Communication method and apparatus, and storage medium and program product

Abstract

The present application relates to the technical field of communications. Disclosed are a communication method and apparatus, and a storage medium and a program product. The method comprises: an access network device sending first information to a terminal device, wherein the first information indicates at least one first resource block, and the at least one first resource block belongs to a first resource block set and a second resource block set; on the basis of the first information, the access network device sending DMRSs to the terminal device on the first resource block set and the second resource block set; and on the basis of an artificial intelligence (AI) model, the terminal device respectively processing the DMRSs received on the first resource block set and the second resource block set. An access network device indicates at least one resource block, such that both the number of resource blocks comprised in a first resource block set and the number of resource blocks comprised in a second resource block set are consistent with the number of resource blocks required for inference performed by an AI model. Therefore, a terminal device can process, on the basis of the AI model, DMRSs received on the first resource block set and the second resource block set, thereby improving the performance of DMRS processing.

Description

Communication methods, devices, storage media and software products

[0001] This application claims priority to Chinese Patent Application No. 202411934334.6, filed on December 25, 2024, entitled "Communication Method, Apparatus, Storage Medium and Program Product", 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, apparatus, storage medium, and program product. Background Technology

[0003] The demodulation reference signal (DMRS) can be used for channel estimation. Since the DMRS carries almost no useful information, its overhead is often considered to balance channel estimation performance with available time-frequency resources for data transmission. Typically, the DMRS is sparse in both the time and frequency domains. Therefore, after estimating the channel at the time-frequency resource unit containing the DMRS using a channel estimation algorithm, it is also necessary to estimate the wireless channel on the time-frequency spatial resources where the DMRS has not been transmitted. This channel estimation can be achieved using artificial intelligence (AI) models.

[0004] Typically, AI DMRS channel estimation is performed at the physical resource bundle (PRB) / resource block group (RBG) granularity because the same precoding (beamforming) is used on a PRB / RBG. A PRB / RBG consists of several resource blocks (RBs). However, when the number of RBs in any PRB / RBG within the bandwidth used by the physical downlink shared channel (PDSCH) received by the terminal device does not match the granularity of the RBs in the PRB / RBG estimated by the AI ​​module, inference cannot be performed using the AI ​​model, and the system will fall back to traditional non-AI DMRS estimation, resulting in a degraded DMRS estimation performance. Summary of the Invention

[0005] This application provides a communication method, apparatus, storage medium, and program product, so that when a terminal device processes the received DMRS based on an AI model, the number of resource blocks included in each resource block set meets the capabilities of the artificial intelligence model.

[0006] Firstly, this application provides a communication method that can be applied to a receiving device. The receiving device can be a terminal device or a communication module within the terminal device, or a circuit or chip applied to the terminal device (such as a modem chip (also known as a baseband chip), or a system-on-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip). Taking the application of this method to a terminal device as an example...

[0007] The method includes: a terminal device receiving first information, the first information indicating at least one first resource block, the at least one first resource block belonging to a first resource block set and a second resource block set; receiving DMRS on the first resource block set and the second resource block set based on the first information, the precoding corresponding to the first resource block set and the precoding corresponding to the second resource block set being the same; and processing the DMRS received on the first resource block set and the second resource block set respectively based on an artificial intelligence model.

[0008] Using this method, the access network device instructs at least one resource block so that the resource blocks included in the first resource block set and the second resource block set both satisfy the input dimension conditions of the AI ​​model. This enables the terminal device to process the DMRS received on the first resource block set and the second resource block set based on the AI ​​model, thereby improving the processing performance of DMRS.

[0009] In conjunction with the first aspect, in one possible design, the number of resource blocks included in both the first resource block set and the second resource block set is greater than or equal to the same set number.

[0010] In this design, the first resource block set reuses at least one resource block from the second resource block set, but without changing the initial number of resource blocks included in the reused second resource block set. The at least one first resource block belongs to both the first and second resource block sets. The number of resource blocks included in both the first and second resource block sets is greater than or equal to the same predetermined number.

[0011] In conjunction with the first aspect, in another possible design, the first information includes an index of the at least one first resource block, or the first information includes a relative index of the at least one first resource block relative to a starting resource block of a first bandwidth, the first bandwidth being the bandwidth used by the terminal device when receiving the physical downlink shared channel (PDSCH).

[0012] By employing this design, by carrying in the first information an index of at least one first resource block, or a relative index of at least one first resource block relative to the starting resource block of the first bandwidth, the terminal device can accurately determine at least one resource block that is being reused.

[0013] In conjunction with the first aspect, in another possible design, the method further includes: the terminal device processing a first inference result corresponding to the at least one first resource block and a second inference result corresponding to the at least one first resource block, wherein the first inference result is obtained based on DMRS inference received on the first resource block set, and the second inference result is obtained based on DMRS inference received on the second resource block set.

[0014] With this design, since at least one resource block belongs to the first resource block set and the second resource block set, the at least one resource block is inferred twice. By processing the first inference result corresponding to at least one first resource block and the second inference result corresponding to at least one first resource block, the inference result can be made more accurate.

[0015] In conjunction with the first aspect, in another possible design, the terminal device processes the first inference result corresponding to the at least one first resource block and the second inference result corresponding to the at least one first resource block, including: the terminal device averaging the first inference result and the second inference result to obtain a third inference result, which serves as the final inference result corresponding to the at least one first resource block.

[0016] In conjunction with the first aspect, in another possible design, the terminal device processes the first inference result corresponding to the at least one first resource block and the second inference result corresponding to the at least one first resource block, including: the terminal device discards the second inference result and uses the first inference result as the final inference result corresponding to the at least one first resource block; or the terminal device discards the first inference result and uses the second inference result as the final inference result corresponding to the at least one first resource block.

[0017] Secondly, this application provides a communication method that can be applied to a transmitting device, which may be an access network device or a communication module within the access network device, or a circuit or chip (such as a modem chip, or a SoC chip or SIP chip containing a modem core) applied to the access network device. Taking the application of this method to an access network device as an example...

[0018] The method includes: an access network device sending first information, the first information indicating at least one first resource block, the at least one first resource block belonging to a first resource block set and a second resource block set; and based on the first information, sending DMRS on the first resource block set and the second resource block set, wherein the precoding corresponding to the first resource block set and the precoding corresponding to the second resource block set are the same.

[0019] In conjunction with the second aspect, in one possible design, the method further includes: the access network device determining that the number of resource blocks included in the third resource block set is less than a set number, the third resource block set being obtained after an initial partitioning of the first bandwidth, the first bandwidth being the bandwidth used by the terminal device when receiving PDSCH; and determining the first resource block set, the first resource block set being obtained after a re-partitioning of the first bandwidth, the first resource block set including the third resource block set and the at least one first resource block.

[0020] In conjunction with the second aspect, in another possible design, the number of resource blocks included in both the first resource block set and the second resource block set is greater than or equal to the same set number.

[0021] In conjunction with the second aspect, in yet another possible design, the first information includes an index of the at least one first resource block, or the first information includes a relative index of the at least one first resource block relative to the starting resource block of the first bandwidth.

[0022] The beneficial effects of the second aspect or any design thereof can be found in the description of the beneficial effects of the first aspect or any design thereof, and will not be repeated here.

[0023] Thirdly, this application provides a communication method that can be applied to a transmitting / receiving device. The transmitting / receiving device can be a terminal device / access network device or a communication module within the terminal device / access network device, or a circuit or chip (such as a modem chip, or a SoC chip or SIP chip containing a modem core) applied to the terminal device / access network device. Taking the application of this method to a terminal device / access network device as an example...

[0024] The method includes: a terminal device / access network device determining the total number of multiple resource block sets included in the first bandwidth, each of the multiple resource block sets including multiple resource blocks, the number of resource blocks included in each resource block set satisfying the capability of an artificial intelligence model, the first bandwidth being the bandwidth used by the terminal device when receiving PDSCH; and performing DMRS transmission on the multiple resource block sets.

[0025] In conjunction with the third aspect, in one possible design, the starting position of the first bandwidth is 0. In the case of the first bandwidth, the first bandwidth includes There are n resource block sets, from the first resource block set to the Nth resource block set. bundle Each resource block set in the -1 resource block set includes L resource blocks, and the Nth resource block set... bundle The resource block set includes One resource block, The value is the size of the first bandwidth.

[0026] In conjunction with the third aspect, in another possible design, the starting position of the first bandwidth is... The first bandwidth includes A set of resource blocks, In the resource block sets, excluding the first resource block set and the second... Outside of a set of resource blocks Each resource block set in the set of L resources includes L resource blocks. The first resource block set in the set of resource blocks includes One resource block, The first in the resource block set The resource block set includes One resource block, The value is the size of the first bandwidth.

[0027] In conjunction with the third aspect, in another possible design, the method is applied to a terminal device, and the method further includes: the terminal device processing the DMRS received on the plurality of resource block sets based on the artificial intelligence model.

[0028] In conjunction with the third aspect, in yet another possible design, the artificial intelligence model is used to process DMRS.

[0029] Fourthly, this application provides a communication device, which may be a terminal device, a device, module, or chip within the terminal device, or a device compatible with the terminal device. In one design, the communication device may include modules corresponding to the methods / operations / steps / actions described in the first aspect. These modules may be hardware circuits, software, or a combination of hardware circuits and software. In another design, the communication device may include a processing module and a communication module.

[0030] One example:

[0031] A communication module is configured to receive first information, the first information indicating at least one first resource block, the at least one first resource block belonging to a first resource block set and a second resource block set; the communication module is further configured to receive DMRS on the first resource block set and the second resource block set based on the first information, the precoding corresponding to the first resource block set and the precoding corresponding to the second resource block set being the same; and a processing module is configured to process the DMRS received on the first resource block set and the second resource block set respectively based on an artificial intelligence model.

[0032] Optionally, the number of resource blocks included in the first resource block set and the second resource block set is greater than or equal to the same set number.

[0033] Optionally, the first information includes an index of the at least one first resource block, or the first information includes a relative index of the at least one first resource block relative to the starting resource block of the first bandwidth, wherein the first bandwidth is the bandwidth used by the terminal device when receiving PDSCH.

[0034] Optionally, the processing module is further configured to process the first inference result corresponding to the at least one first resource block and the second inference result corresponding to the at least one first resource block, wherein the first inference result is obtained based on DMRS inference received on the first resource block set, and the second inference result is obtained based on DMRS inference received on the second resource block set.

[0035] Optionally, the processing module is further configured to average the first inference result and the second inference result to obtain a third inference result, which serves as the final inference result corresponding to the at least one first resource block.

[0036] Optionally, the processing module is further configured to discard the second inference result and use the first inference result as the final inference result corresponding to the at least one first resource block; or the processing module is further configured to discard the first inference result and use the second inference result as the final inference result corresponding to the at least one first resource block.

[0037] Fifthly, this application provides a communication device, which may be an access network device, a device, module, or chip within the access network device, or a device compatible with the access network device. In one design, the communication device may include modules corresponding to the methods / operations / steps / actions described in the second aspect. These modules may be hardware circuits, software, or a combination of hardware circuits and software. In another design, the communication device may include a processing module and a communication module.

[0038] One example:

[0039] A communication module is configured to send first information indicating at least one first resource block, the at least one first resource block belonging to a first resource block set and a second resource block set; and the communication module is further configured to send DMRS on the first resource block set and the second resource block set based on the first information, wherein the precoding corresponding to the first resource block set and the precoding corresponding to the second resource block set are the same.

[0040] Optionally, the processing module is configured to determine that the number of resource blocks included in the third resource block set is less than a set number, wherein the third resource block set is obtained after the initial division of the first bandwidth, and the first bandwidth is the bandwidth used by the terminal device when receiving PDSCH; and the processing module is further configured to determine the first resource block set, wherein the first resource block set is obtained after the re-division of the first bandwidth, and the first resource block set includes the third resource block set and the at least one first resource block.

[0041] Optionally, the number of resource blocks included in the first resource block set and the second resource block set is greater than or equal to the same set number.

[0042] Optionally, the first information includes an index of the at least one first resource block, or the first information includes a relative index of the at least one first resource block relative to the starting resource block of the first bandwidth.

[0043] Sixthly, this application provides a communication device, which may be a terminal device / access network device, or a device, module, or chip within the terminal device / access network device, or a device compatible with the terminal device / access network device. In one design, the communication device may include modules corresponding to the methods / operations / steps / actions described in the third aspect. These modules may be hardware circuits, software, or a combination of hardware circuits and software. In another design, the communication device may include a processing module and a communication module.

[0044] One example:

[0045] The processing module is configured to determine the total number of multiple resource block sets included in the first bandwidth, wherein each resource block set includes multiple resource blocks, and the number of resource blocks included in each resource block set satisfies the capability of the artificial intelligence model, wherein the first bandwidth is the bandwidth used by the terminal device when receiving PDSCH; and to perform DMRS transmission on the multiple resource block sets.

[0046] Optionally, the starting position of the first bandwidth is 0. In the case of the first bandwidth, the first bandwidth includes There are n resource block sets, from the first resource block set to the Nth resource block set. bundle Each resource block set in the -1 resource block set includes L resource blocks, and the Nth resource block set... bundle The resource block set includes One resource block, The value is the size of the first bandwidth.

[0047] Optionally, the starting position of the first bandwidth is The first bandwidth includes A set of resource blocks, In the resource block sets, excluding the first resource block set and the second... Outside of a set of resource blocks Each resource block set in the set of L resources includes L resource blocks. The first resource block set in the set of resource blocks includes One resource block, The first in the resource block set The resource block set includes One resource block, The value is the size of the first bandwidth.

[0048] Optionally, the communication device is a terminal device, and the processing module is further configured to process the DMRS received on the plurality of resource block sets based on the artificial intelligence model.

[0049] Optionally, the artificial intelligence model is used to process DMRS.

[0050] In a seventh aspect, this application provides a communication device, the communication device including a processor for implementing the method described in the first aspect above. The processor is coupled to a memory for storing instructions and data, and when the processor executes the instructions stored in the memory, it can implement the method described in the first aspect above. Optionally, the communication device may further include a memory; the communication device may also include a communication interface for communicating with other devices. Exemplarily, the communication interface may be a transceiver, circuit, bus, module, pin, or other type of communication interface.

[0051] In one possible device, the communication apparatus includes:

[0052] Memory, used to store instructions;

[0053] A communication interface is configured to receive first information indicating at least one first resource block, the at least one first resource block belonging to a first resource block set and a second resource block set; the communication interface is further configured to receive DMRS on the first resource block set and the second resource block set based on the first information, wherein the precoding corresponding to the first resource block set and the precoding corresponding to the second resource block set are the same; and

[0054] The processor is used to process the DMRS received on the first resource block set and the second resource block set respectively based on an artificial intelligence model.

[0055] Optionally, the number of resource blocks included in the first resource block set and the second resource block set is greater than or equal to the same set number.

[0056] Optionally, the first information includes an index of the at least one first resource block, or the first information includes a relative index of the at least one first resource block relative to the starting resource block of the first bandwidth, wherein the first bandwidth is the bandwidth used by the terminal device when receiving PDSCH.

[0057] Optionally, the processor is further configured to process a first inference result corresponding to the at least one first resource block and a second inference result corresponding to the at least one first resource block, wherein the first inference result is obtained based on DMRS inference received on the first resource block set, and the second inference result is obtained based on DMRS inference received on the second resource block set.

[0058] Optionally, the processor is further configured to average the first inference result and the second inference result to obtain a third inference result, which serves as the final inference result corresponding to the at least one first resource block.

[0059] Optionally, the processor is further configured to discard the second inference result and use the first inference result as the final inference result corresponding to the at least one first resource block; or the processor is further configured to discard the first inference result and use the second inference result as the final inference result corresponding to the at least one first resource block.

[0060] Eighthly, this application provides a communication device including a processor for implementing the method described in the second aspect above. The processor is coupled to a memory for storing instructions and data. When the processor executes the instructions stored in the memory, it can implement the method described in the second aspect above. Optionally, the communication device may further include a memory; the communication device may also include a communication interface for communicating with other devices. Exemplarily, the communication interface may be a transceiver, circuit, bus, module, pin, or other type of communication interface.

[0061] In one possible device, the communication apparatus includes:

[0062] Memory, used to store instructions;

[0063] A communication interface is configured to send first information indicating at least one first resource block, the at least one first resource block belonging to a first resource block set and a second resource block set; and the communication interface is further configured to send DMRS on the first resource block set and the second resource block set based on the first information, wherein the precoding corresponding to the first resource block set and the precoding corresponding to the second resource block set are the same.

[0064] Optionally, the processor is configured to determine that the number of resource blocks included in the third resource block set is less than a set number, wherein the third resource block set is obtained after the initial division of the first bandwidth, and the first bandwidth is the bandwidth used by the terminal device when receiving PDSCH; and the processor is further configured to determine the first resource block set, wherein the first resource block set is obtained after the first bandwidth is re-divided, and the first resource block set includes the third resource block set and the at least one first resource block.

[0065] Optionally, the number of resource blocks included in the first resource block set and the second resource block set is greater than or equal to the same set number.

[0066] Optionally, the first information includes an index of the at least one first resource block, or the first information includes a relative index of the at least one first resource block relative to the starting resource block of the first bandwidth.

[0067] Ninthly, this application provides a communication device, the communication device including a processor for implementing the method described in the third aspect above. The processor is coupled to a memory for storing instructions and data, and when the processor executes the instructions stored in the memory, it can implement the method described in the second aspect above. Optionally, the communication device may further include a memory; the communication device may also include a communication interface for communicating with other devices. Exemplarily, the communication interface may be a transceiver, circuit, bus, module, pin, or other type of communication interface.

[0068] In one possible device, the communication apparatus includes:

[0069] Memory, used to store instructions;

[0070] A processor is configured to determine the total number of multiple resource block sets included in a first bandwidth, each of the multiple resource block sets including multiple resource blocks, the number of resource blocks included in each resource block set satisfying the capability of an artificial intelligence model, and the first bandwidth being the bandwidth used by the terminal device when receiving PDSCH; and

[0071] A communication interface for DMRS transmission over the multiple sets of resource blocks.

[0072] Optionally, the starting position of the first bandwidth is 0. In the case of the first bandwidth, the first bandwidth includes There are n resource block sets, from the first resource block set to the Nth resource block set. bundle Each resource block set in the -1 resource block set includes L resource blocks, and the Nth resource block set... bundle The resource block set includes One resource block, The value is the size of the first bandwidth.

[0073] Optionally, the starting position of the first bandwidth is The first bandwidth includes A set of resource blocks, In the resource block sets, excluding the first resource block set and the second... Outside of a set of resource blocks Each resource block set in the set of L resources includes L resource blocks. The first resource block set in the set of resource blocks includes One resource block, The first in the resource block set The resource block set includes One resource block, The value is the size of the first bandwidth.

[0074] Optionally, the communication device is a terminal device, and the processor is further configured to process the DMRS received on the plurality of resource block sets based on the artificial intelligence model.

[0075] Optionally, the artificial intelligence model is used to process DMRS.

[0076] In a tenth aspect, this application provides a communication system including a communication device as described in the fourth aspect; and a communication device as described in the fifth aspect.

[0077] In an eleventh aspect, this application also provides a computer program that, when run on a computer, causes the computer to perform the method provided in any one of the first to third aspects described above.

[0078] In a twelfth aspect, this application also provides a computer program product, including instructions that, when executed on a computer, cause the computer to perform the method provided in any one of the first to third aspects described above.

[0079] In a thirteenth aspect, this application also provides a computer-readable storage medium storing a computer program or instructions that, when executed on a computer, cause the computer to perform the method provided in any one of the first to third aspects described above.

[0080] In a fourteenth aspect, this application also provides a chip for reading a computer program stored in a memory and executing the method provided in any one of the first to third aspects, or the chip includes circuitry for executing the method provided in any one of the first to third aspects.

[0081] In a fifteenth aspect, this application also provides a chip system including a processor for supporting a device in implementing the methods provided in any of the first to third aspects. In one possible design, the chip system further includes a memory for storing programs and data necessary for the device. The chip system may be composed of chips or may include chips and other discrete devices.

[0082] The effects of the solutions provided in any of the second to thirteenth aspects above can be referenced in the corresponding descriptions in the first aspect. Attached Figure Description

[0083] Figure 1 is a schematic diagram of a communication system;

[0084] Figure 2A is a schematic diagram of a neuron structure;

[0085] Figure 2B is a schematic diagram of the layer relationship in a neural network;

[0086] Figure 2C is a schematic diagram of an AI application framework provided in this application;

[0087] Figure 3 is a schematic diagram of another communication system;

[0088] Figures 4A to 4D are schematic diagrams of several network architectures;

[0089] Figure 5 is a schematic diagram of an existing AI DMRS estimate as an example;

[0090] Figure 6 is a flowchart illustrating a communication method provided in an embodiment of this application;

[0091] Figure 7 is a schematic diagram of AI DMRS estimation based on an embodiment of this application;

[0092] Figure 8 is a flowchart illustrating another communication method provided in an embodiment of this application;

[0093] Figures 9 and 10 are schematic diagrams of the communication device provided in this application. Detailed Implementation

[0094] The present application will now be described in further detail with reference to the accompanying drawings.

[0095] The term "at least one" as used in this application refers to one or more items. "More than one item" means two or more items. "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. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. Furthermore, it should be understood that although the terms "first," "second," etc., may be used in this application to describe various objects, these objects should not be limited to these terms. These terms are only used to distinguish the objects from each other.

[0096] The terms "comprising" and "having," and any variations thereof, used in this application as described below, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or optionally include other steps or units inherent to such processes, methods, products, or apparatus. It should be noted that in this application, words such as "exemplary" or "for example" are used to indicate illustrative, exemplary, or descriptive purposes. Any method or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other methods or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0097] The technology provided in this application can be applied to various communication systems, for example, the communication system can be a fourth-generation (4G) communication system. th Generation 4G) communication systems (such as Long Term Evolution (LTE) systems), 5G (5G) th This refers to a generation of communication systems, including 5G, WiMAX (Worldwide Interoperability for Microwave Access), WLAN (Wireless Local Area Network), a converged system of multiple systems, or future communication systems. 5G communication systems can also be called new radio (NR) systems.

[0098] In a communication system, a network element can send signals to or receive signals from another network element. These signals can include information, signaling, or data. The term "network element" can also be replaced by an entity, network entity, device, communication equipment, communication module, node, communication node, etc. This application uses a network element as an example for description. For instance, a communication system may include at least one terminal device and at least one access network device. The access network device can send downlink signals to the terminal device, and / or the terminal device can send uplink signals to the access network device. Furthermore, it is understood that if the communication system includes multiple terminal devices, these terminal devices can also exchange signals; that is, both the signal-sending network element and the signal-receiving network element can be terminal devices.

[0099] The communication method provided in this application embodiment can be applied to wireless communication systems such as 5G, future communications, and satellite communications. Referring to Figure 1, Figure 1 is a simplified schematic diagram of a wireless communication system provided in this application embodiment. As shown in Figure 1, the wireless communication system includes a wireless access network 100. The wireless access network 100 can be a next-generation wireless access network or a traditional (e.g., 5G, 4G) wireless access network. One or more communication devices (120a-120j, collectively referred to as 120) can be interconnected or connected to one or more network devices (110a, 110b, collectively referred to as 110) in the wireless access network 100. Optionally, Figure 1 is only a schematic diagram; the wireless communication system may also include other devices, such as core network devices, wireless relay devices, and / or wireless backhaul devices, which are not shown in Figure 1.

[0100] Optionally, in practical applications, the wireless communication system may include multiple network devices (also known as access network devices) or multiple communication devices simultaneously. A network device may serve one or more communication devices simultaneously. A communication device may also access one or more network devices simultaneously. This application embodiment does not limit the number of communication devices and network devices included in the wireless communication system.

[0101] In this context, a network device can be an entity on the network side used to transmit or receive signals. A network device can also be an access device that allows communication devices to wirelessly connect to the wireless communication system; for example, a network device can be a base station. Base stations can broadly encompass various names listed below, or be interchangeable with them, such as: NodeB, Evolved NodeB (eNB), Next Generation NodeB (gNB), Access Network Equipment in Open Radio Access Network (O-RAN), Relay Station, Access Point, Transmitting and Receiving Point (TRP), Transmitting Point (TP), Master eNB (MeNB), Secondary eNB (SeNB), Multi-Standard Radio (MSR) Node, Home Base Station, Network Controller, Access Node, Radio Node, Access Point (AP), Transmitting Node, Transceiver Node, Baseband Unit (BBU), Radio Remote Unit (RRU), Active Antenna Unit (AAU), Remote Radio Head (RRH), Central Unit (CU), Distributed Unit (DU), Radio Unit (CU), etc. The network equipment includes units (RU), centralized unit control plane (CU-CP) nodes, centralized unit user plane (CU-UP) nodes, positioning nodes, etc. Base stations can be macro base stations, micro base stations, relay nodes, donor nodes, or similar entities, or combinations thereof. Network equipment can also refer to communication modules, modems, or chips installed within the aforementioned equipment or devices. Network equipment can also be mobile switching centers and equipment that performs base station functions in device-to-device (D2D), vehicle-to-everything (V2X), and machine-to-machine (M2M) communications, network-side equipment in 6G networks, and equipment that performs base station functions in future communication systems. Network equipment can support networks with the same or different access technologies. The embodiments of this application do not limit the specific technologies or equipment forms used in the network equipment.

[0102] Network devices can be fixed or mobile. For example, base stations 110a and 110b are stationary and are responsible for wireless transmission and reception in one or more cells from communication device 120. The helicopter or drone 120i shown in Figure 1 can be configured to act as a mobile base station, and one or more cells can move depending on the location of the mobile base station 120i. In other examples, the helicopter or drone (120i) can be configured as a communication device to communicate with base station 110b.

[0103] In this application, the communication device used to implement the above-mentioned network access functions can be an access network device, a network device with some access network functions, or a device capable of supporting the implementation of access network functions, such as a chip system, hardware circuit, software module, or hardware circuit plus software module. This device can be installed in the access network device or used in conjunction with the access network device. In the method of this application, the example of an access network device being used as the communication device to implement the access network device functions is described.

[0104] Communication devices can be user-side entities used to receive or transmit signals, such as mobile phones. Communication devices can be used to connect people, things, and machines. Communication devices can communicate with one or more core networks via network devices. Communication devices include handheld devices with wireless connectivity, other processing devices connected to wireless modems, or vehicle-mounted devices. Communication devices can be portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile devices. Communication devices 120 can be widely used in various scenarios, such as cellular communication, D2D, V2X, point-to-point (P2P), machine-to-machine (M2M), machine-type communication (MTC), Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc.Examples of communication equipment 120 include: 3GPP standard user equipment (UE), fixed equipment, mobile equipment, handheld devices, wearable devices, cellular phones, smartphones, session initialization protocol (SIP) phones, laptops, personal computers, smart books, vehicles, satellites, global positioning system (GPS) devices, target tracking devices, drones, helicopters, aircraft, ships, remote control devices, smart home devices, industrial equipment, personal communication service (PCS) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), wireless network cameras, tablets, handheld computers, mobile internet devices (MIDs), wearable devices such as smartwatches, VR devices, AR devices, wireless terminals in industrial control, terminals in vehicle-to-everything (V2X) systems, wireless terminals in self-driving systems, wireless terminals in smart grids, wireless terminals in transportation safety, and smart city applications. Wireless terminals in cities include smart gas pumps, terminal devices on high-speed trains, and wireless terminals in smart homes, such as smart speakers, smart coffee machines, and smart printers. Communication equipment 120 can be wireless devices in the above scenarios or devices installed on wireless devices, such as communication modules, modems, or chips in the aforementioned devices. Communication equipment can also be called a terminal, terminal device, UE, mobile station (MS), mobile terminal (MT), etc. Communication equipment can also be communication equipment in future wireless communication systems. Communication equipment can be used in dedicated network equipment or general-purpose equipment. The embodiments of this application do not limit the specific technology or specific equipment form used in the communication equipment.

[0105] Optionally, the communication equipment can be used to act as a base station. For example, a UE can act as a scheduling entity, providing sidelink signaling between UEs in V2X, D2D, or P2P, etc. As shown in Figure 1, cellular phone 120a and car 120b communicate with each other using sidelink signaling. Cellular phone 120a communicates with smart home device 120e without relaying communication signals through base station 110b.

[0106] In this application, the communication device used to implement the functions of the communication equipment can be a terminal device, a terminal device having some of the functions of the aforementioned communication equipment, or a device capable of supporting the implementation of the functions of the aforementioned communication equipment, such as a chip system. This device can be installed in the terminal device or used in conjunction with the terminal device. In this application, the chip system can be composed of chips or include chips and other discrete components. The technical solutions provided in this application are described using the example of a terminal device or a UE as the communication device.

[0107] Optionally, wireless communication systems typically consist of cells. Base stations manage the cells and provide communication services to multiple mobile stations (MS) within them. A base station includes a Base Unit (BBU) and a Remote Receiver Unit (RRU). The BBU and RRU can be located in different places; for example, the RRU can be deployed remotely to a high-traffic area, while the BBU is located in a central equipment room. Alternatively, the BBU and RRU can be located in the same equipment room. The BBU and RRU can also be different components within the same rack. Optionally, a cell can correspond to one carrier or a member carrier.

[0108] It is understood that this application can be applied between network devices and communication devices, between network devices, or between communication devices, that is, between primary devices and secondary devices. The primary device can be a network device or a communication device. When the primary device is a network device, the secondary device can be another network device or a communication device. When the primary device is a communication device, the secondary device can be another communication device.

[0109] The following description uses the primary device as a network device, such as an access network device, and the secondary device as a communication device, such as a terminal device, as an example. Downlink corresponds to the communication direction from the primary device to the secondary device, and uplink corresponds to the communication direction from the secondary device to the primary device.

[0110] Protocol layer structure between access network equipment and terminal equipment:

[0111] Communication between access network devices and terminal devices follows a specific protocol layer structure. This protocol layer structure can include a control plane protocol layer structure and a user plane protocol layer structure. For example, the control plane protocol layer structure can include the functions of protocol layers such as the radio resource control (RRC) layer, the packet data convergence protocol (PDCP) layer, the radio link control (RLC) layer, the medium access control (MAC) layer, and the physical layer. Similarly, the user plane protocol layer structure can include the functions of protocol layers such as the PDCP layer, the RLC layer, the MAC layer, and the physical layer. In one possible implementation, a service data adaptation protocol (SDAP) layer can be included above the PDCP layer.

[0112] Optionally, the protocol layer structure between the access network device and the terminal may also include an artificial intelligence (AI) layer for transmitting AI-related data.

[0113] Taking data transmission between access network devices and terminal devices as an example, data transmission needs to pass through user plane protocol layers, such as the SDAP layer, PDCP layer, RLC layer, MAC layer, and physical layer. The SDAP layer, PDCP layer, RLC layer, MAC layer, and physical layer can also be collectively referred to as the access layer. Based on the direction of data transmission, it is divided into sending and receiving; each of these layers is further divided into a sending part and a receiving part. Taking downlink data transmission as an example, after the PDCP layer obtains data from the upper layer, it transmits the data to the RLC layer and MAC layer. The MAC layer then generates a transport block, and finally, it is wirelessly transmitted through the physical layer. Data is encapsulated in corresponding ways at each layer. For example, data received by a layer from the upper layer is considered a service data unit (SDU) of that layer. After encapsulation by that layer, it becomes a protocol data unit (PDU) and is then passed to the next layer.

[0114] For example, the terminal device may also have an application layer and a non-access layer. The application layer can be used to provide services to applications installed on the terminal device. For instance, downlink data received by the terminal device can be sequentially transmitted from the physical layer to the application layer, and then provided to the application by the application layer; or, the application layer can acquire data generated by the application and sequentially transmit the data to the physical layer for transmission to other communication devices. The non-access layer can be used to forward user data, such as forwarding uplink data received from the application layer to the SDAP layer or forwarding downlink data received from the SDAP layer to the application layer.

[0115] Structure of access network equipment:

[0116] Access network equipment can include CUs and DUs. Multiple DUs can be centrally controlled by a single CU. As an example, the interface between the CU and DU can be called an F1 interface. The control plane (CP) interface can be F1-C, and the user plane (UP) interface can be F1-U. CUs and DUs can be distinguished according to the protocol layer of the wireless network: for example, the functions of the PDCP layer and above are located in the CU, and the functions of protocol layers below the PDCP layer (such as RLC and MAC layers) are located in the DU; or, for another example, the functions of the PDCP layer and above are located in the CU, and the functions of protocol layers below the PDCP layer are located in the DU.

[0117] It is understandable that the above division of CU and DU processing functions according to protocol layers is merely an example. Other division methods are also possible. For instance, CUs or DUs can be divided into those with more protocol layer functions, or they can be divided into those with partial protocol layer processing functions. In one design, some functions of the RLC layer and the protocol layer functions above the RLC layer are located in the CU, while the remaining functions of the RLC layer and the protocol layer functions below the RLC layer are located in the DU. In another design, the functions of CUs or DUs can be divided according to service type or other system requirements, such as latency. Functions that need to meet latency requirements are located in the DU, while functions that do not need to meet this latency requirement are located in the CU. In yet another design, the CU can also have one or more core network functions. For example, the CU can be located on the network side for convenient centralized management. In yet another design, the RU of the DU is remotely located. The RU has radio frequency functionality.

[0118] Optionally, DU and RU can be partitioned at the physical layer (PHY). For example, DU can implement higher-level functions in the PHY layer, and RU can implement lower-level functions. Specifically, for transmission, the PHY layer functions may include adding cyclic redundancy check (CRC) codes, channel coding, rate matching, scrambling, modulation, layer mapping, precoding, resource mapping, physical antenna mapping, and / or RF transmission functions. For reception, the PHY layer functions may include CRC, channel decoding, rate matching de-scrambling, demodulation, layer mapping de-mapping, channel detection, resource demapping, physical antenna demapping, and / or RF reception functions. The higher-level functions in the PHY layer may include a subset of the PHY layer's functions, for example, functions closer to the MAC layer, while the lower-level functions in the PHY layer may include another subset of the PHY layer's functions, for example, functions closer to the RF functions. For example, higher-level functions in the PHY layer may include adding CRC codes, channel coding, rate matching, scrambling, modulation, and layer mapping, while lower-level functions in the PHY layer may include precoding, resource mapping, physical antenna mapping, and radio frequency transmission functions; or, higher-level functions in the PHY layer may include adding CRC codes, channel coding, rate matching, scrambling, modulation, layer mapping, and precoding, while lower-level functions in the PHY layer may include resource mapping, physical antenna mapping, and radio frequency transmission functions.

[0119] For example, the functionality of a CU can be implemented by a single entity or by different entities. For instance, the functionality of the CU can be further divided, separating the control plane and user plane and implementing them through different entities: a control plane CU entity (i.e., the CU-CP entity) and a user plane CU entity (i.e., the CU-UP entity). These CU-CP and CU-UP entities can be coupled with a DU to jointly complete the functions of the access network device.

[0120] In the above architecture, signaling generated by the CU can be sent to the terminal device via the DU, or signaling generated by the terminal device can be sent to the CU via the DU. For example, signaling from the RRC or PDCP layer will eventually be processed into physical layer signaling and sent to the terminal device, or it can be transformed from received physical layer signaling. Under this architecture, the RRC or PDCP layer signaling can be considered to be sent via the DU, or via the DU and RU.

[0121] Optionally, any one of DU, CU, CU-CP, CU-UP, and RU mentioned above can be a software module, a hardware structure, or a combination of software and hardware structures, without limitation. The different entities can exist in different forms, without limitation. For example, DU, CU, CU-CP, and CU-UP are software modules, and RU is a hardware structure. These modules and the methods they execute are also within the scope of protection of this application.

[0122] It should be understood that the number and type of each device in the communication system shown in Figure 1 are for illustrative purposes only, and this application is not limited thereto. In actual applications, the communication system may include more terminal devices, more access network devices, and other network elements, such as core network devices and / or network elements used to implement artificial intelligence functions.

[0123] It is understandable that all or part of the functions implemented by one or more of the terminal devices, access network devices, core network devices, or network elements used to implement artificial intelligence functions can be virtualized, that is, implemented through one or more of dedicated or general-purpose processors and corresponding software modules. Among these, the terminal devices and access network devices involve air interface transmission, and the transmit and receive functions of this interface can be implemented in hardware. Core network devices, such as operation administration and maintenance (OAM) network elements, can also be virtualized. Optionally, one or more of the functions of the virtualized terminal devices, access network devices, core network devices, or network elements used to implement artificial intelligence functions can be implemented by cloud devices, such as cloud devices in over-the-top (OTT) systems.

[0124] The method provided in this application can be used for communication between access network equipment and terminal equipment, or for communication between other communication equipment, such as communication between macro base stations and micro base stations in a wireless backhaul link, or communication between two terminal devices in a sidelink (SL), etc., without limitation.

[0125] To facilitate understanding, the AI ​​technologies involved in this application will be introduced below. It should be understood that this introduction is not intended to limit this application.

[0126] (1) AI Model:

[0127] AI models are the concrete implementations of AI technology functions, representing the mapping relationship between the model's inputs and outputs. AI models can be neural networks, linear regression models, decision tree models, support vector machines (SVMs), Bayesian networks, Q-learning models, or other machine learning (ML) models.

[0128] This application relates to an encoder for compressing CSI and a decoder for recovering compressed CSI. The encoder and decoder need to be used in a matched manner, and can be understood as a pair of AI models. In this application, an encoder may include one or more AI models, and the decoder matched by the encoder also includes one or more AI models. The number of AI models included in the encoder and decoder used in the matching process is the same and they correspond one-to-one.

[0129] In one possible design, a set of matching encoders and decoders can be specifically two parts of the same autoencoder (AE). An autoencoder is an unsupervised learning neural network that uses input data as labeled data; therefore, it can also be understood as a self-supervised learning neural network. Autoencoders can be used for data compression and reconstruction. For example, the encoder in an autoencoder can compress (encode) data A to obtain data B; the decoder in the autoencoder can decompress (decode) data B to recover data A. Alternatively, the decoder can be understood as the inverse operation of the encoder.

[0130] (2) Neural Networks:

[0131] Neural networks are a specific implementation of AI or machine learning techniques. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings.

[0132] The idea behind neural networks originates from the neuronal structure of the brain. For example, each neuron performs a weighted summation of its input values ​​and outputs the result through an activation function. Figure 2A shows a schematic diagram of a neuron structure. Assume the neuron's input is x = [x0, x1, ..., x...]. n The weights corresponding to each input are w = [w, w1, ..., w]. n ], where w i As x i The weights are used to assign weights to x. iWeighting is applied. The bias for the weighted sum of the input values ​​is, for example, b. Activation functions can take many forms. Suppose the activation function of a neuron is: y = f(z) = max(0, z), then the output of that neuron is:

[0133] For example, if the activation function of a neuron is y = f(z) = z, then the output of that neuron is: Among them, b and w i x i The activation function can take various values, such as decimals, integers (e.g., 0, positive integers, or negative integers), or complex numbers. Different neurons in a neural network can have the same or different activation functions.

[0134] Neural networks typically consist of multiple layers, each containing one or more neurons. Increasing the depth and / or width of a neural network enhances its expressive power, providing more robust information extraction and abstract modeling capabilities for complex systems. The depth of a neural network refers to the number of layers it comprises, while the number of neurons in each layer can be called the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the processing results to the output layer, which then obtains the network's output. In another implementation, a neural network includes an input layer, hidden layers, and an output layer (see Figure 2B). The input layer processes the received input information through neurons and passes the processing results to the hidden layers. The hidden layers perform calculations on the received processing results and pass these calculations to the output layer or adjacent hidden layers, ultimately resulting in the network's output. A neural network may contain one hidden layer or multiple sequentially connected hidden layers; there is no limitation on this.

[0135] Taking neural networks as an example, the AI ​​model involved in this application can be a deep neural network (DNN). Depending on the construction method of the network, DNN can include feedforward neural networks (FNN), convolutional neural networks (CNN), and recurrent neural networks (RNN).

[0136] (3) Training dataset and inference data:

[0137] Training datasets are used to train AI models. A training dataset can include the AI ​​model's input, or it can include both the AI ​​model's input and the target output. Specifically, a training dataset includes one or more training data points, which can be training samples input to the AI ​​model or the AI ​​model's target output. The target output can also be referred to as the label or labeled samples. The training dataset is a crucial part of machine learning; model training essentially involves learning certain features from the training data to make the AI ​​model's output as close as possible to the target output, minimizing the difference between the AI ​​model's output and the target output. The composition and selection of the training dataset can, to a certain extent, determine the performance of the trained AI model.

[0138] Furthermore, a loss function can be defined during the training process of an AI model (such as a neural network). The loss function describes the difference or discrepancy between the output value of the AI ​​model and the target output value. This application does not limit the specific form of the loss function. The training process of an AI model involves adjusting the model parameters of the AI ​​model so that the value of the loss function is less than a threshold, or so that the value of the loss function meets the target requirements. For example, if the AI ​​model is a neural network, adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers in the neural network, its width, the weights of the neurons, or the parameters in the activation function of the neurons.

[0139] Inference data can be used as input to a trained AI model for inference. During the model's inference process, the inference data is input into the AI ​​model, and the corresponding output, which is the inference result, is obtained.

[0140] (4) AI model design:

[0141] The design of an AI model mainly includes a data collection phase (e.g., collecting training data and / or inference data), a model training phase, and a model inference phase. It can further include an application phase of the inference results. See Figure 2C for an example of an AI application framework. In the aforementioned data collection phase, the data source provides the training dataset and inference data. In the model training phase, the AI ​​model is obtained by analyzing or training the training data provided by the data source. The AI ​​model represents the mapping relationship between the model's input and output. Learning the AI ​​model through model training nodes is equivalent to learning the mapping relationship between the model's input and output using the training data. In the model inference phase, the AI ​​model trained in the model training phase is used to perform inference based on the inference data provided by the data source to obtain the inference result. This phase can also be understood as: inputting inference data into the AI ​​model, obtaining the output through the AI ​​model, which is the inference result. This inference result can indicate the configuration parameters used (executed) by the execution object, and / or the operations performed by the execution object. In the application phase of inference results, the inference results are published. For example, the inference results can be uniformly planned by the actor entity, which can send the inference results to one or more execution objects (e.g., core network devices, access network devices, or terminal devices) for execution. Furthermore, the actor entity can also provide feedback on the model's performance to the data source, facilitating subsequent model updates and training.

[0142] It is understandable that communication systems can include network elements with artificial intelligence (AI) capabilities. The AI ​​model design-related steps described above can be performed by one or more network elements with AI capabilities. In one possible design, AI functions (such as AI modules or AI entities) can be configured within existing network elements in the communication system to implement AI-related operations, such as AI model training and / or inference. For example, these existing network elements could be access network equipment 110 (such as a gNB), terminal equipment 120 or 130, core network equipment, or network management systems. The network management system can divide network management work into three categories based on the actual needs of the operator's network operation: operation, administration, and maintenance. The network management system can also be called an Operation Administration and Maintenance (OAM) network element, or simply OAM. Operation mainly involves the analysis, prediction, planning, and configuration of daily network and service operations; maintenance mainly involves daily operational activities such as testing and fault management of the network and its services. The network management system can detect network operating status, optimize network connectivity and performance, improve network stability, and reduce network maintenance costs. Alternatively, in another possible design, an independent network element can be introduced into the communication system to perform AI-related operations, such as training AI models. This independent network element can be called an AI network element or an AI node, etc., and this application does not limit the name. The AI ​​network element 140 can be directly connected to the access network equipment in the communication system, or it can be indirectly connected to the access network equipment through a third-party network element. Among them, the third-party network element can be a core network element such as an authentication management function (AMF) network element, a user plane function (UPF) network element, an OAM, a cloud server, or other network elements, without limitation. For example, see Figure 3, where the AI ​​network element 140 is introduced into the communication system shown in Figure 1 above.

[0143] In this application, a model can infer one parameter or multiple parameters. The training processes of different models can be deployed on different devices or nodes, or on the same device or node. The inference processes of different models can be deployed on different devices or nodes, or on the same device or node. Taking the model training phase completed by a terminal device as an example, after training the corresponding encoder and decoder, the terminal device sends the model parameters of the decoder to the access network device. Taking the model training phase completed by an access network device as an example, after training the corresponding encoder and decoder, the access network device can instruct the model parameters of the encoder to the terminal device. Taking the model training phase completed by an independent AI network element as an example, after training the corresponding encoder and decoder, the AI ​​network element sends the model parameters of the encoder to the terminal device and the model parameters of the decoder to the access network device. Then, the model inference phase corresponding to the encoder is performed in the terminal device, and the model inference phase corresponding to the decoder is performed in the access network device.

[0144] The model parameters can include one or more of the following: model structure parameters (e.g., number of layers, and / or weights), model input parameters (e.g., input dimension, number of input ports), or model output parameters (e.g., output dimension, number of output ports). The input dimension refers to the size of an input data set; for example, if the input data is a sequence, the corresponding input dimension indicates the length of the sequence. The number of input ports refers to the quantity of input data. Similarly, the output dimension refers to the size of an output data set; for example, if the output data is a sequence, the corresponding output dimension indicates the length of the sequence. The number of output ports refers to the quantity of output data.

[0145] Furthermore, this application also provides several network architectures illustrated in Figures 4A to 4D, taking model training and / or inference in access network devices as an example, and divides the functional modules for model training and / or inference in access network devices.

[0146] As shown in Figure 4A(a), in a first possible implementation, the access network device includes a near real-time access network intelligent controller (RIC) module for model learning and / or inference. For example, the near real-time RIC can obtain network-side and / or terminal-side information from at least one of the CU, DU, and RU, which may include training data or inference data. For example, the near real-time RIC can be used for model training and can also be used for inference with the trained model. Optionally, the near real-time RIC can submit the inference results to at least one of the CU, DU, and RU. Optionally, the CU and DU can exchange inference results. Optionally, the DU and RU can exchange inference results; for example, the near real-time RIC submits the inference results to the DU, which then submits them to the RU.

[0147] As shown in Figure 4A(b), in the second possible implementation, a non-real-time RIC may be included outside the access network equipment in the communication system. Optionally, this non-real-time RIC may be located in the OAM or in the core network equipment. This non-real-time RIC is used for model learning and inference. For example, the non-real-time RIC can obtain network-side and / or terminal-side information from at least one of the CU, DU, and RU, which may include training data or inference data. For example, the non-real-time RIC is used for model training and can also use the trained model for inference. Optionally, the non-real-time RIC can submit the inference results to at least one of the CU, DU, and RU. Optionally, the CU and DU can exchange inference results. Optionally, the DU and RU can exchange inference results, for example, the non-real-time RIC submits the inference results to the DU, and the DU submits them to the RU.

[0148] As shown in Figure 4A(c), in the third possible implementation, the access network device includes a near real-time RIC, and outside the access network device, it also includes a non-real-time RIC. Optionally, the non-real-time RIC can be located in the OAM or in the core network device. In one possible design, the non-real-time RIC can be used for model training. The near real-time RIC can obtain the model parameters of the trained AI model from the non-real-time RIC, and obtain network-side and / or terminal-side information from at least one of the CU, DU, and RU, using this information and the trained AI model to obtain the inference result. Furthermore, the near real-time RIC can also submit the inference result to at least one of the CU, DU, and RU. Optionally, the CU and DU can exchange inference results, and the DU and RU can also exchange inference results, for example, the near real-time RIC submits the inference result to the DU, and the DU submits it to the RU. Alternatively, in one possible design, the near real-time RIC is used for model training and inference using the trained model, while the non-real-time RIC does not participate in the model training or inference; or, the non-real-time RIC is used for model training and inference using the trained model, while the real-time RIC does not participate in the model training or inference. Another possible design involves the near real-time RIC training the model and sending the model parameters of the trained AI model to the non-real-time RIC, which then uses the trained model for inference.

[0149] Figure 4B shows an example network architecture to which the method provided in this application can be applied. Compared to (c) in Figure 4A, the CU in Figure 4B is separated into CU-CP and CU-UP.

[0150] Figure 4C shows an example network architecture to which the method provided in this application can be applied. As shown in Figure 4C, optionally, the access network device includes one or more AI entities, the function of which is similar to the near real-time RIC described above. Optionally, the OAM includes one or more AI entities, the function of which is similar to the non-real-time RIC described above. Optionally, the core network device includes one or more AI entities, the function of which is similar to the non-real-time RIC described above. When both the OAM and core network devices include AI entities, the models trained by their respective AI entities are different, and / or the models used for inference are different. In this application, the difference in models may include at least one of the following: the structural parameters of the model (e.g., the number of layers in the model, and / or weights, etc.), the input parameters of the model, or the output parameters of the model.

[0151] Figure 4D shows an example network architecture to which the method provided in this application can be applied. Compared to Figure 4C, the access network device in Figure 4D is separated into CU and DU. Optionally, the CU may include an AI entity, the function of which is similar to the near real-time RIC described above. Optionally, the DU may include an AI entity, the function of which is similar to the near real-time RIC described above. When both CU and DU include AI entities, the models trained by their respective AI entities are different, and / or the models used for inference are different. Optionally, the CU in Figure 4D can be further split into CU-CP and CU-UP. Optionally, one or more AI models can be deployed in CU-CP. And / or, one or more AI models can be deployed in CU-UP. Optionally, in Figure 4C or Figure 4D, the OAM of the access network device and the OAM of the core network device can be deployed separately and independently.

[0152] As a crucial component of the system design, the reference signal is primarily responsible for tasks including channel state measurement, data demodulation, beam training, and time-frequency parameter tracking. The design of the reference signal mainly involves the design of random sequence generation and time-frequency resource mapping, as well as the power of the corresponding transmitted sequence. These three aspects combine to form a complete reference signal pattern design. In summary, the reference signal pattern design / configuration mentioned in this application can at least include the configuration of the reference signal's position, power, and sequence. As shown in Table 1, the reference signal refers to different signals in uplink and downlink transmission:

[0153] Table 1 shows the specific signals that the reference signals represent in uplink and downlink.

[0154] One function of a reference signal is to aid in channel estimation. As mentioned earlier, during communication, the receiver needs prior knowledge of the wireless channel information in both antennas to perform coherent detection and decoding of the transmitter's data. Since the reference signal carries almost no useful information, its overhead is often considered to balance channel estimation performance with available time-frequency resources for data transmission. Typically, the reference signal is sparse in both the time and frequency domains. Therefore, after estimating the channel at the time-frequency resource unit containing the reference signal using a channel estimation algorithm, it is also necessary to estimate the wireless channel in the time-frequency domain resources where the reference signal has not been transmitted. Common channel estimation algorithms include least squares (LS), linear minimum mean square error (LMMESE), and compressed sensing (CS) algorithms.

[0155] DMRS in existing standards:

[0156] As mentioned above, 5G systems contain many reference signals with different functions. Among them, the DMRS (Digital Modulation Reference Signal) is used for data demodulation, meaning it can be used to estimate the channel response of the time-frequency resources occupied by the data. There is a trade-off between the accuracy of channel estimation and the density / overhead of the DMRS. If the channel exhibits significant frequency selectivity (i.e., the channel varies greatly in the frequency domain), the DMRS density in the frequency domain should be increased. Similarly, if the channel varies rapidly in the time domain, more resources need to be allocated in the time domain to deploy the reference signal. After determining the DMRS density in the time-frequency domain, we need to further consider the position of the DMRS within the time-frequency resource block. For example, under stationary channel conditions, to reduce interpolation errors and implementation complexity, a uniform distribution of DMRS signals in the frequency and time domains can be adopted. Since the DMRS itself does not transmit any data signals useful to users, it needs to be allocated with an appropriate density to maximize throughput.

[0157] The method for generating the DMRS random sequence depends on the specific waveform used. Currently, 5G supports two waveforms: cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform-spread-orthogonal frequency division multiplexing (DFT-s-OFDM). The method for mapping the DMRS sequence to physical time-frequency resource units is also clearly defined in standard protocols. Specifically, the location of the DMRS within a single time-frequency resource block (RB) is mainly determined by the following parameters:

[0158] Mapping type: There are Type A and Type B. The two mapping types have different restrictions on the starting symbol position and the number of PDSCH symbols (Type B starts from the third symbol).

[0159] DMRS configuration type: Determines the location of the frequency domain resources of the DMRS (type1 or type2).

[0160] Additional Position of DMRS: Determines whether there is an additional DMRS in the time domain.

[0161] Maximum Length (maxLength): Determines whether it is a single-symbol DMRS or a double-symbol DMRS.

[0162] AI DMRS estimation:

[0163] AI DMRS estimation, which estimates the channel using an AI model, typically involves the received DMRS data and the DMRS itself (or data based on both after processing), with the output being the estimated channel. AI DMRS channel estimation is usually performed at the physical resource block bundle (PRB bundle) / resource block group (RBG) granularity (described below using RBG as an example). Because the same precoding (beamforming) is used on a PRB bundle / RBG, the channel estimated at the DMRS location can be used for the PDSCH data location. According to the standard, the optional PRB bundle size / RBG size can be 2RB, 4RB, or wideband. Figure 5 shows a schematic diagram of an existing AI DMRS estimation example, illustrating the input relationship of AI DMRS estimation at a 4RB granularity. In Figure 5, the terminal device uses 16 RBs to receive PDSCH, which includes four 4 RBs. The AI ​​DMRS model is trained with 4 RB granularity, so each RBG is estimated separately for AI DMRS. A total of four AI DMRS estimations are performed to complete the estimation of DMRS on all 16 RBs.

[0164] RBG allocation in existing standards:

[0165] Depending on the starting position of the RBG, there are usually several allocation scenarios:

[0166] Case 1: Starting position of the bandwidth used by the terminal device to receive PDSCH So when we have 50 RBs (the bandwidth used by the terminal device to receive PDSCH) When the RBG granularity is 4 (L=4), the bandwidth includes 13 RBGs, where each of the first 12 RBGs includes 4 RBs, and the last RBG includes...

[0167] Scenario 2:

[0168] For example, for In a scenario where L=4, 50 RBs are included. but:

[0169] RBG 0 contains

[0170] RBG 12 contains

[0171] Each of the remaining 11 RBGs contains 4 RBs.

[0172] Based on the above analysis, it is generally assumed that different precoding methods are used within different RBGs. Therefore, when an RBG granularity that does not match the input of the AI ​​DMRS estimation model occurs, inference using the AI ​​model cannot be performed, and the model will revert to traditional non-AI DMRS estimation. Otherwise, different RBGs would require different DMRS estimation methods (different RBG granularities), leading to increased complexity. Furthermore, factors such as the DMRS pattern may be designed based on AI DMRS estimation, potentially preventing traditional DMRS estimation. Additionally, the performance of non-AI DMRS estimation may be lower than that of AI DMRS estimation.

[0173] If all models are trained at the minimum granularity (1RB), the problem of RBG granularity mismatch can be solved, but the implementation complexity will increase by a factor of 1. Originally, one inference can demodulate 4RB channels, but if trained at the minimum granularity, one RBG will require 4 inferences.

[0174] In view of this, this application provides a communication scheme in which an access network device instructs at least one resource block such that the resource blocks included in the first resource block set and the second resource block set both satisfy the capabilities of the AI ​​model, thereby enabling the terminal device to process the DMRS received on the first resource block set and the second resource block set based on the AI ​​model, thus improving the processing performance of DMRS.

[0175] Based on the above communication system, the following provides a communication method according to an embodiment of this application:

[0176] Figure 6 shows a flowchart of a communication method provided in an embodiment of this application. Exemplarily, the method may include the following steps:

[0177] S601. The access network device determines that the number of resource blocks included in the third resource block set is less than the set number.

[0178] Access network equipment can configure one or more bandwidths for terminal equipment. For example, access network equipment can configure one or more bandwidths for terminal equipment via RRC signaling. Access network equipment can schedule terminal equipment to receive PDSCH via downlink control information (DCI) and activate a first bandwidth, which is the bandwidth configured by the access network equipment for the terminal equipment to use when receiving PDSCH, and this first bandwidth is one of the aforementioned one or more bandwidths. Specifically, access network equipment can indicate PDSCH resources via DCI, or via information element (IE): Physical Downlink Shared Channel - Configuration (pdsch-config) parameters – resource allocation.

[0179] In this embodiment, in order to better perform channel estimation, the access network device and the terminal device divide the first bandwidth into multiple resource block sets, that is, the first bandwidth includes multiple resource block sets, and the terminal device can perform inference based on the DMRS received on each resource block set.

[0180] In this embodiment, the access network device divides the first bandwidth based on the RBG partitioning method defined in existing standards. When the access network device divides the first bandwidth into multiple resource block sets, there may be one or more third resource block sets containing fewer resource blocks than a predetermined number. Here, we describe one third resource block set as an example. The third resource block set is obtained after the initial partitioning of the first bandwidth. The predetermined number is set to meet the capability requirements of the AI ​​model, or to meet the granularity requirements of the AI ​​model. For example, the predetermined number is the minimum number of resource blocks required for AI model inference.

[0181] It is understandable that each resource block set in the resource block sets other than the third resource block set contains a number of resource blocks greater than or equal to the set number.

[0182] S602. The access network device determines the first resource block set.

[0183] After the access network device determines that the number of resource blocks included in the third resource block set is less than a set number, the access network device needs to redistribute the first bandwidth to determine the first resource block set. This first resource block set is obtained after redistributing the first bandwidth, and it includes the third resource block set and at least one first resource block. The at least one first resource block is a resource block reused from another resource block set adjacent to the third resource block set (here, the second resource block set).

[0184] It is understood that the first resource block set reuses at least one resource block from the second resource block set, but does not change the initial number of resource blocks included in the reused second resource block set. The at least one first resource block belongs to both the first and second resource block sets. The number of resource blocks included in both the first and second resource block sets is greater than or equal to the same predetermined number.

[0185] The precoding corresponding to the first resource block set is the same as that corresponding to the second resource block set. However, the precoding corresponding to the first resource block set, the second resource block set, and the other resource block sets included in the first bandwidth are different. That is, the first resource block set and the second resource block set are equivalent to a large resource block set (called the fourth resource block set). The access network device calculates the precoding based on the channels within this fourth resource block set, and this precoding is uniformly applied to all time-frequency resources in this fourth resource block set.

[0186] S603. The access network device sends the first information to the terminal device.

[0187] Accordingly, the terminal device receives the first information.

[0188] In this embodiment, the terminal device also allocates the first bandwidth based on the RBG partitioning method defined in the existing standard. However, the access network device reuses at least one resource block from the second resource block set for the third resource block set, which is equivalent to changing the partitioning method. Therefore, after determining the first resource block set, the access network device sends first information to the terminal device. This first information indicates at least one first resource block, that is, it indicates that the first resource block set reuses at least one resource block from the second resource block set, and the terminal device can reuse the channel data within this at least one first resource block for inference of the first resource block set.

[0189] The first information indicates at least one first resource block, and may be implemented in, but is not limited to, the following two ways:

[0190] One implementation involves the first information including an index of at least one first resource block. Each resource block in the first bandwidth has a unique index. By carrying the index of at least one first resource block in the first information, the access network device enables the terminal device to accurately determine at least one resource block in the second resource block set that is multiplexed by the first resource block set. For example, if the first bandwidth includes 16 resource blocks (RBs), the first information could include 4 bits. Assuming the 4th RB is multiplexed by the first resource block set, the first information would be "0011" to indicate that the 4th RB in the first bandwidth is multiplexed by the first resource block set.

[0191] Another implementation is that the first information includes at least one starting resource block of the first resource block relative to the first bandwidth. The relative index. Each resource block in the first bandwidth has a unique index. By carrying at least one relative index of the first resource block relative to the starting resource block of the first bandwidth in the first information, the access network device can enable the terminal device to accurately determine at least one resource block in the second resource block set that is multiplexed by the first resource block set. For example, if the first bandwidth includes 16 RBs and a BWP is 4 RBs, then the first information may include 2 bits, assuming a relative... If the fourth RB is reused by the first resource block set, then the first information is "11". The fourth RB in the first bandwidth relative to the start position of the first BWP is reused by the first resource block.

[0192] It is understandable that the terminal device also divides the first bandwidth based on the RBG partitioning method defined by the existing standard. The number of resource blocks included in the third resource block set obtained by the initial partitioning is less than the set number. After receiving the first information, the terminal device can determine that the first resource block set includes the third resource block set and at least one resource block according to the at least one resource block indicated by the first information.

[0193] For example, the first information may be carried in at least one of the following signaling: RRC signaling, DCI information, and medium access control element (MAC CE).

[0194] S604. The access network device sends DMRS on the first resource block set and the second resource block set based on the first information.

[0195] After the access network device determines that at least one resource block from the second resource block set is multiplexed from the first resource block set, it transmits DMRS on both the first and second resource block sets. The first and second resource block sets use the same precoding.

[0196] Of course, the access network equipment also transmits DMRS on other resource block sets included in the first bandwidth. The precoding corresponding to the first resource block set, the second resource block set, and the other resource block sets included in the first bandwidth is different.

[0197] S605. The terminal device processes the DMRS received on the first resource block set and the second resource block set based on an artificial intelligence model.

[0198] The first inference result is obtained based on the DMRS inference received on the first resource block set; the second inference result is obtained based on the DMRS inference received on the second resource block set.

[0199] Since at least one resource block belongs to the first resource block set and the second resource block set, the at least one resource block is inferred twice. By processing the first inference result corresponding to at least one first resource block and the second inference result corresponding to at least one first resource block, the inference result can be made more accurate.

[0200] The terminal device processes the DMRS received on the first resource block set and the second resource block set based on an artificial intelligence model, which may include, but is not limited to, the following two implementation methods:

[0201] One implementation involves the terminal device averaging the first and second inference results to obtain a third inference result, which serves as the final inference result corresponding to at least one first resource block. Since the at least one resource block is inferred twice, resulting in two inference results for that resource block, averaging these two results to obtain the third inference result, and using it as the final inference result for at least one first resource block, makes the inference results more accurate. The terminal device can include this third inference result in both the inference results of the first resource block set and the inference results of the second resource block set.

[0202] Another implementation involves the terminal device discarding the second inference result and using the first inference result as the final inference result for at least one first resource block; or the terminal device discarding the first inference result and using the second inference result as the final inference result for at least one first resource block. Since the at least one resource block is inferred twice, resulting in two inference results for that resource block, discarding one of the inference results for that resource block can make the inference results more accurate. The terminal device can then include the retained inference results in the inference results of the first resource block set and the inference results of the second resource block set, respectively.

[0203] The following example illustrates this. Figure 7 shows a schematic diagram of AI DMRS estimation based on an embodiment of this application. In this example, resource blocks are described using RBs as an example, and resource block sets are described using RBGs as an example. Based on the existing standard definition of RBG partitioning method, RBG1 in the first bandwidth includes 3 RBs, and RBG2 includes 4 RBs. Assuming the minimum number of resource blocks required for AI model inference is 4 RBs, since RBG1 only includes 3 RBs, it cannot meet the minimum number of resource blocks required for AI model inference, and therefore, inference based on the AI ​​model cannot be performed on RBG1. In this example, RBG1 reuses RB3 from RBG2. RBG1 and RBG2 use the same precoding, which is equivalent to merging RBG1 and RBG2 into a large RBG. Furthermore, the access network device indicates to the terminal device that RB3 is reused. The access network device transmits DMRS on RBG1 and RBG2. Based on the RBG partitioning method defined by the existing standard and the first information, the terminal device determines that RBG1 reuses RB3 in RBG2. After receiving DMRS on RBG1 and RBG2, the terminal device performs two inferences based on the AI ​​model. The first inference is for the DMRS received on RB index = {0,1,2,3}, and the second inference is for the DMRS received on RB index = {3,4,5,6}.

[0204] For example, the above method can be implemented when channel estimation based on an AI model is activated. Before implementing the method provided in the embodiments of this application, the access network device can activate the terminal device to perform channel estimation based on an AI model through DCI or the like.

[0205] According to an embodiment of this application, a communication method is provided in which an access network device instructs at least one resource block so that the resource blocks included in the first resource block set and the second resource block set both satisfy the capabilities of an AI model. This enables the terminal device to process the DMRS received on the first resource block set and the second resource block set based on the AI ​​model, thereby improving the processing performance of the DMRS.

[0206] The above embodiments describe a method for determining the number of resource blocks included in each resource block set based on the RBG partitioning method defined by existing standards for terminal devices and access network devices.

[0207] When the terminal device and the access network device determine the number of resource blocks included in each resource block group based on the RBG partitioning method defined by the existing standard, although the standard defines that "the terminal device generally assumes different precodings within different RBGs", it is not possible to enforce the behavior of the terminal device. Then the terminal device can forcibly extract N RBs (0 < N < AI model input granularity) from other (adjacent) RBGs to supplement the current RBG for the inference of the AI model. Since different precodings are used within different RBGs, this will lead to a decline in channel estimation performance.

[0208] The following embodiments will describe a redefined RBG partitioning method. The terminal device and the access network device determine the number of resource blocks included in each resource block group based on the redefined RBG partitioning method:

[0209] As shown in FIG. 8, it is a schematic flowchart of another communication method provided by an embodiment of the present application. Exemplarily, the method may include the following steps:

[0210] S801a. The access network device determines the total number of multiple resource block groups included in the first bandwidth.

[0211] S801b. The terminal device determines the total number of multiple resource block groups included in the first bandwidth.

[0212] In this embodiment, it can be defined by the protocol that the access network device and the terminal device use the solution of this embodiment to determine the total number of multiple resource block groups included in the first bandwidth (in other words, to determine the number of resource blocks included in each resource block group); alternatively, the access network device and the terminal device can also negotiate to use the solution of this embodiment to determine the total number of multiple resource block groups included in the first bandwidth.

[0213] The bandwidth used by the terminal device to receive PDSCH configured by the access network device for the terminal device is the first bandwidth, and the meaning of the first bandwidth can be referred to the description above.

[0214] In this embodiment, for better channel estimation, the access network device and the terminal device divide the first bandwidth into multiple resource block groups, that is, the first bandwidth includes multiple resource block groups. The precoding corresponding to each resource block group can be different, and the terminal device can perform inference based on the DMRS received on each resource block group respectively. The access network device and the terminal device respectively determine the total number of multiple resource block groups included in the first bandwidth, that is, determine the number of resource blocks included in each resource block group.

[0215] Each of the aforementioned resource block sets comprises multiple resource blocks, and the number of resource blocks in each set satisfies the capabilities of the AI ​​model. For example, the number of resource blocks in each set satisfies the capabilities of the AI ​​model, meaning it meets the granularity requirements of the AI ​​model. For instance, the number of resource blocks in each set is greater than or equal to the minimum number of resource blocks required for the AI ​​model's inference.

[0216] For example, when the access network device and the terminal device initially determine the total number of resource block sets included in the first bandwidth, there may be one or more resource block sets whose number of resource blocks is less than the minimum number of resource blocks required for AI model inference. In this case, the access network device and the terminal device can determine the total number of resource block sets included in the first bandwidth by independently judging and reusing at least one resource block based on the above requirement (the number of resource blocks included in each resource block set is greater than or equal to the minimum number of resource blocks required for AI model inference).

[0217] The following discussion will address different scenarios:

[0218] One scenario is that the starting position of the first bandwidth is 0, in In the case of the first bandwidth, it includes There are n resource block sets, from the first resource block set to the Nth resource block set. bundle Each resource block set in the -1 resource block set includes L resource blocks, and the Nth resource block set... bundle The resource block set includes One resource block, This represents the size of the first bandwidth.

[0219] For example, suppose L = 4RB, 50%4 > 0, There are 12 RBGs, where each RBG from the 1st to the 11th RBGs contains 4 RBs, and the 12th RBG contains 2 + 4 = 6 RBs. Therefore, each RBG in the 12 RBGs contains more than or equal to 4 RBs.

[0220] Another scenario is that the starting position of the first bandwidth is... The first bandwidth includes A set of resource blocks, In the resource block sets, excluding the first resource block set and the second... Outside of a set of resource blocks Each resource block set in the set of L resources includes L resource blocks. The first resource block set in the set of resource blocks includes One resource block, The first in the resource block set The resource block set includes One resource block, This represents the size of the first bandwidth.

[0221] For example, suppose L = 4RB, 50 RBs include There are 11 RBGs, where each RBG in the 2nd to 10th RBGs contains 4 RBs, the 1st RBG contains 2*4-(25%4)=7 RBs, and the 11th RBG contains (25+50)%4+4=7 RBs. Therefore, each RBG in the 11 RBGs contains more than or equal to 4 RBs.

[0222] S802. Terminal equipment and access network equipment perform DMRS transmission on multiple resource block sets.

[0223] After the access network device and the terminal device determine the total number of resource block sets included in the first bandwidth, the access network device transmits DMRS on the resource block sets, and the terminal device receives DMRS on the resource block sets. The precoding for each resource block set can be different.

[0224] S803. The terminal device processes the DMRS received on multiple resource block sets based on an AI model.

[0225] After receiving DMRS from the aforementioned multiple resource block sets, the terminal device processes the DMRS received from the multiple resource block sets based on an AI model. This AI model is used to process the DMRS.

[0226] For example, since the precoding corresponding to each resource block set is different, the terminal device performs inference on the DMRS received on each resource block set based on the AI ​​model. Through inference, in addition to completing the channel estimation at the time-frequency resource unit where the DMRS is located, it can also estimate the radio channel on the time-frequency spatial domain resources where the DMRS has not been transmitted.

[0227] For example, the above method can be implemented when channel estimation based on an AI model is activated.

[0228] According to an embodiment of this application, a communication method is provided in which the terminal device and the access network device determine the number of resource blocks included in each resource block set based on the redefined RBG partitioning method, so that the number of resource blocks included in each resource block set meets the capability of the AI ​​model. Thus, the terminal device can perform channel estimation based on the AI ​​model, thereby improving the performance of channel estimation.

[0229] In this application, the phrase "sending information to... (e.g., a terminal device)" or the related illustrations in the accompanying drawings can be understood as the destination of the information being the terminal device. This can include sending information directly or indirectly to the terminal device. Similarly, the phrase "receiving information from... (e.g., a terminal device)" or "receiving information from... (e.g., a terminal device)" or the related illustrations in the accompanying drawings can be understood as the source of the information being the terminal device. This can include receiving information directly or indirectly from the terminal device. Information may undergo necessary processing between the source and destination, such as format changes, but the destination can understand the valid information from the source. Similar expressions in this application can be interpreted similarly, and will not be elaborated further here.

[0230] It is understood that this application uses terminal equipment and access network equipment as examples to illustrate the interaction, but this application does not limit the entities that can be used to illustrate the interaction. For example, the terminal equipment in the method provided by this application can also be a chip, chip system, or processor applied to the terminal equipment, or it can be a logical node, logical module, or software that can implement all or part of the terminal equipment; the access network equipment in the method provided by this application can also be a chip, chip system, or processor applied to the access network equipment, or it can be a logical node, logical module, or software that can implement all or part of the access network equipment functions.

[0231] It is understood that, in order to achieve the functions in the above embodiments, the terminal device and access network device include hardware structures and / or software modules corresponding to perform each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, 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 scenario and design constraints of the technical solution.

[0232] Based on the same concept, referring to Figure 9, this application provides a communication device 900, which includes a processing module 901 and a communication module 902. The communication device 900 can be a terminal device, or a communication device applied to or used in conjunction with a terminal device to implement a communication method executed on the terminal device side; alternatively, the communication device 900 can be an access network device, or a communication device applied to or used in conjunction with an access network device to implement a communication method executed on the access network device side.

[0233] The communication module can also be called a transceiver module, transceiver, transceiver unit, or transceiver device. The processing module can also be called a processor, processing board, processing unit, or processing device. Optionally, the communication module is used to perform the sending and receiving operations on the terminal device side or access network device side in the above method. The device in the communication module that implements the receiving function can be regarded as a receiving unit, and the device in the communication module that implements the sending function can be regarded as a sending unit. That is, the communication module includes a receiving unit and a sending unit.

[0234] When the communication device 900 is applied to a terminal device, the processing module 901 can be used to implement the processing functions of the terminal device in the example of FIG6 or FIG8, and the communication module 902 can be used to implement the sending and receiving functions of the terminal device in the example of FIG6 or FIG8. Alternatively, the communication device can be understood with reference to the fourth or sixth aspect of the invention and the possible designs in the fourth or sixth aspect.

[0235] When the communication device 900 is applied to an access network device, the processing module 901 can be used to implement the processing functions of the access network device in the example of Figure 6 or Figure 8, and the communication module 902 can be used to implement the transmitting and receiving functions of the access network device in the example of Figure 6 or Figure 8. Alternatively, the communication device can be understood with reference to the fifth or sixth aspect of the invention and the possible designs in the fifth or sixth aspect.

[0236] Furthermore, it should be noted that the aforementioned communication module and / or processing module can be implemented through virtual modules. For example, the processing module can be implemented through software functional units or virtual devices, and the communication module can be implemented through software functions or virtual devices. Alternatively, the processing module or communication module can also be implemented through physical devices. For example, if the device is implemented using a chip / chip circuit, the communication module can be an input / output circuit and / or a communication interface, performing input operations (corresponding to the aforementioned receiving operation) and output operations (corresponding to the aforementioned sending operation); the processing module is an integrated processor, microprocessor, or integrated circuit.

[0237] The module division in this application is illustrative and represents only one logical functional division. In actual implementation, other division methods are possible. Furthermore, the functional modules in the various examples of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0238] Based on the same technical concept, this application also provides a communication device 1000. For example, the communication device 1000 may be a chip or a chip system. Optionally, in this application, the chip system may be composed of chips, or may include chips and other discrete devices.

[0239] The communication device 1000 can be used to implement the function of any network element in the communication system described in the foregoing examples. The communication device 1000 may include at least one processor 1010. Optionally, the processor 1010 is coupled to a memory, which may be located within the device, integrated with the processor, or located outside the device. For example, the communication device 1000 may also include at least one memory 1020. The memory 1020 stores the computer programs, computer programs or instructions, and / or data necessary for implementing any of the above examples; the processor 1010 may execute the computer programs stored in the memory 1020 to perform the methods in any of the above examples.

[0240] The communication device 1000 may also include a communication interface 1030, through which the communication device 1000 can interact with other devices. For example, the communication interface 1030 may be a transceiver, circuit, bus, module, pin, or other type of communication interface. When the communication device 1000 is a chip-based device or circuit, the communication interface 1030 may also be an input / output circuit, capable of inputting information (or receiving information) and outputting information (or sending information). The processor may be an integrated processor, microprocessor, integrated circuit, or logic circuit, and the processor can determine the output information based on the input information.

[0241] The coupling in this application refers to indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, used for information exchange between devices, units, or modules. The processor 1010 may operate in conjunction with the memory 1020 and the communication interface 1030. This application does not limit the specific connection medium between the processor 1010, the memory 1020, and the communication interface 1030.

[0242] Optionally, referring to Figure 10, the processor 1010, memory 1020, and communication interface 1030 are interconnected via bus 1040. Bus 1040 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. Buses can be classified as address buses, data buses, control buses, etc. For ease of illustration, only one thick line is used in Figure 10, but this does not indicate that there is only one bus or one type of bus.

[0243] In this application, the processor can be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0244] In this application, the memory can be non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or it can be volatile memory, such as random-access memory (RAM). Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited to this. The memory in this application can also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0245] In one possible implementation, the communication device 1000 can be applied to an access network device. Specifically, the communication device 1000 can be an access network device or a device capable of supporting the access network device and implementing the functions of the access network device in any of the examples described above. The memory 1020 stores computer programs (or instructions) and / or data that implement the functions of the access network device in any of the examples described above. The processor 1010 can execute the computer program stored in the memory 1020 to complete the method performed by the access network device in any of the examples described above. Applied to an access network device, the communication interface in the communication device 1000 can be used to interact with a terminal device, sending information to the terminal device or receiving information from the terminal device.

[0246] In another possible implementation, the communication device 1000 can be applied to a terminal device. Specifically, the communication device 1000 can be a terminal device or a device capable of supporting the terminal device and implementing the functions of the terminal device in any of the examples described above. The memory 1020 stores computer programs (or instructions) and / or data that implement the functions of the terminal device in any of the examples described above. The processor 1010 can execute the computer program stored in the memory 1020 to complete the method executed by the terminal device in any of the examples described above. Applied to a terminal device, the communication interface in the communication device 1000 can be used to interact with access network devices, sending information to or receiving information from the access network devices.

[0247] Since the communication device 1000 provided in this example can be applied to access network equipment to complete the method executed by the access network equipment, or applied to terminal equipment to complete the method executed by the terminal equipment, the technical effects it can achieve can be referred to the above method example, and will not be repeated here.

[0248] Based on the above examples, this application provides a communication system including an access network device and a terminal device, wherein the access network device and the terminal device can implement the communication method provided in the examples shown in Figure 6 or Figure 8.

[0249] The technical solutions provided in this application can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal device, an access network device, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) 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 media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media, etc.

[0250] In this application, examples may reference each other without logical contradiction. For example, methods and / or terms between method embodiments may reference each other, functions and / or terms between device embodiments may reference each other, and functions and / or terms between device examples and method examples may reference each other.

Claims

1. A communication method, characterized in that, The method includes: Receive first information, the first information indicating at least one first resource block, the at least one first resource block belonging to a first resource block set and a second resource block set; Based on the first information, a demodulation reference signal DMRS is received on the first resource block set and the second resource block set, and the precoding corresponding to the first resource block set and the precoding corresponding to the second resource block set are the same. The DMRS received on the first resource block set and the second resource block set are processed based on an artificial intelligence model.

2. The method as described in claim 1, characterized in that, The number of resource blocks included in the first resource block set and the second resource block set is greater than or equal to the same set number.

3. The method as described in claim 1 or 2, characterized in that, The first information includes an index of the at least one first resource block, or the first information includes a relative index of the at least one first resource block relative to the starting resource block of the first bandwidth, wherein the first bandwidth is the bandwidth used by the terminal device when receiving the Physical Downlink Shared Channel (PDSCH).

4. The method according to any one of claims 1-3, characterized in that, The method further includes: The first inference result corresponding to the at least one first resource block and the second inference result corresponding to the at least one first resource block are processed. The first inference result is obtained based on the DMRS inference received on the first resource block set, and the second inference result is obtained based on the DMRS inference received on the second resource block set.

5. A communication method, characterized in that, The method includes: Send a first message, the first message indicating at least one first resource block, the at least one first resource block belonging to a first resource block set and a second resource block set; Based on the first information, a demodulation reference signal (DMRS) is transmitted on the first resource block set and the second resource block set, wherein the precoding corresponding to the first resource block set and the precoding corresponding to the second resource block set are the same.

6. The method as described in claim 5, characterized in that, The method further includes: The number of resource blocks included in the third resource block set is less than a set number. The third resource block set is obtained after the initial division of the first bandwidth, which is the bandwidth used by the terminal device when receiving the Physical Downlink Shared Channel (PDSCH). The second resource block set is determined, which is obtained after the first bandwidth is re-divided, and the second resource block set includes the third resource block set and the at least one first resource block.

7. The method as described in claim 5 or 6, characterized in that, The number of resource blocks included in both the first resource block set and the second resource block set is greater than or equal to the same set number.

8. The method as described in claim 6 or 7, characterized in that, The first information includes an index of the at least one first resource block, or the first information includes a relative index of the at least one first resource block relative to the starting resource block of the first bandwidth.

9. A communication method, characterized in that, The method includes: The total number of multiple resource block sets included in the first bandwidth is determined, each of the multiple resource block sets includes multiple resource blocks, and the number of resource blocks included in each resource block set satisfies the capability of the artificial intelligence model. The first bandwidth is the bandwidth used by the terminal device when receiving the Physical Downlink Shared Channel (PDSCH). Demodulation reference signal (DMRS) transmission is performed on the multiple resource block sets.

10. The method as described in claim 9, characterized in that, The starting position of the first bandwidth is 0, in In the case of the first bandwidth, the first bandwidth includes There are n resource block sets, from the first resource block set to the Nth resource block set. bundle Each resource block set in the -1 resource block set includes L resource blocks, and the Nth resource block set... bundle The resource block set includes One resource block, The value is the size of the first bandwidth.

11. The method as described in claim 9, characterized in that, The starting position of the first bandwidth is The first bandwidth includes A set of resource blocks, In the resource block sets, excluding the first resource block set and the second... Outside of a set of resource blocks Each resource block set in the set of L resources includes L resource blocks. The first resource block set in the set of resource blocks includes One resource block, The first in the resource block set The resource block set includes One resource block, The value is the size of the first bandwidth.

12. The method according to any one of claims 9-11, characterized in that, The method is applied to a terminal device, and the method further includes: The DMRS received on the multiple resource block sets is processed based on the artificial intelligence model.

13. The method according to any one of claims 9-12, characterized in that, The artificial intelligence model is used to process DMRS.

14. A communication device, characterized in that, Used to implement the method as described in any one of claims 1-13.

15. A communication device, characterized in that, include: A processor coupled to a memory, the processor being configured to invoke computer program instructions stored in the memory to perform the method as described in any one of claims 1-13.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-13.

17. A computer program product, characterized in that, Includes instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-13.

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