Data compression and transmission method, and apparatus and storage medium

Abstract

A data compression and transmission method, and an apparatus and a storage medium. The method comprises: a first communication apparatus determining a compression parameter corresponding to the data type of first data; then, on the basis of the determined compression parameter, performing data compression on the first data, so as to obtain compressed information, wherein the compressed information comprises base information and coefficient information, or only the coefficient information, the base information is used for expressing the first data, and the coefficient information comprises an expression coefficient of at least one piece of sub-information of the base information for the first data; and then sending the compressed information, such that the compressed information of the first data that is obtained by means of compression has a relatively high compression effect, such as a relatively high compression rate and a relatively low compression loss.

Description

Data compression transmission method and device, and storage medium

[0001] The present application claims priority to the Chinese patent application No. 202411392477.9, filed on September 30, 2024, and entitled "Data compression transmission method and device, and storage medium", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the field of communication technology, and in particular to a data compression transmission method and device, and storage medium. BACKGROUND

[0003] In some communication scenarios, communication devices can compress data to be transmitted based on a dictionary learning technology, and transmit the compressed data. Due to the universality of compression based on the dictionary learning technology, it is widely used for data compression of different data, such as point cloud data, artificial intelligence (AI) data, channel state information (CSI), radio frequency map (RF map), etc.

[0004] However, when different data is compressed based on the dictionary learning technology, the compression effect often varies, that is, the compression effect may be poor when some data is compressed. SUMMARY

[0005] The data compression transmission method, device, and storage medium provided by the embodiments of the present application can achieve high-reliability compression transmission for different data to be compressed, thereby improving the universality and compression effect of data compression based on the dictionary learning technology.

[0006] In a first aspect, the present application provides a data compression transmission method. The method can be executed by a first communication device. In the absence of special description, the first communication device in the present application can refer to a communication device itself (for example, a network device, a terminal device), a component in the communication device (for example, a processor, a chip, or a chip system, etc.), or a logic module or software capable of realizing all or part of the functions of the communication device.

[0007] In the method, the first communication device determines a compression parameter corresponding to a data type of the first data, and then performs data compression on the first data based on the determined compression parameter to obtain compression information, the compression information including: basis information and coefficient information, or the coefficient information, the basis information being used to express the first data, and the coefficient information including an expression coefficient of at least one sub-information of the basis information to the first data, and then transmitting the compression information, so that the compression information of the compressed first data has a higher compression effect, such as a higher compression rate and a lower compression loss.

[0008] In a possible implementation, the compression parameter includes a first parameter, and the first parameter indicates the basis information. The basis information corresponding to the data type of the first data has a better expression ability to the first data, and based on this, the first communication device performs compression on the first data based on the corresponding basis information, which can improve the compression effect of the first data.

[0009] In a possible implementation, the first parameter can indicate whether the basis information includes first basis information and second basis information, and the first basis information and the second basis information are used to express the first data. The two basis information can have a better expression ability to the first data, so as to further improve the compression effect.

[0010] In a possible implementation, the compression parameter includes a second parameter, and the second parameter indicates an execution order of the first compression processing and the second compression processing. The first compression processing is performed first and then the second compression processing is performed, which can effectively reduce the quantization interval and improve the quantization precision. The second compression processing is performed first and then the first compression processing is performed, which is more conducive to screening useful data for some data types, and can avoid affecting the unbiased characteristics of the data by quantizing the screened coefficients for other data types, so as to ensure the performance reliability of the model. Therefore, the execution order of the first compression processing and the second compression processing can be related to the data type of the first data. The first communication device performs compression processing on the first data based on the order of the compression processing indicated by the second parameter corresponding to the data type of the first data, which can improve the compression effect.

[0011] In a possible implementation, the compression parameter includes a parameter indicating a threshold value, and the threshold value is used to screen the coefficient information. The first communication device performs coefficient screening based on the threshold value corresponding to the data type of the first data to obtain the coefficient information, which can reduce the data amount of the compression information and further improve the compression rate.

[0012] In a possible implementation, the compression parameter includes a parameter indicating a quantization range. The first communication device performs quantization compression based on the quantization range corresponding to the data type of the first data, which can further improve the compression effect.

[0013] In a possible implementation, the first data can be processed before being compressed, and the processed data can be compressed to obtain the compression information, so as to achieve a better compression effect. For example, the compression parameter includes a third parameter, which indicates that the first data is processed by K data processing processes, where the K data processing processes include at least one of the following:

[0014] the first data is divided into N sub-data; or

[0015] a dimension of the first data is converted from a first dimension to a second dimension, and the dimension includes a number of rows and a number of columns of the first data; or

[0016] a plurality of second data units in the first data are divided into M data groups; or

[0017] the first data is normalized.

[0018] It can be understood that K, M, and N are positive integers.

[0019] In a possible implementation, when the K data processing processes include dividing the first data into N sub-data, the compression parameter includes a fourth parameter, which indicates a data length of each sub-data or the number N of the sub-data. It should be understood that the data length of each sub-data and / or the number N of the sub-data can be associated with a data type of the first data, that is, different data segmentation manners can be used for different data types to reduce loss of data characteristics of the first data after data segmentation.

[0020] In a possible implementation, when the K data processing processes include converting the dimension of the first data from the first dimension to the second dimension, the compression parameter includes a fifth parameter, which indicates the second dimension. The second dimension to which the first data is to be converted can be associated with the data type of the first data, that is, different dimensions can be converted for different data types to improve the compression effect.

[0021] In a possible implementation, when the K data processing processes include dividing the plurality of second data units in the first data into M data groups, the compression parameter can include a sixth parameter, which indicates a dimension or a value range of the second data units in each data group. The dimension or the value range of the second data units in each data group can be associated with the data type of the first data. Therefore, the first communication apparatus processes the first data based on the dimension or the value range corresponding to the data type of the first data, so that the processed first data has a better compression effect in a subsequent compression process.

[0022] In a possible implementation, the third parameter is further used to indicate an execution position of each data processing in the data compression process, and / or an execution order between at least two data processing. The execution position of each data processing in the data compression, and the execution order between different data processing, can be associated with the data type of the first data. When the first communication device performs data processing and data compression on the first data, the first data is executed according to the order corresponding to the data type of the first data, so that the compression effect of the first data can be improved.

[0023] Optionally, the third parameter is further used to indicate a number K of data processing, where K is a positive integer.

[0024] In a possible implementation, in order to synchronize part or all of the compression parameters between the first communication device and the second communication device, the first communication device can receive first indication information, where the first indication information is used to indicate the compression parameters, or the first communication device can send second indication information, where the second indication information is used to indicate the compression parameters.

[0025] Optionally, the first indication information is carried in radio resource control (RRC) signaling.

[0026] In a second aspect, a data compression transmission method is provided. The method can be performed by a second communication device. In the case where no special description is given, the second communication device in the present application can refer to a communication device itself (for example, a network device, a terminal device), a component in the communication device (for example, a processor, a chip, or a chip system), or a logic module or software capable of realizing all or part of the functions of the communication device.

[0027] In the method, the second communication device receives compression information of the first data, where the compression information includes base information and coefficient information, or the coefficient information, the base information is used to express the first data, and the coefficient information includes a coefficient of at least one sub-information of the base information to the first data, and the first data is recovered according to the compression information and a compression parameter corresponding to the data type of the first data.

[0028] In a possible implementation, the compression parameter includes a first parameter, and the first parameter is used to indicate the base information.

[0029] In a possible implementation, the first parameter is further used to indicate whether the base information includes first base information and second base information, and the first base information and the second base information are used to express the first data.

[0030] In a possible implementation, the compression parameter includes a second parameter, and the second parameter is used to indicate an execution order of a first compression processing and a second compression processing; the first compression processing is used to filter to obtain the coefficient information; and the second compression processing is used to quantitatively compress.

[0031] In a possible implementation, the compression parameter comprises a parameter indicating a threshold value used for screening the coefficient information.

[0032] In a possible implementation, the compression parameter comprises a parameter indicating a quantization range.

[0033] In a possible implementation, the compression parameter comprises a third parameter indicating K data processing procedures performed on the first data; wherein the K data processing procedures comprise at least one of the following:

[0034] segmenting a plurality of data units of the first data to obtain N sub-data;

[0035] converting a data dimension of the first data from a first dimension to a second dimension, the data dimension comprising a number of rows and a number of columns of the first data;

[0036] dividing a plurality of data units in the first data into M data groups; or

[0037] performing normalization processing on the first data.

[0038] It can be understood that K, M and N are positive integers.

[0039] In a possible implementation, the compression parameter comprises a fourth parameter indicating a data length of each sub-data or the number N of sub-data.

[0040] In a possible implementation, the compression parameter comprises a fifth parameter indicating the second dimension.

[0041] In a possible implementation, the compression parameter comprises a sixth parameter indicating a data dimension or a value range of the data units in each data group.

[0042] In a possible implementation, the third parameter is further used for indicating an execution position of the data processing in the data compression process, and / or an execution order between at least two data processing procedures.

[0043] In a possible implementation, the third parameter is further used for indicating the number K of data processing procedures, wherein K is a positive integer.

[0044] In a possible implementation, the method further comprises: sending, by the second communication apparatus, first indication information, the first indication information being used for indicating the compression parameter.

[0045] In a possible implementation, the first indication information is carried in radio resource control (RRC) signaling.

[0046] In a possible implementation, the method further includes: receiving, by the second communication device, second indication information, the second indication information being used for indicating the compression parameter.

[0047] In a third aspect, the present application provides a communication device, including a module for performing the method in the first aspect or any possible implementation, or including a module for performing the method in the second aspect or any possible implementation.

[0048] In a fourth aspect, the present application provides a communication device, including a processor for performing the method in the first aspect, the second aspect or any possible implementation by running a computer program or by a logic circuit.

[0049] In a possible implementation, the communication device further includes a memory for storing the computer program.

[0050] In a possible implementation, the communication device further includes a communication interface for inputting and outputting signals.

[0051] In a fifth aspect, the present application provides a chip, including a processor for calling and running computer instructions from a memory, so that a device installed with the chip performs the method in the first aspect, the second aspect or any possible implementation.

[0052] In a sixth aspect, the present application provides a communication system, including a first communication device for performing the method in the first aspect or any possible implementation, and a second communication device for performing the method in the second aspect or any possible implementation.

[0053] In a seventh aspect, the present application provides a computer readable storage medium for storing computer program instructions, the computer program instructions causing a computer to perform the method in the first aspect, the second aspect or any possible implementation.

[0054] In an eighth aspect, the present application provides a computer program for causing a computer to perform the method in the first aspect, the second aspect or any possible implementation.

[0055] In a ninth aspect, the present application provides a computer program product including computer program instructions for causing a computer to perform the method in the first aspect, the second aspect or any possible implementation.

[0056] The beneficial effects of the second aspect to the ninth aspect and the possible implementations can refer to the beneficial effects brought by the first aspect and the possible implementations of the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0057] FIG. 1 is a schematic diagram of an architecture of a mobile communication system to which embodiments of the present application are applied.

[0058] FIG. 2 is a schematic diagram of a dictionary learning framework provided by an embodiment of the present application.

[0059] FIG. 3 is a schematic diagram of an interaction flow of a data compression transmission method provided by an embodiment of the present application.

[0060] FIG. 4 is a schematic diagram of another interaction flow of a data compression transmission method provided by an embodiment of the present application.

[0061] FIG. 5 is a schematic diagram of data compression provided by an embodiment of the present application.

[0062] FIG. 6 is a schematic diagram of another data compression provided by an embodiment of the present application.

[0063] FIG. 7 is a schematic diagram of data processing provided by an embodiment of the present application.

[0064] FIG. 8 is a schematic diagram of another data processing provided by an embodiment of the present application.

[0065] FIG. 9 is a schematic diagram of another data processing provided by an embodiment of the present application.

[0066] FIG. 10 is a schematic block diagram of a communication apparatus provided by an embodiment of the present application.

[0067] FIG. 11 is another schematic block diagram of a communication apparatus provided by an embodiment of the present application. DETAILED DESCRIPTION

[0068] The technical solutions in the present application will be described below with reference to the accompanying drawings.

[0069] FIG. 1 is a schematic diagram of an architecture of a mobile communication system to which embodiments of the present application are applied. As shown in FIG. 1, the mobile communication system includes a core network device 110, a network device 120, and at least one terminal device (such as terminal device 130 and terminal device 140 in FIG. 1). The terminal device is connected to the network device in a wireless manner, and the network device is connected to the core network device in a wireless or wired manner. The core network device and the network device can be independent and different physical devices, or can be a same physical device in which functions of the core network device and logical functions of the network device are integrated, or can be a physical device in which part of the functions of the core network device and part of the functions of the network device are integrated. The terminal device can be fixed or movable. FIG. 1 is only a schematic diagram, and the communication system can further include other network devices, such as a wireless relay device and a wireless backhaul device, which are not shown in FIG. 1. Embodiments of the present application do not limit the number of the core network device, the network device, and the terminal device included in the mobile communication system.

[0070] In embodiments of the present application, the network device can be any device having a wireless transceiving function. The network device includes, but is not limited to, an evolved Node B (eNB), a home evolved Node B (HNB), a baseband unit (BBU), an access point (AP) in a wireless fidelity (WiFi) system, a wireless relay node, a wireless backhaul node, a transmission point (TP) or a transmission and reception point (TRP), a mobile switching center, a device-to-device (D2D) device, a vehicle-to-everything (V2X) device, a machine-to-machine (M2M) device, a device serving as a base station in unmanned aerial vehicle (UAV) communication, a network device in a non-terrestrial network (NTN) communication system (i.e., a device that can be deployed on a high-altitude platform, a satellite, or a high-altitude aircraft), a gNB in a 5th generation (5G) mobile communication system, one or a group of antenna panels (including multiple antenna panels) of a base station in a 5G system, or a network node constituting a gNB or a transmission point, such as a BBU or a distributed unit (DU), without specific limitation in embodiments of the present application.

[0071] In some deployments, a gNB can include a centralized unit (CU) and a DU. The CU and the DU implement partial functions of the gNB respectively, and the CU and the DU can communicate through an F1 interface. The gNB can also include an active antenna unit (AAU). The AAU can implement partial physical layer processing functions, radio frequency processing, and related functions of an active antenna.

[0072] It can be understood that the network device can be a device including one or more of the CU node, the DU node, and the AAU node. In addition, the CU can be divided into a network device in a radio access network (RAN) or a network device in a core network (CN), which is not limited in the present application.

[0073] In the embodiments of the present application, the terminal device can also be referred to as a user equipment (UE), an access terminal, a user unit, a user station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent, or a user device.

[0074] The terminal device can be a device providing voice / data connectivity to a user, for example, a handheld device with wireless connection function, a vehicle-mounted device, etc. Currently, some examples of the terminal can be a mobile phone, a pad, a computer (such as a notebook computer, a palm computer, etc.) with wireless transceiver function, a drone, a customer-premises equipment (CPE), a smart point of sale (POS) machine, a mobile internet device (MID), a virtual reality (VR) device, an augmented reality (AR) device, 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, a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a 5G network, or a terminal device in a system evolved after 5G, etc.

[0075] The network device and the terminal device can communicate through a licensed spectrum, can communicate through an unlicensed spectrum, or can communicate through both the licensed spectrum and the unlicensed spectrum. The network device and the terminal device can communicate through a spectrum below 6 GHz, can communicate through a spectrum at and above 6 GHz, or can communicate through both the spectrum below 6 GHz and the spectrum at and above 6 GHz. Embodiments of the present application do not limit the spectrum resources used between the network device and the terminal device.

[0076] It should be understood that the present application does not limit the specific forms of the network device and the terminal device.

[0077] The communication method provided in the application can be applied to various communication systems, for example, a Long Term Evolution (LTE) system, a 5G mobile communication system, and a mobile communication system evolved after 5G. The 5G mobile communication system or the future communication system can include non-standalone (NSA) and / or standalone (SA).

[0078] The communication method provided in the application can also be applied to machine type communication (MTC), Long Term Evolution-machine (LTE-M), a device to device (D2D) network, a machine to machine (M2M) network, an internet of things (IoT) network, or other networks.

[0079] The goal of dictionary learning is to extract the essential features of a thing, realize dimension reduction of information of the thing, and reduce the interference of unimportant information of the thing on the definition of the thing. When data compression is performed based on the dictionary learning technology, a dictionary of source data (or original data) is first obtained, the dictionary can also be called a base, the dictionary includes the essential features of the source data, and then the source data is expressed based on the dictionary. The expression can be understood as a description of the source data by the dictionary based on a weight, so that the projection of the source data under the dictionary is sparse, to realize the effect of data compression. The dictionary learning is exemplarily described below in combination with FIG. 2.

[0080] FIG. 2 is a schematic diagram of a dictionary learning framework provided in an embodiment of the application. As shown in FIG. 2, the source data Y can be represented by an R-row-by-C-column matrix, so the source data can also be called a source matrix. Through a numerical iteration process of dictionary learning, a dictionary matrix D of the source data Y is obtained. The dictionary matrix D, or a base, can be a C'-row-by-R-column matrix, and each row / column in the dictionary matrix D can be called a base vector. The dictionary matrix D includes the features of the source data, for example, the features of the source data are expressed by each base vector.

[0081] In the process of data compression, any column (such as c1 column) in the source matrix can be expressed by the basis vectors in the dictionary matrix D and the weight of each basis vector. As shown in FIG. 2, each element in the sparse vector corresponds to a basis vector in the dictionary matrix D, and the value of each element is the weight (or coefficient) of the corresponding basis vector expressing the c1 column of the source matrix. The first element in the sparse vector corresponds to a basis vector that has the expression ability for the c1 column of the source matrix, and the second element corresponds to a basis vector that does not have the expression ability for the c1 column of the source matrix. The sparse vectors used to express each column of the source matrix can form a sparse matrix. The more the second elements in the sparse matrix, the less the information with low correlation to the target task occupies the resources. It is considered that the sparse performance of the sparse matrix is better. By expressing the source data Y through the elements with expression ability in the sparse matrix, the expression ability for the source data Y can be improved while reducing the storage resources and transmission resources, that is, the compression ratio of the data compression based on the dictionary learning technology is higher and the data loss is smaller.

[0082] The information obtained by projecting the source data under the dictionary in the form of a matrix (i.e., a sparse matrix) is only an example of description, and the data form of the information obtained by projecting the source data under the dictionary is not limited in the application, for example, it can also be a numerical sequence. In the following, the elements with expression ability in the sparse matrix are summarized as coefficient information.

[0083] The above examples are only described by taking the dictionary (or basis) in the form of a vector as an example, but the data form of the dictionary (or basis) is not limited in the application, for example, it can also be a numerical sequence. In order to unify, the dictionary (or basis) is expressed as basis information in the following, and the basis information can include the dictionary (or basis), or it can be understood that the basis information is a generalization of various data forms of the dictionary (or basis).

[0084] The above source data can also be called original data or data to be compressed. Since the embodiments of the application are to compress and transmit the data to be transmitted, the data to be compressed in the embodiments of the application is also called data to be transmitted. In order to unify, the data to be compressed is uniformly called data to be compressed in the following. In the embodiments of the application, the data to be compressed can be first data, or sub-data obtained by processing the first data, or a data group, etc.

[0085] The first data is not limited in the application, for example, the first data can include one or more of the following examples:

[0086] I. Point cloud data: refers to a collection of spatial points in a multi-dimensional (e.g., three-dimensional) coordinate system. For example, data is collected by a machine vision sensor and recorded in the form of points, each spatial point containing three-dimensional coordinates, which can contain color information (RGB), position information, reflection intensity information (intensity), etc. In a point cloud scenario, a terminal device (e.g., 130 and / or 140 in FIG. 1) can collect data through a sensor and transmit the collected data to a network device (e.g., 120 in FIG. 1), and the network device (e.g., 120 in FIG. 1) can reconstruct the point cloud data.

[0087] II. AI data: model parameters, AI gradients, or intermediate results transmitted in the federated learning process can be referred to as AI data. Of course, AI data is only one possible naming method, and the present application does not limit the name. For example, it can also be referred to as model data, AI model data, etc. In a federated learning scenario, a terminal device (e.g., 130 and / or 140 in FIG. 1) can transmit locally updated AI model data to a network device (e.g., 120 in FIG. 1) to update a global AI data.

[0088] III. CSI: information about the state of the wireless channel between the sender and the receiver in a wireless network (e.g., a 5G network), which can include a channel matrix H, a channel quality indicator (CQI), a rank indicator (RI), and a pre-coding matrix indication (PMI), etc. For example, a terminal device (e.g., 130 and / or 140 in FIG. 1) can measure and obtain CSI based on a reference signal and send the CSI to a network device (e.g., 120 in FIG. 1). The present application does not limit the use of CSI, which can be used for adaptive coding and modulation, beamforming, optimization of multiple input multiple output (MIMO) systems, and resource allocation, etc.

[0089] IV. RF map: refers to a map reflecting signal propagation in a specific area by measuring and analyzing the strength, coverage range and other characteristics of the radio frequency signal, wherein the RF map can be referred to as an electromagnetic map or a radio frequency map, and the application does not limit the naming. Building an accurate RF map is the cornerstone of many applications such as resource allocation, interference control, positioning and network planning. Generally speaking, the electromagnetic map can be represented by power spectrum density (PSD) or received signal strength indicator (RSSI) and the like, which can be affected by different factors such as propagation loss, reflection and diffraction of buildings, random shadowing and small-scale fading. Taking RSSI measurement as an example, in the process of constructing the RF map, the terminal device can receive the reference signal to measure the RSSI to obtain the measurement result of the RSSI on each subcarrier, that is, the RF map measured by the terminal device (such as 130 and / or 140 in FIG. 1), and the terminal device can feed back the RF map to the network device (such as 120 in FIG. 1), and then the network device constructs the final RF map according to the RF map fed back by one or more terminal devices.

[0090] Regardless of the type of data, data compression can be achieved based on the above dictionary learning technology. In order to ensure that the dictionary learning technology can achieve high reliability of compression transmission for different types of data, the embodiment of the application determines the compression parameter corresponding to the data type of the first data, and then compresses the first data based on the determined compression parameter, so that the compressed information of the first data has a high compression effect, such as a high compression rate and a low compression loss.

[0091] It should be understood that only uplink transmission is described in the above examples, but the application is not limited thereto, for example, any type of data described above can also be compressed and transmitted in the communication scenario of downlink transmission or sidelink transmission.

[0092] In the embodiment of the application, the first data is reconstructed or can be expressed as: the first data is recovered or the first data is constructed, and in order to express the meaning after exchanging the order of the sentence, the meaning is also consistent, for example, the first data is reconstructed, the first data is recovered or the first data is constructed, etc. In order to unify, the following is expressed as recovering the first data or recovering the first data.

[0093] In the embodiments of the present application, the same or similar items or elements with basically the same functions and effects are distinguished by "first", "second", and the like, without limiting the sequence and the number of signals. Those skilled in the art can understand that "first", "second", and the like do not limit the number and execution sequence, and "first", "second", and the like do not necessarily mean different. In addition to distinguishing different data as described above, different parameters, dimensions, compression processing, indication information, base information, and the like can also be distinguished.

[0094] The communication method provided by the embodiments of the present application will be described below with reference to the accompanying drawings.

[0095] The method provided by the embodiments of the present application will be described below with reference to the interaction between the first communication device and the second communication device for the sake of understanding and illustration. When the data compression transmission method provided by the embodiments of the present application is applied to uplink transmission, the first communication device can be any terminal device in the communication system shown in FIG. 1, such as terminal device 130 or terminal device 140, and the second communication device can be network device 120 in the communication system shown in FIG. 1; when the data compression transmission method provided by the embodiments of the present application is applied to downlink transmission, the first communication device can be network device 120 in the communication system shown in FIG. 1, and the second communication device can be any terminal device in the communication system shown in FIG. 1, such as terminal device 130 or terminal device 140; when the data compression transmission method provided by the embodiments of the present application is applied to sidelink transmission, the first communication device and the second communication device can be any two terminal devices in the communication system shown in FIG. 1, such as terminal device 130 and terminal device 140.

[0096] It should also be understood that this should not constitute any limitation on the execution subject of the method provided by the present application. As long as the method provided by the embodiments of the present application can be executed by running the program with the code of the method provided by the embodiments of the present application, it can be the execution subject of the method provided by the embodiments of the present application. For example, any of the above communication devices can be implemented as a terminal device or a component in a terminal device, such as a chip, a chip system, or other functional modules capable of calling and executing programs; any of the above communication devices can be implemented as a network device or a component in a network device, such as a chip, a chip system, or other functional modules capable of calling and executing programs.

[0097] FIG. 3 is an interaction flow diagram of a data compression transmission method provided by an embodiment of the present application. In combination with FIG. 3, the method 200 includes the following or all processes:

[0098] S210, the first communication device determines the compression parameter corresponding to the data type of the first data.

[0099] S220, the first communication device compresses the first data according to the compression parameter to obtain compressed information

[0100] S230, the first communication device sends the compressed information to the second communication device. Correspondingly, the second communication device receives the compressed information from the first communication device.

[0101] S240, the second communication device recovers the first data according to the compressed information and the compression parameter corresponding to the data type of the first data.

[0102] The present application does not limit the data type of the first data to be transmitted. For example, the first data can be point cloud data, AI data, CSI, RF map, etc. in the foregoing examples.

[0103] In order to achieve better compression effect, for different data types of data to be compressed, the compression processing process, the execution order of the compression processing process and / or the parameters used in the compression processing may be different. Therefore, in the embodiment of the present application, when the first data is compressed, the data type of the first data needs to be determined. Based on the compression parameter corresponding to the data type of the first data, part or all of the compression processing process, the execution order of the compression processing process or the parameters used in the compression processing can be determined, and then the first data is compressed based on the compression parameter, which will effectively improve the compression effect of the first data.

[0104] As an example, the first communication device can determine the compression parameter of the data type of the first data from at least one data type and the compression parameter corresponding to each data type. Wherein, the at least one data type and the compression parameter corresponding to each data type can be preset in the first communication device, or be indicated by the protocol or other communication devices (such as the second communication device or other network devices), which is not limited in the present application. As another example, the compression parameter corresponding to the data type of the first data can be directly indicated by other communication devices, such as the second communication device or other network devices determining the compression parameter corresponding to the data type of the first data and sending configuration information to the first communication device to configure the compression parameter.

[0105] For example, referring to S310 in FIG. 4, the second communication device can send first indication information to the first communication device, the first indication information being used to indicate the compression parameter, and correspondingly, the second communication device receives the first indication information from the first communication device. Optionally, the first indication information can be carried in the radio resource control (RRC) signaling.

[0106] For example, referring to S320 in FIG. 4, the first communication device can send second indication information to the second communication device, where the second indication information is used to indicate the compression parameter, and correspondingly, the first communication device receives the first indication information from the second communication device. Optionally, the second indication information can be carried in uplink control information (UCI).

[0107] It should be understood that S310 and S320 can be executed alternatively, or both S310 and S320 can be executed. For example, the second communication device can send the first indication information to the first communication device to indicate the compression parameter expected to be adopted by the first communication device, and the first communication device can send the second indication information to the second communication device to indicate the compression parameter actually adopted.

[0108] It should also be understood that the second indication information and the compression information can be independent of each other, such as being sent after being encapsulated in protocols respectively, and the application does not limit the transmission order between the second indication information and the compression information; or the second indication information and the compression information can be sent after being encapsulated in protocols together, and the application does not limit this.

[0109] In the scenario where the first communication device and the second communication device implement sidelink transmission, the network device can configure the compression parameter to the first communication device, and optionally, the network device can also configure the compression parameter to the second communication device, so as to realize synchronization of the compression parameter between the first communication device and the second communication device.

[0110] The compression parameter indicated by the first indication information can include part or all of the parameters in the following examples. When the first indication information indicates part of the parameters in the following examples, the parameters not indicated can be generated by the first communication device, preset, agreed by protocols, or indicated by other communication devices, and the application does not limit this. Similarly, the compression parameter indicated by the second indication information can include part or all of the parameters in the following examples, and when the second indication information indicates part of the parameters in the following examples, the parameters not indicated can be generated by the second communication device, preset, agreed by protocols, or indicated by other communication devices, and the application does not limit this.

[0111] Further, the first communication device can compress the first data according to the determined compression parameter to obtain compressed information. Alternatively, the first communication device can express the first data by the base information to obtain compressed information including coefficient information, so as to compress the first data. The coefficient information includes expression coefficients of at least one sub-information in the base information to the first data, in other words, the coefficient information includes at least one element, the at least one element has expression ability to the first data, and each element is an expression coefficient of the corresponding sub-information to the first data. Alternatively, the element in the coefficient information can be a non-zero element in a sparse matrix obtained based on dictionary learning, or the element in the coefficient information can be obtained by element screening of the sparse matrix, and how to screen the coefficient information will be described below.

[0112] The compressed information can further include the base information used to express the first data. The base information in the compressed information can include sub-information of the base information, or can further include a size of the base information; or the compressed information can include an identifier of the base information, such as an index of the base information. It should be understood that when the compressed information does not include the base information, the base information can be preset, or agreed by a protocol, or synchronized between the first communication device and the second communication device through other signaling and / or data, such as that the second communication device sends indication information to the first communication device to indicate the base information, or the first communication device sends indication information to the second communication device to indicate the base information, or other communication devices configure the first communication device and the second communication device.

[0113] Alternatively, the compressed information obtained by expressing the first data by the base information to realize data compression can further include information indicating a position of at least one sub-information expressing the first data in the base information, so as to determine the at least one sub-information by the second communication device, and further accurately recover the first data.

[0114] For example, the compression parameter can include a parameter related to the base information, such as a first parameter indicating the base information. It can be understood that different data types of data have different data characteristics, and therefore the base information used to express the characteristics also has differences, in other words, the same base information has different expression abilities to different data types of data. Based on this, the base information corresponding to the data type of the first data has better expression ability to the first data, which can improve the compression effect of the first data.

[0115] In a possible implementation, the base information used to express the first data indicated by the first parameter can be one or more, and the number of the base information is associated with the data type of the first data. For example, when the first data is CSI, the object characteristics of the CSI make the first data have information in multiple domains, such as an angle domain, a time delay domain, a space domain, and the like. The CSI can be expressed by using multiple base information, so that the expression coefficients have better sparsity. For example, the first parameter indicates that the base information used to express the first data is multiple, which can indicate whether the base information includes first base information and second base information, or indicate at least one of the first base information or the second base information, or indicate the number of the base information.

[0116] In an example, the base information indicated by the first parameter can be used to left multiply the first data to obtain the coefficient information. As shown in (a) of FIG. 5, the base information D includes L rows of sub-information, the first data Y is a matrix of R rows and C columns, and the base information D left multiplies the first data Y to obtain a sparse matrix X of L rows and C columns, where the element x (i,j) is the expression coefficient of the jth column of the first data for the ith sub-information of the base information D. Optionally, the expression process of the base information on the first data can be represented by the following pseudo code:

[0117] wherein X s represents the sparse matrix obtained by expressing the first data based on the base information, or represents the sparse matrix obtained by expressing the s-th data based on the base information. The s-th data can be the s-th data in a plurality of data determined based on the first data, such as the s-th sub-data in N sub-data determined based on the first data, or the s-th data group in M data groups determined based on the first data. P represents the p-th column in the L columns of the base information.

[0118] Optionally, the absolute value of the element in the sparse matrix X is associated with the expression ability of the element on the first data. For example, the larger the absolute value of the element, the stronger the expression ability of the element on the first data. Based on this, the elements with stronger expression ability in the sparse matrix X can be grouped to form the coefficient information, that is, the compression of the first data is implemented.

[0119] Optionally, the base information described above can be replaced by the transpose D T of the base information, or the conjugate transpose D H of the base information.

[0120] Optionally, the base information described above can right multiply the first data, which is not limited in the present application. Optionally, the compression parameter can further include a parameter indicating the expression manner of the base information on the first data, such as a parameter indicating that the base information left multiplies or right multiplies the first data.

[0121] In another example, the first base information and the second base information indicated by the first parameter can be used to left multiply and right multiply the first data respectively to obtain the coefficient information. Referring to (b) shown in FIG. 5, the first base information D1 includes L1 rows of sub-information, the second base information D2 includes L2 columns of sub-information, and the first data Y is a matrix of R rows by C columns. The first base information D1 left multiplies the first data Y, and the second base information D2 right multiplies the first data Y to obtain a sparse matrix X of L rows by C columns, where the element x (i,j) is the expression coefficient of the jth column of the first data by the ith sub-information of the first base information D1 and the ith sub-information of the C sub-information of the second base information D2. Optionally, the expression process of the first data by the first base information and the second base information can be represented by the following pseudo code:

[0122] where p represents the pth column of the L1 columns of the first base information, and q represents the qth row of the L2 rows of the second base information.

[0123] Optionally, the absolute value of the element in the sparse matrix X is associated with the expression ability of the element to the first data. For example, the larger the absolute value of the element, the stronger the expression ability of the element to the first data. Based on this, the elements with stronger expression ability in the sparse matrix X can be grouped to form the coefficient information, that is, to compress the first data.

[0124] Optionally, the first base information can be replaced by the transpose D1 T of the first base information, or the conjugate transpose D1 H of the first base information.

[0125] Optionally, the second base information can be replaced by the transpose D2 T of the second base information, or the conjugate transpose D2 H of the second base information.

[0126] Optionally, the first communication device can generate base information, such as generating base information corresponding to the first data based on a dictionary learning technique, and then express the first data through the base information to obtain the coefficient information. Optionally, the first communication device can process the first data based on the dictionary learning technique to obtain the base information and the coefficient information. In this case, the first parameter can indicate the number of base information, and the first communication device can generate a corresponding number of base information.

[0127] Optionally, the first parameter can indicate each sub-information in the base information and / or the size of the base information (such as the number of rows and / or columns of the base information), or the first parameter can indicate the identification of the base information, such as the index of the base information.

[0128] For example, referring to (a) of FIG. 6, the first communication device can obtain the basis information D and the sparse matrix X of the first data Y based on the dictionary learning technology. Further, the first communication device can quantize the basis information D and perform coefficient screening on the sparse matrix X to obtain coefficient information, and then quantize the coefficient information.

[0129] The application does not limit the execution order of the first compression processing and the second compression processing.

[0130] For example, referring to (a) of FIG. 6, the first communication device can obtain the basis information D and the sparse matrix X of the first data Y based on the dictionary learning technology. Further, the first communication device can quantize the basis information D and perform coefficient screening on the sparse matrix X to obtain coefficient information, and then quantize the coefficient information.

[0131] For example, referring to (a) of FIG. 6, the first communication device can obtain the basis information D and the sparse matrix X of the first data Y based on the dictionary learning technology. Further, the first communication device can quantize the basis information D and perform coefficient screening on the sparse matrix X to obtain coefficient information, and then quantize the coefficient information.

[0132] It can be understood that performing the first compression processing first and then performing the second compression processing can effectively reduce the quantization interval and improve the quantization accuracy. Performing the second compression processing first and then performing the first compression processing is more conducive to screening useful data for some data types, for example, for data with obvious geometric characteristics (such as point cloud data), performing quantization compression based on the position relationship of the coefficients in the data space first and then performing coefficient screening can obtain coefficient information that is more helpful to reduce compression loss. For other data types, quantizing the screened coefficients can avoid affecting the unbiased characteristics of the data, for example, AI data is more suitable for performing the second compression processing first and then performing the first compression processing to ensure the unbiased characteristics of the first data, thereby ensuring the performance reliability of the model. Therefore, the execution order of the first compression processing and the second compression processing can be related to the data type of the first data.

[0133] Based on this, the compression parameter can comprise a second parameter indicating an execution order of the first compression processing and the second compression processing. As mentioned above, the compression parameter can be indicated by the first indication information and / or the second indication information. When the first indication information is carried in the RRC signaling, the second parameter can be indicated by a dictCompressionOrder field in the RRC signaling.

[0134] As an example of the first compression processing, the first communication device can filter the coefficient information based on a threshold value. For example, the first communication device can take the elements in the sparse matrix whose absolute values are greater than or equal to the threshold value as the elements in the coefficient information. Referring to (a) in FIG. 6, the coefficient information filtered based on the threshold value comprises two kinds of elements in the sparse matrix X that have higher expressiveness for the first data. The coefficient filtering process shown in (b) in FIG. 6 is similar, and will not be described herein for brevity.

[0135] Optionally, the threshold value corresponding to different data types can be different.

[0136] Optionally, the process of filtering the coefficients based on the threshold value by the first communication device can be expressed by the following pseudo code:

[0137] wherein the set Z[L sel ] comprises the elements filtered based on the threshold value, L sel is the number of the filtered elements, T sel is the threshold value, B[i,j] = 1 is used to identify the element X s [i,j] that has the expressiveness for the first data based on the threshold value, and B[i,j] = 0 is used to identify the element X s [i,j] that does not have the expressiveness for the first data based on the threshold value.

[0138] Based on this, the compression parameter can comprise a parameter indicating the threshold value.

[0139] As an example of the second compression processing, the first communication device can perform quantization processing on the sequence to be quantized (such as the basis information, the coefficient information or the information of the sparse matrix) based on a quantization range. The quantization range can be represented by a minimum value U min and a maximum value U max of the quantizer, for example, the quantization range of the basis information can be [-1, 1]. For example, the first communication device can perform quantization processing on each element in the sequence to be quantized U based on the quantization range, which can be expressed by the following pseudo code:

[0140] for f = 0 to F-1

[0141] end for

[0142] wherein U[f] represents the f-th element of the F elements of the sequence to be quantized, V[f] represents the quantized value of the f-th element of the F elements of the sequence to be quantized, V represents the quantized sequence, Q represents the quantization precision, is an offset, i.e., the quantization point offset is a step, or the quantization point is the midpoint of the step. It should be noted that the value of the offset is not limited in the present application, for example, it can also be 0.

[0143] Therefore, the compression parameter can include a parameter indicating the quantization range. For example, the maximum value and the minimum value can be directly indicated, or the number of quantization bits can be indicated, and then the quantization range is determined based on the number of quantization bits, such as a preset U min = 0, the first communication device can determine U min and the number of quantization bits based on U max . Optionally, when the first indication information is carried in the RRC signaling, the second parameter can be indicated by the quantization parameter (quantParameter) field in the RRC signaling.

[0144] Optionally, the quantization ranges corresponding to different data types can be different.

[0145] Optionally, the first communication device can determine the quantization range based on the sequence to be quantized. For example, the maximum value of the element in the sequence to be quantized is determined as U max , for example, U max may be equal to the value of the element, or U max may be equal to the sum of the value of the element and a preset offset, or U max may be equal to the product of the value of the element and a preset coefficient; for example, the minimum value of the element in the sequence to be quantized is determined as U min , and U min is determined in a manner similar to the determination of U max . For the sake of brevity, the process of determining the quantization range by traversing the elements in the sequence to be quantized is expressed by the following pseudo code:

[0146] Optionally, the first communication device and the second communication device can synchronize the manner of obtaining the quantization range, for example, the first indication information and / or the second indication information can indicate that the quantization range is determined based on the sequence to be quantized.

[0147] As another example of the second compression processing, the first communication device can perform quantization compression based on the geometric properties of the coefficients, or the positional relationship of the coefficients in the data space. For example, the first communication device can perform quantization processing using an octree method, which is a data structure for describing a three-dimensional space. Based on the octree method, a three-dimensional data space can be divided into eight sub-regions, such as dividing a cube into eight smaller cubes, and each sub-region can be further subdivided as needed until a certain condition is met or the required precision is reached. Then, one or more coefficients falling within the sub-region can be represented by a reference point in the sub-region, such as the center point of the cube. The quantization compression implemented in this way can preserve more geometric features in the data to be transmitted, and can reduce compression loss for data with geometric features, such as point cloud data.

[0148] Optionally, the at least one quantization compression method used in the second compression processing of the first data is related to the data type of the first data. Optionally, when the first data is subjected to the second compression processing using at least two quantization compression methods, the execution order between the quantization compression methods is related to the data type of the first data.

[0149] Accordingly, the compression parameter can include a parameter indicating at least one quantization compression method of the second compression processing, such as a parameter indicating the quantization compression method based on the numerical quantization range of the above example and / or the quantization compression method based on the geometric properties of the above example. When indicating more than two quantization compression methods, the compression parameter can further include a parameter indicating the execution order between different quantization compression methods.

[0150] In some embodiments, the first data can be subjected to data processing before being subjected to data compression, and then the processed data can be subjected to data compression to obtain the compression information described above, so as to achieve better compression effect. The data processing method is not limited in the present application, and several possible examples are provided as follows:

[0151] Example one, the first communication device divides the plurality of first data units of the first data to obtain N sub-data. The present application does not limit the division method of the first data units in the first data. For example, when the first data is a matrix, a column / row of data in the first data can be a first data unit. For another example, when the first data is a numerical sequence, a fixed length (such as 2 bits) of data is a first data unit.

[0152] For example, referring to FIG. 7, the first data Y includes C columns of data (i.e., C first data units), and the first communication device can divide every h first data units into a sub-data. For example, after division, the first column of data y1 to the hth column of data yh belong to the first sub-data, the h+1th column of data yh+1 to the 2hth column of data y2h belong to the second sub-data, and so on. h The h+1th column of data yh+1 to the 2hth column of data y2h belong to the second sub-data, and so on. h+1to the 2hth column of data y 2h belongs to the 2th sub-data, …, the C-h+1th column of data y C- h+1 to the Cth column of data y C belongs to the C / hth sub-data.

[0153] For ease of understanding, the process of dividing the first data into N sub-data is exemplarily illustrated by pseudo code as follows:

[0154] In the above process, each sub-data is defined first, such as each sub-data is defined as an all zero matrix of h columns and R rows, wherein the column number H of the first N-1 sub-data is equal to the preset value h, and the column number H of the Nth sub-data is equal to C-(N-1)×h, that is, the number of first data units in the Nth sub-data is less than or equal to h. Further, in the process of filling elements, a plurality of first data units in the first data are sequentially divided into each sub-data.

[0155] It can be understood that the above data division according to h first data units is only one possible example, and the present application does not limit this, such as different sub-data can include different numbers of first data units.

[0156] In the above example one, the first communication device divides the first data into N sub-data, and then compresses each sub-data to obtain the compressed information of the first data. It can be understood that the data amount of the first data is large and the spatial distribution is wide, so it is difficult to obtain a sparse matrix with good sparsity when expressing the first data based on basis information, which leads to a complex processing process, large time delay and large compression loss based on the decomposition of the first data to obtain basis information and coefficient information. However, data compression of each sub-data obtained by division can effectively reduce the processing complexity and improve the compression effect.

[0157] Optionally, the basis information used for data compression of each sub-data can be the same or different, which is not limited by the present application. Optionally, the compressed information of the first data can include the compressed information of each sub-data, and the compressed information of each sub-data can include the basis information for expressing the sub-data and the coefficient information for expressing the sub-data based on the basis information, or the compressed information of each sub-data can include the coefficient information for expressing the sub-data based on the basis information.

[0158] Optionally, the first parameter in the compression parameter can indicate the base information of each sub-data. Optionally, each sub-data can be expressed by one or more base information, such as the first base information and the second base information in the above example, that is, the first parameter can indicate one or more base information of each sub-data.

[0159] Optionally, the compression parameter can include a fourth parameter, which indicates the data length of each sub-data and / or the number N of sub-data. The first communication device and the second communication device can synchronize the relationship between each sub-data of the compressed transmission and the first data based on the fourth parameter, so that the second communication device can recover the first data based on the fourth parameter. Optionally, when the first indication information is carried in the RRC signaling, the fourth parameter can be indicated by the dataSegmentParameter field in the RRC signaling. The data length of each sub-data and / or the number N of sub-data can be associated with the data type of the first data, that is, different data segmentation methods can be used for different data types to reduce the loss of the data characteristics of the first data after sub-data segmentation.

[0160] Example two, the first communication device converts the data dimension of the first data from a first dimension to a second dimension, which includes the number of rows and the number of columns of the first data. In other words, the first data is reshaped.

[0161] The application does not limit the conversion method of the data dimension. In the first implementation manner, the dimension conversion can be realized by matrix transposition. For example, referring to (a) shown in FIG. 8, the first communication device can obtain the R-row-by-C-column matrix Y T by matrix transposition of the C-row-by-R-column matrix Y

[0162] In the second implementation manner, the first communication device can split or combine each data unit of the first data to realize the dimension conversion. For example, the first communication device can split at least part of the row data and / or the column data in the first data; for another example, the first communication device can combine at least part of the row data and / or the column data in the first data; for another example, the first communication device can split part of the row data and / or the column data in the first data and combine another part of the row data and / or the column data; for another example, the first communication device can sequentially perform the splitting of at least part of the row data and / or the column data and the combining of at least part of the row data and / or the column data in the first data in a sequence.

[0163] Referring to (b) shown in FIG. 8, the first data is a P-row-by-Q-column matrix, each column data in the first data is split, and the split data forms an R-row-by-C-column matrix, where R is less than P and C is greater than Q. In addition to the above, each row data in the first data can be split, or at least one row data and / or at least one column data in the first data can be combined.

[0164] In the third implementation manner, the first communication apparatus can flatten the first data into one-dimensional data from the first dimension, and reshape the one-dimensional data into data of the second dimension, as shown in (c) of FIG. 8.

[0165] For ease of understanding, the process of converting the data dimension of the first data from the first dimension to the second dimension is exemplarily described below by using pseudo code:

[0166] In the pseudo code, W is the first data converted into the second dimension (R rows multiplied by C columns), and the initial state of W is defined as an all zero matrix of C columns and R rows in the above process, and then the flattened first data U[d] is filled into W element by element.

[0167] The pseudo code above is to put the elements in the flattened first data into the matrix W in sequence by column, but the application is not limited thereto, for example, the elements in the flattened first data can also be put into the matrix W in sequence by row. The compression parameter can include a parameter indicating that the matrix in the second dimension is constructed by row or by column during the dimension conversion. When the first indication information is carried in the RRC signaling, the dataReshapeMode field in the RRC signaling can be used to indicate whether the matrix in the second dimension is constructed by row or by column during the dimension conversion.

[0168] Optionally, the compression parameter can include a fifth parameter indicating the second dimension. The second dimension can be associated with the data type of the first data, so as to reduce the loss of the data characteristics of the first data after the dimension conversion. Optionally, the first dimension of the first data can be synchronized between the first communication apparatus and the second communication apparatus, so as to facilitate the second communication apparatus to recover the first data. The first dimension can be indicated by the first communication apparatus and the second communication apparatus through the transmission of the indication information, or the synchronization of the second dimension and the dimension conversion manner can be used to realize the synchronization of the first dimension.

[0169] In Example Three, the first communication apparatus divides a plurality of second data units in the first data into M data groups. The application does not limit the division manner of the second data units in the first data. For example, when the first data is an RF map, the multipath information in each grid belongs to a second data unit, and when the first data is point cloud data, the data of the sampling points in each sub-region belongs to a second data unit.

[0170] In some scenarios, the data dimensions of different second data units in the first dataset differ. For example, when the first dataset is an RF map, the number of paths in the multipath information of each grid varies, leading to differences in data dimensions. Similarly, the number of sampling points in different sub-regions of point cloud data also varies, resulting in differences in data dimensions. Understandably, the values ​​of different second data units in the first dataset also differ. Especially when the data dimensions or values ​​of the second data units differ significantly, compressing the entire first dataset will not simultaneously achieve a high compression rate and reduce compression loss. Therefore, based on Example 3 above, multiple second data units in the first dataset are divided into M data groups according to their data dimensions and / or numerical values. This ensures that different second data units within the same data group are the same or similar in data dimensions and / or values. Furthermore, by representing the corresponding data group using the base information of each data group, the compression effect can be improved.

[0171] Referring to Figure 9, taking the RF map as the first data as an example, in the 4-row by 8-column grid area of ​​the RF map, multipath information with the same path number is represented by the same pattern. Figure 9 shows that the data to be compressed is divided according to the path number, and three data groups (data group #0, data group #1 and data group #2) are determined. In some possible implementations, some grid areas may not include any path information. For example, the grid areas in the 1st row and 6th column and the 3rd row and 7th column of Figure 9 do not include any path information. Since there is no path information, these grid areas that do not include any path can be excluded from any data group, can be divided into a separate data group, or can be divided into a certain data group.

[0172] To facilitate understanding, the following two pseudocode examples illustrate how multiple second data units of the first data are divided into M data groups.

[0173] I. Classified by data dimension:

[0174] Among them, data unit s d If the dimension is x1 rows and x2 columns, then the dimension of this data element is defined as: size(s) d ) = x1x2; (further definition) A set is used to represent a group of data, l d The data dimension of each data unit within a set. Find U max and U min The implementation method for determining the quantization range can be found in the previous example, which will not be repeated here for the sake of brevity.

[0175] II. Classification based on numerical value (or numerical range):

[0176] wherein, is a set of threshold values, is an empty set.

[0177] Optionally, the base information used when each data group is compressed can be the same or different, which is not limited in the present application. Optionally, the compression information of the first data can include the compression information of each data group, and the compression information of each data group can include base information used to express the data group and coefficient information used to express the data group based on the base information, or the compression information of each data group can include coefficient information used to express the data group based on the base information.

[0178] Optionally, the first parameter in the compression parameter can indicate the base information of each sub-data. Optionally, each data group can be expressed by one or more base information, such as the first base information and the second base information in the above example, that is, the first parameter can indicate one or more base information of each data group.

[0179] Optionally, the compression parameter can include a sixth parameter, and the sixth parameter indicates the data dimension or the value range of the second data unit in each data group. The first communication device and the second communication device can synchronize the relationship between each data group of the compressed transmission and the first data based on the sixth parameter, so that the second communication device recovers the first data based on the sixth parameter. Optionally, when the first indication information is carried in the RRC signaling, the sixth parameter can be indicated by the (dataRegroupMode) field in the RRC signaling. Whether the M data groups are divided based on the data dimension or based on the value range can be associated with the data type of the first data. For example, when the first data is RF map or point cloud data, the data dimension difference between the data units is large, and the plurality of second data units in the first data are divided into M data groups based on the data dimension. Optionally, the data dimension or the value range of the second data unit in each data group can be associated with the data type of the first data.

[0180] In Example Four, the first communication device can normalize the first data, or in other words, change the values of the elements in the first data. For example, the first communication device can subtract a constant from each element in the first data, where the constant can be a positive number or a complex number, and the constant can be the same or different for different elements. The normalization of the first data by the first communication device can avoid the situation where the elements in the first data have different numerical ranges, and some elements are identified as unimportant data in the subsequent process of determining the sparse matrix based on dictionary learning, resulting in a large compression loss. For example, when the first data is point cloud data, the z-axis coordinate of the point cloud data in the three-dimensional coordinate (x, y, z) has a small value (tends to zero), and in this case, the z-axis data is easily identified as unimportant data. Normalizing the first data can effectively increase the absolute value of the z-axis data.

[0181] For ease of understanding, the numerical transformation process of the elements in the first data is exemplarily illustrated by pseudo code as follows:

[0182] for a=0 to A-1

[0183] V[a]=U[a]-G[a];

[0184] end for

[0185] where G[a] is a constant.

[0186] Optionally, the compression parameter can also include a constant used in the normalization process, such as G[a] described above.

[0187] It should be understood that the first data unit in Example One and the second data unit in Example Three can be defined by different data unit division methods, and can be understood as different types of data units. Of course, the first data unit and the second data unit can also be defined by the same data unit division method. The first data unit and the second data unit can be summarized as data units.

[0188] For example, the first communication device can perform K data processing processes on the first data, where the K data processing processes can include any one or more of the four examples described above, and the K data processing processes can include multiple executions of the same data processing process. For example, the first communication device can divide the first data into M' data groups according to the data dimension based on Example Three described above, and then divide the data in at least one of the M' data groups according to the numerical range, and finally obtain M data groups. The K data processing processes are associated with the data type of the first data. For example, when the first data is an RF map, the first communication device needs to divide the multiple second data units in the first data into M data groups. It should be understood that M is a positive integer.

[0189] According to the above, the compression parameter can comprise a third parameter, which indicates the K data processing processes. For example, the third parameter can indicate the K data processing processes from the K' data processing processes, such as the third parameter comprises a parameter corresponding to each data processing process in the K' data processing processes, and the parameter corresponding to each data processing process indicates whether the data processing process belongs to the K data processing processes, i.e., whether the data processing process is used for data processing of the first data.

[0190] For example, when the first indication information is carried in the RRC signaling, the bit of the dictProcess-Segment field in the RRC signaling is the first value, which indicates that the data processing process of data segmentation in example one is used; the bit of the dictProcess-Reshape field in the RRC signaling is the first value, which indicates that the data processing process of dimension conversion in example two is used; the bit of the dictProcess-Regroup field in the RRC signaling is the first value, which indicates that the data processing process of dividing data groups in example three is used. The first value can be 0 or 1, which is not limited in the present application. Optionally, when the bit of the above field is the second value, it indicates that the corresponding data processing process is not used for data processing of the first data, and the second value is different from the first value.

[0191] Optionally, the third parameter is also used to indicate the execution position of each data processing process in the K data processing processes in the data compression process. For example, the data processing process is executed before the dictionary learning (i.e., the first data is expressed by the base information), or before the coefficient screening, or before the quantization compression. Optionally, in the RRC signaling, when the data processing process of data segmentation is executed before the dictionary learning, dictProcess-Segment-position: {0}; when the data processing process of data segmentation is executed after the dictionary learning and before the coefficient screening, dictProcess-Segment-position: {1}, etc. The field used to indicate the execution position of data reshaping can be dictProcess-Reshape-position, and the indication manner can refer to the dictProcess-Segment-position field, which is not described herein for the sake of brevity. The field used to indicate the execution position of data reorganization can be dictProcess-Regroup-position, and the indication manner can refer to the dictProcess-Segment-position field.

[0192] Optionally, the third parameter is further used to indicate the execution order between different data processing procedures in the K data processing procedures. For example, when the first indication information is carried in the RRC signaling, a dictionary process order field (dictProcessOrder) can be used to indicate the execution order between different data processing procedures in the K data processing procedures.

[0193] Optionally, the third parameter is further used to indicate the number K of data processing procedures, where K is a positive integer.

[0194] As a possible implementation, the third parameter can indicate at least one of the K data processing procedures, the execution order and the number K of data processing procedures from K' data processing procedures, where the K' data processing procedures can be as shown in Table 1. The K' data processing procedures can be agreed upon by protocols; or the K' data processing procedures can be preconfigured, for example, the network device configures the K' data processing procedures to the first communication device and the second communication device; or the K' data processing procedures are preset in the first communication device and / or the second communication device, and the first communication device and the second communication device synchronize the K' data processing procedures when the K' data processing procedures are preset in one of the communication devices.

[0195] Table 1

[0196] In the above implementation, the third parameter can indicate at least one of the K data processing procedures, the execution order and the number K of data processing procedures from the K' data processing procedures shown in Table 1. For example, the third parameter can include information shown in Table 2:

[0197] Table 2

[0198] For example, the third parameter can indicate whether to perform data processing procedures by 1 bit, and in the case of performing data processing procedures, indicate the number K of data processing procedures to be performed and the index of the data processing procedures to be performed, such as index 2 and index 0, where index 2 is before index 0, indicating that the first data is first data reorganized, i.e., the multiple second data units in the first data are divided into M data groups, and the first data is segmented, i.e., the multiple data units in each data group are segmented.

[0199] After receiving the compressed information, the second communication device can recover the first data according to the compression parameter corresponding to the data type of the first data and the compressed information. It should be understood that the process of decompressing the compressed information by the second communication device is the inverse process of compressing the first data by the first communication device. Generally, the recovered first data is different from the first data before compression, but it should be understood that the closer the recovered first data is to the first data, the smaller the compression loss caused by data compression transmission.

[0200] For example, the second communication device determines the compression parameter corresponding to the data type of the first data, which has the same or similar implementation as the compression parameter determined by the first communication device. As an example, the second communication device can determine the compression parameter corresponding to the data type of the first data from at least one data type and the compression parameter corresponding to each data type. The at least one data type and the compression parameter corresponding to each data type can be preset in the second communication device, or determined by a protocol or indicated by another communication device (such as the first communication device or another network device), which is not limited in the present application. As another example, the compression parameter corresponding to the data type of the first data can be directly indicated by another communication device, such as the first communication device or another network device, which determines the compression parameter corresponding to the data type of the first data and sends configuration information to the first communication device to configure the compression parameter.

[0201] Therefore, in the embodiments of the present application, the first communication device determines the compression parameter corresponding to the data type of the first data, and then compresses the first data based on the determined compression parameter, so that the compressed information of the first data has a higher compression effect, such as a higher compression rate and a lower compression loss.

[0202] It can be understood that, in order to implement the functions in the above embodiments, the network device and the terminal include corresponding hardware structures and / or software modules for performing various functions. Those skilled in the art should easily realize that, in combination with the units and method steps of the examples described in the embodiments disclosed in the present application, the present application can be realized in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in hardware or computer software driven hardware depends on the specific application scenario and design constraints of the technical solution.

[0203] FIG. 10 is a schematic block diagram of a communication device according to an embodiment of the present application. In one possible implementation, the communication device 400 can include a module or unit corresponding to each of the methods performed by the first communication device or the second communication device in the above method embodiments. The unit can be a hardware circuit, software, or a combination of hardware circuit and software.

[0204] In a possible implementation, as shown in FIG. 10, the apparatus 400 can include a transceiver module 410 and a processing module 420.

[0205] Optionally, the communication apparatus 400 can correspond to the first communication apparatus in the above method embodiments.

[0206] When the communication apparatus 400 is configured to perform the method at the side of the first communication apparatus, the processing module 420 can be configured to determine a compression parameter corresponding to a data type of first data, and compress the first data according to the compression parameter to obtain compressed information, the compressed information including base information and coefficient information, or coefficient information, the base information being used to express the first data, and the coefficient information including an expression coefficient of at least one sub-information of the base information to the first data; and the transceiver module 410 can be configured to send the compressed information.

[0207] Optionally, the compression parameter includes a first parameter, and the first parameter is used to indicate the base information.

[0208] Optionally, the first parameter is further used to indicate whether the base information includes first base information and second base information, and the first base information and the second base information are used to express the first data.

[0209] Optionally, the compression parameter includes a second parameter, and the second parameter is used to indicate an execution order of a first compression process and a second compression process, the first compression process being used to filter to obtain the coefficient information, and the second compression process being used for quantization compression.

[0210] Optionally, the compression parameter includes a third parameter, and the third parameter is used to indicate K data processing processes performed on the first data, and the K data processing processes include at least one of the following:

[0211] dividing a plurality of first data units of the first data to obtain N sub-data; or

[0212] converting a data dimension of the first data from a first dimension to a second dimension, the data dimension including a number of rows and a number of columns of the first data; or

[0213] dividing a plurality of second data units in the first data into M data groups.

[0214] Optionally, the compression parameter includes a fourth parameter, and the fourth parameter is used to indicate a data length of each of the sub-data or a number N of the sub-data.

[0215] Optionally, the compression parameter includes a fifth parameter, and the fifth parameter is used to indicate the second dimension.

[0216] Optionally, the compression parameter comprises a sixth parameter, the sixth parameter indicating a data dimension or a value range of the second data unit in each data group.

[0217] Optionally, the third parameter is further used to indicate an execution position of the data processing in the data compression process, and / or an execution order between at least two data processing.

[0218] Optionally, the third parameter is further used to indicate a number K of the data processing processes.

[0219] Optionally, the transceiver 410 is further configured to receive first indication information, the first indication information being used to indicate the compression parameter.

[0220] Optionally, the first indication information is carried in radio resource control (RRC) signaling.

[0221] Optionally, the transceiver 410 is further configured to send second indication information, the second indication information being used to indicate the compression parameter.

[0222] Optionally, the communication apparatus 400 can correspond to the second communication apparatus in the above method embodiments.

[0223] When the communication apparatus 400 is configured to execute the method of the second communication apparatus, the transceiver 410 can be configured to receive compression information of first data, the compression information comprising base information and coefficient information, or the coefficient information, the base information being used to express the first data, the coefficient information comprising an expression coefficient of at least one sub-information of the base information to the first data; and the processing module 420 can be configured to restore the first data according to the compression information and a compression parameter corresponding to a data type of the first data.

[0224] Optionally, the compression parameter comprises a first parameter, the first parameter indicating the base information.

[0225] Optionally, the first parameter is further used to indicate whether the base information comprises first base information and second base information, the first base information and the second base information being used to express the first data.

[0226] Optionally, the compression parameter comprises a second parameter, the second parameter indicating an execution order of a first compression processing and a second compression processing; the first compression processing being used to filter to obtain the coefficient information; and the second compression processing being used for quantization compression.

[0227] Optionally, the compression parameter comprises a third parameter, the third parameter indicating K data processing processes on the first data; wherein the K data processing processes comprise at least one of the following:

[0228] The plurality of data units of the first data are divided into N sub-data; or

[0229] The data dimension of the first data is converted from a first dimension to a second dimension, and the data dimension includes the number of rows and the number of columns of the first data; or

[0230] The plurality of data units in the first data are divided into M data groups.

[0231] Optionally, the compression parameter includes a fourth parameter, and the fourth parameter indicates the data length of each sub-data or the number N of sub-data.

[0232] Optionally, the compression parameter includes a fifth parameter, and the fifth parameter indicates the second dimension.

[0233] Optionally, the compression parameter includes a sixth parameter, and the sixth parameter indicates the data dimension or the value range of the data unit in each data group.

[0234] Optionally, the third parameter is further used to indicate the execution position of the data processing in the data compression process, and / or the execution order between at least two data processes.

[0235] Optionally, the third parameter is further used to indicate the number K of data processing processes.

[0236] Optionally, the transceiver module 410 is further used to send first indication information, and the first indication information is used to indicate the compression parameter.

[0237] Optionally, the first indication information is carried in radio resource control (RRC) signaling.

[0238] Optionally, the transceiver module 410 is further used to receive second indication information, and the second indication information is used to indicate the compression parameter.

[0239] It should be understood that the specific processes performed by each module have been described in detail in the above method embodiments, and thus will not be described here again for the sake of brevity.

[0240] The transceiver module 410 in the communication apparatus 400 can be implemented through a transceiver, for example, can correspond to the transceiver 520 in the communication apparatus 500 shown in FIG. 11, and the processing module 420 in the communication apparatus 400 can be implemented through at least one processor, for example, can correspond to the processor 510 in the communication apparatus 500 shown in FIG. 11.

[0241] When the communication apparatus 400 is a chip or a chip system configured in a communication device (such as a terminal device or a network device), the transceiver module 410 in the communication apparatus 400 can be implemented through an input / output interface, a circuit, etc., and the processing module 420 in the communication apparatus 400 can be implemented through a processor, a microprocessor or an integrated circuit, etc. integrated on the chip or the chip system.

[0242] Figure 11 is another schematic block diagram of a communication device according to an embodiment of the present application. As shown in Figure 11, the communication device 500 can include a processor 510. The processor 510 can be configured to perform the method performed by the first communication device or the second communication device in the above method embodiments.

[0243] In some possible implementation, the communication device 500 can include a transceiver 520. The transceiver 520 can communicate with the processor 510 via an internal connection path. The processor 510 can control the transceiver 520 to send and / or receive signals.

[0244] In some possible implementation, the communication device 500 can include a memory 530. The memory 530 can communicate with the processor 510 via an internal connection path. The memory 530 and the processor 510 can be integrated together or separately arranged. The memory 530 can also be a memory outside the device. The memory 530 is configured to store instructions, and the processor 510 is configured to execute the instructions stored in the memory 530 to perform the method of the first communication device or the second communication device in the above method embodiments.

[0245] It should be understood that the communication device 500 can correspond to the first communication device or the second communication device in the above method embodiments, and can be configured to perform each step and / or procedure performed by the first communication device or the second communication device in the above method embodiments. Optionally, the memory 530 can include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory can also include a non-volatile random access memory. The memory 530 can be one separate device or integrated in the processor 510. The processor 510 can be configured to execute the instructions stored in the memory 530, and when the processor 510 executes the instructions stored in the memory, the processor 510 is configured to perform each step and / or procedure of the above method embodiments corresponding to the first communication device or the second communication device.

[0246] Optionally, the communication device 500 is the first communication device in the above embodiments.

[0247] Optionally, the communication device 500 is the second communication device in the above embodiments.

[0248] The transceiver 520 can include a transmitter and a receiver. The transceiver 520 can further include an antenna, and the number of the antenna can be one or more. The processor 510 and the memory 530 and the transceiver 520 can be integrated on different chips. For example, the processor 510 and the memory 530 can be integrated on a baseband chip, and the transceiver 520 can be integrated on a radio frequency chip. The processor 510 and the memory 530 and the transceiver 520 can also be integrated on the same chip. The present application does not make any limitation in this regard.

[0249] Optionally, the communication apparatus 500 is a component, such as a chip, a chip system, etc., configured in the first communication apparatus.

[0250] Optionally, the communication apparatus 500 is a component, such as a chip, a chip system, etc., configured in the second communication apparatus.

[0251] The transceiver 520 can also be a communication interface, such as an input / output interface, a circuit, etc. The transceiver 520, the processor 510 and the memory 530 can be integrated on the same chip, such as a baseband chip.

[0252] The present application also provides a processing apparatus, including at least one processor, the at least one processor is configured to execute a computer program or a logic circuit, so that the processing apparatus executes the method performed by the first communication apparatus or the second communication apparatus in the above method embodiments. The processing apparatus can also include a memory, and the memory is configured to store the computer program.

[0253] The present application also provides a processing apparatus, including a processor and an input / output interface. The input / output interface is coupled to the processor. The input / output interface is configured to input and / or output information. The information includes at least one of instructions and data. The processor is configured to execute a computer program, so that the processing apparatus executes the method performed by the first communication apparatus or the second communication apparatus in the above method embodiments.

[0254] The present application also provides a processing apparatus, including a processor and a memory. The memory is configured to store a computer program, and the processor is configured to call and execute the computer program from the memory, so that the processing apparatus executes the method performed by the first communication apparatus or the second communication apparatus in the above method embodiments.

[0255] It should be understood that the processing device described above can be one or more chips. For example, the processing device can be a field programmable gate array (FPGA), can be an application specific integrated circuit (ASIC), can also be a system on chip (SoC), can also be a central processor unit (CPU), can also be a network processor (NP), can also be a digital signal processor (DSP), can also be a micro controller unit (MCU), can also be a programmable logic device (PLD) or other integrated chip.

[0256] In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as hardware processor execution completion, or executed by hardware and software modules in the processor. The software module can be located in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, register, etc. The storage medium is located in the memory, and the processor reads the information in the memory, and combines the hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0257] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with a signal processing capability. In the implementation process, the steps of the above method embodiments can be completed by an integrated logic circuit or an instruction in the form of software in the processor. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in the storage, and the processor reads the information in the storage, and combines the hardware to complete the steps of the above method.

[0258] It can be understood that the memory in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM) and direct rambus RAM (DR RAM). It should be noted that the memory of the system and method described herein is intended to include but not limited to these and any other suitable types of memory.

[0259] According to the method provided by the embodiment of the application, the application further provides a computer program product, which comprises a computer program or a set of instructions, and when the computer program or the set of instructions run on a computer, the computer program or the set of instructions make the computer execute the method performed by the first communication device or the second communication device in the method embodiments.

[0260] According to the method provided by the embodiment of the application, the application further provides a computer readable storage medium, which stores a program, and when the program runs on a computer, the program makes the computer execute the method performed by the first communication device or the second communication device in the method embodiments.

[0261] According to the method provided by the embodiment of the application, the application further provides a communication system, which can comprise the first communication device or the second communication device described above.

[0262] The terms "component", "module", "system", and the like used in the present specification are used to represent computer-related entities, hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and / or thread of execution, and a component can be localized on one computer and / or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate by way of local and / or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and / or across a network such as the Internet with other systems via the signal).

[0263] Those skilled in the art can clearly understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0264] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the system, device and unit described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0265] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiments is merely a logical function division, and there can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0266] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0267] In addition, each functional unit in the embodiments of the present application can be integrated in one processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit.

[0268] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the essential part of the technical solutions of the present application or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a second communication device, etc.) to execute all or part of the steps of the methods of the embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, and various other media that can store program codes.

Claims

1. A data compression transmission method characterized by, The method comprises: determining a compression parameter corresponding to a data type of first data; compressing the first data according to the compression parameter to obtain compressed information, wherein the compressed information comprises base information and coefficient information, or coefficient information, the base information is used to express the first data, and the coefficient information comprises an expression coefficient of at least one sub-information of the base information to the first data; sending the compressed information.

2. The method of claim 1, wherein, The compression parameter comprises a first parameter, and the first parameter indicates the base information.

3. The method of claim 2, wherein, The first parameter is also used to indicate whether the base information comprises first base information and second base information, and the first base information and the second base information are used to express the first data.

4. The method according to any one of claims 1 to 3, characterized in that, The compression parameter comprises a second parameter, and the second parameter indicates an execution order of a first compression process and a second compression process; the first compression process is used to screen to obtain the coefficient information; the second compression process is used to quantitatively compress.

5. The method according to any one of claims 1 to 4, characterized in that, The compression parameter comprises a third parameter, and the third parameter indicates K data processing processes on the first data; wherein the K data processing processes comprise at least one of the following: a plurality of first data units of the first data are divided to obtain N sub-data; or a data dimension of the first data is converted from a first dimension to a second dimension, and the data dimension comprises a row number and a column number of the first data; or a plurality of second data units in the first data are divided into M data groups; or the first data is subjected to a normalization process.

6. The method of claim 5, wherein, The compression parameter comprises a fourth parameter, and the fourth parameter indicates a data length of each sub-data or a number N of sub-data.

7. The method according to claim 5 or 6, characterized in that, The compression parameter comprises a fifth parameter, and the fifth parameter indicates the second dimension.

8. The method according to any one of claims 5 to 7, characterized in that, The compression parameter comprises a sixth parameter, and the sixth parameter indicates a data dimension or a value range of a second data unit in each data group.

9. The method according to any one of claims 5 to 8, characterized in that, The third parameter is also used to indicate an execution position of the data processing in a data compression process, and / or an execution order between at least two data processes.

10. The method according to any one of claims 5 to 9, characterized in that, The third parameter is also used to indicate the number K of data processing processes.

11. The method according to any one of claims 1 to 10, characterized in that, The method further comprises: receiving first indication information, the first indication information being used to indicate the compression parameter.

12. The method of claim 11, wherein, The first indication information is carried in radio resource control (RRC) signaling.

13. The method according to any one of claims 1 to 12, characterized in that, The method further comprises: sending second indication information, the second indication information being used to indicate the compression parameter.

14. A data compression transmission method characterized by, The method comprises: receiving compressed information of first data, the compressed information comprising base information and coefficient information, or coefficient information, the base information being used to express the first data, and the coefficient information comprising an expression coefficient of at least one sub-information of the base information to the first data; restoring the first data according to the compressed information and a compression parameter corresponding to a data type of the first data.

15. The method of claim 14, wherein, The compression parameter comprises a first parameter, and the first parameter indicates the base information.

16. The method of claim 15, wherein, The first parameter is also used to indicate whether the base information comprises first base information and second base information, and the first base information and the second base information are used to express the first data.

17. The method according to any one of claims 14 to 16, characterized in that, The compression parameter comprises a second parameter, which indicates an execution order of the first compression processing and the second compression processing. The first compression processing is used for screening to obtain the coefficient information. The second compression processing is used for quantization compression.

18. The method according to any one of claims 14 to 17, characterized in that, The compression parameter comprises a third parameter, which indicates K data processing procedures on the first data; wherein the K data processing procedures comprise at least one of the following: a plurality of data units of the first data are divided to obtain N sub-data; or a data dimension of the first data is converted from a first dimension to a second dimension, the data dimension comprising a row number and a column number of the first data; or a plurality of data units in the first data are divided into M data groups; or the first data is normalized.

19. The method of claim 18, wherein, The compression parameter comprises a fourth parameter, which indicates a data length of each sub-data or a number N of sub-data.

20. The method of claim 18 or 19, wherein, The compression parameter comprises a fifth parameter, which indicates the second dimension.

21. The method according to any one of claims 18 to 20, characterized in that, The compression parameter comprises a sixth parameter, which indicates a data dimension or a value range of a data unit in each data group.

22. The method according to any one of claims 18 to 21, characterized in that, The third parameter is further used for indicating an execution position of the data processing in a data compression process, and / or an execution order between at least two data processing.

23. The method according to any one of claims 18 to 22, characterized in that, The third parameter is further used for indicating the number K of the data processing procedures.

24. The method according to any one of claims 14 to 23, characterized in that, Further comprising: sending first indication information, the first indication information being used for indicating the compression parameter.

25. The method of claim 24, wherein, The first indication information is carried in a radio resource control (RRC) signaling.

26. The method according to any one of claims 14 to 25, characterized in that, Further comprising: receiving second indication information, the second indication information being used for indicating the compression parameter.

27. A communications device, characterized by Comprising: a processor, which is used for running a computer program or instructions, so that the method in any one of claims 1 to 26 is executed.

28. The communication apparatus according to claim 27, wherein, The communication device further comprises a memory, which stores the computer program or instructions.

29. A computer-readable storage medium, characterized in that, A computer program or instructions for storing, which are run to execute the method in any one of claims 1 to 26.

30. A computer program product, characterised in that, A computer program or instructions, which are run to execute the method in any one of claims 1 to 26.

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