Method, device and system for compressing map or mapping configuration

Abstract

Example embodiments relate to a method for map compression or mapping information compression. In one aspect, a first device compresses information using relationships between elements in the information, the information including at least one of a first map, a second map, or a mapping configuration between the first map and the second map. The first map represents one of radio environmental information and geometric information, and the second map represents the other of the environmental information and the geometric information. The first device then sends the compressed information to a second device, where the compressed information is smaller in size than the information. Thus, the map and mapping may be compressed to reduce indication overhead. Therefore, perception performance and communication performance are improved, and processing delay and complexity are reduced.

Description

TECHNICAL FIELD

[0001] Exemplary embodiments of the present disclosure relate generally to the field of telecommunications, and in particular to methods for map compression or mapping configuration compression. BACKGROUND

[0002] With the development of communication technology, user equipment (UE) location information has been introduced in cellular communication networks to improve various performance metrics for the network. For example, these performance metrics can include capacity, agility, and efficiency. This improvement can be achieved when the elements of the network utilize the location, behavior, movement pattern, etc. of the UE in the context of a priori information that describes the wireless environment in which the UE operates.

[0003] Sensing systems can be used to help collect UE pose information, including the location of the UE in a global coordinate system, the speed and direction of the UE's movement in the global coordinate system, orientation information, and information about the wireless environment. "Location" is also referred to as "position," and the two terms can be used interchangeably herein. Known sensing systems include radio detection and ranging (RADAR) and light detection and ranging (LIDAR), among others. While sensing systems can be separate from communication systems, using an integrated system to collect information is advantageous to reduce hardware (and cost) in the system as well as the time, frequency, or spatial resources needed to perform both functions. However, the use of communication system hardware to perform sensing of UE pose information and environmental information is a challenging and open problem. Additionally, there is still a need to reduce the overhead of sensing systems. SUMMARY

[0004] In general, exemplary embodiments of the present disclosure provide a solution for compressing one or more maps, one or more mapping configurations, or any combination of maps and mapping configurations.

[0005] In a first aspect, a method is provided. The method comprises compressing information using relationships between elements in the information, wherein the information comprises at least one of a first map, a second map, or a mapping configuration between the first map and the second map, the first map representing one of radio environment information and geometry information, the second map representing the other of radio environment information and geometry information; outputting the compressed information, wherein the compressed information is smaller in size than the information. Thus, the map and mapping can be compressed to reduce the indication overhead. As a result, the sensing performance and communication performance are improved, and the processing latency and complexity are reduced.

[0006] In some embodiments, the mapping configuration indicates at least one of: an index of an element in the first map per element in the second map; an index of an element in the second map per element in the first map; a list of index pairs, wherein an index pair in the list of index pairs comprises an index of an element in the first map and an index of an element in the second map; an element in the first map per element in the second map; an element in the second map per element in the first map; or a list of element pairs, wherein an element pair in the list of element pairs comprises an element in the first map and an element in the second map. In this way, the mapping configuration can be indicated in a variety of alternative ways. Additionally, the mapping configuration can also indicate the first map and the second map in an implicit way.

[0007] In some embodiments, an element in the first map or the second map representing radio environment information can have at least one of: a multipath or ray tracing information type, a channel matrix information type characterizing a channel, a beamforming information type, a reference signal information type, or a channel quality or state information type. In this way, different RF-maps (including specific types of RF-map elements) can be flexibly provided according to different scenarios and sensing / communication tasks.

[0008] In some embodiments, an element in the first map or the second map representing geometry information can have at least one of: a two-dimensional (2D) location area type; a three-dimensional (3D) location area type; a geographical coordinate type; or a processed data type associated with the geometry information. In this way, different G-maps (including specific types of G-map elements) can be flexibly provided based on different scenarios and sensing / communication tasks.

[0009] In some embodiments, a first element in the map representing radio environment information in the first map or the second map has a first element type, and a second element in the map has a second element type, wherein the first element type can be the same as or different from the second element type, a first size of the first element can be the same as or different from a second size of the second element, and / or a first value range of the first element can be the same as or different from a second value range of the second element. In this way, the RF-map can include multiple elements with different element types. Then, through the mapping configuration, the first device can obtain radio environment information of different aspects. In addition, the RF-map can be divided into different sizes, shapes, or types.

[0010] In some embodiments, the elements in the first map or the second map representing radio environment information can have one or more element types. In this way, sufficient radio environment information can be obtained directly.

[0011] In some embodiments, a third element in the map representing geometric information in the first map or the second map can have a third element type, and a fourth element in the map has a fourth element type, wherein the third element type is the same as or different from the fourth element type; and / or a third size or shape of the third element is the same as or different from a fourth size or shape of the fourth element. In this way, the division of the G-map can be uniform or non-uniform.

[0012] In some embodiments, the compression information can include compressing at least one of the first map, the second map, or the mapping configuration into multiple layers with different compression levels. In this way, the first map, the second map, or the mapping configuration can be hierarchically represented, making information transmission more flexible.

[0013] In some embodiments, the method can further include sending at least one of the number of compression levels, at least one compression parameter of each compression level, the number of map elements of each compression level, the size of the map of each compression level, or the mapping method using different compression levels to the second device. In this way, information indicating the hierarchical map or the hierarchical mapping configuration is sent to the second device.

[0014] In some embodiments, the compression information can include: mapping one of the first map, the compressed first map, or the layer of the first map to one of the second map, the compressed second map, or the layer of the second map; mapping one of the first map, the compressed first map, or the layer of the first map to a plurality of compressed second maps with different compression levels; mapping a plurality of compressed first maps with different compression levels to a plurality of compressed second maps with different compression levels; or mapping a plurality of compressed first maps with different compression levels to one of the second map, the compressed second map, or the layer of the second map. In this way, the first map can be flexibly mapped to the second map.

[0015] In some embodiments, the compression information can include: dividing the first map, the compressed first map, or the layer of the first map into a plurality of portions; and mapping the plurality of portions to a plurality of compressed second maps with different compression levels. In this way, the first map can be flexibly mapped to the second map.

[0016] In some embodiments, the compression information can include: selecting a plurality of elements from the first map, the compressed first map, or the layer of the first map; and mapping the plurality of elements to a plurality of compressed second maps with different compression levels. In this way, the first map can be flexibly mapped to the second map.

[0017] In some embodiments, the compression information can include: generating a mapping between the compressed first map with a first compression level and the compressed second map with a second compression level, wherein the first compression level is the same as or different from the second compression level. In this way, the first map can be flexibly mapped to the second map.

[0018] In some embodiments, the information can be represented by at least one of: a multi-dimensional matrix; a tree; a list; or an array. In this way, the first map, the second map, or the mapping configuration can be flexibly represented, thereby improving communication performance.

[0019] In some embodiments, the information can be represented by a multi-dimensional matrix, and compression of the information can be performed based on at least one of: projection; matrix transformation; vector quantization; scalar quantization; or entropy encoding. In this way, the first map, the second map, or the mapping configuration can be compressed in several alternative ways.

[0020] In some embodiments, the method can further include: sending at least one compression parameter to the second device, the at least one compression parameter including at least one of a projection method, a transformation method, a transformation basis, a number of quantization bits, or an entropy encoding method. In this way, information associated with compression parameters of the multi-dimensional matrix is indicated to the second device.

[0021] In some embodiments, the information can be represented by a tree, the compressed information can include at least one of a compressed tree structure or compressed tree node information. In this way, information associated with the tree is indicated to the second device.

[0022] In some embodiments, multiple nodes of the tree can be compressed individually or jointly. In this way, the tree can be compressed in several alternative ways.

[0023] In some embodiments, the method can further include sending, to the second device, at least one compression parameter including a tree depth. In this way, information associated with a compression parameter of the tree is indicated to the second device.

[0024] In some embodiments, the information can be represented by a list or an array, the compressed information can include at least one of: compressing numerical values of the list or array based on entropy coding; compressing non-numerical values of the list or array based on differential compression. In this way, the list or array can be compressed in several alternative ways.

[0025] In some embodiments, the compressed information can further include at least one of: compressing numerical values based on differential compression before entropy coding; or compressing non-numerical values based on projection, matrix transformation, quantization, or entropy coding in parallel with differential compression. In this way, the list or array can be compressed in several alternative ways.

[0026] In some embodiments, the method can further include sending, to the second device, at least one compression parameter including at least one of a recombination method, a projection method, a transformation method, a transformation basis, a number of quantization bits, or an entropy coding method selected for differential compression. In this way, information associated with a compression parameter of the list or array is indicated to the second device.

[0027] In some embodiments, the compressed information can further include encoding a residual between two elements using a prediction method, where the two elements can be in the same map / mapping or different maps / mappings; and compressing the encoded residual based on at least one of projection, matrix transformation, vector quantization, scalar quantization, or entropy coding. In this way, the information can be compressed using a prediction method.

[0028] In some embodiments, the method can further include sending, to the second device, at least one compression parameter including at least one of a prediction mode or a reference element index. In this way, information associated with the prediction method is indicated to the second device.

[0029] In a second aspect, a method is provided. The method comprises: obtaining compressed information; obtaining, based on the compressed information, information comprising at least one of a first map, a second map, or a mapping configuration between the first map and the second map, the first map representing one of radio environment information and geometry information, the second map representing the other of the environment information and the geometry information, the compressed information being smaller in size than the information. Thus, a second device can obtain the information from the compressed information received from a first device. Thus, the perception performance and the communication performance are improved, the indication overhead, the processing latency and the complexity are reduced.

[0030] In some embodiments, the mapping configuration indicates at least one of: an index of an element in the first map per element in the second map; an index of an element in the second map per element in the first map; a list of index pairs, wherein an index pair in the above index pairs comprises an index of an element in the first map and an index of an element in the second map; an element in the first map per element in the second map; an element in the second map per element in the first map; or a list of element pairs, wherein an element pair in the above element pairs comprises an element in the first map and an element in the second map. In this way, the mapping configuration can be indicated in a variety of alternative ways. In addition, the mapping configuration can also indicate the first map and the second map in an implicit way.

[0031] In some embodiments, an element in the first map or the second map representing radio environment information can have at least one of: a multipath or ray tracing information type; a channel matrix information type characterizing a channel; a beamforming information type; a reference signal information type; or a channel quality or state information type. In this way, different RF-maps (including specific types of RF-map elements) can be flexibly provided according to different scenarios and perception / communication tasks.

[0032] In some embodiments, an element in the first map or the second map representing geometry information can have at least one of: a two-dimensional (2D) location area type; a three-dimensional (3D) location area type; a geographical coordinate type; or a processed data type associated with the geometry information. In this way, different G-maps (including specific types of G-map elements) can be flexibly provided according to different scenarios and perception / communication tasks.

[0033] In some embodiments, a first element in the first map or the second map representing radio environment information has a first element type, a second element in the map has a second element type, wherein the first element type can be the same as or different from the second element type, a first size of the first element can be the same as or different from a second size of the second element, and / or a first value range of the first element can be the same as or different from a second value range of the second element. In this way, the RF-map can include multiple elements with different element types. Then, through the mapping configuration, the first device can obtain radio environment information of different aspects. Furthermore, the first map can be divided into different sizes, shapes, or types.

[0034] In some embodiments, the elements in the first map or the second map representing radio environment information can have one or more element types. In this way, sufficient radio environment information can be obtained directly.

[0035] In some embodiments, a third element in the first map or the second map representing geometry information has a third element type, a fourth element in the map has a fourth element type, wherein the third element type can be the same as or different from the fourth element type; and / or a third size or shape of the third element can be the same as or different from a fourth size or shape of the fourth element. In this way, the division of the G-map can be uniform or non-uniform.

[0036] In some embodiments, obtaining the compressed information can include that the second device receives the compressed information from the first device. In this way, the second device can obtain the compressed information in several alternative ways.

[0037] In some embodiments, at least one of the first map, the second map, or the mapping configuration can be compressed into multiple layers with different compression levels. In this way, the first map, the second map, or the mapping configuration can be represented hierarchically, making information transmission more flexible.

[0038] In some embodiments, obtaining the information can include that at least one of a number of compression levels, at least one compression parameter of each compression level, a number of map elements of each compression level, a map size of each compression level, or a mapping method utilizing different compression levels is received from the first device; and the information is obtained according to at least one of the number of compression levels, the at least one compression parameter of each compression level, the number of map elements of each compression level, the map size of each compression level, or the mapping method utilizing different compression levels. In this way, the second device obtains information of a hierarchical map or a hierarchical mapping configuration to determine the first map, the second map, or the mapping configuration.

[0039] In some embodiments, one of the first map, the compressed first map, or the layers of the first map is mapped to one of the second map, the compressed second map, or the layers of the second map; one of the first map, the compressed first map, or the layers of the first map is mapped to a plurality of compressed second maps with different compression levels; a plurality of compressed first maps with different compression levels are mapped to a plurality of compressed second maps with different compression levels; or a plurality of compressed first maps with different compression levels are mapped to one of the second map, the compressed second map, or the layers of the second map. In this way, the first map can be flexibly mapped to the second map.

[0040] In some embodiments, the first map, the compressed first map, or the layers of the first map can be divided into a plurality of portions, and the plurality of portions can be mapped to a plurality of compressed second maps with different compression levels. In this way, the first map can be flexibly mapped to the second map.

[0041] In some embodiments, a plurality of elements can be selected from the first map, the compressed first map, or the layers of the first map, and the plurality of elements can be mapped to a plurality of compressed second maps with different compression levels. In this way, the first map can be flexibly mapped to the second map.

[0042] In some embodiments, the compressed first map with a first compression level can be mapped to the compressed second map with a second compression level, the first compression level can be the same as or different from the second compression level. In this way, the first map can be flexibly mapped to the second map.

[0043] In some embodiments, the information can be represented by at least one of: a multi-dimensional matrix; a tree; a list; or an array. In this way, the first map, the second map, or the mapping configuration can be flexibly represented, thereby improving communication performance.

[0044] In some embodiments, the information can be represented by a multi-dimensional matrix, and the information can be compressed based on at least one of: a projection; a matrix transformation; a vector quantization; a scalar quantization; or an entropy encoding. In this way, the first map, the second map, or the mapping configuration can be compressed in several alternative ways.

[0045] In some embodiments, obtaining the information can comprise: receiving at least one compression parameter from the first device, the at least one compression parameter comprising at least one of a projection method, a transformation method, a transformation basis, a number of quantization bits, or an entropy encoding method; and obtaining the information according to the at least one compression parameter. In this way, the second device obtains information associated with compression parameters of the multi-dimensional matrix to determine the first map, the second map, or the mapping configuration.

[0046] In some embodiments, the information can be represented by a tree, and the compressed information can include at least one of a compressed tree structure or compressed tree node information. In this way, the second device obtains information associated with the tree to determine the first map, the second map, or the mapping configuration.

[0047] In some embodiments, a plurality of nodes of the tree can be compressed individually or jointly. In this way, the tree can be compressed in several alternative ways.

[0048] In some embodiments, obtaining the information can include receiving at least one compression parameter including a tree depth from the first device, and obtaining the information based on the at least one compression parameter. In this way, the second device obtains information associated with a compression parameter of the tree to determine the first map, the second map, or the mapping configuration.

[0049] In some embodiments, the information can be represented by a list or an array, numerical values of the list or the array can be compressed based on entropy coding, and non-numerical values of the list or the array can be compressed based on differential compression. In this way, the list or the array can be compressed in several alternative ways.

[0050] In some embodiments, the method can include at least one of: prior to the entropy coding, the numerical values are compressed based on the differential compression; or in parallel with the differential compression, the non-numerical values are compressed based on a projection, a matrix transformation, a quantization, or an entropy coding. In this way, the list or the array can be compressed in several alternative ways.

[0051] In some embodiments, obtaining the information can include receiving at least one compression parameter from the first device, the at least one compression parameter including at least one of a recombination method, a projection method, a transformation method, a transformation basis, a number of quantization bits, or an entropy coding method selected for the differential compression, and obtaining the information based on the at least one compression parameter. In this way, the second device obtains information associated with a compression parameter of the list or the array to determine the first map, the second map, or the mapping configuration.

[0052] In some embodiments, a residual between two elements can be encoded with a prediction method, the two elements can be in a same map / mapping or different maps / mappings, and the encoded residual can be compressed based on at least one of a projection, a matrix transformation, a vector quantization, a scalar quantization, or an entropy coding. In this way, the information can be compressed using a prediction method.

[0053] In some embodiments, obtaining the information can comprise receiving at least one compression parameter from the first device, the at least one compression parameter comprising at least one of a prediction mode or a reference element index, and obtaining the information based on the at least one compression parameter. In this way, the information associated with the prediction method is used to determine the first map, the second map, or the mapping configuration.

[0054] In some embodiments, obtaining the information can further comprise one of decoding the compressed information, or decompressing the compressed information. In this way, the second device can determine the first map, the second map, or the mapping configuration to improve the communication performance.

[0055] In a third aspect, a first device is provided. The first device comprises an interface and a processor communicatively coupled with the interface. The processor is configured to compress information using a relationship between elements in the information, wherein the information comprises at least one of a first map, a second map, or a mapping configuration between the first map and the second map, the first map representing one of radio environment information and geometry information, the second map representing the other of the radio environment information and the geometry information, and output the compressed information via the interface, wherein a size of the compressed information is smaller than a size of the information. Thus, the map and the mapping can be compressed to reduce the indication overhead. Thus, the perception performance and the communication performance are improved, and the processing latency and complexity are reduced.

[0056] In a fourth aspect, a second device is provided. The second device comprises an interface and a processor communicatively coupled with the interface. The processor is configured to obtain compressed information, and obtain information based on the compressed information, the information comprising at least one of a first map, a second map, or a mapping configuration between the first map and the second map, the first map representing one of radio environment information and geometry information, the second map representing the other of the radio environment information and the geometry information, and a size of the compressed information is smaller than a size of the information. Thus, the second device can obtain the information from the compressed information received from the first device. Thus, the perception performance and the communication performance are improved, and the indication overhead, the processing latency and complexity are reduced.

[0057] In a fifth aspect, a non-transitory computer readable medium comprising a computer program stored thereon, which, when executed on at least one processor, causes the at least one processor to carry out the method of any one of the first aspect or the second aspect.

[0058] In a sixth aspect, an apparatus is provided comprising at least one processing circuitry configured to carry out the method of any one of the first aspect or the second aspect.

[0059] In a seventh aspect, there is provided a computer program product tangibly stored on a computer readable medium and comprising computer executable instructions that, when executed, cause an apparatus to perform the method of any of the first or second aspects.

[0060] It is to be understood that the Summary is not intended to identify key or essential features of embodiments of the disclosure, nor is it intended to limit the scope of the disclosure. Other aspects of the disclosure will become readily apparent to those skilled in the art on review of the following description. BRIEF DESCRIPTION OF DRAWINGS

[0061] Some example embodiments will be described with reference to the accompanying drawings, in which: Figure 1A An example communication system in which example embodiments of the disclosure can be implemented is illustrated; Figure 1B An example communication system in which example embodiments of the disclosure can be implemented is illustrated; Figure 1C Examples of electronic devices (EDs) and base stations relevant to some embodiments of the disclosure are illustrated; Figure 1D Examples of units or modules in devices relevant to some embodiments of the disclosure are illustrated; Figure 1E Examples of sensing management functions (SMFs) relevant to some embodiments of the disclosure are illustrated; Figure 2 An example signalling diagram illustrating example procedures in accordance with some embodiments of the disclosure is illustrated; Figures 3A-3B An example representation of mapping configuration between RF-maps and G-maps in accordance with some embodiments of the disclosure is illustrated; Figure 4 Example hierarchical compression in accordance with some embodiments of the disclosure is illustrated; Figures 5A-5C Example mapping configuration in accordance with some embodiments of the disclosure is illustrated; Figure 6 Example matrices in accordance with some embodiments of the disclosure are illustrated; Figure 7A Example matrix partitioning in accordance with some embodiments of the disclosure is illustrated; Figure 7B Another example matrix partitioning in accordance with some embodiments of the disclosure is illustrated; Figure 7C Example list or array representation in accordance with some embodiments of the disclosure is illustrated; Figure 8 An exemplary tree representation is shown in accordance with some embodiments of the present disclosure; Figure 9A An exemplary intra prediction is shown in accordance with some embodiments of the present disclosure; Figure 9B An exemplary inter prediction is shown in accordance with some embodiments of the present disclosure; Figure 10 A flow diagram of a method implemented at a first device is shown in accordance with some embodiments of the present disclosure; Figure 11 A flow diagram of a method implemented at a second device is shown in accordance with some embodiments of the present disclosure; Figure 12 A simplified block diagram of a device suitable for implementing embodiments of the present disclosure is shown.

[0062] Throughout the drawings, identical or similar reference numerals can designate identical or similar elements. DETAILED DESCRIPTION

[0063] The principles of the present disclosure will now be described with reference to some exemplary embodiments. It should be understood that the embodiments are described only for the purpose of explanation and to help understand and implement the present disclosure, and do not suggest any limitation on the scope of the present disclosure. The inventive concept described herein can be implemented in various ways other than those specifically described below.

[0064] In the following description and claims, unless otherwise defined, all technical and scientific terms used herein are to be taken as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0065] References in the present disclosure to “one embodiment”, “an embodiment”, “exemplary embodiment”, etc., indicate that the embodiment described can include a particular feature, structure, or characteristic, but every embodiment can not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that adapt or modify such feature, structure, or characteristic in connection with other embodiments, whether or not such adapt or modification is explicitly described, is within the knowledge of those of skill in the art.

[0066] It should be understood that, although terms “first” and “second” and the like can be used herein to describe various elements, these elements should not be limited by the terms “first” and “second”. The terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the example embodiments. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0067] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising”, “has” and / or “having”, “includes” and / or “including” when used herein, specify the presence of stated features, elements and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof.

[0068] Figure 1A An example communication system 100A in which example embodiments of the present disclosure can be implemented is shown. Referring to Figure 1A , as a non-limiting illustrative example, a simplified schematic diagram of a communication system is provided. The communication system 100A includes a wireless access network 120. The wireless access network 120 can be a next generation (e.g., sixth generation (6G) or beyond) wireless access network, or a legacy (e.g., 5G, 4G, 3G, or 2G) wireless access network. One or more communication electronic devices (EDs) 110a, 110b, 110c, 110d, 110e, 110f, 110g, 110h, 110i, 110j (generally referred to as 110) can be interconnected, or connected to one or more network nodes (170a, 170b, generally referred to as 170) in the wireless access network 120. A core network 130 can be part of the communication system, can rely on, or be independent of, the radio access technology used in the communication system 100A. Further, the communication system 100A includes a public switched telephone network (PSTN) 140, the Internet 150, and other networks 160.

[0069] Figure 1BAn exemplary communication system in which exemplary embodiments of the present disclosure can be implemented is shown. Generally, the communication system 100B enables multiple wireless or wireline elements to communicate with one another. The communication system 100B can be used to provide voice, data, video, signaling, and / or text content, among other content, through broadcast, multicast, and unicast, among other techniques. The communication system 100B can operate by sharing resources, such as a carrier frequency bandwidth, among its constituent elements. The communication system 100B can include terrestrial communication systems and / or non-terrestrial communication systems. The communication system 100B can provide a wide range of communication services and applications (e.g., earth monitoring, remote sensing, passive sensing and positioning, navigation and tracking, autonomous delivery and mobility, among others). The communication system 100B can provide high availability and robustness through joint operation of terrestrial and non-terrestrial communication systems. For example, integration of non-terrestrial communication systems (or components thereof) into terrestrial communication systems can result in a heterogeneous network that can be viewed as comprising multiple tiers. The heterogeneous network can achieve better overall performance compared to traditional communication networks through efficient multi-link joint operation, more flexible function sharing, and faster physical layer link switching between terrestrial and non-terrestrial networks.

[0070] The terrestrial and non-terrestrial communication systems can be viewed as subsystems of a communication system. In Figure 1B In the example shown, the communication system 100B includes electronic devices (EDs) 110a, 110b, 110c, 1 lOd (generally referred to as EDs 110), radio access networks (RANs) 120a, 120b, a non-terrestrial communication network 120c, a core network 130, a public switched telephone network (PSTN) 140, the Internet 150, and other networks 160. The RANs 120a, 120b include respective base stations (BSs) 170a, 170b, which can be generally referred to as terrestrial transmit and receive points (T-TRPs) 170a, 170b. The non-terrestrial communication network 120c includes an access node 172, which can be generally referred to as a non-terrestrial transmit and receive point (NT-TRP) 172.

[0071] Alternatively or additionally, any of the EDs 110 can be used to connect with, access, or communicate with any of the T-TRPs 170a, 170b and the NT-TRP 172, the Internet 150, the core network 130, the PSTN 140, other networks 160, or any combination of the foregoing. In some examples, the ED 110a can communicate uplink and / or downlink transmissions with the T-TRP 170a over a terrestrial air interface 190a. In some examples, the EDs 110a, 110b, 110c, and 110d can also communicate directly with one another over one or more sidelink air interfaces 190b. In some examples, the ED 110d can communicate uplink and / or downlink transmissions with the NT-TRP 172 over a non-terrestrial air interface 190c.

[0072] The air interfaces 190a and 190b can use similar communication techniques, such as any applicable wireless access technology. For example, the communication system 100B can implement one or more channel access methods in the air interfaces 190a and 190b, such as code division multiple access (CDMA), space division multiple access (SDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), Direct Fourier Transform spread OFDMA (DFT-OFDMA), or single-carrier FDMA (SC-FDMA). The air interfaces 190a and 190b can utilize other high-dimensional signal spaces, which can involve combinations of orthogonal and / or non-orthogonal dimensions.

[0073] The non-terrestrial air interface 190c can implement communication between the ED 110d and the one or more NT-TRPs 172 over a wireless link or simple link. For some examples, the link is a dedicated connection for unicast transmissions, a connection for broadcast transmissions, or a connection between a group of EDs 110 and the one or more NT-TRPs 172 for groupcast transmissions.

[0074] The RANs 120a and 120b are in communication with the core network 130 in order to provide the EDs 110a, 110b, and 110c with access to various services such as voice, data, etc. The RANs 120a and 120b and / or the core network 130 can be in direct or indirect communication with one or more other RANs (not shown) that can or can not be using the same radio access technology to communicate with the core network 130. The core network 130 can also serve as a gateway for the RANs 120a and 120b or EDs 110a, 110b, and 110c, or both, to access other networks (such as PSTN 140, the Internet 150, and other networks 160) via a SGSN / MME, a GGSN, or both. In addition, some or all of the EDs 110a, 110b, and 110c can include functionality for communicating over different wireless links with different wireless networks using different wireless technologies and / or protocols. Instead of, or in addition to, wireless communication, the EDs 110a, 110b, and 110c can communicate with service providers or switches (not shown) and the Internet 150 via wired communication channels. The PSTN 140 can include a circuit- switched telephone network for providing plain old telephone service (POTS). The Internet 150 can include a network of computers and sub-networks (intranets) or both, and incorporate protocols such as Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), etc. The EDs 110a, 110b, and 110c can be multi-mode devices capable of operating according to a plurality of wireless access technologies, and incorporate multiple transceivers as needed to support these technologies.

[0075] Any or all of the EDs 110 and BSs 170 can be a sensing node in the system 100B. A sensing node is a network entity that senses by transmitting and receiving sensing signals. Some sensing nodes are communication devices that both communicate and sense. However, it is possible for some sensing nodes to not communicate, but to be dedicated to sensing. The sensing agent 174 is an example of a sensing node that is dedicated to sensing. Unlike the EDs 110 and BSs 170, the sensing agent 174 does not transmit nor receive communication signals. However, the sensing agent 174 can transmit configuration information, sensing information, signaling information, or other information within the communication system 100B. The sensing agent 174 can communicate with the core network 130 to transmit information with the rest of the communication system 100B. For example, the sensing agent 174 can determine the location of the ED 110a and transmit that information to the base station 170a through the core network 130. While the sensing agent 174 is shown as a separate entity from the EDs 110 and BSs 170, it is possible for the sensing agent 174 to be an ED 110 or a BS 170.Figure 2 Only one sensing agent 174 is shown, but any number of sensing agents can be implemented in the communication system 100B. In some embodiments, one or more sensing agents can be implemented at one or more RAN 120s.

[0076] Figure 1C Examples of electronic devices (EDs) and base stations related to some embodiments of this disclosure are shown. Figure 1C As shown, another example of an ED 110 and base stations 170a, 170b, and / or 170c is provided. The ED 110 is used to connect people, objects, machines, etc. The ED 110 can be widely used in various scenarios, such as cellular communication, device-to-device (D2D), vehicle-to-everything (V2X), peer-to-peer (P2P), machine-to-machine (M2M), machine-type communication (MTC), Internet of Things (IoT), virtual reality (VR), augmented reality (AR), mixed reality (MR), metaverse, digital twins, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearable devices, smart transportation, smart cities, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc.

[0077] Each ED 110 represents any applicable end-user device for wireless operation, which can include (or can be referred to as) a user equipment / device (UE), a wireless transmit / receive unit (WTRU), a mobile station, a fixed or mobile subscriber unit, a cellular phone, a station (STA), a machine type communication (MTC) device, a personal digital assistant (PDA), a smartphone, a laptop, a computer, a tablet, a wireless sensor, a consumer electronics device, a smart book, a vehicle, a car, a truck, a bus, a train, or an IoT device, a wearable device (e.g., a watch, a head-mounted device, glasses), an industrial device, or a means for communicating (e.g., a communication module, a modem, or a chip), among others. Future generations of EDs 110 can be referred to using other terminology. Each base station 170a and 170b is a T-TRP, hereinafter referred to as T-TRP 170. Figure 1C Also shown in the middle is an NT-TRP, hereinafter referred to as NT-TRP 172. Each ED 110 connected to the T-TRP 170 and / or the NT-TRP 172 can be dynamically or semi-statically turned on (i.e., established, activated, or enabled), turned off (i.e., released, deactivated, or disabled), and / or configured in response to one or more of connection availability and connection necessity.

[0078] The ED 110 includes a transmitter 111 and a receiver 113 coupled to one or more antennas 204. Only one antenna 204 is shown in the figure. One, some, or all of the antennas 204 can also be a panel. The transmitter 111 and the receiver 113 can be, for example, integrated as a transceiver. The transceiver is used to modulate data or other content for transmission by at least one antenna 204 or a network interface controller (NIC). The transceiver is also used to demodulate data or other content received by the at least one antenna 204. Each transceiver includes any applicable structure for generating signals for wireless or wired transmission and / or for processing signals received through wireless or wired means. Each antenna 204 includes any applicable structure for transmitting and / or receiving wireless or wired signals.

[0079] The ED 110 includes at least one memory 115. The memory 115 stores instructions and data used, generated, or collected by the ED 110. For example, the memory 115 could store software

[0080] The ED 110 can also include one or more input / output devices (not shown) or interfaces (such as a wired interface to the Internet 150 in FIG. 1). The input / output devices support interaction with a user or other devices or systems. Each input / output device includes any suitable structure for providing information to or from a user, including a network interface communication, such as through a speaker, microphone, keypad, keyboard, display, or touch screen.

[0081] ED 110 includes a processor 117 to perform operations including operations related to preparing transmissions for uplink transmissions to NT-TRPs 172 and / or T-TRPs 170, operations related to processing downlink transmissions received from NT-TRPs 172 and / or T-TRPs 170, and operations related to processing sidelink transmissions to and from another ED 110. The processing operations related to preparing transmissions for uplink transmissions can include operations such as encoding, modulating, transmit beamforming, and generating symbols for transmission. The processing operations related to processing downlink transmissions can include operations such as receive beamforming, demodulating, and decoding received symbols. According to embodiments, receiver 113 can receive downlink transmissions, possibly using receive beamforming, and processor 117 can extract signaling (e.g., by detecting and / or decoding the signaling) from the downlink transmissions. For example, the signaling can be reference signals transmitted by NT-TRPs 172 and / or T-TRPs 170. In some embodiments, processor 117 implements transmit beamforming and / or receive beamforming according to beam pointing indications (e.g., beam angle information (BAI)) received from T-TRPs 170. In some embodiments, processor 117 can perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as operations related to detecting synchronization sequences, decoding and acquiring system information, and the like. In some embodiments, processor 117 can perform channel estimation, e.g., using reference signals received from NT-TRPs 172 and / or T-TRPs 170.

[0082] Although not shown, processor 117 can form part of transmitter 111 and / or part of receiver 113. Although not shown, memory 115 can form part of processor 117.

[0083] Processor 117, processing components of transmitter 111, and processing components of receiver 113 can all be implemented by the same or different one or more processors to execute instructions stored in memory (e.g., memory 115). Alternatively, some or all of processor 117, processing components of transmitter 111, and processing components of receiver 113 can be implemented using a programmed field-programmable gate array (FPGA), a graphical processing unit (GPU), a Central Processing Unit (CPU), or an application-specific integrated circuit (ASIC), among other specialized circuits.

[0084] In some implementations, T-TRP 170 can have other names, such as a base station, a base transceiver station (BTS), a wireless base station, a network node, a network equipment, a network-side device, a transmission / reception node, a NodeB, an evolved NodeB (eNodeB or eNB), a home eNodeB, a next Generation NodeB (gNB), a transmission point (TP), a site controller, an access point (AP), a wireless router, a relay, a remote radio head, a ground node, a ground network device, a ground base station, a base band unit (BBU), a remote radio unit (RRU), an active antenna unit (AAU), a remote radio head (RRH), a central unit (CU), a distributed unit (DU), a positioning node, etc. T-TRP 170 can be a macro BS, a micro BS, a relay node, a host node, etc., or a combination thereof. T-TRP 170 can refer to the above-mentioned devices or means (e.g., a communication module, a modem, or a chip) in the above-mentioned devices.

[0085] In some embodiments, various parts of T-TRP 170 can be distributed. For example, some modules of T-TRP 170 can be located at a remote end of a device that houses antennas 256 of T-TRP 170, can be coupled to the device that houses antennas 256 of T-TRP 170 through a communication link (not shown), such as a common public radio interface (CPRI), which is sometimes referred to as front-haul. Thus, in some embodiments, the term T-TRP 170 can also refer to modules on the network side that perform processing operations for ED 110 position determination, resource allocation (scheduling), message generation and encoding / decoding, etc., which are not necessarily part of the device that houses antennas 256 of T-TRP 170. These modules can also be coupled to other T-TRPs. In some embodiments, T-TRP 170 can actually be multiple T-TRPs that work together to serve ED 110, for example, by using coordinated multipoint transmission.

[0086] The T-TRP 170 includes at least one transmitter 181 and at least one receiver 183 coupled to one or more antennas 256. Only one antenna 256 is shown in the figure. One, some or all of the antennas 256 can also be panels. The transmitter 181 and receiver 183 can be integrated as a transceiver. The T-TRP 170 also includes a processor 182 for performing operations, including operations related to preparing transmissions for downlink transmissions to the ED 110, processing uplink transmissions received from the ED 110, preparing transmissions for backhaul transmissions to the NT-TRP 172, and processing transmissions received from the NT-TRP 172 over the backhaul. Processing operations related to preparing transmissions for downlink or backhaul transmissions can include operations such as encoding, modulation, precoding (e.g., multiple input multiple output (MIMO) precoding), transmit beamforming, and generating symbols for transmission. Processing operations related to processing transmissions received in uplink or over the backhaul can include operations such as receive beamforming, demodulating received symbols, and decoding received symbols. The processor 182 can also perform operations related to network access (e.g., initial access) and / or downlink synchronization, such as generating contents of a synchronization signal block (SSB), generating system information, etc. In some embodiments, the processor 182 also generates beam pointing indications, such as the BAI, which the scheduler 184 can schedule for transmission. The processor 182 performs other network-side processing operations described herein, such as determining a location of the ED 110, determining a deployment location of the NT-TRP 172, etc. In some embodiments, the processor 182 can generate signaling, such as to configure one or more parameters of the ED 110 and / or one or more parameters of the NT-TRP 172. Any signaling generated by the processor 182 is transmitted by the transmitter 181. It should be noted that “signaling” as used herein can also be referred to as control signaling. Dynamic signaling can be transmitted in a control channel such as a physical downlink control channel (PDCCH), and static or semi-static higher layer signaling can be included in data packets transmitted in a data channel such as a physical downlink shared channel (PDSCH).

[0087] A scheduler 184 can be coupled to the processor 182. The scheduler 184 can be included in the T-TRP 170 or can operate separately. The scheduler 184 can schedule uplink, downlink, and / or backhaul transmissions including issuing scheduling grants and / or configuring grant-free (“configured grant”) resources. The T-TRP 170 also includes memory 185 that stores information and data. The memory 185 stores instructions and data used, generated, or collected by the T-TRP 170. For example, the memory 185 can store software

[0088] Although not shown, the processor 182 can form part of the transmitter 181 and / or part of the receiver 183. Further, although not shown, the processor 182 can implement the scheduler 184. Although not shown, the memory 185 can form part of the processor 182.

[0089] The processor 182, the scheduler 184, the processing components of the transmitter 181, and the processing components of the receiver 183 can each be implemented by the same or different one or more processors configured to execute instructions stored in a memory (e.g., the memory 185). Alternatively, some or all of the processor 182, the scheduler 184, the processing components of the transmitter 181, and the processing components of the receiver 183 can be implemented using FPGA, GPU, CPU, or ASIC, among other specialized circuits.

[0090] While NT-TRP 172 is shown by way of example only as a drone, NT-TRP 172 can be implemented in any applicable non-ground-based form, such as a high-altitude platform, a satellite, a high-altitude platform and unmanned aircraft as an international mobile telecommunication base station, which will be discussed below. In addition, in some implementations, NT-TRP 172 can have other names, such as a non-ground node, a non-ground network device, or a non-ground base station. NT-TRP 172 includes a transmitter 186 and a receiver 187 coupled to one or more antennas 108. Only one antenna 108 is shown in the figure. One, some or all of the antennas can also be panels. The transmitter 186 and the receiver 187 can be integrated as a transceiver. NT-TRP 172 also includes a processor 188 for performing operations, including operations related to preparing transmissions for downlink transmissions to ED 110, processing uplink transmissions received from ED 110, preparing transmissions for backhaul transmissions to T-TRP 170, and processing transmissions received from T-TRP 170 over the backhaul. Processing operations related to preparing transmissions for downlink or backhaul transmissions can include operations such as encoding, modulating, precoding (e.g., MIMO precoding), transmit beamforming, and generating symbols for transmission. Processing operations related to processing transmissions received in uplink or over the backhaul can include operations such as receive beamforming, demodulating received symbols, and decoding received symbols. In some embodiments, processor 188 implements transmit beamforming and / or receive beamforming according to beam pointing information (e.g., BAI) received from T-TRP 170. In some embodiments, processor 188 can generate signaling, such as to configure one or more parameters of ED 110. In some embodiments, NT-TRP 172 implements physical layer processing, but not higher layer functions such as functions of medium access control (MAC) or radio link control (RLC) layers. Since this is by way of example only, more generally, NT-TRP 172 can implement higher layer functions in addition to physical layer processing.

[0091] NT-TRP 172 also includes a memory 189 that stores information and data. Although not shown, processor 188 can form part of transmitter 186 and / or part of receiver 187. Although not shown, memory 189 can form part of processor 188.

[0092] The processor 188, processing components of the transmitter 186, and processing components of the receiver 187 can all be implemented by the same or different one or more processors that are used to execute instructions stored in a memory (e.g., the memory 189). Alternatively, some or all of the processor 188, processing components of the transmitter 186, and processing components of the receiver 187 can be implemented using programmed FPGAs, GPUs, CPUs, or specialized circuitry such as ASICs, etc. In some embodiments, the NT-TRP 172 can actually be multiple NT-TRPs that operate together to serve the ED 110, e.g., through coordinated multipoint transmission.

[0093] The T-TRP 170, NT-TRP 172, and / or ED 110 can include other components, which for brevity, are not depicted.

[0094] Figure 1D Examples of units or modules in a device related to some embodiments of the disclosure are shown. One or more steps of the method embodiments provided herein can be performed by Figure 1D corresponding units or modules of the. Figure 1D Units or modules in a device such as the ED 110, T-TRP 170, or NT-TRP 172 are shown. For example, a signal can be transmitted by a transmitting unit or module. A signal can be received by a receiving unit or module. A signal can be processed by a processing unit or module. Other steps can be performed by an artificial intelligence (AI) module or a machine learning (ML) module. The corresponding units or modules can be implemented using hardware, one or more components or devices executing software, or a combination thereof. For example, one or more units or modules can be integrated circuitry such as a programmed FPGA, GPU, CPU, or ASIC. It should be understood that if these modules are implemented as software executed with a processor, the modules can be retrieved by the processor as needed, individually or collectively, for processing, in one or more instances, and it should also be understood that these modules can themselves include instructions for further deployment and instantiation.

[0095] Other details regarding the ED 110, T-TRP 170, and NT-TRP 172 are known to those of skill in the art. Accordingly, these details are omitted here.

[0096] Sensing nodes can combine sensing-based technologies with reference signal-based technologies to enhance UE attitude determination. This type of sensing node can also be called a sensing management function (SMF). In some networks, the SMF can also be called a location management function (LMF). The SMF can be implemented as a physically independent entity located at the core network 130 connected to multiple BS 170s. In other aspects of this application, the SMF can be implemented as a logical entity co-located within the BS 170 through logic executed by the processor 182. Figure 1E Examples of sensing management functions (SMFs) related to some embodiments of this disclosure are shown.

[0097] like Figure 1E As shown, when implemented as a physically independent entity, the SMF 176 includes at least one processor 194, at least one transmitter 192, at least one receiver 196, one or more antennas 195, and at least one memory 199. Transceivers (not shown) may be used in place of transmitters 192 and receivers 196. A scheduler 198 may be coupled to the processor 194. The scheduler 198 may be included within the SMF 176 or may operate separately from the SMF. The processor 194 implements various processing operations of the SMF 176, such as signal encoding, data processing, power control, input / output processing, or any other function. The processor 194 may also be used to implement some or all of the functions and / or embodiments described in more detail above. Each processor 194 includes any suitable processing or computing device for performing one or more operations. For example, each processor 194 may include a microprocessor, microcontroller, digital signal processor, field-programmable gate array, or application-specific integrated circuit.

[0098] Attitude determination techniques based on reference signals belong to the "active" attitude estimation paradigm. In this paradigm, the party inquiring about attitude information (e.g., the UE) participates in the process of determining the inquirer's attitude. The inquirer can send or receive (or send and receive) signals specific to the attitude determination process. Positioning techniques based on Global Navigation Satellite Systems (GNSS), such as GPS, are other examples of the active attitude estimation paradigm.

[0099] In contrast, radar-based sensing technologies, for example, can be considered a "passive" attitude determination paradigm. In passive attitude determination, the target is completely unaware of the attitude determination process.

[0100] By integrating sensing and communication in one system, the system does not need to operate based on a single paradigm only. Therefore, combining sensing-based techniques with reference-signal-based techniques can enable enhanced pose determination.

[0101] For example, the enhanced pose determination can include obtaining UE channel subspace information, which is particularly useful for UE channel reconstruction at the sensing node, especially for beam-based operation and communication. The UE channel subspace is a subset of the entire algebraic space defined on the spatial domain, in which the entire channel from the TP to the UE is located. Therefore, the UE channel subspace can define the TP-to-UE channel very precisely. Signals transmitted on other subspaces have negligible contribution to the UE channel. Knowing the UE channel subspace helps to reduce the workload required for UE-end channel measurement and network-end channel reconstruction. Therefore, combining sensing-based techniques with reference-signal-based techniques can greatly reduce the overhead of UE channel reconstruction compared to traditional methods. The subspace information can also facilitate subspace-based sensing to reduce sensing complexity and improve sensing accuracy.

[0102] Sensing systems can be used to help collect UE pose information, including the UE’s location in a global coordinate system, the UE’s moving speed and direction in the global coordinate system, orientation information, and information about the wireless environment. “Location” is also referred to as “position,” and the two terms can be used interchangeably herein. Well-known sensing systems include radio detection and ranging (RADAR) and light detection and ranging (LIDAR), among others. While sensing systems can be separate from communication systems, using an integrated system to collect information can help reduce hardware (and cost) in the system and the time, frequency, or spatial resources required to perform both functions. However, using communication system hardware to perform sensing of UE pose information and environmental information is extremely challenging and an open problem. The difficulty of this problem is related to factors such as limited resolution of communication systems, dynamic nature of the environment, and the large number of objects whose electromagnetic properties and locations need to be estimated.

[0103] Therefore, sensing-communication integration (also referred to as communication-sensing integration) is a desirable feature in existing and future communication systems. Furthermore, it is desirable to design the information exchanged between the UE and the sensing system or sensing coordinator, as well as the corresponding interaction protocol, to facilitate practical implementation of sensing-communication integration.

[0104] Furthermore, terrestrial networks and non-terrestrial networks can enable a range of new services and applications, such as earth monitoring, remote sensing, passive sensing and positioning, navigation and tracking, automated delivery and mobility. Terrestrial network-based sensing and non-terrestrial network-based sensing can provide intelligent context-aware networks to enhance UE experience. For example, terrestrial network-based sensing and non-terrestrial network-based sensing can involve opportunities for positioning and sensing applications based on a set of new features and service capabilities. Applications such as THz imaging and spectroscopy have the potential to provide continuous, real-time physiological information for future digital health technologies through dynamic, non-invasive, non-contact measurements. Simultaneous localization and mapping (SLAM) methods can not only enable advanced cross reality (XR) applications, but also enhance navigation for autonomous objects such as vehicles and drones. In terrestrial networks and non-terrestrial networks, measured channel data and sensing and positioning data can be acquired through large bandwidth, new spectrum, dense networks, and more light-of-sight (LOS) links. From these data, a radio environment map can be drawn, in which channel information is linked with its corresponding positioning or environmental information to provide enhanced physical layer design based on the map.

[0105] As a base station or other network device can collect and use its own channel and / or sensing data or UE’s channel and / or sensing data, the base station or other network device can have a larger field of view, a longer sensing distance, more detailed global information, and a higher resolution environmental map. If the network provides a radio environment map to a UE, the map can help the UE to improve its sensing function, such as to improve sensing accuracy or to reduce sensing complexity, and the map can also assist the UE communication, such as MIMO or beamforming process. In addition, when the UE’s location / geographical information changes, or the surrounding environment changes, the radio environment map corresponding to the UE can also change. If the network can provide the UE with the latest radio environment map information according to these changes, the UE’s processing latency or processing complexity can be reduced. At the same time, the performance of sensing or communication operation can be improved accordingly.

[0106] According to some embodiments of the present disclosure, a solution for map compression or mapping configuration compression is provided. In one aspect, a first device compresses information using relationships between elements in the information, the information comprising at least one of a first map, a second map, or a mapping configuration between the first map and the second map. The first map represents one of radio environment information and geometry information, and the second map represents the other of the radio environment information and the geometry information. Then, the first device sends the compressed information to a second device, where the size of the compressed information is smaller than the above-mentioned information. Therefore, the map and the mapping can be compressed to reduce the indication overhead. As a result, the sensing performance and the communication performance are improved, and the processing latency and complexity are reduced.

[0107] Figure 2 A signaling diagram is shown, which illustrates an exemplary procedure according to some embodiments of the present disclosure. The procedure 200 can involve a first device 201 and a second device 202. Figure 2 The first device 201 in the communication system 100A can be an example of the communication electronic device 110 in Figure 1A The first device 201 can also be an example of the network node 170 in the communication system 100A. Figure 1A The first device 201 can also be an example of the communication electronic device 110 in the communication system 100A. Figure 2 The second device 202 in the communication system 100A can be an example of the communication electronic device 110 in Figure 1A The second device 202 can also be an example of the communication electronic device 110 in the communication system 100A. Figure 1A The second device 202 can also be an example of the network node 170 in the communication system 100A. It should be understood that although the processing procedure 200 has been described in the communication system 100A, the procedure can equally be applied to other communication scenarios. Figure 1A

[0108] In the processing procedure 200, the first device 201 compresses 210 information using relationships between elements in the information, the information comprising at least one of a first map, a second map, or a mapping configuration between the first map and the second map, or any combination of two or more of the above. The first map represents one of radio environment information and geometry information, and the second map represents the other of the radio environment information and the geometry information. In other words, the first device 201 can compress radio environment information, geometry information, a mapping between the radio environment information and the geometry information, or any combination of two or more of the above. Compressing information can refer to encoding the information, where the size of the encoded information is smaller than the information. Additionally, the information can comprise indication information to indicate whether the first map, the second map, or the mapping configuration is included in the above-mentioned information. Alternatively, the time-frequency resources and the interaction time between the first device 201 and the second device 202 can indirectly indicate each map or mapping configuration. According to the time-frequency resources and the interaction time, an entity can determine whether the first map, the second map, or the mapping is included in the information; therefore, the information does not need to carry the indication information again.

[0109] ​For example, the first map can represent a radio environment map, a radio frequency map, a radio map, a radio-based map, a radio signal-based map, a wireless signal-based map, or other similar meaning map. The first map element can have several representations, such as ray tracing information, multipath information, channel H information, channel state and / or quality information, beamforming information, reference signal information, or channel quality indicator (CQI) information. The first map can be an RF-map. The second map can represent a location / geometric / geographical information or map, can represent some intermediate results after processing the location / geometric / geographical information, or can represent other similar meaning map. The second map can be a grid-based map or other format map. Each element / grid in the second map includes corresponding geometric / geographical information. The second map can be a G-map. The "map" represents an indication form, and can also be represented by a list, a matrix, a group, a set, a range, an area, a relationship, a lookup table, information, or other names. The "mapping" represents a relationship, and can also be represented by a relationship, a match, a lookup table, or other names. Each map (RF-map, G-map, or other map) includes N elements, where N is greater than or equal to 1. The elements in the map can have different sizes or shapes. The shape of the element can be regular or irregular. In other words, the division of the map can be uniform or non-uniform. The elements in the map can have the same type / shape or different types and / or shapes.

[0110] The mapping configuration can also be referred to as a mapping. The mapping can include one or more mapping elements. Some mapping examples between the G-map and the RF-map are provided below, but the present disclosure is not limited to these examples. In addition, the examples in the present embodiment are illustrated using regular G-map or RF-map elements, but these methods are also applicable to irregular G-map or RF-map elements. In a first example, each RF-map element can have an index, which can be explicitly configured based on the element order or implicitly obtained. Each element in the G-map will be mapped to an element in the RF-map. As shown in FIG. 3, the first G-map element in the G-map 301 is mapped to the RF-map element with index 1 in the RF-map 305, the second G-map element is mapped to the RF-map element with index 5, the third G-map element is mapped to the RF-map element with index 1, the fourth G-map element is mapped to the RF-map element with index 0, and so on. According to the foregoing mapping example, the mapping 303 itself can be a map or an index map, as shown in FIG. 3. Figure 3A Figure 3A ​intermediate the mapping indicates that the G-map element with index 0 in G-map 307 corresponds to the RF-map element with index 1 in RF-map 309, the G-map element with index 1 corresponds to the RF-map element with index 5, the G-map element with index 2 corresponds to the RF-map element with index 1, the G-map element with index 3 corresponds to the RF-map element with index 0, and so on. Based on the aforementioned mapping example, the mapping can be represented by a list or array: {(0, 1), (1, 5), (2, 1), (3, 0)...}, where each mapping element (i, j) represents the mapping or relationship between the G-map element index i and the RF-map element index j.

[0111] In a second example, each RF-map element has an index, and the index can be explicitly configured or implicitly obtained based on the element order. Each G-map element also has an index, and the index can be explicitly configured or implicitly obtained based on the element order. As shown in Figure 3B the mapping indicates that the G-map element with index 0 in G-map 307 corresponds to the RF-map element with index 1 in RF-map 309, the G-map element with index 1 corresponds to the RF-map element with index 5, the G-map element with index 2 corresponds to the RF-map element with index 1, the G-map element with index 3 corresponds to the RF-map element with index 0, and so on. Based on the aforementioned mapping example, the mapping can be represented by a list or array: {(0, 1), (1, 5), (2, 1), (3, 0)...}, where each mapping element (i, j) represents the mapping or relationship between the G-map element index i and the RF-map element index j.

[0112] Additionally, the mapping configuration can include or be represented as: an index of an element in the first map per element in the second map, an index of an element in the second map per element in the first map, a list of index pairs, where an index pair in the list of index pairs includes an index of an element in the first map and an index of an element in the second map, an element in the first map per element in the second map, an element in the second map per element in the first map, a list of element pairs, where an element pair in the list of element pairs includes an element in the first map and an element in the second map, or any combination of two or more of the above.

[0113] Without any limitation, an element in the first map or the second map representing radio environment information can have one or more types and / or modalities. For example, an element in the first map or the second map representing radio environment information can have at least one of: a multipath or ray tracing information type, a channel matrix information type characterizing a channel, a beamforming information type, a reference signal information type, or a channel quality or state information type. In an example, an element in the first map or the second map representing radio environment information (which can also be referred to as an RF-map element) can have the following representation.

[0114] The RF-map elements can include ray-tracing or multipath information. For example, each path / ray can be represented by information about the amplitude, delay, angle, etc. of the path / ray. That is, the RF-map elements can include information about one or more paths / rays (e.g., a set of {amplitude, delay, angle,...}). Additionally or alternatively, the RF-map elements can include channel H information. The channel H information can be represented by a vectorized format, a matrix-based format, or a scalar value. Additionally or alternatively, the RF-map elements can include beamforming information. For example, each beam can be represented by information about the beam angle, beam gradient, beam width, etc. of the beam. That is, the RF-map elements can include information about one or more beams (e.g., a set of {beam angle, beam gradient, beam width,...}). Additionally or alternatively, the RF-map elements can include reference signal information. For example, each RF-map element can include information about one or more reference signals. Additionally or alternatively, the RF-map elements can include one or more channel quality indicators (CQIs). Additionally or alternatively, the RF-map elements can be a direct or indirect representation of channel state and / or quality, such as a CQI, an MCS, an SNR, an MSC range, an SNR range, etc.

[0115] In some embodiments, the elements in the RF-map have one or more element types. Additionally, a first element in a first map or a second map (i.e., RF-map) representing radio environment information has a first element type, and a second element in the map (i.e., RF-map) has a second element type, the first element type being the same as or different from the second element type. In an example, the elements in the RF-map can have different types and / or modalities. For example, a first element in the RF-map has a first plurality of types and / or modalities, and a second element in the RF-map has a second plurality of types and / or modalities. Then in this case, at least a portion of the first plurality of types and / or modalities can be different from the second plurality of types and / or modalities. In a specific example, the first element can include multipath information, and the second element can include channel H information. In another example, a third element can include beamforming information. These elements in the RF-map can include different types of elements (or different number of element types).

[0116] Additionally or alternatively, the first size of the first element in the RF-map can be the same or different from the second size of the second element in the RF-map, regardless of whether the element types are the same. In some embodiments, the first size can be different from the second size in terms of dimensions if the element types of the first element and the second element are the same. The term "size" as used herein refers to a measure or metric of an element in the map in different aspects. That is, the term "size" as used herein can be understood in a broader sense. For example, the size can refer to a measure or metric of at least one of the following aspects: dimensions, compression ratio / bits, type order, number of parameters in the element, etc. Without limitation, the size can refer to other similar metrics of the element.

[0117] For example, the first element, the second element, and another third element have channel H information type. That is, the first element has a dimension of 512 x 64 x 80. The second element has a dimension of 256 x 128. The third element has a dimension of vector 1 x 100. In this example, the sizes of these elements are different, either in terms of the number of dimensions or in terms of the size of a given dimension.

[0118] Additionally or alternatively, in some embodiments, the first size can be different from the second size in terms of number of bits, compression or quantization ratio, or compression or quantization level. That is, the compression or quantization ratio / level of the elements are different. In an example, the first element has channel H information type, the quantized bits of channel H information are compressed or quantized to 5 bits of information. The second element is channel H information type, the channel H information is compressed or quantized to 4 bits of information. If the original quantization level of the channel H information is 16 bits of information (i.e., the information is originally stored with 16 bits), the compression ratios associated with the quantization of the first element and the second element are 3.2 and 4, respectively. Thus, the compression or quantization ratio / level of the elements can be different even for the same element type. While quantization and compression are often referring to different but related concepts, in the context of the previous example, the two terms can be used interchangeably for certain purposes. Additionally, in another example, the first element is multipath information type, the amplitude, delay, and angle information of each path are compressed or quantized to 6 bits, 8 bits, and 5 bits, respectively. The second element can be beamforming information type, the beam angle, beam gradient, and beam width information of each beam are compressed or quantized to 6 bits, 5 bits, and 7 bits, respectively. The quantization level of the elements can also be different for different element types. The quantization level can be different even for the angle in path information and the angle in beamforming information, for example.

[0119] Additionally or alternatively, the first size can differ from the second size in the order of information types in each element. In an example, the first element can be {channel H information, beamforming information}, and the second element can be {beamforming information, channel H information}. That is, the elements can include multiple types, and the order of the types can also be different.

[0120] Additionally or alternatively, the first size can differ from the second size in the number of parameters in the element. In an example, the first element can have beamforming information with a beam number of 5. The second element can have beamforming information with a beam number of 3. Thus, the elements include different numbers of parameters. In another example, the first element has a ray tracing type and a channel quality type, the ray tracing type including 4 rays / paths, whereas the second element can have only the ray tracing type, the ray tracing type including 2 rays / paths.

[0121] Additionally or alternatively, in some embodiments, the first value range of the first element can be the same as or different from the second value range of the second element. In an example, in the case that the elements in the RF-map have the same type, the first value range of the first element in the RF-map can be the same as or different from the second value range of the second element in the RF-map, depending on whether the division of the RF-map is uniform or not. In another example, the first element type is reference signal information, and the value range is 0-20 dB; the second element type is reference signal information, and the value range is 0-30 dB. The value ranges of the elements are different. In another example, the element types of the first element and the second element are different, and the physical dimensions are also different, thus, the first value range and the second value range are essentially different due to the different physical dimensions.

[0122] In some embodiments, the elements in the first map or the second map representing the geometry information have at least one of: a two-dimensional (2D) location area type; a three-dimensional (3D) location area type; a geographic coordinate type; a processed data type associated with the geographic / geometry information; or any combination of two or more of the above. For example, the first map or the second map representing the geometry / geographic information (which can also be referred to as a G-map) can also represent some intermediate results of the processed geometry / geographic information, etc. The G-map can be a grid-based map or a map represented in other formats. The G-map can include M G-map elements / grids, where M > 1. The G-map elements / grids can indicate 2D / 3D locations, 2D / 3D areas or regions, geometry information about the surrounding environment, geographic coordinates, other geometry / geographic information, or pre-processed geometry / geographic information.

[0123] In some embodiments, a third element in a map (i.e., G-map) representing geometric information in the first map or the second map has a third element type, and a fourth element in the map (i.e., G-map) has a fourth element type. The third element type can be the same as or different from the fourth element type, and / or a third size or shape of the third element can be the same as or different from a fourth size or shape of the fourth element.

[0124] In an example, one element in the G-map can include 3D position region information and geographic coordinate information, and another element in the G-map can include geometric information about the surrounding environment. That is, the elements in the G-map can include different types of information and / or different numbers of information types.

[0125] In some embodiments, the sizes of the elements in the G-map can be different in terms of element dimensions. For example, one element in the G-map is of a 2D position region type with a dimension of 100 x 200; another element in the G-map is of a 2D position region type with a dimension of 200 x 200. Additionally or alternatively, one element in the G-map is of a 2D position region type with a dimension of 100 x 200; another element in the G-map is of a 3D position region type with a dimension of 50 x 250 x 100.

[0126] Additionally or alternatively, the sizes of the elements in the G-map can be different in terms of compression or quantization ratios / levels. For example, one element in the G-map is of a 2D position region type with 2D position region information compressed or quantized to 8 bits. Another element in the G-map can be of a 3D position region type with 3D position region information compressed or quantized to 12 bits. Another element in the G-map is of a geographic coordinate type with geographic coordinates (x, y, z) compressed or quantized to 16 bits. Thus, the compression or quantization ratios / levels of the elements can be different.

[0127] Additionally or alternatively, the sizes of the elements in the G-map can be different in terms of orders of information types in each element. For example, one element in the G-map includes {2D position region, geographic coordinate}. Another element in the G-map includes {geographic coordinate, 2D position region}. That is, the elements in the G-map can include multiple types, and the orders of the types can be different.

[0128] Additionally or alternatively, the sizes of the elements in the G-map can be different in terms of numbers of parameters of the elements. For example, one element in the G-map is of a 2D geographic coordinate type including 3 sets of coordinates (x, y). Another element in the G-map is of a 2D geographic coordinate type including 4 sets of coordinates (x, y). That is, the elements in the G-map can include different numbers of parameters. In this way, the description of geometric / geographic information can be flexibly provided to the UE.

[0129] In some embodiments, the first device may compress a first map, a second map, a mapping configuration, or any combination of two or more of these into multiple layers with different compression levels. For example, the map may be compressed hierarchically into several layers, such as from a coarse map to a refined map. It should be understood that a coarse map can be any relatively coarse map in a hierarchical or multi-layered map, and a refined map can be any relatively fine map in a hierarchical or multi-layered map. A refined map refers to a map obtained by compressing the first or second map at a lower compression level. A coarse map refers to a map obtained by compressing the first or second map at a higher compression level. The size of a refined map may be larger than the size of a coarse map. It should be understood that the term "size" as used herein refers to a measurement or metric of the map in different aspects. For example, a refined map may have more elements than a coarse map. Without limitation, size can refer to other similar metrics of elements. Each layer can be incrementally compressed based on the previous layer. The current layer can be differentially compressed using the resized previous layer. Different layers may use different compression levels or parameters.

[0130] like Figure 4 As shown, map 410 can be compressed into at least one of coarse map 420, refined map 430, or refined map 440. Refined map 430 can be compressed based on coarse map 420. Refined map 440 can be compressed based on either refined map 430 or coarse map 420. Coarse map 420 can be a layer with a higher compression level, and layers with higher compression levels can use fewer quantization bits. Refined map 440 can be a layer with a lower compression level, and layers with lower compression levels may use more quantization bits. In other words, layers with lower compression levels can be quantized to more bits, and layers with higher compression levels can be quantized to fewer bits. In another example, layers with higher compression levels can use smaller maps (smaller size or fewer elements), and layers with lower compression levels can use larger maps. Maps (first map, second map, or both) and mappings (between the first and second maps) can both be hierarchically represented as several layers with different compression levels.

[0131] In some embodiments, the first device can map one of the first map, the compressed first map, or the layers of the first map to one of the second map, the compressed second map, or the layers of the second map. In some embodiments, the first device can map one of the first map, the compressed first map, or the layers of the first map to a plurality of compressed second maps with different compression levels. In some embodiments, the first device can map a plurality of compressed first maps with different compression levels to a plurality of compressed second maps with different compression levels. In some embodiments, the first device can map a plurality of compressed first maps with different compression levels to one of the second map, the compressed second map, or the layers of the second map.

[0132] Alternatively, the first device can divide the compressed first map into a plurality of parts. Then, the first device can map the plurality of parts to a plurality of compressed second maps with different compression levels. For example, the RF-map can be hierarchically represented or compressed into a number of layers, e.g., from a coarse map to one or more refined maps. The G-map can also be hierarchically represented by a number of layers or compressed into the number of layers, such as from a coarse map to one or more refined maps. As Figure 5A illustrated, the coarse G-map 505 can be mapped to the coarse RF-map 510. The refined G-map can be divided into different parts 515 and 520, where each part 515 and 520 of the refined G-map can be mapped to a different refined RF-map 525 and 530. The combination of the coarse RF-map 510 and the refined RF-maps 525 and 530 can be obtained by compressing the RF-map using different compression levels. In Figure 5A the example, the part 515 of the refined G-map is mapped to the refined RF-map 525, and the part 520 of the refined G-map is mapped to the refined RF-map 530.

[0133] Alternatively, each RF-map can be hierarchically represented by a number of layers or compressed into the number of layers, e.g., from a coarse map to one or more refined maps. Each G-map can also be hierarchically represented by a number of layers or compressed into a number of layers, e.g., from a coarse map to one or more refined maps. As Figure 5B illustrated, the coarse G-map 535 is mapped to the coarse RF-map 550. The refined G-map can be mapped to the refined RF-map. As Figure 5B illustrated, the refined G-map 555 is mapped to the refined RF-map 570. Furthermore, different coarse G-maps can be mapped to different coarse RF-maps. A plurality of coarse G-maps (from the same G-map, or from different G-maps) can be mapped to the same coarse RF-map. As Figure 5BAs shown, coarse G-maps 535, 540, and 545 are mapped to coarse RF-map 550. Different refined G-maps can be mapped to different refined RF-maps. As shown, refined G-maps 555 and 560 are mapped to refined RF-map 570, and refined G-map 565 is mapped to refined RF-map 575. Multiple refined G-maps (from the same G-map, or from different G-maps) can be mapped to the same refined RF-map. As shown, refined G-maps 555 and 560 are mapped to refined RF-map 570. Figure 5B As shown, refined G-maps 555 and 560 are mapped to refined RF-map 570, and refined G-map 565 is mapped to refined RF-map 575. Multiple refined G-maps (from the same G-map, or from different G-maps) can be mapped to the same refined RF-map. As shown, refined G-maps 555 and 560 are mapped to refined RF-map 570. Figure 5B As shown, refined G-maps 555 and 560 are mapped to refined RF-map 570.

[0134] Additionally, the first device can select a plurality of elements from the compressed first map. Then, the first device can map the plurality of elements to a plurality of compressed second maps with different compression levels. For example, the RF-maps can be represented by several tiers or compressed into several layers, e.g., from a coarse map to one or more refined maps. The G-maps can also be represented by tiers or compressed into several layers, e.g., from a coarse map to one or more refined maps. Different elements or grids in the same refined G-map can be mapped to different refined RF-maps. In some cases, a subset of elements or grids in a refined G-map can be selected to be mapped to a refined RF-map, while certain elements or grids in the refined G-map are not mapped. As shown, Figure 5C As shown, a subset of elements 580 and 585 of refined G-maps are mapped to refined RF-maps 590 and 595, respectively.

[0135] Alternatively or additionally, to compress information, the first device can generate a mapping between a compressed first map and a compressed second map, the compressed first map having a first compression level, the compressed second map having a second compression level. The first compression level and the second compression level can be the same or different. In other words, the compression level of the compressed first map and the compression level of the compressed second map can be the same or different.

[0136] Embodiments of the present disclosure are not necessarily limited to a two-tier map scenario with only one coarse map and one refined map. The refined RF-maps and the refined G-maps can each be associated with different tiers of multiple tiers of each map. For example, the refined RF-map is the 3rd tier of a 5-tier RF-map, and the refined G-map is the 2nd tier of a 3-tier G-map. The coarse RF-map and the coarse G-map can also come from different tiers. Additionally, the coarse RF-map and the coarse G-map can be of different sizes, and the refined RF-map and the refined G-map can be of different sizes.

[0137] In addition to the compressed bits, some compression parameters can need to be indicated between the encoder (BS) and the decoder (UE) to indicate the mapping method with different compression levels, e.g., hierarchical mapping method. With hierarchical mapping, multiple mappings can be generated.

[0138] It should be appreciated that the G-Map refers to the RF-Map, e.g., each G-Map element / grid refers to an element index in the RF-Map. The summary of the invention described herein can also be applied to the RF-Map refers to the G-Map or pair-wise mapping indication. For example, each RF-Map element can refer to one or more element indexes in the G-Map. The mapping can include one or more matching pairs, each matching pair indicates a (G-Map element, RF-Map element) pair.

[0139] In some embodiments, the information can be represented by a multi-dimensional matrix, a tree, a list, an array, or any combination of two or more of the above. In other words, the first map, the second map, or the mapping configuration can be represented by a multi-dimensional matrix, a tree, a list, an array, or any combination of two or more of the above.

[0140] In an example, the RF-Map, the G-Map, or other maps can be represented by a multi-dimensional matrix including a plurality of map elements. The map elements can have several representations, e.g., ray tracing / multipath information, channel H information, channel state and / or quality information, beamforming information, reference signal information, CQI, as previously described. Each element has an index, which can be an explicit index or an implicit index. The multi-dimensional matrix can be uniformly partitioned; as Figure 6 As shown, the map 610 is uniformly partitioned. The multi-dimensional matrix can be non-uniformly partitioned; as Figure 6 As shown, the map 620 is non-uniformly partitioned.

[0141] If the multi-dimensional matrix is uniformly partitioned, the index of each element or grid can be indicated to represent the map. The index can be configured explicitly or implicitly according to the element order. As Figure 7A As shown, the map 710 is uniformly partitioned, the number of grids in each dimension, the number of elements in each dimension, or the size or length of elements in each dimension can be indicated to indicate the partition.

[0142] If the multi-dimensional matrix is non-uniformly partitioned, the range or bounding box for each element can be indicated to represent the map. The partition can be represented based on a tree partitioning, e.g., quad-tree, octree,.... For example, taking a 2D map or 2D matrix as an example, as Figure 7BAs shown, the partitions of map 720 are indicated using {{start x0, y0, x-range d0, y-range d0'}, {start x1, y1, x-range d1, y-range d1'}...}, where (x0, y0) represents the start location of the first partition / range / bounding box (i.e., element 721) with implicit or explicit index 0, "x-range d0" represents the size or length in the x-dimension, and "y-range d0" represents the size or length in the y-dimension. (x1, y1) represents the start location of the second partition / range / bounding box (i.e., element 722) with implicit or explicit index 1. In this case, the map is represented based on a list or array. In another example, the partitions can be represented using a quadtree. As follows Figure 7B As shown, map 730 is represented by quadtree 740. Node 741 in quadtree 740 corresponds to element 731 in map 730, node 742 in quadtree 740 corresponds to element 732 in map 730, and node 743 in quadtree 740 corresponds to element 733 in map 730. In quadtree 740, number 1 in a node of quadtree 740 indicates further partitioning, and number 0 in a node indicates termination of partitioning. For example, node 741 with number 0 cannot be further partitioned, and node 744 with number 1 can be further partitioned. If the map is a 3D map / matrix, an octree can be used to represent the partitions. Higher order trees (e.g., octree 750) can be used to represent the partitions of a multi-dimensional map or matrix. In this case, the map is represented based on a tree.

[0143] For ease of illustration, 2D graphs are used to discuss and illustrate some of the concepts disclosed herein. However, the matrix can also be a multi-dimensional matrix, and the above-described methods apply. It should be understood that these 2D graphs are for illustration purposes and are not intended to be limiting. The subject matter described herein can be implemented in various ways other than described below.

[0144] A map (RF-map, G-map, or other map) can also be represented by a list or array that includes a plurality of map elements. A map element can have a number of representations, each element having an index (explicit, or implicit), as described previously. Element i has a number of element representations. Some examples are given below.

[0145] In an example, element i is {(x, y, z)}, representing a location, position, or coordinate. Optionally, an element index "i" can be included, and the representation of element i becomes {index i, (x, y, z)}. For example, this map element can be used to indicate a G-map location.

[0146] In another example, element i is {{amplitude 0, delay 0, angle 0}, {amplitude 1, delay 1, angle 1}, …, {amplitude n i , delay n i , angle n i}}. The parameter {amplitude x, delay x, angle x} represents the amplitude, delay, angle of a path or ray (e.g., one path / ray) in a set of paths or rays. Optionally, the number of paths n i may be included, where n i is the number of paths or rays. In addition, the element index “i” can also be included. The representation of element i can be {index i, number of paths n i , {amplitude 0, delay 0, angle 0}, {amplitude 1, delay 1, angle 1}, …, {amplitude n i , delay n i , angle n i}}. For example, this map element can be used to indicate an RF-map element, such as one or more paths / rays.

[0147] For element i, the element type can be included. For example, element i can be {type RAY, number of paths n i , {amplitude 0, delay 0, angle 0}, {amplitude 1, delay 1, angle 1}, …, {amplitude n i , delay n i , angle n i}}, where type RAY indicates that the element type is a set of paths or rays. In another example, element j can be {type BEAM, number of beams n j , {angle 0, gradient 0, width 0}, …, {angle n j , gradient n j , width n j}}, where type BEAM indicates that the element type is beamforming information (each beam can include information about the beam angle, beam gradient, beam width, etc. of the beam. The element can include one or more beams). Optionally, the number of paths n i or the number of beams n j may be included in element i, for example, the representation of element j becomes {index j, type BEAM, number of beams n j , {angle 0, gradient 0, width 0}, …, {angle n j , gradient n j , width n j}}.

[0148] For element i, the element size can be included. For example, element i can be {type H, size M t x N t, value / compressed value…}, where "M t ×N t " is the element size / length / dimension, and "value / compressed value…" indicates the original value or compressed value of the channel H information included in the element.

[0149] Based on the above representation of the map elements, a map represented by a list or an array can be represented as {number of elements k, {element 0, element 1, …, element k}}, where the map includes k elements. The "number of elements k" value can be optional information. As shown in FIG. 7, element 770 can have eight dimensions, where the number of elements (i.e., the value of k) is 8. Element 780 can have six dimensions, where the value of k is 6. Element 790 can have five dimensions, where the value of k is 5. Figure 7C

[0150] In some embodiments, the information can be represented by a multi-dimensional matrix, and the compression of the information is based on projection, matrix transformation, vector quantization, scalar quantization, entropy encoding, or any combination of two or more of the above.

[0151] For example, to compress or encode the matrix content X, the following methods can be used individually or in combination. In one method, the projection or transformation of X is performed based on a certain base or dictionary, or based on one of the discrete cosine transform, the discrete Fourier transform, or the fast Fourier transform. For example, a base or dictionary can be used to project or transform X into Y according to Y = UX, thereby reducing the matrix dimension or obtaining a more sparse matrix. In another method, vector / scalar quantization is performed on X or a pre-processed X (e.g., after the projection or transformation). Vector quantization quantizes multiple elements together by exploiting the relationship between the multiple elements. The selection of the number of quantization bits for scalar quantization is associated with the range of the elements, and scalar quantization employs either fixed-bit quantization or dynamic quantization. In the case of fixed-bit quantization, the number of quantization bits for all elements is the same. In the case of dynamic quantization, some elements use fewer quantization bits, while other elements use slightly more quantization bits. In yet another method, entropy encoding is performed. In addition, vector / scalar quantization can be combined with projection, matrix transformation, or entropy encoding to compress the elements. For example, the bit sequence generated by quantization (e.g., the quantization index sequence generated by vector quantization, or other quantization bit sequence or quantization index sequence generated by scalar quantization) will exhibit unequal probability distribution (non-uniform distribution), which is more conducive to improving the performance of entropy encoding. Therefore, entropy decoding, inverse quantization, and inverse transformation (e.g., inverse discrete cosine transform or inverse discrete Fourier transform) can be used in the decoding process or decompression process. The above compression methods can be used for G-maps or RF-maps represented by uniformly divided matrices, for mapping between G-maps and RF-maps, and so on. ​

[0152] In some embodiments, information can be represented by a tree, compressed information can include compressed tree structure, compressed tree node information, or any combination of the above. For example, a matrix or map partition can be represented by an octree or quadtree or higher order tree. As shown, a map 810 can be represented by a tree 820. A node 821 in the quadtree 820 corresponds to an element 811 in the map 810, a node 822 in the quadtree 820 corresponds to an element 812 in the map 810, and a node 823 in the quadtree 820 corresponds to an element 813 in the map 810. A number 1 in a node indicates further partitioning, and a number 0 in a node indicates termination of partitioning. Some tree nodes can include map elements, for example, a node numbered 0 includes a map element. Compressed information can include compressed tree structure, compressed tree node information, or map element information. In some embodiments, multiple nodes of a tree can be compressed individually or jointly. For example, tree nodes or map elements can be compressed individually or jointly. The above compression methods can be used for G-Map or RF-Map represented by a non-uniformly divided matrix partitioned based on a tree. Figure 8

[0153] In some embodiments, information can be represented by a list or array. To compress information, a first device can compress numerical values in a list or array based on entropy coding. Alternatively, information can also be represented by a list or array. To compress information, a first device can compress non-numerical values in a list or array based on differential compression. Additionally, to compress information, a first device can also compress numerical values based on differential compression before entropy coding. Additionally or alternatively, to compress information, a first device can also compress non-numerical values based on projection, matrix transformation, quantization, or entropy coding in parallel with differential compression. In an example, elements in a list are channel matrices, for example, {H0, H1, … H i}. In this example, projection or transformation can be applied before or after calculating residuals for differential compression in addition to differential compression.

[0154] For example, a list can be {element number k, {element 0, element 1, …, element k}}, where k is the number of elements, and the “element number k” value is optionally included information. The representation of element i is {path number n i , {amplitude 0, delay 0, angle 0}, {amplitude 1, delay 1, angle 1}, …, {amplitude n i , delay n i , angle n i}}. Compression methods can be introduced based on this example. Compression methods are similar for lists with other types of elements.

[0155] ​The number values can be put together and then compressed or encoded by entropy coding. For example, {n0, n1,... n i} can be entropy coded, or first compressed by difference compression and then entropy coded. The non-number values can be put together and compressed or encoded by difference compression. Alternatively, difference compression can be used in combination with quantization, entropy coding, etc. For example, {amplitude 0, amplitude 1,... amplitude n i} can be put together, and then the first device can perform difference compression and get the residuals {amplitude 1 - amplitude 0, amplitude 2 - amplitude 1,... amplitude n i - amplitude n i–1}. Then, quantization (for lossy compression) and / or entropy coding can be applied. The values {delay 0, delay 1,... delay n i} can be compressed similarly.

[0156] In another example, if the elements in the list are channel matrices and {H0, H1,... Hi} have been obtained, in addition to difference compression, a projection or transformation can be applied before the residuals are calculated or after the residuals are calculated. In yet another example, if the elements in the list are bounding boxes or ranges, for example, {num = n, {start x0, y0, range d0, d0'}, {start x1, y1, range d1, d1'},..., {start xn, yn, range dn, dn'}}, the bounding boxes or ranges can be difference compressed, compressing the "x"s together and similarly compressing the "d"s together. The compressed information can include compressed number values and a set of compressed non-number values. The compression methods described above can be used for G-maps or RF-maps represented by lists, or for mappings represented by lists, or for pair-wise mappings, etc.

[0157] In some embodiments, the first device can encode the residuals between two elements with a prediction method, where the two elements are in the same map / mapping or in different maps / mappings. The first device can compress the encoded residuals based on at least one of a projection, a matrix transformation, a vector quantization, a scalar quantization, an entropy coding, or any combination of two or more of the above.

[0158] For example, intra prediction and inter prediction can be used to compress matrix content. As Figure 9AAs shown, if elements 901 and 903 are close, e.g., the distance between elements 901 and 903 is small, or the mean-square error (MSE) is small, one element can be differentially compressed with respect to the other. In this case, only the residual (i.e., the difference between the two elements) needs to be encoded, reducing the number of compressed bits. Similar compression methods (projection / transform, quantization, entropy encoding, etc.) described above can be used to compress the residual.

[0159] Inter-prediction can be used to compress matrix elements from different matrices, maps, or layers. For example, the matrix elements can be from matrices at different times or nodes, maps at different resolutions, or different layers in the same matrix, etc. As shown, Figure 9B As shown, if elements 901 and 903 are close, e.g., the distance between elements 901 and 903 is small, or the mean-square error (MSE) is small, one element can be differentially compressed with respect to the other. In this case, only the residual (i.e., the difference between the two elements) needs to be encoded, reducing the number of compressed bits. Similar compression methods (projection / transform, quantization, entropy encoding, etc.) described above can be used to compress the residual.

[0160] If intra-prediction or inter-prediction is used, some compression parameters can be indicated between the encoder (BS) and the decoder (UE). The compression parameters can include the prediction mode (intra or inter), the reference index (e.g., the index of the reference element used in intra / inter prediction), etc. It should be understood that the intra-prediction and inter-prediction described herein can also be applied to trees, lists, arrays, or another representation type of map and mapping configuration, using the matrix as an example.

[0161] Referring back to Figure 2 , the first device 201 outputs 220 compressed information 230 to the second device 202, the size of the compressed information being smaller than the information. On the other side of the communication, the second device 202 obtains 240 the compressed information. Alternatively, the second device 202 can obtain the compressed information by receiving the compressed information from the first device 201. Alternatively, the second device 202 can obtain the compressed information from a third device (e.g., at least one terminal device or network function).

[0162] The second device 202 obtains 250 the information based on the compressed information. The information includes one of the first map, the second map, or the mapping configuration, or any combination of two or more of the above. In some embodiments, to obtain the information, the second device can decode the compressed information. In some embodiments, to obtain the information, the second device can decompress the compressed information.

[0163] Alternatively, the first device can also send to the second device the number of compression levels, at least one compression parameter for each compression level, the number of map elements for each compression level, the map size for each compression level, the mapping method with different compression levels, or any combination of two or more of the above. For example, in addition to the compressed bits, some compression parameters can also need to be indicated between the encoder (BS) and the decoder (UE). The compression parameters can include the number of levels, the compression parameters for each level, the map size / map element number for each level, or the mapping method with different compression levels.

[0164] Accordingly, the second device can receive from the first device the number of compression levels, at least one compression parameter for each compression level, the number of map elements for each compression level, the map size for each compression level, the mapping method with different compression levels, or any combination of two or more of the above. The second device can obtain the information according to the number of compression levels, at least one compression parameter for each compression level, the number of map elements for each compression level, the map size for each compression level, the mapping method with different compression levels, or any combination of two or more of the above. In an example, obtaining the information can include decompressing the compressed information and decoding the compressed information, the size of which is smaller than the information.

[0165] Additionally, the first device can send to the second device at least one compression parameter. The at least one compression parameter can include a projection method, a transformation method, a transformation basis, a number of quantization bits, an entropy encoding method, or any combination of two or more of the above. In an example, the transformation basis can include a basis for matrix transformation. In another example, the transformation basis can include a codebook for vector quantization.

[0166] For example, in addition to the compressed bits, some compression parameters can also need to be indicated between the encoder (BS) and the decoder (UE). The compression parameters can include the transformation method and the transformation basis, the number of quantization bits, the entropy encoding method, etc.

[0167] Then, the second device can receive from the first device at least one compression parameter. The at least one compression parameter can include a projection method, a transformation method, a transformation basis, a number of quantization bits, an entropy encoding method, or any combination of two or more of the above. Then, the second device can obtain the information according to the at least one compression parameter.

[0168] Additionally, the first device can send to the second device at least one compression parameter. The at least one compression parameter can include a tree depth. For example, the tree depth and other compression parameters need to be indicated. Accordingly, the second device can receive from the first device at least one compression parameter, which can include a tree depth.

[0169] Alternatively, the first device can also send at least one compression parameter to the second device. The at least one compression parameter can include a reorganization method, a projection method, a transformation method, a transformation basis, a number of quantization bits, an entropy coding method, or any combination of two or more of the above for differential compression. For example, in addition to the compressed bits, some compression parameters can need to be indicated between the encoder (BS) and the decoder (UE). Some compression parameters can include a number of quantization bits, an entropy coding method, a reorganization method for differential compression, etc.

[0170] On the other side of the communication, the second device can receive at least one compression parameter from the first device. The at least one compression parameter can include a reorganization method, a projection method, a transformation method, a transformation basis, a number of quantization bits, an entropy coding method, or any combination of two or more of the above for differential compression. The second device can obtain information according to the at least one compression parameter.

[0171] Additionally or alternatively, the first device can also send at least one compression parameter to the second device. The at least one compression parameter can include a prediction mode, an index of a reference element, or a combination of the above.

[0172] On the other side of the communication, the second device can receive at least one compression parameter from the first device. The at least one compression parameter can include at least one of a prediction mode or a reference element index. The second device can obtain information according to the at least one compression parameter.

[0173] In view of the above, RF-map is used to represent a radio environment map, a radio frequency map, a radio map, a radio-based map, a radio signal-based map, a wireless signal-based map, or a map of other similar meanings. G-map is used to represent location / geometric / geographical information or a map, or some intermediate results after processing of the location / geometric / geographical information, or a map of other similar meanings. "Map" represents a form of indication, which can also be represented by a list, a matrix, a group, a set, a range, an area, a relationship, a lookup table, information, and other names. Among them, "mapping" represents a relationship, which can also be represented by a relationship, a matching, a lookup table, and other names.

[0174] Exemplary embodiments of the present disclosure are described by the interaction and processing procedures between a user equipment (UE) and a base station (BS). The information and protocol flows exchanged in these procedures can also be represented by Figures 1A-1EThe other network nodes described in the above can be replaced by, for example, the ED 110 and the TRP 170, the ED 110 and the core network, the ED 110 and the ED 110, the TRP 170 and the TRP 170. The UE in the processes described by some embodiments of the present disclosure can be replaced by the sensing node mentioned in the above. The BS in the processes described by some embodiments of the present disclosure can be replaced by the sensing coordinator. The sensing coordinator is a node in the network that can assist the sensing operation. These nodes can be independent nodes dedicated to sensing operations, or other nodes that perform sensing operations in parallel with communication operations (for example, the TRP 170, the ED 110, or the core network node in the above). Figures 1A-1E The sensing nodes mentioned in the above can be replaced. The BS in the processes described by some embodiments of the present disclosure can be replaced by the sensing coordinator. The sensing coordinator is a node in the network that can assist the sensing operation. These nodes can be independent nodes dedicated to sensing operations, or other nodes that perform sensing operations in parallel with communication operations (for example, the TRP 170, the ED 110, or the core network node in the above). Figures 1A-1E The TRP 170, the ED 110, or the core network node in the above).

[0175] Generally, the map (RF-map or G-map) and the mapping (between the G-map and the RF-map) can be represented using a multi-dimensional matrix, a tree, a list, or an array. Several compression methods are proposed for compressing the above map or mapping to reduce the map / mapping indication overhead. The compression methods include: compressing the map / mapping represented by a multi-dimensional matrix; compressing the map / mapping represented by a tree; compressing the map / mapping represented by a list / array. In addition, a hierarchical representation and compression method for the map / mapping are also proposed. It should be understood that the present disclosure is also applicable to the map or mapping compression in other scenarios. For example, the present disclosure can also be applied to Wi-Fi, ultra wide band (UWB), and other short-range communications. That is, the BS in the processes described by the present disclosure can be replaced by an access point (AP).

[0176] Figure 10 A flowchart of an exemplary method 1000 implemented at a first device of some embodiments of the present disclosure is shown. For discussion purposes, reference will be made to Figure 1A The method 1000 will be described from the perspective of the communication electronic device 110 or the network node 170. It should be understood that the method 1000 can include additional actions not shown, and / or some of the shown actions can be omitted, without the scope of the present disclosure being limited thereto.

[0177] At block 1010, the first device compresses information using relationships between elements in the information, wherein the information includes at least one of a first map, a second map, or a mapping between the first map and the second map, the first map representing one of radio environment information and geometric information, the second map representing the other of the environment information and the geometric information. At block 1020, the first device transmits the compressed information to a second device, wherein the compressed information is smaller in size than the information.

[0178] In some embodiments, the mapping configuration indicates at least one of: an index of an element in the first map per element in the second map; an index of an element in the second map per element in the first map; a list of index pairs, where an index pair in the list of index pairs comprises an index of an element in the first map and an index of an element in the second map; a list of elements in the first map per element in the second map; a list of elements in the second map per element in the first map; or a list of element pairs, where an element pair in the list of element pairs comprises an element in the first map and an element in the second map.

[0179] In some embodiments, an element in the first map or the second map representing radio environment information can have at least one of: a multipath or ray tracing information type; a channel matrix information type characterizing a channel; a beamforming information type; a reference signal information type; or a channel quality or state information type.

[0180] In some embodiments, an element in the first map or the second map representing geometry information can have at least one of: a two-dimensional (2D) location area type; a three-dimensional (3D) location area type; a geographic coordinate type; or a processed data type associated with the geometry information.

[0181] In some embodiments, a first element in a map in the first map or the second map representing radio environment information has a first element type, a second element in the map has a second element type, where the first element type can be the same as or different from the second element type, a first size of the first element can be the same as or different from a second size of the second element, and / or a first value range of the first element can be the same as or different from a second value range of the second element.

[0182] In some embodiments, an element in the first map or the second map representing radio environment information can have one or more element types. In some embodiments, a third element in a map in the first map or the second map representing geometry information has a third element type, a fourth element in the map has a fourth element type, where the third element type is the same as or different from the fourth element type; and / or a third size or shape of the third element is the same as or different from a fourth size or shape of the fourth element.

[0183] In some embodiments, to compress information, the first device can compress at least one of the first map, the second map, or the mapping configuration into a plurality of layers with different compression levels. In some embodiments, the first device can further send to the second device at least one of a number of compression levels, at least one compression parameter per compression level, a number of map elements per compression level, a map size per compression level, or a mapping method utilizing different compression levels.

[0184] In some embodiments, to compress the information, the first device can map one of the first map, the compressed first map, or the layer of the first map to one of the second map, the compressed second map, or the layer of the second map; map one of the first map, the compressed first map, or the layer of the first map to a plurality of compressed second maps with different compression levels; map a plurality of compressed first maps with different compression levels to a plurality of compressed second maps with different compression levels; or map a plurality of compressed first maps with different compression levels to one of the second map, the compressed second map, or the layer of the second map.

[0185] In some embodiments, to compress the information, the first device can divide the first map, the compressed first map, or the layer of the first map into a plurality of portions; and map the plurality of portions to a plurality of compressed second maps with different compression levels.

[0186] In some embodiments, to compress the information, the first device can select a plurality of elements from the first map, the compressed first map, or the layer of the first map; and map the plurality of elements to a plurality of compressed second maps with different compression levels.

[0187] In some embodiments, to compress the information, the first device can generate a mapping between the compressed first map with a first compression level and the compressed second map with a second compression level, where the first compression level is the same as or different from the second compression level.

[0188] In some embodiments, the information can be represented by at least one of: a multi-dimensional matrix; a tree; a list; or an array. In some embodiments, the information can be represented by a multi-dimensional matrix, and the compression of the information can be performed based on at least one of: a projection; a matrix transformation; a vector quantization; a scalar quantization; or an entropy encoding.

[0189] In some embodiments, the first device can further send, to the second device, at least one compression parameter including at least one of a projection method, a transformation method, a transformation basis, a number of quantization bits, or an entropy encoding method.

[0190] In some embodiments, the information can be represented by a tree, and the compressed information can include at least one of a compressed tree structure or compressed tree node information. In some embodiments, the first device can further send, to the second device, at least one compression parameter including a tree depth.

[0191] In some embodiments, the information can be represented by a list or an array, and to compress the information, the first device can perform at least one of: compressing numerical values of the list or the array based on entropy coding; compressing non-numerical values of the list or the array based on differential compression.

[0192] In some embodiments, to compress the information, the first device can perform at least one of: compressing numerical values based on differential compression before entropy coding; or compressing non-numerical values based on projection, matrix transformation, quantization or entropy coding in parallel with differential compression.

[0193] In some embodiments, the first device can further send at least one compression parameter to the second device, the at least one compression parameter comprising at least one of a reorganization method, a projection method, a transformation method, a transformation basis, a number of quantization bits or an entropy coding method selected for the differential compression.

[0194] In some embodiments, to compress the information, the first device can further encode a residual between two elements using a prediction method, wherein the two elements can be in a same map / mapping or different maps / mappings; compress the encoded residual based on at least one of projection, matrix transformation, vector quantization, scalar quantization or entropy coding.

[0195] In some embodiments, the first device can further send at least one compression parameter to the second device, the at least one compression parameter comprising at least one of a prediction mode or a reference element index.

[0196] Figure 11 A flowchart of an exemplary method 1100 implemented at the second device to illustrate some embodiments of the present disclosure is shown. For discussion purposes, reference will be made to the system 100 of FIG. 1. Figure 1A The method 1100 is described from the perspective of the communication electronic device 110 or the network node 170. It should be understood that the method 1100 can include additional actions not shown and / or can omit some of the actions shown without departing from the scope of the present disclosure.

[0197] At block 1110, the second device obtains the compressed information. At block 1120, the second device obtains the information based on the compressed information, the information comprising at least one of a first map, a second map or a mapping configuration between the first map and the second map, wherein the first map represents one of radio environment information and geometry information, the second map represents the other of the environment information and the geometry information, and the compressed information is smaller in size than the information.

[0198] In some embodiments, the mapping configuration can indicate at least one of: an index of an element in the first map per element in the second map; an index of an element in the second map per element in the first map; a list of index pairs, where an index pair among the index pairs comprises an index of an element in the first map and an index of an element in the second map; a list of elements per element in the first map in the second map; a list of elements per element in the second map in the first map; or a list of element pairs, where an element pair among the element pairs comprises an element in the first map and an element in the second map.

[0199] In some embodiments, an element in the first map or the second map representing radio environment information can have at least one of: a multipath or ray tracing information type; a channel matrix information type characterizing a channel; a beamforming information type; a reference signal information type; or a channel quality or state information type.

[0200] In some embodiments, an element in the first map or the second map representing geometry information can have at least one of: a two-dimensional (2D) location area type; a three-dimensional (3D) location area type; a geographic coordinate type; or a processed data type associated with the geometry information.

[0201] In some embodiments, a first element in a map in the first map or the second map representing radio environment information has a first element type, a second element in the map has a second element type, where the first element type can be the same as or different from the second element type, a first size of the first element can be the same as or different from a second size of the second element, and / or a first value range of the first element can be the same as or different from a second value range of the second element.

[0202] In some embodiments, an element in the first map or the second map representing radio environment information can have one or more element types. In some embodiments, a third element in a map in the first map or the second map representing geometry information has a third element type, a fourth element in the map has a fourth element type, where the third element type can be the same as or different from the fourth element type; and / or a third size or shape of the third element can be the same as or different from a fourth size or shape of the fourth element.

[0203] In some embodiments, obtaining the compressed information can comprise: receiving the compressed information from the first device.

[0204] In some embodiments, at least one of the first map, the second map, or the mapping configuration can be compressed into a plurality of layers having different compression levels.

[0205] In some embodiments, to obtain the information, the second device can receive, from the first device, at least one of a number of compression levels, at least one compression parameter for each compression level, a number of map elements for each compression level, a map size for each compression level, or a mapping method utilizing different compression levels; and obtain the information based on at least one of the number of compression levels, the at least one compression parameter for each compression level, the number of map elements for each compression level, the map size for each compression level, or the mapping method utilizing different compression levels.

[0206] In some embodiments, one of the first map, the compressed first map, or the layers of the first map is mapped to one of the second map, the compressed second map, or the layers of the second map; one of the first map, the compressed first map, or the layers of the first map is mapped to a plurality of compressed second maps having different compression levels; a plurality of compressed first maps having different compression levels are mapped to a plurality of compressed second maps having different compression levels; or a plurality of compressed first maps having different compression levels are mapped to one of the second map, the compressed second map, or the layers of the second map.

[0207] In some embodiments, the first map, the compressed first map, or the layers of the first map can be split into a plurality of portions, which can be mapped to a plurality of compressed second maps having different compression levels.

[0208] In some embodiments, a plurality of elements can be selected from the first map, the compressed first map, or the layers of the first map, which can be mapped to a plurality of compressed second maps having different compression levels.

[0209] In some embodiments, the compressed first map having a first compression level can be mapped to the compressed second map having a second compression level, the first compression level can be the same as or different from the second compression level.

[0210] In some embodiments, the information can be represented by at least one of a multi-dimensional matrix, a tree, a list, or an array. In some embodiments, the information can be represented by a multi-dimensional matrix, and the information can be compressed based on at least one of a projection, a matrix transformation, a vector quantization, a scalar quantization, or an entropy encoding.

[0211] In some embodiments, to obtain the information, the second device can receive, from the first device, at least one compression parameter including at least one of a projection method, a transformation method, a transformation basis, a number of quantization bits, or an entropy encoding method; and obtain the information based on the at least one compression parameter.

[0212] In some embodiments, the information can be represented by a tree, the compressed information can include at least one of a compressed tree structure or compressed tree node information. In some embodiments, a plurality of nodes of the tree can be compressed individually or jointly.

[0213] In some embodiments, to obtain the information, the second device can receive, from the first device, at least one compression parameter including a tree depth; and obtain the information according to the at least one compression parameter.

[0214] In some embodiments, the information can be represented by a list or an array, numerical values of the list or the array can be compressed based on entropy coding, and non-numerical values of the list or the array can be compressed based on difference compression.

[0215] In some embodiments, the numerical values are compressed based on the difference compression before the entropy coding; or the non-numerical values are compressed based on the projection, the matrix transformation, the quantization or the entropy coding in parallel with the difference compression.

[0216] In some embodiments, to obtain the information, the second device can receive, from the first device, at least one compression parameter including at least one of a reorganization method, a projection method, a transformation method, a transformation basis, a number of quantization bits or an entropy coding method selected for the difference compression; and obtain the information according to the at least one compression parameter.

[0217] In some embodiments, a prediction method can be utilized to encode a residual between two elements, the two elements can be in a same map / mapping or different maps / mappings, and the encoded residual can be compressed based on at least one of a projection, a matrix transformation, a vector quantization, a scalar quantization or an entropy coding.

[0218] In some embodiments, to obtain the information, the second device can receive, from the first device, at least one compression parameter including at least one of a prediction mode or a reference element index; and obtain the information according to the at least one compression parameter. In some embodiments, to obtain the information, the second device can decode the compressed information, or decompress the compressed information.

[0219] Figure 12 A simplified block diagram of a device 1200 (also referred to as apparatus 1200) suitable for implementing embodiments of the present disclosure is shown. The device 1200 can be considered as Figure 1A Another example implementation of the communication electronic device 110 or the network node 170 is shown. Thus, the device 1200 can be implemented at or as at least a part of the above-mentioned devices.

[0220] As shown, the device 1200 includes a processor 1210, a memory 1220 coupled to the processor 1210, a suitable transmitter (TX) and receiver (RX) 1240 coupled to the processor 1210, and a communication interface coupled to the TX / RX 1240. The TX / RX 1240 can also be referred to as a transceiver. The TX / RX 1240 can be coupled to the processor 1210 through any suitable interface for inputting and outputting signals to and from the processor. The memory 1210 stores at least a portion of a program 1230. The TX / RX 1240 is used for bidirectional communications. The TX / RX 1240 has at least one antenna to facilitate communication, but in practice, an access node or base station mentioned in the present disclosure can have several antennas. The communication interface can represent any interface needed to communicate with other network elements, such as an X2 or Xn interface for bidirectional communications between eNBs or gNBs, an S1 interface for communications between a Mobility Management Entity (MME) / Serving Gateway (S-GW) and eNBs or gNBs, an Un interface for communications between eNBs or gNBs and relay nodes (RN), a Uu interface for communications between eNBs or gNBs and terminal devices, or a PC5 interface for communications between two terminal devices.

[0221] It is assumed that the program 1230 includes program instructions that, when executed by the associated processor 1210, enable the device 1200 to operate in accordance with the embodiments of the present disclosure, as described herein with reference to the Figures 1A-11 Embodiments herein can be implemented by computer software executable by the processor 1210 of the device 1200, or by hardware, or by a combination of software and hardware. The processor 1210 can be configured to implement various embodiments of the present disclosure. Further, the processor 1210 in combination with the memory 1220 can form processing means 1250 for implementing various embodiments of the present disclosure.

[0222] The memory 1220 can be of any type suitable to the local technical requirements, and can be implemented using any suitable data storage technology, such as nonvolatile computer-readable memory devices, semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory (as non-limiting examples). While only one memory 1220 is shown in the device 1200, there can be several physically distinct memory modules in the device 1200. The processor 1210 can be of any type suitable to the local technical requirements, and can include one or more of general-purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), and processors based on multi-core processor architectures, as non-limiting examples. The device 1200 can have multiple processors such as a dedicated integrated circuit chip that is time-slaved to a clock that is synchronized with a master processor.

[0223] Components included in the apparatuses and / or devices of the present disclosure can be implemented in various ways including software, hardware, firmware, or any combination of the three. In one embodiment, one or more units can be implemented using software and / or firmware, e.g., using machine-executable instructions stored on a storage medium. In addition to or instead of machine-executable instructions, part or all of the units in the apparatuses and / or devices can be implemented by hardware logic components such as Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0224] In general, the various embodiments of the disclosure can be implemented in hardware or special-purpose circuits, software, logic or any combination thereof. Some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software which can be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the disclosure are illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques termination or methods described herein can be implemented in, as non-limiting examples, hardware, software, firmware, special-purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0225] The present disclosure also provides at least one computer program product which is tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as those included in program modules, which, when executed in a device on a target real or virtual processor, operate to perform processes or methods as described above with reference to any of the Figures 2-11 In general, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The functionality of the program modules can be combined or split between program modules as desired in various embodiments. Machine executable instructions for a program module can be executed within a local or distributed device. In a distributed device, program modules can be located in local and remote memory storage devices.

[0226] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general or special purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow charts and / or block diagrams. The program code can be executed entirely on a machine, partly on a machine (as a stand-alone software package), partly on a machine and partly on a remote machine, or entirely on a remote machine or server.

[0227] The program code mentioned above can be embodied on a machine-readable medium, which can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. It can include, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above. More specific examples of the machine-readable storage medium will include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0228] Moreover, while operations can be depicted in the drawings in a particular, sequential order, this should not be understood as a requirement that such operations be performed in the order in which they are depicted, or that all of the operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous. Likewise, while several specific embodiments have been described above, these are not intended to be limiting, for the scope of the disclosure, but to be included as being within the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments or in any suitable sub-combination. Moreover, although the application has been described in detail with reference to particular embodiments, those skilled in the art will understand that various other changes, substitutions, and alterations can be made hereto without departing from the spirit and scope of the application as defined by the appended claims.

[0229] Although the present disclosure has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject of the appended claims need not be limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example implementations of implementing the claims.

[0230] When these functions are implemented in the form of software function units and sold or used as independent products, these functions can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts of the prior art that make contributions or the parts of the technical solutions can be implemented in the form of a software product. The software product is stored in a storage medium and includes a number of instructions for instructing a computer device (which can be a personal computer, a server or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. The above storage medium includes any medium that can store program codes, such as a USB flash disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0231] The above description is only some specific implementation manners of the present application, and is not intended to limit the protection scope of the present application. Any changes or replacements of the technical contents disclosed in the present application that are easily conceived by those skilled in the art are within the protection scope of the present application. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A method comprising: The information is compressed using relationships between elements in the information, wherein the information includes at least one of a first map, a second map, or a mapping configuration between the first map and the second map, wherein the first map represents one of radio environment information and geometric information, and the second map represents the other of the radio environment information and the geometric information; as well as Output compressed information, wherein the size of the compressed information is smaller than the information itself.

2. The method according to claim 1, wherein compressing the information comprises: The first map, the second map, or the mapping configuration is compressed into multiple layers with different compression levels.

3. The method according to claim 1 or 2, further comprising: Sending at least one of the following from the first device to the second device: the number of compression levels, at least one compression parameter for each compression level, the number of map elements for each compression level, the map size for each compression level, or a mapping method using different compression levels.

4. The method according to any one of claims 1 to 3, wherein compressing the information comprises: Map one of the first map, the compressed first map, or a layer of the first map to the second map, the compressed second map, or a layer of the second map; Map one of the first map, the compressed first map, or a layer of the first map to multiple compressed second maps with different compression levels; Map multiple compressed first maps with different compression levels to multiple compressed second maps with different compression levels; or Multiple compressed first maps with different compression levels are mapped to a second map, a compressed second map, or a layer of the second map.

5. The method according to any one of claims 1 to 4, wherein compressing the information comprises: The first map, the compressed first map, or the layers of the first map are divided into multiple parts; as well as The multiple parts are mapped to multiple compressed second maps with different compression levels.

6. The method according to any one of claims 1 to 4, wherein compressing the information comprises: Select multiple elements from the first map, the compressed first map, or a layer of the first map; as well as The multiple elements are mapped to multiple compressed second maps with different compression levels.

7. The method according to any one of claims 1 to 6, wherein compressing the information comprises: A mapping is generated between a compressed first map and a compressed second map, wherein the first map has a first compression level and the second map has a second compression level, and wherein the first compression level and the second compression level are the same as or different.

8. The method according to any one of claims 1 to 7, wherein the information is represented by at least one of the following: Multidimensional matrix; Tree; List; or Array.

9. The method of claim 8, wherein the information is represented by the multidimensional matrix, and the compression of the information is performed based on at least one of the following: projection; Matrix transformations; Vector quantization; Scalar quantization; or Entropy coding.

10. The method of claim 9, further comprising: At least one compression parameter is sent from the first device to the second device, the at least one compression parameter including at least one of projection method, transformation method, transformation basis, quantization bit number or entropy coding method.

11. The method of claim 8, wherein the information is represented by the tree, and the compressed information includes at least one of a compressed tree structure or compressed tree node information.

12. The method of claim 11, wherein the plurality of nodes of the tree are compressed individually or jointly.

13. The method according to claim 11 or 12, further comprising: Send at least one compression parameter, including the tree depth, from the first device to the second device.

14. The method of claim 8, wherein the information is represented by the list or the array, and compressing the information comprises at least one of the following: Compressing the numerical values ​​of the list or array based on entropy coding; or The non-numeric values ​​of the list or array are compressed based on differential compression.

15. The method of claim 14, wherein compressing the information further comprises at least one of the following: The digital value is compressed based on the differential compression prior to the entropy encoding; or In parallel with the differential compression, the non-digital values ​​are compressed based on projection, matrix transformation, quantization, or entropy coding.

16. The method according to claim 14 or 15, further comprising: At least one compression parameter is sent from the first device to the second device, the at least one compression parameter including at least one of the following: a recombination method, a projection method, a transformation method, a transformation basis, a number of quantization bits, or an entropy coding method for the differential compression selection value.

17. The method according to any one of claims 1 to 16, wherein compressing the information further comprises: The residual between two elements is encoded using a prediction method, wherein the two elements are in the same map / mapping or in different maps / mappings; as well as The encoded residual is compressed based on at least one of the projection, the matrix transformation, the vector quantization, the scalar quantization, or the entropy coding.

18. The method of claim 17, further comprising: At least one compression parameter is sent from the first device to the second device, the at least one compression parameter including at least one of a prediction mode or a reference element index.

19. The method according to any one of claims 1 to 18, wherein the mapping configuration indicates at least one of the following: For each element in the second map, find its index in the first map; For each element in the first map, find its index in the second map; A list of index pairs, wherein each index pair includes an index of an element in the first map and an index of an element in the second map; Elements in the second map, and elements in the first map; Elements in the first map, elements in the second map; or A list of element pairs, wherein each element pair includes elements from the first map and elements from the second map.

20. The method according to any one of claims 1 to 19, wherein an element in the first or second map representing the radio environment information has at least one of the following: Multipath or ray tracing information type, The type of channel matrix information that characterizes the channel. Beamforming information type Reference signal information type, or Channel quality or status information type.

21. The method according to any one of claims 1 to 20, wherein the elements in the first or second map representing the geometric information have at least one of the following: Two-dimensional (2D) location region type; 3D location region type; Geographic coordinate type; or The processed data type associated with the geometric information.

22. A method comprising: Obtain compressed information; as well as Information is obtained based on the compressed information, the information including at least one of a first map, a second map, or a mapping configuration between the first map and the second map, wherein the first map represents one of radio environment information and geometric information, the second map represents the other of the radio environment information and the geometric information, and the size of the compressed information is smaller than the information itself.

23. The method of claim 22, wherein obtaining the compressed information comprises: The compressed information is received from the first device by the second device.

24. The method of claim 22 or 23, wherein at least one of the first map, the second map, or the mapping configuration is compressed into multiple layers with different compression levels.

25. The method according to any one of claims 22 to 24, wherein obtaining the information comprises: Receive from the first device the number of compression levels, at least one compression parameter for each compression level, the number of map elements for each compression level, the map size for each compression level, or at least one of the mapping methods using different compression levels; as well as The information is obtained based on at least one of the following: the number of compression levels, the at least one compression parameter for each compression level, the number of map elements for each compression level, the map size for each compression level, or the mapping method using different compression levels.

26. The method according to any one of claims 22 to 25, wherein one of the following is present: A first map, a compressed first map, or a layer of the first map is mapped to a second map, a compressed second map, or a layer of the second map; A first map, a compressed first map, or a layer of the first map is mapped to multiple compressed second maps with different compression levels; Multiple compressed first maps with different compression levels are mapped to multiple compressed second maps with different compression levels; or Multiple compressed first maps with different compression levels are mapped to a second map, a compressed second map, or a layer of the second map.

27. The method according to any one of claims 22 to 26, wherein the first map, the compressed first map, or the layer of the first map is divided into multiple parts, and the multiple parts are mapped to multiple compressed second maps with different compression levels.

28. The method of any one of claims 22 to 27, wherein a plurality of elements are selected from the first map, a compressed first map, or a layer of the first map, and the plurality of elements are mapped to a plurality of compressed second maps having different compression levels.

29. The method according to any one of claims 22 to 28, wherein a compressed first map having a first compression level is mapped to a compressed second map having a second compression level, the first compression level being the same as or different from the second compression level.

30. The method according to any one of claims 22 to 29, wherein the information is represented by at least one of the following: Multidimensional matrix; Tree; List; or Array.

31. The method of claim 30, wherein the information is represented by the multidimensional matrix, and the information is compressed based on at least one of the following: projection; Matrix transformations; Vector quantization; Scalar quantization; or Entropy coding.

32. The method of claim 31, wherein obtaining the information comprises: The first device receives at least one compression parameter, the at least one compression parameter including at least one of projection method, transformation method, transformation basis, number of quantization bits or entropy coding method; as well as The information is obtained based on the at least one compression parameter.

33. The method of claim 30, wherein the information is represented by the tree, and the compressed information includes at least one of a compressed tree structure or compressed tree node information.

34. The method of claim 33, wherein the plurality of nodes of the tree are compressed individually or jointly.

35. The method of claim 33 or 34, wherein obtaining the information comprises: Receive at least one compression parameter including the tree depth from the first device; as well as The information is obtained based on the at least one compression parameter.

36. The method of claim 30, wherein the information is represented by the list or the array, wherein at least one of the following exists: The numerical values ​​of the list or array are compressed based on entropy encoding; or The non-numeric values ​​of the list or array are compressed based on differential compression.

37. The method of claim 36, wherein at least one of the following is present: The digital value is compressed based on the differential compression prior to the entropy encoding; or In parallel with the differential compression, the non-digital values ​​are compressed based on projection, matrix transformation, quantization, or entropy coding.

38. The method of claim 36 or 37, wherein obtaining the information comprises: The device receives at least one compression parameter, which includes at least one of the following: a recombination method, a projection method, a transformation method, a transformation basis, a number of quantization bits, or an entropy coding method for the differential compression selection value. as well as The information is obtained based on the at least one compression parameter.

39. The method of any one of claims 22 to 38, wherein the residual between two elements is encoded using a prediction method, the two elements being in the same map / mapping or in different maps / mappings, and the encoded residual is compressed based on at least one of the projection, the matrix transformation, the vector quantization, the scalar quantization, or the entropy coding.

40. The method of claim 39, wherein obtaining the information comprises: Receive at least one compression parameter from the first device, the at least one compression parameter including at least one of a prediction mode or an index of a reference element; The information is obtained based on the at least one compression parameter.

41. The method according to any one of claims 22 to 40, wherein obtaining the information further comprises one of the following: Decode the compressed information; or Decompress the compressed information.

42. The method according to any one of claims 22 to 41, wherein the mapping configuration indicates at least one of the following: For each element in the second map, find its index in the first map; For each element in the first map, find its index in the second map; A list of index pairs, wherein each index pair includes an index of an element in the first map and an index of an element in the second map; Elements in the second map, and elements in the first map; Elements in the first map, elements in the second map; or A list of element pairs, wherein each element pair includes elements from the first map and elements from the second map.

43. The method according to any one of claims 22 to 42, wherein an element in the first or second map representing the radio environment information has at least one of the following: Multipath or ray tracing information type, The type of channel matrix information that characterizes the channel. Beamforming information type Reference signal information type, or Channel quality or status information type.

44. The method according to any one of claims 22 to 43, wherein the elements in the first or second map representing the geometric information have at least one of the following: Two-dimensional (2D) location region type; 3D location region type; Geographic coordinate type; or The processed data type associated with the geometric information.

45. A first device, comprising: interface; as well as The processor that is communicatively coupled to the interface, The processor is configured as follows: The information is compressed using relationships between elements in the information, wherein the information includes at least one of a first map, a second map, or a mapping configuration between the first map and the second map, the first map representing one of radio environment information and geometric information, and the second map representing the other of the radio environment information and the geometric information; as well as The compressed information is output via the interface, wherein the size of the compressed information is smaller than that of the original information.

46. ​​A second device, comprising: interface; as well as The processor that is communicatively coupled to the interface, The processor is configured as follows: Obtain compressed information; as well as Information is obtained based on the compressed information, which includes at least one of a first map, a second map, or a mapping configuration between the first map and the second map, wherein the first map represents one of radio environment information and geometric information, the second map represents the other of the radio environment information and the geometric information, and the size of the compressed information is smaller than the information itself.

47. A computer-readable medium comprising a computer program stored thereon, which, when executed on at least one processor, causes the at least one processor to perform the method according to any one of claims 1 to 44.

48. An apparatus comprising at least one processor configured to cause the apparatus to perform the method according to any one of claims 1 to 44.

49. A computer program product comprising computer-executable instructions that, when executed, cause a device to perform the method according to any one of claims 1 to 44.

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