Method and apparatus for communication, device, storage medium, and program product

Abstract

Embodiments of the present application provide a method for communication, a device, a storage medium, and a program product. In the method, a data compression apparatus sends a first data matrix. Then, the data compression apparatus sends a projection result of a second data matrix and a projection residual. The projection result is determined on the basis of a determined projection matrix of the second data matrix relative to the first data matrix, and the projection residual is determined on the basis of the second data matrix and the projection result. In this way, the correlation between the first data matrix and the second data matrix can be fully utilized, thereby improving compression efficiency, and saving the transmission bandwidth or storage resources.

Description

Method, apparatus, device, storage medium and program product for communication

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on November 14, 2023, with application number 202311524435.1 and application name “A method, apparatus, device, storage medium and program product for communication”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] Embodiments of the present application generally relate to the field of communications, and more particularly to a method for communications, a terminal device, a network device, a computer-readable storage medium, and a computer program product. Background Art

[0003] Native air interface data can include synaesthesia data, artificial intelligence (AI) model data, and channel state information (CSI) data from multi-antenna systems. Native data is high-dimensional and large in volume, and its interaction and transmission consume significant air interface resources. Data compression is required to reduce air interface resource usage or conserve storage resources. Other data, such as application interaction data on the internet, also requires compression to conserve network bandwidth or storage resources.

[0004] Summary of the Invention

[0005] The embodiments of this application provide a technical solution for data compression and decompression, which can provide higher compression efficiency and save transmission resources. In addition to being applicable to native air interface data, the embodiments of this disclosure can also be applied to, for example, Internet interactive data or other data, and can be widely used in terminal devices or network-side devices in future wireless communication scenarios.

[0006] In a first aspect, a communication method is provided. This method can be performed by a data compression device. Unless otherwise specified, the data compression device in the embodiments of the present application can refer to the data compression device itself (for example, implemented as a terminal device or network device), or a component within the data compression device (for example, a processor, chip, or chip system), or a logic module or software that can implement all or part of the functions of the data compression device. The following description uses the data compression device as an example. In this method, the data compression device sends a first data matrix. Furthermore, the data compression device sends the projection result and projection residual of a second data matrix. The projection result is determined based on the projection matrix determined by the second data matrix relative to the first data matrix, and the projection residual is determined based on the second data matrix and the projection result. In this way, the correlation between the first data matrix and the second data matrix can be fully utilized to improve compression efficiency and save transmission bandwidth or storage resources.

[0007] In a second aspect, a communication method is provided. This method can be performed by a data decompression device. Unless otherwise specified, the data decompression device in the embodiments of the present application can refer to the data decompression device itself (for example, implemented as a terminal device or network device), a component within the data decompression device (for example, a processor, chip, or chip system), or a logic module or software that implements all or part of the functions of the data decompression device. The following description uses the data decompression device as an example. In this method, the data decompression device receives a first data matrix. Furthermore, the data decompression device receives a projection result and a projection residual of a second data matrix. Furthermore, the data decompression device obtains a second data matrix or an approximate matrix of the second data matrix based on the first data matrix, the projection result, and the projection residual. The projection result is determined based on a projection matrix determined by the second data matrix relative to the first data matrix, and the projection residual is determined based on the second data matrix and the projection result. In this way, the data compressed by the data compression device can be decompressed and restored, fully utilizing the correlation between the first data matrix and the second data matrix, improving compression efficiency, and saving transmission bandwidth or storage resources.

[0008] In some implementations, the projection matrix is ​​determined as follows: the data compression device selects a predetermined number of columns from a reference matrix to form the projection matrix, where the reference matrix is ​​the first data matrix or a submatrix of the first data matrix. This allows for a simpler construction of the projection matrix, saving computational effort.

[0009] In some implementations, the projection matrix is ​​determined as follows: the data compression device determines a projection basis matrix based on a reference matrix and selects a predetermined number of columns in the projection basis matrix to form the projection matrix. The reference matrix is ​​the first data matrix or a submatrix of the first data matrix. In this manner, a high-energy basis can be extracted using LRMA, fully extracting features of the reference matrix and reducing compression errors.

[0010] In some implementations, determining the projection basis matrix based on the reference matrix includes performing matrix decomposition on the reference matrix to determine the projection basis matrix. In this way, features of the reference matrix can be fully extracted and compression errors can be reduced.

[0011] In some implementations, the matrix decomposition includes any one of the following: low-rank matrix approximation (LRMA) decomposition, SVD decomposition, or QR decomposition. In this way, the matrix decomposition can be flexibly implemented.

[0012] In some implementations, the data compression device further transmits one or more of the following: first indication information indicating the number of columns in the projection matrix; second indication information indicating whether the projection matrix is ​​based on a reference matrix or a projection basis matrix obtained by performing LRMA decomposition on the reference matrix; or third indication information indicating the position of the projection matrix columns in the reference matrix or the projection basis matrix. This allows synchronization between the data compression device and the data decompression device, facilitating accurate decompression.

[0013] In some implementations, the data decompression device further receives one or more of the following: first indication information indicating the number of columns in the projection matrix; second indication information indicating whether the projection matrix is ​​based on a reference matrix or a projection basis matrix obtained by performing LRMA decomposition on the reference matrix; or third indication information indicating the position of the columns of the projection matrix in the reference matrix or the projection basis matrix. In this way, synchronization between the data compression device and the data decompression device can be achieved, facilitating accurate decompression.

[0014] In some implementations, determining the projection matrix by the data compression device includes: the data compression device selecting a subspace of a predetermined number of dimensions in a column space spanned by a reference matrix, wherein the projection matrix represents the subspace, and wherein the reference matrix is ​​the first data matrix or a submatrix of the first data matrix. In this manner, the second data matrix can be accurately fitted, reducing compression errors.

[0015] In some implementations, the subspace is selected so that the norm of the projection residual after the second data matrix is ​​projected onto the projection matrix is ​​minimized. In this way, the second data matrix can be accurately fitted and compression errors can be reduced.

[0016] In some implementations, determining the projection matrix by the data compression device includes: the data compression device performing QR decomposition on a reference matrix to determine a Q matrix. Furthermore, the data compression device performs singular value decomposition on the product of the transpose of the Q matrix and the data matrix to obtain an eigenvector matrix. Furthermore, the data compression device determines a subspace selection matrix based on a predetermined number of preceding columns of the eigenvector matrix. Furthermore, the data compression device determines a projection matrix based on the subspace selection matrix and the reference matrix. In this manner, the generated projection matrix can accurately fit the second data matrix, reducing compression errors.

[0017] In some implementations, the data compression device determines the projection matrix including: the data compression device performs QR decomposition on the reference data C to obtain a Q matrix, where the QR decomposition formula is C=Q×R, The columns are orthogonal, Furthermore, the data compression device performs the matrix Q T G performs SVD decomposition to obtain the U matrix, where G is the second data matrix and the formula is Q T G=U∑V T Then, the data compression device obtains the first d columns of the matrix U, which are recorded as Furthermore, the data compression device obtains the subspace selection matrix Then, the data compression device obtains the projection matrix P * =C×S * In this way, the generated projection matrix can accurately fit the second data matrix and reduce the compression error.

[0018] In some implementations, the data compression device determines the projection matrix, including: the data compression device solves the following optimization problem, where G is the second data matrix, C is the reference matrix In this way, the generated projection matrix can accurately fit the second data matrix and reduce compression errors.

[0019] In some implementations, the method performed by the data compression device further includes sending fourth indication information, where the fourth indication information is used to indicate a subspace selection matrix, where the subspace selection matrix is ​​used together with a reference matrix to determine a projection matrix. In this way, the subspace selection matrix can be accurately indicated to the data decompression device, facilitating accurate decompression.

[0020] In some implementations, the method performed by the data decompression device further includes receiving fourth indication information, the fourth indication information being used to indicate a subspace selection matrix, where the subspace selection matrix is ​​used together with a reference matrix to determine a projection matrix. In this manner, the subspace selection matrix can be accurately indicated by the data compression device, facilitating accurate decompression.

[0021] In some implementations, the method performed by the data compression device further includes: receiving feedback information regarding the first data matrix; and determining a second data matrix based on the feedback information and the first data. In this manner, at least a portion of the first data matrix is ​​accurately selected to compress the second data matrix, thereby improving compression accuracy.

[0022] In some implementations, the method performed by the data compression device further includes: sending fifth indication information for the first data matrix; and determining the second data matrix based on the fifth indication information and the first data matrix.

[0023] In some implementations, the method performed by the data decompression device further includes: sending feedback information regarding the first data matrix; and determining a second data matrix based on the feedback information and the first data. In this manner, at least a portion of the first data matrix is ​​accurately selected for decompression of the second data matrix, thereby improving decompression accuracy.

[0024] In some implementations, the method performed by the data decompression device further includes: receiving fifth indication information for the first data matrix; and determining the second data matrix based on the fifth indication information and the first data matrix.

[0025] In some implementations, the feedback information or the fifth indication information includes one or more of the following: subset indication information indicating the first data matrix or a submatrix of the first data matrix, or confidence information indicating the confidence level of the first data. In this way, a portion of the first data matrix can be accurately indicated, facilitating accurate compression of the second data matrix.

[0026] In some implementations, the first data matrix has a first granularity, the second data matrix has a second granularity, and the first granularity is greater than the second granularity. In this way, the data compression method can be applied to data of different granularities, expanding its scope of application.

[0027] In some implementations, the first data matrix is ​​data sampled from a geographic space at a first resolution, the second data is data sampled from a geographic space at a second resolution higher than the first resolution, and the subset indication information indicates spatial location information of a subset of the first data. Additionally or alternatively, the method performed by the data compression device may also employ: the first data matrix is ​​a plurality of cluster centers of a plurality of data classes determined by clustering the original data, the second data matrix is ​​data contained in one or more of the plurality of data classes, and the subset indication information indicates one or more cluster centers of the plurality of cluster centers corresponding to the one or more data classes. In this way, the second data matrix can be collected and compressed using different resolutions or clustering methods, thereby improving compression accuracy and reducing errors.

[0028] In some implementations, the reference matrix is ​​determined based on one or more of: feedback information for the first data matrix, a submatrix of the first data matrix, or the first data matrix. This allows for flexible generation of the reference matrix, facilitating data compression.

[0029] In some implementations, the second data matrix is ​​not grouped and corresponds entirely to the reference matrix, or the second data matrix is ​​divided into multiple data groups, each of which corresponds to a sub-matrix of the reference matrix. This allows for flexible processing of the second data matrix in either a grouped or ungrouped manner, facilitating appropriate compression processing.

[0030] In some implementations, the method performed by the data compression device further includes sending one or more of the following: first configuration information indicating whether feedback information for the first data matrix is ​​index-based or bitmap-based, second configuration information indicating whether confidence feedback for the first data matrix is ​​enabled, third configuration information indicating whether the first data matrix is ​​based on geographic location or clustering, fourth configuration information indicating whether a reference matrix is ​​determined based on feedback information for the first data matrix or based on the first data matrix, or fifth configuration information indicating whether the second data matrix as a whole corresponds to the reference matrix or whether multiple data groups divided from the second data matrix correspond to multiple sub-matrices of the reference matrix. In this way, the data compression device can accurately send the configuration information to the data decompression device, facilitating synchronization between the two parties and facilitating accurate decompression by the data decompression device.

[0031] In some implementations, the method performed by the data compression device further includes: sending at least one of first configuration information, second configuration information, third configuration information, fourth configuration information, or fifth configuration information before sending the first data matrix. This facilitates synchronization between the data compression device and the data decompression device, and facilitates accurate decompression by the data decompression device.

[0032] In some implementations, the method performed by the data decompression device further includes receiving one or more of the following: first configuration information indicating whether feedback information for the first data matrix is ​​index-based or bitmap-based, second configuration information indicating whether confidence feedback for the first data matrix is ​​enabled, third configuration information indicating whether the first data matrix is ​​based on geographic location or clustering, fourth configuration information indicating whether a reference matrix is ​​determined based on feedback information for the first data matrix or based on the first data matrix, or fifth configuration information indicating whether the second data matrix as a whole corresponds to the reference matrix or whether multiple data groups divided from the second data matrix correspond to multiple sub-matrices of the reference matrix. In this way, the data decompression device can accurately receive the configuration information from the data compression device, facilitating synchronization between the two parties and facilitating accurate decompression by the data decompression device.

[0033] In some implementations, the method performed by the data decompression device further includes: receiving at least one of first configuration information, second configuration information, third configuration information, fourth configuration information, or fifth configuration information before receiving the first data matrix. This facilitates synchronization between the data compression device and the data decompression device, and facilitates accurate decompression by the data decompression device.

[0034] In some implementations, the method performed by the data compression device further includes transmitting one or more of the following: sixth configuration information indicating whether the projection matrix is ​​based on the column space or a subspace of the column space of the reference matrix, or seventh configuration information identifying the reference matrix from the first data matrix. In this manner, the method for generating the projection matrix can be accurately identified, facilitating synchronization between the data compression device and the data decompression device and enabling the data decompression device to perform accurate decompression.

[0035] In some implementations, the data compression device sends at least one of the sixth configuration information or the seventh configuration information before sending the projection result and projection residual. This allows accurate identification of the projection matrix generation method, facilitates synchronization between the data compression device and the data decompression device, and facilitates accurate decompression by the data decompression device.

[0036] In some implementations, the method performed by the data decompression device further includes receiving one or more of the following: sixth configuration information indicating whether the projection matrix is ​​based on the column space or a subspace of the column space of the reference matrix, or seventh configuration information identifying the reference matrix from the first data matrix. In this manner, the method for generating the projection matrix can be accurately identified, facilitating synchronization between the data compression device and the data decompression device and enabling the data decompression device to perform accurate decompression.

[0037] In some implementations, the data decompression device receives at least one of the sixth configuration information or the seventh configuration information before receiving the projection result and the projection residual. This allows accurate identification of the projection matrix generation method, facilitates synchronization between the data compression device and the data decompression device, and facilitates accurate decompression by the data decompression device.

[0038] In a third aspect, a device is provided. The device can refer to the data compression device itself (for example, implemented as a terminal device or network device), or a component within the data compression device (for example, a processor, chip, or chip system), or a logic module or software that can implement all or part of the functions of the data compression device. The following description uses the data compression device as an example. The device includes: a first data matrix sending module for sending the first data matrix, and a result sending module for sending the projection result and projection residual of the second data matrix. The projection result is determined based on the projection matrix determined by the second data matrix relative to the first data matrix, and the projection residual is determined based on the second data matrix and the projection result. In this way, the correlation between the first data matrix and the second data matrix can be fully utilized to improve compression efficiency and save transmission bandwidth or storage resources.

[0039] In a fourth aspect, a device is provided. This device can refer to the data decompression device itself (for example, implemented as a network device or terminal device), a component within the data decompression device (for example, a processor, chip, or chip system), or a logic module or software that implements all or part of the functions of the data decompression device. The following description uses the data decompression device as an example. The device includes: a first data matrix receiving module for receiving a first data matrix; a result receiving module for receiving a projection result and projection residual of a second data matrix; and a second data matrix acquisition module for acquiring a second data matrix or an approximation of the second data matrix based on the first data matrix, the projection result, and the projection residual. The projection result is determined based on a projection matrix determined by the second data matrix relative to the first data matrix, and the projection residual is determined based on the second data matrix and the projection result. In this manner, data compressed by the data compression device can be decompressed and recovered, fully utilizing the correlation between the first and second data matrices, improving compression efficiency, and saving transmission bandwidth or storage resources.

[0040] In a fifth aspect, a system is provided, comprising the apparatus of the third and fourth aspects. In this way, the correlation between the first data matrix and the second data matrix can be fully utilized to improve compression efficiency and save transmission bandwidth or storage resources.

[0041] In a sixth aspect, a device is provided. This device may be the data compression device or data decompression device in the above-mentioned method embodiments, or a chip provided in the data compression device or data decompression device. The device includes a processor and a memory. The memory is used to store a computer program or instructions. When the processor executes the computer program or instructions, the data compression device or data decompression device executes the method performed by the data compression device or data decompression device in the above-mentioned method embodiments.

[0042] In the seventh aspect, an embodiment of the present application provides a computer-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are executed, the method performed by the data compression device or the data decompression device in the above aspects is implemented.

[0043] In a ninth aspect, a computer program product is provided, which includes: a computer program code, which, when run, enables the method performed by the data compression device or the data decompression device in the above aspects to be executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] FIG1A illustrates a communication system in which embodiments of the present application may be implemented.

[0045] FIG1B is a block diagram of compression and transmission of native data over an air interface.

[0046] FIG1C is a schematic diagram of a low-rank matrix approximation.

[0047] FIG1D is a schematic diagram of a first data transmission scenario.

[0048] FIG. 1E is a schematic diagram of a second data transmission scenario.

[0049] FIG2 is a flowchart of data compression and decompression in an embodiment of the present application.

[0050] FIG3A is a flowchart of transmitting a first data matrix and a second data matrix in an embodiment of the present application.

[0051] FIG3B is a schematic diagram of transmitting a first data matrix and a second data matrix in an embodiment of the present application.

[0052] FIG4A is a schematic diagram of performing projection compression using a given projection matrix in an embodiment of the present application.

[0053] FIG4B is a flowchart of performing projection compression on a given projection matrix in an embodiment of the present application.

[0054] FIG5A is a schematic diagram of generating a projection matrix by column selection or LRMA decomposition in an embodiment of the present application.

[0055] FIG5B is a flowchart of generating a projection matrix for data compression by column selection or LRMA decomposition in an embodiment of the present application.

[0056] FIG6A is a schematic diagram of subspace projection in an embodiment of the present application.

[0057] FIG6B is a flowchart of generating a projection matrix by subspace projection for data compression in an embodiment of the present application.

[0058] FIG. 7A is a schematic diagram of a radio frequency map (RF map) in an embodiment of the present application.

[0059] FIG7B is a schematic diagram of a location-based retrieval method for coarse / fine-grained data division in an embodiment of the present application.

[0060] FIG7C is a schematic diagram of a clustering-based retrieval method for coarse / fine-grained data division in an embodiment of the present application.

[0061] FIG7D is a flowchart of data compression based on retrieval in an embodiment of the present application.

[0062] FIG8A is a schematic diagram of unpacked data compression in an embodiment of the present application.

[0063] FIG8B is a schematic diagram of packet data compression in an embodiment of the present application.

[0064] FIG9 is a flow chart of integrated signaling interaction in an embodiment of the present application.

[0065] FIG10A is a schematic diagram showing the effect of compressing radio frequency map data in an embodiment of the present application.

[0066] FIG10B is a schematic diagram showing the effect of compressing the channel matrix in an embodiment of the present application.

[0067] FIG11 is a flow chart of a data compression method in an embodiment of the present application.

[0068] FIG12 is a flow chart of a data decompression method in an embodiment of the present application.

[0069] FIG13 shows a simplified block diagram of an example device of a possible implementation method of an embodiment of the present application.

[0070] FIG14 shows a simplified block diagram of a communication device according to a possible implementation method in an embodiment of the present application.

[0071] FIG15 shows a simplified block diagram of a network device in a possible implementation manner of an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the embodiments of the present application will be further described in detail with reference to the accompanying drawings. The specific operation methods and functional descriptions in the method embodiments can also be applied to the device embodiments or system embodiments.

[0073] Raw air interface data primarily includes interaural data, AI model data, and CSI data from multi-antenna systems. Raw data is high-dimensional and large in volume, and its interaction and transmission consume significant air interface resources. Data compression technology can significantly reduce air interface resource usage while meeting certain distortion or mission accuracy requirements. Data compression is necessary to reduce air interface resource usage. Other data, such as application interaction data on the internet, also requires compression to conserve network bandwidth and storage resources.

[0074] Figure 1A illustrates a communication system in which data compression and decompression can be implemented according to an embodiment of the present application. As shown in Figure 1A , the communication method provided by an embodiment of the present application can be applied to a wireless communication system 100. In wireless communication system 100, terminal device 101 and network device 103 are shown. Data compression can be implemented in terminal device 101, and data decompression can be implemented in network device 103. Alternatively, data compression can be implemented in network device 103, and data decompression can be implemented in terminal device 101. In wireless communication system 100, network device 103, such as a base station (BS), provides communication services to terminal device 101, such as a mobile station (MS). The base station includes a baseband unit (BBU) and a remote radio unit (RRU). The BBU and RRU can be placed in different locations, for example: a remote RRU in a high-traffic area or a central equipment room. Alternatively, the BBU and RRU can be placed in the same equipment room. Alternatively, the BBU and RRU can be different components within the same rack. Those skilled in the art will appreciate that data compression and decompression can also be implemented between two terminal devices using sidelink communication, or between two network devices, or between two devices using a wired link. Data compression and decompression can also be used to store data in media such as hard disks, Flash, read-only memory (ROM), and random access memory (RAM), and the embodiments of the present disclosure are not limited to this.

[0075] The wireless communication systems in the embodiments of the present application include but are not limited to: Narrow Band-Internet of Things (NB-IoT), Global System for Mobile Communications (GSM), Enhanced Data rate for GSM Evolution (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access 2000 (CDMA2000), Time Division-Synchronization Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), three major application scenarios of 5G mobile communication systems: eMBB, URLLC, and eMTC, as well as 6G.

[0076] It should be understood that the above wireless communication system is applicable to both high-frequency scenarios (above 6G) such as millimeter waves and low-frequency scenarios (sub6G). Application scenarios of wireless communication systems include, but are not limited to, fifth-generation systems (5G), new radio (NR) communication systems, and future evolved public land mobile network (PLMN) systems.

[0077] The terminal device 101 shown above can be a user equipment (UE), a terminal, an access terminal, a terminal unit, a terminal station, a mobile station (MS), a remote station, a remote terminal, a mobile terminal, a wireless communication device, a terminal agent or a terminal device, etc. The terminal device 110 can also be a communication chip with a communication module, or a vehicle with a communication function, or an on-board device (such as an on-board communication device, an on-board communication chip), etc. The terminal device 101 can have a wireless transceiver function, which can communicate with one or more network devices of one or more communication systems (such as wireless communication) and receive network services provided by the network devices, where the network devices include but are not limited to the illustrated network device (103). It can be understood by those skilled in the art that the data compression and decompression scenario shown in FIG1A can also be applied between network devices and network devices, or between terminal devices and terminal devices, etc., and the present disclosure does not limit this.

[0078] Among them, the terminal device 101 can be a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA) device, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a wearable device, a terminal device in a future 5G network, or a terminal device in a future evolved PLMN network, etc.

[0079] The terminal device 101 can specifically be a mobile phone, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc.

[0080] In addition, the terminal device 101 can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted. The terminal device 101 can also be deployed on the water surface (such as a ship, etc.). The terminal device 101 can also be deployed in the air (such as an airplane, a balloon, and a satellite, etc.). The network device (103) can be an access network device (or access network point). Among them, the access network device refers to a device that provides network access functions, such as a radio access network (RAN) base station, etc. The network device (103) can specifically include a base station (BS), or a base station and a wireless resource management device for controlling the base station, etc. The network device (103) can also include a relay station (relay device), an access point, a base station in a 5G network or an NR base station, a base station in a future evolved PLMN network, etc. The network device (103) can be a wearable device or a vehicle-mounted device. The network device (103) can also be a communication chip with a communication module.

[0081] For example, the network device (103) includes but is not limited to: a base station (g nodeB, gNB) in 5G, an evolved node B (eNB) in a long term evolution (LTE) system, a radio network controller (RNC), a wireless controller under a cloud radio access network (CRAN) system, a base station controller (BSC), a home base station (for example, home evolved nodeB, or home node B, HNB), a baseband unit (BBU), a transmission point (TRP), a transmitting point (TP), a mobile switching center, and may also be an evolutionary NB (eNB or eNodeB) in LTE, a base station device in a future 5G network or an access network device in a future evolved PLMN network, or a wearable device or a vehicle-mounted device.

[0082] In some deployments, network devices may include a centralized unit (CU) and a distributed unit (DU). The network device may also include an active antenna unit (AAU). The CU implements some of the network device's functions, while the DU implements some of the network device's functions. For example, the CU is responsible for processing non-real-time protocols and services, and implementing the functions of the radio resource control (RRC) and packet data convergence protocol (PDCP) layers. The DU is responsible for processing physical layer protocols and real-time services, and implementing the functions of the radio link control (RLC), media access control (MAC), and physical (PHY) layers. The AAU implements some physical layer processing functions, RF processing, and active antenna-related functions. Because RRC layer information ultimately becomes PHY layer information, or is converted from PHY layer information, in this architecture, higher-layer signaling, such as RRC layer signaling, can also be considered to be sent by the DU, or by the DU+AAU. It is understandable that the network device may be a device including one or more of a CU node, a DU node, and an AAU node. In addition, the CU may be divided into a network device in an access network (radio access network, RAN), or the CU may be divided into a network device in a core network (core network, CN), and the embodiments of the present application do not limit this. Examples of network devices include, but are not limited to, Node B (NodeB or NB), evolved NodeB (eNodeB or eNB), next generation NodeB (gNB), transmit receive point (TRP), remote radio unit (RRU), radio head (RH), remote radio head (RRH), IAB node, low power node, such as a micro-micro node, a micro-micro node, a reconfigurable intelligent surface (RIS), a network controlled repeater, and the like.

[0083] In addition, the network device (103) can be connected to a core network (CN) device, which can be used to provide core network services for the access network device (103) and the terminal device (101). The core network device can correspond to different devices in different systems. For example, in 3G, the core network device can correspond to the serving GPRS support node (SGSN) of the general packet radio service (GPRS) and / or the gateway GPRS support node (GGSN) of GPRS. In 4G, the core network device can correspond to the mobility management entity (MME) and / or the serving gateway (S-GW). In 5G, the core network device can correspond to the access and mobility management function (AMF), the session management function (SMF) or the user plane function (UPF).

[0084] Figure 1B is a block diagram of compression and transmission of air interface raw data. In block diagram 110, at the transmitting end, the perception data, AI data, and CSI data undergo data identification and filtering 113, data transformation and quantization 115, data selection and channel mapping 117 in physical source coding 111, and then pass through channel coding 119 and enter the receiving end through the channel. At the receiving end, the data received from the channel undergoes channel decoding 121 and source decoding 123 to obtain data for the task. The channel can be, for example, a wireless channel 121. It will be understood by those skilled in the art that the channel can also be a wired channel, and the embodiments of the present disclosure are not limited to this. It will be understood by those skilled in the art that this scenario of compressed data transmission through the channel can also be replaced by a compressed data storage scenario in the medium, and the embodiments of the present disclosure are not limited to this.

[0085] Depending on the specific scenario, raw air interface data may contain various forms of redundancy. Mining this redundancy can enable data compression. For example, a large amount of content in the perceived raw signal has little impact on subsequent tasks, so discarding it can significantly reduce the data volume. The perceived point cloud data is correlated in time and space, and the channel matrix has strong correlations in the frequency and spatial angle domains.

[0086] Low Rank Matrix Approximation (LRMA) is a method for data compression by mining data correlation, and its principle is shown in Figure 1C. Based on the Eckart-Young-Mirsky theory or truncated singular value decomposition (SVD), the matrix A with m rows and n columns is approximately equal to the matrix B with m rows and k columns multiplied by the matrix E with k rows and n columns. The columns of the matrix B are orthogonal and can be used as a projection subspace. The projection error of each column of A on this projection subspace approaches the minimum value. The compression method based on LRMA can mine the correlation redundancy between the columns in the data matrix A. In many scenarios of native data compression, the system can transmit data multiple times instead of just once. For example, the CSI scenario may use periodic interval feedback. As shown in the first scenario (CSI scenario) of FIG1D , terminal device 141 performs two channel measurements 146 and 156 based on reference signals sent by network device 143, such as first reference signal 145 and second reference signal 155, respectively, to obtain first channel data 150 and second channel data 160. For example, first channel data 150 and second channel data 160 may be frequency-domain transfer functions of the channel between terminal device 141 and network device 143. In scenarios where terminal device 141 is not moving very fast, first channel data 150 and second channel data 160 may be temporally correlated. In the point cloud or RF map scenario (second scenario) shown in FIG1E , a hierarchical transmission effect can be achieved through interaction. Network device 173 first transmits large-scale coarse-grained data 175 to terminal device 171, and then transmits small-scale fine-grained data 185 based on the terminal device's perception measurements 176 and feedback 180. This significantly reduces the amount of transmitted data. In this scenario, coarse-grained data 175 and fine-grained data 185 are spatially correlated. In Figure 1D and Figure 1E, the terminal devices 161 and 171 can be an implementation of the terminal device 101 in Figure 1A, and the network devices 163 and 173 can be an implementation of the network device 103 in Figure 1A. In addition to the spatial granularity of the point cloud data and the radio frequency map data in Figure 1E, the channel data in Figure 1D can also have frequency domain granularity. For example, the first channel data 150 is coarse-grained data in the frequency domain, and the second channel data 160 is fine-grained data in the frequency domain. The second channel data 160 with fine frequency domain granularity is generated by the terminal device 141 based on the feedback interaction (not shown in Figure 1D). It can be understood by those skilled in the art that there may also be other granularity resolution methods, such as time domain granularity, etc., and the embodiments of the present disclosure are not limited to this.

[0087] In the disclosed embodiment, due to the correlation between first channel data 150 and second channel data 160, and the correlation between coarse-grained data 175 and fine-grained data 185, at least a portion of first channel data 150 or coarse-grained data 175 can be selected as a reference for compressing second channel data 160 or fine-grained data 185. The first granularity of coarse-grained data 175 is greater than the second granularity of fine-grained data 185. In the disclosed embodiment, the above data can be in matrix form. First channel data 150 and coarse-grained data 175 can be collectively referred to as a first data matrix, and second channel data 160 and fine-grained data 185 can be collectively referred to as a second data matrix. Those skilled in the art will appreciate that the above data can also be in vector form, which is a specific form of a matrix.

[0088] FIG2 is a flowchart of data compression and decompression in an embodiment of the present application, which specifically describes how to perform efficient data compression and decompression in a multiple transmission scenario of data, such as air interface native data.

[0089] In process 200, the data compression device 201 sends (204) a first data matrix 205. The data compression device 201 sends (208) a projection result and a projection residual 210 of a second data matrix. The projection result is determined based on a projection matrix determined by the second data matrix relative to the first data matrix, and the projection residual is determined based on the second data matrix and the projection result. The data decompression device 203 receives the first data matrix 205 and the projection result and the projection residual 210 of the second data matrix. At 215, the data decompression device 203 obtains a second data matrix or an approximate matrix of the second data matrix based on the first data matrix, the projection result, and the projection residual. In this way, the correlation between the first data matrix and the second data matrix can be fully utilized to improve compression efficiency and save transmission bandwidth or storage resources. The data compression device 201 can correspond to the terminal device 101 in Figure 1A, and the data compression device 203 can correspond to the network device 103 in Figure 1A. Alternatively, the data compression device 201 may correspond to the network device 103 in FIG. 1A , and the data compression device 203 may correspond to the terminal device 101 in FIG. 1A .

[0090] Figure 3A is a flow chart of transmitting a first data matrix and a second data matrix in an embodiment of the present application. The data compression device 301 and the data decompression device 303 in Figure 3A may be implementations of the data compression device 201 and the data decompression device 203 in Figure 2, respectively.

[0091] In process 300, the data compression device 301 sends (304) the first data matrix 305 to the data decompression device 303. At 310, the data compression device 301 selects a d-dimensional subspace from the k-dimensional space spanned by the selected reference data or reference matrix, and projects the second data matrix onto this d-dimensional subspace, and subsequently compresses the projection residual. The reference matrix is ​​a submatrix of the first data matrix 305. The submatrix can be a part or all of the first data matrix 305. The data compression device 301 sends (313) the second data matrix to the data decompression device 303, specifically, it can send the projection part or projection result, and the compression result of the projection residual. The data compression device 301 can also send the compression result of the projection part or projection result. The compression result of the projection part and the compression result of the projection residual can be further quantized. In this way, the correlation between the second data and the first data can be used to efficiently compress the second data, reduce the transmission bandwidth or save storage space.

[0092] 3B is a schematic diagram of transmitting the first data matrix and the second data matrix in an embodiment of the present application. In embodiment 320, the data compression device performs LRMA decomposition, such as matrix decomposition, on the reference matrix C 325 (m rows and k columns) in the first data matrix to obtain the product 330 of the projection matrix (s columns) and the projection coefficient (s rows). The projection matrix can represent a basis (Base) or a subspace, and the projection matrix can be a coefficient matrix. At 335, the data compression device selects a d-dimensional subspace from the k-dimensional space spanned by the reference data as the projection matrix (d columns) for the second data matrix G. This d-dimensional subspace can select d columns from the k columns of the reference matrix C, or it can select d columns from the projection matrix of the s columns. The data compression device projects the second data matrix G of m rows and n columns onto the selected d-dimensional subspace, or the projection matrix of d columns, to obtain a projection part or projection result 345 of d rows and n columns, and a projection residual or orthogonal part 350. The data compression device subsequently compresses the projection residual or orthogonal portion 350, for example, using the LRMA method, dictionary compression, transform domain compression, differencing, quantization, or other methods to obtain a compressed result. The data compression device may also compress the projection portion or projection result 345 by methods such as quantization or entropy coding to obtain a compressed result. The correlation between the first data matrix and the second data matrix is ​​used to select a d-dimensional subspace, and the second data matrix is ​​projected. The projection result and the projection result can be used to characterize the second data matrix G 340 with a smaller error. Those skilled in the art will appreciate that, in addition to matrix decomposition, other methods such as dictionary learning can also be used to obtain the projection matrix, and this disclosure is not limited to this.

[0093] In an embodiment of the present disclosure, a second data matrix, such as fine-grained data, is projected onto a space spanned by a first data matrix, such as coarse-grained data, to assist in compression, and the projection operation is performed onto a space spanned by a reference matrix. Given a projection matrix, the second data matrix can be subjected to projective decomposition, selected by an appropriate method, or constructed on an optimal subspace of the space spanned by the reference matrix for projection. In this way, the correlation between the first and second data matrices can be utilized to compress the second data matrix, reducing compression errors. In an embodiment of the present disclosure, coarse-grained data (corresponding to the first data matrix) can be retrieved based on spatial location or clustering, and the fine-grained data can be compressed through feedback interaction. The feedback can be the retrieval results or confidence level of the coarse-grained data. In this way, the correlation between the coarse-grained data and the fine-grained data (corresponding to the second data matrix) is utilized to accurately compress the fine-grained data, reducing compression errors. The data compression device and the data decompression device can also exchange the correspondence between the coarse-grained data and the fine-grained data, as well as reference the indication of the coarse-grained data, thereby achieving synchronization between the data compression device and the data decompression device, facilitating accurate decompression.

[0094] FIG4A is a schematic diagram of projecting compression using a given projection matrix in an embodiment of the present application. Specifically, embodiment 400 implements projection of a second data matrix G given a projection matrix P. At least a portion of a reference matrix or a basis of the reference matrix constitutes a projection matrix P405 (dimension m*d), the column space of which constitutes a projection space. The projection matrix P is used to perform projective decomposition on the second data matrix G 410 to be compressed, obtaining a projection portion or result portion 415 (P T P) -1 P T G. According to the second data matrix G 410 and the projection part or result part: 415, the orthogonal part 420G-(P T P) -1 P T G. The orthogonal part is subsequently compressed to obtain a compressed result. The subsequent compression can be any of the following: LRMA decomposition, dictionary compression, transform domain compression, differential calculation, quantization, or other compression methods, which are not limited in the embodiments of the present disclosure. The projection part or the result part can also be compressed to obtain a compressed result. In this embodiment, the first data matrix or reference matrix can be coarse-grained, and the second data matrix G 410 can be fine-grained.

[0095] FIG4B is a flowchart of projection compression of a given projection matrix in an embodiment of the present application, and corresponds to FIG4A . The data compression device 431 and the data decompression device 433 can be specific implementations of the data compression device 201 and the data decompression device 203 in FIG2 , respectively. In embodiment 430, the data compression device 431 sends (434) a first data matrix 435 to the data decompression device 433. At 440, the data compression device 431 projects the second data matrix using the projection matrix P, and subsequently compresses the projection residual (orthogonal part). The data compression device 431 sends (443) a second data matrix 445 to the data decompression device 433. Specifically, the projection result matrix (P T P) -1 P T G, and the projection residual G-(P T P) -1 P T The data compression device 431 may also send the compression result of the projection result matrix to the data decompression device 433 .

[0096] FIG5A is a schematic diagram of generating a projection matrix by column selection or LRMA decomposition in an embodiment of the present application. Specifically, embodiment 500 may be a specific method for generating the projection matrix P in FIG4A .

[0097] In the embodiment of the present disclosure, the projection matrix P 520 can be selected from the d columns in the reference matrix C 505 with m rows and k columns. The reference matrix C is the first data matrix or a submatrix of the first data matrix. The selection method can be to solve a combinatorial optimization problem. When k is not large, a traversal search can be performed on the k columns, or a heuristic search can be used to select d columns from the k columns. The projection matrix P 520 can also be selected from the d columns of the projection basis 510 (m*s matrix) when the reference data matrix C is subjected to LRMA decomposition. For example, after performing SVD decomposition on the reference matrix, the d columns corresponding to the largest d singular values ​​of the SVD can be directly selected. In this way, two different methods can be flexibly used to obtain the projection matrix P. The method of directly selecting d columns in the reference matrix has a small amount of calculation, while the method of performing SVD decomposition on the reference matrix can reduce the projection error.

[0098] FIG5B is a flowchart of generating a projection matrix for data compression by column selection or LRMA decomposition in an embodiment of the present application, corresponding to the projection matrix generation method shown in FIG5A . Compared with FIG4B , in the embodiment 530 of FIG5B , the data compression device 431 also sends a first indication information, i.e., a projection matrix dimension d indication 535 , a second indication information, i.e., a projection matrix source indication 540 (1 bit), and a third indication information, i.e., a projection matrix column indication 545 (k bits) to the data decompression device 433 . The number of columns or dimension d of the projection matrix is ​​a parameter related to the compression rate (data volume) and can be dynamically indicated. In actual use, the data compression device 431 and the data decompression device 433 can configure some possible values ​​in advance, for example, all possible values ​​of d are 1, 2, 3, 4, 5, 6, 7, and 8, and then dynamically select or indicate one of the values ​​using a number of bits. For example, the aforementioned 8 possible values ​​of d require 3 bits to indicate. The data compression device 431 may also send a projection matrix source indication 540 (1 bit) to the data decompression device 433 to indicate whether the projection matrix P is derived from the reference matrix or the basis after LRMA decomposition of the reference matrix. The projection matrix source indication 540 may be dynamically indicated each time it is transmitted, or may be semi-statically indicated and selected at intervals. When the projection matrix P is derived directly from the reference matrix, a projection matrix column indication 545 is used to indicate which d columns are selected from the k columns of the reference matrix as the projection matrix P. A k-bit bitmap may be used to identify the selected d columns, where 1≤d≤k. When the projection matrix P is derived from the basis after LRMA decomposition of the reference matrix, a projection matrix column indication 545 is used to indicate the number of columns of the projection matrix P, where 1≤d≤s. In the disclosed embodiment, the data compression device 431 and the data decompression device 433 may also be fixedly configured to select between using the d columns of the reference matrix and the d columns of the basis after LRMA decomposition of the reference matrix to compress and decompress the second data matrix. In this way, the correlation between the first and second data matrices can be exploited, and a flexible compression method can be selected to compress and decompress the second data matrix. The indication information synchronizes the data compression and decompression devices, facilitating accurate data compression and decompression. In the disclosed embodiment, the first data matrix or reference matrix C 505 can be coarse-grained, while the second data matrix can be fine-grained.

[0099] Figure 6A is a schematic diagram of subspace projection in an embodiment of the present application. In embodiment 600, the data compression device can select the best-performing d-dimensional subspace in the column space 605 spanned by the first data matrix to project the second data matrix 610. The selection of the d-dimensional subspace is related to the second data matrix 610, thereby reducing the projection error and, in turn, the compression error.

[0100] In the disclosed embodiment, the reference matrix C (m*k dimensions) referenced by the second data matrix G (m*n dimensions) to be compressed is the first data matrix or a submatrix of the first data matrix. A d-dimensional subspace of the reference matrix C is selected, and a projection matrix is ​​obtained. The selection criterion is to minimize the Frobenius norm of the orthogonal part after projection, that is, the error of the projection of the second data matrix G on the subspace approaches the minimum value. This can be solved by solving an optimization problem, and the specific steps are as follows:

[0101] The data compression device first selects the optimal subspace

[0102] where R k×d is a real space of k*d dimensional matrix, which is a floating point number. T is the transpose of the P matrix, is the Frobenius norm operation, and argmin is the minimum value optimization.

[0103] The subspace selection matrix S is selected from the previous step * Get the optimal projection subspace, that is, get the projection matrix P * =C×S *

[0104] Project and decompose the second data matrix G on the optimal projection subspace into the following two parts:

[0105] Projection part: (P *T P * ) -1 P *T G

[0106] Orthogonal part: GP(P T P) -1 P T G

[0107] Finally, the orthogonal part of the projection is compressed using methods such as LRMA compression, dictionary compression, transform domain compression, etc. The projection part can also be compressed using methods such as quantization and entropy coding.

[0108] In the embodiment of the present disclosure, the optimal subspace S * It is optimized for the second data matrix G so that the projection error of G in this subspace is minimized. When a new second data matrix G' appears, S can be recalculated. * , thus obtaining the optimal substrate every time.

[0109] In the embodiment of the present disclosure, for the second data matrix, the data to be transmitted includes three parts: the subspace selection matrix S *, the subspace projection part of the second data matrix, and the subsequent compression result of the projection residual of the second data matrix. FIG6B is a flowchart of the subspace projection generation projection matrix for data compression in an embodiment of the present application, and FIG6A and the above-mentioned acquisition of the subspace selection matrix S * , and obtain the projection matrix embodiment. Compared with FIG5B, in sending (623) the second data matrix 625, the subspace selection matrix S is added. * In the embodiment of the present disclosure, the subspace selection matrix S of the data compression device 431 is * Under the condition of , the projection matrix source indication 540 (1 bit) and the projection matrix column indication 545 (k bits) may not be sent. * The basis characteristics of the projection subspace have been fully described, and the source of the projection matrix and the column position of the projection matrix no longer need to be indicated.

[0110] In the embodiment of the present disclosure, the second data matrix G may be a fine-grained matrix, and the first data matrix and the reference matrix may be coarse-grained matrices.

[0111] In the embodiments of the present disclosure, the specific solution method for the above optimization problem will be described later. The optimal subspace selection problem is to select a d-dimensional subspace in the k-dimensional column space spanned by the reference data matrix C, so that the residual error of the second data matrix G after projection into the d-dimensional subspace is as small as possible, that is,

[0112] This problem has a closed-form solution. The detailed solution steps are as follows:

[0113] First, perform QR decomposition on the reference matrix C:

[0114] C=Q×R, The columns are orthogonal, Q is a real matrix with m rows and k columns, and R is a real matrix with k rows and k columns.

[0115] Substituting C=QR into P=CS, we get P=QRS. RS can be written as The original problem is equivalent to the following optimization problem:

[0116] The above formula is equivalent to Find the optimal d-dimensional projection subspace. The matrix Q can be T Do SVD decomposition on G and get Q T G=UΣV T

[0117] Optimal is equal to the first d columns of matrix U, so the optimal solution to the original problem is And further get the projection matrix P* =C×S *

[0118] To summarize the above solution process, we first project the data, then perform SVD decomposition to obtain the optimal subspace. This allows us to use a closed-form solution to obtain the optimal projected subspace associated with the second data matrix, reducing the projection error of the second data matrix and, consequently, the compression error.

[0119] In the embodiment of the present disclosure, the first data matrix, the reference matrix, and the second data matrix may have different granularities, for example, different spatial resolutions in a radio frequency map scenario.

[0120] Figure 7A is a schematic diagram of a radio frequency map (RF map) in an embodiment of the present application. Radio frequency map is an air interface native data related to geographic location information, which can describe the electromagnetic propagation characteristics in the environment and can be used to assist communication and positioning. As shown in embodiment 700, the radio frequency map data divides the space into small grids according to a certain resolution, and each grid is represented by a location point, usually the center point of the grid. The radio frequency map data can record the electromagnetic propagation environment information at the representative position, such as multipath information (such as angle, delay, power, etc.), scaler information (such as capacity, channel quality information CQI), whether the electromagnetic propagation between certain base stations is a line of sight (LOS) or a non-line of sight (NLOS), a deterministic H matrix, the geographic location coordinates represented by the grid, etc. The radio frequency map data can be a summary of the above information at the grid point position, or a part of the above information, describing the electromagnetic propagation characteristics of the entire area. Similar information can also be used in point cloud data scenarios.

[0121] In an embodiment of the present disclosure, for example, for the scenario shown in FIG. 1E , two methods, position-based and cluster-based, can be used to divide and retrieve coarse-grained data and fine-grained data. FIG. 7B shows a position-based method, while FIG. 7C shows a cluster-based method. In the embodiment 710 of FIG. 7B , the coarse-grained data 715 sent by the data compression device is data obtained by spatial low-resolution sampling of the original data, corresponding to the first data matrix. The fine-grained data 725 (corresponding to the second data matrix) sent by the data compression device is obtained based on the feedback result 720 of spatial position retrieval of the coarse-grained data 715 fed back from the data decompression device. The first granularity of the coarse-grained data 715 is greater than the second granularity of the fine-grained data 725. The data decompression device can feedback the index of a subset 720 of the received coarse-grained data 715, or can use a bitmap to indicate the subset 720. The index method and the bitmap method can be selected from either one, and a preconfigured method can be used. The data compression device can retrieve the fine-grained data 725 to be sent next based on the index of the subset 720 of the fed-back coarse-grained data, such as the position index in Figure 7B. In this way, an association can be established between the fine-grained data 725 and the coarse-grained data 715 through the position index or bitmap, making full use of the spatial correlation between the two types of data, performing data compression on the area of ​​interest, improving compression efficiency, and reducing data transmission resources or data storage space. In the embodiment of the present disclosure, the coarse-grained data 715 and the fine-grained data 725 can be in vector form or matrix form, where a vector is a special form of a matrix.

[0122] In the disclosed embodiment, the data decompression device may also use the confidence level of the coarse-grained data 715 for feedback. For example, the data decompression device may classify the confidence level into several levels and then provide feedback on the confidence level of each coarse-grained data 715. The data compression device then determines which coarse-grained data 715 to retrieve based on the feedback confidence level, thereby obtaining the fine-grained data 725 to be subsequently transmitted. Location-based or confidence-based methods can be used in radio frequency map data or point cloud data scenarios. Compared to retrieving subsets, using confidence levels allows for more accurate feedback.

[0123] In embodiment 730 of FIG7C , the data compression device clusters the raw data 735, and the obtained coarse-grained data 740 (corresponding to the first data matrix) is used as the cluster center of the raw data 735, and the coarse-grained data 740 is sent to the data decompression device. Points of different shapes in 735 represent raw data in different classes. For example, points of shapes such as regular triangles, squares, and diamonds represent raw data in different classes. In embodiment 730, the coarse-grained data 740 includes 6 cluster center points. The data decompression device retrieves 3 cluster center points 745 of interest from the coarse-grained data 740 and feeds them back to the data compression device. In the data compression device, fine-grained data 750 (corresponding to the second data matrix) is obtained based on the retrieval result 745 of the coarse-grained data fed back by the data decompression device. The first granularity of the coarse-grained data 740 is greater than the second granularity of the fine-grained data 750. This clustering method can be used in radio frequency map data scenarios or point cloud scenarios. In an embodiment of the present disclosure, similar to the scenario of FIG7B , the data decompression device may also provide feedback on the confidence of the coarse-grained data 735, which will not be described in detail in this disclosure. By clustering, the data transmission resources or data storage space of the coarse-grained data can be reduced. By generating fine-grained data through feedback on clustering, the spatial correlation between the coarse-grained data and the fine-grained data can be fully utilized, data compression can be performed on the area of ​​interest, compression efficiency can be improved, and data transmission resources or data storage space can be reduced. In an embodiment of the present disclosure, the coarse-grained data 740 and the fine-grained data 750 can be in vector form or in matrix form, and a vector is a special form of a matrix.

[0124] Figure 7D is a flowchart of retrieval-based data compression in an embodiment of the present application. Process 760 in Figure 7D corresponds to the embodiments in Figures 7A, 7B, and 7C, specifically illustrating the signaling used in these embodiments. Data compression device 761 and data decompression device 763 may be specific implementations of data compression device 201 and data decompression device 203, respectively.

[0125] In process 760, the data compression device 761 sends (764) a coarse-grained data matrix 765 to the data decompression device 763. The data decompression device 763 analyzes and retrieves the coarse-grained data matrix 765, and feeds back (768) the retrieval result or confidence 770 of the coarse-grained data matrix. At 775, the data compression device 761 projects the fine-grained data matrix G and subsequently compresses the projection residual (orthogonal part). The data compression device 761 sends (778) a projection matrix dimension d indication 780, a projection matrix source indication 782 (1 bit), and a projection matrix column indication 784 (k bits) to the data decompression device 763 to configure the data decompression device 763 to achieve synchronization of data compression and decompression. The data compression device 761 sends (788) a fine-grained data matrix 790 to the data decompression device 763. The fine-grained data matrix 790 may include: a subspace selection matrix S * , the projection result matrix (P *T P * ) -1 P *T G, and the projection residual GP(P T P) -1 P T G is compressed by subsequent compression. The fine-grained data matrix 790 may also include the projection result matrix (P *T P * ) -1 P *T In this way, a complete data transmission and signaling transmission mechanism can be established between the data compression device 761 and the data decompression device 763, and the correlation between coarse-grained data and fine-grained data can be used to perform data compression and decompression, thereby improving compression efficiency and reducing data transmission resources or data storage space.

[0126] Those skilled in the art will understand that, in addition to the coarse-grained data and fine-grained data of different spatial resolutions used for radio frequency maps or point cloud scenarios shown in the embodiments of Figures 7A-7C, coarse-grained data and fine-grained data can also be used for scenarios such as the frequency domain resolution of the channel data in Figure 1D, or other time domain resolutions, and the embodiments of the present disclosure are not limited to this.

[0127] The disclosed embodiment also discloses compressing fine-grained data using the correspondence between fine-grained data and coarse-grained data, and referring to the indication of coarse-grained data, for example, for the scenarios shown in FIG. 1E , FIG. 7A , FIG. 7B , and FIG. 7C .

[0128] There are two possible correspondences between fine-grained data and coarse-grained data: fine-grained data is not compressed in groups, and fine-grained data is compressed in groups, as shown in FIG8A and FIG8B respectively.

[0129] FIG8A is a schematic diagram of unpacked data compression in an embodiment of the present application.

[0130] In embodiment 800, reference coarse-grained data C 805 and fine-grained data G 810 to be compressed are not grouped. During compression, all fine-grained data G 810 are compressed together, and the compression process corresponds to, or refers to, the same set of coarse-grained data, that is, the overall reference coarse-grained data 805. The reference coarse-grained data 805 can be in two different forms. The current reference coarse-grained data can be all the coarse-grained data fed back by the data decompression device, or it can be a subset of the coarse-grained data sent by the data compression device in the previous round. Under the condition of using a subset of the coarse-grained data, the subset can be indicated, for example, by means of a subscript index or a bitmap. In this way, the fine-grained data can be compressed with reference to all the coarse-grained data or a subset of the coarse-grained data, making full use of the coarse-grained data and improving the compression effect.

[0131] FIG8B is a schematic diagram of packet data compression in an embodiment of the present application.

[0132] In embodiment 820, the data compression device groups the fine-grained data 830, for example, into the three groups shown in FIG8B . Each group of fine-grained data corresponds to a group in the coarse-grained data 825. Specifically, the fine-grained data 830 currently sent by the data compression device can be divided into multiple groups, and each group refers to a coarse-grained data fed back by the data decompression device. For example, corresponding to the clustering embodiment of FIG7C , grouping can be performed based on clustering, and the reference coarse-grained data of each group can be the cluster center of the group. Each group of data in the fine-grained data 830 can refer to a subset of the coarse-grained data 825 sent in the previous round. In the embodiment of the present disclosure, the subset can be indicated, for example, by using a subscript index or a bitmap. In this way, the compression of fine-grained data can refer to the part of the coarse-grained data that is most relevant to it, thereby reducing computational complexity and improving compression efficiency.

[0133] Figure 9 is a flow chart of integrated signaling interaction in an embodiment of the present application. In Figure 9, embodiment 900 summarizes the signaling used in the above embodiments. Data compression device 901 and data decompression device 903 can be specific implementations of data compression device 201 and data decompression device 203.

[0134] The first data matrix (coarse-grained data matrix) 910, block 920, and second data matrix (fine-grained data matrix) 960 in FIG9 correspond to the first data matrix 405, block 410, and second data matrix 625 in FIG6B , and the coarse-grained data matrix 765, confidence level 770, block 775, and fine-grained data matrix 790 in FIG7D , respectively. The retrieval results of the coarse-grained data and confidence level 910 in FIG9 correspond to the retrieval results of the coarse-grained data matrix in FIG7D , and will not be further described in this disclosure.

[0135] In process 900, the sixth configuration information, or projection space mode indication 935, is used to select one of the following two methods for projection: using the column space of the reference matrix (reference coarse-grained data) for projection, or using the optimal subspace of the column space of the reference matrix (reference coarse-grained data) for projection. The seventh configuration information identifies the reference matrix from the first data matrix.

[0136] The mode and parameters 905 to be configured, which are sent (904) by the data decompression device 903 to the data compression device 901, include: first configuration information, second configuration information, third configuration information, fourth configuration information, and fifth configuration information. The mode and parameters 905 to be configured may also be sent (904) by the data compression device 901 to the data decompression device 903 to synchronize the data compression device 901 with the data decompression device 903.

[0137] Configuration signaling related to data partitioning and retrieval may include first configuration information, second configuration information, and third configuration information. The first configuration information, or subset feedback mode configuration information, is used to identify whether the data decompression device 903 uses a subscript index or a bitmap when feeding back a coarse-grained data subset. The second configuration information, or confidence feedback enable configuration information, is used to identify whether the data decompression device 903 adds confidence feedback when feeding back a coarse-grained data subset. The third configuration information, or data partition mode configuration information, is used to select between a geographic location-based method and a cluster-based method.

[0138] Configuration signaling related to the data correspondence relationship and the reference coarse-grained data indication may include fourth configuration information and fifth configuration information. The fourth configuration information or data correspondence mode configuration information is used to select between group correspondence and non-group correspondence. The fifth configuration information or coarse-grained data reference mode configuration information is used to select between the following two reference modes: compressing the current fine-grained data (or a certain group of fine-grained data) with reference to the coarse-grained data fed back by the data decompression device 903; or compressing the current fine-grained data (or a certain group of fine-grained data) with reference not to the coarse-grained data fed back by the data decompression device 903, but all the coarse-grained data sent by the previous round of data compression device 901, or a subset of all the coarse-grained data.

[0139] In the embodiment of the present disclosure, the data compression device 901 and the data decompression device 903 can be well configured and synchronized through the above-mentioned signaling, so as to facilitate accurate compression and decompression.

[0140] Figure 10A is a schematic diagram of the effect of compressing radio frequency map data in an embodiment of the present application. In embodiment 1000, a comparison is made between the distortion of LRMA in an implementation and the distortion of LRMA using projection in an embodiment of the present disclosure.

[0141] The simulation corresponding to embodiment 1000 adopts a coarse data and fine-grained data generation mode based on geographic location for RF map data, corresponding to the scenario of Figure 1E. For coarse-grained data, RF map data of 4 geographic locations at a downsampled spatial resolution are used, with 10 diameters for each location. For fine-grained data, RF map data of 12 locations at the original spatial resolution are used, with 10 diameters for each location. Benchmark 1005 is to directly perform LRMA compression on the fine-grained data. In the embodiment of the present disclosure, corresponding to 1010, the coarse-grained data of all 4 locations are used as the reference coarse-grained data, and the projection matrix is ​​selected from the first d columns of the projection basis after LRMA decomposition of the reference coarse-grained data, and dynamic optimization is performed on d. Curves 1005 and 1010 are rate distortion (RD) curves for compressing the pitch angle, the horizontal axis is the number of floating-point numbers after compression, and the vertical axis is the distortion. It can be seen from the embodiment 1000 that, corresponding to the same vertical axis distortion, the number of compressed floating-point numbers in the embodiment 1010 of the present disclosure is smaller than that in the reference 1005 , achieving a good data compression effect.

[0142] Figure 10B is a schematic diagram of the effect of compressing the channel matrix in an embodiment of the present application. In embodiment 1020, a comparison is made between the distortion of LRMA based on an implementation and the distortion of LRMA using projection in an embodiment of the present disclosure.

[0143] The simulation corresponding to embodiment 1020 is mainly for the channel matrix data of a large-scale multiple input multiple output (MIMO) antenna system, corresponding to the scenario of Figure 1D. The simulation environment is a single user, and data is fed back once every 20 transmission time intervals (TTIs), and each TTI contains 14 orthogonal frequency division multiplexing (OFDM) symbols. 64 resource blocks (RBs)*12 sub-carriers (SCs)=768 frequency points are used, and the sub-carrier spacing is 3K Hz. The number of antennas is 32 for reception and 1024 for transmission. Benchmark 1025 corresponds to reconstructing the data of the current TTI into a 32768*768 matrix, directly performing LRMA compression, and processing the real and imaginary parts separately. In 1030 of the embodiment of the present disclosure, the data fed back last time, such as the data 20 TTIs ago, is used as a reference, and the projection matrix is ​​selected from the first d columns of the projection basis after the reference data is decomposed by LRMA, and d is fixed to 40. Curves 1025 and 1030 are rate distortion (RD) curves for compressing the channel transmission data matrix H. The horizontal axis represents the number of floating-point numbers after compression, and the vertical axis represents distortion. As can be seen from Example 1020, for the same vertical axis distortion, the number of floating-point numbers after compression in Example 1030 of the present disclosure is smaller than that in the baseline 1025, achieving good data compression results.

[0144] As can be seen from FIG. 10A and FIG. 10B , the compression using the correlation between the coarse-grained data and the fine-grained data, or the first data matrix and the second data matrix, achieves good results.

[0145] Figure 11 is a flowchart of a data compression method according to an embodiment of the present application. This method can be performed by a data compression device 201. Unless otherwise specified, the data compression device 201 in the embodiments of the present application can refer to the data compression device itself (e.g., implemented as a terminal device or network device), a component within the data compression device (e.g., a processor, chip, or chip system), or a logic module or software that implements all or part of the data compression device's functions. The following description uses the data compression device 201 as an example. In method 1100, at 1110, the data compression device 201 transmits a first data matrix. At 1120, the data compression device 201 transmits the projection result and projection residual of a second data matrix. The projection result is determined based on the projection matrix determined by the second data matrix relative to the first data matrix, and the projection residual is determined based on the second data matrix and the projection result. This allows full utilization of the correlation between the first and second data matrices, improving compression efficiency and conserving transmission bandwidth or storage resources.

[0146] In some implementations, other operations performed at the data compression device 201 described in conjunction with FIG. 2 to FIG. 10 in the embodiments of the present disclosure are also included.

[0147] Figure 12 is a flow chart of the data decompression method in an embodiment of the present application. This method can be executed by the data decompression device 203. Unless otherwise specified, the data decompression device 203 in the embodiment of the present application can refer to the data decompression device itself (for example, implemented as a terminal device, network device), or a component in the data decompression device (for example, a processor, chip, or chip system, etc.), or it can also be a logic module or software that can implement all or part of the functions of the data decompression device. The following description takes the execution subject as the data decompression device 203 as an example.

[0148] In method 1200, at 1210, the data decompression device 203 receives a first data matrix. At 1220, the data decompression device 203 receives a projection result and a projection residual of a second data matrix. At 1230, the data decompression device 203 obtains a second data matrix or an approximate matrix of the second data matrix based on the first data matrix, the projection result, and the projection residual. The projection result is determined based on a projection matrix of the second data matrix relative to the first data matrix, and the projection residual is determined based on the second data matrix and the projection result. In this way, the data compressed by the data compression device can be decompressed and restored, fully utilizing the correlation between the first data matrix and the second data matrix, improving compression efficiency, and saving transmission bandwidth or storage resources.

[0149] In some implementations, other operations performed at the data decompression device 203 as described in conjunction with FIG. 2 to FIG. 10 in the embodiments of the present disclosure are also included.

[0150] Figures 13 and 14 are schematic diagrams of the structures of possible communication devices provided by embodiments of the present application. These communication devices can implement the functions of the terminal device or network device in the above-mentioned method embodiment, and therefore can also achieve the beneficial effects possessed by the above-mentioned method embodiment. In an embodiment of the present application, the communication device can be a terminal device 101 as shown in Figure 1A, or it can be a network device 103 as shown in Figure 1A, or it can be a module (for example, a processor, a chip, or a chip system, etc.) applied to a terminal device or a network device, or it can also be a logic module or software that can implement all or part of the functions of a terminal device or a network device. The terminal device or the network device can be implemented as a data compression device or a data decompression device.

[0151] As shown in Figure 13, the communication device 1300 includes a transceiver module 1301 and a processing module 1302. The communication device 1300 can be used to implement the functions of the data compression device or the data decompression device in the method embodiments shown in Figures 2, 11, and 12 above.

[0152] When communication device 1300 is used to implement the functions of the data compression device in the method embodiments depicted in FIG2 and FIG11 , transceiver module 1301 is configured to transmit a first data matrix and a projection result and projection residual of a second data matrix. Processing module 1302 is configured to determine a projection result based on a projection matrix determined by the second data matrix relative to the first data matrix, and to determine a projection residual based on the second data matrix and the projection result.

[0153] When communication device 1300 is used to implement the functions of the data decompression device in the method embodiments depicted in Figures 2 and 12: transceiver module 1301 is configured to receive a first data matrix and a projection result and projection residual of a second data matrix. Processing module 1302 is configured to obtain a second data matrix or an approximate matrix of the second data matrix based on the first data matrix, the projection result, and the projection residual. The projection result is determined based on a projection matrix determined by the second data matrix relative to the first data matrix, and the projection residual is determined based on the second data matrix and the projection result.

[0154] As shown in Figure 14, communication device 1400 includes a processor 1410 and an interface circuit 1420. Processor 1410 and interface circuit 1420 are coupled to each other. It is understood that interface circuit 1420 can be a transceiver or an input / output interface. Optionally, communication device 1400 may also include a memory 1430 for storing instructions executed by processor 1410, input data required by processor 1410 to execute instructions, or data generated after processor 1410 executes instructions.

[0155] When the communication device 1400 is used to implement the method in the above method embodiment, the processor 1410 is used to execute the functions of the above processing module 1302 , and the interface circuit 1420 is used to execute the functions of the above transceiver module 1301 .

[0156] When the communication device is a chip used in a terminal device, the terminal device chip implements the functions of the terminal device in the above method embodiments. The terminal device chip receives information from other modules in the terminal device (such as a radio frequency module or antenna), and the information is sent by the network device to the terminal device; or the terminal device chip sends information to other modules in the terminal device (such as a radio frequency module or antenna), and the information is sent by the terminal device to the network device.

[0157] When the communication device is a chip used in a network device, the network device chip implements the network device functions of the above method embodiments. The network device chip receives information from other modules in the network device (such as a radio frequency module or antenna), and the information is sent by the terminal device to the network device; or the network device chip sends information to other modules in the network device (such as a radio frequency module or antenna), and the information is sent by the network device to the terminal device.

[0158] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0159] When the apparatus in the embodiment of the present application is a network device, the apparatus may be as shown in FIG15 . The apparatus may include one or more radio frequency units, such as a remote radio unit (RRU) 1510 and one or more baseband units (BBU) (also referred to as digital units, DU) 1520. The RRU 1510 may be referred to as a transceiver module, which may include a transmitting module and a receiving module, or the transceiver module may be a module capable of performing both transmitting and receiving functions. The transceiver module may correspond to the transceiver module 1301 in FIG13 , that is, it may perform the actions performed by the transceiver module 1301. Optionally, the transceiver module may also be referred to as a transceiver, a transceiver circuit, or a transceiver, etc., and may include at least one antenna 1511 and a radio frequency unit 1512. The RRU 1510 portion is primarily used for transmitting and receiving radio frequency signals and converting radio frequency signals into baseband signals. The BBU 1510 portion is primarily used for baseband processing, controlling the base station, etc. The RRU 1510 and the BBU 1520 may be physically arranged together or physically separated, that is, a distributed base station.

[0160] The BBU 1520 is the control center of the base station, also known as a processing module, which may correspond to the processing module 1302 in Figure 13 and is primarily used to perform baseband processing functions such as channel coding, multiplexing, modulation, and spread spectrum. Furthermore, the processing module can execute actions typically performed by the processing module 1302. For example, the BBU (processing module) may be used to control the base station to execute the network device operation procedures described in the aforementioned method embodiments.

[0161] In one example, the BBU 1520 can be composed of one or more single boards, and multiple single boards can jointly support a wireless access network with a single access standard (such as an LTE network), or can separately support wireless access networks with different access standards (such as an LTE network, a 5G network, or other networks). The BBU 1520 also includes a memory 1521 and a processor 1522. The memory 1521 is used to store necessary instructions and data. The processor 1522 is used to control the base station to perform necessary actions, such as controlling the base station to execute the operation process of the network device in the above method embodiment. The memory 1521 and the processor 1522 can serve one or more single boards. That is, a memory and a processor can be set separately on each single board. Alternatively, multiple single boards can share the same memory and processor. In addition, necessary circuits can also be set on each single board.

[0162] An embodiment of the present application provides a communication system. The communication system may include the data compression device and data decompression device involved in the embodiment shown in Figure 2 above, such as terminal device 101 or network device 103. Optionally, the terminal device and network device in the communication system may execute the communication method shown in any of Figures 2, 11, and 12.

[0163] The present application also provides a circuit that can be coupled to a memory and can be used to execute the processes related to the terminal device or network device in any of the above method embodiments. The chip system may include the chip and other components such as a memory or a transceiver.

[0164] It should be understood that the processor mentioned in the embodiments of the present application may be a CPU, or may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0165] It should also be understood that the memory mentioned in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0166] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) is integrated into the processor.

[0167] It should be noted that the memory described herein is intended to include, but not be limited to, these and any other suitable types of memory.

[0168] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0169] It will be appreciated that the modules and algorithmic steps of each example described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians 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.

[0170] It can be clearly understood that for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed communication methods and devices can be implemented in other ways. For example, the device embodiments described above are schematic. For example, the division of the modules is a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0172] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network elements. Some or all of these elements may be selected to achieve the purpose of this embodiment according to actual needs.

[0173] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0174] If this function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the contributing part or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method of each embodiment of the present application. The aforementioned computer-readable storage medium can be any available medium that can be accessed by a computer. By way of example and not limitation, computer-readable media may include random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), universal serial bus flash disk, mobile hard disk, or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer.

[0175] As used herein, the term "including" and similar terms should be understood as open inclusion, i.e., "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. can refer to different or the same objects and are used to distinguish the objects referred to without implying a specific spatial order, temporal order, order of importance, etc. of the objects referred to. In some embodiments, values, processes, selected items, determined items, devices, means, components, assemblies, etc. are referred to as "best", "lowest", "highest", "minimum", "maximum", etc. It should be understood that such descriptions are intended to indicate that a selection can be made from a number of available functional options, and that such a selection need not be better, lower, higher, smaller, larger, or otherwise preferred than other options in other aspects or all aspects. As used herein, the term "determine" can encompass a variety of actions. For example, "determine" can include calculating, computing, processing, deriving, investigating, searching (e.g., searching in a table, database, or another data structure), ascertaining, etc. Furthermore, "determining" may include receiving (eg, receiving information), accessing (eg, accessing data in a memory), etc. Furthermore, "determining" may include resolving, selecting, choosing, establishing, etc.

Claims

1. A method comprising: sending a first data matrix; as well as Sending the projection result and projection residual of the second data matrix; wherein the projection result is determined according to a projection matrix determined by the second data matrix relative to the first data matrix, The projection residual is determined according to the second data matrix and the projection result.

2. A method comprising: receiving a first data matrix; receiving a projection result and a projection residual of a second data matrix; as well as Based on the first data matrix, the projection result and the projection residual, obtaining the second data matrix or an approximate matrix of the second data matrix, wherein the projection result is determined according to a projection matrix determined by the second data matrix relative to the first data matrix, The projection residual is determined according to the second data matrix and the projection result.

3. The method according to claim 1 or 2, wherein the projection matrix is ​​determined according to the following manner: A predetermined number of columns are selected in a reference matrix to form the projection matrix, the reference matrix being a sub-matrix of the first data matrix or the first data matrix.

4. The method according to claim 1 or 2, wherein the projection matrix is ​​determined according to the following manner: Determine a projection basis matrix based on a reference matrix; and selecting a predetermined number of columns in the projection basis matrix to form the projection matrix, The reference matrix is ​​the first data matrix or a submatrix of the first data matrix.

5. The method according to claim 4, wherein determining the projection basis matrix according to the reference matrix comprises: A matrix decomposition is performed on the reference matrix to determine a projection basis matrix.

6. The method according to claim 5, wherein the matrix decomposition comprises any one of the following: Low Rank Matrix Approximation (LRMA) decomposition, SVD decomposition, or QR decomposition.

7. The method according to any one of claims 1 or 3-6, further comprising sending at least one of the following: First indication information, used to indicate the number of columns of the projection matrix; Second indication information, used to indicate whether the projection matrix is ​​based on the reference matrix or on a projection basis matrix obtained by performing LRMA decomposition on the reference matrix; or The third indication information is used to indicate the position of the column of the projection matrix in the reference matrix or the projection base matrix.

8. The method according to any one of claims 2 to 6, further comprising receiving at least one of the following: First indication information, used to indicate the number of columns of the projection matrix; Second indication information, used to indicate whether the projection matrix is ​​based on the reference matrix or on a projection basis matrix obtained by performing LRMA decomposition on the reference matrix; or The third indication information is used to indicate the position of the column of the projection matrix in the reference matrix or the projection base matrix.

9. The method of claim 1 , wherein determining the projection matrix comprises: selecting a subspace of a predetermined number of dimensions in the column space spanned by the reference matrix, wherein the projection matrix represents the subspace, The reference matrix is ​​the first data matrix or a submatrix of the first data matrix. 10 . The method according to claim 9 , wherein the subspace is selected so as to minimize the norm of the projection residual after the second data matrix is ​​projected onto the projection matrix.

11. The method of claim 9 or 10, wherein determining the projection matrix comprises: Performing QR decomposition on the reference matrix to determine a Q matrix; Performing singular value decomposition on the product of the transpose of the Q matrix and the data matrix to obtain an eigenvector matrix; determining a subspace selection matrix based on the first predetermined number of columns of the eigenvector matrix; as well as The projection matrix is ​​determined based on the subspace selection matrix and the reference matrix.

12. The method of claim 9 or 10, wherein determining the projection matrix comprises: Perform QR decomposition on the reference data C to obtain a Q matrix; C=Q×R, The columns are orthogonal, For the matrix Q T Perform SVD decomposition on G to obtain the U matrix, where G is the second data matrix; Q T G=U∑V T Get the first d columns of matrix U, denoted as Get the subspace selection matrix Get the projection matrix P * =C×S * .

13. The method of claim 9 or 10, wherein determining the projection matrix comprises: Solve the following optimization problem, where G is the second data matrix and C is the reference matrix 14. The method according to any one of claims 1, 9-11, further comprising: Fourth indication information is sent, where the fourth indication information is used to indicate a subspace selection matrix, wherein the subspace selection matrix is ​​used to determine the projection matrix together with the reference matrix.

15. The method according to any one of claims 2, 9-11, further comprising: Fourth indication information is received, where the fourth indication information is used to indicate a subspace selection matrix, wherein the subspace selection matrix is ​​used to determine the projection matrix together with the reference matrix.

16. The method according to claim 1, further comprising: receiving feedback information for the first data matrix; as well as The second data matrix is ​​determined based on the feedback information and the first data matrix.

17. The method according to claim 1, further comprising: Sending fifth indication information for the first data matrix; as well as Based on the fifth indication information and the first data matrix, the second data matrix is ​​determined.

18. The method according to claim 2, further comprising: Feedback information for the first data matrix is ​​sent, where the feedback information is used together with the first data matrix to determine the second data matrix.

19. The method according to claim 2, further comprising: receiving fifth indication information for the first data matrix; The fifth indication information is used together with the first data matrix to determine the second data matrix.

20. The method according to any one of claims 16 to 19, wherein the feedback information or the fifth indication information comprises at least one of the following: Subset indication information, used to indicate the first data matrix or a sub-matrix of the first data matrix; or Confidence information is used to indicate the confidence of the first data matrix.

21. The method of any one of claims 1-20, wherein the first data matrix has a first granularity, the second data matrix has a second granularity, and the first granularity is greater than the second granularity.

22. The method of claim 21, wherein at least one of the following: The first data is sampled data of a geographic space using a first resolution, the second data matrix is ​​sampled data of the geographic space using a second resolution higher than the first resolution, and the subset indication information indicates the subset of the first data matrix. Spatial location information; or The first data matrix is ​​a plurality of cluster centers of a plurality of data classes determined by performing clustering on the original data, the second data matrix is ​​the data contained in one or more data classes among the plurality of data classes, and the subset indication information indicates one or more cluster centers among the plurality of cluster centers corresponding to the one or more data classes.

23. The method according to any one of claims 1 to 22, wherein the reference matrix is ​​determined based on at least one of: Feedback information for the first data matrix; a submatrix of the first data matrix; or The first data matrix.

24. The method according to any one of claims 2 to 23, wherein: The second data matrix is ​​not grouped but corresponds to the reference matrix as a whole; or The second data matrix is ​​divided into a plurality of data groups, and the plurality of data groups respectively correspond to a plurality of sub-matrices of the reference matrix.

25. The method according to any one of claims 1, 3-7, 9-14, 16, 17, 20-24, further comprising sending at least one of the following: first configuration information, used to indicate whether the feedback information for the first data matrix is ​​based on an index or a bitmap; second configuration information, used to indicate whether confidence feedback for the first data matrix is ​​enabled; third configuration information, used to indicate whether the first data matrix is ​​based on geographic location or clustering; fourth configuration information, used to indicate whether the reference matrix is ​​determined based on feedback information for the first data matrix or based on the first data matrix; or The fifth configuration information is used to indicate whether the second data matrix as a whole corresponds to the reference matrix, or whether the multiple data groups divided into the second data matrix correspond to multiple sub-matrices of the reference matrix respectively.

26. The method of claim 25, wherein at least one of the first configuration information, the second configuration information, the third configuration information, the fourth configuration information, or the fifth configuration information is transmitted before the first data matrix is ​​transmitted.

27. The method of any one of claims 2, 8, 15, 18-24, further comprising receiving at least one of: first configuration information, used to indicate whether the feedback information for the first data matrix is ​​based on an index or a bitmap; second configuration information, used to indicate whether confidence feedback for the first data matrix is ​​enabled; third configuration information, used to indicate whether the first data matrix is ​​based on geographic location or clustering; fourth configuration information, used to indicate whether the reference matrix is ​​determined based on feedback information for the first data matrix or based on the first data matrix; or The fifth configuration information is used to indicate whether the second data matrix as a whole corresponds to the reference matrix, or whether the multiple data groups divided into the second data matrix correspond to multiple sub-matrices of the reference matrix respectively.

28. The method of claim 27, wherein at least one of the first configuration information, the second configuration information, the third configuration information, the fourth configuration information, or the fifth configuration information is received before the first data matrix is ​​transmitted.

29. The method of any one of claims 1, 3-7, 9-14, 16, 17, 20-26, further comprising sending at least one of the following: Sixth configuration information, used to indicate whether the projection matrix is ​​based on the column space of the reference matrix or on a subspace of the column space; or The seventh configuration information is used to identify the reference matrix from the first data matrix. 30 . The method according to claim 29 , wherein at least one of the sixth configuration information or the seventh configuration information is sent before sending the projection result and the projection residual.

31. The method of any one of claims 2, 8, 18-24, 27, 28, further comprising receiving at least one of: Sixth configuration information, used to indicate whether the projection matrix is ​​based on the column space of the reference matrix or on a subspace of the column space; or The seventh configuration information is used to identify the reference matrix from the first data matrix. 32 . The method according to claim 31 , wherein at least one of the sixth configuration information or the seventh configuration information is received before the projection result and the projection residual are sent.

33. An apparatus comprising: A first data matrix sending module, used for sending a first data matrix; as well as A result sending module, used for sending the projection result and projection residual of the second data matrix; wherein the projection result is determined according to a projection matrix determined by the second data matrix relative to the first data matrix, The projection residual is determined according to the second data matrix and the projection result.

34. An apparatus comprising: A first data matrix receiving module, used for receiving a first data matrix; A result receiving module, used for receiving the projection result and projection residual of the second data matrix; as well as A second data matrix acquisition module is used to acquire the second data matrix or an approximate matrix of the second data matrix based on the first data matrix, the projection result and the projection residual. wherein the projection result is determined according to a projection matrix determined by the second data matrix relative to the first data matrix, The projection residual is determined according to the second data matrix and the projection result.

35. A system comprising the apparatus of claims 33 and 34.

36. An apparatus comprising: A processor, and a memory storing instructions, wherein when the instructions are executed by the processor, the terminal device executes the method according to any one of claims 1 to 32.

37. A computer-readable storage medium storing instructions, which, when executed by an electronic device, causes the electronic device to perform the method according to any one of claims 1 to 32.

38. A computer program product, comprising instructions, which, when executed by an electronic device, cause the electronic device to perform the method according to any one of claims 1 to 32.

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