Communication method and apparatus, chip module, storage medium, and program product

Abstract

A communication method and apparatus, a chip module, a storage medium, and a program product. The method comprises: acquiring first information, the first information comprising a first attribute and at least one reference wireless data set, and each reference wireless data set among the at least one reference wireless data set comprising reference wireless data and attributes associated with the reference wireless data; and, on the basis of a first model, processing the first attribute and the at least one reference wireless data set, and acquiring second information, the first model being used for predicting at least one type of wireless data, and the second information comprising predicted wireless data associated with the first attribute. Thus, a multifunctional wireless prediction model is provided, which supports prediction of various attributes and various wireless data, so that models do not need to be separately designed for prediction of each type of wireless data, thereby improving prediction efficiency.

Description

Communication method, apparatus, chip module, storage medium and program product

[0001] The present application claims priority to the Chinese patent application No. 202410474302.6, filed on April 18, 2024, and entitled "Communication method, apparatus, chip module, storage medium and program product", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the field of communication technology, and in particular to a communication method, apparatus, chip module, storage medium and program product. BACKGROUND

[0003] Introducing artificial intelligence (AI) / machine learning (ML) technology into the wireless air interface can be used for compression and reconstruction of wireless channel information, beam management and positioning enhancement.

[0004] Current AI models are respectively for each wireless AI task. Models for different tasks respectively collect data for training and subsequent model management, which will result in a large number of models in the wireless communication system and high complexity of model management. SUMMARY

[0005] The present application provides a communication method, apparatus, chip module, storage medium and program product to provide a multifunctional wireless prediction model supporting prediction of multiple attributes and multiple wireless data.

[0006] In a first aspect, a communication method is provided, the method comprising: obtaining first information, the first information comprising a first attribute and at least one reference wireless data set, each reference wireless data set in the at least one reference wireless data set comprising a reference wireless data and an attribute associated with the reference wireless data; and processing the first attribute and the at least one reference wireless data set based on a first model to obtain second information, the first model being used to predict at least one wireless data, the second information comprising a predicted wireless data associated with the first attribute.

[0007] In this aspect, by obtaining the first attribute and the at least one reference wireless data set, and processing the first attribute and the at least one reference wireless data set based on the first model to obtain the predicted wireless data associated with the first attribute, the multifunctional wireless prediction model can support prediction of multiple attributes and multiple wireless data, without the need to design a model for each wireless data prediction, thereby improving the prediction efficiency.

[0008] With reference to the first aspect, in a possible implementation, the reference wireless data associated attribute or the first attribute comprises at least one of the following: a location, a time, a frequency.

[0009] In this implementation, prediction of wireless data of multiple attributes can be supported, improving prediction efficiency and management efficiency.

[0010] With reference to the first aspect, in another possible implementation, the type of the reference wireless data or the predicted wireless data comprises at least one of the following: path loss, channel quality indication, angle of arrival, angle of departure, timing advance, signal to interference plus noise ratio, data rate.

[0011] In this implementation, prediction of multiple types of wireless data is supported, improving prediction efficiency and management efficiency.

[0012] With reference to the first aspect, in yet another possible implementation, the first information further comprises first indication information, the first indication information being used to indicate the type of the predicted wireless data and / or the type of the first attribute.

[0013] In this implementation, since the values of different attributes or different wireless data can be the same, the first indication information can accurately indicate which attribute and / or which wireless data is predicted this time. The first indication information can indicate the mapping relationship between the type of the predicted wireless data and the type of the first attribute.

[0014] With reference to the first aspect, in yet another possible implementation, the method further comprises: obtaining an identifier of the first model and at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports prediction of at least one type of wireless data of at least one attribute.

[0015] In this implementation, the model can be managed, and the model can support at least one function, so that when the model is used to predict at least one type of wireless data of a certain attribute, a corresponding model can be selected for prediction according to the identifier of the model.

[0016] With reference to the first aspect, in yet another possible implementation, the method further comprises: receiving second indication information, the second indication information being used to indicate that processing is based on the first model, and the second indication information comprising an identifier of the first model.

[0017] In this implementation, the model can be managed, and a corresponding model can be selected for prediction according to the identifier of the model.

[0018] With reference to the first aspect, in a further possible implementation of the first aspect, the method further includes: sending third information, the third information indicating at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports predicting at least one attribute of at least one wireless data.

[0019] In this implementation, when predicting based on the first model, the communication apparatus can indicate the identity of the first model and at least one function supported by the first model.

[0020] With reference to the first aspect, in a further possible implementation of the first aspect, the method further includes: obtaining fourth information, the fourth information being used to indicate activating at least one function supported by the first model; or the fourth information being used to indicate deactivating at least one function supported by the first model.

[0021] With reference to the first aspect, in a further possible implementation of the first aspect, before the processing, based on the first model, the first attribute and the at least one set of reference wireless data to obtain the second information, the method further includes: processing the attribute associated with the reference wireless data or the first attribute included in each set of reference wireless data in the at least one set of reference wireless data to obtain an attribute of a first data length; and processing the reference wireless data included in each set of reference wireless data in the at least one set of reference wireless data to obtain reference wireless data of a second data length.

[0022] In this implementation, since the first model has a data length requirement for the attribute associated with the reference wireless data or the first attribute, before the processing, based on the first model, the first attribute and the at least one set of reference wireless data to obtain the second information, the attribute associated with the reference wireless data or the first attribute included in each set of reference wireless data in the at least one set of reference wireless data can also be processed to obtain an attribute of a first data length.

[0023] The first model can also have a data length requirement for the reference wireless data, and therefore, before the processing, based on the first model, the first attribute and the at least one set of reference wireless data to obtain the second information, the reference wireless data included in each set of reference wireless data in the at least one set of reference wireless data can also be processed to obtain reference wireless data of a second data length.

[0024] With reference to the first aspect, in a possible implementation of the first aspect, the method further includes: adding a first vector to the first data length attribute, the first vector being used to identify the attribute associated with the reference wireless data; and adding a second vector to the second data length reference wireless data, the second vector being used to identify the reference wireless data.

[0025] In this implementation, each set of reference wireless data and the attribute associated with the reference wireless data share the same position embeding, which is used to identify whether the input is an attribute or reference wireless data. For example, a first vector can be added to the first data length attribute obtained after processing by the neural network, and the first vector is used to identify the attribute associated with the reference wireless data. A second vector can also be added to the second data length reference wireless data obtained after processing by the neural network, and the second vector is used to identify the reference wireless data.

[0026] The second aspect provides a communication apparatus for implementing the communication method in the first aspect or any possible implementation of the first aspect. The apparatus can be a device, a module (for example, a processor, a chip, or a chip system) applied to the device, or a logic node, a logic module, or software capable of implementing all or part of the device functions.

[0027] In a possible implementation, the communication apparatus in the second aspect includes units for performing the method in the first aspect or any possible implementation of the first aspect.

[0028] The communication apparatus includes a processing unit and can further include a transceiver unit.

[0029] The processing unit is configured to obtain first information, the first information including a first attribute and at least one set of reference wireless data, each set of reference wireless data including reference wireless data and an attribute associated with the reference wireless data; and the processing unit is configured to process the first attribute and the at least one set of reference wireless data based on a first model to obtain second information, the first model being used to predict at least one kind of wireless data, and the second information including predicted wireless data associated with the first attribute.

[0030] Optionally, the attribute associated with the reference wireless data or the first attribute includes at least one of the following: a location, a time, and a frequency.

[0031] Optionally, the type of the reference wireless data or the predicted wireless data comprises at least one of the following: path loss, channel quality indication, angle of arrival, angle of departure, timing advance, signal to interference plus noise ratio, data rate.

[0032] Optionally, the first information further comprises first indication information, the first indication information being used to indicate the type of the predicted wireless data and / or the type of the first attribute.

[0033] Optionally, the processing unit is further configured to obtain an identity of the first model and at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports predicting at least one attribute based on at least one wireless data.

[0034] Optionally, the transceiver is configured to receive second indication information, the second indication information being used to indicate that the processing is based on the first model, the second indication information comprising the identity of the first model.

[0035] Optionally, the transceiver is further configured to send third information, the third information indicating the at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports predicting at least one attribute based on at least one wireless data.

[0036] Optionally, the processing unit is further configured to obtain fourth information, the fourth information being used to indicate activating the at least one function supported by the first model; or the fourth information being used to indicate deactivating the at least one function supported by the first model.

[0037] Optionally, the processing unit is further configured to process the attribute associated with the reference wireless data or the first attribute comprised in each of the at least one set of reference wireless data to obtain a first data length of attribute; and the processing unit is further configured to process the reference wireless data comprised in each of the at least one set of reference wireless data to obtain a second data length of reference wireless data.

[0038] Optionally, the processing unit is further configured to add a first vector to the first data length of attribute, the first vector being used to identify the attribute associated with the reference wireless data; and the processing unit is further configured to add a second vector to the second data length of reference wireless data, the second vector being used to identify the reference wireless data.

[0039] In a possible implementation, the communication device in the second aspect includes a processing circuit coupled with a memory; the processing circuit is configured to enable the device to perform the corresponding functions in the communication method described above. The memory is used to store the programs (instructions) and / or data necessary for the device. Optionally, the communication device can also include a communication interface for enabling communication between the device and other network elements. Optionally, the memory can be located inside the communication device or outside the communication device. Illustratively, the processing circuit can be a processor or a circuit for processing in the processor.

[0040] When the communication device in the second aspect is a chip, the sending unit can be an output unit such as an output circuit or a communication interface; the receiving unit can be an input unit such as an input circuit or a communication interface. When the communication device is a terminal device, the sending unit can be a transmitter or a transmitter; the receiving unit can be a receiver or a receiver.

[0041] In a third aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program or instructions, when the computer program or instructions are executed, the method in the first aspect or any implementation of the first aspect is implemented.

[0042] In a fourth aspect, a computer program product is provided, and the computer program product includes instructions, when the instructions are executed on a communication device, the communication device is enabled to perform the method in the first aspect or any implementation of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0043] FIG. 1 is a simplified schematic diagram of a wireless communication system provided by an embodiment of the present application;

[0044] FIG. 2 is a schematic diagram of an architecture of an open radio access network;

[0045] FIGS. 3A-3D are schematic diagrams of settings of near real-time RIC and non-real-time RIC in a network architecture;

[0046] FIG. 4 is a schematic diagram of a neuron structure;

[0047] FIG. 5 is a schematic diagram of a neural network;

[0048] FIG. 6 is a schematic diagram of an AI application framework;

[0049] FIG. 7 is a schematic diagram of an architecture of another communication system provided by an embodiment of the present application;

[0050] FIG. 8 is a schematic diagram of an AI / ML management architecture;

[0051] FIG. 9 is a schematic diagram of a single-function model;

[0052] FIG. 10 is a schematic diagram of function management of a model;

[0053] FIG. 11 is a schematic diagram of a communication method according to an embodiment of the present application;

[0054] FIG. 12 is a schematic diagram of a multifunctional wireless prediction model according to an embodiment of the present application;

[0055] FIG. 13 is a schematic diagram of an internal structure of a multifunctional wireless prediction model according to an embodiment of the present application;

[0056] FIG. 14 is a schematic diagram of association between a model and a function according to an embodiment of the present application;

[0057] FIG. 15 is a schematic diagram of a multifunctional radio map model according to an embodiment of the present application;

[0058] FIG. 16 is a schematic diagram of a multifunctional wireless information prediction model according to an embodiment of the present application;

[0059] FIG. 17 is a schematic diagram of time domain wireless data prediction according to an embodiment of the present application;

[0060] FIG. 18A is a schematic diagram of independent processing of multiple attributes input to a model according to an embodiment of the present application;

[0061] FIG. 18B is a schematic diagram of cascading of multiple attributes input to a model according to an embodiment of the present application;

[0062] FIG. 19 is a schematic diagram of a structure of a communication device according to an embodiment of the present application;

[0063] FIG. 20 is a schematic diagram of a structure of another communication device according to an embodiment of the present application. DETAILED DESCRIPTION

[0064] The embodiments of the present application will be described below with reference to the accompanying drawings.

[0065] The technology provided by the present application can be applied to various communication systems. For example, the communication system can be a fourth generation (4G) communication system (such as a long term evolution (LTE) system), a fifth generation (5G) communication system, a worldwide interoperability for microwave access (WiMAX) system, a wireless local area network (WLAN) system, a satellite communication system, a converged system of multiple systems, or a future communication system such as a sixth generation (6G) communication system. th th The technology provided by the present application can be applied to various communication systems. For example, the communication system can be a fourth generation (4G) communication system (such as a long term evolution (LTE) system), a fifth generation (5G) communication system, a worldwide interoperability for microwave access (WiMAX) system, a wireless local area network (WLAN) system, a satellite communication system, a converged system of multiple systems, or a future communication system such as a sixth generation (6G) communication system.​th A 5G communication system or new radio (NR) system can be regarded as a communication system of a pre-6th generation (6G) or 6G.

[0066] A network element in a communication system can send or receive a signal to or from another network element. The signal can include information, signaling, data, etc. The network element can also be replaced by an entity, a network entity, a device, a terminal device, a communication module, a node, a communication node, etc. The network element is taken as an example for description in the present application. For example, the communication system can include at least one terminal device and at least one network device. The network device can send a downlink signal to the terminal device, and / or the terminal device can send an uplink signal to the network device. In addition, it can be understood that if the communication system includes multiple terminal devices, the terminal devices can also send signals to each other, that is, the sending network element and the receiving network element of the signal can be terminal devices.

[0067] Referring to FIG. 1, FIG. 1 is a simplified schematic diagram of a wireless communication system provided by an embodiment of the present application. As shown in FIG. 1, the wireless communication system includes a wireless access network 100. The wireless access network 100 can be a next generation (for example, 6G or higher version) wireless access network, or a traditional (for example, 5G, 4G) wireless access network. One or more terminal devices (120a-120j, collectively referred to as 120) can be connected to each other, or connected to one or more network devices (110a, 110b, collectively referred to as 110) in the wireless access network 100. Optionally, FIG. 1 is only a schematic diagram, and the wireless communication system can also include other devices, such as core network devices, wireless relay devices, and / or wireless backhaul devices, etc., which are not shown in FIG. 1.

[0068] Optionally, in actual application, the wireless communication system can simultaneously include multiple network devices (also referred to as access network devices), and can also simultaneously include multiple terminal devices. One network device can simultaneously serve one or more terminal devices. One terminal device can also simultaneously access one or more network devices. Embodiments of the present application do not limit the number of terminal devices and network devices included in the wireless communication system.

[0069] The network device can be an entity for transmitting or receiving signals on the network side. The network device can be an access device through which a terminal device accesses the wireless communication system in a wireless manner. For example, the network device can be a base station. The base station can be referred to as a radio access network (RAN) node, a NodeB, an evolved NodeB (eNB), a next generation NodeB (gNB), a network device in an open radio access network (O-RAN), a relay station, an access point, a transmitting and receiving point (TRP), a transmitting point (TP), a master eNB (MeNB), a secondary eNB (SeNB), a multi-standard radio (MSR) node, a home base station, a network controller, an access node, a radio node, an access point (AP), a transmission node, a transceiver node, a building baseband unit (BBU), a remote radio unit (RRU), an active antenna unit (AAU), a remote radio head (RRH), a centralized unit (CU), a distributed unit (DU), a radio unit (RU), a CU control plane (CU-CP) node, a CU user plane (CU-UP) node, a positioning node, a RAN intelligent controller (RIC), and the like. The base station can be a macro base station, a micro base station, a relay node, a donor node, or the like, or a combination thereof. The network device can also refer to a communication module, a modem, or a chip for being disposed in the foregoing devices or apparatuses. The network device can also be a mobile switching center, a device-to-device (D2D) device, a vehicle-to-everything (V2X) device, a machine-to-machine (M2M) device, a device assuming a base station function in device-to-device (D2D) communication, a device assuming a base station function in vehicle-to-everything (V2X) communication, a device assuming a base station function in machine-to-machine (M2M) communication, a network side device in a 6G network, a device assuming a base station function in a future communication system, and the like.The network device can support networks of the same or different access technologies. Embodiments of the present application do not limit the specific technology and specific device form adopted by the network device.

[0070] The network device can be fixed or mobile. For example, the base stations 110a, 110b are stationary and are responsible for wireless transmission and reception in one or more cells from the terminal device 120. The helicopter or drone 120i shown in FIG. 1 can be configured to act as a mobile base station, and one or more cells can move according to the location of the mobile base station 120i. In other examples, the helicopter or drone (120i) can be configured to function as a terminal device that communicates with the base station 110b.

[0071] In the present application, the communication apparatus for implementing the access network function as described above can be a network device, a network device having part of the function of the access network, or an apparatus capable of supporting the implementation of the access network function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module, which can be installed in the network device or used in conjunction with the network device. In the method of the present application, the communication apparatus for implementing the function of the network device is described by way of example of the network device.

[0072] The terminal device can be an entity on the user side for receiving or transmitting signals, such as a mobile phone. The terminal device can be used to connect people, things and machines. The terminal device can communicate with one or more core networks through a network device. The terminal device includes a handheld device with a wireless connection function, another processing device connected to a wireless modem, or a vehicle-mounted device, etc. The terminal device can be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device. The terminal device 120 can be widely used in various scenarios, such as cellular communication, D2D, V2X, point-to-point (P2P), machine-to-machine (M2M), machine type communication (MTC), internet of things (IoT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, remote medical treatment, smart power grid, smart furniture, smart office, smart wear, smart transportation, smart city, unmanned aerial vehicle, robot, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and movement, etc.Some examples of the terminal device 120 are a user equipment (UE) of a 3GPP standard, a fixed device, a mobile device, a handheld device, a wearable device, a cellular phone, a smart phone, a session initiated protocol (SIP) phone, a notebook, a personal computer, a smart book, a vehicle, a satellite, a global positioning system (GPS) device, a target tracking device, a drone, a helicopter, an aircraft, a ship, a remote control device, a smart home device, an industrial device, a personal communication service (PCS) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a wireless webcam, a tablet, a palm computer, a mobile internet device (MID), a wearable device such as a smart watch, a VR device, an AR device, a wireless terminal in industrial control, a terminal in Internet of Things (IoT) system, a wireless terminal in self driving, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city such as a smart fuel dispenser, a terminal device on a high-speed train, and a wireless terminal in a smart home such as a smart speaker, a smart coffee machine, a smart printer, etc. The terminal device 120 can be a wireless device in the above various scenarios or an apparatus for being arranged in a wireless device, e.g., a communication module, a modem, or a chip in the above devices. The terminal device can also be referred to as a terminal, a terminal device, a UE, a mobile station (MS), a mobile terminal (MT), etc. The terminal device can also be a terminal device in a future wireless communication system. The terminal device can be used in a dedicated network device or a general-purpose device. The embodiments of the present application do not limit specific technologies and specific device forms adopted by the terminal device.

[0073] Optionally, the terminal device can be used to act as a base station. For example, a UE can act as a scheduling entity that provides sidelink signals between UEs in V2X, D2D, or P2P, etc. As shown in FIG. 1, the cellular phone 120a and the car 120b communicate with each other using sidelink signals. The cellular phone 120a and the smart home device 120e communicate without relaying the communication signals through the base station 110b.

[0074] In this application, the communication device for realizing the function of the terminal device can be a terminal device, can also be a terminal device with part of the function of the above terminal device, or can be a device capable of supporting the realization of the function of the above terminal device, such as a chip system, which can be installed in the terminal device or matched with the terminal device. In this application, the chip system can be composed of a chip, or can include a chip and other discrete devices. In the technical solutions provided in this application, the communication device is taken as an example to describe the terminal device or UE.

[0075] Optionally, a wireless communication system is usually composed of a cell, a base station provides management of the cell, and the base station provides communication services to a plurality of mobile stations (MSs) in the cell. The base station includes a baseband unit (BBU) and a remote radio unit (RRU). The BBU and the RRU can be placed in different places, for example, the RRU is pulled away and placed in a high traffic area, and the BBU is placed in a central machine room. The BBU and the RRU can also be placed in the same machine room. The BBU and the RRU can also be different components under one rack. Optionally, one cell can correspond to one carrier or member carrier.

[0076] In some deployments, the network device mentioned in the embodiments of the present application can be a device including a CU, or a DU, or a device including a CU and a DU, or a control plane CU node (centralized unit-control plane, CU-CP) and a user plane CU node (centralized unit-user plane, CU-UP) and a DU node. For example, the network device can include a gNB-CU-CP, a gNB-CU-UP and a gNB-DU.

[0077] In some deployments, a plurality of RAN nodes assist the terminal device to implement wireless access, and different RAN nodes respectively implement part of the functions of the base station. For example, the RAN node can be a CU, a DU, a CU-CP, a CU-UP, or an RU, etc. The CU and the DU can be separately arranged, or can be included in the same network element, such as a BBU. The RU can be included in a radio frequency device or a radio frequency unit, such as an RRU, an AAU or an RRH.

[0078] The RAN node can support one or more types of fronthaul interfaces, different fronthaul interfaces respectively corresponding to DUs and RUs having different functions. If the fronthaul interface between the DU and the RU is a common public radio interface (CPRI), the DU is configured to implement one or more of baseband functions, and the RU is configured to implement one or more of radio frequency functions. If the fronthaul interface between the DU and the RU is another interface, relative to the CPRI, one or more of the partial baseband functions of the downlink and / or uplink, such as, for the downlink, one or more of precoding, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / add cyclic prefix (CP), are moved from the DU to the RU for implementation, and for the uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / remove cyclic prefix are moved from the DU to the RU for implementation. In a possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the splitting manner between the DU and the RU is different, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0079] Taking eCPRI Cat A as an example, for downlink transmission, with layer mapping as the cut, the DU is configured to implement layer mapping and one or more functions (i.e., one or more of encoding, rate matching, scrambling, modulation, layer mapping) before layer mapping, while other functions (e.g., one or more of resource element (RE) mapping, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / add cyclic prefix (CP)) after layer mapping are implemented in the RU. For uplink transmission, with de-RE mapping as the cut, the DU is configured to implement de-mapping and one or more functions (i.e., one or more of decoding, de-rate matching, de-scrambling, de-modulation, inverse discrete Fourier transform (IDFT), channel equalization, de-RE mapping) before de-mapping, while other functions (e.g., one or more of digital BF or FFT / CP removal) after de-mapping are implemented in the RU. It can be understood that the function description of the DU and the RU corresponding to various types of eCPRI can refer to the eCPRI protocol, which is not described here.

[0080] In a possible design, the processing unit in the BBU for implementing baseband functions is referred to as a base band high (BBH) unit, and the processing unit in the RRU / AAU / RRH for implementing baseband functions is referred to as a base band low (BBL) unit.

[0081] The CU (or CU-CP and CU-UP), DU or RU can also have different names in different systems, but those skilled in the art can understand their meanings. For example, as shown in the architecture diagram of the open radio access network (O-RAN) in FIG. 2, the architecture includes at least a non-real-time RAN intelligent controller: it is a function in the service management and orchestration framework (SMO), which drives the content transmitted through the A1 interface. It consists of a non-real-time RIC framework and non-real-time RIC applications (rApps). Near-real-time RAN intelligent controller: an O-RAN network function that enables near-real-time control and optimization of RAN elements and resources through fine-grained data collection and operations over the E2 interface. It can include AI / ML workflows, including model training, inference and updating. Open cloud (O-Cloud): a cloud computing platform that includes a series of physical infrastructure nodes that meet the needs of O-RAN to host related O-RAN functions (such as Near-RT RIC, O-CU-CP, O-CU-UP and O-DU), supported software components (such as operating systems, virtual machine monitors, container runtimes, etc.) and appropriate management and orchestration functions. In the ORAN system, the CU can also be referred to as an open-centralized unit (O-CU), the DU can also be referred to as an open-distributed unit (O-DU), the CU-CP can also be referred to as an O-CU-CP, the CU-UP can also be referred to as an O-CU-UP, and the RU can also be referred to as an O-RU. Any one of the CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0082] In the embodiments of the present application, the device for implementing the function of the network device can be a network device; it can also be a device capable of supporting the network device to implement the function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module. The device can be installed in the network device or used in combination with the network device. In the embodiments of the present application, only the device for implementing the function of the network device is taken as an example to illustrate the network device, and the scheme of the embodiments of the present application is not limited.

[0083] It can be understood that the present application can be applied between a network device and a terminal device.

[0084] Protocol layer structure between a network device and a terminal device:

[0085] The communication between a network device and a terminal device follows a certain protocol layer structure. The protocol layer structure can include a control plane protocol layer structure and a user plane protocol layer structure. For example, the control plane protocol layer structure can include the functions of protocol layers such as a radio resource control (RRC) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, a medium access control (MAC) layer, and a physical layer. For example, the user plane protocol layer structure can include the functions of protocol layers such as a PDCP layer, an RLC layer, a MAC layer, and a physical layer, and in one possible implementation, a service data adaptation protocol (SDAP) layer can also be included above the PDCP layer.

[0086] Optionally, the protocol layer structure between a network device and a terminal device can also include an artificial intelligence (AI) layer for transmitting AI function related data.

[0087] Taking the data transmission between a network device and a terminal device as an example, the data transmission needs to pass through the user plane protocol layers, such as the SDAP layer, the PDCP layer, the RLC layer, the MAC layer, and the physical layer. Among them, the SDAP layer, the PDCP layer, the RLC layer, the MAC layer, and the physical layer can also be collectively referred to as the access layer. According to the transmission direction of the data, each layer is divided into a sending part and a receiving part. Taking the following downlink data transmission as an example, the PDCP layer obtains data from the upper layer, transmits the data to the RLC layer and the MAC layer, generates a transport block by the MAC layer, and then transmits the data through the physical layer for wireless transmission. The data is encapsulated in each layer. For example, the data received by a certain layer from the upper layer of the layer is regarded as the service data unit (SDU) of the layer, which is encapsulated into a protocol data unit (PDU) after the encapsulation of the layer, and then is transmitted to the next layer.

[0088] Exemplarily, the terminal device can further have an application layer and a non-access layer. The application layer can be configured to provide services to applications installed in the terminal device. For example, downlink data received by the terminal device can be sequentially transmitted from the physical layer to the application layer, and then provided to the applications by the application layer. For another example, the application layer can obtain data generated by the applications, and sequentially transmit the data to the physical layer for sending to other communication devices. The non-access layer can be configured to forward user data. For example, uplink data received from the application layer can be forwarded to the SDAP layer, or downlink data received from the SDAP layer can be forwarded to the application layer.

[0089] It should be understood that the number and types of devices in the communication system shown in FIG. 1 are merely illustrative, and the present application is not limited thereto. In actual applications, more terminal devices, more network devices, and other network elements, such as core network devices and / or network elements for implementing artificial intelligence functions, can also be included in the communication system.

[0090] It can be understood that all or part of the functions of one or more of the terminal device, the network device, the core network device, or the network element for implementing artificial intelligence functions can be virtualized, that is, implemented by one or more of a special processor or a general processor and a corresponding software module. Among them, the terminal device and the network device involve air interface transmission, and the transceiver function of the interface can be realized by hardware. The core network device, such as the operation administration and maintenance (OAM) network element, can be virtualized. Optionally, one or more functions of the virtualized terminal device, network device, core network device, or network element for implementing artificial intelligence functions can be implemented by a cloud device, such as a cloud device in an over the top (OTT) system.

[0091] In order to support AI technology in a wireless network, an AI node can also be introduced in the network.

[0092] Optionally, the AI node can be deployed in one or more of the following positions in the communication system: network device, terminal device, or core network device, etc., or the AI node can also be deployed separately, for example, deployed in a position other than any of the above devices, such as a host or a cloud server in an over the top (OTT) system. The AI node can communicate with other devices in the communication system, which can be one or more of the following: network device, terminal device, or network element of the core network, etc.

[0093] It can be understood that the number of AI nodes is not limited in the present application. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on functions, such as different AI nodes being responsible for different functions.

[0094] It can also be understood that the AI nodes can be independent devices, can be integrated into the same device to implement different functions, or can be network elements in a hardware device, or can be software functions running on a dedicated hardware, or virtualized functions instantiated on a platform (for example, a cloud platform), and the specific form of the AI nodes is not limited in the present application.

[0095] The AI nodes can be AI network elements or AI modules.

[0096] These network element nodes, such as one or more devices in a core network device, an access network node (RAN node), a terminal, or an OAM, are provided with one or more AI modules. The access network node can be a separate RAN node, or can include multiple RAN nodes, such as a CU and a DU. The CU and / or the DU can also be provided with one or more AI modules. Optionally, the CU can also be split into a CU-CP and a CU-UP. The CU-CP and / or the CU-UP are provided with one or more AI models.

[0097] The AI module is used to implement the corresponding AI function. The AI modules deployed in different network elements can be the same or different. The model of the AI module can implement different functions according to different parameter configurations. The model of the AI module can be configured based on one or more of the following parameters: a structural parameter (such as at least one of a number of neural network layers, a neural network width, a connection relationship between layers, a weight of a neuron, an activation function of a neuron, or a bias in the activation function), an input parameter (such as a type of input parameter and / or a dimension of the input parameter), or an output parameter (such as a type of output parameter and / or a dimension of the output parameter). The bias in the activation function can also be referred to as a bias of the neural network.

[0098] One AI module can have one or more models. One model can infer an output, which includes one parameter or multiple parameters. The learning process, the training process, or the inference process of different models can be deployed in different nodes or devices, or can be deployed in the same node or device.

[0099] Still referring to FIG. 2, the RIC is included in the communication system. For example, the RIC can be the AI module described above, which is used to implement AI-related functions. The RIC includes a near-real time RIC (near-RT RIC) and a non-real time RIC (Non-RT RIC). The non-real time RIC mainly processes non-real-time information, such as data that is not sensitive to latency, which can be on the order of seconds. The near-real time RIC mainly processes near-real-time information, such as data that is relatively sensitive to latency, which can be on the order of tens of milliseconds.

[0100] The near-real time RIC is used for model training and inference. For example, it is used to train an AI model, and inference is performed using the AI model. The near-real time RIC can obtain network-side and / or terminal-side information from RAN nodes (such as CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data. Optionally, the near-real time RIC can deliver inference results to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, the near-real time RIC delivers inference results to a DU, which then delivers them to an RU.

[0101] The non-real time RIC is also used for model training and inference. For example, it is used to train an AI model, and inference is performed using the AI model. The non-real time RIC can obtain network-side and / or terminal-side information from RAN nodes (such as CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data, and 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, the non-real time RIC delivers inference results to a DU, which then delivers them to an RU.

[0102] The near-real time RIC and the non-real time RIC can also be separately set up as a network element. Alternatively, the near-real time RIC and the non-real time RIC can also be part of other devices. For example, the near-real time RIC can be set up in a RAN node (such as a CU or a DU), while the non-real time RIC can be set up in an OAM, a cloud server, a core network device, or another network device.

[0103] For example, the near-real time RIC and the non-real time RIC can be set up in the network architecture as shown in FIGS. 3A-3D:

[0104] As shown in (a) of FIG. 3A, in a first possible implementation, a near-real time RIC module is included in a network device, which is used for model learning and / or inference.

[0105] As shown in (b) of FIG. 3A, in a second possible implementation, in the communication system, the network device can further include a non-real-time RIC, which can be located in the OAM or the core network device.

[0106] As shown in (c) of FIG. 3A, in a third possible implementation, the network device includes a near-real-time RIC, and the network device further includes a non-real-time RIC. Optionally, the non-real-time RIC can be located in the OAM or the core network device.

[0107] In FIG. 3B, the CU is separated into a CU-CP and a CU-UP, compared with (c) of FIG. 3A. The near-real-time RIC and the non-real-time RIC are the same as those in (c) of FIG. 3A.

[0108] As shown in FIG. 3C, optionally, the network device can include one or more AI entities, which have a function similar to the near-real-time RIC described above. Optionally, the OAM can include one or more AI entities, which have a function similar to the non-real-time RIC described above. Optionally, the core network device can include one or more AI entities, which have a function similar to the non-real-time RIC described above. When the OAM and the core network device both include AI entities, the AI entities of the OAM and the core network device have different trained models and / or different inference models. In this application, the different models can include at least one of the following: different structural parameters of the model (such as the number of layers of the model, and / or the weight value, etc.), different input parameters of the model, or different output parameters of the model.

[0109] In FIG. 3D, the network device is separated into a CU and a DU, compared with FIG. 3C. Optionally, the CU can include an AI entity, which has a function similar to the near-real-time RIC described above. Optionally, the DU can include an AI entity, which has a function similar to the near-real-time RIC described above. When the CU and the DU both include AI entities, the AI entities of the CU and the DU have different trained models and / or different inference models. Optionally, the CU in FIG. 3D can be further split into a CU-CP and a CU-UP. Optionally, the CU-CP can be deployed with one or more AI models. And / or, the CU-UP can be deployed with one or more AI models. Optionally, in FIG. 3C or FIG. 3D, the OAM of the network device and the OAM of the core network device can be separately deployed.

[0110] For ease of understanding, the AI technology involved in this application will be introduced as follows. It can be understood that this introduction does not limit the application.

[0111] (1) AI model

[0112] AI refers to the intelligence exhibited by machines created by humans. Generally, artificial intelligence refers to the technology that presents human intelligence through ordinary computer programs. Artificial intelligence can be defined as machines or computers that mimic humans and have cognitive functions related to human thinking, such as learning and problem solving. Artificial intelligence can learn from past experiences, make reasonable decisions, and respond quickly. The goal of artificial intelligence is to understand intelligence by building computer programs with symbolic reasoning or reasoning.

[0113] Machine learning (ML) is one way to achieve artificial intelligence, that is, to solve problems in artificial intelligence by means of machine learning. Machine learning theory mainly designs and analyzes some algorithms that allow computers to automatically "learn". Machine learning algorithms are a class of algorithms that automatically analyze rules from data and use rules to predict unknown data. Because learning algorithms involve a lot of statistical theory, machine learning is particularly closely related to inferential statistics, also known as statistical learning theory.

[0114] Machine learning can be divided into supervised learning, unsupervised learning, and reinforcement learning.

[0115] Supervised learning uses the collected sample values and sample labels to learn the mapping relationship from sample values to sample labels using machine learning algorithms, and uses machine learning models to express the learned mapping relationship. The process of training a machine learning model is the process of learning this mapping relationship. For example, in signal detection, the noisy received signal is the sample, and the true constellation point corresponding to the signal is the label. Machine learning expects to learn the mapping relationship between samples and labels through training, that is, to learn a signal detector. During training, the model parameters are optimized by calculating the error between the predicted value of the model and the true label. Once the mapping relationship is learned, the learned mapping can be used to predict the label of each new sample. The mapping relationship learned by supervised learning can include linear mapping and nonlinear mapping. According to the type of label, the learned task can be divided into classification tasks and regression tasks.

[0116] Unsupervised learning only uses collected sample values to discover the internal patterns of samples using algorithms. In unsupervised learning, there is a class of algorithms that use samples themselves as a supervisory signal, that is, the model learns the mapping relationship from samples to samples, which is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the predicted value of the model and the sample itself. Self-supervised learning can be used for signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

[0117] Reinforcement learning is different from supervised learning, which is a kind of algorithm for learning a policy for solving a problem by interacting with the environment. Unlike supervised and unsupervised learning, the reinforcement learning problem does not have explicit "correct" action label data. The algorithm needs to interact with the environment to obtain the reward signal of the environment feedback, and then adjust the decision action to obtain a larger reward signal value. For example, in the following power control, the reinforcement learning model adjusts the downlink transmission power of each user according to the system total throughput rate of the wireless network, and then expects to obtain a higher system throughput rate. 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 action" label cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action". Reinforcement learning is achieved through iterative interaction with the environment.

[0118] An AI model is an algorithm or computer program that can implement an AI function, and is a specific implementation of an AI technology function. The AI model represents the mapping relationship between the input and output of the model. The type of AI model can be a neural network, a linear regression model, a decision tree model, a support vector machine (SVM), a Bayesian network, a Q-learning model, or other machine learning models.

[0119] (2) Deep neural network (DNN)

[0120] A deep neural network is a specific implementation form of AI or machine learning technology. According to the universal approximation theorem, a neural network can theoretically approximate any continuous function, thereby enabling the neural network to learn any mapping. Traditional communication systems need to rely on rich expert knowledge to design communication modules, while a deep learning communication system based on a DNN can automatically discover the implicit pattern structure from a large amount of data set, establish the mapping relationship between the data, and obtain better performance than traditional modeling methods.

[0121] The idea of a DNN originates from the neuron structure of the brain tissue. For example, each neuron performs a weighted sum operation on its input values and outputs the operation result through an activation function. As shown in FIG. 4, it is a schematic diagram of a neuron structure. Assuming that the input of the neuron is x = [x0, x1, …, x n ], and the weights corresponding to each input are w = [w0, w1, …, w n ], where w i is the weight of x i , which is used to weight x iThe inputs are weighted. The bias for the weighted sum of the inputs according to the weights is, for example, b. The form of the activation function can be various. Assuming that the activation function of a neuron is y = f(z) = max(0, z), the output of the neuron is:

[0122] For example, the activation function of a neuron is y = f(z) = z, the output of the neuron is:

[0123] where b, w i , x i may be various possible values such as a decimal, an integer (for example, 0, a positive integer, or a negative integer), or a complex number. The activation functions of different neurons in a neural network can be the same or different.

[0124] A neural network generally includes multiple layers, and each layer can include one or more neurons. By increasing the depth and / or width of the neural network, the expressiveness of the neural network can be improved, and the neural network can provide stronger information extraction and abstract modeling capabilities for complex systems. The depth of the neural network can refer to the number of layers included in the neural network, and the number of neurons included in each layer can be referred to as the width of the layer. In an implementation manner, the neural network includes an input layer and an output layer. The input layer of the neural network processes the received input information through neurons, and transmits the processing result to the output layer, and the output layer obtains the output result of the neural network. In another implementation manner, the neural network includes an input layer, a hidden layer, and an output layer, and the schematic diagram of the neural network can be referred to in FIG. 5. The input layer of the neural network processes the received input information through neurons, and transmits the processing result to the hidden layer in the middle. The hidden layer calculates the received processing result to obtain a calculation result, and transmits the calculation result to the output layer or the adjacent hidden layer, and finally the output layer obtains the output result of the neural network. The neural network can include one hidden layer or multiple sequentially connected hidden layers, which is not limited.

[0125] According to the construction manner of the network, the DNN can include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN). The FNN network shown in FIG. 5 is characterized in that the neurons in adjacent layers are completely connected two by two, which makes the FNN usually need a large amount of storage space and leads to a high calculation complexity.

[0126] CNN is a kind of neural network specially designed to deal with data with similar grid structure. For example, time series data and image data can be considered as similar grid structure data. CNN does not use all input information at once for operation, but uses a fixed size window to extract part of the information for convolution operation, which greatly reduces the calculation of model parameters. In addition, according to the different types of window extraction information (such as people and objects in the same picture are different types of information), each window can use different convolution kernel operation, which makes CNN better extract the features of input data.

[0127] RNN is a kind of DNN network that uses feedback time series information. Its input includes the new input value at the current time and the output value of itself at the previous time. RNN is suitable for obtaining sequence features with temporal correlation, and is particularly suitable for speech recognition, channel coding and decoding and other applications.

[0128] The above FNN, CNN and RNN are common neural network structures, which are constructed based on neurons. As mentioned above, each neuron performs weighted summation operation on its input value, and the weighted summation result is output through a nonlinear function. Therefore, the weights of the weighted summation operation of the neurons in the neural network and the nonlinear function are called the parameters of the neural network. Taking the neuron with max{0, x} as the nonlinear function as an example, the operation of the neuron is The parameters of the neuron performing the operation are the weights w = [w0, …, w n ], the bias of the weighted summation b, and the nonlinear function max{0, x}. The parameters of all neurons in a neural network constitute the parameters of the neural network.

[0129] (3) Training data set and inference data

[0130] The training data set is used for training the AI model, and the training data set can include the input of the AI model or the input and target output of the AI model. Among them, the training data set includes one or more training data, and the training data can be a training sample input into the AI model or a target output of the AI model. Among them, the target output can also be called a label or a label sample. The training data set is one of the important parts of machine learning. Model training is essentially learning some features from the training data, so that the output of the AI model is as close as possible to the target output, such as the difference between the output of the AI model and the target output is as small as possible. The composition and selection of the training data set can determine the performance of the AI model trained to a certain extent.

[0131] In addition, in the training process of an AI model (e.g., a neural network), a loss function can be defined. The loss function describes the gap or difference between the output value of the AI model and the target output value. The present application does not limit the specific form of the loss function. The training process of the AI model is a process of adjusting the model parameters of the AI model so that the value of the loss function is less than a threshold, or so that the value of the loss function meets the target requirement. For example, the AI model is a neural network, and adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers of the neural network, the width, the weight of the neuron, or the parameter of the activation function of the neuron.

[0132] The inference data can be input into the trained AI model as input for inference of the AI model. In the model inference process, the inference data is input into the AI model, and the corresponding output is obtained as the inference result.

[0133] (4) Design of AI model

[0134] The design of the AI model mainly includes a data collection link (e.g., collecting training data and / or inference data), a model training link, and a model inference link. Further, it can also include an inference result application link. Referring to FIG. 6, an AI application framework is illustrated. In the foregoing data collection link, a data source is used to provide a training data set and inference data. In the model training link, the AI model is obtained by analyzing or training the training data provided by the data source. The AI model represents the mapping relationship between the input and the output of the model. The AI model is learned by the model training node, which is equivalent to learning the mapping relationship between the input and the output of the model by using the training data. In the model inference link, the AI model trained by the model training link is used to perform inference based on the inference data provided by the data source, and the inference result is obtained. This link can also be understood as follows: the inference data is input into the AI model, and the output obtained by the AI model is the inference result. The inference result can indicate the configuration parameter used (executed) by the execution object, and / or the operation executed by the execution object. In the inference result application link, the inference result is published, for example, the inference result can be uniformly planned by an execution entity, for example, the execution entity can send the inference result to one or more execution objects (e.g., core network equipment, network equipment, or terminal equipment, etc.) for execution. For another example, the execution entity can also feed back the performance of the model to the data source, so as to facilitate subsequent implementation of model update training.

[0135] It can be understood that the network element with artificial intelligence function can be included in the communication system. The above-mentioned AI model design related links can be executed by one or more network elements with artificial intelligence function. In one possible design, the AI function (such as AI module or AI entity) can be configured in the existing network element in the communication system to implement the AI related operation, such as the training and / or inference of the AI model. For example, the existing network element can be a network device (such as gNB), a terminal device, a core network device, or a network management device, etc. Among them, the network management device can divide the management work of the network into three categories according to the actual needs of the operator network operation: operation, administration, and maintenance. The network management device can also be called OAM network element, abbreviated as OAM. The operation mainly completes the analysis, prediction, planning and configuration work of the daily network and service; the maintenance mainly completes the daily operation activities of the test and fault management of the network and its service, and the network management device can detect the network running state, optimize the network connection and performance, improve the network running stability, and reduce the network maintenance cost. Or in another possible design, a separate network element can also be introduced in the communication system to execute the AI related operation, such as training the AI model. The separate network element can be called AI network element or AI node, etc., and the name is not limited in the present application. The AI network element can be directly connected with the network device in the communication system, or can be indirectly connected with the network device through a third party device and the network device. Among them, the third party device can be an authentication management function (AMF) network element, a user plane function (UPF) network element, etc. core network element, OAM, cloud server or other network element, which is not limited. For example, referring to FIG. 7, another architecture of the communication system provided by the present application is shown, which includes a network device 710, a terminal device 720, 730, and an AI network element 740 is introduced in the communication system.

[0136] In the present application, one model can infer one parameter, or infer multiple parameters. The training processes of different models can be deployed in different devices or nodes, or can be deployed in the same device or node. The inference processes of different models can be deployed in different devices or nodes, or can be deployed in the same device or node.

[0137] The model parameters can include one or more of the following: a structure parameter (e.g., a number of layers of the model, and / or a weight value, etc.) of a model, an input parameter (e.g., an input dimension, a number of input ports) of the model, or an output parameter (e.g., an output dimension, a number of output ports) of the model. It can be understood that the input dimension can refer to a size of an input data, for example, when the input data is a sequence, the input dimension corresponding to the sequence can indicate a length of the sequence. The number of input ports can refer to a number of input data. Similarly, the output dimension can refer to a size of an output data, for example, when the output data is a sequence, the output dimension corresponding to the sequence can indicate a length of the sequence. The number of output ports can refer to a number of output data.

[0138] Introducing artificial intelligence / machine learning techniques into wireless air interface can be used for compression and reconstruction of wireless channel information, beam management, and positioning enhancement. AI / ML based on neural networks is trained by data driving to improve the performance of wireless tasks. As shown in the AI / ML management architecture diagram of FIG. 8, the AI / ML system includes data collection, model training, model management / performance monitoring, model inference, model storage, and inference control of the model (including activation, deactivation, fallback, switching, and selection).

[0139] The development of AI / ML technology has changed the AI in the field of natural language processing: pre-training of large models through pre-training of diversified massive data can realize the application of multiple downstream tasks.

[0140] Current AI models are designed for each wireless AI task, as shown in the single-function model diagram of FIG. 9. Model 1 predicts the path loss between the transmitting or receiving device based on the location information of the transmitting or receiving device; model 2 predicts the communication rate based on the location information of the transmitting or receiving device; and model K predicts the beam direction based on the time information. The models for different tasks collect data for training and subsequent model management.

[0141] As shown in the function management diagram of the model of FIG. 10, one function can be implemented by multiple models for different scenarios, and each model has a specific applicable range. At this time, the function can be generally managed by activating or deactivating the function, and the specific model under each function needs to be managed in relation to the model under the premise of activating the function.

[0142] Current models are designed for each function, which cannot utilize the correlation between functions and will result in a large number of models in the wireless communication system and high complexity of model management.

[0143] Therefore, the application provides a communication scheme, which obtains a first attribute and at least one reference wireless data set, processes the first attribute and the at least one reference wireless data set based on a first model, and obtains predicted wireless data associated with the first attribute, thereby providing a multifunctional wireless prediction model, supporting prediction of multiple attributes and multiple wireless data, without the need to design a model for each wireless data prediction, and improving the prediction efficiency.

[0144] The communication method provided by the embodiments of the application will be described in detail below based on the communication system.

[0145] As shown in FIG. 11, it is a flowchart of the communication method provided by the embodiments of the application. The method may, for example, include the following steps:

[0146] S1101. Obtain first information.

[0147] As shown in FIG. 12, it is a schematic diagram of the multifunctional wireless prediction model provided by the embodiments of the application, and the first model in FIG. 12 is a multifunctional wireless prediction model. The first model supports at least one function, i.e., the first model supports prediction of at least one wireless data of at least one attribute. The first model includes two parts of input and output. The input includes at least one attribute and at least one reference wireless data set; and the output includes predicted wireless data associated with the at least one attribute. The attribute includes, but is not limited to, at least one of the following: position, time, frequency (such as resource block index (RB index)), etc. Each reference wireless data set in the at least one reference wireless data set includes reference wireless data and an attribute associated with the reference wireless data. The reference wireless data includes, but is not limited to, at least one of the following: path loss, channel quality indication (CQI), angle-of-arrival (AOA), angle-of-departure (AOD), time advanced (TA), signal to interference plus noise ratio (SINR), data rate, etc. Prediction of multiple attributes and multiple wireless data can be realized based on the same model (or the same model parameters).

[0148] Therefore, when the first model is used for training or reasoning, first, the input information of the first model, i.e., the first information, needs to be obtained.

[0149] As described above, the first model can obtain the predicted wireless data associated with the at least one attribute based on the at least one attribute and the at least one set of reference wireless data. This embodiment is described by taking an example of inputting the first attribute (one or more of any of the above attributes) and the at least one set of reference wireless data (the first information includes the first attribute and the at least one set of reference wireless data) to obtain the predicted wireless data associated with the first attribute, and obtaining the predicted wireless data associated with multiple attributes can refer to the method of this embodiment.

[0150] Exemplarily, the reference wireless data can be historical wireless data. Each set of reference wireless data in the at least one set of reference wireless data includes reference wireless data and an attribute associated with the reference wireless data. The attribute associated with the reference wireless data refers to the value of the attribute associated with the reference wireless data. For example, the attribute is a location, the at least one set of reference wireless data refers to at least one set of reference wireless data of at least one location, and the attribute associated with the reference wireless data refers to the location associated with the reference wireless data. For another example, the attribute is a time, the at least one set of reference wireless data refers to at least one set of reference wireless data of at least one time, and the attribute associated with the reference wireless data refers to the time associated with the reference wireless data.

[0151] As shown in FIG. 13, it is an internal structure diagram of a multifunctional wireless prediction model provided by an embodiment of the present application. The input includes reference wireless data 1 and an attribute associated with the reference wireless data 1 (for example, assuming that the first attribute is a location, the attribute associated with the reference wireless data 1 is the location associated with the reference wireless data 1), reference wireless data 2 and an attribute associated with the reference wireless data 2 (the attribute associated with the reference wireless data 2 is the location associated with the reference wireless data 2). The figure shows two sets of reference wireless data, and actually more reference wireless data and attributes associated with the reference wireless data can be included. The first attribute, i.e., an attribute associated with the predicted wireless data, is also input, and the predicted wireless data, i.e., the predicted wireless data associated with the first attribute, is also input.

[0152] Further, the attribute associated with the reference wireless data can be a number, and the attribute values associated with the reference wireless data can be the same under different attributes (e.g., the first attribute is location, the attribute associated with the reference wireless data 1 is 1 (the location associated with the reference wireless data 1), and the attribute associated with the reference wireless data 2 is 2 (the location associated with the reference wireless data 2); the first attribute is time, the attribute associated with the reference wireless data 1 is also 1 (the time associated with the reference wireless data 1), and the attribute associated with the reference wireless data 2 is also 2 (the time associated with the reference wireless data 2)). Therefore, in order to distinguish the input reference wireless data and the attribute associated with the reference wireless data, a first indication information (i.e., the first information further includes the first indication information) can also be input, which is used to indicate the type of the predicted wireless data and / or the type of the first attribute, i.e., to indicate which attribute and / or which wireless data is predicted this time. Exemplarily, the first indication information can be a mode index.

[0153] By configuring a set of model parameters for the first model, the prediction of at least one wireless data can be realized.

[0154] In some communication scenarios, the first model can be deployed at the base station side, and the base station can receive the first information reported by the UE. In other communication scenarios, the first model can be deployed at the UE side, and the UE can collect the first information by itself or receive the first information sent by the base station.

[0155] Exemplarily, the first model can be a decoder transformer, a recurrent neural network (RNN), a long short-term memory (LSTM), or other neural networks supporting sequence prediction tasks, etc.

[0156] When training the first model, different input lengths are predicted to minimize the average loss as the optimization objective.

[0157] S1102. Based on the first model, the first attribute and the at least one set of reference wireless data are processed to obtain second information.

[0158] After obtaining the first attribute and the at least one set of reference wireless data, the first attribute and the at least one set of reference wireless data are input into a first model. The first attribute and the at least one set of reference wireless data can be processed based on the first model to obtain second information. For example, the first attribute and the at least one set of reference wireless data can be processed based on the first model to obtain predicted wireless data associated with the first attribute. The first model is used to predict at least one type of wireless data. The second information includes the predicted wireless data associated with the first attribute, i.e., the predicted wireless data corresponding to the attribute.

[0159] For example, when the first attribute is a location, a plurality of sets of reference wireless data (e.g., path loss, CQI, AOA, AOD, TA, SINR, data rate, etc.) of different locations can be input into the first model to obtain predicted wireless data (e.g., path loss, CQI, AOA, AOD, TA, SINR, data rate, etc.) of a certain location. For another example, when the first attribute is a time, a plurality of sets of reference wireless data (e.g., path loss, CQI, AOA, AOD, TA, SINR, data rate, etc.) of different times can be input into the first model to obtain predicted wireless data (e.g., path loss, CQI, AOA, AOD, TA, SINR, data rate, etc.) of a certain time in the future. That is, the first model can support the prediction of at least one type of wireless data of at least one attribute, or in other words, the first model supports at least one function.

[0160] Because the model in this embodiment can support multiple wireless functions, the model can be managed based on the model (rather than managing a plurality of models based on functions as shown in FIG. 10). As shown in FIG. 14, a model and function association diagram provided by an embodiment of the present application is shown. The model is identified and assigned a model identifier, such as model 1 and model 2 in FIG. 14. The description or meta information of the model 1 and the model 2 includes at least one function supported by the model (which can be a list of functions supported by the model). For example, the model 1 supports function 1 (e.g., location-associated path loss), function 2 (e.g., location-associated AOA), and function 3 (e.g., time-associated TA); and the model 2 supports function 4 (e.g., frequency-associated path loss) and function 5 (e.g., frequency-associated CQI).

[0161] In addition, since the first model has a data length requirement for the attribute associated with the reference wireless data or the first attribute, before processing the first attribute and the at least one set of reference wireless data based on the first model to obtain the second information, the attribute associated with the reference wireless data or the first attribute included in each set of reference wireless data in the at least one set of reference wireless data can also be processed to obtain the attribute of the first data length. As shown in FIG. 13, the attribute associated with the reference wireless data or the first attribute can be processed by a multilayer perceptron (MLP) to obtain the attribute of the first data length.

[0162] Similarly, the first model can also have a data length requirement for the reference wireless data, and therefore, before processing the first attribute and the at least one set of reference wireless data based on the first model to obtain the second information, the reference wireless data included in each set of reference wireless data in the at least one set of reference wireless data can also be processed to obtain the reference wireless data of the second data length. As shown in FIG. 13, the reference wireless data can also be processed by an MLP to obtain the reference wireless data of the second data length.

[0163] In addition, as shown in FIG. 13, the reference wireless data and the attribute associated with the reference wireless data can be jointly embedded together with the first indication information described above. That is, each set of reference wireless data and the attribute associated with the reference wireless data share the same position embedding, which is used to identify whether the input is an attribute or reference wireless data. Exemplarily, the first vector can be added to the attribute of the first data length obtained after the MLP processing, and the first vector is used to identify the attribute associated with the reference wireless data. The second vector can also be added to the reference wireless data of the second data length obtained after the MLP processing, and the second vector is used to identify the reference wireless data.

[0164] When predicting based on the first model, the communication apparatus can obtain the identifier of the first model and at least one function supported by the first model. If the model is deployed on the UE side, the base station can perform model management, and the base station can receive the identifier of at least one model and at least one function supported by each model in the at least one model reported by the UE. When the base station instructs the UE to predict the wireless data associated with the first attribute, the base station can send second indication information, for example, the second indication information can be used to instruct processing based on the first model, and the second indication information includes the identifier of the first model. If the model is deployed on the base station side, the base station can provide prediction services to the UE, and the base station can instruct the UE about the functions supported by different models. For example, the base station can send third information to the UE, and the third information indicates at least one function supported by the first model.

[0165] In addition, the model can be updated / rolled back, etc. based on the model identification. The rollback refers to, in some cases, when the model performance deteriorates, the traditional algorithm or the default algorithm / model can be used to process the data.

[0166] For any model, the function thereof can also be activated / deactivated. Specifically, fourth information can be acquired, which is used to indicate activation of at least one function supported by the first model; or the fourth information is used to indicate deactivation of at least one function supported by the first model. For example, the model is deployed on the UE side, and the base station can send the fourth information to the UE; for another example, the model is deployed on the base station side, and the UE can send the fourth information to the base station.

[0167] According to the communication method provided by the embodiment of the present application, the first attribute and the at least one reference wireless data set are acquired, and the first attribute and the at least one reference wireless data set are processed based on the first model to obtain the predicted wireless data associated with the first attribute. Through the multifunctional wireless prediction model, the prediction of multiple attributes and multiple wireless data can be supported, and the model does not need to be designed for each wireless data prediction, thereby improving the prediction efficiency.

[0168] The following describes how to obtain the predicted wireless data associated with a certain attribute through two examples:

[0169] In one example, a multifunctional radio map model is used to predict multiple wireless data of a specific location. As shown in FIG. 15, it is a schematic diagram of a multifunctional radio map model according to an embodiment of the present application. The input of the model includes location information and at least one reference wireless data set (two reference wireless data sets are shown in FIG. 15: the first reference wireless data set includes reference wireless data 1 (such as path loss, CQI, AOA, AOD, TA, SINR, data rate, etc.) and the attribute associated with the reference wireless data 1 (such as the first location), and the second reference wireless data set includes reference wireless data 2 (such as path loss, CQI, AOA, AOD, TA, SINR, data rate, etc.) and the attribute associated with the reference wireless data 2 (such as the second location).

[0170] The location information and the at least one reference wireless data set can be processed based on the multifunctional radio map model to obtain multiple predicted wireless data (such as path loss, CQI, AOA, AOD, TA, SINR, data rate, etc.) associated with the location information (such as the third location).

[0171] Further, the position information, the position information associated with the reference wireless data 1, and the position information associated with the reference wireless data 2 can be processed by a neural network such as MLP to obtain the position information, the position information associated with the reference wireless data 1, and the position information associated with the reference wireless data 2 with a unified data length, respectively.

[0172] Further, the reference wireless data 1 and the reference wireless data 2 can also be processed by a neural network such as MLP to obtain the reference wireless data 1 and the reference wireless data 2 with a unified data length, respectively.

[0173] In addition, the position information, the position information associated with the reference wireless data 1, and the position information associated with the reference wireless data 2 after the data length is unified can be added with a first vector for identifying the position information.

[0174] In addition, the reference wireless data 1 and the reference wireless data 2 after the data length is unified can be added with a second vector for identifying the reference wireless data 1 and the reference wireless data 2.

[0175] In addition, in order to distinguish the input reference wireless data and the attribute associated with the reference wireless data, a model index can also be input to the multi-functional radio map model to indicate that the type and / or attribute of the predicted wireless data is the position information.

[0176] After the position information and the at least one set of reference wireless data are processed based on the multi-functional radio map model to obtain the predicted wireless data associated with the position information, the predicted wireless data can also be processed by a neural network such as MLP to obtain the predicted wireless data with a required data length. For example, the multi-functional radio map model outputs a high-dimensional vector, and after being processed by the neural network such as MLP, the high-dimensional vector can be mapped to a dimension with the required data length.

[0177] When the multi-functional radio map model is trained, the difference between the predicted wireless data of each position and the label (target wireless data, such as the true value of the wireless data or the measured wireless data) can be used as the loss function of the model, such as mean-square error (MSE) or mean absolute error loss (MAE).

[0178] When the multi-functional radio map model is used for inference, the number of the input set of reference wireless data can be determined according to performance requirements and the like.

[0179] In the present example, when the model is trained / monitored, the communication device can collect reference wireless data at different locations in the same scenario. For example, when the model is deployed at the base station side, the base station can configure the UE to perform measurement and report of multiple reference wireless data. Alternatively, the UE can actively report the location information and at least one reference wireless data measured at different locations.

[0180] When the model is used for inference, if the model is deployed at the base station side, the UE can report the location information and at least one reference wireless data measured at different locations, the base station can collect the location information and at least one reference wireless data measured at different locations reported by the UE as input, and the base station can predict the wireless data associated with a certain location. If the model is deployed at the UE side, the UE can use the location information and at least one reference wireless data measured at different locations collected by itself as input of the model, or request the base station for at least one reference wireless data at a specific location as input of the model, to predict the wireless data associated with a certain location. Further, the UE can report the predicted wireless data to the base station.

[0181] The multi-functional radio map model provided in the present example can support prediction of multiple wireless data, i.e., support multiple radio map functions. The correlation of multiple wireless data is fully utilized, and the complexity of model management is reduced.

[0182] In another example, multiple wireless data at a specific time is predicted by a multi-functional wireless information prediction model. As shown in FIG. 16, it is a schematic diagram of a multi-functional wireless information prediction model according to an example of the present application, wherein the input of the model includes time information and at least one reference wireless data set (two reference wireless data sets are shown in the figure: the first reference wireless data set includes reference wireless data 1 (such as path loss, CQI, AOA, AOD, TA, SINR, data rate, etc.) and the attribute associated with reference wireless data 1 (such as the first time), and the second reference wireless data set includes reference wireless data 2 (such as path loss, CQI, AOA, AOD, TA, SINR, data rate, etc.) and the attribute associated with reference wireless data 2 (such as the second time).

[0183] Based on the multi-functional wireless information prediction model, the time information and at least one reference wireless data set can be processed to obtain multiple predicted wireless data associated with the time information (such as path loss, CQI, AOA, AOD, TA, SINR, data rate, etc. at the third time).

[0184] Further, the time information, the time information associated with the reference wireless data 1, and the time information associated with the reference wireless data 2 can be processed by a neural network such as MLP to obtain the time information, the time information associated with the reference wireless data 1, and the time information associated with the reference wireless data 2 with a unified data length, respectively.

[0185] Further, the reference wireless data 1 and the reference wireless data 2 can also be processed by a neural network such as MLP to obtain the reference wireless data 1 and the reference wireless data 2 with a unified data length, respectively.

[0186] In addition, the time information, the time information associated with the reference wireless data 1, and the time information associated with the reference wireless data 2 after the data length is unified can be added with a first vector for identifying the time information.

[0187] In addition, the reference wireless data 1 and the reference wireless data 2 after the data length is unified can also be added with a second vector for identifying the reference wireless data 1 and the reference wireless data 2.

[0188] In addition, in order to distinguish the input reference wireless data and the attribute associated with the reference wireless data, a model index can also be input to the above multifunctional wireless information prediction model to indicate that the type and / or attribute of the predicted wireless data is time information.

[0189] After the time information and the at least one set of reference wireless data are processed based on the multifunctional wireless information prediction model to obtain the predicted wireless data associated with the time information, the predicted wireless data can also be processed by a neural network such as MLP to obtain the predicted wireless data with a required data length. For example, the multifunctional wireless information prediction model outputs a high-dimensional vector, which can be mapped to a dimension with a required data length after being processed by a neural network such as MLP.

[0190] When the multifunctional wireless information prediction model is trained, the difference between the wireless data predicted by each time and the label (target wireless data, such as the true value of the wireless data or the measured wireless data) can be used as the loss function of the model, such as MSE or MAE.

[0191] When the multifunctional wireless information prediction model is used for inference, the number of the input set of reference wireless data can be determined according to performance requirements and the like.

[0192] In the present example, when the model is trained / model performance is monitored, the communication device can collect reference wireless data at different times in the same scene. For example, when the model is deployed at the base station side, the base station can configure the UE to perform measurement and report of multiple reference wireless data. Alternatively, the UE can actively report the time information and at least one reference wireless data measured at different times.

[0193] In inference using the model, as shown in FIG. 17, which is a schematic diagram of time-domain wireless data prediction according to an embodiment of the present application, if the model is deployed at the base station side, the UE can report time information and at least one reference wireless data measured at different times, the base station can collect the time information and at least one reference wireless data measured at different times reported by the UE as input, and the base station can predict wireless data associated with a certain time. If the model is deployed at the UE side, the UE can collect time information and at least one reference wireless data measured at different times as input of the model, or request at least one reference wireless data at a specific time from the base station as input of the model, to predict wireless data associated with a certain time. Further, the UE can report the predicted wireless data to the base station.

[0194] The multi-functional wireless information prediction model provided in the example can support prediction of multiple wireless data, i.e., support multiple wireless information prediction functions. The correlation of multiple wireless data is fully utilized, and the complexity of model management is reduced.

[0195] The above embodiments describe how to obtain predicted wireless data associated with a certain attribute based on at least one reference wireless data set of the attribute. In actual use, multiple attributes can be processed based on the above multi-functional model to obtain predicted wireless data associated with multiple attributes.

[0196] Specifically, each reference wireless data can include multiple attributes, such as location, time, frequency, etc., i.e., wireless data can be predicted based on location, time, or frequency information at the same time. Multiple attributes can be input to the model after being processed independently or in cascade. As shown in FIG. 18A, which is a schematic diagram of inputting multiple attributes to the model independently according to an embodiment of the present application, each attribute is processed independently by MLP and then processed subsequently with at least one reference wireless data set. As shown in FIG. 18B, which is a schematic diagram of inputting multiple attributes to the model in cascade according to an embodiment of the present application, multiple attribute information is cascaded, processed by MLP, and then processed subsequently with at least one reference wireless data set.

[0197] Based on the same idea of the above communication method, the embodiments of the present application further provide a communication device for implementing the above method. The communication device can be the communication device in the above method embodiments, or a component applicable to the communication device. It can be understood that the communication device comprises the hardware structure and / or software module corresponding to each function in order to implement the above functions. Those skilled in the art should easily realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in the form of hardware or 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 the present application.

[0198] The embodiments of the present application can divide the functions of the communication device according to the above method embodiments, for example, each function module can be divided according to each function, or two or more functions can be integrated in one processing unit. The integrated module can be realized in the form of hardware or software function module. It should be noted that the division of the modules in the embodiments of the present application is illustrative, and is only a logical function division. There can be another division method in actual implementation.

[0199] Based on the same idea of the above communication method, the present application further provides a communication device as follows:

[0200] As shown in FIG. 19, a structure schematic diagram of a communication device provided by the embodiments of the present application is shown in FIG. 19, the communication device 1900 comprises a transceiver unit 1901 and a processing unit 1902; wherein:

[0201] The processing unit 1902 is configured to acquire first information, the first information comprising a first attribute and at least one reference wireless data set, each reference wireless data set in the at least one reference wireless data set comprising a reference wireless data and an attribute associated with the reference wireless data; and the processing unit 1902 is configured to process the first attribute and the at least one reference wireless data set based on a first model to acquire second information, the first model being used to predict at least one wireless data, and the second information comprising a predicted wireless data associated with the first attribute.

[0202] Optionally, the attribute associated with the reference wireless data or the first attribute comprises at least one of the following: position, time, frequency.

[0203] Optionally, the type of the reference wireless data or the predicted wireless data comprises at least one of the following: path loss, channel quality indication, angle of arrival, angle of departure, timing advance, signal to interference plus noise ratio, data rate.

[0204] Optionally, the first information further comprises first indication information, the first indication information being used to indicate the type of the predicted wireless data and / or the type of the first attribute.

[0205] Optionally, the processing unit 1902 is further configured to obtain an identifier of the first model and at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports predicting at least one type of wireless data of at least one attribute.

[0206] Optionally, the transceiver 1901 is configured to receive second indication information, the second indication information being used to indicate that processing is based on the first model, and the second indication information comprises the identifier of the first model.

[0207] Optionally, the transceiver 1901 is further configured to send third information, the third information indicating the at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports predicting at least one type of wireless data of at least one attribute.

[0208] Optionally, the processing unit 1902 is further configured to obtain fourth information, the fourth information being used to indicate activating the at least one function supported by the first model, or the fourth information being used to indicate deactivating the at least one function supported by the first model.

[0209] Optionally, the processing unit 1902 is further configured to process the attribute associated with the reference wireless data or the first attribute included in each of the at least one set of reference wireless data to obtain a first data length of attribute, and the processing unit 1902 is further configured to process the reference wireless data included in each of the at least one set of reference wireless data to obtain a second data length of reference wireless data.

[0210] Optionally, the processing unit 1902 is further configured to add a first vector to the first data length of attribute, the first vector being used to identify the attribute associated with the reference wireless data, and the processing unit 1902 is further configured to add a second vector to the second data length of reference wireless data, the second vector being used to identify the reference wireless data.

[0211] The specific implementation of the transceiver 1901 and the processing unit 1902 can refer to the description in the above method embodiments.

[0212] Further, it should be noted that the aforementioned transceiver and / or processing unit can be implemented by virtual modules, for example, the processing unit can be implemented by a software function unit or a virtual device, and the transceiver can be implemented by a software function or a virtual device. Alternatively, the processing unit or the transceiver can also be implemented by an entity circuit, for example, if the device is implemented by a chip / chip circuit, the transceiver can be an input / output circuit and / or a communication interface, which performs an input operation (corresponding to the aforementioned receiving operation) and an output operation (corresponding to the aforementioned sending operation); and the processing unit is a processing circuit, such as an integrated processor or a microprocessor or an integrated circuit.

[0213] The division of modules in the present application is illustrative, and is only a logical function division. In actual implementation, there can be another division manner. In addition, each function module in each example of the present application can be integrated in one processor, or can be a separate physical existence, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module.

[0214] As shown in FIG. 20, FIG. 20 is a structural schematic diagram of another communication device provided by an embodiment of the present application. The communication device 2000 includes one or more processing circuits 2001 (one processing circuit is shown in the figure). Optionally, the communication device 2000 can also include a memory 2003 (indicated by a dashed line in the figure). The memory 2003 is used to store instructions executed by the processing circuit 2001, or to store input data required by the processing circuit 2001 to run instructions, or to store data generated after the processing circuit 2001 runs instructions. Optionally, the communication device 2000 can also include an interface circuit 2002 (indicated by a dashed line in the figure), and the processing circuit 2001 and the interface circuit 2002 are coupled with each other. It can be understood that the interface circuit 2002 can be a transceiver or an input / output interface.

[0215] The processing circuit can be a processor or a circuit for processing in the processor.

[0216] The processing circuit 2001 is configured to obtain first information, the first information including a first attribute and at least one reference wireless data set, each reference wireless data set in the at least one reference wireless data set including a reference wireless data and an attribute associated with the reference wireless data; and the processing circuit 2001 is configured to process the first attribute and the at least one reference wireless data set based on a first model to obtain second information, the first model being used to predict at least one wireless data, and the second information including a predicted wireless data associated with the first attribute.

[0217] Optionally, the first attribute or the attribute associated with the reference wireless data comprises at least one of: a location, a time, a frequency.

[0218] Optionally, the type of the reference wireless data or the predicted wireless data comprises at least one of: a path loss, a channel quality indication, an angle of arrival, an angle of departure, a timing advance, a signal to interference plus noise ratio, a data rate.

[0219] Optionally, the first information further comprises first indication information, the first indication information being used to indicate the type of the predicted wireless data and / or the type of the first attribute.

[0220] Optionally, the processing circuit 2001 is further configured to obtain an identification of the first model and at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports predicting at least one type of wireless data of at least one attribute.

[0221] Optionally, the interface circuit 2002 is configured to receive second indication information, the second indication information being used to indicate that processing is based on the first model, the second indication information comprising the identification of the first model.

[0222] Optionally, the interface circuit 2002 is further configured to send third information, the third information indicating the at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports predicting at least one type of wireless data of at least one attribute.

[0223] Optionally, the processing circuit 2001 is further configured to obtain fourth information, the fourth information being used to indicate activating the at least one function supported by the first model; or the fourth information being used to indicate deactivating the at least one function supported by the first model.

[0224] Optionally, the processing circuit 2001 is further configured to process the attribute associated with the reference wireless data or the first attribute included in each of the at least one set of reference wireless data to obtain a first data length of attribute; and the processing circuit 2001 is further configured to process the reference wireless data included in each of the at least one set of reference wireless data to obtain a second data length of reference wireless data.

[0225] Optionally, the processing circuit 2001 is further configured to add a first vector to the first data length of attribute, the first vector being used to identify the attribute associated with the reference wireless data; and the processing circuit 2001 is further configured to add a second vector to the second data length of reference wireless data, the second vector being used to identify the reference wireless data.

[0226] The embodiment of the present application further provides a computer readable storage medium, wherein a computer program or instructions are stored in the computer readable storage medium, and the computer program or instructions are executed to realize the method in the above embodiment.

[0227] The embodiment of the present application further provides a computer program product containing instructions, which, when run on a computer, cause the computer to execute the method in the above embodiment.

[0228] The embodiment of the present application further provides a communication system, comprising the communication device.

[0229] The embodiment of the present application further provides a circuit, which is coupled with a memory, and is used to execute the method shown in the above embodiment. The circuit can include a chip circuit.

[0230] Optionally, the embodiment of the present application further provides a chip system, comprising at least one processor and an interface, wherein the at least one processor is coupled with a memory through the interface, and when the at least one processor runs the computer program or instructions in the memory, the chip system executes the method in any method embodiment. Optionally, the chip system can be composed of a chip, or can include the chip and other discrete devices, and the embodiment of the present application does not make a specific limitation.

[0231] The memory in the present application can also be a circuit or other any device capable of realizing a storage function, used to store program instructions and / or data. The memory can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but is not limited to this. For example, the memory can be a non-volatile memory, such as a digital versatile disc (DVD), a hard disk drive (HDD) or a solid-state drive (SSD), etc., and can also be a volatile memory, such as a random-access memory (RAM).

[0232] In the following description of the present application, the terms “include” and “have” and any variations thereof mentioned in the present application are intended to cover the non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0233] It should be understood that, in the description of the present application, unless otherwise specified, " / " represents that the objects associated in front and back are in an "or" relationship, for example, A / B can represent A or B; wherein A, B can be singular or plural. And, in the description of the present application, unless otherwise specified, "multiple" means two or more than two. "At least one of the following" or the like means any combination of the items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c can be single or multiple. In addition, in order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the same items or similar items with basically the same function and role are distinguished by using "first", "second", etc. The skilled in the art can understand that "first", "second", etc. do not limit the quantity and execution order, and "first", "second", etc. do not necessarily mean different. At the same time, in the embodiments of the present application, "exemplary" or "for example" means to serve as an example, illustration or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the use of "exemplary" or "for example" is intended to present the relevant concept in a specific manner, for understanding.

[0234] In the above embodiments, all or part can be realized by software, hardware, firmware or any combination thereof. When realized by software, all or part can be realized in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode.

[0235] Although the present application is described in conjunction with the embodiments thereof, other changes and modifications to the described embodiments can be understood and effected by those skilled in the art in view of the foregoing description, the drawings and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plurality. A single processor or other unit can fulfil the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0236] It can be understood that various digital numbers involved in the embodiments of the present application are only distinguished for convenience of description, and are not used to limit the scope of the embodiments of the present application. The size of the serial number of the above processes does not mean the order of execution, and the execution order of the processes should be determined according to its function and inherent logic.

[0237] In the above embodiments, the description of each embodiment is focused on, and the part not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0238] The components in the device of the embodiments of the present application can be combined, divided and deleted according to actual needs. Those skilled in the art can combine or combine the features of different embodiments and different embodiments described in the specification.

[0239] In the present application, the examples can be referred to each other without logical contradiction, for example, the methods and / or terms between the method embodiments can be referred to each other, for example, the functions and / or terms between the device embodiments can be referred to each other, for example, the functions and / or terms between the device examples and the method examples can be referred to each other.

Claims

1. A communication method characterized by comprising: The method comprises: obtaining first information, the first information comprising a first attribute and at least one reference wireless data set, each reference wireless data set in the at least one reference wireless data set comprising a reference wireless data and an attribute associated with the reference wireless data; processing the first attribute and the at least one reference wireless data set based on a first model to obtain second information, the first model being used to predict at least one wireless data, the second information comprising a predicted wireless data associated with the first attribute.

2. The method of claim 1, wherein, The attribute associated with the reference wireless data or the first attribute comprises at least one of the following: position, time, frequency.

3. The method of claim 1 or 2, wherein, The type of the reference wireless data or the predicted wireless data comprises at least one of the following: path loss, channel quality indicator, angle of arrival, angle of departure, timing advance, signal to interference plus noise ratio, data rate.

4. The method according to any one of claims 1 to 3, characterized in that, The first information further comprises first indication information, the first indication information being used to indicate the type of the predicted wireless data and / or the type of the first attribute.

5. The method of any one of claims 1-4, wherein, The method further comprises: obtaining an identifier of the first model and at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports predicting at least one wireless data of at least one attribute.

6. The method of claim 5, wherein, The method further comprises: receiving second indication information, the second indication information being used to indicate that processing is based on the first model, the second indication information comprising the identifier of the first model.

7. The method of any one of claims 1-5, wherein, The method further comprises: sending third information, the third information indicating the at least one function supported by the first model, the at least one function supported by the first model being used to indicate that the first model supports predicting at least one wireless data of at least one attribute.

8. The method of claim 5 or 7, wherein, The method further comprises: obtaining fourth information, the fourth information being used to indicate activating the at least one function supported by the first model; or the fourth information being used to indicate deactivating the at least one function supported by the first model.

9. The method of any one of claims 1-8, wherein, Before the processing of the first attribute and the at least one reference wireless data set based on the first model to obtain the second information, the method further comprises: processing the attribute associated with the reference wireless data or the first attribute comprised in each reference wireless data set in the at least one reference wireless data set to obtain a first data length of attribute; processing the reference wireless data comprised in each reference wireless data set in the at least one reference wireless data set to obtain a second data length of reference wireless data.

10. The method of claim 9, wherein, Before the processing of the first attribute and the at least one reference wireless data set based on the first model to obtain the second information, the method further comprises: adding a first vector to the first data length of attribute, the first vector being used to identify the attribute associated with the reference wireless data; adding a second vector to the second data length of reference wireless data, the second vector being used to identify the reference wireless data.

11. A communications device, characterized by A device comprising means for performing the method of any one of claims 1-10.

12. A communications device, characterized by comprising processing circuitry and interface circuitry for receiving signals from and transmitting signals to other communication devices other than the communication device, and the processing circuitry is configured to implement the method of any one of claims 1-10 by means of logic circuitry or by means of executing code instructions.

13. The communication apparatus according to claim 12, wherein The communication device is a chip.

14. A chip module, characterized by comprising a transceiver component and a chip, the chip being configured to implement the method of any one of claims 1-10.

15. A computer readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the method of any one of claims 1-10.

16. A computer program product, characterised in that, The computer program product comprises program instructions involved, which, when executed, implement the method of any one of claims 1-10.

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