WO2024250559A1

WO2024250559A1 - Method, apparatus, and system for compression of map or mapping configuration

Info

Publication number: WO2024250559A1
Application number: PCT/CN2023/130356
Authority: WO
Inventors: Mengyao Ma; Jianglei Ma; Xiaoyan Bi
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2023-06-09
Filing date: 2023-11-08
Publication date: 2024-12-12
Anticipated expiration: 2025-12-09

Abstract

Example embodiments relate to methods for map compression or mapping information compression. In an aspect, a first device compresses information using a relationship among elements in the information, and 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 represents one of radio environment information and geometry information, and the second map represents the other one of the environment information and the geometry information. Then the first device transmits the compressed information to a second device, wherein a size of the compressed information is smaller than the information. As such, the map and mapping can be compressed so as to reduce the indication overhead. Therefore, the sensing performance and communication performance are improved, and the processing delay and complexity are reduced.

Description

METHOD, APPARATUS, AND SYSTEM FOR COMPRESSION OF MAP OR MAPPING CONFIGURATION

FIELD

Example embodiments of the present disclosure generally relate to the field of telecommunication and in particular, to methods for map compression or mapping configuration compression.

BACKGROUND

With the development of communication technology, user equipment (UE) position information has been introduced in cellular communication networks to improve various performance metrics for the network. Such performance metrics may, for example, include capacity, agility, and efficiency. The improvement may be achieved when elements of the network exploit the position, the behavior, the mobility pattern, etc., of the UE in the context of a priori information describing a wireless environment in which the UE is operating.

A sensing system may be used to help gather UE pose information, including its location in a global coordinate system, its velocity and direction of movement in the global coordinate system, orientation information, and the information about the wireless environment. “Location” is also known as “position” and these two terms may be used interchangeably herein. Examples of well-known sensing systems include radio detection and ranging (RADAR) and light detection and ranging (LIDAR) . While the sensing system can be separate from the communication system, it could be advantageous to gather the information using an integrated system, which reduces the hardware (and cost) in the system as well as the time, frequency, or spatial resources needed to perform both functionalities. However, using the communication system hardware to perform sensing of UE pose and environment information is a highly challenging and open problem. In addition, the overhead of the sensing system still needs to be reduced.

SUMMARY

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

In a first aspect, there is provided a method. The method comprises compressing information using a relationship among 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 represents one of radio environment information and geometry information, and the second map represents the other one of the environment information and the geometry information; and outputting compressed information, wherein a size of the compressed information is smaller than the information. As such, the map and mapping can be compressed so as to reduce the indication overhead. Therefore, the sensing performance and communication performance are improved, and the processing delay and complexity are reduced.

In some embodiments, the mapping configuration indicates at least one of the following: 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 among the 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 among the element pairs comprises an element in the first map and an element in the second map. In this way, the mapping configuration may be indicated in multiple alternative manners. In addition, the mapping configuration may also indicate the first map and second map in an implicit way.

In some embodiments, an element in the first map or the second map representing the radio environment information may be of at least one of the following: a multi-path 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 status 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.

In some embodiments, an element in the first map or the second map representing the geometry information may be of at least one of the following: 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 sensing/communication tasks.

In some embodiments, a first element in a map of the first map or the second map representing the radio environment information is of a first element type and a second element in the map is of a second element type, and wherein the first element type and the second element type may be the same or different, a first size of the first element and a second size of the second element may be the same of different, and/or a first value range of the first element and a second value range of the second element may be the same or different. In this way, the RF-map may include a plurality of elements that have different element types. Then, by means of the mapping configuration, the first device may obtain radio environment information in different aspects. Furthermore, the RF-map may be divided in different sizes, shapes or types.

In some embodiments, an element in the first map or the second map representing the radio environment information may be of one or more element types. In this way, sufficient radio environment information may be obtained directly.

In some embodiments, a third element in a map of the first map or the second map representing the geometry information may be of a third element type and a fourth element in the map is of a fourth element type, and wherein the third element type and the fourth element type are the same or different; and/or a third size or shape of the third element and a fourth size or shape of the fourth element are the same or different. In this way, the G-map may be divided in an even or uneven manner.

In some embodiments, compressing the information may comprise compressing the at least one of the first map, the second map, or the mapping configuration into a plurality of layers with different compression levels. In this way, the first map, the second map, or the mapping configuration may be hierarchically represented, and the transmission of the information is more flexible.

In some embodiments, the method may further comprise transmitting, to the second device, at least one of a number of the 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 method of mapping with different compression levels. In this way, the information of the hierarchical map or hierarchical mapping configuration is indicated to the second device.

In some embodiments, compressing the information may comprise mapping one of a first map, a compressed first map, or a layer of the first map to one of a second map, a compressed second map, or a layer of the second map; mapping one of a first map, a compressed first map, or a 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 a second map, a compressed second map, or a layer of the second map. In this way, the first map may be mapped to the second map flexibly.

In some embodiments, compressing the information may comprise splitting the first map, a compressed first map, or a layer of the first map into a plurality of parts; and mapping the plurality of parts to a plurality of compressed second maps with different compression levels. In this way, the first map may be mapped to the second map flexibly.

In some embodiments, compressing the information may comprise selecting a plurality of elements from a first map, a compressed first map, or a 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 may be mapped to the second map flexibly.

In some embodiments, compressing the information may comprise generating a mapping between a compressed first map with a first compression level and a compressed second map with a second compression level, wherein the first compression level and the second compression level are the same or different. In this way, the first map may be mapped to the second map flexibly.

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

In some embodiments, the information may be represented by the multi-dimensional matrix, and the compressing of the information may be performed based on at least one of the following: a projection; a matrix transformation; a vector quantization; a scalar quantization; or entropy coding. In this way, the first map, the second map, or the mapping configuration may be compressed in several alternative manners.

In some embodiments, the method may further comprise transmitting, to the second device, at least one compression parameter comprising at least one of a projection method, a transform method, a transform base, quantization bits, or an entropy coding method. In this way, the information associated with the compression parameter of a multi-dimensional matrix is indicated to the second device.

In some embodiments, the information may be represented by the tree, and the compressed information may comprise at least one of a compressed tree structure or compressed tree node information. In this way, the information associated with the tree is indicated to the second device.

In some embodiments, a plurality of nodes of the tree may be compressed separately or jointly. In this way, the tree may be compressed in several alternative manners.

In some embodiments, the method may further comprise transmitting, to the second device, at least one compression parameter comprising a tree depth. In this way, the information associated with the compression parameter of the tree is indicated to the second device.

In some embodiments, the information may be represented by the list or the array, and compressing the information may comprise at least one of the following: compressing number values of the list or the array based on entropy coding; or compressing non-number values of the list or the array based on a differential compression. In this way, the list or the array may be compressed in several alternative manners.

In some embodiments, compressing the information may further comprise at least one of the following: compressing the number values based on the differential compression before the entropy coding; or compressing the non-number values based on a projection, a matrix transformation, a quantization or entropy coding in parallel with the differential compression. In this way, the list or the array may be compressed in several alternative manners.

In some embodiments, the method may further comprise transmitting, to the second device, at least one compression parameter comprising at least one of a re-organizing method of selecting values for the differential compression, a projection method, a transform method, a transform base, quantization bits, or an entropy coding method. In this way, the information associated with the compression parameter of the list or the array is indicated to the second device.

In some embodiments, compressing the information may further comprise encoding a residual between two elements with a prediction method, wherein the two elements may be in a same map/mapping or different maps/mappings; and compressing the encoded residual based on at least one of the projection, the matrix transformation, the vector quantization, the scalar quantization, or the entropy coding. In this way, a prediction method is used to compress the information.

In some embodiments, the method may further comprise transmitting, to the second device, at least one compression parameter comprising at least one of a prediction mode, or an index of a reference element. In this way, the information associated with the prediction method is indicated to the second device.

In a second aspect, there is provided a method. The method comprising: obtaining compressed information; and 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 represents one of radio environment information and geometry information, the second map represents the other one of the environment information and the geometry information, and a size of the compressed information is smaller than the information. As such, the second device may obtain information based on the compressed information received from the first device. Therefore, the sensing performance and communication performance are improved, and the indication overhead, the processing delay and complexity are reduced.

In some embodiments, a first element in a map of the first map or the second map representing the radio environment information is of a first element type and a second element in the map is of a second element type, and wherein the first element type and the second element type may be the same or different, a first size of the first element and a second size of the second element may be the same of different, and/or a first value range of the first element and a second value range of the second element may be the same or different. In this way, the RF-map may include a plurality of elements that have different element types. Then, by means of the mapping configuration, the first device may obtain radio environment information in different aspects. Furthermore, the first map may be divided in different sizes, shapes or types.

In some embodiments, a third element in a map of the first map or the second map representing the geometry information is of a third element type and a fourth element in the map is of a fourth element type, and wherein the third element type and the fourth element type may be the same or different; and/or a third size or shape of the third element and a fourth size or shape of the fourth element may be the same or different. In this way, the G-map may be divided in an even or uneven manner.

In some embodiments, obtaining the compressed information may comprise: receiving, by a second device, the compressed information from a first device. In this way, the second device may obtain the compressed information in several alternative manners.

In some embodiments, the at least one of the first map, the second map, or the mapping configuration may be compressed into a plurality of layers with different compression levels. In this way, the first map, the second map, or the mapping configuration may be hierarchically represented, and the transmission of the information is more flexible.

In some embodiments, obtaining the information may comprise receiving, from the first device, 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 method of mapping with different compression levels; and obtaining the information based on the 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 a method of mapping with different compression levels. In this way, the second device obtains the information of the hierarchical map or hierarchical mapping configuration to determine the first map, the second map or the mapping configuration.

In some embodiments, one of a first map, a compressed first map, or a layer of the first map is mapped to one of a second map, a compressed second map, or a layer of the second map; one of a first map, a compressed first map, or a layer 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 is mapped to a plurality of compressed second maps with different compression levels; or a plurality of compressed first maps with different compression levels is mapped to one of a second map, a compressed second map, or a layer of the second map. In this way, the first map may be mapped to the second map flexibly.

In some embodiments, the first map, a compressed first map, or a layer of the first map may be split into a plurality of parts, and the plurality of parts may be mapped to a plurality of compressed second maps with different compression levels. In this way, the first map may be mapped to the second map flexibly.

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

In some embodiments, a compressed first map with a first compression level may be mapped to a compressed second map with a second compression level, and the first compression level and the second compression level may be the same or different. In this way, the first map may be mapped to the second map flexibly.

In some embodiments, the information may be represented by the multi-dimensional matrix, and the information may be compressed based on at least one of the following: a projection; a matrix transformation; a vector quantization; a scalar quantization; or entropy coding. In this way, the first map, the second map, or the mapping configuration may be compressed in several alternative manners.

In some embodiments, obtaining the information may comprise receiving, from the first device, at least one compression parameter comprising at least one of a projection method, a transform method, a transform base, quantization bits, or an entropy coding method; and obtaining the information based on the at least one compression parameter. In this way, the second device obtains the information associated with the compression parameter of a multi-dimensional matrix to determine the first map, the second map or the mapping configuration.

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

In some embodiments, obtaining the information may comprise receiving, from the first device, at least one compression parameter comprising a tree depth; and obtaining the information based on the at least one compression parameter. In this way, the second device obtains the information associated with the compression parameter of the tree to determine the first map, the second map or the mapping configuration.

In some embodiments, the information may be represented by the list or the array, and number values of the list or the array may be compressed based on entropy coding, or non-number values of the list or the array may be compressed based on a differential compression. In this way, the list or the array may be compressed in several alternative manners.

In some embodiments, the method may comprise at least one of the following: the number values are compressed based on the differential compression before the entropy coding; or the non-number values are compressed based on a projection, a matrix transformation, a quantization or entropy coding in parallel with the differential compression. In this way, the list or the array may be compressed in several alternative manners.

In some embodiments, obtaining the information may comprise receiving, from the first device, at least one compression parameter comprising at least one of a re-organizing method of selecting values for the differential compression, a projection method, a transform method, a transform base, quantization bits, or an entropy coding method; and obtaining the information based on the at least one compression parameter. In this way, the second device obtains the information associated with the compression parameter of the list or the array to determine the first map, the second map or the mapping configuration.

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

In some embodiments, obtaining the information may comprise receiving, from the first device, at least one compression parameter comprising at least one of a prediction mode, or an index of a reference element; 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.

In some embodiments, obtaining the information may further comprise one of the following: decoding the compressed information; or decompressing the compressed information. In this way, the second device may determine the first map, the second map or the mapping configuration to improve communication performance.

In a third aspect, there is provided a first device. The first device comprises an interface and a processor communicatively coupled with the interface. The processor is configured to compress information using a relationship among 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 represents one of radio environment information and geometry information, and the second map represents the other one of the environment information and the geometry information; and output compressed information to via the interface, wherein a size of the compressed information is smaller than the information. As such, the map and mapping can be compressed so as to reduce the indication overhead. Therefore, the sensing performance and communication performance are improved, and the processing delay and complexity are reduced.

In a fourth aspect, there is provided a second device. The second device comprises a interface and a processor communicatively coupled with the interface. The processor is configured to obtain compressed information; and obtain, 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 represents one of radio environment information and geometry information, the second map represents the other one of the environment information and the geometry information, and a size of the compressed information is smaller than the information. As such, the second device may obtain information based on the compressed information received from the first device. Therefore, the sensing performance and communication performance are improved, and the indication overhead, the processing delay and complexity are reduced.

In a fifth aspect, there is provided a non-transitory computer readable medium comprising computer program stored thereon, the computer program, when executed on at least one processor, causing the at least one processor to perform the method of any one of the first aspect or second aspect.

In a sixth aspect, there is provided an apparatus comprising at least one processing circuit configured to perform the method of any one of the first aspect or second aspect.

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

It is to be understood that the summary section is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

Some example embodiments will now be described with reference to the accompanying drawings, in which:

Fig. 1A illustrates an example communication system in which example embodiments of the present disclosure may be implemented;

Fig. 1B illustrates an example communication system in which example embodiments of the present disclosure may be implemented;

Fig. 1C illustrates an example of an electronic device (ED) and base stations related to some embodiments of the present disclosure;

Fig. 1D illustrates an example of units or modules in a device related to some embodiments of the present disclosure;

Fig. 1E illustrates an example of a sensing management function (SMF) related to some embodiments of the present disclosure;

Fig. 2 illustrates an example signaling chart illustrating an example process according to some embodiments of the present disclosure;

Figs. 3A-3B illustrate example representations of the mapping configuration between RF-map and G-map according to some embodiments of the present disclosure;

Fig. 4 illustrates an example hierarchically compression according to some embodiments of the present disclosure;

Figs. 5A-5C illustrate example mapping configurations according to some embodiments of the present disclosure;

Fig. 6 illustrates example matrixes according to some embodiments of the present disclosure;

Fig. 7A illustrates an example matrix division according to some embodiments of the present disclosure;

Fig. 7B illustrates another example matrix division according to some embodiments of the present disclosure;

Fig. 7C illustrates an example list or array representation according to some embodiments of the present disclosure;

Fig. 8 illustrates an example tree representation according to some embodiments of the present disclosure;

Fig. 9A illustrates an example intra-prediction according to some embodiments of the present disclosure;

Fig. 9B illustrates an example inter-prediction according to some embodiments of the present disclosure;

Fig. 10 illustrates a flowchart of a method implemented at a first device according to some embodiments of the present disclosure;

Fig. 11 illustrates a flowchart of a method implemented at a second device according to some embodiments of the present disclosure; and

Fig. 12 illustrates a simplified block diagram of an apparatus that is suitable for implementing embodiments of the present disclosure.

Throughout the drawings, the same or similar reference numerals represent the same or similar elements.

DETAILED DESCRIPTION

Principles of the present disclosure will now be described with reference to some example embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. The disclosure described herein can be implemented in various manners other than the ones specifically described below.

In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

References in the present disclosure to “one embodiment” , “an embodiment” , “an example embodiment” , and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. The term “another embodiment” is to be read as “at least one other embodiment. ” Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is within the knowledge of one skilled in the art to adapt or modify such feature, structure, or characteristic in connection with other embodiments, whether or not such adaptations are explicitly described.

It shall be understood that although the terms “first” and “second” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These 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 example embodiments. As used herein, the term “and/or” includes any and all combinations of one or more of the listed terms.

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” , “comprising” , “has” , “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.

Fig. 1A illustrates an example communication system 100A in which example embodiments of the present disclosure may be implemented. Referring to FIG. 1A, as an illustrative example without limitation, a simplified schematic illustration of a communication system is provided. The communication system 100A comprises a radio access network 120. The radio access network 120 may be a next generation (e.g. sixth generation (6G) or later) radio access network, or a legacy (e.g. 5G, 4G, 3G or 2G) radio access network. One or more communication electronic device (ED) 110a, 110b, 110c, 110d, 110e, 110f, 110g, 110h, 110i, 110j (generically referred to as 110) may be interconnected to one another or connected to one or more network nodes (170a, 170b, generically referred to as 170) in the radio access network 120. A core network 130 may be a part of the communication system and may be dependent or independent of the radio access technology used in the communication system 100A. Also the communication system 100A comprises a public switched telephone network (PSTN) 140, the internet 150, and other networks 160.

Fig. 1B illustrates an example communication system in which example embodiments of the present disclosure may be implemented. In general, the communication system 100B enables multiple wireless or wired elements to communicate data and other content. The purpose of the communication system 100B may be to provide content, such as voice, data, video, signaling and/or text, via broadcast, multicast and unicast, etc. The communication system 100B may operate by sharing resources, such as carrier spectrum bandwidth, between its constituent elements. The communication system 100B may include a terrestrial communication system and/or a non-terrestrial communication system. The communication system 100B may provide a wide range of communication services and applications (such as earth monitoring, remote sensing, passive sensing and positioning, navigation and tracking, autonomous delivery and mobility, etc. ) . The communication system 100B may provide a high degree of availability and robustness through a joint operation of a terrestrial communication system and a non-terrestrial communication system. For example, integrating a non-terrestrial communication system (or components thereof) into a terrestrial communication system can result in what may be considered a heterogeneous network comprising multiple layers. Compared to conventional communication networks, the heterogeneous network may achieve better overall performance through efficient multi-link joint operation, more flexible functionality sharing, and faster physical layer link switching between terrestrial networks and non-terrestrial networks.

The terrestrial communication system and the non-terrestrial communication system could be considered sub-systems of the communication system. In the example shown in FIG. 1B, the communication system 100B includes electronic devices (ED) 110a, 110b, 110c, 110d (generically referred to as ED 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 may be generically referred to as terrestrial transmit and receive points (T-TRPs) 170a-170b. The non-terrestrial communication network 120c includes an access node 172, which may be generically referred to as a non-terrestrial transmit and receive point (NT-TRP) 172.

Any ED 110 may be alternatively or additionally configured to interface, access, or communicate with any T-TRP 170a-170b and NT-TRP 172, the Internet 150, the core network 130, the PSTN 140, the other networks 160, or any combination of the preceding. In some examples, ED 110a may communicate an uplink and/or downlink transmission over a terrestrial air interface 190a with T-TRP 170a. In some examples, the EDs 110a, 110b, 110c and 110d may also communicate directly with one another via one or more sidelink air interfaces 190b. In some examples, ED 110d may communicate an uplink and/or downlink transmission over a non-terrestrial air interface 190c with NT-TRP 172.

The air interfaces 190a and 190b may use similar communication technology, such as any suitable radio access technology. For example, the communication system 100B may implement one or more channel access methods, 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) in the air interfaces 190a and 190b. The air interfaces 190a and 190b may utilize other higher dimension signal spaces, which may involve a combination of orthogonal and/or non-orthogonal dimensions.

The non-terrestrial air interface 190c can enable communication between the ED 110d and one or multiple NT-TRPs 172 via a wireless link or simply a link. For some examples, the link is a dedicated connection for unicast transmission, a connection for broadcast transmission, or a connection between a group of EDs 110 and one or multiple NT-TRPs 172for multicast transmission.

The RANs 120a and 120b are in communication with the core network 130 to provide the EDs 110a 110b, and 110c with various services such as voice, data, and other services. The RANs 120a and 120b and/or the core network 130 may be in direct or indirect communication with one or more other RANs (not shown) , which may or may not be directly served by core network 130, and may or may not employ the same radio access technology as RAN 120a, RAN 120b or both. The core network 130 may also serve as a gateway access between (i) the RANs 120a and 120b or EDs 110a 110b, and 110c or both, and (ii) other networks (such as the PSTN 140, the Internet 150, and the other networks 160) . In addition, some or all of the EDs 110a 110b, and 110c may include functionality for communicating with different wireless networks over different wireless links using different wireless technologies and/or protocols. Instead of wireless communication (or in addition thereto) , the EDs 110a 110b, and 110c may communicate via wired communication channels to a service provider or switch (not shown) , and to the Internet 150. PSTN 140 may include circuit switched telephone networks for providing plain old telephone service (POTS) . Internet 150 may include a network of computers and subnets (intranets) or both, and incorporate protocols, such as Internet Protocol (IP) , Transmission Control Protocol (TCP) , User Datagram Protocol (UDP) . EDs 110a 110b, and 110c may be multimode devices capable of operation according to multiple radio access technologies, and incorporate multiple transceivers necessary to support such.

Any or all of the EDs 110 and BS 170 may be sensing nodes in the system 100B. Sensing nodes are network entities that perform sensing by transmitting and receiving sensing signals. Some sensing nodes are communication equipment that perform both communications and sensing. However, it is possible that some sensing nodes do not perform communications, and are instead dedicated to sensing. The sensing agent 174 is an example of a sensing node that is dedicated to sensing. Unlike the EDs 110 and BS 170, the sensing agent 174 does not transmit or receive communication signals. However, the sensing agent 174 may communicate configuration information, sensing information, signaling information, or other information within the communication system 100B. The sensing agent 174 may be in communication with the core network 130 to communicate information with the rest of the communication system 100B. By way of example, the sensing agent 174 may determine the location of the ED 110a, and transmit this information to the base station 170a via the core network 130. Although only one sensing agent 174 is shown in FIG. 2, any number of sensing agents may be implemented in the communication system 100B. In some embodiments, one or more sensing agents may be implemented at one or more of the RANs 120.

Fig. 1C illustrates an example of an electronic device (ED) and a base station related to some embodiments of the present disclosure. As shown in Fig. 1C, another example of an ED 110 and a base station 170a, 170b and/or 170c is provided. The ED 110 is used to connect persons, objects, machines, etc. The ED 110 may be widely used in various scenarios, for example, cellular communications, device-to-device (D2D) , vehicle to everything (V2X) , peer-to-peer (P2P) , machine-to-machine (M2M) , machine-type communications (MTC) , Internet of things (IOT) , virtual reality (VR) , augmented reality (AR) , mixed reality (MR) , metaverse, digital twin, industrial control, self-driving, remote medical, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc.

Each ED 110 represents any suitable end user device for wireless operation and may include such devices (or may 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 telephone, 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, wearable devices such as a watch, head mounted equipment, a pair of glasses, an industrial device, or apparatus (e.g. communication module, modem, or chip) in the forgoing devices, among other possibilities. Future generation EDs 110 may be referred to using other terms. Each base station 170a and 170b is a T-TRP and will hereafter be referred to as T-TRP 170. Also shown in FIG. 1C, a NT-TRP will hereafter be referred to as NT-TRP 172. Each ED 110 connected to T-TRP 170 and/or 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 of more of: connection availability and connection necessity.

The ED 110 includes a transmitter 111 and a receiver 113 coupled to one or more antennas 204. Only one antenna 204 is illustrated. One, some, or all of the antennas 204 may alternatively be panels. The transmitter 111 and the receiver 113 may be integrated, e.g. as a transceiver. The transceiver is configured to modulate data or other content for transmission by at least one antenna 204 or network interface controller (NIC) . The transceiver is also configured to demodulate data or other content received by the at least one antenna 204. Each transceiver includes any suitable structure for generating signals for wireless or wired transmission and/or processing signals received wirelessly or by wire. Each antenna 204 includes any suitable structure for transmitting and/or receiving wireless or wired signals.

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 instructions or modules configured to implement some or all of the functionality and/or embodiments described herein and that are executed by one or more processing unit (s) (e.g., a processor 117) . Each memory 115 includes any suitable volatile and/or non-volatile storage and retrieval device (s) . Any suitable type of memory may be used, such as random access memory (RAM) , read only memory (ROM) , hard disk, optical disc, subscriber identity module (SIM) card, memory stick, secure digital (SD) memory card, on-processor cache, and the like.

The ED 110 may further 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 permit interaction with a user or other devices in the network. Each input/output device includes any suitable structure for providing information to or receiving information from a user, such as through operation as a speaker, a microphone, a keypad, a keyboard, a display, or a touch screen, including network interface communications.

The ED 110 includes the processor 117 for performing operations including those operations related to preparing a transmission for uplink transmission to the NT-TRP 172 and/or the T-TRP 170, those operations related to processing downlink transmissions received from the NT-TRP 172 and/or the T-TRP 170, and those operations related to processing sidelink transmission to and from another ED 110. Processing operations related to preparing a transmission for uplink transmission may include operations such as encoding, modulating, transmit beamforming, and generating symbols for transmission. Processing operations related to processing downlink transmissions may include operations such as receive beamforming, demodulating and decoding received symbols. Depending upon the embodiment, a downlink transmission may be received by the receiver 113, possibly using receive beamforming, and the processor 117 may extract signaling from the downlink transmission (e.g. by detecting and/or decoding the signaling) . An example of signaling may be a reference signal transmitted by the NT-TRP 172 and/or by the T-TRP 170. In some embodiments, the processor 117 implements the transmit beamforming and/or the receive beamforming based on the indication of beam direction, e.g. beam angle information (BAI) , received from the T-TRP 170. In some embodiments, the processor 117 may perform operations relating to network access (e.g. initial access) and/or downlink synchronization, such as operations relating to detecting a synchronization sequence, decoding and obtaining the system information, etc. In some embodiments, the processor 117 may perform channel estimation, e.g. using a reference signal received from the NT-TRP 172 and/or from the T-TRP 170.

Although not illustrated, the processor 117 may form part of the transmitter 111 and/or part of the receiver 113. Although not illustrated, the memory 115 may form part of the processor 117.

The processor 117, the processing components of the transmitter 111 and the processing components of the receiver 113 may each be implemented by the same or different one or more processors that are configured to execute instructions stored in a memory (e.g. in the memory 115) . Alternatively, some or all of the processor 117, the processing components of the transmitter 111 and the processing components of the receiver 113 may each be implemented using dedicated circuitry, such as a programmed field-programmable gate array (FPGA) , a graphical processing unit (GPU) , a Central Processing Unit (CPU) or an application-specific integrated circuit (ASIC) .

The T-TRP 170 may be known by other names in some implementations, such as a base station, a base transceiver station (BTS) , a radio base station, a network node, a network device, a device on the network side, a transmit/receive node, a Node B, 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 station, a remote radio head, a terrestrial node, a terrestrial network device, a terrestrial 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, among other possibilities. The T-TRP 170 may be a macro BS, a pico BS, a relay node, a donor node, or the like, or combinations thereof. The T-TRP 170 may refer to the forgoing devices or refer to apparatus (e.g. a communication module, a modem, or a chip) in the forgoing devices.

In some embodiments, the parts of the T-TRP 170 may be distributed. For example, some of the modules of the T-TRP 170 may be located remote from the equipment that houses the antennas 256 for the T-TRP 170, and may be coupled to the equipment that houses the antennas 256 over a communication link (not shown) sometimes known as front haul, such as common public radio interface (CPRI) . Therefore, in some embodiments, the term T-TRP 170 may also refer to modules on the network side that perform processing operations, such as determining the location of the ED 110, resource allocation (scheduling) , message generation, and encoding/decoding, and that are not necessarily part of the equipment that houses the antennas 256 of the T-TRP 170. The modules may also be coupled to other T-TRPs. In some embodiments, the T-TRP 170 may actually be a plurality of T-TRPs that are operating together to serve the ED 110, e.g. through the use of coordinated multipoint transmissions.

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 illustrated. One, some, or all of the antennas 256 may alternatively be panels. The transmitter 181 and the receiver 183 may be integrated as a transceiver. The T-TRP 170 further includes a processor 182 for performing operations including those related to: preparing a transmission for downlink transmission to the ED 110, processing an uplink transmission received from the ED 110, preparing a transmission for backhaul transmission to the NT-TRP 172, and processing a transmission received over backhaul from the NT-TRP 172. Processing operations related to preparing a transmission for downlink or backhaul transmission may include operations such as encoding, modulating, precoding (e.g. multiple input multiple output (MIMO) precoding) , transmit beamforming, and generating symbols for transmission. Processing operations related to processing received transmissions in the uplink or over backhaul may include operations such as receive beamforming, demodulating received symbols and decoding received symbols. The processor 182 may also perform operations relating to network access (e.g. initial access) and/or downlink synchronization, such as generating the content of synchronization signal blocks (SSBs) , generating the system information, etc. In some embodiments, the processor 182 also generates an indication of beam direction, e.g. BAI, which may be scheduled for transmission by a scheduler 184. The processor 182 performs other network-side processing operations described herein, such as determining the location of the ED 110, determining where to deploy the NT-TRP 172, etc. In some embodiments, the processor 182 may generate signaling, e.g. 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 sent by the transmitter 181. Note that “signaling” , as used herein, may alternatively be called control signaling. Dynamic signaling may be transmitted in a control channel, e.g. a physical downlink control channel (PDCCH) , and static or semi-static higher layer signaling may be included in a packet transmitted in a data channel, e.g. in a physical downlink shared channel (PDSCH) .

The scheduler 184 may be coupled to the processor 182. The scheduler 184 may be included within or operated separately from the T-TRP 170. The scheduler 184 may schedule uplink, downlink, and/or backhaul transmissions, including issuing scheduling grants and/or configuring scheduling-free ( “configured grant” ) resources. The T-TRP 170 further includes a memory 185 for storing information and data. The memory 185 stores instructions and data used, generated, or collected by the T-TRP 170. For example, the memory 185 could store software instructions or modules configured to implement some or all of the functionality and/or embodiments described herein and that are executed by the processor 182.

Although not illustrated, the processor 182 may form part of the transmitter 181 and/or part of the receiver 183. Also, although not illustrated, the processor 182 may implement the scheduler 184. Although not illustrated, the memory 185 may form part of the processor 182.

The processor 182, the scheduler 184, the processing components of the transmitter 181 and the processing components of the receiver 183 may each be implemented by the same or different one or more processors that are configured to execute instructions stored in a memory, e.g. in 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 may be implemented using dedicated circuitry, such as a FPGA, a GPU, a CPU, or an ASIC.

Although the NT-TRP 172 is illustrated as a drone only as an example, the NT-TRP 172 may be implemented in any suitable non-terrestrial form, such as high altitude platforms, satellite, high altitude platform as international mobile telecommunication base stations and unmanned aerial vehicles, which forms will be discussed hereinafter. Also, the NT-TRP 172 may be known by other names in some implementations, such as a non-terrestrial node, a non-terrestrial network device, or a non-terrestrial base station. The NT-TRP 172 includes a transmitter 186 and a receiver 187 coupled to one or more antennas 108. Only one antenna 108 is illustrated. One, some, or all of the antennas may alternatively be panels. The transmitter 186 and the receiver 187 may be integrated as a transceiver. The NT-TRP 172 further includes a processor 188 for performing operations including those related to: preparing a transmission for downlink transmission to the ED 110, processing an uplink transmission received from the ED 110, preparing a transmission for backhaul transmission to T-TRP 170, and processing a transmission received over backhaul from the T-TRP 170. Processing operations related to preparing a transmission for downlink or backhaul transmission may include operations such as encoding, modulating, precoding (e.g. MIMO precoding) , transmit beamforming, and generating symbols for transmission. Processing operations related to processing received transmissions in the uplink or over backhaul may include operations such as receive beamforming, demodulating received symbols and decoding received symbols. In some embodiments, the processor 188 implements the transmit beamforming and/or receive beamforming based on beam direction information (e.g. BAI) received from the T-TRP 170. In some embodiments, the processor 188 may generate signaling, e.g. to configure one or more parameters of the ED 110. In some embodiments, the NT-TRP 172 implements physical layer processing, but does not implement higher layer functions such as functions at the medium access control (MAC) or radio link control (RLC) layer. As this is only an example, more generally, the NT-TRP 172 may implement higher layer functions in addition to physical layer processing.

The NT-TRP 172 further includes a memory 189 for storing information and data. Although not illustrated, the processor 188 may form part of the transmitter 186 and/or part of the receiver 187. Although not illustrated, the memory 189 may form part of the processor 188.

The processor 188, the processing components of the transmitter 186 and the processing components of the receiver 187 may each be implemented by the same or different one or more processors that are configured to execute instructions stored in a memory, e.g. in the memory 189. Alternatively, some or all of the processor 188, the processing components of the transmitter 186 and the processing components of the receiver 187 may be implemented using dedicated circuitry, such as a programmed FPGA, a GPU, a CPU, or an ASIC. In some embodiments, the NT-TRP 172 may actually be a plurality of NT-TRPs that are operating together to serve the ED 110, e.g. through coordinated multipoint transmissions.

The T-TRP 170, the NT-TRP 172, and/or the ED 110 may include other components, but these have been omitted for the sake of clarity.

Fig. 1D illustrates an example of units or modules in a device related to some embodiments of the present disclosure. One or more steps of the embodiment methods provided herein may be performed by corresponding units or modules, according to Fig. 1D. Fig. 1D illustrates units or modules in a device, such as in the ED 110, in the T-TRP 170, or in the NT-TRP 172. For example, a signal may be transmitted by a transmitting unit or by a transmitting module. A signal may be received by a receiving unit or by a receiving module. A signal may be processed by a processing unit or a processing module. Other steps may be performed by an artificial intelligence (AI) or machine learning (ML) module. The respective units or modules may be implemented using hardware, one or more components or devices that execute software, or a combination thereof. For instance, one or more of the units or modules may be an integrated circuit, such as a programmed FPGA, a GPU, a CPU, or an ASIC. It will be appreciated that where the modules are implemented using software for execution by a processor for example, the modules may be retrieved by a processor, in whole or part as needed, individually or together for processing, in single or multiple instances, and that the modules themselves may include instructions for further deployment and instantiation.

Additional details regarding the EDs 110, the T-TRP 170, and the NT-TRP 172 are known to those of skill in the art. As such, these details are omitted here.

A sensing node may combine sensing-based techniques with reference signal-based techniques to enhance UE pose determination. This type of sensing node may also be known as a sensing management function (SMF) . In some networks, the SMF may also be known as a location management function (LMF) . The SMF may be implemented as a physically independent entity located at the core network 130 with connection to the multiple BSs 170. In other aspects of the present application, the SMF may be implemented as a logical entity co-located inside a BS 170 through logic carried out by the processor 182. Fig. 1E illustrates an example of a sensing management function (SMF) related to some embodiments of the present disclosure.

As shown in FIG. 1E, the SMF 176, when implemented as a physically independent entity, 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. A transceiver, not shown, may be used instead of the transmitter 192 and receiver 196. A scheduler 198 may be coupled to the processor 194. The scheduler 198 may be included within or operated separately from the SMF 176. The processor 194 implements various processing operations of the SMF 176, such as signal coding, data processing, power control, input/output processing, or any other functionality. The processor 194 can also be configured to implement some or all of the functionality and/or embodiments described in more detail above. Each processor 194 includes any suitable processing or computing device configured to perform one or more operations. Each processor 194 could, for example, include a microprocessor, microcontroller, digital signal processor, field programmable gate array, or application specific integrated circuit.

A reference signal-based pose determination technique belongs to an “active” pose estimation paradigm. In an active pose estimation paradigm, the enquirer of pose information (i.e., the UE) takes part in process of determining the pose of the enquirer. The enquirer may transmit or receive (or both) a signal specific to pose determination process. Positioning techniques based on a global navigation satellite system (GNSS) such as Global Positioning System (GPS) are other examples of the active pose estimation paradigm.

In contrast, a sensing technique, based on radar for example, may be considered as belonging to a “passive” pose determination paradigm. In a passive pose determination paradigm, the target is oblivious to the pose determination process.

By integrating sensing and communications in one system, the system need not operate according to only a single paradigm. Thus, the combination of sensing-based techniques and reference signal-based techniques can yield enhanced pose determination.

The enhanced pose determination may, for example, include obtaining UE channel sub-space information, which is particularly useful for UE channel reconstruction at the sensing node, especially for a beam-based operation and communication. The UE channel sub-space is a subset of the entire algebraic space, defined over the spatial domain, in which the entire channel from the TP to the UE lies. Accordingly, the UE channel sub-space defines the TP-to-UE channel with very high accuracy. The signals transmitted over other sub-spaces result in a negligible contribution to the UE channel. Knowledge of the UE channel sub-space helps to reduce the effort needed for channel measurement at the UE and channel reconstruction at the network-side. Therefore, the combination of sensing-based techniques and reference signal-based techniques may enable the UE channel reconstruction with much less overhead as compared to traditional methods. Sub-space information can also facilitate sub-space based sensing to reduce sensing complexity and improve sensing accuracy.

A sensing system may be used to help gather UE pose information, including its location in a global coordinate system, its velocity and direction of movement in the global coordinate system, orientation information, and information about the wireless environment. “Location” is also known as “position” and these two terms may be used interchangeably herein. Examples of well-known sensing systems include radio detection and ranging (RADAR) and light detection and ranging (LIDAR) . While the sensing system can be separate from the communication system, it could be advantageous to gather the information using an integrated system, which reduces the hardware (and cost) in the system as well as the time, frequency, or spatial resources needed to perform both functionalities. However, using the communication system hardware to perform sensing of UE pose and environment information is a highly challenging and open problem. The difficulty of the problem relates to factors such as the limited resolution of the communication system, the dynamicity of the environment, and the huge number of objects whose electromagnetic properties and position are to be estimated.

Accordingly, integrated sensing and communication (also known as integrated communication and sensing) is a desirable feature in existing and future communication systems. And it is desirable to design the information exchanged between UE and the sensing system or sensing coordinator and corresponding interaction protocols, for practical implementations of integrated sensing and communication.

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

Because the base stations or other network devices can collect and use their own channel and/or sensing data or channel and/or sensing data of a UE, the base station or other network devices may have a larger field of view, a longer sensing distance, more detailed global information, and a higher resolution environmental map. If the network provides the radio environmental map to the UE, the map can help the UE improve its sensing function, e.g. improve sensing accuracy or reduce sensing complexity, or assist UE communication, such as MIMO or beamforming procedures. In addition, when the location/geographical information of UE changes, or the surrounding environment changes, the radio environmental map corresponding to the UE may also change. If the network can provide the most up-to-date knowledge of radio environmental map to the UE according to these changes, the processing delay or processing complexity of the UE can be reduced. Meanwhile, the performance of sensing or communication operations can be improved accordingly.

According to some embodiments of the present disclosure, there is provided a solution for map compression or mapping configuration compression. In an aspect, a first device compresses information using a relationship among elements in the information, and 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 represents one of radio environment information and geometry information, and the second map represents the other one of the radio environment information and the geometry information. Then, the first device transmits compressed information to a second device, wherein a size of the compressed information is smaller than the information. As such, the map and mapping can be compressed so as to reduce the indication overhead. Therefore, the sensing performance and communication performance are improved, and the processing delay and complexity are reduced.

Fig. 2 illustrates a signaling chart illustrating an example process according to some embodiments of the present disclosure. The process 200 may involve the first device 201 and the second device 202. The first device 201 in Fig. 2 may be an example of the network node 170 in Fig. 1A, and the first device 201 may also be an example of the communication electronic device 110 in Fig. 1A. The second device 202 in Fig. 2 may be an example of the communication electronic device 110 in Fig. 1A, and the second device 202 may also be an example of the network node 170 in Fig. 1A. It would be appreciated that although the process flow 200 has been described in the communication system 100A of Fig. 1A, this process may be likewise applied to other communication scenarios.

In the process flow 200, the first device 201 compresses 210 information using a relationship among elements in the information, and the information comprises a first map, a second map, a mapping configuration between the first map and the second map, or any combination of two or more of the above-mentioned items. The first map represents one of radio environment information and geometry information, and the second map represents the other one of the environment information and the geometry information. In other words, the first device 201 may compress the radio environment information, the geometry information, the mapping between the radio environment information and the geometry information, or any combination of two or more of the above-mentioned items. Compressing the information may refer to encoding the information, and the size of the encoded information is smaller than the information. Additionally, the information may comprise indication information to indicate that whether the first map, the second map or the mapping configuration is included in the information. Alternatively, the time-frequency resources and interaction time between the first device 201 and the second device 202 may implicitly indicate each map or mapping configuration. Based on the time-frequency resources and 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 also carry the indication information.

For example, the first map may represent the radio environmental map, radio frequency map, radio map, radio-based map, radio-signal-based map, wireless-signal-based map, or other maps with similar meanings. The first map element may have several representations, such as ray tracing information, multi-path information, channel, H, information, channel status and/or quality information, beamforming information, reference signal information, or channel quality indicator (CQI) information. The first map may be an RF-map. The second map may represent the location/geometry/geographic information or map, or some intermediate results after processing of location/geometry/geography information, or other maps with similar meanings. The second map may be a grid-based map or a map in other formats. Each element/grid in the second map includes the corresponding geometry/geography information. The second map may be a G-map. The “map” represents a form of indication, and may also be known by other names such as list, matrix, group, set, range, area, relationship, lookup table, information, etc. The “mapping” represents a relationship, and may also be known by other names such as relationship, matching, lookup table, etc. Each map (RF-map, or G-map, or other maps) includes N elements, where N is greater than or equal to 1. The elements in the map can have different sizes or shapes. The element shape can be regular or irregular. In other words, the map may be divided evenly or unevenly. The elements in the map can be of the same type/modality, or different types and/or modalities.

The mapping configuration may also be referred to as a mapping. The mapping may include one or multiple mapping elements. Some mapping examples between a G-map and a RF-map are provided below, but the present disclosure is not limited to these examples. In addition, the examples in this embodiment use regular G-map or RF-map elements for illustration, but these methods are also applicable to irregular G-map or RF-map elements. In the first example, each RF-map element can have an index, and the index may be configured explicitly or implicitly obtained based on the order of the elements. Each element of G-map will be mapped to one element in RF-map. As shown in Fig. 3A, the first G-map element of G-map 301 is mapped to the RF-map element of RF-map 305 with index 1, 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, and the fourth G-map element is mapped to the RF-map element with index 0, and so on. Based on the preceding mapping examples, the mapping 303 itself can become a map, or an index map, as shown in the middle of the Fig. 3A: a 4x4 map 303 with mapping elements {1, 5, 1, 0, 2, 3…1, 5} . Alternatively, the mapping may be represented by a list/array: {1, 5, 1, 0, 2, 3…1, 5} , where the i-th mapping element in the list represents the corresponding RF-map element index of the i-th G-map element.

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

Additionally, the mapping configuration may comprise, 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 among the 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; 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; or any combination of two or more of the above-mentioned items.

Without any limitation, the element in the first map or the second map representing the radio environment information may be of one or more types and/or modalities. For example, the element in the first map or the second map representing the radio environment information may be at least one of: a multi-path 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 status information type. In an example, the element in the first map or the second map representing the radio environment information (which may be also referred to as RF-map element) may have the following representations.

An RF-map element may include ray tracing or multi-path information. For example, each path/ray may be represented by information about the amplitude, delay, angle, etc. of the path/ray. Then the RF-map element can include information about one or multiple paths/rays, e.g. a set of {amplitude, delay, angle…} . In addition or alternatively, an RF-map element can include channel, H, information. The channel, H, information can be represented by a vectorized format, matrix-based format, or a scalar value. In addition or alternatively, an RF-map element can include beamforming information. For example, each beam may be represented by information about the angle, beam gradient, beam width, etc. of the beam. Then the RF-map element can include information about one or multiple beams, e.g. a set of {angle, beam gradient, beam width …} . In addition or alternatively, an RF-map element can include reference signal information. For example, each RF-map element can include information about one or multiple reference signals. In addition or alternatively, an RF-map element may include one or multiple CQI (channel quality indicator) . In addition or alternatively, an RF-map element may be a direct or indirect representation of the channel status and/or quality, such as CQI, MCS, SNR, a range of MSC, a range of SNR, etc.

In some embodiments, an element in the RF-map is of one or more element types. Additionally, a first element in the first map or the second map representing the radio environment information (i.e., RF-map) is of a first element type and a second element in the map (i.e., RF-map) is of a second element type, and the first element type and the second element type are the same or different. In an example, elements in the RF-map may be of different types and/or modalities. For example, the first element in the RF-map is of a first plurality of types and/or modalities and the second element in the RF-map is of a second plurality of types and/or modalities. In this case, at least a part of the first plurality of types and/or modalities may be different from the second plurality of types and/or modalities. In a specific example, the first element may include multi-path information and the second element may include channel, H, information. In a further example, a third element may include beamforming information. These elements in the RF-map can include different types of elements (or a different number of types of elements) .

In addition or alternatively, a first size of a first element in the RF-map may be the same as or different from a second size of a second element in the RF-map regardless of whether or not the element types are the same. In some embodiments, if the element types of the first and second elements are the same, the first size may be different, in respect of its dimensions, from the second size. The term “size” used herein represents a measurement or metric of an element in a map in different aspects. That is, the term “size” used herein can be understood in a broader sense. For example, the size may represent a measurement or metric for at least one aspect of the following: the dimension, compression ratio/bits, orders of types, the number of parameters in an element, and the like. Without any limitation, the size may represent other similar metrics of the element.

For example, the first element, the second element and a further third element are of the channel, H, information type. Then the dimensions of first element are 512 x 64 x 80. The dimensions of the second element are 256 x 128. The dimension of the third element is a 1 x 100 vector. In this example, the size of these elements are different, both in terms of the number of dimensions and in terms of sizes of a given dimension.

In addition or alternatively, in some embodiments, the first size may be different from the second size in terms of bits, ratio or level of compression or quantization. That is, the compression or quantization ratio/levels of elements are different. In an example, the first element is of the channel, H, information type and the quantization bits of channel, H, information is compressed or quantized to 5 bits of information. The second element is the channel H information type and the channel H information is compressed or quantized to 4 bits of information. If the original quantization level for channel H information is 16 bits of information (i.e., the information is originally stored in 16 bits) , the compression ratio associated with quantization of the first element and the second element is 3.2 and 4, respectively. Accordingly, even for a same element type, the compression or quantization ratio/levels of elements can be different. While quantization and compression in general refer to different yet related concepts, the terms are interchangeable for certain purposes in the context of the preceding example. In addition, in another example, the first element is of the multi-path information type and the amplitude, delay, and angle information of each path are compressed or quantized to 6, 8, and 5 bits, respectively. The second element may be the beamforming information type and the angle, beam gradient, and beam width information of each beam are compressed or quantized to 6, 5, and 7 bits, respectively. The quantization levels of elements may also be different for different element types. Even for the example of angle in path information and angle in beamforming information, the quantization levels may be different.

In addition or alternatively, the first size may be different from the second size in terms of the orders of types of information in each element. In an example, the first element may be {channel H information, beamforming information} , and the second element may be {beamforming information, channel H information} . That is, the elements can include multiple types, and the orders of types can be different.

In addition or alternatively, the first size may be different from the second size in terms of the number of parameters of an element. In an example, the first element may be of beamforming information and the number of beams is 5. The second element may be of beamforming information and the number of beams is 3. Accordingly, the elements include different numbers of parameters. In another example, the first element is of the ray tracing type and the channel quality type and the ray tracing type includes 4 rays/paths, however, the second element may be only of the ray tracing type and the ray tracing type includes 2 rays/paths.

In addition or alternatively, in some embodiments, a first value range of the first element and a second value range of the second element are the same or different. In an example, in the case that the elements in the RF-map are of the same type, a first value range of the first element in the RF-map and a second value range of the second element in the RF-map may be the same or different, and this may depend on whether the RF-map is divided evenly. In a further example, the first element type is reference signal information and the value range is 0 to 20 dB; the second element type is reference signal information and the value range is 0 to 30 dB. The value range of elements are different. In another example, the first element and the second element are of different element types and their physical dimensions are different; accordingly, the first value range is inherently different from the second value range because of the different physical dimensions.

In some embodiments, the element in the first map or the second map representing the geometry information is of at least one of: a two-dimensional (2D) location area type; a three-dimensional (3D) location area type; a geographical coordinate type; a processed data type associated with the geography/geometry information, or any combination of two or more of the above-mentioned items. For example, the first map or the second map representing the geometry/geography information (which may be also referred to as the G-map) may also represent some intermediate results after processing of geometry/geography information, etc. The G-map can be a grid-based map or be a map represented in other formats. The G-map may include M G-map elements/grids, where M ≥ 1. The G-map element/grid can indicate 2D/3D locations, or a 2D/3D region or areas, or the geometric information about the surrounding environment, or geographical coordinates, or other geometry/geography information or preprocessed geometry/geography information.

In some embodiments, a third element in a map of the first map or the second map representing the geometry information (i.e., G-map) is of a third element type, and a fourth element in the map (i.e., G-map) is of a fourth element type. The third element type and the fourth element type may be the same or different and/or a third size or shape of the third element and a fourth size or shape of the fourth element may be the same or different.

In an example, an element in the G-map may include 3D location area information and geographical coordinate information, and another element in the G-map may include geometric information about the surrounding environment. That is, the elements in the G-map may include different types of information and/or a different number of types of information.

In some embodiments, the sizes of elements in the G-map may be different in respect of the dimensions of the elements. For example, an element in the G-map is a 2D location area type and the dimensions are 100 x 200; another element in the G-map is a 2D location area type and the dimensions are 200 x 200. In addition or alternatively, an element in the G-map is a 2D location area type and the dimensions are 100 x 200; another element in the G-map is a 3D location area type and the dimensions are 50 x 250 x 100.

In addition or alternatively, the sizes of elements in the G-map may be different in terms of compression or quantization ratio/levels. For example, an element in the G-map is of the 2D location area type and the 2D location area information is compressed or quantized to 8 bits. Another element in the G-map may be the 3D location area type and the 3D location area information is compressed or quantized to 12 bits. A further element in the G-map is of the geographical coordinate type and the geographical coordinate (x, y, z) is compressed or quantized to 16 bits. Thus, the compression or quantization ratio/levels of elements may be different.

In addition or alternatively, the sizes of elements in the G-map may be different in terms of the order of types of information in each element. For example, an element in the G-map includes {2D location area, geographical coordinate} . Another element in the G-map includes {geographical coordinate, 2D location area} . That is, the elements in the G-map can include multiple types, and the orders of types can be different.

In addition or alternatively, the sizes of elements in the G-map may be different in the number of parameters of the element. For example, an element in the G-map is of the 2D geographical coordinate type and includes 3 sets of coordinates (x, y) . Another element in the G-map is of the 2D geographical coordinate type and includes 4 sets of coordinates (x, y) . That is, the elements in the G-map may include different numbers of parameters. In this way, the description of the geometry/geography information may be flexibly provided to UE.

In some embodiments, the first device may compress the first map, the second map, the mapping configuration, or any combination of two or more of the above-mentioned items into a plurality of layers with different compression levels. For example, the map may be hierarchically compressed into several layers, e.g. from a coarse map to a refined map. It is to be understood that a coarse map may be any relatively coarser map in a hierarchical map or a multi-layer map, and a refined map be any relatively finer map in a hierarchical map or a multi-layer map. The refined map refers a map obtained by compressing the first map or the second map at a lower compression level. The coarse map refers a map obtained by compressing the first map or the second map at a higher compression level. The size of the refined map may be larger than the size of the coarse map. It is to be understood that the term “size” used herein represents a measurement or metric of a map in different aspects. For example, the refined map may have more elements than the coarse map. Without any limitation, the size may represent other similar metrics of the element. Each layer may be incrementally compressed based on the previous layer. A resized previous layer may be used to differentially compress a current layer. Different compression levels or parameters may be used for different layers.

As shown in Fig. 4, map 410 may be compressed into at least one of coarse map 420, refined map 430, or refined map 440. Refined map 430 may be compressed based on coarse map 420. Refined map 440 may be compressed based on refined map 430 or coarse map 420. Coarse map 420 may be the layer with higher compression level, and the layer with higher compression level may use fewer quantization bits. Refined map 440 may be the layer with lower compression level, and the layer with lower compression level may use more quantization bits. In other words, the layer with lower compression level may be quantized to more bits, and the layer with higher compression level may be quantized to fewer bits. In another example, the layer with higher compression level may use a smaller map (smaller size or smaller element numbers) , and the layer with lower compression level may use a larger map. Both the map (first map, or second map, or both) and the mapping (between first map and second map) may be hierarchically represented into several layers with different compression levels.

In some embodiments, the first device may map one of a first map, a compressed first map, or a layer of the first map to one of a second map, a compressed second map, or a layer of the second map. In some embodiments, the first device may map one of a first map, a compressed first map, or a layer of the first map to a plurality of compressed second maps with different compression levels. In some embodiments, the first device may 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 may map a plurality of compressed first maps with different compression levels to one of a second map, a compressed second map, or a layer of the second map.

Alternatively, the first device may split a compressed first map into a plurality of parts. Then the first device may map the plurality of parts to a plurality of compressed second maps with different compression levels. For example, an RF-map may be hierarchically represented by or compressed into several layers, such as from a coarse map to one or multiple refined maps. The G-map may also be hierarchically represented by or compressed into several layers, such as from a coarse map to one or multiple refined maps. As shown in Fig. 5A, coarse G-map 505 can be mapped to a coarse RF-map 510. The refined G-map may be split 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 coarse RF-map 510 and a combination of the refined RF-map 525 and 530 may be obtained by compressing an RF-map using different compression levels. In the example of Fig. 5A, part 515 of the refined G-map is mapped to refined RF-map 525 and part 520 of the refined G-map is mapped to refined RF-map 530.

Alternatively, each RF-map may be hierarchically represented by or compressed into several layers, e.g. from a coarse map to one or multiple refined maps. Each G-map may also be hierarchically represented by or compressed into several layers, e.g. from coarse map to one or multiple refined maps. As shown in Fig. 5B, coarse G-map 535 is mapped to coarse RF-map 550. A refined G-map may be mapped to a refined RF-map. As shown in Fig. 5B, refined G-map 555 is mapped to refined RF-map 570. Furthermore, different coarse G-maps may be mapped to different coarse RF-maps. Multiple coarse G-maps (from the same G-map, or from different G-maps) may be mapped to the same coarse RF-map. As shown in Fig. 5B, coarse G-maps 535, 540 and 545 are mapped to coarse RF-map 550. Different refined G-maps may be mapped to different refined RF-maps. As shown in Fig. 5B, 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) may be mapped to the same refined RF-map. As shown in Fig. 5B, refined G-maps 555 and 560 are mapped to refined RF-map 570.

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

Alternatively or additionally, in order to compress the information, the first device may generate a mapping between a compressed first map with a first compression level and a compressed second map with a second compression level. The first compression level and the second compression level may be same or different. In other words, the compression level of the compressed first map and the compression level of the compressed second map may be same or different.

Embodiments of the present disclosure are not necessarily limited to a two-layer-map scenario with only one coarse map and one refined map. The refined RF-map and the refined G-map may each be associated with a different layer of a plurality of layers of each map. For example, the refined RF-map is the 3rd layer in a 5-layer RF-map, and the refined G-map is the 2-layer in a 3-layer G-map. The coarse RF-map and the coarse G-map may also come from different layers. In addition, the coarse RF-map and the coarse G-map may have different sizes, and the refined RF-map and the refined G-map may have different sizes.

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

It is to be understood that G-map referring to RF-map is used as an example, e.g. each G-map element/grid refers to one element index in the RF-map. The disclosure described herein may also be applied to RF-map referring to G-map, or paired mapping indication. For example, each RF-map element may refer to one or multiple element indices in the G-map. The mapping may include one or multiple match-pairs, each pair indicates a (G-map element, RF-map element) pair.

In some embodiments, the information may be represented by a multi-dimensional matrix, a tree, a list, an array, or any combination of two or more of the above-mentioned items. In another words, the first map, second map, or the mapping configuration may be represented by a multi-dimensional matrix, a tree, a list, an array, or any combination of two or more of the above-mentioned items.

In an example, the RF-map, G-map, or other maps may be represented by a multi-dimensional matrix, which comprises multiple map elements. The map element can have several representations, e.g. ray tracing/multi-path information, channel, H, information, channel status and/or quality information, beamforming information, reference signal information, CQI as introduced previously. Each element has an index, and the index may be an explicit index or an implicit index. The multi-dimensional matrix may be evenly divided; as shown in Fig. 6, map 610 is evenly divided. The multi-dimensional matrix may be unevenly divided; as shown in Fig. 6, map 620 is unevenly divided.

If the multi-dimensional matrix is evenly divided, an index for each element or grid may be indicated to represent the map. The index may be configured explicitly or implicitly based on the order of the elements. As shown in Fig. 7A, map 710 is evenly divided, and the number of grids in each dimension, the number of elements in each dimension, or the size or length for the element in each dimension may be indicated for the partitions.

If the multi-dimensional matrix is unevenly divided, a range or a box for each element may be indicated to represent the map. The division may be represented based on tree partitions (e.g. quadtree, octree …) . For example, using a 2D map or a 2D matrix as an example, as shown in Fig. 7B, { {start x0, y0, x range d0, y range d0’ } , {start x1, y1, x range d1, y range d1’ } …} is indicated for the partitions of map 720, where (x0, y0) means the starting position of the first partition/range/box with implicit or explicit index 0 (i.e., the element 721) , “x range d0” means the size or length in x dimension, “y range d0” means the size or length in y dimension. (x1, y1) means the starting position of the second partition/range/box with implicit or explicit index 1 (i.e., the element 722) . In this case, the map is represented based on a list or an array. In another example, a quadtree may be used to represent the partitions. As shown in following Fig. 7B, map 730 is represented by quadtree 740. The node 741 in quadtree 740 corresponds to element 731 in map 730, the node 742 in quadtree 740 corresponds to element 732 in map 730, and the node 743 in quadtree 740 corresponds to element 733 in map 730. In the quadtree 740, the number 1 in the node of quadtree 740 means further-partition, and the number 0 in the node means end-partition. For example, the node 741 with number 0 cannot be further partitioned, and the node 744 with number 1 may be further partitioned. If the map is a 3D map/matrix, an octree may be used to represent the partitions. A higher-order tree can be used to represent the partition of a multi-dimensional map or matrix, e.g., the octree 750. In this case, the map is represented based on a tree.

For easier illustration, 2D figures are used to discuss and illustrate some concepts disclosed herein. However, the matrix can also be a multi-dimensional matrix, and the above method is also applicable. It is to be understood that these 2D figures are for the purpose of illustration and are not intended to be limiting. The disclosure described herein can be implemented in various manners other than the ones described below.

A map (RF-map, or G-map, or other maps) may also be represented by a list or an array, which comprises multiple map elements. The map element can have several representations, and each element has an index (explicit or implicit) , as introduced previously. There are several element representations for element i. Some examples are given as follows.

In an example, element i: { (x, y, z) } representing location, position, or coordinate. Optionally, the element index “i” can be included, then the representation of element i becomes {index i, (x, y, z) } . For example, such a map element can be used to indicate a G-map location.

In another example, element i: { {amp 0, delay 0, angle 0} , {amp 1, delay 1, angle 1} , …, {amp n_i, delay n_i, angle n_i} } . The parameter {amp x, delay x, angle x} represents amplitude, delay, angle of one path or a ray in a set of paths or rays, e.g. multi-paths/multi-rays. Optionally, a path number n_i may be included, where n_i is the number of the paths or rays. Additionally, the element index “i” may be included. The representation of element i may be {index i, path number n_i, {amp 0, delay 0, angle 0} , {amp 1, delay 1, angle 1} , …, {amp n_i, delay n_i, angle n_i} } . For example, such a map element can be used to indicate an RF-map element, e.g. one or multiple paths/rays.

An element type can be included for element i. For example, element i can be {type RAY, path number ni, {amp 0, delay 0, angle 0} , {amp 1, delay 1, angle 1} , …, {amp n_i, delay n_i, angle n_i} } , and type RAY represents that the element type is multi-paths or multi-rays. In another example, element j can be {type BEAM, beam number n_j, {angle 0, gradient 0, width 0} , …, {angle n_j, gradient n_j, width n_j} } , and type BEAM represents that the element type is a beamforming information (Each beam may include information about the angle, beam gradient, beam width, etc. of the beam. The element can include one or multiple beams) . Optionally, the path number n_i, or the beam number n_j may be included in element i, e.g. the representation of element j becomes {index j, type BEAM, beam number n_j, {angle 0, gradient 0, width 0} , …, {angle nj, gradient n_j, width n_j} } .

An element size can be included for element i. For example, element i can be {type H, size M_t x N_t, value/compressed values…} , where “M_t x N_t” is the element size/length/dimension, and “value/compressed values…” represents the original values or the compressed values of channel, H, information included in this element.

Based on the above representation of a map element, the map represented by a list or array can be expressed as: {Element number k, {Element 0, Element 1, …, Element k} } , where the map includes k elements. The “Element number k” value can be optionally included information. As shown in Fig. 7C, the element 770 may have eight dimensions, where the number of elements (i.e., the value of k) is 8. The element 780 may have six dimensions, where the value of k is 6. The element 790 may have five dimensions, where the value of k is 5.

In some embodiments, the information may be represented by the multi-dimensional matrix, and compression of the information is performed based on a projection, a matrix transformation, a vector quantization, a scalar quantization, entropy coding or any combination of two or more of the above-mentioned items.

For example, in order to compress or encode matrix content X, following approaches may be used individually or in combination. In an approach, projection or transform X is performed based on some base or dictionary, or is based on one of a discrete cosine transform, a discrete Fourier transform, or a fast Fourier transform. For example, a base or dictionary may be used to project or transform X to Y based on Y=UX, so as to reduce the matrix dimension, or obtain a sparser matrix. In another approach, a vector/scalar quantization is performed on X or the preprocessed X (e.g. after projection or transform) . The vector quantization utilizes relationships between multiple elements to quantify the multiple elements together. The selection of quantization bits for the scalar quantization is associated with a range of the elements, and the scalar quantization utilizes fixed-bit quantization or dynamic quantization. Regarding fixed-bit quantization, the number of quantization bits for all elements are the same. Regarding dynamic quantization, some elements use fewer quantization bits, while others use slightly more quantization bits. In yet another approach, entropy coding is performed. In addition, the vector/scalar quantization may be combined with projection, matrix transformation, or entropy coding to compress the elements. For example, the bit sequence generated by the quantization (e.g., quantization index sequence generated by the vector quantization, or other quantization bit sequences or quantization index sequences generated by the scalar quantization) will exhibit an unequal probability distribution (non-uniform distribution) , which is more conducive to improving entropy coding performance. Accordingly, entropy decoding, de-quantization, inverse-transform (e.g., inverse discrete cosine transform or inverse discrete Fourier transform) may be used in the decoding process or decompression process. The above compression approaches can be used for a G-map or an RF-map represented by an evenly-divided matrix, or can be used for the mapping between the G-map and the RF-map, and so on.

In some embodiments, the information may be represented by the tree, and the compressed information may comprise a compressed tree structure, compressed tree node information, or any combination of two of the above-mentioned items. For example, the matrix or map partition may be represented by octree, or quadtree, or a higher-order tree. As shown in Fig. 8, the map 810 may be represented by tree 820. The node 821 in quadtree 820 corresponds to element 811 in map 810, the node 822 in quadtree 820 corresponds to element 812 in map 810, and the node 823 in quadtree 820 corresponds to element 813 in map 810. The number 1 in the node means further-partition, and the number 0 in the node means end-partition. Some tree nodes may include the map element, for example, the node with number 0 include a map element. The compressed information may include: compressed tree structure, compressed tree node information or map element information. In some embodiments, a plurality of nodes of the tree may be compressed separately or jointly. For example, the tree nodes or map elements can be compressed separately, or can be put together for compression. The above compression approaches can be used for a G-map or an RF-map represented by an unevenly-divided matrix partitioned based on a tree.

In some embodiments, the information may be represented by the list or the array. In order to compress the information, the first device may compress number values of the list or the array based on entropy coding. Alternatively, the information may be represented by the list or the array. In order to compress the information, the first device may compress non-number values of the list or the array based on a differential compression. Additionally, in order to compress the information, the first device may further compress the number values based on the differential compression before the entropy coding. Alternatively or additionally, in order to compress the information, the first device may further compress the non-number values based on a projection, a matrix transformation, a quantization or entropy coding in parallel with the differential compression. In an example, the elements in the list is a channel matrix, such as {H₀, H₁, …H_i} . In this example, a projection or a transform can be applied in addition to differential compression, either before or after calculating the residual for the differential compression.

For example, the list may be {Element number k, {Element 0, Element 1, …, Element k} } , where k is the element number, and the “Element number k” value is optionally included information. The representation of the element i is {path number n_i, {amp 0, delay 0, angle 0} , {amp 1, delay 1, angle 1} , …, {amp n_i, delay n_i, angle n_i} } , and the compression method may be introduced based on this example. The compression for the list with other types of elements are similar.

The “number values” may be put together and then compressed or encoded via entropy coding. For example, entropy coding may be applied on {n0, n1, …n_i} , or differential compression may be applied first and then entropy coding may be applied. The “non-number values” may be put together, and compressed or encoded via differential compression. Alternatively, differential compression may also be jointly used with quantization, entropy coding, etc. For example, {amp 0, amp 1, …amp n_i} may be put together, and then the first device may perform differential compression and get the residual {amp 1 -amp 0, amp 2 -amp 1, …amp n_i -amp n_i-1} . Then quantization (for lossy compression) and/or entropy coding may be applied. The values {delay 0, delay 1, …delay n_i} may be similarly compressed.

In another example, if the element in the list is a channel matrix and {H0, H1, …Hi} is obtained, in addition to differential compression, projection or transform can be applied before calculating the residual or after calculating the residual. In yet another example, if the element in the list is a box or range, e.g. {num=n, {start x0, y0, range d0, d0’ } , {start x1, y1, range d1, d1’} , …, {start xn, yn, range dn, dn’} } , the box or range may be differentially compressed, and “x” are compressed together, “d” are compressed together similarly. The compressed information may include compressed “number values” and a set of compressed “non-number values” . The above compression approaches can be used for a G-map or an RF-map represented by a list, or for a mapping represented by a list, or for a paired-mapping, etc.

In some embodiments, the first device may encode a residual between two elements with a prediction method, wherein the two elements are in a same map/mapping or are in different maps/mappings. Based on at least one of the projection, the matrix transformation, the vector quantization, the scalar quantization, or the entropy coding, or any combination of two or more of the above-mentioned items, the first device may compress the encoded residual.

For example, intra-prediction and inter-prediction may be used to compress the matrix content. As shown in Fig. 9A, if elements 901 and 903 are close, for example, the distance between elements 901 and 903 is small, or mean-square error (MSE) is small, one element can be differentially compressed based on the other one. In this case, only the residual (i.e., the difference between the two elements) needs to be encoded so as to reduce the compressed bits. A similar compression approach mentioned above (projection/transform, quantization, entropy coding, etc. ) may be used to compress the residual.

Inter-prediction can be used to compress the matrix elements from different matrixes, maps, or layers. For example, the matrix elements may come from the matrix in different time or nodes, the map with different resolution, or different layers in the same matrix, etc. As shown in Fig. 9B, if the elements 905 and 907 from different matrix/map/layer are close, for example, the distance between elements 905 and 907 is small, or the MSE is small, one element may be differentially compressed based on the other one. In this case, only the residual (i.e., the difference between the two elements) needs to be encoded so as to reduce the compressed bits. A similar compression approach mentioned above (projection/transform, quantization, entropy coding etc. ) may be used to compress the residual.

If intra-prediction or inter-prediction is used, some compression parameters may be indicated between the encoder (BS) and decoder (UE) . The compression parameters may comprise the prediction mode (intra or inter) , the reference index (e.g. the index of the referenced element used in intra/inter-prediction) , etc. It is to be understood that a matrix is presented as an example, and the intra-prediction and the inter-prediction described herein may also be applied to a tree, a list, an array, or another representation type of the map and the mapping configuration.

Reference is made back to Fig. 2, the first device 201 outputs 220 the compressed information 230 to the second device 202, and the size of the compressed information is 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 may obtain the compressed information by receiving the compressed information from the first device 201. Alternatively, the second device 202 may obtain the compressed information from a third device, e.g. at least one terminal device, or a network function.

Based on the compressed information, the second device 202 obtains 250 information. The information comprises the first map, the second map, or the mapping configuration, or any combination of two or more of the above-mentioned items. In some embodiments, in order to obtain the information, the second device may decode the compressed information. In some embodiments, in order to obtain the information, the second device may decompress the compressed information.

Alternatively, the first device may further transmit the number of the compression levels, 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, the method of mapping with different compression levels, or any combination of two or more of the above-mentioned items to the second device. For example, in addition to the compressed bits, some compression parameters may need to be indicated between the encoder (BS) and decoder (UE) . The compression parameters may comprise the number of levels, the compression parameters of each level, the map size/number of map elements in each level, or the method of mapping with different compression levels.

Correspondingly, the second device may receive the number of compression levels, 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, the method of mapping with different compression levels, or any combination of two or more of the above-mentioned items from the first device. Based on the number of compression levels, 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, the method of mapping with different compression levels, or any combination of two or more of the above-mentioned items, the second device may obtain the information. In an example, obtaining the information may comprise decompress the compressed information and decode the compressed information, and the size of the encoded information or compressed information is smaller than the information.

Additionally, the first device may transmit at least one compression parameter to the second device. The at least one compression parameter may comprise a projection method, a transform method, a transform base, quantization bits, an entropy coding method, or any combination of two or more of the above-mentioned items. In an example, the transform base may comprise a base for matrix transformation. In another example, the transform base may comprise a codebook for vector quantization.

For example, in addition to the compressed bits, some compression parameters may need to be indicated between the encoder (BS) and decoder (UE) . The compression parameters may comprise transform approach and base, quantization bits, entropy coding method, etc.

Then the second device may receive at least one compression parameter from the first device. The at least one compression parameter may comprise a projection method, a transform method, a transform base, quantization bits, an entropy coding method, or any combination of two or more of the above-mentioned items. Then the second device may obtain the information based on the at least one compression parameter.

In addition, the first device may transmit at least one compression parameter to the second device. The at least one compression parameter may comprise a tree depth. For example, tree depths, and other compress parameters need to be indicated. Correspondingly, the second device may receive the at least one compression parameter from the first device, and the at least one compression parameter may comprise a tree depth.

Alternatively, the first device may further transmit at least one compression parameter to the second device. The at least one compression parameter may comprise a re-organizing method of selecting values for the differential compression, a projection method, a transform method, a transform base, quantization bits, an entropy coding method, or any combination of two or more of the above-mentioned items. For example, in addition to the compressed bits, some compression parameters may need to be indicated between the encoder (BS) and decoder (UE) . Some compression parameters may comprise quantization bits, entropy coding approach, the re-organizing approach for selecting the values for differential compression, and so on.

On the other side of the communication, the second device may receive at least one compression parameter from the first device. The at least one compression parameter may comprise a re-organizing method of selecting values for the differential compression, a projection method, a transform method, a transform base, quantization bits, an entropy coding method, or any combination of two or more of the above-mentioned items. Based on the at least one compression parameter, the second device may obtain the information.

Alternatively or additionally, the first device may further transmit at least one compression parameter to the second device. The at least one compression parameter may comprise a prediction mode, an index of a reference element, or a combination of the above-mentioned items.

On the other side of the communication, the second device may receive at least one compression parameter from the first device. The at least one compression parameter may comprise at least one of a prediction mode, or an index of a reference element. Based on the at least one compression parameter, the second device may obtain the information.

In view of the above, the RF-map is used to represent a radio environmental map, a radio frequency map, a radio map, a radio-based map, a radio-signal-based map, a wireless-signal-based map, or other maps with similar meanings. The G-map is used to represent location/geometry/geographic information or map, or some intermediate results after processing of location/geometry/geography information, or other maps with similar meanings. “Map” represents a form of indication, and can also be known by other names such as list, matrix, group, set, range, area, relationship, lookup table, information, etc. Among them, “mapping” represents a relationship, and can also be known by other names such as relationship, matching, lookup table, etc.

Example embodiments of the present disclosure are described by interaction and processing procedures between the user equipment (UE) and the base station (BS) . The exchanged information and protocol flows in these procedures can also be performed by other network nodes described in Figs. 1A-1E, for example, between ED 110 and TRP 170, between ED 110 and core network, between ED 110 and ED 110, between TRP 170 and TRP 170. The UE in the procedure described in some embodiments of the present disclosure may be replaced with a sensing node mentioned in Figs. 1A-1E. The BS in the procedure described in some embodiments of the present disclosure may be replaced with a sensing coordinator. Sensing coordinators are nodes in a network that can assist in the sensing operation. These nodes can be stand-alone nodes dedicated to just sensing operations or may be other nodes (for example TRP 170, ED 110, or core network node in Fig. 1A-1E) performing the sensing operations in parallel with communication operations.

In general, the map (RF-map or G-map) and the mapping (between G-map and RF-map) can be represented using a multi-dimensional matrix, a tree, a list, or an array. Several compression approaches are presented to compress the above map or mapping, so as to reduce the map/mapping indication overhead. The compression approaches comprise: compressing the map/mapping represented by multi-dimensional matrix; compressing the map/mapping represented by tree; compressing the map/mapping represented by list/array. In addition, a hierarchical representation and compression approach for the map/mapping is presented. It is to be understood that the present disclosure is also applicable to the compression of maps or mapping in other scenarios. For example, the present disclosure can be also applied to Wi-Fi, ultra wide band (UWB) and other short range communications. Then The BS in the procedure described in the present disclosure may be replaced with access points (APs) .

Fig. 10 shows a flowchart of an example method 1000 implemented at a first device in accordance with some embodiments of the present disclosure. For the purpose of discussion, the method 1000 will be described from the perspective of the communication electronic device 110 or the network node 170 with reference to Fig. 1A. It is to be understood that the method 1000 may include additional acts not shown and/or may omit some shown acts, and the scope of the present disclosure is not limited in this regard.

At block 1010, the first device compresses information using a relationship among 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 represents one of radio environment information and geometry information, and the second map represents the other one of the environment information and the geometry information. At block 1020, the first device transmits the compressed information to a second device, wherein a size of the compressed information is smaller than the information.

In some embodiments, the mapping configuration indicates at least one of the following: 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 among the 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 among the element pairs comprises an element in the first map and an element in the second map.

In some embodiments, an element in the first map or the second map representing the radio environment information may be of at least one of the following: a multi-path 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 status information type.

In some embodiments, an element in the first map or the second map representing the geometry information may be of at least one of the following: 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 some embodiments, a first element in a map of the first map or the second map representing the radio environment information is of a first element type and a second element in the map is of a second element type, and wherein the first element type and the second element type may be the same or different, a first size of the first element and a second size of the second element may be the same of different, and/or a first value range of the first element and a second value range of the second element may be the same or different.

In some embodiments, an element in the first map or the second map representing the radio environment information may be of one or more element types. In some embodiments, a third element in a map of the first map or the second map representing the geometry information is of a third element type and a fourth element in the map is of a fourth element type, and wherein the third element type and the fourth element type are the same or different; and/or a third size or shape of the third element and a fourth size or shape of the fourth element are the same or different.

In some embodiments, in order to compress the information, the first device may compress the 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 may further transmit, to the second device, at least one of a number of the 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 method of mapping with different compression levels.

In some embodiments, in order to compress the information, the first device may map one of a first map, a compressed first map, or a layer of the first map to one of a second map, a compressed second map, or a layer of the second map; map one of a first map, a compressed first map, or a 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 a second map, a compressed second map, or a layer of the second map.

In some embodiments, in order to compress the information, the first device may split the first map, a compressed first map, or a layer of the first map into a plurality of parts; and map the plurality of parts to a plurality of compressed second maps with different compression levels.

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

In some embodiments, in order to compress the information, the first device may generate a mapping between a compressed first map with a first compression level and a compressed second map with a second compression level, wherein the first compression level and the second compression level are the same or different.

In some embodiments, the information may be represented by at least one of the following: a multi-dimensional matrix; a tree; a list; or an array. In some embodiments, the information may be represented by the multi-dimensional matrix, and the compressing of the information may be performed based on at least one of the following: a projection; a matrix transformation; a vector quantization; a scalar quantization; or entropy coding.

In some embodiments, the first device may further transmit, to the second device, at least one compression parameter comprising at least one of a projection method, a transform method, a transform base, quantization bits, or an entropy coding method.

In some embodiments, the information may be represented by the tree, and the compressed information may comprise at least one of a compressed tree structure or compressed tree node information. In some embodiments, the first device may further transmit, to the second device, at least one compression parameter comprising a tree depth.

In some embodiments, the information may be represented by the list or the array, and in order to compress the information, the first device may at least one of the following: compressing number values of the list or the array based on entropy coding; or compressing non-number values of the list or the array based on a differential compression.

In some embodiments, in order to compress the information, the first device may at least one of the following: compressing the number values based on the differential compression before the entropy coding; or compressing the non-number values based on a projection, a matrix transformation, a quantization or entropy coding in parallel with the differential compression.

In some embodiments, the first device may further transmit, to the second device, at least one compression parameter comprising at least one of a re-organizing method of selecting values for the differential compression, a projection method, a transform method, a transform base, quantization bits, or an entropy coding method.

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

In some embodiments, the first device may further transmit, to the second device, at least one compression parameter comprising at least one of a prediction mode, or an index of a reference element.

Fig. 11 shows a flowchart of an example method 1100 implemented at a second device in accordance with some embodiments of the present disclosure. For the purpose of discussion, the method 1100 will be described from the perspective of the communication electronic device 110 or the network node 170 with reference to Fig. 1A. It is to be understood that the method 1100 may include additional acts not shown and/or may omit some shown acts, and the scope of the present disclosure is not limited in this regard.

At block 1110, the second device obtains compressed information. At block 1120, the second device obtains, 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, wherein the first map represents one of radio environment information and geometry information, the second map represents the other one of the environment information and the geometry information, and a size of the compressed information is smaller than the information.

In some embodiments, the mapping configuration may indicate at least one of the following: 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 among the 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 among the element pairs comprises an element in the first map and an element in the second map.

In some embodiments, an element in the first map or the second map representing the radio environment information may be of one or more element types. In some embodiments, a third element in a map of the first map or the second map representing the geometry information is of a third element type and a fourth element in the map is of a fourth element type, and wherein the third element type and the fourth element type may be the same or different; and/or a third size or shape of the third element and a fourth size or shape of the fourth element may be the same or different.

In some embodiments, obtaining the compressed information may comprise: receiving the compressed information from a first device.

In some embodiments, the at least one of the first map, the second map, or the mapping configuration may be compressed into a plurality of layers with different compression levels.

In some embodiments, in order to obtain the information, the second device may receive, from the first device, 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 method of mapping with different compression levels; and obtain the information based on the 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 a method of mapping with different compression levels.

In some embodiments, one of a first map, a compressed first map, or a layer of the first map is mapped to one of a second map, a compressed second map, or a layer of the second map; one of a first map, a compressed first map, or a layer 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 is mapped to a plurality of compressed second maps with different compression levels; or a plurality of compressed first maps with different compression levels is mapped to one of a second map, a compressed second map, or a layer of the second map.

In some embodiments, the first map, a compressed first map, or a layer of the first map may be split into a plurality of parts, and the plurality of parts may be mapped to a plurality of compressed second maps with different compression levels.

In some embodiments, a plurality of elements may be selected from the first map, a compressed first map, or a layer of the first map, and the plurality of elements may be mapped to a plurality of compressed second maps with different compression levels.

In some embodiments, a compressed first map with a first compression level may be mapped to a compressed second map with a second compression level, and the first compression level and the second compression level may be the same or different.

In some embodiments, the information may be represented by at least one of the following: a multi-dimensional matrix; a tree; a list; or an array. In some embodiments, the information may be represented by the multi-dimensional matrix, and the information may be compressed based on at least one of the following: a projection; a matrix transformation; a vector quantization; a scalar quantization; or entropy coding.

In some embodiments, in order to obtain the information, the second device may receive, from the first device, at least one compression parameter comprising at least one of a projection method, a transform method, a transform base, quantization bits, or an entropy coding method; and obtain the information based on the at least one compression parameter.

In some embodiments, the information may be represented by the tree, and the compressed information may comprise at least one of a compressed tree structure or compressed tree node information. In some embodiments, a plurality of nodes of the tree may be compressed separately or jointly.

In some embodiments, in order to obtain the information, the second device may receive, from the first device, at least one compression parameter comprising a tree depth; and obtain the information based on the at least one compression parameter.

In some embodiments, the information may be represented by the list or the array, and number values of the list or the array may be compressed based on entropy coding, or non-number values of the list or the array may be compressed based on a differential compression.

In some embodiments, the number values are compressed based on the differential compression before the entropy coding; or the non-number values are compressed based on a projection, a matrix transformation, a quantization or entropy coding in parallel with the differential compression.

In some embodiments, in order to obtain the information, the second device may receive, from the first device, at least one compression parameter comprising at least one of a re-organizing method of selecting values for the differential compression, a projection method, a transform method, a transform base, quantization bits, or an entropy coding method; and obtain the information based on the at least one compression parameter.

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

In some embodiments, in order to obtain the information, the second device may receive, from the first device, at least one compression parameter comprising at least one of a prediction mode, or an index of a reference element; and obtain the information based on the at least one compression parameter. In some embodiments, in order to obtain the information, the second device may decode the compressed information; or decompress the compressed information.

FIG. 12 illustrates a simplified block diagram of a device 1200 (also termed as an apparatus 1200) that is suitable for implementing embodiments of the present disclosure. The device 1200 can be considered as a further example implementation of the communication electronic device 110 or the network node 170 as shown in FIG. 1A. Accordingly, the device 1200 can be implemented at or as at least a part of the above devices.

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 may also be known as a transceiver. The TX/RX 1240 may be coupled to the processor 1210 via any suitable interface configured for inputting signals into, and outputting signals from, the processor. The memory 1210 stores at least a part of a program 1230. The TX/RX 1240 is for bidirectional communications. The TX/RX 1240 has at least one antenna to facilitate communication, though in practice an access node or base station mentioned in this disclosure may have several antennas. The communication interface may represent any interface that is necessary for communication with other network elements, such as an X2 or Xn interface for bidirectional communications between eNBs or gNBs, an S1 interface for communication between a Mobility Management Entity (MME) /Serving Gateway (S-GW) and the eNB or gNB, a Un interface for communication between the eNB or gNB and a relay node (RN) , a Uu interface for communication between the eNB or gNB and a terminal device, or PC5 interface for communication between two terminal devices.

The program 1230 is assumed to include 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 discussed herein with reference to Figs. 1A to 11. The embodiments herein may 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 may be configured to implement various embodiments of the present disclosure. Furthermore, a combination of the processor 1210 and memory 1220 may form processing means 1250 adapted to implement various embodiments of the present disclosure.

The memory 1220 may be of any type suitable to the local technical network and may be implemented using any suitable data storage technology, such as a non-transitory computer readable storage medium, 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 may be several physically distinct memory modules in the device 1200. The processor 1210 may be of any type suitable to the local technical network, and may include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 1200 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

The components included in the apparatuses and/or devices of the present disclosure may be implemented in various manners, including software, hardware, firmware, or any combination thereof. In one embodiment, one or more units may be implemented using software and/or firmware, for example, machine-executable instructions stored on the storage medium. In addition to or instead of machine-executable instructions, parts or all of the units in the apparatuses and/or devices may be implemented, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include 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) , and the like.

Generally, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representation, it will be appreciated that the blocks, apparatus, systems, technique terminal devices or methods described herein may 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.

The present disclosure also provides at least one computer program product 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, being executed in a device on a target real or virtual processor, to carry out the process or method as described above with reference to any of FIGS. 2 to 11. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

The above program code may be embodied on a machine readable medium, which may be any tangible medium that may contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine readable medium may be a machine readable signal medium or a machine readable storage medium. A machine readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine readable storage medium would 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 foregoing.

Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific embodiment details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable sub-combination.

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

When the functions are implemented in the form of a software functional unit and sold or used as an independent product, the functions may be stored in a computer-readable storage medium. Based on such an understanding, the technical solutions of this application essentially, or the part contributing to the prior art, or some of the technical solutions may be implemented in a form of a software product. The software product is stored in a storage medium, and includes several instructions for instructing a computer device (which may be a personal computer, a server, or a network device) to perform all or some of the steps of the methods described in the embodiments of this application. The foregoing storage medium includes: any medium that can store program code, such as a USB flash drive, a removable hard disk, a read-only memory (Read-Only Memory, ROM) , a random access memory (Random Access Memory, RAM) , a magnetic disk, or an optical disc.

The foregoing descriptions are merely specific implementations of this application, but are not intended to limit the protection scope of this application. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in this application shall fall within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

A method comprising:

compressing information using a relationship among 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 represents one of radio environment information and geometry information, and the second map represents the other one of the environment information and the geometry information; and

outputting compressed information, wherein a size of the compressed information is smaller than the information.
The method of claim 1, wherein compressing the information comprises:

compressing the at least one of the first map, the second map, or the mapping configuration into a plurality of layers with different compression levels.
The method of claim 1 or 2, further comprising:

transmitting, from a first device to a second device, at least one of a number of the 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 method of mapping with different compression levels.
The method of any of claims 1-3, wherein compressing the information comprises:

mapping one of a first map, a compressed first map, or a layer of the first map to one of a second map, a compressed second map, or a layer of the second map;

mapping one of a first map, a compressed first map, or a 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 a second map, a compressed second map, or a layer of the second map.
The method of any of claims 1-4, wherein compressing the information comprises:

splitting the first map, a compressed first map, or a layer of the first map into a plurality of parts; and

mapping the plurality of parts to a plurality of compressed second maps with different compression levels.
The method of any of claims 1-4, wherein compressing the information comprises:

selecting a plurality of elements from the first map, a compressed first map, or a layer of the first map; and

mapping the plurality of elements to a plurality of compressed second maps with different compression levels.
The method of any of claims 1-6, wherein compressing the information comprises:

generating a mapping between a compressed first map with a first compression level and a compressed second map with a second compression level, wherein the first compression level and the second compression level are the same or different.
The method of any of claims 1-7, wherein the information is represented by at least one of the following:

a multi-dimensional matrix;

a tree;

a list; or

an array.
The method of claim 8, wherein the information is represented by the multi-dimensional matrix, and the compressing of the information is performed based on at least one of the following:

a projection;

a matrix transformation;

a vector quantization;

a scalar quantization; or

entropy coding.
The method of claim 9, further comprising:

transmitting, from a first device to a second device, at least one compression parameter comprising at least one of a projection method, a transform method, a transform base, quantization bits, or an entropy coding method.
The method of claim 8, wherein the information is represented by the tree, and the compressed information comprises at least one of a compressed tree structure or compressed tree node information.
The method of claim 11, wherein a plurality of nodes of the tree are compressed separately or jointly.
The method of claim 11 or 12, further comprising:

transmitting, from a first device to a second device, at least one compression parameter comprising a tree depth.
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 number values of the list or the array based on entropy coding; or

compressing non-number values of the list or the array based on a differential compression.
The method of claim 14, wherein compressing the information further comprises at least one of the following:

compressing the number values based on the differential compression before the entropy coding; or

compressing the non-number values based on a projection, a matrix transformation, a quantization or entropy coding in parallel with the differential compression.
The method of claim 14 or 15, further comprising:

transmitting, from a first device to a second device, at least one compression parameter comprising at least one of a re-organizing method of selecting values for the differential compression, a projection method, a transform method, a transform base, quantization bits, or an entropy coding method.
The method of any of claims 1-16, wherein compressing the information further comprises:

encoding a residual between two elements with a prediction method, wherein the two elements are in a same map/mapping or different maps/mappings; and

compressing the encoded residual based on at least one of the projection, the matrix transformation, the vector quantization, the scalar quantization, or the entropy coding.
The method of claim 17, further comprising:

transmitting, from a first device to a second device, at least one compression parameter comprising at least one of a prediction mode, or an index of a reference element.
The method of any of claims 1-18, wherein the mapping configuration indicates at least one of the following:

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 among the 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 among the element pairs comprises an element in the first map and an element in the second map.
The method of any of claims 1-19, wherein an element in the first map or the second map representing the radio environment information is of at least one of the following:

a multi-path 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 status information type.
The method of any of claims any of claims 1-20, wherein an element in the first map or the second map representing the geometry information is of at least one of the following:

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.
A method comprising:

obtaining compressed information; and

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, wherein the first map represents one of radio environment information and geometry information, the second map represents the other one of the environment information and the geometry information, and a size of the compressed information is smaller than the information.
The method of claim 22, wherein obtaining the compressed information comprises:

receiving, by a second device, the compressed information from a first device.
The method of claim 22 or 23, wherein the at least one of the first map, the second map, or the mapping configuration are compressed into a plurality of layers with different compression levels.
The method of any of claims 22-24, wherein obtaining the information comprises:

receiving, from the first device, at least one of a number of the 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 method of mapping with different compression levels; and

obtaining the information based on the at least one of the number of the 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 method of mapping with different compression levels.
The method of any of claims 22-25, wherein one of:

one of a first map, a compressed first map, or a layer of the first map is mapped to one of a second map, a compressed second map, or a layer of the second map;

one of a first map, a compressed first map, or a layer 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 is mapped to a plurality of compressed second maps with different compression levels; or

a plurality of compressed first maps with different compression levels is mapped to one of a second map, a compressed second map, or a layer of the second map.
The method of any of claims 22-26, wherein the first map, a compressed first map, or a layer of the first map is split into a plurality of parts, and the plurality of parts is mapped to a plurality of compressed second maps with different compression levels.
The method of any of claims 22-27, wherein a plurality of elements is selected from the first map, a compressed first map, or a layer of the first map, and the plurality of elements is mapped to a plurality of compressed second maps with different compression levels.
The method of any of claims 22-28, wherein a compressed first map with a first compression level is mapped to a compressed second map with a second compression level, and the first compression level and the second compression level are the same or different.
The method of any of claims 22-29, wherein the information is represented by at least one of the following:

a multi-dimensional matrix;

a tree;

a list; or

an array.
The method of claim 30, wherein the information is represented by the multi-dimensional matrix, and the information is compressed based on at least one of the following:

a projection;

a matrix transformation;

a vector quantization;

a scalar quantization; or

entropy coding.
The method of claim 31, wherein obtaining the information comprises:

receiving, from the first device, at least one compression parameter comprising at least one of a projection method, a transform method, a transform base, quantization bits, or an entropy coding method; and

obtaining the information based on the at least one compression parameter.
The method of claim 30, wherein the information is represented by the tree, and the compressed information comprises at least one of a compressed tree structure or compressed tree node information.
The method of claim 33, wherein a plurality of nodes of the tree are compressed separately or jointly.
The method of claim 33 or 34, wherein obtaining the information comprising:

receiving, from the first device, at least one compression parameter comprising a tree depth; and

obtaining the information based on the at least one compression parameter.
The method of claim 30, wherein the information is represented by the list or the array, wherein at least one of the following:

number values of the list or the array are compressed based on entropy coding; or

non-number values of the list or the array are compressed based on a differential compression.
The method of claim 36, wherein at least one of the following:

the number values are compressed based on the differential compression before the entropy coding; or

the non-number values are compressed based on a projection, a matrix transformation, a quantization or entropy coding in parallel with the differential compression.
The method of claim 36 or 37, wherein obtaining the information comprising:

receiving, from the first device, at least one compression parameter comprising at least one of a re-organizing method of selecting values for the differential compression, a projection method, a transform method, a transform base, quantization bits, or an entropy coding method; and

obtaining the information based on the at least one compression parameter.
The method of any of claims 22-38, wherein a residual between two elements is encoded with a prediction method, and the two elements are in a same map/mapping or 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.
The method of claim 39, wherein obtaining the information comprising:

receiving, from the first device, at least one compression parameter comprising at least one of a prediction mode, or an index of a reference element; and

obtaining the information based on the at least one compression parameter.
The method of any of claims 22-40, wherein obtaining the information further comprising one of the following:

decoding the compressed information; or

decompressing the compressed information.
The method of any of claims 22-41, wherein the mapping configuration indicates at least one of the following:

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 among the 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 among the element pairs comprises an element in the first map and an element in the second map.
The method of any of claims 22-42, wherein an element in the first map or the second map representing the radio environment information is of at least one of the following:

a multi-path 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 status information type.
The method of any of claims any of claims 22-43, wherein an element in the first map or the second map representing the geometry information is of at least one of the following:

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.
A first device comprising:

an interface; and

a processor communicatively coupled with the interface,

wherein the processor is configured to:

compress information using a relationship among 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 represents one of radio environment information and geometry information, and the second map represents the other one of the environment information and the geometry information; and

output the compressed information via the interface, wherein a size of the compressed information is smaller than the information.
A second device comprising:

an interface; and

a processor communicatively coupled with the interface,

wherein the processor is configured to:

obtain compressed information; and

obtain, 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, wherein the first map represents one of radio environment information and geometry information, the second map represents the other one of the environment information and the geometry information, and a size of the compressed information is smaller than the information.
A non-transitory computer readable medium comprising a computer program stored thereon, the computer program, when executed on at least one processor, causing the at least one processor to perform the method of any of claims 1-44.
An apparatus comprising at least one processor configured to cause the apparatus to perform the method of any of claims 1-44.
A computer program product comprising computer-executable instructions which, when executed, cause an apparatus to perform the method of any of claims 1-44.