Communication methods and devices

Abstract

This application relates to the field of communication technology and discloses a communication method and apparatus. The method includes: a first device sending a first request to a second device, the first request being for requesting the acquisition of first wireless data; and the second device sending a first response to the first device, the first response including the first wireless data, the first wireless data being associated with second wireless data. Using the solution of this application, the first device can acquire the first wireless data from the second device, thereby completing its own missing wireless data and improving the efficiency of wireless data acquisition.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a communication method and apparatus. Background Technology

[0002] With the development of communication technology, artificial intelligence (AI) and machine learning (ML) technologies have been introduced into wireless communication systems. For example, AI technology can be used for wireless channel information compression and reconstruction, beam management, and positioning enhancement.

[0003] Different types of wireless data are required at various stages of AI / ML model development. Terminal and network devices can collect different types of wireless data. Wireless data used for AI / ML model training or monitoring typically consists of wireless data samples composed of wireless data pairs that serve as model inputs and outputs. These wireless data samples may consist of different types of wireless data from different devices. Due to issues such as acquisition conditions, privacy, and real-time requirements of terminal or network devices, these devices may not be able to obtain complete wireless data samples; that is, some data that makes up the wireless data sample may be missing.

[0004] Therefore, how to obtain the necessary wireless data samples to improve the efficiency of wireless data acquisition is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a communication method and apparatus to obtain the required wireless data samples and improve the efficiency of wireless data acquisition.

[0006] Firstly, a communication method is provided, which can be applied to a first device, which may be the first device itself or a communication module within the first device, or a circuit or chip applied to the first device (such as a modem chip (also known as a baseband chip), or a system-on-a-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip). Taking the application of this method to a first device as an example...

[0007] In this method, a first device sends a first request to request first wireless data; and receives a first response including the first wireless data, which is associated with second wireless data.

[0008] Using this method, the first device can obtain the first wireless data from the second device, thereby completing its own missing wireless data and improving the efficiency of wireless data acquisition.

[0009] In one possible implementation, the first request includes at least one of the following: the second wireless data, the first attribute, and the first identifier.

[0010] Using this method, the first device can send second wireless data to the second device, so that the second device can locate the first wireless data associated with the second wireless data. The first attribute includes at least one of the following: collection time and collection location. For example, the first attribute may include the collection time and collection location of the second wireless data. Based on the collection time and collection location of the second wireless data, the second device can obtain the first wireless data collected at the same collection time and collection location. Alternatively, the first attribute may include the collection time and collection location of the first wireless data. The second device uses a first identifier to identify the attributes of each wireless data (or data sample): data sample, first attribute, and data sample identifier. The second device can obtain the first wireless data corresponding to the first identifier.

[0011] In another possible implementation, the first attribute includes at least one of the following: acquisition time, acquisition location.

[0012] In another possible implementation, the first wireless data and the second wireless data include at least one of the following: uplink channel information, downlink channel information, location information, transmit power, sensor data, moving speed, moving direction, and antenna parameters.

[0013] For example, the first wireless data is downlink channel information, and the second wireless data is uplink channel information;

[0014] The first wireless data is uplink channel information, and the second wireless data is downlink channel information;

[0015] The first wireless data is downlink channel information, and the second wireless data is downlink channel information;

[0016] The first wireless data is uplink channel information, and the second wireless data is uplink channel information;

[0017] The first wireless data is uplink channel information, and the second wireless data is the location information of the first device;

[0018] The first wireless data is uplink channel information, and the second wireless data is the transmit power of the first device;

[0019] The first wireless data includes at least one of the following: uplink channel information, downlink channel information, location information of the first device, sensor data of the first device, speed of the first device, direction of movement of the first device, antenna parameters of the second device, and the second wireless data is the location information of the first device.

[0020] Using this method, based on the second wireless data, the first wireless data associated with the second wireless data can be obtained, enabling the first device to obtain more complete wireless data.

[0021] In another possible implementation, the first wireless data is obtained based on the second wireless data.

[0022] Using this method, the second wireless data can be supplemented based on the second wireless data, allowing the first device to obtain more complete wireless data.

[0023] In another possible implementation, the second wireless data includes wireless channel information for multiple locations, the first request includes a first location, the multiple locations do not include the first location, and the first wireless data is the wireless channel information for the first location.

[0024] Using this method, the second wireless data can be supplemented based on the second wireless data, allowing the first device to obtain more complete wireless data.

[0025] In another possible implementation, the second wireless data includes wireless channel information at multiple times, the first request includes a first time, the multiple times do not include the first time, and the first wireless data is the wireless channel information at the first time.

[0026] Using this method, the second wireless data can be supplemented based on the second wireless data, allowing the first device to obtain more complete wireless data.

[0027] In another possible implementation, the first wireless data is obtained based on the second wireless data, including: the first wireless data is obtained by performing at least one of the following operations based on the second wireless data: interpolation calculation, averaging, and prediction based on an artificial intelligence model.

[0028] Using this method, the second wireless data can be supplemented based on the second wireless data, allowing the first device to obtain more complete wireless data.

[0029] In another possible implementation, the first wireless data is collected by a second or third device, and the second wireless data is collected by the first device.

[0030] Secondly, a communication method is provided, which can be applied to a second device, which may be a second device or a communication module in a second device, or a circuit or chip applied to the second device (such as a modem chip, or a SoC chip or SIP chip containing a modem core). Taking the application of this method to a second device as an example.

[0031] In this method, a second device receives a first request for requesting to acquire first wireless data; acquires the first wireless data based on the first request; and sends a first response including the first wireless data, which is associated with second wireless data.

[0032] In one possible implementation, the first request includes at least one of the following: the second wireless data, the first attribute, and the first identifier.

[0033] In another possible implementation, the first attribute includes at least one of the following: acquisition time, acquisition location.

[0034] In another possible implementation, the first wireless data and the second wireless data include at least one of the following: uplink channel information, downlink channel information, location information, transmit power, sensor data, moving speed, moving direction, and antenna parameters.

[0035] For example, the first wireless data is downlink channel information, and the second wireless data is uplink channel information;

[0036] The first wireless data is uplink channel information, and the second wireless data is downlink channel information;

[0037] The first wireless data is downlink channel information, and the second wireless data is downlink channel information;

[0038] The first wireless data is uplink channel information, and the second wireless data is uplink channel information;

[0039] The first wireless data is uplink channel information, and the second wireless data is the location information of the first device;

[0040] The first wireless data is uplink channel information, and the second wireless data is the transmit power of the first device;

[0041] The first wireless data includes at least one of the following: uplink channel information, downlink channel information, location information of the first device, sensor data of the first device, speed of the first device, direction of movement of the first device, antenna parameters of the second device, and the second wireless data is the location information of the first device.

[0042] In another possible implementation, the first wireless data is obtained based on the second wireless data.

[0043] In another possible implementation, the second wireless data includes wireless channel information for multiple locations, the first request includes a first location, the multiple locations do not include the first location, and the first wireless data is the wireless channel information for the first location.

[0044] In another possible implementation, the second wireless data includes wireless channel information at multiple times, the first request includes a first time, the multiple times do not include the first time, and the first wireless data is the wireless channel information at the first time.

[0045] In another possible implementation, the first wireless data is obtained based on the second wireless data, including: the first wireless data is obtained by performing at least one of the following operations based on the second wireless data: interpolation calculation, averaging, and prediction based on an artificial intelligence model.

[0046] In another possible implementation, the first wireless data is collected by a second or third device, and the second wireless data is collected by the first device.

[0047] In yet another possible implementation, the method further includes: sending first information, the first information including the first wireless data and the second wireless data.

[0048] The beneficial effects of the second aspect or any embodiment of the second aspect can be referred to the description of the first aspect.

[0049] Thirdly, a communication device is provided for implementing the communication method in any of the first, second, or first and second embodiments described above. The device may be a device, a module applied to a device (e.g., a processor, chip, or chip system), or a logic node, logic module, or software capable of implementing all or part of the device's functions.

[0050] In one possible implementation, the communication device in the third aspect includes a unit for executing the methods in the first aspect, the second aspect, or any one of the first and second aspects.

[0051] The communication device includes a transceiver unit and may also include a processing unit.

[0052] Wherein, when the above-mentioned communication device is used to execute the method in the first aspect or any of the embodiments of the first aspect, the transceiver unit is used to send a first request, the first request being used to request to obtain first wireless data; and the transceiver unit is further used to receive a first response, the first response including the first wireless data, the first wireless data being associated with second wireless data.

[0053] Optionally, the first request includes at least one of the following: the second wireless data, the first attribute, and the first identifier.

[0054] Optionally, the first attribute includes at least one of the following: collection time and collection location.

[0055] Optionally, the first wireless data and the second wireless data include at least one of the following: uplink channel information, downlink channel information, location information, transmit power, sensor data, moving speed, moving direction, and antenna parameters.

[0056] Optionally, the first wireless data is obtained based on the second wireless data.

[0057] Optionally, the second wireless data includes wireless channel information for multiple locations, the first request includes a first location, the multiple locations do not include the first location, and the first wireless data is the wireless channel information of the first location.

[0058] Optionally, the second wireless data includes wireless channel information at multiple times, the first request includes a first time, the multiple times do not include the first time, and the first wireless data is the wireless channel information at the first time.

[0059] Optionally, the first wireless data is obtained based on the second wireless data, including: the first wireless data is obtained by performing at least one of the following operations based on the second wireless data: interpolation calculation, averaging, and prediction based on an artificial intelligence model.

[0060] Optionally, the first wireless data is collected by a second or third device, and the second wireless data is collected by the device.

[0061] Wherein, when the above-mentioned communication device is used to execute the method in the second aspect or any of the embodiments of the second aspect, the transceiver unit is used to receive a first request, the first request being used to request the acquisition of first wireless data; based on the first request, acquire the first wireless data; and the transceiver unit is further used to send a first response, the first response including the first wireless data, the first wireless data being associated with second wireless data.

[0062] Optionally, the first request includes at least one of the following: the second wireless data, the first attribute, and the first identifier.

[0063] Optionally, the first attribute includes at least one of the following: collection time and collection location.

[0064] Optionally, the first wireless data and the second wireless data include at least one of the following: uplink channel information, downlink channel information, location information, transmit power, sensor data, moving speed, moving direction, and antenna parameters.

[0065] Optionally, the first wireless data is obtained based on the second wireless data.

[0066] Optionally, the second wireless data includes wireless channel information for multiple locations, the first request includes a first location, the multiple locations do not include the first location, and the first wireless data is the wireless channel information of the first location.

[0067] Optionally, the second wireless data includes wireless channel information at multiple times, the first request includes a first time, the multiple times do not include the first time, and the first wireless data is the wireless channel information at the first time.

[0068] Optionally, the first wireless data is obtained based on the second wireless data, including: the first wireless data is obtained by performing at least one of the following operations based on the second wireless data: interpolation calculation, averaging, and prediction based on an artificial intelligence model.

[0069] Optionally, the first wireless data is collected by the device or a third device, and the second wireless data is collected by the first device.

[0070] Optionally, the transceiver unit is further configured to transmit first information, the first information including the first wireless data and the second wireless data.

[0071] In another possible implementation, the communication device in the third aspect described above includes processing circuitry coupled to a memory; the processing circuitry is configured to enable the device to perform the corresponding functions in the communication method described above. The memory is used to couple with the processing circuitry and stores necessary programs (instructions) and / or data for the device. Optionally, the communication device may further include a communication interface for enabling communication between the device and other network elements. Optionally, the memory may be located inside or outside the communication device. Exemplarily, the processing circuitry may be a processor or circuitry within a processor for processing.

[0072] When the communication device in the third aspect above is a chip, the transmitting 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 transmitting unit can be a transmitter or a receiver; the receiving unit can be a receiver or a receiver.

[0073] Fourthly, a computer-readable storage medium is provided, wherein a computer program or instructions are stored therein, and when the computer program or instructions are executed, the method described in the first aspect, the second aspect, or any one of the first aspect and the second aspect is implemented.

[0074] Fifthly, a computer program product containing instructions is provided, which, when executed on a communication device, causes the communication device to perform the method described in the first aspect, the second aspect, or any one of the first aspect and the second aspect. Attached Figure Description

[0075] Figure 1 A simplified schematic diagram of a wireless communication system provided in an embodiment of this application;

[0076] Figure 2 This is a schematic diagram of the architecture of another communication system provided in an embodiment of this application;

[0077] Figures 3A to 3D This diagram illustrates the configuration of near-real-time RICs and non-real-time RICs in a network architecture.

[0078] Figure 4 A schematic diagram of a neuron structure;

[0079] Figure 5 This is a schematic diagram of a neural network;

[0080] Figure 6 This is a schematic diagram of a wireless air interface AI / ML framework;

[0081] Figure 7 A flowchart illustrating a communication method provided in an embodiment of this application;

[0082] Figure 8 This is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0083] Figure 9 This is a schematic diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0084] The embodiments of this application are described below with reference to the accompanying drawings.

[0085] The technology provided in this application can be applied to various communication systems, for example, the communication system can be a fourth-generation (4G) communication system. th Generation 4G) communication systems (such as Long Term Evolution (LTE) systems), 5G (5G) th This refers to various communication systems, including generational (5G) communication systems, worldwide interoperability for microwave access (WiMAX), wireless local area network (WLAN) systems, satellite communication systems, integrated systems of multiple systems, and future communication systems. Among these, 5G communication systems can also be called new radio (NR) systems.

[0086] In a communication system, a network element can send signals to or receive signals from another network element. These signals can include information, signaling, or data. The term "network element" can also be replaced by an entity, network entity, device, terminal device, communication module, node, communication node, etc. This application uses a network element as an example for description. For instance, a communication system may include at least one terminal device and at least one network device. The network device can send downlink signals to the terminal device, and / or the terminal device can send uplink signals to the network device. Furthermore, it is understood that if the communication system includes multiple terminal devices, these terminal devices can also exchange signals; that is, both the signal-transmitting network element and the signal-receiving network element can be terminal devices.

[0087] See Figure 1 , Figure 1 This is a simplified schematic diagram of a wireless communication system provided in an embodiment of this application. Figure 1 As shown, the wireless communication system includes a wireless access network 100. The wireless access network 100 can be a future wireless access network or a traditional (e.g., 5G, 4G) wireless access network. One or more terminal devices (120a-120j, collectively referred to as 120) can be interconnected or connected to one or more network devices (110a, 110b, collectively referred to as 110) within the wireless access network 100. Optionally, Figure 1 This is just an illustration; the wireless communication system may also include other devices, such as core network equipment, wireless relay equipment, and / or wireless backhaul equipment. Figure 1 It is not shown in the middle.

[0088] Optionally, in practical applications, the wireless communication system may include multiple network devices (also known as access network devices) and multiple terminal devices simultaneously. A network device can serve one or more terminal devices simultaneously. A terminal device can also access one or more network devices simultaneously. This application embodiment does not limit the number of terminal devices and network devices included in the wireless communication system.

[0089] In this context, a network device can be an entity on the network side used to transmit or receive signals. A network device can also be an access device that allows terminal devices to wirelessly connect to the wireless communication system; for example, a network device can be a base station. Base stations can broadly encompass various names listed below, or be interchangeable with them, such as: radio access network (RAN) node, Node B, evolved Node B (eNB), next generation Node B (gNB), network equipment in open radio access network (O-RAN), relay station, access point, transmit / receive point (TRP), transmitting point (TP), master eNB (MeNB), secondary eNB (SeNB), multi-standard radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, building baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), centralized unit (CU), and distributed unit (CU). Network equipment includes units (DU), radio units (RU), centralized unit control plane (CU-CP) nodes, centralized unit user plane (CU-UP) nodes, positioning nodes, RAN intelligent controllers (RIC), etc. Base stations can be macro base stations, micro base stations, relay nodes, donor nodes, or similar entities, or combinations thereof. Network equipment can also refer to communication modules, modems, or chips installed within the aforementioned equipment or devices. Network equipment can also be mobile switching centers and equipment that performs base station functions in device-to-device (D2D), vehicle-to-everything (V2X), and machine-to-machine (M2M) communications; network-side equipment in future communication networks; and equipment that performs base station functions in future communication systems. Network equipment can support networks using the same or different access technologies.The embodiments of this application do not limit the specific technology or device form used in the network device.

[0090] Network devices can be fixed or mobile. For example, base stations 110a and 110b are stationary and are responsible for wireless transmission and reception from one or more cells of terminal device 120. Figure 1 The helicopter or drone 120i shown can be configured to act as a mobile base station, and one or more cells can move depending on the location of the mobile base station 120i. In other examples, the helicopter or drone (120i) can be configured as a terminal device to communicate with base station 110b.

[0091] In this application, the communication device used to implement the above-mentioned network access function can be a network device, a network device with partial network access function, or a device capable of supporting the implementation of network access function, such as a chip system, hardware circuit, software module, or hardware circuit plus software module. This device can be installed in or used in conjunction with a network device. In the method of this application, a network device is used as an example to illustrate the communication device used to implement the network device function.

[0092] A terminal device can be a user-side entity used to receive or transmit signals, such as a mobile phone. Terminal devices can be used to connect people, things, and machines. Terminal devices can communicate with one or more core networks via network devices. Terminal devices include handheld devices with wireless connectivity, other processing devices connected to a wireless modem, or vehicle-mounted devices. Terminal devices can be portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile devices. Terminal devices 120 can be widely used in various scenarios, such as cellular communication, D2D, V2X, point-to-point (P2P), machine-to-machine (M2M), machine-type communication (MTC), Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc.Examples of terminal equipment 120 include: 3GPP standard user equipment (UE), fixed equipment, mobile equipment, handheld devices, wearable devices, cellular phones, smartphones, session-initiated protocol (SIP) phones, laptops, personal computers, smart books, vehicles, satellites, global positioning system (GPS) devices, target tracking devices, drones, helicopters, aircraft, ships, remote control devices, smart home devices, industrial equipment, personal communication service (PCS) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), wireless network cameras, tablets, handheld computers, mobile internet devices (MIDs), wearable devices such as smartwatches, VR devices, AR devices, wireless terminals in industrial control, terminals in vehicle-to-everything (V2X) systems, wireless terminals in self-driving systems, wireless terminals in smart grids, wireless terminals in transportation safety, and smart city applications. Wireless terminals in various scenarios include smart gas pumps, high-speed rail terminals, and smart home terminals such as smart speakers, smart coffee machines, and smart printers. Terminal device 120 can be a wireless device in these scenarios or a device for installing on a wireless device, such as a communication module, modem, or chip. Terminal device can also be called a terminal, terminal equipment, UE, mobile station (MS), mobile terminal (MT), etc. Terminal device can also be a terminal device in future wireless communication systems. Terminal device can be used in dedicated network equipment or general-purpose equipment. The embodiments of this application do not limit the specific technology or device form used in the terminal device.

[0093] Optionally, the terminal device can be used to act as a base station. For example, the UE can act as a scheduling entity, providing sidelink signaling between UEs in V2X, D2D, or P2P, etc. Figure 1 As shown, cellular phone 120a and car 120b communicate with each other using a side link signal. Cellular phone 120a communicates with smart home device 120e without needing to relay communication signals through base station 110b.

[0094] In this application, the communication device used to implement the functions of the terminal device can be a terminal device, a terminal device having some of the functions of the aforementioned terminal device, or a device capable of supporting the implementation of the functions of the aforementioned terminal device, such as a chip system. This device can be installed in the terminal device or used in conjunction with the terminal device. In this application, the chip system can be composed of chips or include chips and other discrete components. The technical solutions provided in this application are described using the example of a terminal device or UE as the communication device.

[0095] Optionally, wireless communication systems typically consist of cells. Base stations manage the cells and provide communication services to multiple mobile stations (MS) within them. A base station includes a baseband unit (BBU) and a remote radio unit (RRU). The BBU and RRU can be located in different places; for example, the RRU can be deployed remotely to a high-traffic area, while the BBU is located in a central equipment room. Alternatively, the BBU and RRU can be located in the same equipment room. The BBU and RRU can also be different components within the same rack. Optionally, a cell can correspond to one carrier or a member carrier.

[0096] In some deployments, the network devices mentioned in the embodiments of this application may be devices including CUs, DUs, or devices including both CUs and DUs, or devices with control plane CU nodes (centralized unit-control plane (CU-CP)) and user plane CU nodes (centralized unit-user plane (CU-UP)) and DU nodes. For example, the network devices may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

[0097] In some deployments, multiple RAN nodes assist terminal devices in achieving wireless access, with different RAN nodes each implementing some of the base station's functions. For example, RAN nodes can be CUs, DUs, CU-CPs, CU-UPs, or RUs. CUs and DUs can be configured separately or included in the same network element, such as a BBU. RUs can be included in radio frequency equipment or radio frequency units, such as RRUs, AAUs, or RRHs.

[0098] RAN nodes can support one or more types of fronthaul interfaces. Different fronthaul interfaces correspond to DUs and RUs with different functions. If the fronthaul interface between the DU and RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and RU is another type of interface, relative to CPRI, some downlink and / or uplink baseband functions, such as, for downlink, precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix addition, are moved from the DU to the RU; for uplink, digital beamforming (BF), or one or more of fast Fourier transform (FFT) / cyclic prefix removal, are moved from the DU to the RU. In one possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the segmentation between DU and RU differs, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, and F.

[0099] Taking eCPRI Cat A as an example, for downlink transmission, layer mapping is used as the dividing line. DU is configured to implement one or more functions preceding layer mapping (i.e., coding, rate matching, scrambling, modulation, and layer mapping), while other functions following layer mapping (e.g., resource element (RE) mapping, digital beamforming (BF), or one or more inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition) are moved to RU. For uplink transmission, de-RE mapping is used as the dividing line. DU is configured to implement one or more functions preceding de-mapping (i.e., decoding, rate matching de-matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and de-RE mapping), while other functions following de-mapping (e.g., digital BF or FFT / CP removal) are moved to RU. It is understandable that the functional descriptions of the DU and RU corresponding to various types of eCPRI can be found in the eCPRI protocol, and will not be elaborated here.

[0100] In one possible design, the processing unit in the BBU used to implement baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU / AAU / RRH used to implement baseband functions is called the baseband low (BBL) unit.

[0101] In this embodiment, the apparatus for implementing the functions of a network device can be a network device itself; it can also be an apparatus capable of supporting the network device in implementing those functions, such as a chip system, hardware circuit, software module, or a hardware circuit plus a software module. This apparatus can be installed in the network device or used in conjunction with the network device. In this embodiment, the example of a network device being used to implement the functions of a network device is provided only and does not constitute a limitation on the solutions described in this embodiment.

[0102] It is understood that this application can be applied between network devices and terminal devices.

[0103] Protocol layer structure between network devices and terminal devices:

[0104] Communication between network devices and terminal devices follows a specific protocol layer structure. This protocol layer structure can include a control plane protocol layer structure and a user plane protocol layer structure. For example, the control plane protocol layer structure can include the functions of protocol layers such as the radio resource control (RRC) layer, the packet data convergence protocol (PDCP) layer, the radio link control (RLC) layer, the medium access control (MAC) layer, and the physical layer. Similarly, the user plane protocol layer structure can include the functions of protocol layers such as the PDCP layer, the RLC layer, the MAC layer, and the physical layer. In one possible implementation, a service data adaptation protocol (SDAP) layer can be included above the PDCP layer.

[0105] Optionally, the protocol layer structure between network devices and terminal devices can also include an artificial intelligence (AI) layer for transmitting AI-related data. Introducing AI / machine learning (ML) technology into the wireless air interface can be used for wireless channel information compression and reconstruction, beam management, and positioning enhancement. Neural network-based AI / ML improves the performance of wireless tasks through data-driven training.

[0106] Taking data transmission between network devices and terminal devices as an example, data transmission needs to pass through user plane protocol layers, such as the SDAP layer, PDCP layer, RLC layer, MAC layer, and physical layer. The SDAP layer, PDCP layer, RLC layer, MAC layer, and physical layer can also be collectively referred to as the access layer. Based on the direction of data transmission, it is divided into sending and receiving; each of these layers is further divided into a sending part and a receiving part. Taking downlink data transmission as an example, after the PDCP layer obtains data from the upper layer, it transmits the data to the RLC layer and MAC layer. The MAC layer then generates a transport block, and finally, it is wirelessly transmitted through the physical layer. Data is encapsulated in corresponding ways at each layer. For example, data received by a layer from the upper layer is considered a Service Data Unit (SDU) of that layer. After encapsulation by that layer, it becomes a Protocol Data Unit (PDU) and is then passed to the next layer.

[0107] For example, the terminal device may also have an application layer and a non-access layer. The application layer can be used to provide services to applications installed on the terminal device. For instance, downlink data received by the terminal device can be sequentially transmitted from the physical layer to the application layer, and then provided to the application by the application layer. Alternatively, the application layer can acquire data generated by the application and sequentially transmit the data to the physical layer for transmission to other communication devices. The non-access layer can be used to forward user data, such as forwarding uplink data received from the application layer to the SDAP layer, or forwarding downlink data received from the SDAP layer to the application layer.

[0108] It should be understood that Figure 1 The number and type of devices in the communication system shown are for illustrative purposes only. This application is not limited to this. In actual applications, the communication system may include more terminal devices, more network devices, and other network elements, such as core network devices and / or network elements used to implement artificial intelligence functions.

[0109] It is understandable that all or part of the functions implemented by one or more of the terminal devices, network devices, core network devices, or network elements used to implement artificial intelligence functions can be virtualized, that is, implemented through one or more of dedicated or general-purpose processors and corresponding software modules. Among these, the transmit and receive functions of the terminal devices and network devices, which involve air interface transmission, can be implemented in hardware. Core network devices, such as operation administration and maintenance (OAM) network elements, can also be virtualized. Optionally, one or more of the functions of the virtualized terminal devices, network devices, core network devices, or network elements used to implement artificial intelligence functions can be implemented by cloud devices, such as cloud devices in over-the-top (OTT) systems.

[0110] To support AI technology in wireless networks, AI nodes may also be introduced into the network.

[0111] Optionally, the AI ​​node can be deployed in one or more of the following locations within the communication system: network devices, terminal devices, or core network devices, etc. Alternatively, the AI ​​node can be deployed independently, for example, in a location other than any of the aforementioned devices, such as in the host or cloud server of an over-the-top (OTT) system. The AI ​​node can communicate with other devices in the communication system, which can be, for example, one or more of the following: network devices, terminal devices, or core network elements, etc.

[0112] It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, these nodes can be divided based on function, such as different AI nodes being responsible for different functions.

[0113] It can also be understood that AI nodes can be independent devices, integrated into the same device to implement different functions, or they can be network components in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of the AI ​​nodes described above. For example, see [link to example]. Figure 2 This is a schematic diagram of another communication system architecture provided in an embodiment of this application. The communication system includes network device 210, terminal devices 220 and 230, and also introduces AI network element 240.

[0114] AI nodes can be AI network elements or AI modules.

[0115] These network element nodes, such as core network equipment, access network nodes (RAN nodes), terminals, or one or more devices in OAM, are equipped with one or more AI modules. The access network node can be a single RAN node or can include multiple RAN nodes, for example, including CU and DU. The CU and / or DU can also be equipped with one or more AI modules. Optionally, the CU can be further divided into CU-CP and CU-UP. One or more AI models are configured in the CU-CP and / or CU-UP.

[0116] AI modules are used to implement corresponding AI functions. AI modules deployed in different network elements can be the same or different. Depending on the parameter configuration, the AI ​​module can implement different functions. The AI ​​module model can be configured based on one or more of the following parameters: structural parameters (e.g., at least one of the following: number of neural network layers, neural network width, inter-layer connections, neuron weights, neuron activation function, or biases in the activation function), input parameters (e.g., the type and / or dimension of the input parameters), or output parameters (e.g., the type and / or dimension of the output parameters). The biases in the activation function can also be referred to as the neural network biases.

[0117] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or they can be deployed on the same node or device.

[0118] The communication system includes a RAN intelligent controller. For example, this RIC can be the aforementioned AI module, used to implement AI-related functions. This RIC includes near-real-time RICs (near-RT RICs) and non-real-time RICs (non-RT RICs). Non-real-time RICs primarily process non-real-time information, such as data that is not sensitive to latency, with latency in the order of seconds. Near-real-time RICs primarily process near-real-time information, such as data that is relatively sensitive to latency, with latency in the order of tens of milliseconds.

[0119] Near real-time (NRT) RICs are used for model training and inference. For example, they are used to train AI models and then use those models for inference. NRT RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data. Optionally, the NRT RIC can deliver inference results to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, the NRT RIC delivers inference results to a DU, which then forwards them to an RU.

[0120] Non-real-time RICs are also used for model training and inference. For example, they can be used to train AI models and then use those models for inference. Non-real-time RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs; for example, a non-real-time RIC delivers inference results to a DU, which then forwards them to an RU.

[0121] Near real-time RICs and non-real-time RICs can also be configured as separate network elements. Alternatively, near real-time RICs and non-real-time RICs can also be part of other devices. For example, near real-time RICs can be set in RAN nodes (e.g., CU, DU), while non-real-time RICs can be set in OAM, cloud servers, core network devices, or other network devices.

[0122] For example, the configuration of near real-time RICs and non-real-time RICs in the network architecture can be as follows: Figures 3A to 3D As shown:

[0123] like Figure 3A As shown in (a), in the first possible implementation, the network device includes a near real-time RIC module for model learning and / or inference.

[0124] like Figure 3A As shown in (b), in the second possible implementation, a non-real-time RIC may be included outside the network device in the communication system. Optionally, the non-real-time RIC may be located in the OAM or in the core network device.

[0125] like Figure 3A As shown in (c), in the third possible implementation, the network device includes a near real-time RIC, and outside the network device, it also includes a non-real-time RIC. Optionally, the non-real-time RIC can be located in the OAM or in the core network device.

[0126] relatively Figure 3A (c) in the middle Figure 3B The CU is separated into CU-CP and CU-UP. Near real-time RIC and non-real-time RIC settings are... Figure 3A (c) is the same.

[0127] like Figure 3C As shown, optionally, the network device includes one or more AI entities, the functions of which are similar to the near real-time RIC described above. Optionally, the OAM includes one or more AI entities, the functions of which are similar to the non-real-time RIC described above. Optionally, the core network device includes one or more AI entities, the functions of which are similar to the non-real-time RIC described above. When both the OAM and core network devices include AI entities, the models trained by their respective AI entities are different, and / or the models used for inference are different. In this application, different models may include at least one of the following differences: model structural parameters (e.g., the number of layers, and / or weights), model input parameters, or model output parameters.

[0128] relatively Figure 3C , Figure 3D The network devices are separated into CU and DU. Optionally, the CU may include AI entities, whose functions are similar to the near real-time RIC described above. Optionally, the DU may include AI entities, whose functions are similar to the near real-time RIC described above. When both CU and DU include AI entities, the models trained on their respective AI entities are different, and / or the models used for inference are different. Optionally, further... Figure 3D The CU is split into CU-CP and CU-UP. Optionally, one or more AI models can be deployed in CU-CP. And / or, one or more AI models can be deployed in CU-UP. Optionally, Figure 3C or Figure 3D In this context, the OAM of network devices and the OAM of core network devices can be deployed separately and independently.

[0129] To facilitate understanding, the AI ​​technologies involved in this application will be introduced below. It should be understood that this introduction is not intended to limit this application.

[0130] (1) AI Model:

[0131] AI refers to the intelligence exhibited by machines created by humans. Generally, artificial intelligence refers to the technology of using ordinary computer programs to represent human intelligence. Artificial intelligence can be defined as a machine or computer that imitates humans and possesses cognitive functions related to human thinking, such as learning and problem-solving. Artificial intelligence can learn from past experiences, make rational decisions, and respond quickly. The goal of artificial intelligence is to understand intelligence by constructing computer programs that demonstrate symbolic reasoning or logical reasoning.

[0132] Machine learning (ML) is a pathway to achieving artificial intelligence, that is, using machine learning as a means to solve problems in artificial intelligence. Machine learning theory primarily involves designing and analyzing algorithms that allow computers to automatically "learn." Machine learning algorithms are a class of algorithms that automatically analyze data to obtain patterns and use these patterns to predict unknown data. Because learning algorithms involve a large amount of statistical theory, machine learning is closely related to inferential statistics and is also known as statistical learning theory.

[0133] Machine learning can be divided into supervised learning, unsupervised learning, and reinforcement learning.

[0134] Supervised learning, based on collected sample values ​​and labels, uses machine learning algorithms to learn the mapping relationship between sample values ​​and labels, and expresses this learned mapping relationship using a machine learning model. The process of training the 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 corresponding real constellation point is the label. Machine learning aims to learn the mapping relationship between samples and labels through training, that is, to enable the machine learning model to learn a signal detector. During training, the model parameters are optimized by calculating the error between the model's predicted values ​​and the real labels. Once the mapping relationship is learned, it can be used to predict the label of each new sample. The mapping relationship learned in supervised learning can include linear mappings and nonlinear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0135] Unsupervised learning relies solely on collected sample values, using algorithms to discover inherent patterns within the samples. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals; that is, the model learns the mapping relationship from sample to sample, which is called self-supervised learning. During training, model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used for signal compression and decompression recovery applications; common algorithms include autoencoders and generative adversarial networks.

[0136] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels. The algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and the optimal decision action. However, because the label of the "correct action" cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action." Reinforcement learning training is achieved through iterative interaction with the environment.

[0137] An AI model is an algorithm or computer program that enables AI functionality. It represents the specific implementation of AI technology and characterizes the mapping relationship between the model's input and output. AI models can be neural networks, linear regression models, decision tree models, support vector machines (SVM), Bayesian networks, Q-learning models, or other machine learning models.

[0138] (2) Deep neural network (DNN):

[0139] Deep neural networks (DNNs) are a specific implementation of AI or machine learning techniques. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings. Traditional communication systems rely on extensive expert knowledge to design communication modules, while DNN-based deep learning communication systems can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.

[0140] The idea behind DNNs (Dual Neural Networks) originates from the neuronal structure of the brain. For example, each neuron performs a weighted summation of its input values ​​and outputs the result through an activation function. Figure 4The diagram shown is a schematic representation of a neuron structure. Assume the neuron's input is x = [x0, x1, ..., x...]. n The weights corresponding to each input are w = [w0, w1, ..., w]. n ], where w i As x i The weights are used to assign weights to x. i Weighting is applied. The bias for the weighted summation of the input values ​​is, for example, b. Activation functions can take many forms. Assuming a neuron's activation function is y = f(z) = max(0, z), then the neuron's output is:

[0141] For example, if the activation function of a neuron is y = f(z) = z, then the output of that neuron is:

[0142] Among them, b and w i x i The activation function can take various values, such as decimals, integers (e.g., 0, positive integers, or negative integers), or complex numbers. Different neurons in a neural network can have the same or different activation functions.

[0143] Neural networks typically consist of multiple layers, each containing one or more neurons. Increasing the depth and / or width of a neural network enhances its expressive power, providing more robust information extraction and abstract modeling capabilities for complex systems. The depth of a neural network refers to the number of layers it comprises, while the number of neurons in each layer is called its width. In one implementation, the neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the results to the output layer, which then outputs the neural network's output. In another implementation, the neural network includes an input layer, hidden layers, and an output layer, as described in [reference needed]. Figure 5 This is a schematic diagram of a neural network. The input layer of the neural network processes the received input information through neurons and passes the processing results to the hidden layers. The hidden layers perform calculations on the received processing results and obtain their own results. These hidden layers then pass their calculation results to the output layer or adjacent hidden layers, and finally, the output layer obtains the output of the neural network. A neural network can include one hidden layer or multiple hidden layers connected sequentially; there is no limitation on this.

[0144] Depending on how the network is constructed, DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Figure 5 The image shows an FNN network, characterized by complete pairwise connections between neurons in adjacent layers. This makes FNNs typically require a large amount of storage space and result in high computational complexity.

[0145] CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data and image data can be considered grid-like data. CNNs do not use all the input information at once for computation; instead, they use a fixed-size window to extract a portion of the information for convolution operations, which significantly reduces the computational cost of model parameters. Furthermore, depending on the type of information extracted by the window (such as people and objects in an image representing different types of information), each window can use different convolution kernels, allowing CNNs to better extract features from the input data.

[0146] Recurrent Neural Networks (RNNs) are a type of distributed neural network (DNN) that utilizes feedback time-series information. Their input includes the current input value and their own output value from the previous time step. RNNs are well-suited for acquiring temporally correlated sequence features, and are particularly applicable to applications such as speech recognition and channel coding / decoding.

[0147] The FNN, CNN, and RNN mentioned above are common neural network structures, all built upon neurons. As introduced above, each neuron performs a weighted summation operation on its input values, and the result is passed through a nonlinear function to produce the output. We call the weights of the weighted summation operation and the nonlinear function in the neural network the parameters of the neural network. Taking a neuron with max{0, w} as the nonlinear function as an example, we perform... The parameters of the operated neuron are weights w = [w0, ..., w0]. n The weighted summation bias is b, and the nonlinear function is max{0, x}. The parameters of all neurons in a neural network constitute the parameters of that neural network.

[0148] (3) Training dataset and inference data:

[0149] Neural network-based AI / ML improves the performance of wireless tasks through data-driven training. Training datasets are used to train AI models and can include the AI ​​model's input, or both the input and target output. A training dataset includes one or more training data points, which can be training samples input to the AI ​​model or the model's target output. The target output can also be referred to as the label or labeled sample. The training dataset is a crucial part of machine learning; model training essentially involves learning certain features from the training data to make the AI ​​model's output as close as possible to the target output, minimizing the difference between the AI ​​model's output and the target output. The composition and selection of the training dataset can, to a certain extent, determine the performance of the trained AI model.

[0150] Furthermore, a loss function can be defined during the training process of an AI model (such as a neural network). The loss function describes the difference or discrepancy between the output value of the AI ​​model and the target output value. This application does not limit the specific form of the loss function. The training process of an AI model involves adjusting the model parameters of the AI ​​model so that the value of the loss function is less than a threshold, or so that the value of the loss function meets the target requirements. For example, if the AI ​​model is a neural network, adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers in the neural network, its width, the weights of the neurons, or the parameters in the activation function of the neurons.

[0151] Inference data can be used as input to a trained AI model for inference. During the model's inference process, the inference data is input into the AI ​​model, and the corresponding output, which is the inference result, is obtained.

[0152] (4) AI model design:

[0153] The design of an AI model mainly includes data collection (e.g., collecting training and / or inference data), model training, and model inference. It can also further include the application of the inference results. See also Figure 6This diagram illustrates a wireless air interface AI / ML framework. The AI / ML system includes data collection, model training, model management / performance monitoring, model inference, model storage, and inference control (including activation, deactivation, rollback, switching, and selection). In the data collection stage, a data source provides training and inference datasets. In the model training stage, the AI ​​model is obtained by analyzing or training the training data provided by the data source. The AI ​​model represents the mapping relationship between the model's input and output. Learning the AI ​​model through model training nodes is equivalent to learning the mapping relationship between the model's input and output using the training data. In the model inference stage, the AI ​​model trained in the model training stage is used to perform inference based on the inference data provided by the data source, obtaining the inference result. This stage can also be understood as: inputting inference data into the AI ​​model, obtaining the output through the AI ​​model, which is the inference result. This inference result can indicate the configuration parameters used (executed) by the execution object, and / or the operations performed by the execution object. In the application phase of inference results, the inference results are published. For example, the inference results can be uniformly planned by the execution entity (actor), which can send the inference results to one or more execution objects (e.g., core network devices, network devices, or terminal devices) for execution. Furthermore, the execution entity can also provide feedback on model performance to the data source, facilitating subsequent model updates and training. Model management monitors model performance based on collected monitoring data and performs actions such as model / function activation / deactivation, switching, selection, and rollback based on performance. The data collected by the data collection unit is used in different AI / ML stages, such as training data for AI / ML training.

[0154] It is understandable that communication systems can include network elements with artificial intelligence (AI) capabilities. The AI ​​model design-related steps described above can be performed by one or more network elements with AI capabilities. In one possible design, AI functions (such as AI modules or AI entities) can be configured within existing network elements in the communication system to implement AI-related operations, such as AI model training and / or inference. For example, these existing network elements could be network devices (such as gNBs), terminal devices, core network devices, or network management systems. The network management system can divide network management work into three categories based on the actual needs of the operator's network operation: operation, administration, and maintenance. The network management system can also be called an OAM (Operation and Administration) network element, or simply OAM. Operation mainly involves the analysis, prediction, planning, and configuration of daily network and service operations; maintenance mainly involves daily operational activities such as testing and fault management of the network and its services. The network management system can detect network operating status, optimize network connectivity and performance, improve network stability, and reduce network maintenance costs. Alternatively, in another possible design, independent network elements can be introduced into the communication system to perform AI-related operations, such as training AI models. This independent network element can be called an AI network element or an AI node, etc., and this application does not limit the name. The AI ​​network element can be directly connected to network devices in the communication system, or it can be indirectly connected to network devices through third-party devices. The third-party devices can be core network elements such as authentication management function (AMF) network elements and user plane function (UPF) network elements, OAM, cloud servers, or other network elements, without limitation.

[0155] In this application, a model can infer one parameter or multiple parameters. The training processes of different models can be deployed on different devices or nodes, or on the same device or node. The inference processes of different models can be deployed on different devices or nodes, or on the same device or node.

[0156] The model parameters can include one or more of the following: model structure parameters (e.g., number of layers, and / or weights), model input parameters (e.g., input dimension, number of input ports), or model output parameters (e.g., output dimension, number of output ports). The input dimension refers to the size of an input data set; for example, if the input data is a sequence, the corresponding input dimension indicates the length of the sequence. The number of input ports refers to the quantity of input data. Similarly, the output dimension refers to the size of an output data set; for example, if the output data is a sequence, the corresponding output dimension indicates the length of the sequence. The number of output ports refers to the quantity of output data.

[0157] The development of AI / ML technology has changed AI in fields such as natural language processing: pre-trained large models can be applied to a variety of downstream tasks through pre-training on diverse and massive amounts of data.

[0158] Current AI models are designed for specific wireless AI tasks. For example, they can predict path loss between transmitting and receiving devices based on their location information; predict communication rates based on their location information; and predict beam direction based on time information. Data is collected for training and subsequent model management for each model used in different tasks.

[0159] AI-based air interface or RAN systems require different types of wireless data at various stages of the AI ​​model. For example, model training needs a large amount of diverse data, model inference needs real-time data, and model monitoring needs a large amount of near real-time data. For wireless or air interface tasks, wireless data is collected through the wireless air interface, which may involve the network sending measurement signals, the UE performing measurements and reporting, or the UE sending reference signals and the network performing measurements. The collected data undergoes preprocessing and other operations before being used for AI model training, monitoring, inference, or analysis.

[0160] Wireless data used for operations such as AI model training or monitoring typically consists of data samples composed of wireless data pairs that serve as model inputs and outputs. Existing technologies collect wireless data at the UE or BS and filter it based on the quality of the collected data, retaining only the data that meets the quality requirements.

[0161] Wireless data samples may consist of different types of wireless data from different devices. Due to issues such as UE or BS acquisition conditions, privacy, and real-time requirements, the UE / BS may not be able to obtain complete wireless data samples, meaning that some data that makes up the wireless data sample may be missing.

[0162] Therefore, this application provides a communication scheme in which a first device can obtain first wireless data from a second device to complete its own missing wireless data, thereby improving the efficiency of wireless data acquisition.

[0163] like Figure 7 The diagram shown is a flowchart illustrating a communication method provided in an embodiment of this application. Exemplarily, the method may include the following steps:

[0164] S701. The first device sends a first request to the second device.

[0165] For example, the first device can be a terminal device and the second device can be a network device; or, both the first and second devices can be network devices; or, both the first and second devices can be terminal devices; or, the first device can be a network device and the second device can be a terminal device. This application does not limit the device types of the first and second devices. Here, taking the second device (network device) providing wireless data association / completion functionality as an example, the network device can aggregate different types of wireless data collected by different devices (e.g., the first device and the third device).

[0166] For example, the first device may be a first device or a communication module within the first device, or a circuit or chip applied to the first device (such as a modem chip, or a SoC chip or SIP chip containing a modem core). Taking the application of this method to the first device as an example. The second device may be a second device or a communication module within the second device, or a circuit or chip applied to the second device (such as a modem chip, or a SoC chip or SIP chip containing a modem core). Taking the application of this method to the second device as an example.

[0167] The first request is used to request the acquisition of first wireless data. For example, the first request is used to request the association / completeness of second wireless data; therefore, the first request can also be called a wireless data association / completeness request. For example, the first wireless data is collected by a second or third device, and the second wireless data is collected by the first device.

[0168] For example, the first request includes at least one of the following: second wireless data, a first attribute, and a first identifier. The first device can send the second wireless data to the second device so that the second device can locate the first wireless data associated with the second wireless data. The first attribute includes at least one of the following: collection time and collection location. For example, the first attribute may include the collection time and collection location of the second wireless data, and the second device can obtain the first wireless data collected at the same collection time and collection location based on the collection time and collection location of the second wireless data. Another example is that the first attribute may include the collection time and collection location of the first wireless data. The second device uses a first identifier to identify the attributes of each wireless data (or data sample): data sample, first attribute, and identifier of the data sample. The second device can obtain the first wireless data corresponding to the first identifier based on the first identifier.

[0169] Furthermore, prior to step S701, the first device may also interact with the second device on related capabilities, for example, the second device may send or broadcast the current wireless data association / completion capability to the first device.

[0170] S702. After receiving the first request, the second device acquires the first wireless data based on the first request.

[0171] The second device identifies all the collected data samples. The aforementioned first request includes a first identifier, and the second device can obtain the first wireless data corresponding to the first identifier based on the first identifier.

[0172] The aforementioned first request may also include a first attribute, which the second device can use to obtain the first wireless data. For example, the first attribute may include the collection time and location of the second wireless data, and the second device can obtain the first wireless data collected at the same collection time and location based on the collection time and location of the second wireless data. Alternatively, the first attribute may include the collection time and location of the first wireless data, and the second device can obtain the first wireless data obtained at that collection time and location.

[0173] The first request may also include second wireless data, and the second device may also locate the first wireless data associated with the second wireless data based on the second wireless data.

[0174] For example, the second device may obtain the first wireless data from a local device or a third device. The second device may collect the first wireless data in real time based on a first request, or the first wireless data may have been collected in advance before the execution of this embodiment.

[0175] The first wireless data is associated with the second wireless data.

[0176] In one example, the first wireless data is associated with the second wireless data, which could mean that the second device acquires the first wireless data that is associated with the second wireless data. The first wireless data and the second wireless data can be different types of wireless data or the same type of wireless data. The first wireless data and the second wireless data can include at least one of the following: uplink channel information, downlink channel information, location information, transmit power, sensor data, moving speed, moving direction, and antenna parameters.

[0177] For example, the first wireless data is associated with the second wireless data, including at least one of the following (1) to (5):

[0178] (1) The first radio data is downlink channel information, and the second radio data is uplink channel information. For example, the first device can be a network device, which measures the received uplink reference signal corresponding to timestamp 1, frame number 1, and / or UE location 1 to obtain uplink channel information. The first device can send a first request, which includes a first identifier, which is at least one of the following: timestamp 1, frame number 1, and UE location 1. The second device can be a terminal device or another network device, which obtains downlink channel information corresponding to the first identifier based on the first identifier. The downlink channel information can be obtained by the second device or a third device (e.g., the third device can be another terminal device) by measuring the received downlink reference signal corresponding to timestamp 1, frame number 1, and / or UE location 1. The first device can evaluate uplink and downlink channel prediction by obtaining the uplink and downlink channel information corresponding to timestamp 1, frame number 1, and / or UE location 1.

[0179] (2) The first radio data is uplink channel information, and the second radio data is downlink channel information. For example, the first device can be a terminal device, which measures the downlink reference signal received corresponding to timestamp 1, frame number 1, and / or UE location 1 to obtain downlink channel information. The first device can send a first request, which includes a first identifier, which is at least one of the following: timestamp 1, frame number 1, and UE location 1. The second device can be a network device, which obtains the uplink channel information corresponding to the first identifier based on the first identifier. The uplink channel information can be obtained by the second device or a third device (e.g., the third device can be another network device) by measuring the uplink reference signal received corresponding to timestamp 1, frame number 1, and / or UE location 1. The first device can evaluate uplink and downlink channel prediction by obtaining the uplink and downlink channel information corresponding to timestamp 1, frame number 1, and / or UE location 1.

[0180] (3) The first wireless data is uplink channel information, and the second wireless data is the location information of the first device. The first device can send a first request, which includes a first identifier, which may be timestamp 1. Based on the first identifier, the second device obtains the uplink channel information corresponding to the first identifier. The uplink channel information may be obtained by the second device by measuring the uplink reference signal received at timestamp 1, which may be sent by the first device. After receiving the uplink channel information sent by the second device, the first device can associate the uplink channel information with the location information of the first device to characterize the association between the location of the first device and the uplink channel information of the second device.

[0181] (4) The first wireless data is uplink channel information, and the second wireless data is the transmit power of the first device. The first device may send a first request, which includes a first identifier, which is at least one of the following: timestamp 1, UE location 1. The second device obtains uplink channel information corresponding to the first identifier based on the first identifier. The uplink channel information may be obtained by the second device by measuring the uplink reference signal received corresponding to timestamp 1 and / or UE location 1, and the uplink reference signal may be sent by the first device. After receiving the uplink channel information sent by the second device, the first device may associate the uplink channel information with the transmit power of the first device to characterize the association between the transmit power of the first device and the uplink channel information obtained by the first device based on the uplink reference signal sent by the first device based on the transmit power.

[0182] (5) The first wireless data includes at least one of the following: uplink channel information, downlink channel information, UE location information, UE sensor data, UE movement speed, UE movement direction, and antenna parameters. The second wireless data is the UE location information. The first device can send a first request, which includes a first identifier, which may be timestamp 1. Based on the first identifier, the second device obtains at least one of the following data corresponding to the first identifier: uplink channel information, downlink channel information, UE sensor data, UE movement speed, UE movement direction, and antenna parameters. The uplink channel information may be obtained by the third device measuring the uplink reference signal received at timestamp 1, which may be sent by the first device; the downlink channel information may be obtained by the UE measuring the downlink reference signal received at timestamp 1. After receiving the above at least one data sent by the second device, the first device can associate the at least one data with the UE location information to characterize the association between the UE location information and the uplink / downlink channel information obtained by the UE based on the uplink / downlink reference signals sent / received by the UE based on the location, movement speed, and movement direction.

[0183] The above are examples of the association between first wireless data and second wireless data. It is understood that the association between first wireless data and second wireless data can be more than just the above examples, and this application does not limit the association relationship between first wireless data and second wireless data.

[0184] In another example, the first wireless data is associated with the second wireless data; this could be because the second device completes the second wireless data, meaning the first wireless data is derived from the second wireless data. The first and second wireless data can be of the same type.

[0185] For example, a first device collects second wireless data, which includes wireless channel information for multiple locations. However, the first device is missing wireless channel information for a first location. Therefore, the first device sends a first request to a second device. The first request includes the first location, but the first location is not included in the multiple locations. Based on the collected radio map data, the second device completes the second wireless data, obtains the wireless channel information for the first location, and sends a first response to the first device. The first response includes the first wireless data, which is the wireless channel information for the first location.

[0186] For example, a first device collects second wireless data, which includes wireless channel information at multiple times. However, the first device is missing wireless channel information at the first time. Therefore, the first device sends a first request to the second device. The first request includes the first time, but the first time is not included in the multiple times. Based on the collected time-series channel data, the second device completes the second wireless data, obtains the wireless channel information at the first time, and sends a first response to the first device. The first response includes the first wireless data, which is the wireless channel information at the first time.

[0187] For example, the first wireless data is obtained by performing at least one of the following operations based on the second wireless data: interpolation calculation, averaging, and prediction based on an artificial intelligence model. For instance, the first request includes a first attribute, which includes the collection time and / or collection location. The second device performs interpolation calculation on the wireless data at the collection time and / or collection location, or the wireless data near the collection time and / or collection location, to obtain the wireless data corresponding to the requested collection time and / or collection location. As another example, if the first device lacks wireless channel information at a first location, the second device can acquire wireless data from several locations near the first location and average or calculate the median of the wireless data from these locations as the wireless data for the first location. As yet another example, if the first device lacks wireless channel information at a first time, the second device can acquire wireless data from several times near the first time and average or calculate the median of the wireless data from these times as the wireless data for the first time. As yet another example, if the first device lacks wireless channel information at a first time, the second device can acquire wireless channel information from several historical times prior to the first time and input the wireless channel information from these historical times into a trained AI model to obtain the wireless channel information for the first time.

[0188] S703. The second device sends a first response to the first device.

[0189] After the second device acquires the first wireless data (i.e., successfully acquires the first wireless data), it sends a first response to the first device. This first response is a response to the aforementioned first request. The first response includes the first wireless data. Further, the first response may also include the identifier of the first wireless data, first attributes, etc.

[0190] Furthermore, the first response may include not only the first wireless data, but also indications of the data quality of the first wireless data. For example, it may include differences between the attributes of the first wireless data and the requested first attribute, such as the difference in acquisition time or location, or the confidence level of the model's prediction of the first wireless data. This facilitates the first device's understanding of the data quality of the first wireless data and allows for further processing of the first wireless data.

[0191] For example, if the second device fails to acquire the first wireless data, the first response is used to indicate that the acquisition of the first wireless data has failed.

[0192] Furthermore, the method may also include the following steps:

[0193] S704. The second device sends the first information to the fourth device.

[0194] For example, the fourth device may be an over-the-top (OTT) device.

[0195] After receiving the second wireless data from the first device and acquiring the first wireless data, the second device correlates wireless data from different sources to construct complete wireless data. The second device can send first information to the fourth device. This first information includes the first and second wireless data. Furthermore, the first information also includes other wireless data (e.g., wireless data from other acquisition times or locations). The fourth device is used to acquire and store the complete wireless data.

[0196] Optionally, the second device may not send the aforementioned first information to the fourth device, but instead, the first device and the third device (e.g., the wireless data collector) may send the collected wireless data to the fourth device respectively. Therefore, step S704 is optional. Figure 7 The middle part is indicated by a dashed line.

[0197] For example, before step S703, the fourth device may also send a second request to the second device, which is used to request complete wireless data (e.g., to request first wireless data and second wireless data). After receiving the second request, the second device sends the aforementioned first information to the fourth device. Alternatively, the aforementioned first request may also request the fourth device to send complete wireless data, that is, the first device requests the second device to send the associated / completed wireless data to the fourth device.

[0198] According to a communication method provided in an embodiment of this application, a first device can obtain first wireless data from a second device to complete its missing wireless data, thereby improving the efficiency of wireless data acquisition.

[0199] In this application, the phrase "sending information to... (e.g., a second device)" or the related illustrations in the accompanying drawings can be understood as the destination of the information being the second device. This can include sending information directly or indirectly to the second device. Similarly, the phrase "receiving information from... (e.g., a second device)" or "receiving information from... (e.g., a second device)" or the related illustrations in the accompanying drawings can be understood as the source of the information being the second device. This can include receiving information directly or indirectly from the second device. Information may undergo necessary processing between the source and destination, such as format changes, but the destination can understand the valid information from the source. Similar expressions in this application can be interpreted similarly, and will not be elaborated further here.

[0200] It is understood that this application uses the first device and the second device as examples to illustrate the execution of the interaction, but this application does not limit the execution of the interaction. For example, the first device in the method provided by this application can also be a chip, chip system, or processor applied to the first device, or it can also be a logic node, logic module, or software that can implement all or part of the functions of the first device; the second device in the method provided by this application can also be a chip, chip system, or processor applied to the second device, or it can also be a logic node, logic module, or software that can implement all or part of the functions of the second device.

[0201] It is understood that, in order to achieve the functions in the above embodiments, the first device and the second device include hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0202] Based on the same concept as the above-described communication method, embodiments of this application also provide a communication device for implementing the above-described method. This communication device can be the communication device described in the above method embodiments, or a component that can be used in a communication device. It is understood that, in order to achieve the above-described functions, the communication device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0203] This application embodiment can divide the communication device into functional modules according to the above method embodiment. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing unit. The integrated modules can be implemented in hardware or as software functional modules. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0204] Based on the same concept as the above communication method, this application also provides the following communication device:

[0205] like Figure 8 The diagram shown is a structural schematic of a communication device provided in an embodiment of this application. The communication device 800 includes a transceiver unit 801 and a processing unit 802; wherein:

[0206] When the communication device 800 is used to implement the functions in the first device, the transceiver unit 801 is used to perform... Figure 7 At least one operation performed by the first device in steps S701 and S703 of the illustrated embodiment.

[0207] When the communication device 800 is used to implement the functions in the second device, the transceiver unit 801 is used to perform... Figure 7 In the illustrated embodiment, at least one operation performed by the second device in steps S701 and S703, and the processing unit 802 for performing... Figure 7 Step S702 in the illustrated embodiment.

[0208] For specific implementation details of the transceiver unit 801 and the processing unit 802, please refer to the description in the above method embodiments.

[0209] Furthermore, it should be noted that the aforementioned transceiver unit and / or processing unit can be implemented through virtual modules. For example, the processing unit can be implemented through software functional units or virtual devices, and the transceiver unit can be implemented through software functions or virtual devices. Alternatively, the processing unit or transceiver unit can also be implemented through physical circuits. For example, if the device is implemented using a chip / chip circuit, the transceiver unit can be an input / output circuit and / or a communication interface, performing input operations (corresponding to the aforementioned receiving operation) and output operations (corresponding to the aforementioned sending operation); the processing unit is a processing circuit, such as an integrated processor, microprocessor, or integrated circuit.

[0210] The module division in this application is illustrative and represents only one logical functional division. In actual implementation, other division methods are possible. Furthermore, the functional modules in the various examples of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0211] like Figure 9The diagram shows a structural schematic of another communication device provided in an embodiment of this application. The communication device 900 includes one or more processing circuits 901 (one processing circuit is illustrated in the figure). Optionally, the communication device 900 may also include a memory 903 (shown as a dashed line in the figure). The memory 903 is used to store instructions executed by the processing circuit 901, or to store input data required for the processing circuit 901 to execute instructions, or to store data generated after the processing circuit 901 executes instructions. Optionally, the communication device 900 may also include an interface circuit 902 (shown as a dashed line in the figure), with the processing circuit 901 and the interface circuit 902 coupled to each other. It is understood that the interface circuit 902 can be a transceiver or an input / output interface.

[0212] The processing circuit can be a processor or a circuit within a processor used for processing.

[0213] When the communication device 900 is used to implement the functions in the first device, the interface circuit 902 is used to execute... Figure 7 At least one operation performed by the first device in steps S701 and S703 of the illustrated embodiment.

[0214] When the communication device 900 is used to implement the functions in the second device, the interface circuit 902 is used to perform... Figure 7 In the illustrated embodiment, at least one operation performed by the second device in steps S701 and S703, and the processing circuit 901 for performing... Figure 7 Step S702 in the illustrated embodiment.

[0215] This application also provides a computer-readable storage medium storing a computer program or instructions that, when executed, implement the methods described in the above embodiments.

[0216] This application also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the methods described in the above embodiments.

[0217] This application also provides a communication system, including the communication device described above.

[0218] This application also provides a circuit coupled to a memory, which is used to perform the methods shown in the above embodiments. This circuit may include a chip circuit.

[0219] Optionally, embodiments of this application also provide a chip system, including: at least one processor and an interface, wherein the at least one processor is coupled to a memory via the interface, and when the at least one processor executes a computer program or instructions in the memory, the chip system performs the method in any of the above method embodiments. Optionally, the chip system may be composed of chips, or may include chips and other discrete devices; embodiments of this application do not specifically limit this.

[0220] The processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), neural network processing units (NPUs), artificial intelligence processors, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor, any conventional processor, or one or more integrated circuits used to control the execution of a program for controlling the method provided in any of the above embodiments. The memory mentioned above can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM), etc. Some or all steps of the communication method in the embodiments of this application can be implemented by a GPU or NPU, or by a GPU or NPU in conjunction with other processors.

[0221] The memory in this application can also be a circuit or any other device capable of performing storage functions, used to store program instructions and / or data. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. For example, memory can be non-volatile memory, such as digital versatile disc (DVD), hard disk drive (HDD), or solid-state drive (SSD), or it can be volatile memory, such as random-access memory (RAM).

[0222] The terms “comprising” and “having”, and any variations thereof, used in the following description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0223] It should be understood that in the description of this application, unless otherwise stated, " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B can represent A or B; where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" do not necessarily imply difference. In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0224] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This 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 this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0225] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0226] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers described above does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

[0227] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0228] The components in the device described in this application embodiment can be combined, divided, or removed according to actual needs. Those skilled in the art can combine or integrate the different embodiments and features described in this specification.

[0229] In this application, examples may reference each other without logical contradiction. For example, methods and / or terms between method embodiments may reference each other, functions and / or terms between device embodiments may reference each other, and functions and / or terms between device examples and method examples may reference each other.

Claims

1. A communication method, characterized in that, Applied to a first device, the method includes: Send a first request, the first request being used to request the acquisition of first wireless data; Receive a first response, the first response including the first wireless data, the first wireless data being associated with second wireless data.

2. The method as described in claim 1, characterized in that, The first request includes at least one of the following: the second wireless data, the first attribute, and the first identifier.

3. The method as described in claim 2, characterized in that, The first attribute includes at least one of the following: collection time, collection location.

4. The method according to any one of claims 1-3, characterized in that, The first wireless data and the second wireless data include at least one of the following: uplink channel information, downlink channel information, location information of the first device, transmit power, sensor data, moving speed, moving direction, and antenna parameters.

5. The method according to any one of claims 1-4, characterized in that, The first wireless data is obtained based on the second wireless data.

6. The method as described in claim 5, characterized in that, The second wireless data includes wireless channel information for multiple locations. The first request includes a first location, but the multiple locations do not include the first location. The first wireless data is the wireless channel information for the first location.

7. The method as described in claim 5 or 6, characterized in that, The second wireless data includes wireless channel information at multiple times, the first request includes a first time, the multiple times do not include the first time, and the first wireless data is the wireless channel information at the first time.

8. The method according to any one of claims 5-7, characterized in that, The first wireless data is obtained based on the second wireless data, including: the first wireless data is obtained by performing at least one of the following operations based on the second wireless data: interpolation calculation, averaging, and prediction based on an artificial intelligence model.

9. The method according to any one of claims 1-8, characterized in that, The first wireless data is collected by the second or third device, and the second wireless data is collected by the first device.

10. A communication method, characterized in that, Applied to a second device, the method includes: Receive a first request, the first request being used to request the acquisition of first wireless data; Based on the first request, obtain the first wireless data; Send a first response, the first response including the first wireless data, the first wireless data being associated with second wireless data.

11. The method as described in claim 10, characterized in that, The first request includes at least one of the following: the second wireless data, the first attribute, and the first identifier.

12. The method as described in claim 11, characterized in that, The first attribute includes at least one of the following: collection time, collection location.

13. The method according to any one of claims 10-12, characterized in that, The first wireless data and the second wireless data include at least one of the following: uplink channel information, downlink channel information, location information, transmit power, sensor data, moving speed, moving direction, and antenna parameters.

14. The method according to any one of claims 10-13, characterized in that, The first wireless data is obtained based on the second wireless data.

15. The method as described in claim 14, characterized in that, The second wireless data includes wireless channel information for multiple locations, the first request includes a first location, the multiple locations do not include the first location, and the first wireless data is the wireless channel information of the first location.

16. The method as described in claim 14 or 15, characterized in that, The second wireless data includes wireless channel information at multiple times, the first request includes a first time, the multiple times do not include the first time, and the first wireless data is the wireless channel information at the first time.

17. The method according to any one of claims 14-16, characterized in that, The first wireless data is obtained based on the second wireless data, including: the first wireless data is obtained by performing at least one of the following operations based on the second wireless data: interpolation calculation, averaging, and prediction based on an artificial intelligence model.

18. The method according to any one of claims 10-17, characterized in that, The first wireless data is collected by the second or third device, and the second wireless data is collected by the first device.

19. The method according to any one of claims 10-18, characterized in that, The method further includes: Send a first message, which includes the first wireless data and the second wireless data.

20. A communication device, characterized in that, The apparatus is used to perform the method as described in any one of claims 1-9, or the apparatus is used to perform the method as described in any one of claims 10-19.

21. A communication device, characterized in that, The device includes a processor and an interface circuit. The interface circuit is used to receive signals from other communication devices and transmit them to the processor or to send signals from the processor to other communication devices. The processor is used to implement the method as described in any one of claims 1-9, or to implement the method as described in any one of claims 10-19, through logic circuits or execution code instructions.

22. A communication system, characterized in that, It includes a first device and a second device, the first device being used to perform the method as described in any one of claims 1-9, and the second device being used to perform the method as described in any one of claims 10-19.

23. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions, which, when executed by a communication device, implement the method as described in any one of claims 1-19.

24. A computer program product containing instructions, characterized in that, When the instructions are executed on the communication device, they implement the method as described in any one of claims 1-19.

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