Communication method and apparatus

Abstract

The present application relates to the technical field of communications. Provided are a communication method and apparatus. The method comprises: a first communication apparatus determining a downlink signal and a first matrix on the basis of a first model, wherein an input parameter of the first model is determined on the basis of a first channel and a first symbol, the first channel is a transmission channel between the first communication apparatus and a second communication apparatus, the first symbol is a symbol to be transmitted between the first communication apparatus and the second communication apparatus, and the dimensions of the first matrix are the same as the dimensions of a linear precoding matrix; and sending the downlink signal and first information, wherein the first information is configured to indicate the first matrix. In the present application, when using non-linear precoding, a first communication apparatus transmits a first matrix, the dimensions of which are the same as those of a linear precoding matrix, such that after receiving first information that indicates the first matrix, a receive end can assist in channel estimation, thereby improving the signal demodulation accuracy.

Description

A communication method and apparatus

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202411937633.5, filed on December 25, 2024, entitled "A Communication Method and Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of communication technology, and in particular to a communication method and apparatus. Background Technology

[0004] In the 5th generation (5G) new radio (NR), massive multiple-input multiple-output (MIMO) antenna technology plays a crucial role in the system's spectral efficiency. To utilize the spatial freedom provided by MIMO antenna technology, network devices perform downlink precoding when transmitting downlink data.

[0005] When using linear precoding, the calculation of precoding weights is only related to the transmission channel between the network device and the terminal, without considering the impact of data symbols. However, when using nonlinear precoding, the calculation of precoding weights considers not only the transmission channel but also the data symbols to be transmitted, which can more effectively eliminate signal interference. However, when using nonlinear precoding, it cannot be guaranteed that the demodulation reference signal (DMRS) and data symbols within the same resource block (RB) or resource block group (RBG) use the same precoding, increasing the complexity of terminal channel estimation and signal demodulation. Summary of the Invention

[0006] This application provides a communication method and apparatus to reduce the complexity of terminal channel estimation and signal demodulation when using nonlinear precoding.

[0007] Firstly, this application provides a communication method that can be applied to a first communication device. For example, the executing entity may be the first communication device, a component within the first communication device (e.g., a communication module, processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the first communication device. For example, the first communication device may be a network device; this application does not limit the specific form of the first communication device. The execution is as follows:

[0008] The downlink signal and the first matrix are determined based on the first model. The input parameters of the first model are determined based on the first channel and the first symbol. The first channel is the transmission channel between the first communication device and the second communication device. The first symbol is the symbol to be transmitted between the first communication device and the second communication device. The dimension of the first matrix is ​​the same as the dimension of the linear precoding matrix. The linear precoding matrix is ​​obtained by the first communication device precoding the first symbol. The downlink signal and the first information are transmitted. The first information is used to indicate the first matrix.

[0009] In this application, when the first communication device employs nonlinear precoding, it transmits a first matrix with the same dimension as the linear precoding matrix. This allows the receiving end to receive the first information indicating the first matrix, which can then assist in channel estimation, improve the accuracy of signal demodulation, and reduce the complexity of terminal channel estimation and signal demodulation.

[0010] In one possible implementation, the first communication device also receives second information, which is used to indicate the historical channel parameters of the first channel.

[0011] Based on this, the first communication device can determine the historical channel parameters of the first channel and determine the downlink signal and the first matrix based on one or more of the historical channel parameters of the first channel.

[0012] In one possible implementation, the input parameters include a first historical channel parameter and a first symbol, wherein the first historical channel parameter is one of the historical channel parameters of the first channel.

[0013] In one possible implementation, the input parameters include the prediction parameters of the first channel and the first symbol. The prediction parameters are the future channel parameters of the first channel within a future time period. The future channel parameters of the first channel are predicted by the channel prediction model based on the second historical channel parameters, which are one or more of the historical channel parameters of the first channel.

[0014] Using the prediction parameters of the first channel as input parameters of the first model can eliminate the influence of historical channel parameters, making the first matrix obtained by nonlinear precoding more reliable.

[0015] In one possible implementation, the first communication device further transmits third information, which indicates third historical channel parameters. The historical channel parameters of the first channel include the third historical channel parameters, which are related to the first model.

[0016] Based on this, the first communication device can inform the second communication device of the historical channel parameters used by the first model or the historical channel parameters used by the channel prediction model, so that the second communication device can align the input parameters of the second model.

[0017] In one possible implementation, the third historical channel parameter being related to the first model includes: the third historical channel parameter being the first historical channel parameter input to the first model; or, the third historical channel parameter being the second historical channel parameter input to the channel prediction model.

[0018] Based on this, the first communication device can inform the second communication device of the historical channel parameters used by the first model or the historical channel parameters used by the channel prediction model, so that the second communication device can align the input parameters of the second model.

[0019] In one possible implementation, the first communication device also sends a fourth message, which indicates the prediction time period corresponding to the prediction parameters.

[0020] Based on this, it can be guaranteed that the prediction results of the channel prediction model on the first communication device side and the second communication device side are the same.

[0021] In one possible implementation, the second information is the identifier of the channel state information-reference signal (CSI-RS) report.

[0022] Compared to directly transmitting the historical channel parameters of the first channel, this can reduce the bit overhead of information transmission.

[0023] In one possible implementation, the first communication device performs vector transformation and compression on the first matrix to obtain a first vector, and the first information is the first vector.

[0024] Based on this, the bit overhead of air interface transmission can be reduced.

[0025] Secondly, this application provides a communication method that can be applied to a second communication device. For example, the executing entity may be the second communication device, a component within the second communication device (e.g., a communication module, processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the second communication device. For example, the second communication device may be a terminal device; this application does not limit the specific form of the second communication device. The execution is as follows:

[0026] The device receives downlink signals and first information, the first information being used to indicate a first matrix; it decodes data bits in the downlink signals based on a second model, the input parameters of which are determined based on the downlink signals, the first matrix, and a first channel, the first channel being the transmission channel between the first communication device and the second communication device.

[0027] In this application, when the second communication device decodes data bits, it receives the first information indicating the first matrix, which enables rapid channel estimation and signal demodulation, improves data processing efficiency, and reduces the complexity of terminal channel estimation and signal demodulation.

[0028] In one possible implementation, the second communication device also sends second information, which is used to indicate the historical channel parameters of the first channel.

[0029] In one possible implementation, the second communication device also receives third information, which is used to indicate third historical channel parameters. The historical channel parameters of the first channel include the third historical channel parameters, which are related to a first model, which is a model on the side of the first communication device.

[0030] In one possible implementation, the third historical channel parameter being related to the first model includes: the third historical channel parameter being the first historical channel parameter input to the first model; or, the third historical channel parameter being the second historical channel parameter input to the channel prediction model; wherein the first historical channel parameter is one of the historical channel parameters of the first channel, and the second historical channel parameter is one or more of the historical channel parameters of the first channel.

[0031] In one possible implementation, the input parameters include first historical channel parameters, downlink signals, and a first matrix.

[0032] In one possible implementation, the input parameters include the prediction parameters of the first channel, the downlink signal, and the first matrix. The prediction parameters are the future channel parameters of the first channel within a future time period, which are predicted by the channel prediction model based on the second historical channel parameters.

[0033] In one possible implementation, the second communication device also receives fourth information, which indicates the prediction time period corresponding to the prediction parameters.

[0034] In one possible implementation, the second piece of information is the identifier of the CSI-RS report.

[0035] In one possible implementation, the first information is a first vector, and the second communication device further decompresses and transforms the first vector to obtain a first matrix.

[0036] Thirdly, embodiments of this application provide a communication device, which may be a first communication device or a second communication device. The communication device has the functions described in the first to second aspects above. For example, the communication device includes modules, units, or means corresponding to the steps involved in the first to second aspects above. These functions, units, or means can be implemented by software, hardware, or hardware executing corresponding software.

[0037] In one possible design, the communication device includes a processing unit and a transceiver unit. The transceiver unit can be used to send and receive signals to enable communication between the communication device and other devices. The processing unit can be used to perform some internal operations of the communication device. The transceiver unit can be called an input / output unit, a communication unit, etc., and can be a transceiver; the processing unit can be a processor. When the communication device is a module (e.g., a chip) in a communication device, the transceiver unit can be an input / output interface, input / output circuit, or input / output pins, etc., and can also be called an interface, communication interface, or interface circuit, etc.; the processing unit can be a processor, processing circuit, or logic circuit, etc.

[0038] In another possible design, the communication device includes a processor and may further include a transceiver for transmitting and receiving signals. The processor executes program instructions to perform the methods in any of the possible designs or implementations of the first to second aspects described above. The communication device may also include one or more memories coupled to the processor, which may store necessary computer programs or instructions for implementing the functions involved in the first to second aspects described above. The processor can execute the computer programs or instructions stored in the memory, and when the computer programs or instructions are executed, the communication device implements the methods in any of the possible designs or implementations of the first to second aspects described above.

[0039] In another possible design, the communication device includes a processor that can be coupled to a memory. The memory can store necessary computer programs or instructions for implementing the functions described in the first to second aspects above. The processor can execute the computer programs or instructions stored in the memory, causing the communication device to implement the methods in any possible design or implementation of the first to second aspects above, when the computer programs or instructions are executed.

[0040] In another possible design, the communication device includes a processor and an interface circuit, wherein the processor is used to communicate with other devices through the interface circuit and to perform the methods in any possible design or implementation of the first to second aspects described above.

[0041] Understandably, in the third aspect described above, the processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, integrated circuit, etc.; when implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. Furthermore, there can be one or more processors, and one or more memories. The memory can be integrated with the processor or separated from it. In specific implementations, the memory can be integrated with the processor on the same chip or disposed on different chips. This application does not limit the type of memory or the arrangement of the memory and processor.

[0042] Fourthly, embodiments of this application provide a communication system including the aforementioned first communication device and second communication device. The first communication device can be used to execute the method in the first aspect, and the second communication device can be used to execute the method in the second aspect. Furthermore, it should be noted that in each aspect, there may be processes executed interactively by multiple devices or network elements; the corresponding processes cannot be executed by a single device or network element. Instead, the corresponding processes are executed primarily through the interaction of corresponding devices or network elements, which will not be elaborated upon here.

[0043] Fifthly, this application provides a chip system including a processor and potentially a memory, for implementing the methods described in the first to second aspects above. The chip system may be composed of chips or may include chips and other discrete devices.

[0044] Sixthly, this application also provides a computer-readable storage medium storing computer-readable instructions that, when executed on a computer, cause the computer to perform the methods described in the first to second aspects.

[0045] In a seventh aspect, this application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods of the embodiments of the first to second aspects described above.

[0046] The technical effects that can be achieved by the second to seventh aspects mentioned above can be referred to the description of the technical effects that can be achieved by the corresponding possible design schemes in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0047] Figure 1 shows a schematic diagram of a communication system;

[0048] Figure 2 shows a schematic diagram of a communication system;

[0049] Figure 3 shows a schematic diagram of a communication system;

[0050] Figure 4 shows a schematic diagram of a communication system;

[0051] Figure 5 shows a schematic diagram of a communication system;

[0052] Figure 6 shows a schematic diagram of a downlink physical layer air interface data processing flow;

[0053] Figure 7 shows a flowchart of a communication method provided in an embodiment of this application;

[0054] Figure 8 shows a schematic diagram of a channel prediction parameter provided in an embodiment of this application;

[0055] Figure 9 shows a flowchart of a communication method provided in an embodiment of this application;

[0056] Figure 10 shows a schematic diagram of a communication scenario provided by an embodiment of this application;

[0057] Figure 11 shows a flowchart of a communication method provided in an embodiment of this application;

[0058] Figure 12 shows a schematic diagram of a communication scenario provided by an embodiment of this application;

[0059] Figure 13 is a schematic diagram of a communication device structure provided in an embodiment of this application;

[0060] Figure 14 is a schematic diagram of a communication device structure provided in an embodiment of this application;

[0061] Figure 15 is a schematic diagram of a communication device structure provided in an embodiment of this application;

[0062] Figure 16 is a schematic diagram of a communication device structure provided in an embodiment of this application;

[0063] Figure 17 is a schematic diagram of a communication device structure provided in an embodiment of this application. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0065] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these dozen or more items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0066] In the embodiments of this application, "send" and "receive" indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which may include direct transmission via the air interface or indirect transmission by other units or modules via the air interface. "Receive information from YY" can be understood as the source of the information being YY, which may include direct reception from YY via the air interface or indirect reception from YY by other units or modules via the air interface. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface. In other words, sending and receiving can occur between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via a bus, wiring, or interface. It is understood that information may undergo necessary processing, such as encoding and modulation, between the source and destination of information transmission, but the destination can understand the valid information from the source. Similar expressions in this application can be understood in a similar way and will not be elaborated further.

[0067] In the embodiments of this application, "when," "if," and "if" all refer to the device taking corresponding actions under certain objective circumstances, not a time limit, nor do they require the device to perform a judgment action, nor do they imply any other limitations. Unless otherwise specified, "if" and "if" are interchangeable, and "when" and "in the case of" are interchangeable. "When" and "if" / "if" are interchangeable. In the embodiments of this application, "*" can be used to represent "multiplication."

[0068] The ordinal numbers such as "first" and "second" mentioned in the embodiments of this application are used to distinguish multiple objects and are not used to limit the size, content, order, timing, priority, or importance of the multiple objects. For example, the first sequence and the second sequence refer to two different sequences, and do not indicate that the content, priority, or importance of these two sequences are different. Words such as "exemplary" or "for example" are used to indicate that they are examples, illustrations, or explanations. Any embodiment or design that is described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design solutions. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0069] Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a list of units is not necessarily limited to those units, but may include other units not expressly listed or inherent to those processes, methods, products, or apparatuses. The methods and apparatuses provided in the embodiments of this application are based on the same or similar technical concepts. Since the principles by which the methods and apparatuses solve the problems are similar, implementations of the apparatus and methods can be referred to mutually, and repeated details will not be elaborated further.

[0070] The technical solutions provided in this application can be applied to 5G systems, or to future communication systems or other similar communication systems. Furthermore, the technical solutions provided in this application can be applied to cellular links, public land mobile networks (PLMNs), machine-to-machine (M2M) networks, Internet of Things (IoT) networks, or other networks. They can also be applied to links between devices, such as device-to-device (D2D) links. D2D links can also be called sidelinks, which are also referred to as secondary links or auxiliary links. In this application, the above terms all refer to links established between devices of the same type, and their meanings are the same. The so-called "same type of devices" can be links between terminal devices, links between base stations, links between relay nodes, etc., and this application does not limit this.

[0071] Figure 1 is a schematic diagram of the architecture of the communication system 1000 used in an embodiment of this application. As shown in Figure 1, the communication system includes a wireless access network 100 and a core network 200. Optionally, the communication system 1000 may also include an Internet 300. The wireless access network 100 may include at least one wireless access network device (110a and 110b in Figure 1) and at least one terminal (120a-120j in Figure 1). The terminal is connected to the wireless access network device wirelessly, and the wireless access network device is connected to the core network wirelessly or via a wired connection. The core network device and the wireless access network device may be independent physical devices, or the functions of the core network device and the logical functions of the wireless access network device may be integrated on the same physical device, or a single physical device may integrate some of the functions of the core network device and some of the functions of the wireless access network device. Terminals and wireless access network devices can be interconnected via wired or wireless connections. Figure 1 is only a schematic diagram; the communication system may also include other network devices, such as wireless relay devices and wireless backhaul devices, which are not shown in Figure 1.

[0072] Wireless access network equipment can be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next-generation NodeB (gNB) in a 5G mobile communication system, a next-generation base station in a future communication system, a base station in a future mobile communication system, or an access node in a wireless-fidelity (WiFi) system; it can also be a module or unit that performs some of the functions of a base station. In some deployments, a gNB can include a centralized unit (CU) and a distributed unit (DU). The CU implements some of the functions of the gNB, and the DU implements some of the functions of the gNB. For example, the CU is responsible for handling non-real-time protocols and services. For example, it implements radio resource control (RRC), service data adaptation protocol (SDAP) functions, and packet data convergence protocol (PDCP) layer functions. The DU is responsible for handling physical layer protocols and real-time services. For example, it can implement the functions of the radio link control (RLC) layer, medium access control (MAC) layer, and physical (PHY) layer. The gNB can also include an active antenna unit (AAU). The AAU implements some physical layer processing functions, radio frequency processing, and related functions of the active antenna. Since the information in the RRC layer ultimately becomes the information in the PHY layer, or is derived from the information in the PHY layer, in this architecture, higher-layer signaling (e.g., RRC layer signaling) can also be considered to be sent by the DU, or by the DU and AAU. It is understood that the network device can be one or more of the following: CU node, DU node, and AAU node. Furthermore, the CU can be a network device in the radio access network (RAN), or a network device in the core network (CN); this application does not limit this. Additionally, in the embodiments of this application, the network device provides services to the cell, and the terminal device communicates with the network device through the transmission resources (e.g., frequency domain resources, or spectrum resources) used by the cell. The cell can be the cell corresponding to network equipment (such as a base station). The cell can belong to a macro base station or to a base station corresponding to a small cell.For example, small cells may include: metro cells, micro cells, pico cells, femto cells, etc. Because small cells have the characteristics of small coverage area and low transmission power, they can provide high-speed data transmission services. Furthermore, in other possible cases, the network device can be other devices that provide wireless communication functions for terminal devices. The embodiments of this application do not limit the specific technology or device form used in the network device.

[0073] A terminal can also be referred to as a terminal device, user equipment (UE), mobile station, or mobile terminal (MT). Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), the Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, and smart cities. Terminals can be mobile phones, tablets, computers with wireless transceiver capabilities, wearable devices, vehicles, drones, helicopters, airplanes, ships, robots, robotic arms, smart home devices, etc. The embodiments of this application do not limit the specific technologies or device forms used in the terminals.

[0074] Network devices and terminals can be fixed in location or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can be deployed on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of the network devices and terminals.

[0075] The roles of network devices and terminals can be relative. For example, the helicopter or drone 120i in Figure 1 can be configured as a mobile network device. For terminals 120j that access the wireless access network 100 via 120i, drone 120i is a network device; however, for network device 110a, 120i is a terminal, meaning that 110a and 120i communicate via a wireless air interface protocol. Of course, 110a and 120i can also communicate via a network device-to-network device interface protocol. In this case, relative to 110a, 120i is also a network device. Therefore, both network devices and terminals can be collectively referred to as communication devices. 110a and 110b in Figure 1 can be called communication devices with network device functions, and 120a-120j in Figure 1 can be called communication devices with terminal functions.

[0076] In the embodiments of this application, the functions of the network device can be executed by modules (such as chips) within the network device, or by a control subsystem that includes network device functions. This control subsystem, including network device functions, can be a control center in the aforementioned application scenarios such as smart grids, industrial control, intelligent transportation, and smart cities. Similarly, the functions of the terminal can be executed by modules (such as chips or modems) within the terminal, or by a device that includes terminal functions.

[0077] Figure 2 is a schematic diagram of a communication system applicable to an embodiment of this application. As shown in Figure 2, the communication system includes terminal devices and network devices. Figure 2 is an example with two terminal devices and one network device, but the actual number of terminal devices and network devices is not limited. Any of the terminal devices in Figure 2 can be used as an example of a second communication device, and the network device can be used as an example of a first communication device.

[0078] Network devices can send downlink signals to terminal devices, and / or terminal devices can send uplink signals to network devices. Optionally, terminal devices can deploy models, and network devices can monitor these models through interaction with the terminal devices. Alternatively, the model can be deployed in other devices that communicate with the terminal devices. In this case, network devices can also monitor the models through interaction with the terminal devices.

[0079] Referring to Figure 3, a schematic diagram of a communication system applicable to an embodiment of this application is shown. Compared to the communication system shown in Figure 2, the communication system shown in Figure 3 further includes AI network elements. The AI ​​network elements are used to perform AI-related operations, such as building training datasets or training AI models. The terminal device involved in Figure 3 can be considered as an example of a first communication device, and the network device can be considered as an example of a second communication device.

[0080] For example, network devices can send data related to the training of AI models to AI network elements, which then construct training datasets and train the models. For instance, data related to model training may include data reported by terminal devices. AI network elements can send the results of AI model-related operations to network devices, which then forward them to terminal devices. For instance, the results of model-related operations may include at least one of the following: the trained model, model evaluation results, or test results.

[0081] Optionally, part of the trained AI model can be deployed on a network device, and another part on a terminal device. Alternatively, the trained AI model can be deployed on a network device. Alternatively, the trained AI model can be deployed on a terminal device. Alternatively, the AI ​​network element can also be set as a module in the network device and / or terminal device, for example, in the network device or terminal device shown in Figure 3.

[0082] Figure 3 illustrates the example of an AI network element directly connected to a network device. In other scenarios, the AI ​​network element can also be connected to a terminal device. Alternatively, the AI ​​network element can be connected to both a network device and a terminal device simultaneously. Furthermore, the AI ​​network element can also be connected to a network device through a third-party network element. This application does not limit the connection relationship between the AI ​​network element and other network elements.

[0083] Figures 2 and 3 are simplified illustrations for ease of understanding only. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in Figures 2 and 3.

[0084] The architecture of access network equipment will be illustrated below with reference to the structural diagrams of the communication system shown in Figures 4 and 5.

[0085] As shown in Figure 4, devices in the communication system are connected via interfaces (e.g., NG, Xn) or air interfaces. These devices, such as core network devices, access network nodes (e.g., RAN devices), terminal devices, or one or more devices in the operation, administration, and maintenance (OAM) systems, are equipped with one or more AI modules. Figure 5 illustrates one device with one AI module, but the actual number of AI modules is not limited. An access network node can be a single RAN node or can include multiple RAN nodes, such as CUs and DUs. The CU and / or DU can also be equipped with one or more AI modules. The terminal device shown in Figure 4 can be considered an example of a first communication device, and one or more devices in the CU or DU core network device or access network node (RAN node) can be considered an example of a second communication device. Optionally, a CU can be further divided into CU-CP and CU-UP. One or more AI models are configured in the CU-CP and / or CU-UP.

[0086] The AI ​​module is used to implement corresponding functions. The AI ​​modules deployed on any two devices in one or more devices can be completely identical, partially identical, or completely different; this application embodiment does not specifically limit this. The AI ​​module is used to implement corresponding AI functions. The AI ​​modules deployed on different devices can be the same or different. Depending on the different parameter configurations, the AI ​​module can implement different functions. The AI ​​module model can be configured based on one or more of the following parameters: structural parameters (e.g., at least one of the following: number of neural network layers, neural network width, inter-layer connections, neuron weights, neuron activation function, or bias in the activation function), input parameters (e.g., the type and / or dimension of the input parameters), or output parameters (e.g., the type and / or dimension of the output parameters). The bias in the activation function can also be referred to as the neural network bias.

[0087] An AI module may include one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or they can be deployed on the same node or device.

[0088] In one possible implementation, the AI ​​module can be a RAN intelligent controller (RIC), such as a near-real-time RIC (near-RT RIC) or a non-real-time RIC (non-RT RIC). For example, a near-real-time RIC is located in a RAN node (e.g., in a CU and / or DU), while a non-real-time RIC is located in an OAM, a cloud server, a core network device, or other network device. The RIC can obtain subsets from multiple end devices from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU), reassemble them into a training dataset, and train the model based on the training dataset.

[0089] For example, near real-time RIC and non-real-time RIC can also be set up as separate network elements.

[0090] As shown in Figure 5, the communication system includes a Resource Interchange (RIC). For example, the RIC can be the AI ​​module shown in Figure 4, used to implement AI-related functions. The RIC includes near-real-time RICs and non-real-time RICs. Near-real-time RICs and / or non-real-time RICs can be used as examples of a second communication device. Real-time RICs primarily process near-real-time information, such as data that is relatively sensitive to latency, with latency on the order of tens of milliseconds. Non-real-time RICs primarily process non-real-time information, such as data that is not sensitive to latency, with latency on the order of seconds.

[0091] Near real-time (NRT) RICs are used for model training and inference. For example, they are used to train AI models and then use those models for inference. NRT RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminal devices. This information can be used as training data or inference data. Optionally, the NRT RIC can deliver inference results to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, the NRT RIC delivers inference results to a DU, which then forwards them to an RU.

[0092] Non-real-time RICs are also used for model training and inference. For example, they can be used to train AI models and then use those models for inference. Non-real-time RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs; for example, a non-real-time RIC delivers inference results to a DU, which then forwards them to an RU.

[0093] Near real-time RICs and non-real-time RICs can also be configured as separate network elements. Alternatively, near real-time RICs and non-real-time RICs can also be part of other devices. For example, near real-time RICs can be set in RAN nodes (e.g., CUs and / or DUs), while non-real-time RICs can be set in OAMs, cloud servers, core network devices, or other network devices.

[0094] Figures 2 to 5 above are examples of communication systems used in the embodiments of this application, and do not actually limit the communication systems that can be applied to the embodiments of this application.

[0095] To better illustrate the solution of this application, the technical terms involved in this application are explained below:

[0096] 1) Machine learning (ML)

[0097] Machine learning aims to endow machines with human-like intelligence by using computer hardware and software to simulate certain intelligent human behaviors. This includes machine learning and many other methods. Machine learning can be categorized into supervised learning, unsupervised learning, and reinforcement learning.

[0098] Supervised learning learns the mapping relationship between samples and labels, expressing this learned mapping relationship in a model. The process of training the model can be viewed as learning this mapping relationship. For example, in signal detection, noisy signals can be used as samples, and the corresponding ground truth points are used as labels. Machine learning aims to learn the mapping relationship between samples and labels through training, thus enabling the model to learn how to detect signals. During model training, the error between the model's predictions and the labels is used to optimize the model's parameters. After training, the model can be used to predict the label of each new sample. The mapping relationships learned in supervised learning include linear and nonlinear mappings. Based on the type of label, the learning task can be divided into classification and regression tasks.

[0099] Unsupervised learning relies on sample values ​​to allow algorithms to discover or learn inherent patterns within the samples themselves. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationships from samples to samples; this type of learning is therefore called self-supervised learning. During model training, the model parameters are optimized by calculating the error between the model's predictions and the samples. Self-supervised learning can be used for signal compression and decompression recovery applications. Models suitable for self-supervised learning include autoencoders and generative adversarial networks.

[0100] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with its environment. Unlike supervised and unsupervised learning, reinforcement learning does not have a pre-defined "correct" label. The algorithm needs to interact with the environment, obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, a reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and the optimal decision action. However, because the "correct" label cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct" label. Training in reinforcement learning is achieved through iterative interaction with the environment.

[0101] 2) Machine learning model

[0102] A model is a concrete implementation of one or more functions, representing the mapping relationship between the model's input and output. A model can include one or more parameters. A substructure (or submodule) of a model can include one or more parameters. For example, f(x) = a*x^2 + b can be considered a model, where a and b correspond to the model's parameters, which can be obtained through training. The process of training a model can be viewed as optimizing its parameters. The process of using the model to implement the corresponding function can be considered as the model's inference process. The output of the model during inference can be called the inference result.

[0103] In the fields of machine learning (ML) and artificial intelligence (AI), a model can be understood as an algorithm or system that, after being trained and learned from input data, is capable of making predictions or performing tasks. Models include, for example, ML models, AI models, algorithms, features, or functions. An AI model can be at least one of the following: linear regression model, logistic regression model, decision tree model, support vector machine (SVM), neural network model, clustering model, Bayesian network, Q-learning model, generative adversarial network, or other machine learning models, without limitation. A neural network model is a mathematical model that mimics the behavioral characteristics of animal neural networks to perform distributed parallel information processing. A neural network model can be one or more of the following: feedforward neural network (FNN), convolutional neural network (CNN), and recurrent neural network (RNN), without specific limitation.

[0104] Neural networks are a typical type of model. For example, deep neural networks (DNNs) are a specific implementation of machine learning. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings.

[0105] 3) Downlink physical layer air interface data processing flow

[0106] As shown in Figure 6, the information bits to be transmitted by the network device are processed by the encoding and modulation modules to obtain data symbols (S). The reference signal sequence (taking the DMRS sequence as an example) is mapped to generate pilot symbols (P). The network device performs precoding based on the channel parameters between the network device and the terminal, for example, linear precoding or nonlinear precoding. Then, based on the data symbols and / or reference symbols, and the result of the precoding process, a downlink signal is obtained. The network device transmits this downlink signal through the air interface. This downlink signal is transmitted to the air interface of the terminal through the channel between the network device and the terminal. During the transmission of the downlink signal through the channel, noise is superimposed to obtain signal Y. The time-frequency resource position corresponding to the DMRS in signal Y is estimated by the terminal to obtain the relevant channel parameters. Based on these parameters, the channel parameters at the time-frequency position of the data symbol are predicted. Then, based on these channel parameters, equalization, demodulation, decoding, and other operations are performed to obtain the estimated information bits. Among them, the channel parameters estimated by the DMRS on the terminal side are the equivalent channel between the network device and the terminal. Optionally, when signal interference is strong, the bit sequence of information to be transmitted by the network device can be interleaved after passing through the encoding module to reduce interference. Correspondingly, the terminal side performs deinterleaving processing after demodulation. This is merely an example and not a specific limitation.

[0107] 4) Linear precoding

[0108] When linear precoding is used, the calculation of the precoding weight W is only related to the transmission channel between the network device and the terminal, and it is applied to the data symbol (S) in the form of linear multiplication to obtain the downlink data signal, or applied to the pilot symbol (P) in the form of linear multiplication to obtain the downlink reference signal. Taking the downlink data signal as an example, the downlink data signal Tx = W*S.

[0109] 5) Nonlinear precoding

[0110] Nonlinear precoding changes the multiplication relationship between precoding and data symbols (or pilot symbols). Instead, it fits the downlink signal through a nonlinear function, taking into account the transmitted symbols (data symbols and / or pilot symbols). This can be expressed as Tx = f(H,S), where H is the channel parameter between the network device and the terminal. f(.) can be a fitting function learned through a neural network.

[0111] When using nonlinear precoding, corresponding channel parameters are used for reference symbols and data symbols respectively. Compared with linear precoding, which uses the same channel parameters for reference symbols and data symbols, this method is more suitable for symbol transmission requirements and can effectively eliminate interference.

[0112] However, when using nonlinear precoding, it is impossible to guarantee that the DMRS and data symbols within the same RB or RBG use the same precoding, which increases the complexity of terminal channel estimation and signal demodulation.

[0113] Based on this, this application provides a communication method to output a first matrix of equivalent linear precoding weights W during nonlinear precoding to assist channel estimation and improve the accuracy of signal demodulation. The first matrix is ​​equivalent to an approximate solution of the linear precoding. This communication method is applicable to the communication systems illustrated in Figures 1-5 above. It can be applied to a first communication device and a second communication device, and can also be implemented based on the interaction between the first and second communication devices. The first communication device can be the first communication device itself, a component within the first communication device (e.g., a communication module, processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the first communication device. For example, the first communication device can be a network device; this application does not limit the specific form of the first communication device. Similarly, the second communication device can be the second communication device itself, a component within the second communication device (e.g., a communication module, processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the second communication device. For example, the second communication device can be a terminal device; this application does not limit the specific form of the second communication device. In this application, a first model is deployed in the first communication device, and a second model is deployed in the second communication device. The first model can be used for nonlinear precoding processing and is an AI model, such as a CNN model. The second model can be used for signal decoding processing and is also an AI model, such as a CNN model. Optionally, a channel prediction model is also deployed in both the first and second communication devices. The first model can be deployed in an AI module or GPU chip within a network device, or in a host or cloud server of an over-the-top (OTT) system. The second model can be deployed in an AI module or CPU chip within a terminal, or in a host or cloud server to which the terminal is subscribed. The specific types and deployment locations of the first and second models are not specifically limited here. Referring to Figure 7, the following steps are performed:

[0114] Step 701: The first communication device determines the downlink signal and the first matrix based on the first model.

[0115] The input parameters of the first model are determined based on a first channel and a first symbol. The first channel is the transmission channel between the first and second communication devices, and the first symbol is the symbol to be transmitted (data symbol and reference symbol, or data symbol) between the first and second communication devices. The dimension of the first matrix is ​​the same as the dimension of the linear precoding matrix, which is obtained by the first communication device precoding the first symbol. The first matrix can be pre-trained using a traditional linear precoding algorithm in a supervised learning manner. For example, a linear precoding matrix based on eigenzero forcing (EZF) can be used as the initial label to train the first model, and then the parameters in the first model can be adjusted based on feedback from the terminal.

[0116] For example, the network device has 8 data streams to transmit, 64 antenna elements, and an 8-row, 64-column linear precoding matrix. The downlink signal can be any type of signal transmitted from the network device to the terminal, such as a sensing signal or synchronization signal. This downlink signal can be an orthogonal frequency division multiplexing (OFDM) signal, a discrete fourier transform spread orthogonal frequency division multiplexing with frequency-domain spectral shaping (DFT-S-OFDM) signal, or other signals, without limitation. The equivalent linear precoding weights of the first matrix can be understood as an approximate solution to the linear precoding.

[0117] In one possible implementation, the input parameters of the first model can be determined based on historical channel parameters of the first channel. For example, the second communication device can send second information to the first communication device before executing step 701, the second information indicating historical channel parameters of the first channel. Based on this, the first communication device can specify the historical channel parameters of the first channel and determine the downlink signal and the first matrix based on one or more of the historical channel parameters of the first channel. For example, the first historical channel parameter is one of the historical channel parameters of the first channel, and the first communication device can select a first historical communication parameter and a first symbol as input parameters of the first model.

[0118] For example, the second information can be an identifier of the CSI-RS report fed back by the second communication device. This reduces the bit overhead of information transmission compared to directly transmitting the historical channel parameters of the first channel. Typically, the CSI-RS report includes the channel parameters of the first channel within a historical time period. For example, CSI-RS report 1 includes H1, where H1 indicates the channel parameters of the first channel from time 1 to time 2. CSI-RS report 2 includes H1, H2, and H3, where H1 indicates the channel parameters of the first channel from time 1 to time 2, H2 indicates the channel parameters of the first channel from time 3 to time 4, and H3 indicates the channel parameters of the first channel from time 4 to time 6.

[0119] For example, the second information may also be historical channel parameters of the first channel.

[0120] For example, the second information can also be other information, such as uplink control information (UCI).

[0121] The second information is not specifically limited here. In addition to the historical channel parameters of the first channel, the second information also includes other information, such as the precoding corresponding to the historical channel. The specific information included in the second information is not specifically limited here.

[0122] In another possible implementation, the input parameters of the first model can be determined based on the predicted parameters of the first channel. In addition to deploying the first model, the first communication device also deploys a channel prediction model, which is used to predict the future channel parameters of the first channel within a future time period. Typically, when a channel prediction model is deployed on the first communication device side, a channel prediction model is also deployed on the second communication device side. The prediction results of the channel prediction models on the first and second communication device sides are the same; the same channel prediction model can be used, or different channel prediction models can be used, without specific limitations here. The channel prediction model is based on the historical channel parameters of the first channel. For example, the second communication device can send second information to the first communication device, which is used to indicate the historical channel parameters of the first channel. The second information can be understood with reference to the above description and will not be repeated here. Based on this, the channel prediction model in the first communication device can predict the predicted parameters of the first channel within a future time period based on the historical channel parameters of the first channel. The input parameters of the first model include the prediction parameters of the first channel and the first symbol. The prediction parameters of the first channel are the future channel parameters of the first channel within a future time period. The future channel parameters of the first channel are predicted by the channel prediction model based on second historical channel parameters, which are one or more of the historical channel parameters of the first channel. For example, the historical channel parameters of the first channel include H1, H2, and H4, and the channel prediction model can predict the prediction parameter H5' of the first channel within a future time period based on H1, H2, and H4. Alternatively, the historical channel parameters of the first channel include H1, and the channel prediction model can predict the prediction parameter H2' of the first channel within a future time period based on H1. This is merely an illustrative example and not a specific limitation.

[0123] To ensure that the prediction results of the channel prediction models on the first and second communication device sides are the same, the first communication device can send fourth information to the second communication device. This fourth information indicates the prediction time period corresponding to the prediction parameters. For example, this fourth information can be downlink control information (DCI), RRC signaling, or media access control element (MAC CE) signaling, without specific limitations. The fourth information may include the prediction time period corresponding to the prediction parameters, for example, future time m, 2m, and 3m. The fourth information may also include indices of the prediction time periods corresponding to the prediction parameters, for example, index 1 and index 2, where index 1 corresponds to future time m and index 2 corresponds to future time 2m. This is only an example and not a specific limitation. As shown in Figure 8, the channel prediction model on the first communication device side uses the historical channel parameters H1, H2, and H4 of the first channel within the historical time period L1 to predict the future channel parameters of the first channel corresponding to future time periods L2, L3, and L4. The fourth information may include the future time periods L2, L3, and L4. This is only an example and not a specific limitation.

[0124] The first communication device also sends third information to the second communication device. This third information indicates third historical channel parameters, which are related to the first model. When the input parameters of the first model are the aforementioned first historical channel parameters and the first symbol, the third historical channel parameters are related to the first model; that is, the historical channel parameters used as input parameters of the first model are the first historical channel parameters. When the input parameters of the first model are the aforementioned prediction parameters of the first channel and the first symbol, the third historical channel parameters are related to the first model; that is, the historical channel parameters used as input parameters of the channel prediction model are the second historical channel parameters. For example, the third information can be the identifier of a CSI-RS report. Furthermore, if a CSI-RS report includes multiple historical channel parameters, the historical channel parameter closest to the current time can be defaulted as the third historical channel parameter. For example, if the third information is the identifier of CSI-RS report 3, which includes H1, H2, and H3, where H3 is the historical channel parameter closest to the current time, then the third historical parameter is H3. Based on this, the first communication device can inform the second communication device of the historical channel parameters used by the first model or the historical channel parameters used by the channel prediction model, so that the second communication device can quickly demodulate the channel parameters.

[0125] Step 702: The first communication device sends downlink signals and first information, the first information being used to indicate the first matrix.

[0126] Accordingly, the second communication device receives the downlink signal and the first information. For example, due to noise, the downlink signal received by the second communication device is a signal superimposed with noise.

[0127] For example, the first information is a first matrix.

[0128] For example, the first communication device performs vector transformation (e.g., row and column transformation, transpose, etc.) and compression on the first matrix to obtain a first vector, and the first information is the first vector. Compared with directly transmitting the first matrix, the bit overhead of air interface transmission can be reduced.

[0129] The downlink signal and the first information can be transmitted using the same signaling or different signaling methods, which is not specifically limited here. For example, the downlink signal can be transmitted via DCI1, or via RRC signaling 1, and the first information can be transmitted via DCI2, or both the downlink signal and the first information can be transmitted using RRC signaling 2, etc. This is only an example and does not specifically limit which signaling bearer the downlink signal and the first information are transmitted using.

[0130] Furthermore, the aforementioned third and fourth information can also be transmitted using DCI, RRC signaling, etc., and can be transmitted simultaneously with downlink signals and first information or not simultaneously. This application does not limit the transmission timing of the third information, fourth information, downlink signals, and first information.

[0131] Step 703: The second communication device decodes the data bits in the downlink signal based on the second model.

[0132] The input parameters of the second model are determined based on the downlink signal, the first matrix, and the first channel, which is the transmission channel between the first and second communication devices. If the first information is a first vector, the second communication device decompresses and transforms the first vector to obtain the first matrix or an estimated value (or approximation) of the first matrix. The output parameters of the second model can be estimated information bits or estimated data symbols, which are not specifically limited here.

[0133] In one possible implementation, the first communication device further sends third information to the second communication device. This third information indicates third historical channel parameters, which are related to the first model. When the input parameters of the first model are the aforementioned first historical channel parameters and the first symbol, the third historical channel parameters are related to the first model; that is, the historical channel parameters used as input parameters of the first model are the first historical channel parameters. The first communication device can indicate the first historical channel parameters to the second communication device. Then, the input parameters of the second model are the first historical channel parameters, the downlink signal, and the first matrix; or, the product of the first historical channel parameters and the first matrix, and the downlink signal.

[0134] When the input parameters of the first model are the prediction parameters of the first channel and the first symbol, the third historical channel parameters are related to the first model; that is, the historical channel parameters used as input parameters of the channel prediction model are the second historical channel parameters. Similarly, the second communication device also deploys a channel prediction model, and the input parameters of the channel prediction model on the second communication device side are the second historical channel parameters. The input parameters of the second model are the prediction parameters of the first channel, the downlink signal, and the first matrix, or the multiplication value of the prediction parameters of the first channel and the first matrix and the downlink signal. To ensure that the prediction results of the channel prediction models on the first and second communication device sides are the same, the first communication device can send fourth information to the second communication device. The fourth information is used to indicate the prediction time period corresponding to the prediction parameters. This can be understood by referring to the relevant description of the fourth information above, and will not be repeated here.

[0135] In this application, when the first communication device employs nonlinear precoding, it transmits a first matrix with the same dimension as the linear precoding matrix, so that after the receiving end receives the first information indicating the first matrix, it can assist in channel estimation and improve the accuracy of signal demodulation.

[0136] The following sections will explain the specific solutions proposed in this application, categorized by specific circumstances.

[0137] Case 1: The input parameters of the first model are determined based on the historical channel parameters of the first channel.

[0138] The following explanation uses a gNB as the first communication device and a UE as the second communication device as an example. The execution is as follows, referring to Figure 9:

[0139] Step 901: The UE sends the second information to the gNB.

[0140] The second information is used to indicate the historical channel parameters of the first channel (i.e., the transmission channel between the UE and the gNB), for example, H. This can be understood by referring to the relevant description in step 701 above, and will not be repeated here.

[0141] Step 902, gNB determines the downlink signal Tx and the first matrix W based on the first model.

[0142] The description of the input parameters of the first model in step 701 above, which are the first historical channel parameters and the first symbol correlation, can be used for reference and will not be repeated here.

[0143] Step 903: gNB sends downlink signal Tx and first vector W' to UE.

[0144] Wherein, W' is the first vector, which is obtained by vector transformation and compression of the first matrix W.

[0145] This can be understood by referring to the description in step 702 above.

[0146] Step 904: gNB sends third information to UE, which is used to indicate the first historical channel parameters.

[0147] This can be understood by referring to the relevant description of the third information above, and will not be repeated here. For example, the first historical channel parameter is H1.

[0148] The execution order of steps 903 and 904 is not limited, and steps 903 and 904 may also be the same step, that is, gNB sends Tx, W' and third information to UE.

[0149] Step 905: The UE decodes the data bits in the downlink signal based on the second model.

[0150] The input parameters of the second model are the first historical channel parameters, the downlink signal Tx, and the estimated value of the first matrix. The estimated value of the first matrix is ​​obtained by the UE after decompressing and transforming the first vector.

[0151] The second model and decoding process can be understood by referring to the description in step 703 above, which will not be repeated here.

[0152] As shown in Figure 10, the first model takes the first symbol S and the first historical channel parameter H1 as inputs, and outputs the downlink signal Tx and the first vector W'. Tx is processed by channel transmission and noise superimposed to obtain the signal y. The UE decompresses W' to obtain the estimated value of the first matrix. H1 and The result of multiplication is used as the input parameter for the second model, which outputs data bits.

[0153] Case 2: The input parameters of the first model are determined based on the prediction parameters of the first channel.

[0154] The following explanation uses a gNB as the first communication device and a UE as the second communication device as an example. The execution is as follows, referring to Figure 11:

[0155] Step 1101: The UE sends the second information to the gNB.

[0156] The second information is used to indicate the historical channel parameters of the first channel (i.e., the transmission channel between the UE and the gNB), for example, H. This can be understood by referring to the relevant description in step 701 above, and will not be repeated here.

[0157] Step 1102: gNB determines the prediction parameters H' of the first channel based on the channel prediction model.

[0158] This can be understood by referring to the description related to the input parameters of the channel prediction model in step 701 above, which are the second historical channel parameters, and will not be repeated here. For example, the second historical channel parameter is H2.

[0159] Step 1103: gNB uses the prediction parameters H' of the first channel and the first symbol as input parameters of the first model to output the downlink signal Tx and the first matrix W.

[0160] The description of the input parameters of the first model as the prediction parameters of the first channel in step 701 above can be used for understanding, and will not be repeated here.

[0161] Step 1104: gNB sends downlink signal Tx and first vector W' to UE.

[0162] Where W' is the first vector, which is obtained by vector transformation and compression of the first matrix.

[0163] This can be understood by referring to the description in step 702 above.

[0164] Step 1105: gNB sends third information to UE, which is used to indicate the second historical channel parameters.

[0165] This can be understood by referring to the relevant description in the third piece of information above, and will not be repeated here. Based on this, the UE can determine the input parameters of the UE side-channel prediction model.

[0166] Step 1106: gNB sends fourth information to UE, which is used to indicate the prediction time period corresponding to the prediction parameters.

[0167] This can be understood by referring to the relevant description in the fourth information above, and will not be repeated here. Based on this, the UE can determine the prediction time period of the UE side-channel prediction model.

[0168] The execution order of steps 1104 to 1106 is not limited, and steps 1104, 1105 and 1106 may also be the same step, that is, the gNB sends Tx, W', third information and fourth information to the UE.

[0169] Step 1107: The UE uses the information prediction model based on the fourth information and the third information to predict the prediction parameters of the first channel.

[0170] Step 1108: The UE decodes the data bits in the downlink signal based on the second model.

[0171] The input parameters of the second model are the prediction parameters H' of the first channel, the downlink signal Tx, and the estimated value of the first matrix. The estimated value of the first matrix is ​​obtained by the UE after decompressing and transforming the first vector.

[0172] The second model and decoding process can be understood by referring to the description in step 703 above, which will not be repeated here.

[0173] As shown in Figure 12, the channel prediction model on the gNB side takes the second historical channel parameter H2 as input and outputs the prediction parameter H' of the first channel. The first model takes data symbol S and H' as input and outputs downlink signal Tx and first vector W'. Tx is processed by channel transmission noise to obtain signal y, and the UE decompresses W' to obtain the estimated value of the first matrix. The UE receives the second historical channel parameter H2 and outputs the prediction parameter H' of the first channel based on the UE-side channel prediction model. H' and... The result of multiplication is used as the input parameter for the second model, which outputs data bits.

[0174] When using the solution provided in this application, the gNB outputs a first vector when outputting the downlink signal, in order to improve the demodulation accuracy of the UE.

[0175] The foregoing primarily describes the solutions provided by the embodiments of this application from the perspective of device interaction. It is understood that, in order to achieve the above functions, each device may include corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0176] The embodiments of this application can divide the device into functional units according to the above method examples. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0177] In the case of using integrated units, FIG13 shows a possible exemplary block diagram of the communication device involved in the embodiments of this application. As shown in FIG13, the communication device 1300 may include a processing unit 1301 and a transceiver unit 1302. The processing unit 1301 is used to control and manage the operation of the communication device 1300. The transceiver unit 1302 is used to support communication between the communication device 1300 and other devices. Optionally, the transceiver unit 1302 may include a receiving unit and / or a transmitting unit, respectively used to perform receiving and transmitting operations. Optionally, the communication device 1300 may also include a storage unit for storing the program code and / or data of the communication device 1300. The transceiver unit may be called an input / output unit, a communication unit, etc., and the transceiver unit may be a transceiver; the processing unit may be a processor. When the communication device is a module (e.g., a chip) in a communication device, the transceiver unit may be an input / output interface, an input / output circuit, or an input / output pin, etc., and may also be called an interface, a communication interface, or an interface circuit, etc.; the processing unit may be a processor, a processing circuit, or a logic circuit, etc. For example, the device can be the first communication device or the second communication device described above.

[0178] More detailed descriptions of the processing unit 1301 and the transceiver unit 1302 can be obtained directly from the relevant descriptions in the above method embodiments, and will not be repeated here.

[0179] In one embodiment, the communication device 1300 is a first communication device, and the processing unit 1301 is used to determine a downlink signal and a first matrix based on a first model. The input parameters of the first model are determined based on a first channel and a first symbol. The first channel is a transmission channel between the first communication device and the second communication device, and the first symbol is a symbol to be transmitted between the first communication device and the second communication device. The dimension of the first matrix is ​​the same as the dimension of the linear precoding matrix, and the linear precoding matrix is ​​obtained by the first communication device performing precoding processing on the first symbol. The transceiver unit 1302 is used to send the downlink signal and first information, and the first information is used to indicate the first matrix.

[0180] In one possible implementation, the transceiver unit 1302 is also used to receive second information, which indicates the historical channel parameters of the first channel.

[0181] In one possible implementation, the input parameters include a first historical channel parameter and a first symbol, wherein the first historical channel parameter is one of the historical channel parameters of the first channel.

[0182] In one possible implementation, the input parameters include the prediction parameters of the first channel and the first symbol. The prediction parameters are the future channel parameters of the first channel within a future time period. The future channel parameters of the first channel are predicted by the channel prediction model based on the second historical channel parameters, which are one or more of the historical channel parameters of the first channel.

[0183] In one possible implementation, the transceiver unit 1302 is further configured to transmit third information, which indicates third historical channel parameters. The historical channel parameters of the first channel include the third historical channel parameters, which are related to the first model.

[0184] In one possible implementation, the third historical channel parameter being related to the first model includes: the third historical channel parameter being the first historical channel parameter input to the first model; or, the third historical channel parameter being the second historical channel parameter input to the channel prediction model.

[0185] In one possible implementation, the transceiver unit 1302 is also used to send fourth information, which indicates the prediction time period corresponding to the prediction parameters.

[0186] In one possible implementation, the second piece of information is the identifier of the CSI-RS report.

[0187] In one possible implementation, the processing unit 1301 is further configured to perform vector transformation and compression processing on the first matrix to obtain a first vector, wherein the first information is the first vector.

[0188] In another embodiment, the communication device 1300 is a second communication device, the transceiver unit 1302 is used to receive downlink signals and first information, the first information is used to indicate the first matrix; the processing unit 1301 is used to decode the data bits in the downlink signals based on a second model, the input parameters of the second model are determined based on the downlink signals, the first matrix and the first channel, the first channel is the transmission channel between the first communication device and the second communication device.

[0189] In one possible implementation, the transceiver unit 1302 is also used to transmit second information, which indicates the historical channel parameters of the first channel.

[0190] In one possible implementation, the transceiver unit 1302 is further configured to receive third information, which indicates third historical channel parameters. The historical channel parameters of the first channel include the third historical channel parameters, which are related to a first model, which is a model on the side of the first communication device.

[0191] In one possible implementation, the third historical channel parameter being related to the first model includes: the third historical channel parameter being the first historical channel parameter input to the first model; or, the third historical channel parameter being the second historical channel parameter input to the channel prediction model; wherein the first historical channel parameter is one of the historical channel parameters of the first channel, and the second historical channel parameter is one or more of the historical channel parameters of the first channel.

[0192] In one possible implementation, the input parameters include first historical channel parameters, downlink signals, and a first matrix.

[0193] In one possible implementation, the input parameters include the prediction parameters of the first channel, the downlink signal, and the first matrix. The prediction parameters are the future channel parameters of the first channel within a future time period, which are predicted by the channel prediction model based on the second historical channel parameters.

[0194] In one possible implementation, the transceiver unit 1302 is also used to receive fourth information, which indicates the prediction time period corresponding to the prediction parameters.

[0195] In one possible implementation, the second piece of information is the identifier of the CSI-RS report.

[0196] In one possible implementation, the first information is a first vector, and the processing unit 1301 is also used to decompress the first vector and transform the vector to obtain a first matrix.

[0197] In one possible design, when the communication device 1300 is a terminal device or a communication module within a terminal device, the function of the processing unit 1301 can be implemented by one or more processors. Specifically, the processor may include a modem chip, or a system-on-a-chip (SoC) chip or a SIP chip containing a modem core. The function of the transceiver unit 1302 can be implemented by transceiver circuitry.

[0198] In one possible design, when the communication device 1300 is a circuit or chip responsible for communication functions in a terminal device, such as a modem chip or a system-on-a-chip (SoC) or SIP chip containing a modem core, the function of the processing unit 1301 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processor cores. The function of the transceiver unit 1302 can be implemented by the interface circuitry or data transceiver circuitry on the aforementioned chip.

[0199] When the aforementioned communication device is a module applied in a base station, the base station module implements the functions of the base station in the above method embodiments. The base station module receives information from other modules (such as radio frequency modules or antennas) in the base station, which is information sent by the UE to the base station; or, the base station module sends information to other modules (such as radio frequency modules or antennas) in the base station, which is information sent by the base station to the UE. Here, the base station module can be the baseband chip of the base station, or it can be a DU or other modules. The DU can be an O-DU under an open-radio access network (O-RAN) architecture.

[0200] Figure 14 is an exemplary block diagram of a communication device provided in an embodiment of this application. For example, the communication device 10 may include a chip system 110, a memory 120, a bus 130, a power management module 140, or a transceiver 150, etc.

[0201] The chip system 110 can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed through integrated logic circuits in the hardware of the chip system 110 or through software instructions.

[0202] As an example and not a limitation, chip system 110 may include circuitry or chips responsible for signal processing (such as a modem chip, also known as a baseband chip, or a system-on-chip (SoC) chip or system-in-package (SIP) chip containing a modem core).

[0203] Optionally, the chip system 110 may also include a memory (such as a cache) for storing instructions and data. In some embodiments, the memory in the chip system 110 is a cache memory. This memory can store instructions or data that the chip system 110 has just used or that are used repeatedly. If the chip system 110 needs to use the instruction or data again, it can directly retrieve it from the memory. This avoids repeated accesses, reduces the waiting time of the chip system 110, and thus improves the efficiency of the system.

[0204] In some embodiments, the chip system 110 may include one or more interfaces. Interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a SIM interface, and / or a USB interface, etc.

[0205] Memory 120 may include random access memory (RAM) and read-only memory (ROM). Memory 120 may store computer-readable, computer-executable code, including instructions that, when executed, cause the processor to perform the various functions described in this application.

[0206] Optionally, the code may include instructions for implementing various aspects of the embodiments of this application. The code may be stored in a non-transitory computer-readable medium such as system memory or other types of memory. In some cases, the code may not be directly executable by the chip system 110, but may enable a computer (e.g., at compile and execution time) to perform the functions described in this application. In some cases, memory 120 may in particular contain a basic I / O system that controls basic hardware or software operations, such as interaction with peripheral components or devices.

[0207] For example, the chip system 110 executes various functional applications and data processing of the communication device 10 by running instructions stored in the memory 120. For instance, when the communication device 10 transfers files with other devices (which may also be terminal devices or network devices), the chip system 110 of the communication device 10 can call the computer-executable program code stored in the memory 120 to implement the encoding or decoding methods provided in the embodiments of this application.

[0208] In addition, the memory 120 can be integrated into the chip system 110 or independent of the chip system 110.

[0209] Bus 130 may be a universal serial bus (USB) used to support communication between the various parts of the communication device 10.

[0210] The power management module 140 is used to receive charging input from the charger. Optionally, the power management module 140 can also supply power to the communication device 10 while charging it (e.g., the battery module of the communication device 10). By way of example and not limitation, the power management module 140 can also supply power to other devices besides the communication device 10.

[0211] Transceiver 150 can communicate bidirectionally via one or more antennas, wired links, or wireless links. For example, transceiver 150 can represent a wireless transceiver and can communicate bidirectionally with another wireless transceiver. Transceiver 150 may also include a modem for modulating packets and providing the modulated packets to the antenna for transmission, and for demodulating packets received from the antenna. Transceiver 150 may include a receiver and a transmitter, the receiver performing the function of receiving information and the transmitter performing the function of transmitting information.

[0212] In some cases, a wireless device may include a single antenna. However, in other cases, the device may have more than one antenna, such as antenna 1 and antenna 2 shown in FIG. 14, which may be capable of simultaneously transmitting or receiving multiple wireless transmissions. Exemplarily, antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in communication device 10 can be used to cover one or more communication frequency bands. Different antennas can also be multiplexed to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In other embodiments, the antennas can be used in conjunction with a tuning switch. Communication device 10 can transfer files to other devices via wireless communication functions.

[0213] In one design, the communication device 10 may correspond to the first communication device in the above method embodiments. The communication device 10 may implement the steps or processes executed by the first communication device in the above method embodiments, wherein the transceiver 150 may be used to perform the transmission and reception related operations of the first communication device in the above method embodiments; and the chip system 110 may be used to perform the processing related operations of the first communication device in the above method embodiments.

[0214] In another design, the communication device 10 may correspond to the second communication device in the above method embodiments. The communication device 10 may implement the steps or processes performed by the second communication device in the above method embodiments, wherein the transceiver 150 may be used to perform the transmission and reception related operations of the second communication device in the above method embodiments; and the chip system 110 may be used to perform the processing related operations of the second communication device in the above method embodiments.

[0215] Under this design, the communication device 10 may include modules such as a short-range communication module 164, a sensor 161, a display 162, or a camera 163, as shown in Figure 14.

[0216] The short-range communication module 164 may include modules that support short-range communication, such as WiFi and Bluetooth.

[0217] Sensor 161 may include pressure sensors, gyroscope sensors, barometric pressure sensors, magnetic sensors, accelerometers, distance sensors, proximity sensors, fingerprint sensors, temperature sensors, touch sensors, ambient light sensors, bone conduction sensors, etc.

[0218] Display 162 is used to display images, videos, etc. The display includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a Miniled LED, a MicroLED, a Micro-OLED, a quantum dot light-emitting diode (QLED), etc. For example, in this embodiment, the display can be used to display the interface required by the communication device 10. Exemplarily, the communication device 10 implements display functions through a GPU, a display, and an application processor. The GPU is a microprocessor for image processing, connected to the display and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The chip system 110 may include one or more GPUs that execute program instructions to generate or modify display information.

[0219] Camera 163 is used to acquire images, videos, etc.

[0220] It is understood that the structure shown in FIG14 does not constitute a specific limitation on the communication device 10. In some embodiments, the communication device 10 may also include more or fewer components than those shown in FIG14, or combine some components, or split some components, or have different component arrangements, etc. Alternatively, some components shown in FIG14 may be implemented in hardware, software, or a combination of software and hardware, and the communication device 10 may be based on the structure given in FIG13 with or without additional components.

[0221] Figure 15 is a schematic block diagram of a communication device provided in an embodiment of this application. The communication device 20 may include a baseband unit 210, which can communicate with external devices via a cellular radio frequency (RF) transceiver 220 (e.g., if the communication device 20 is a terminal device, the baseband unit 210 can communicate with network devices via the cellular RF transceiver 220; or, if the communication device 20 is a network device, the baseband unit 210 can communicate with terminal devices and / or core network devices via the cellular RF transceiver 220).

[0222] Baseband unit 210 may include computer-readable medium / memory. Baseband unit 210 is responsible for general processing, including the execution of software stored on the computer-readable medium / memory. When executed by baseband unit 210, the software causes baseband unit 210 to perform the various functions described above. The computer-readable medium / memory may also be used to store data manipulated by baseband unit 210 during software execution.

[0223] The baseband unit 210 further includes a receiving unit 201, a management unit 202, and a transmitting unit 203. The management unit 202 includes the one or more sub-units shown in FIG. 15. The units within the management unit 202 may be stored in a computer-readable medium / memory and / or configured as hardware within the baseband unit 210. The receiving unit 201 and the transmitting unit 203 may be referred to as transceiver units.

[0224] Figure 16 is a schematic block diagram of a chip system provided in an embodiment of this application. The chip system 30 includes, but is not limited to, a modem chip, also known as a baseband chip, or a system-on-chip (SoC) chip or a system-in-package (SIP) chip containing a modem core.

[0225] The chip system (or processing system) includes a processor 310, a memory 320, and an input / output interface 330.

[0226] The processor 310 can be a processing circuit in the chip system (including at least one processor, such as processor 311 and processor 312 as shown in FIG. 16). The processor 310 can be coupled to the memory 320, and call the instructions in the memory 320, so that the chip system can implement the methods and functions of the various embodiments of this application. The input / output interface 330 can be an input / output circuit in the chip system, which outputs the information processed by the chip system, or inputs the data or signaling information to be processed into the chip system for processing.

[0227] As one approach, the chip system is used to implement the operations performed by the first or second communication device in the various method embodiments described above.

[0228] For example, processor 310 is used to implement the processing-related operations performed by the first communication device or the second communication device in the above method embodiments, as described in the foregoing embodiments; input / output interface 330 is used to implement the sending and / or receiving-related operations performed by the first communication device or the second communication device in the above method embodiments, as described in the foregoing embodiments.

[0229] Figure 17 is a schematic block diagram of a chip system provided in an embodiment of this application. The chip system 40 (or processing system) includes an input / output interface 410 and logic circuitry 420. The input / output interface 410 can be an input / output circuit within the chip system, outputting processed information or inputting data or signaling information to be processed into the chip system for processing; details can be found in the descriptions of the foregoing embodiments. The logic circuitry 420 is used to execute the aforementioned communication method; details can also be found in the descriptions of the foregoing embodiments.

[0230] As one approach, the chip system is used to implement the operations performed by the first or second communication device in the various method embodiments described above.

[0231] For example, logic circuit 420 is used to implement processing-related operations performed by the first communication device or the second communication device in the above method embodiments; input / output interface 410 is used to implement sending and / or receiving-related operations performed by the first communication device or the second communication device in the above method embodiments.

[0232] This application also provides a computer-readable storage medium storing computer instructions for implementing the methods executed by the first communication device or the second communication device in the above-described method embodiments.

[0233] For example, when the computer program is executed by the computer, it enables the computer to implement the methods executed by the first communication device or the second communication device in the various embodiments of the above methods.

[0234] This application also provides a computer program product comprising instructions which, when executed by a computer, implement the methods performed by the first communication device or the second communication device in the above-described method embodiments.

[0235] This application also provides a communication system, including the aforementioned first communication device and / or second communication device.

[0236] The explanations and beneficial effects of the relevant contents in any of the devices provided above can be found in the corresponding method embodiments provided above, and will not be repeated here.

[0237] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, compact disc read-only memory (CD-ROM), or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Additionally, the ASIC can reside in a first communication device. Alternatively, the processor and storage medium can exist as discrete components in an access network device or terminal.

[0238] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. A computer program is a set of instructions that directs each step of an action of an electronic computer or other device with message processing capabilities. It is typically written in a programming language and runs on a target architecture. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be volatile or non-volatile, or it can include both types of storage media.

[0239] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0240] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers described above does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

Claims

1. A communication method, characterized in that, Applied to a first communication device, comprising: The downlink signal and the first matrix are determined based on the first model. The input parameters of the first model are determined based on the first channel and the first symbol. The first channel is the transmission channel between the first communication device and the second communication device. The first symbol is the symbol to be transmitted between the first communication device and the second communication device. The dimension of the first matrix is ​​the same as the dimension of the linear precoding matrix. The linear precoding matrix is ​​obtained by the first communication device performing precoding processing on the first symbol. The downlink signal and first information are sent, the first information being used to indicate the first matrix.

2. The method according to claim 1, characterized in that, The method further includes: Receive second information, which indicates the historical channel parameters of the first channel.

3. The method according to claim 2, characterized in that, The input parameters include a first historical channel parameter and the first symbol, wherein the first historical channel parameter is one of the historical channel parameters of the first channel.

4. The method according to claim 2, characterized in that, The input parameters include the prediction parameters of the first channel and the first symbol. The prediction parameters are the future channel parameters of the first channel within a future time period. The future channel parameters of the first channel are predicted by the channel prediction model based on the second historical channel parameters, which are one or more of the historical channel parameters of the first channel.

5. The method according to claim 3 or 4, characterized in that, The method further includes: Send a third message, which is used to indicate a third historical channel parameter. The historical channel parameter of the first channel includes the third historical channel parameter, and the third historical channel parameter is related to the first model.

6. The method according to claim 5, characterized in that, The third historical channel parameters and the first model are related to: The third historical channel parameter is the first historical channel parameter input to the first model; or... The third historical channel parameter is the second historical channel parameter input to the channel prediction model.

7. The method according to claim 4, characterized in that, The method further includes: Send a fourth message, which indicates the prediction time period corresponding to the prediction parameter.

8. The method according to any one of claims 2-7, characterized in that, The second piece of information is the identifier of the Channel State Information Reference Signal (CSI-RS) report.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: The first matrix is ​​transformed and compressed to obtain the first vector, and the first information is the first vector.

10. A communication method, characterized in that, Applied to a second communication device, including: Receive downlink signals and first information, wherein the first information is used to indicate a first matrix; The data bits in the downlink signal are decoded based on the second model. The input parameters of the second model are determined based on the downlink signal, the first matrix, and the first channel, which is the transmission channel between the first communication device and the second communication device.

11. The method according to claim 10, characterized in that, The method further includes: Send a second message, which indicates the historical channel parameters of the first channel.

12. The method according to claim 11, characterized in that, The method further includes: Receive third information, the third information being used to indicate third historical channel parameters, the historical channel parameters of the first channel including the third historical channel parameters, the third historical channel parameters being related to a first model, the first model being a model on the side of the first communication device.

13. The method according to claim 12, characterized in that, The third historical channel parameters and the first model are related to: The third historical channel parameter is the first historical channel parameter input to the first model; or... The third historical channel parameter is the second historical channel parameter input to the channel prediction model; Wherein, the first historical channel parameter is one of the historical channel parameters of the first channel, and the second historical channel parameter is one or more of the historical channel parameters of the first channel.

14. The method according to claim 13, characterized in that, The input parameters include the first historical channel parameters, the downlink signal, and the first matrix.

15. The method according to claim 13, characterized in that, The input parameters include the prediction parameters of the first channel, the downlink signal, and the first matrix. The prediction parameters are the future channel parameters of the first channel within a future time period. The future channel parameters of the first channel are predicted by the channel prediction model based on the second historical channel parameters.

16. The method according to claim 15, characterized in that, The method further includes: Receive fourth information, which is used to indicate the prediction time period corresponding to the prediction parameter.

17. The method according to any one of claims 11-16, characterized in that, The second piece of information is the identifier of the Channel State Information Reference Signal (CSI-RS) report.

18. The method according to any one of claims 10-17, characterized in that, The first information is a first vector, and the method further includes: The first vector is decompressed and transformed to obtain the first matrix.

19. A communication device, characterized in that, Includes a module for performing the method as described in any one of claims 1-9.

20. A communication device, characterized in that, Includes modules for performing the method as described in any one of claims 10-18.

21. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions, which, when executed by a communication device, implement the method as described in any one of claims 1-9, or the method as described in any one of claims 10-18.

22. A computer program product, characterized in that, The computer program product includes: computer instructions that, when executed on a computer, cause the method as described in any one of claims 1-9 to be implemented, or to implement the method as described in any one of claims 10-18.

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