes instructions. Optionally, the processor 1610 and the memory 1630 may be provided separately or integrated together.

[0338] When the aforementioned communication device is a chip applied to a network-side device, the network-side device chip implements the functions of the network-side device in the above method embodiments. The network-side device chip receives information from a terminal or control node, which can be understood as the information being first received by other modules (such as radio frequency modules or antennas) in the network-side device, and then sent to the network-side device chip by these modules. The network-side device chip sends information to a terminal, which can be understood as the information being forwarded to other modules (such as radio frequency modules or antennas) in the network-side device, and then sent to the terminal or control node by these modules.

[0339] When the aforementioned communication device is a chip applied to a terminal, the terminal chip implements the functions of the terminal in the above method embodiments. The terminal chip receives information from network-side devices, which can be understood as the information being first received by other modules in the terminal (such as an RF module or antenna), and then sent to the terminal chip by these modules. The terminal chip sends information to network-side devices, which can be understood as the information being first sent to other modules in the terminal (such as an RF module or antenna), and then sent to the network-side devices by these modules.

[0340] When the aforementioned communication device is a chip applied to a control node, the control node chip implements the functions of the control node in the above method embodiments. The control node chip receives information from network-side devices, which can be understood as the information being first received by other modules (such as radio frequency modules or antennas) in the control node, and then sent to the control node chip by these modules. The control node chip sends information to network-side devices, which can be understood as the information being sent down to other modules (such as radio frequency modules or antennas) in the control node, and then sent to the network-side devices by these modules.

[0341] This application also provides a computer-readable storage medium storing computer instructions that, when executed, cause the computer to perform the method described in any one of the embodiments shown in Figures 2 to 14.

[0342] This application also provides a computer program product, which includes computer program code. When the computer program code is run, it causes the computer to perform the method described in any one of the embodiments shown in Figures 2 to 14.

[0343] In this application, entity A sends information to entity B, either directly or indirectly through other entities. Similarly, entity B receives information from entity A, either directly or indirectly through other entities. Entities A and B can be RAN nodes or terminals, or modules within RAN nodes or terminals. Information transmission and reception can be between RAN nodes and terminals, such as between a base station and a terminal; between two RAN nodes, such as between a CU and a DU; or between different modules within a single device, such as between a terminal chip and other modules of the terminal, or between a base station chip and other modules of the base station.

[0344] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0345] The method steps in the embodiments of this application can be implemented in hardware or in software instructions executable by a processor. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. The storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a network-side device, terminal, or control node. The processor and storage medium can also exist as discrete components in a network-side device, terminal, or control node.

[0346] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0347] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0348] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0349] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. In the textual description of this application, the character " / " generally indicates an "or" relationship between the preceding and following related objects; in the formulas of this application, the character " / " indicates a "division" relationship between the preceding and following related objects. "Including at least one of A, B, and C" can mean: including A; including B; including C; including A and B; including A and C; including B and C; including A, B, and C.

[0350] The terms "first" and "second," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of operations or units is not limited to the listed operations or units, but may optionally include operations or units not listed, or may optionally include other operations or units inherent to these processes, methods, products, or apparatuses.

[0351] In this application, "send" and "receive" refer to the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which can include direct transmission via the air interface or indirect transmission via the air interface from other units or modules. "Receive information from YY" can be understood as the source of the information being YY, which can include direct reception from YY via the air interface or indirect reception from YY via the air interface from other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface. In other words, sending and receiving can occur between devices, such as between a base station and a terminal, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via a bus, wiring, or interface. It is understood that information may undergo necessary processing, such as encoding and modulation, between the source and destination of the information transmission, but the destination can understand the valid information from the source. Similar expressions in this application can be understood in a similar way and will not be elaborated further.

[0352] In this application, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information (as described below, the instruction information) is called the information to be instructed. In specific implementations, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed; it can also instruct a part of the information to be instructed, while the other parts of the information to be instructed are known or pre-agreed upon. For example, the instruction can be implemented by using a pre-agreed (e.g., protocol predefined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. This application does not limit the specific method of instruction. It is understood that for the sender of the instruction information, the instruction information can be used to instruct the information to be instructed; for the receiver of the instruction information, the instruction information can be used to determine the information to be instructed.

[0353] It is understood that the various numerical designations used in the embodiments of this application are for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

Claims

A communication method characterized by comprising: The method includes: Obtain channel measurement information of the terminal, wherein the channel measurement information is associated with the location information of the terminal; Based on the channel measurement information and the first model, the channel information of the terminal is obtained, and the channel information is used for the target task; The first model is obtained by training a training dataset, which includes sample channel measurement information of at least one terminal and the corresponding sample channel information. The method of claim 1, wherein The acquisition of channel measurement information of the terminal includes: Send first configuration information to the terminal, wherein the first configuration information is used to configure the first measurement resource; The receiving terminal obtains channel measurement information on the first measurement resource; or, based on the reference signal sent by the terminal on the first measurement resource, the receiving terminal determines the channel measurement information of the terminal. The method of claim 1, wherein The measurement information obtained from the terminal includes: Send second configuration information to the terminal, the second configuration information being used to configure second measurement resources; Send third configuration information to the terminal, the third configuration information being used to configure third measurement resources; The terminal receives channel measurement information obtained on a second measurement resource and a third measurement resource. The channel measurement information includes first measurement information on the second measurement resource and second measurement information on the third measurement resource; or, the channel measurement information includes information fused by the terminal from the first and second measurement information. Based on the reference signals transmitted by the terminal on the second measurement resource and the reference signals transmitted by the terminal on the third measurement resource, the channel measurement information of the terminal is determined; or, Based on the measurement information obtained by the terminal on the second measurement resource and the reference signal sent by the terminal on the third measurement resource, the channel measurement information of the terminal is determined. A communication method characterized by comprising: The method includes: Receive second configuration information sent by the base station, the second configuration information being used to configure second measurement resources; Receive third configuration information sent by the base station, the third configuration information being used to configure third measurement resources; The system sends measurement information obtained from second and third measurement resources to the base station. This measurement information includes first measurement information from the second measurement resource and second measurement information from the third measurement resource; alternatively, the measurement information includes information obtained by the terminal through fusion of the first and second measurement information. Send reference signals on the second and third measurement resources to the base station; or, The measurement information obtained on the second measurement resource and the reference signal on the third measurement resource are sent to the base station. A model training method, characterized in that, The method includes: Obtain a training dataset, which includes sample channel measurement information of at least one terminal and corresponding sample channel information. The sample channel measurement information is associated with the location information of the terminal, and the sample channel information is used for the target task. A first model is obtained based on the training dataset. The first model is used to represent the mapping relationship between the terminal's channel measurement information and channel information. The method according to claim 5, characterized in that The acquisition of the training dataset includes: Receive measurement reports from each terminal in at least one terminal, the measurement reports including the location information of the terminal and sample channel measurement information associated with the location information; For each terminal, the terminal's location information is input into the second model to obtain sample channel information corresponding to the location information. The second model is used to represent the mapping relationship between the terminal's location information and the channel information of the location information. A training dataset is constructed based on the sample channel measurement information associated with the location information of each terminal and the sample channel information corresponding to the location information of each terminal. The method according to claim 5, characterized in that The acquisition of the training dataset includes: Receive measurement reports reported by each terminal in at least one terminal, the measurement reports including the terminal's location information, sample channel measurement information associated with the location information, and sample channel information corresponding to the location information; A training dataset is constructed based on the received measurement reports. The method according to claims 6-7, characterized in that Also includes: Receive activation requests for the MDT minimized road test process; The configuration information for the MDT process is sent to each terminal, and the configuration information is used to instruct the terminal to report the measurement report. The method of claim 8, wherein After obtaining the first model based on the training dataset, the process further includes: Receive deactivation requests from the MDT process. The method according to claim 6, characterized in that Also includes: Send a request to obtain the second model; Receive and deploy the second model. A model training method, characterized in that, The method includes: The terminal receives a request to acquire a second model from the base station. The second model represents the mapping relationship between the terminal's location information and the channel information of the location information. Send the second model to the base station; An activation request for the MDT process is sent to the base station; the activation request is used to trigger the base station to send configuration information of the MDT process to each terminal among at least one terminal, and the configuration information is used to instruct the terminal to report a measurement report. The method of claim 11, wherein Also includes: Send a deactivation request for the MDT procedure to the base station. The method of claim 11, wherein Also includes: Receive measurement reports sent by the base station and determine the training dataset based on the measurement reports; Obtain the first model based on the training dataset; The first model is used to represent the mapping relationship between the terminal's channel measurement information and channel information; The first model is sent to the base station. A communication device, characterized by It includes modules for performing the method as described in any one of claims 1-4, or modules for performing the method as described in any one of claims 5-13. A communication device, characterized by The device includes a processor and an interface circuit. The interface circuit is used to receive signals from other communication devices besides the communication device and transmit them to the processor, or to send signals from the processor to other communication devices besides the communication device. The processor uses logic circuits or execution code instructions to cause the communication device to implement the method as described in any one of claims 1-4, or to implement the method as described in any one of claims 5-13. The apparatus of claim 15, wherein The communication device is a chip or chip system. A computer-readable storage medium, characterized by, The storage medium stores a computer program or instructions, which, when executed by a communication device, implement the method as described in any one of claims 1-4, or the method as described in any one of claims 5-13.

Images