Sensing method and apparatus

Abstract

A sensing method and apparatus. The method comprises: a first device inputting first compressed information into a decompression model, so as to obtain second sensing information output by the decompression model, wherein the first compressed information is related to first sensing information, the first sensing information comprises information that indicates first power corresponding to at least one spatial position, and the second sensing information comprises information that indicates second power corresponding to the at least one spatial position; and on the basis of a loss function, the first device updating parameters of a compression model and / or the decompression model, wherein the loss function comprises a distortion function between the first sensing information and the second sensing information, the distortion function is related to a power difference corresponding to the at least one spatial position and the maximum first power indicated by the first sensing information, and the power difference corresponding to one of the at least one spatial position refers to the difference between the first power and the second power. The distortion function in the embodiments of the present application enables relatively accurate power recovery at a receiving end, thereby facilitating an improvement in sensing precision.

Description

A sensing method and device

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 202411217058.1, filed on August 31, 2024, entitled "A Sensing Method and Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of sensing technology, and in particular to a sensing method and apparatus. Background Technology

[0004] For sensing services, the location or shape of a target in the environment can be sensed by utilizing the reflection, scattering, or diffraction of signals transmitted by user equipment (UE) or base stations as they propagate through space. Taking a scenario where both the UE and base station participate in sensing as an example, the UE can measure the signals reflected, scattered, or diffracted by the target and report the measurement results to the base station, which then realizes the sensing of the target.

[0005] However, the measurement result is a large data volume, and the UE will incur significant transmission overhead when reporting the measurement result. Summary of the Invention

[0006] This application provides a sensing method and apparatus to reduce transmission overhead. The sensing method and apparatus can also be considered a communication method and apparatus, or an integrated sensing and communication method and apparatus, or a model training method and apparatus.

[0007] In a first aspect, a first sensing method is provided, which can be applied to a first device. The first device is, for example, a first equipment, or other equipment including the functions of a first equipment, or a circuit, or a chip system. The first device is, for example, a terminal-side device, also referred to as a terminal device. The terminal device is, for example, a terminal device, or other equipment including the functions of a terminal device, or a circuit, or a chip system (or a chip, such as a modem chip, also known as a baseband chip, or a system-on-chip (SoC) chip containing a modem core or a system-in-package (SIP) chip) or other functional module, which can implement the functions of the terminal device, and is, for example, disposed in the terminal device. Alternatively, the first device is, for example, a network-side device, also referred to as a network device. The network device is, for example, an access network device, or other equipment including the functions of an access network device, or a circuit, or a chip system (or a chip) or other functional module, which can implement the functions of a network device, and is, for example, disposed in the network device. The access network device can be a non-ORAN architecture or an ORAN architecture; or, the access network device can be a SU, CU, DU, or RU under an ORAN architecture. The access network device may be located on the ground, or it may be a satellite, or located on a satellite. In the following description, the first device is taken as an example. According to the above description, the first device is, for example, a terminal device or an access network device. The method includes: inputting first compression information into a decompression model to obtain second sensing information output by the decompression model, wherein the first compression information is related to the first sensing information, the first sensing information includes information indicating a first power corresponding to at least one spatial location, and the second sensing information includes information indicating a second power corresponding to the at least one spatial location; updating the parameters of the compression model and / or the decompression model according to a loss function, wherein the loss function includes a distortion function between the first sensing information and the second sensing information, the distortion function being related to the power difference corresponding to the at least one spatial location and the maximum first power indicated by the first sensing information, wherein the power difference corresponding to one of the at least one spatial locations refers to the difference between the first power and the second power.

[0008] Embodiments of the present application can train a compression model and a decompression model, so that the compression model can be used to compress the first perception information. The compressed first perception information has a smaller data volume compared to the uncompressed first perception information, thereby reducing the transmission overhead. The first perception information may include the first power corresponding to at least one spatial position. The loss function used for training in embodiments of the present application may be related to the power difference corresponding to the at least one spatial position and the maximum power indicated by the first perception information. For example, among the first powers corresponding to the at least one spatial position, the larger the first power, the smaller the error brought by compression, so that the receiver can accurately recover the larger first power, thereby making the determined position of the perception target more accurate and facilitating the improvement of the perception accuracy.

[0009] In an alternative embodiment, the distortion function includes a weighting coefficient corresponding to the at least one spatial position, and the weighting coefficient is related to the largest first power indicated by the first perception information. Embodiments of the present application can correct the power error corresponding to the spatial position through the weighting coefficient. For example, each spatial position may correspond to a weighting coefficient, so that different spatial positions can correct the power error through their respective weighting coefficients, making the correction granularity finer.

[0010] In an alternative embodiment, the distortion function satisfies the following relationship: where L d represents the value of the distortion function, p' k represents the second power corresponding to the kth spatial position indicated by the second perception information, p k represents the first power corresponding to the kth spatial position indicated by the first perception information, 0 < k < K, K represents the number of the at least one spatial position, and w k represents the weighting coefficient corresponding to the kth spatial position. This is one embodiment of the distortion function. There may be other embodiments in addition. In addition, this formula may also have other variations. For example, one or more constants may be added to the formula, and this is not limited.

[0011] In one optional implementation, the k-th spatial location among the at least one spatial location corresponds to a k-th weighting coefficient. This k-th weighting coefficient is related to the first power indicated by the first sensing information corresponding to the k-th spatial location, and to the maximum and / or minimum first power indicated by the first sensing information. The weighting coefficient can be related to the maximum and / or minimum first power indicated by the first sensing information. For example, among the weighting coefficients corresponding to the at least one spatial location, the spatial location with a larger corresponding first power can have a larger weighting coefficient. This results in a smaller power error corrected by the weighting coefficient, making the receiver's recovery of larger first powers more accurate. Consequently, the determined location of the sensing target can be more accurate, which is beneficial for improving sensing accuracy.

[0012] In one optional implementation, the k-th weighting coefficient satisfies the following relationship: Where, p k p represents the first power indicated by the first sensing information corresponding to the k-th spatial location. min p represents the minimum first power indicated by the first sensing information. max This represents the maximum first power indicated by the first sensed information. This is one implementation of the weighting coefficients; other implementations are also possible. Furthermore, this formula can be modified in other ways, such as by adding one or more constants, etc., without limitation.

[0013] In one optional implementation, the value of the k-th weighting coefficient is the value corresponding to the power range in which the first power at the k-th spatial location falls, and the power range is determined based on the maximum and / or minimum first power indicated by the first sensing information. This is another implementation of the weighting coefficient; other implementations are also possible.

[0014] In one optional implementation, the loss function further includes a compression ratio function between the first compressed information and the first perceived information. The loss function may include one or more other functions besides the distortion function; there is no limitation on this.

[0015] In an optional implementation, the method further includes: inputting the first perceptual information into the compression model to obtain the first compressed information output by the compression model; or, receiving the first compressed information. For example, the training process can be completed independently by the first device, and this training method can be an offline training method. In the offline training method, the first device can first obtain the first perceptual information, and then obtain the first compressed information based on the first perceptual information. For example, the first perceptual information is obtained by the first device from a training sample library, and the first perceptual information can be a sample in the training sample library. Alternatively, the training process can also be completed by the first device and the second device, or understood as the second device assisting the first device in completing the training, and this training method can be an online training method. In the online training method, for example, the second device can obtain the first perceptual information, and after obtaining the first compressed information based on the first perceptual information, it can send the first compressed information to the first device. It is evident that the embodiments of this application can employ either an offline training method or an online training method, making the training process quite flexible.

[0016] In one optional implementation, the first compressed information is received, and the method further includes: receiving a first sensing signal; and determining the first sensing information based on the first sensing signal. If an online training method is used, the actual first sensing information is obtained by the second device, which then obtains the first compressed information. The second device sends the first compressed information to the first device, but may not send the first sensing information to the first device. Therefore, the first device can obtain the first sensing information in a corresponding manner. For example, the first device can generate the first sensing information based on the received first sensing signal. Due to the dissimilarity of uplink and downlink channels, the first sensing information generated by the first device can be considered the same as the first sensing information on the second device's side, or it can be understood that the first sensing information generated by the first device can replace the first sensing information on the second device's side. This approach can reduce the reporting volume of the second device and save transmission overhead.

[0017] In an optional implementation, the method further includes sending updated information about the compression model. After model training is complete, the first device can send the trained compression model to the second device, and the trained decompression model is maintained, for example, by the first device, so that the first device and the second device can compress and decompress the perceived information according to the trained compression model and decompression model.

[0018] In a second aspect, a second perception method is provided, which can be applied to a second device. The second device is, for example, a second device, or other devices including the functions of the second device, or a circuit, or a chip system. The second device is, for example, a terminal-side device or a network-side device. For the introduction of the terminal-side device or the network-side device, reference can be made to the first aspect. The method includes: sending first compression information, where the first compression information is used to determine a loss function, and the loss function includes a distortion function between first perception information and second perception information. The first perception information and the second perception information are related to the first compression information. The first perception information includes information indicating the first power corresponding to at least one spatial position, and the second perception information includes information indicating the second power corresponding to the at least one spatial position. The distortion function is related to the power difference corresponding to the at least one spatial position and the maximum first power included in the first perception information, where the power difference corresponding to one spatial position in the at least one spatial position refers to the difference between the first power and the second power.

[0019] In an optional implementation manner, the distortion function includes a weighting coefficient corresponding to the at least one spatial position, and the weighting coefficient is related to the maximum first power indicated by the first perception information.

[0020] In an optional implementation manner, the distortion function satisfies the following relationship: where, L d represents the value of the distortion function, p ′ k represents the second power corresponding to the k-th spatial position indicated by the second perception information, p k represents the first power corresponding to the k-th spatial position indicated by the first perception information, 0 < k < K, K represents the number of the at least one spatial position, and w k represents the weighting coefficient corresponding to the k-th spatial position.

[0021] In an optional implementation manner, the k-th spatial position in the at least one spatial position corresponds to the k-th weighting coefficient, and the k-th weighting coefficient is related to the first power corresponding to the k-th spatial position indicated by the first perception information, and the maximum first power and / or the minimum first power indicated by the first perception information.

[0022] In an optional implementation manner, the k-th weighting coefficient satisfies the following relationship: where, p k represents the first power corresponding to the k-th spatial position indicated by the first perception information, p min represents the minimum first power indicated by the first perception information, pmax This indicates the maximum first power indicated by the first sensing information.

[0023] In one optional implementation, the value of the k-th weighting coefficient is the value corresponding to the power range in which the first power at the k-th spatial location is located, and the power range is determined based on the maximum and / or minimum first power indicated by the first sensing information.

[0024] In an optional implementation, the loss function further includes a compression ratio function between the first compressed information and the first perceived information.

[0025] In one optional implementation, the method further includes: receiving a second sensing signal; generating the first sensing information based on the second sensing signal; and inputting the first sensing information into a compression model to obtain the first compression information.

[0026] In an alternative implementation, the method further includes sending a first sensing signal.

[0027] In an alternative implementation, the method further includes receiving updated information about the compression model.

[0028] For the technical effects of the second aspect or various alternative implementation methods, please refer to the introduction of the technical effects of the first aspect or corresponding implementation methods.

[0029] Thirdly, an apparatus is provided. The apparatus can be the first apparatus described in the first aspect above. The apparatus possesses the functions of the first apparatus described above. For example, the apparatus is capable of implementing the functions described in the first aspect above. For instance, the apparatus includes modules, units, or means corresponding to performing the operations involved in the first aspect above. These modules, units, or means can be implemented through software, hardware, or a combination of software and hardware. The apparatus is, for example, a terminal device, or other device including terminal device functions, or a chip system (or chip or circuit) or other functional module capable of implementing the functions of a terminal device, and is, for example, disposed in a terminal device. Alternatively, the apparatus is, for example, a network device, or other device including network device functions, or a chip system (or chip or circuit) or other functional module capable of implementing the functions of a network device, and is, for example, disposed in a network device. The network device includes, for example, one or more of the following: core network equipment, access network equipment, SU, CU, DU, or RU. In an optional implementation, the apparatus includes a baseband device and a radio frequency device. In another optional implementation, the device includes a processing unit (sometimes also called a processing module) and a transceiver unit (sometimes also called a transceiver module). The transceiver unit is capable of both sending and receiving functions. When the transceiver unit performs the sending function, it can be called a sending unit (sometimes also called a sending module); when the transceiver unit performs the receiving function, it can be called a receiving unit (sometimes also called a receiving module). The sending unit and the receiving unit can be the same functional module, which is called the transceiver unit and can perform both sending and receiving functions; alternatively, the sending unit and the receiving unit can be different functional modules, and the transceiver unit is a collective term for these functional modules.

[0030] In one optional implementation, the processing unit is configured to input first compression information into the decompression model to obtain second sensing information output by the decompression model, wherein the first compression information is related to the first sensing information, the first sensing information includes information indicating a first power corresponding to at least one spatial location, and the second sensing information includes information indicating a second power corresponding to the at least one spatial location; the processing unit is further configured to update the parameters of the compression model and / or the decompression model according to a loss function, wherein the loss function includes a distortion function between the first sensing information and the second sensing information, the distortion function being related to the power difference corresponding to the at least one spatial location and the maximum first power indicated by the first sensing information, wherein the power difference corresponding to one of the at least one spatial locations refers to the difference between the first power and the second power.

[0031] In an alternative embodiment, the device further includes a storage unit (sometimes also called a storage module), the processing unit being coupled to the storage unit and executing programs or instructions in the storage unit to enable the device to perform the functions of the first device described in the first aspect above.

[0032] Fourthly, an apparatus is provided. The apparatus can be the second apparatus described in the second aspect above. The apparatus possesses the functions of the second apparatus described above. For example, the apparatus is capable of implementing the functions described in the second aspect above. For instance, the apparatus includes modules, units, or means corresponding to the operations described in the second aspect above. These modules, units, or means can be implemented in software, hardware, or a combination of software and hardware. The apparatus is, for example, a network device, or other device including network device functions, or a chip system (or chip or circuit) or other functional module capable of implementing the functions of a network device. This chip system or functional module is, for example, disposed within a network device. The network device includes, for example, core network equipment and / or access network equipment. Optionally, the network device includes, for example, one or more of the following: core network equipment, access network equipment, SU, CU, DU, or RU. In one optional implementation, the apparatus includes a baseband device and a radio frequency device. In another optional implementation, the apparatus includes a processing unit (sometimes also called a processing module) and a transceiver unit (sometimes also called a transceiver module). For details on the implementation of the send / receive unit, please refer to the relevant introduction in the third section.

[0033] In one optional implementation, the transceiver unit (or the transmitting unit) is configured to transmit first compressed information, the first compressed information being used to determine a loss function, wherein the loss function includes a distortion function between first sensing information and second sensing information, the first sensing information and the second sensing information being related to the first compressed information, the first sensing information including information indicating a first power corresponding to at least one spatial location, the second sensing information including information indicating a second power corresponding to the at least one spatial location, the distortion function being related to the power difference corresponding to the at least one spatial location and the maximum first power included in the first sensing information, wherein the power difference corresponding to one of the at least one spatial locations refers to the difference between the first power and the second power.

[0034] In an alternative embodiment, the device further includes a storage unit (sometimes also called a storage module), the processing unit being coupled to the storage unit and executing programs or instructions in the storage unit to enable the device to perform the functions of the second device described in the second aspect above.

[0035] Fifthly, an apparatus is provided, the apparatus comprising a memory and one or more processors. The memory is used to store part or all of a computer program or instructions necessary for implementing the functions involved in the first or second aspect described above. The one or more processors are executable to carry out the computer program or instructions, such that, when executed, the apparatus implements the methods in any possible design or implementation of the first or second aspect described above.

[0036] In one possible design, the device may further include interface circuitry, wherein the processor is configured to communicate with other devices or components via the interface circuitry.

[0037] In one possible design, the device may also include the memory.

[0038] The aforementioned device may be a terminal, or a communication module in the terminal, or a chip in the terminal responsible for communication functions such as a modem chip (also known as a baseband chip) or a SoC or SIP chip containing a modem module.

[0039] A sixth aspect provides an apparatus comprising a memory and one or more processors. The memory is used to store part or all of a computer program or instructions necessary for implementing the functions described in the first or second aspect above. The one or more processors are executable to carry out the computer program or instructions, such that, when executed, the apparatus implements the methods in any possible design or implementation of the first or second aspect above.

[0040] In one possible design, the device may further include interface circuitry, wherein the processor is configured to communicate with other devices or components via the interface circuitry.

[0041] In one possible design, the device may also include the memory.

[0042] The aforementioned device may be a network device, a communication module in a network device, or a chip in a network device that is responsible for communication functions, such as a modem chip (also known as a baseband chip) or a SoC or SIP chip that contains a modem module.

[0043] A seventh aspect provides a communication system including a first device. The first device is configured to perform the method described in the first aspect. For example, the first device can be implemented using the devices described in the third, fifth, or sixth aspects.

[0044] Optionally, the communication system further includes a second device for performing the method described in the second aspect above. For example, the second device can be implemented using the device described in the fourth, fifth, or sixth aspects.

[0045] Eighthly, a computer-readable storage medium is provided for storing a computer program or instructions that, when executed, cause the method performed by the first or second means in the preceding aspects to be implemented.

[0046] Ninthly, a computer program product containing instructions is provided, which, when the computer program or instructions are run on a computer, causes the methods described in the above aspects to be implemented.

[0047] In a tenth aspect, a chip system is provided, including a processor and an interface, the processor being configured to call and execute instructions from the interface to enable the chip system to implement the methods described above. Attached Figure Description

[0048] Figure 1 is a schematic diagram of a structure of an access network device in an embodiment of this application;

[0049] Figure 2A is a schematic diagram of a single-station sensing mode;

[0050] Figure 2B is a schematic diagram of the dual-station sensing mode;

[0051] Figures 3 and 4 are schematic diagrams of two application scenarios of the embodiments of this application;

[0052] Figures 5 and 7 are flowcharts of two sensing methods provided in the embodiments of this application;

[0053] Figure 6 is a schematic diagram of the compression model and decompression model in the embodiments of this application;

[0054] Figure 8 is a schematic diagram of at least one spatial location in an embodiment of this application;

[0055] Figure 9 is a schematic diagram of a device provided in an embodiment of this application;

[0056] Figure 10 is a schematic diagram of another device provided in an embodiment of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0058] In this application embodiment, the number of nouns, unless otherwise specified, refers to "singular nouns or plural nouns," that is, "one or more." "At least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. For example, A / B means: A or B. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c means: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0059] The ordinal numbers such as "first" and "second" mentioned in the embodiments of this application are used to distinguish multiple objects, and are not used to limit the size, content, order, timing, priority, or importance of the multiple objects. Furthermore, the numbering of steps in the various embodiments described in this application is only to distinguish different steps and is not used to limit the order in which the steps are performed.

[0060] The following explanations of some terms or concepts used in the embodiments of this application are provided to facilitate understanding by those skilled in the art.

[0061] In this embodiment, the terminal device is a device with wireless transceiver capabilities, which can be a fixed device, a mobile device, a handheld device (e.g., a mobile phone), a wearable device, an in-vehicle device, or a wireless device (e.g., a communication module, a modem, or a chip system, etc.) built into the aforementioned devices. The terminal device is used to connect people, objects, machines, etc., and can be widely used in various scenarios, including but not limited to the following: sensing scenarios, cellular communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine / machine-type communications (M2M / MTC) communication, Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, self-driving, remote medical care, smart grid, smart furniture, smart office, smart wearables, smart transportation, smart city, drones, robots, and indoor commercial scenarios (such as mobile phone screen mirroring, file sharing, and mobile phone to VR glasses video transmission). When the terminal equipment is applied to V2X, it can also be called a V2X device, such as a smart car, digital car, unmanned car, driverless car, pilotless car, or automobile, self-driving car, or autonomous car, pure electric vehicle (EV), hybrid electric vehicle (HEV), range-extended electric vehicle (REEV), plug-in hybrid electric vehicle (PHEV), new energy vehicle, or roadside unit (RSU). The terminal equipment can also be a device used in D2D communication, such as an electricity meter or water meter.

[0062] Furthermore, in this embodiment, the terminal device can also be a terminal device in an Internet of Things (IoT) system. IoT is an important component of the future development of information technology. Its main technical feature is to connect objects to the network through communication technology, thereby realizing an intelligent network of human-machine interconnection and object-to-object interconnection.

[0063] The various terminal devices described above, if located in a vehicle (e.g., placed inside or installed inside a vehicle), can all be considered in-vehicle terminal devices, also known as on-board units (OBUs). The terminal device of this application can also be an in-vehicle module, in-vehicle component, in-vehicle chip, or in-vehicle unit built into a vehicle as one or more components or units. The vehicle can implement the methods of this application through the built-in in-vehicle module, in-vehicle component, in-vehicle chip, or in-vehicle unit.

[0064] The terminal equipment may sometimes be referred to as UE, terminal, access station, UE station, remote station, wireless communication equipment, or user equipment, etc.

[0065] In this application embodiment, the device for implementing the terminal device function can be a terminal device, which can be a terminal device or a device capable of supporting the terminal device in implementing the function, such as a chip system. This device can be installed in the terminal device. In the technical solutions provided in this application embodiment, the example of a terminal device being used to implement the terminal device function is used to describe the technical solutions provided in this application embodiment.

[0066] The network devices in this application embodiment include, for example, access network devices (or access network elements) and / or core network devices (or core network elements). The access network devices are devices with wireless transceiver capabilities, used to communicate with the terminal devices. The access network devices include, but are not limited to, base stations (base transceiver stations, BTS, Node B, evolved Node B (eNodeB) / eNB, or the next generation Node B (gNodeB) / gNB), transmission reception points (TRPs), base stations evolved from the 3rd generation partnership project (3GPP), access nodes in wireless fidelity (Wi-Fi) systems, wireless relay nodes, wireless backhaul nodes, etc. The base stations can be: macro base stations, micro base stations, pico base stations, small cells, relay stations, etc. Multiple base stations can support networks using the same access technology or networks using different access technologies. A base station can contain one or more co-located or non-co-located transmission and reception points. The access network equipment can also be a radio controller, centralized unit (CU), and / or distributed unit (DU) in a cloud radio access network (CRAN) scenario. The access network equipment can also be a server, etc. For example, the network equipment in V2X technology can be a roadside unit (RSU). The following description uses a base station as an example to illustrate the access network equipment. A base station can communicate with a terminal device, or it can communicate with a terminal device through a relay station. A terminal device can communicate with multiple base stations in different access technologies. The core network equipment is used to implement functions such as mobility management, data processing, session management, policy and billing. The names of the equipment implementing core network functions may differ in systems using different access technologies; this application does not limit this.Taking the 5th generation (5G) mobile communication technology system as an example, the core network equipment includes, for example, access and mobility management function (AMF), session management function (SMF), policy control function (PCF), or user plane function (UPF), etc.

[0067] In a CU-DU architecture, or in an open RAN (ORAN) system, access network equipment may include one or more logical network elements such as a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). CUs and DUs may be separate entities or included in the same network element, such as a baseband unit (BBU). RUs may be included in radio equipment or radio units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0068] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called an open CU (O-CU), DU can also be called an open DU (O-DU), CU-CP can also be called an open CU-CP (O-CU-CP), CU-UP can also be called an open CU-UP (O-CU-CP), and RU can also be called an open RU (O-RU). For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples in its embodiments. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in the embodiments of this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0069] The CU and DU can be configured according to the protocol layer functions of the wireless network they implement. For example, the CU can be configured to implement the functions of the Packet Data Convergence Protocol (PDCP) layer and above (such as the Radio Resource Control (RRC) layer and / or the Service Data Adaptation Protocol (SDAP) layer); the DU can be configured to implement the functions of protocol layers below the PDCP layer (such as one or more of the Radio Link Control (RLC) layer, Media Access Control (MAC) layer, or Physical (PHY) layer). As another example, the CU can be configured to implement the functions of protocol layers above the PDCP layer (such as the RRC and / or SDAP layers), and the DU can be configured to implement the functions of protocol layers below the PDCP layer (such as one or more of the RLC, MAC, or PHY layers).

[0070] The above CU and DU configurations are merely examples; the functions of the CU and DU can be configured as needed. For instance, the CU or DU can be configured to have more protocol layer functions, or only some protocol layer processing functions. For example, some RLC layer functions and protocol layer functions above the RLC layer can be placed in the CU, while the remaining RLC layer functions and protocol layer functions below the RLC layer can be placed in the DU. Furthermore, the functions of the CU or DU can be divided according to service type or other system requirements, such as by latency. Functions that require low latency can be placed in the DU, while functions that do not require low latency can be placed in the CU.

[0071] DU and RU can cooperate to implement the functions of the PHY layer. A DU can be connected to one or more RUs. The functions of DU and RU can be configured in various ways depending on the design. For example, a DU can be configured to implement baseband functions, and an RU can be configured to implement mid-RF functions. Another example is that a DU can be configured to implement higher-level functions in the PHY layer, and an RU can be configured to implement lower-level functions in the PHY layer, or to implement both lower-level and RF functions. Higher-level functions in the physical layer can include a portion of the physical layer's functions that are closer to the MAC layer, while lower-level functions in the physical layer can include another portion of the physical layer's functions that are closer to the mid-RF side.

[0072] In this embodiment, the access network device can implement sensing functions. One possible structure of the access network device is shown in Figure 1, which can also be considered as the structure of a sensing-fusion access network device. In this structure, a module for implementing sensing-related functions is introduced into the access network device, for example, called a sensing unit (SU), with no limitation on the name. The SU can be a functional module or entity within the access network device; alternatively, the SU can be a functional module or entity located outside the access network device. Figure 1 shows an example where the SU is located within the access network device. The SU can be connected to a first core network element (directly or indirectly) to interact with sensing requirements. The first core network element includes, for example, a sensing function (SF) element, also simply referred to as a sensing element. The SU can also be connected to a second core network element or a CU / DU / RU to transmit sensing-related information or data. The second core network element includes, for example, an AMF and / or a UPF.

[0073] When a UE reports perception information to an access network device (which can be understood as the UE reporting perception information to a Subsystem Unit (SU) within the access network device), this perception information can be transmitted from the UE to the DU, then from the DU to the CU, and from the CU to the SU; alternatively, the perception information can be transmitted from the UE to the DU, and then directly from the DU to the SU; or the perception information can be directly transmitted from the UE to the SU via the s-Uu interface, where the s-Uu interface is the communication interface between the UE and the SU. When an access network device transmits perception information to a core network element (e.g., from the access network device to the SF), the SU can transmit this perception information to the SF via the AMF or UPF, or the CU can transmit this perception information to the SF via the AMF or UPF, or the SU or CU can directly transmit this perception information to the SF.

[0074] In this application embodiment, the apparatus for implementing the functions of a network device can be referred to as a network apparatus. This network apparatus can be a network element, a network device, or an apparatus capable of supporting the network device or network element in implementing the function, such as a chip system. This apparatus can be installed within the network device. In the technical solutions provided in this application embodiment, the apparatus for implementing the functions of a network device is described as a network device (for example, the apparatus for implementing the functions of an access network device is an access network device, and the apparatus for implementing the functions of a core network device is a core network device).

[0075] A sensing signal is a signal used to sense (or detect) a target (or object). Sensing signals can also be called detection signals, linear frequency modulated signals, radar signals, radar sensing signals, radar detection signals, or environmental sensing signals, etc. Sensing signals can be pulse signals or signals from wireless communication systems. For example, a sensing signal can be an orthogonal frequency division multiplexing (OFDM) signal obtained by modulating a specific sequence on a subcarrier. This specific sequence can be any of the following sequences: Zadoff-Chu sequence (ZC sequence), pseudo-random sequence, or predefined sequence. Pseudo-random sequences include any of the following sequences: longest linear feedback shift register sequence (m-sequence) or Gold sequence. Predefined sequences can be, for example, random data symbols, such as random data symbols modulated by quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM).

[0076] An echo signal is a signal generated when a sensed signal is reflected by a target. Both the echo signal and the sensed signal can reflect the parameters of the target. For example, the time delay of the echo signal relative to the sensed signal can reflect the distance of the target relative to the transmitter, and the Doppler shift of the echo signal relative to the sensed signal can reflect the velocity of the target.

[0077] Communication-sensing fusion signals, also known as synthetic-sensing fusion signals, synthetic signals, or integrated synthetic-sensing signals, are signals used for both communication and sensing. When used for communication, the fusion signal carries the communication data or reference signal sequence that needs to be transmitted between communication devices. When used for sensing, the fusion signal can be understood as being used to sense (or detect) targets.

[0078] For example, the signals used for sensing described herein may include sensing signals and / or synesthetic fusion signals, etc.

[0079] The target can be any tangible object in the environment that can reflect electromagnetic waves, such as mountains, forests, or buildings, and can also include mobile objects such as vehicles, drones, pedestrians, and terminal devices. The target can also be referred to as a sensing target, detection target, perceived target, detected target, perceived object, detected object, or sensed device, etc., and the embodiments of this application do not limit it.

[0080] For sensing, based on the different senders and receivers of the sensing signal, sensing modes can be divided into two types: single-site sensing and dual-site sensing. Single-site sensing mode, also known as self-transmitting and self-receiving mode, single-end sensing mode, or monocentric sensing mode, refers to the same device transmitting the sensing signal and receiving the echo signal reflected from the target, as shown in Figure 2A, where both the transmitting and receiving devices are device 1. Dual-site sensing mode, also known as A-transmitting and B-receiving mode or self-transmitting and other-receiving mode, refers to different devices transmitting the sensing signal and receiving the echo signal reflected from the target, as shown in Figure 2B, where the transmitting device is device 2 and the receiving device is device 3. Figures 2A and 2B both use a vehicle as an example of the sensing target. For example, in Figure 2A, device 1 is a base station or UE. In single-site sensing mode, device 1 transmits the sensing signal, and device 1 receives the echo signal generated by the reflection, scattering, or diffraction of the sensing signal by a sensing target in the environment (e.g., the vehicle in Figure 2A) for environmental sensing. For example, in Figure 2B, device 2 is a base station or UE, and device 3 is a base station or UE. In the dual-site sensing mode, device 1 sends a sensing signal, and device 2 receives the echo signal generated by the reflection, scattering, or diffraction of the sensing signal by scatterers in the environment (such as vehicles in Figure 2B) to perform environmental sensing. The sensing scenario in which the UE participates can also be called the UE-assisted sensing scenario.

[0081] This application embodiment can train compression and decompression models, thereby compressing the first sensing information using the compression model. The compressed first sensing information has a smaller data volume compared to the uncompressed first sensing information, thus reducing transmission overhead. The first sensing information may include a first power corresponding to at least one spatial location. The loss function used for training in this application embodiment is related to the power difference corresponding to the at least one spatial location and the maximum power indicated by the first sensing information. For example, among the first powers corresponding to the at least one spatial location, the larger the first power, the smaller the error caused by compression, making the receiver more accurate in recovering larger first powers. This makes the determined location of the sensing target more accurate, which is beneficial to improving sensing accuracy.

[0082] Referring to Figure 3, which is a schematic diagram of a potential sensing network architecture, Figure 3 is based on a 5G core network (5G core, 5GC). The network architecture shown in Figure 3 can also be an application scenario of an embodiment of this application.

[0083] In the architecture shown in Figure 3, a new SF (Sensing Provider) has been added. This SF can be a device or component providing network sensing capabilities, also known as a sensing management function (SMF), or other names. The SF can be deployed on the core network side or the RAN side; Figure 3 shows an example of deployment in the core network. In the network architecture shown in Figure 3, the SF can reuse the interfaces between the location management function (LMF) and other 5GC network elements such as the AMF, network exposure function (NEF), unified data management (UDM), network data analytics function (NWDAF), and PCF for sensing interaction. Sensing signaling between the SF and the radio access network (RAN) or UE can be transmitted through the AMF; sensing measurement data acquired by the RAN or UE can be transmitted to the SF via the control plane, for example, by using the reused long term evolution (LTE) positioning protocol (LPP) or new radio (NR) positioning protocol annex (NRPPa) protocol, or it can be transmitted through the user plane, forwarded to the SF via the UPF, or directly transmitted to the SF.

[0084] The newly added SF in this network architecture can realize basic sensing functions, such as sensing authorization, sensing control, sensing measurement data processing, or result output. Specifically, interfaces are set up and interaction is established between the SF and 5GC network elements such as AMF, NEF, UDM, NWDAF, PCF, LMF, and UPF, as defined below.

[0085] NS1: A new interface between SF and AMF, which can transmit sensing and control signaling. Additionally, this interface can also transmit sensing measurement data in scenarios where sensing measurement data is uploaded to the control plane.

[0086] NS2: A new interface between SF and NEF. This interface can transmit signaling messages between sensing network elements relayed through NEF and application functions (AF) on the service side, and at the same time open the sensing results to the AF.

[0087] NS3: A new interface between SF and UDM. This interface can be used for authentication or authorization, and to obtain UE-aware subscription information, service AMF information, or other information.

[0088] NS4: A new interface between SF and NWDAF. Through this interface, SF and NWDAF can jointly complete artificial intelligence (AI) processing related to perception services.

[0089] NS5: A new interface between SF and PCF. Through this interface, SF can transmit information such as sensing requirements, quality of service (QoS) requirements, or sensing results of sensing services to PCF. PCF can then make decisions to generate policy control and charging (PCC) policies related to sensing services.

[0090] NS6: A new interface between SF and LMF. Through this interface, SF can obtain location-related information, such as the sensing area, the RAN information of the sensing target, and the location information of the sensed UE.

[0091] NS7: A new interface between SF and UPF. Sensing measurement data can be directly transmitted from (R)AN to SF via UPF, or indirectly forwarded to SF via UPF. In scenarios where (R)AN performs sensing, forwarding via UPF can improve the functionality of UPF to support data transmission at the (R)AN granularity.

[0092] In addition to the newly added interfaces mentioned above, existing interfaces (such as N1, N2, N5, N8, N33, etc.) can also support the transmission of information related to sensing services, such as authentication information, sensing service type, sensing service quality requirements, sensing measurement data, or sensing results, etc.

[0093] Figure 3 illustrates an example where the SF (Sensitive Detection) is an independent device. Alternatively, the SF and LMF (Local Management Detector) can be co-located, meaning the network element handling sensing services and the network element handling location services can be the same. Alternatively, the SF can be co-located with other core network elements, such as the AMF (Auxiliary Location Detector). The LMF is the core network element in the 5GC that provides control plane positioning. It can calculate and feedback location information in the 5G network, providing functions such as positioning process management, UE capability acquisition, auxiliary data provision, and UE location estimation. Optionally, if the SF and LMF are co-located, the LMF and the gateway mobile location center (GMLC) can be functionally enhanced to support basic sensing functions. The GMLC can be the first network element within the operator's network to process sensing requests, performing privacy checks or authorization functions, routing sensing requests to the AMF, or performing LMF selection, etc.

[0094] For example, if the SF and LMF are co-located, an additional interface can be added between the LMF and GMLC to transmit information related to awareness services, such as adding an NL9 interface. Additionally, interfaces related to the LMF and GMLC (such as one or more of the following: NL1 interface between AMF and LMF, NL2 interface between AMF and GMLC, NL5 interface between NEF and GMLC, or NL6 interface between UDM and GMLC) can also support the transmission of information related to awareness services, as detailed below.

[0095] N33: The interface between AF and NEF, through which information such as the type of sensing business, business requirements, and sensing results can be transmitted.

[0096] NL5: The interface between NEF and GMLC, through which information such as the type of sensing business, business requirements, and sensing results can be transmitted.

[0097] NL6: The interface between GMLC and UDM, through which privacy inspection data can be transferred.

[0098] NL2: The interface between NEF and AMF, through which information such as the perceived business type, business requirements, and perceived results can be transmitted.

[0099] NL1: The interface between AMF and LMF, through which information such as perceived business type, business requirements, and perceived results can be transmitted.

[0100] NL9: A new interface between GMLC and LMF, through which information such as the type of sensing business, business requirements, and sensing results can be transmitted.

[0101] Referring again to Figure 4, which is a schematic diagram of another potential sensing network architecture based on 5GC, the network architecture shown in Figure 4 can also be another application scenario of the embodiments of this application.

[0102] In the network architecture shown in Figure 4, the SF (Sensitive Detection) is relatively independent of the existing core network elements. The SF does not need to interact with the core network elements, or only needs to perform minimal interaction. For scenarios where there is only a sensing requirement within a specific area, or scenarios where there is only a sensing requirement, this network architecture can provide sensing services without requiring 5GC control or only requiring some network elements to participate in control. Furthermore, by deploying the SF locally, sensing measurement data or results can remain within the campus, thus meeting the enterprise's requirements for the security and privacy of sensing measurement data or results, and reducing sensing latency. This network architecture is relatively simple, flexible, efficient, has few transmission nodes, and is easy to deploy. Optionally, this network architecture can support UE-related sensing requirements, and implementation schemes for functions such as authorization, mobility management, and billing can be considered as needed.

[0103] In this network architecture, the SF can directly establish a connection with the RAN node. Control plane sensing signaling and user plane sensing measurement data can be transmitted via the newly defined interface NS1. When the UE participates in sensing, control plane signaling can be forwarded to the SF via the AMF, and sensing measurement data can be transmitted via NS1. Furthermore, there can also be an interface between the SF and 5GC network elements (such as AMF, NEF, or NWDAF) to control the AF to provide sensing service requirements to the SF through core network functions. The interface between the SF and 5GC network elements is described below.

[0104] NS1: A new interface between the SF and (R)AN, which can transmit sensing control signaling or sensing measurement data. In one implementation, the SF can also be deployed on the RAN side; for example, the SF can be co-located with access network equipment (e.g., a base station), or the SF can be a standalone device within the access network.

[0105] NS2: A new interface that may be added between SF and AMF. This interface can receive awareness service requirements from UE, or transmit signaling between SF and other network elements in the core network, such as transmitting interaction messages between SF and UDM.

[0106] NS3: A potential new interface between SF and NEF. This interface can transmit signaling between SF and the service-side AF via NEF, and can also expose the sensing results to the AF. The interaction between SF and AF may not go through NEF. In actual deployment, NS2 and NS3 may be chosen as one of the two options. That is, the AF can send sensing service requests indirectly to SF or directly to SF (without NEF) via NS2 (NEF); or, the AF can send sensing service requests to SF via N33 (NEF) and NS2 (AMF).

[0107] NS4: A potential new interface between SF and NWDAF, through which SF and NWDAF can jointly perform intelligent analysis and prediction to generate perception results.

[0108] The technical solutions provided in this application can be applied to fourth-generation (4G) mobile communication systems, such as LTE systems, or to 5G systems, such as NR systems, or to next-generation mobile communication systems or other similar communication systems, such as future communication systems, or to existing satellite mobile communication technology systems. No specific limitations are imposed. For example, Figures 3 and 4 are based on 5G. In addition, SF can also be deployed in other networks, such as 6G networks, or other future communication networks.

[0109] The embodiments of this application can be applied to the scenarios shown in Figure 2B, Figure 3 or Figure 4, or they can also be used in other scenarios, such as any scenario involving sensing services.

[0110] The method provided in the embodiments of this application is described below with reference to the accompanying drawings. In various embodiments of this application, the signal used to implement the sensing function or sensing service is transmitted through reflection, scattering, or diffraction. The sensing device (e.g., a network device) can determine the relevant characteristics of the sensing target based on the received signal. For example, it can estimate the time delay, Doppler, or angular spectrum information based on the received signal to determine information such as the distance, angle, power, or speed of the sensing target. Optionally, the network device can also send measurement results to the sensing network element, such as the point cloud information, distance, angle, or speed of the sensing target. In the accompanying drawings corresponding to the various embodiments of this application, all steps indicated by dashed lines are optional steps.

[0111] The various embodiments described herein can train models (e.g., including compression and / or decompression models). After training, the trained models can be used to transmit sensing information. This transmission process can be applied to the network architecture shown in Figures 2B, 3, or 4. For example, the transmission process of sensing information can be completed by a first device and a second device. For example, the first device can be device 2 shown in Figure 2B, and the second device can be device 3 shown in Figure 2B. Another example is that the first device can be (R)AN shown in Figure 3 or 4, or a SU or CU included in (R)AN; the second device can be UE shown in Figure 3 or 4. Yet another example is that the first device can be SF shown in Figure 3 or 4; the second device can be UE shown in Figure 3 or 4.

[0112] Furthermore, the training process of this model can be applied to the network architectures shown in Figures 2A, 2B, 3, or 4. For example, the model training process can be completed by a first device, such as device 1 shown in Figure 2A, device 2 shown in Figure 2B, (R)AN shown in Figure 3 or 4, or SU or CU included in (R)AN, or SF shown in Figure 3 or 4. Alternatively, the model training process can be completed by a first device and a second device, such as device 2 shown in Figure 2B and device 3 shown in Figure 2B; or the first device can be (R)AN shown in Figure 3 or 4, or SU or CU included in (R)AN, and the second device can be UE shown in Figure 3 or 4; or the first device can be SF shown in Figure 3 or 4, and the second device can be UE shown in Figure 3 or 4.

[0113] This application provides a perception method, as shown in Figure 5, which is a flowchart of the method. In the embodiment shown in Figure 5, the model training process can be completed by a single node, such as by a first device. The training method provided in this application embodiment is, for example, an offline training method.

[0114] S501, the first device inputs first compression information into the decompression model and obtains second sensing information. The second sensing information is, for example, the output information of the decompression model.

[0115] The decompression model is the model to be trained. The decompression model and the compression model can be a pair, capable of compressing and decompressing perceptual information. For example, the decompression model and / or the compression model can be the models to be trained. The compression model can be, for example, an artificial intelligence (AI) model, with a structure such as a convolutional autoencoder or a generative adversarial neural network. The decompression model can also be an AI model, with a structure such as a convolutional autoencoder or a generative adversarial neural network. The structure of the compression model can be the same as or different from the structure of the decompression model. The compression model can be used to extract feature vectors or feature value codebooks of perceptual information (e.g., first perceptual information) to compress the first perceptual information; for example, the first compressed information may include the feature vectors or feature value codebooks. The decompression model can be used to inversely transform the feature vectors or feature value codebooks into perceptual information (e.g., second perceptual information), effectively decompressing the first compressed information.

[0116] Please refer to Figure 6, which shows an example of the compression and decompression models. The model shown in Figure 6 is, for example, a convolutional autoencoder. In Figure 6, the compression model may include convolutional layers and activation functions, and the decompression model may also include convolutional layers and decompression functions. However, the positions of the convolutional layers and activation functions differ in the compression and decompression models; therefore, Figure 6 uses the structural differences between the compression and decompression models as an example. The compression model compresses perceptual information to obtain compressed information. This process can be a dimensionality reduction process of the perceptual information, so the number of parameters used to represent the convolutional layers and activation functions in the compression model gradually decreases. The decompression model recovers the compressed information to obtain perceptual information. This process can be an dimensionality increase process of the compressed information, so the number of parameters used to represent the convolutional layers and activation functions in the decompression model gradually increases. The input information of the compression model is, for example, first perceptual information. The compression model compresses the first perceptual information to obtain first compressed information. The first compressed information can be used as the input information of the decompression model. The decompression model processes the first compressed information to obtain the second perceptual information.

[0117] In S501, the first device can input the first compressed information into the decompression model. Alternatively, the first device can input the quantized first compressed information into the decompression model. For example, after obtaining the first compressed information, the first device can quantize the first compressed information and then input the quantized first compressed information into the decompression model. Alternatively, the first device can input the first compressed information after arithmetic encoding and decoding. For example, after obtaining the first compressed information, the first device can quantize the first compressed information, then perform arithmetic encoding and decoding on the quantized first compressed information, and then input the arithmetic-encoded and decoded first compressed information into the decompression model. Another example is that after obtaining the first compressed information, the first device can perform arithmetic encoding and decoding on the first compressed information, and then input the arithmetic-encoded and decoded first compressed information into the decompression model. By quantizing and / or arithmetic encoding and decoding the first compressed information, the data volume of the first compressed information can be further reduced.

[0118] Optionally, the method may further include S502, in which the first device obtains first compression information. The first compression information is related to the first sensing information. S502, for example, occurs before S501.

[0119] One possible way for the first device to obtain the first compressed information includes inputting the first sensing information into a compression model to obtain the first compressed information, as exemplified by S502 in Figure 5. The first compressed information may be, for example, the output information of the compression model. Optionally, in this method, the first sensing information may not be the sensing information obtained by the first device from actually measuring the signal used to achieve the sensing function, but rather a training sample obtained by the first device. For example, the first device may store multiple training samples, or the first device may obtain multiple training samples from other devices, and the first sensing information may be, for example, one of these multiple training samples.

[0120] The first sensing information, also known as sensing imaging information, power spectrum, or sensing power spectrum, may include at least one first indication information, each of which indicates a first power corresponding to a spatial location. This can be understood as the first sensing information including information indicating a first power corresponding to at least one spatial location (i.e., including at least one first indication information). For example, each first indication information may indicate a correspondence between a spatial location and a first power. Optionally, the spatial location may be represented by any two parameters selected from distance, horizontal angle, or vertical angle. That is, each of the at least one spatial location may correspond to any two parameters selected from a certain distance, a certain horizontal angle, or a certain vertical angle, and each spatial location may also correspond to a first power. Taking the spatial location as represented by three parameters—distance, horizontal angle, and vertical angle—as an example, the first sensing information may also be called a three-dimensional power spectrum or three-dimensional sensing power spectrum. Here, the distance may refer to the distance between the spatial location and the first or second device.

[0121] The first perceptual information is, for example, a matrix, and the first compressed information includes, for example, the eigenvectors or eigenvalue codebook of the matrix.

[0122] Each of the at least one spatial location is considered, for example, as a location grid, where each location grid may have a corresponding index or number, and the first sensing information includes at least one first indication information, where each first indication information may include a correspondence between a spatial location index and a first power.

[0123] In the at least one spatial location, the first power corresponding to different spatial locations may be the same or different. The larger the first power corresponding to a certain spatial location, the greater the power of the signal used to realize the sensing function reflected or scattered through that spatial location, and the greater the probability that a sensing target exists at that spatial location; or, the smaller the first power corresponding to a certain spatial location, the smaller the power of the signal used to realize the sensing function reflected or scattered through that spatial location, and the smaller the probability that a sensing target exists at that spatial location.

[0124] The second sensed information can be considered as recovered information of the first compressed information. For example, if the first sensed information is a power spectrum, then the second sensed information can also be a power spectrum, such as being called a recovered power spectrum. The second sensed information may include at least one second indication information, where each second indication information can indicate a second power corresponding to a spatial location. It can be understood that the second sensed information may include information indicating a second power corresponding to at least one spatial location (i.e., including at least one second indication information). For example, each second indication information can indicate a correspondence between a spatial location and a second power. Optionally, the spatial location can be represented by any two parameters among distance, horizontal angle, or vertical angle, that is, each of the at least one spatial location can correspond to any two parameters among a certain distance, a certain horizontal angle, or a certain vertical angle, and each spatial location can also correspond to a second power. Taking the spatial location as represented by the three parameters of distance, horizontal angle, and vertical angle as an example, the second sensed information can also be called a three-dimensional recovered power spectrum or a three-dimensional sensed recovered power spectrum, etc. Here, the distance can refer to the distance between the spatial location and the first device or the second device. The parameters used to characterize the spatial location of the second perceived information can be the same as those used to characterize the spatial location of the first perceived information. For example, the spatial location indicated by the first perceived information can be represented by three parameters: distance, horizontal angle, and vertical angle. Similarly, the spatial location indicated by the second perceived information can also be represented by three parameters: distance, horizontal angle, and vertical angle.

[0125] In the absence of compression error, the first and second sensing information can theoretically be identical; for example, the first and second powers corresponding to each spatial location in at least one spatial location may be the same. However, due to factors such as compression error, the first and second sensing information may be the same, completely different, or not completely identical. For example, the first and second powers corresponding to each spatial location in some or all of the at least one spatial location may be different.

[0126] S503, The first device updates the parameters of the compression model and / or the decompression model according to the loss function.

[0127] The first device can calculate a loss function based on the first and second sensing information, and then update the parameters of the compression model and / or the parameters of the decompression model based on the loss function.

[0128] The loss function may include a distortion function between the first and second sensing information. This distortion function may be correlated with the power difference corresponding to the at least one spatial location and with the maximum power indicated by the first sensing information. The power difference corresponding to any one of the at least one spatial location may refer to the difference between the first power and the second power corresponding to that spatial location. The first power corresponding to that spatial location is included in the first sensing information, and the second power corresponding to that spatial location is included in the second sensing information. The maximum power indicated by the first sensing information refers to the largest value among the at least one first power indicated by the first sensing information corresponding to the at least one spatial location.

[0129] The distortion function is related to the maximum power indicated by the first sensing information. For example, one implementation includes weighting coefficients corresponding to the at least one spatial location, and these weighting coefficients are related to the maximum power indicated by the first sensing information. For instance, each of the at least one spatial location may correspond to a weighting coefficient, so the distortion function may include at least one weighting coefficient, and each of these weighting coefficients may be related to the maximum power indicated by the first sensing information. The distortion function may also be related to the power difference corresponding to the at least one spatial location. For example, if the distortion function includes the power difference corresponding to each of the at least one spatial location, then the distortion function may include at least one power difference. Optionally, in the distortion function, the at least one power difference and the at least one weighting coefficient may have a one-to-one correspondence. Each power difference can characterize the power error at the spatial location corresponding to that power difference, and the power error can be corrected through the weighting coefficients. Any of the at least one weighting coefficients may be greater than 0.

[0130] As an alternative implementation of this distortion function, the distortion function may be in the form of a weighted mean squared error (MSE). For example, this distortion function may satisfy the following relationship:

[0131] Among them, L d This represents the value of the distortion function, w. k p represents the weighting coefficient corresponding to the k-th spatial location among at least one spatial location. k This represents the first power (p) corresponding to the k-th spatial location. k (can be indicated by the first perceived information), p ′ k This represents the second power (p) corresponding to the k-th spatial location. ′ k(which can be indicated by the second sensing information), K represents the number of the at least one spatial position, where 0 < k < K. Among them, p ′ k -p k represents the power difference corresponding to the k-th spatial position.

[0132] The weighting coefficient corresponding to any one of the at least one spatial position can be related to the first power corresponding to this one spatial position indicated by the first sensing information, and related to the maximum power and / or minimum power indicated by the first sensing information. For example, the k-th spatial position among the at least one spatial position corresponds to the k-th weighting coefficient, and the k-th weighting coefficient can be related to the first power corresponding to this k-th spatial position indicated by the first sensing information, and related to the maximum power and / or minimum power indicated by the first sensing information. Among them, the minimum power indicated by the first sensing information refers to the first power with the smallest value among the at least one first power corresponding to the at least one spatial position indicated by the first sensing information.

[0133] As an optional implementation manner, the k-th weighting coefficient satisfies the following relationship, for example:

[0134] Among them, w k represents the k-th weighting coefficient. p k represents the first power corresponding to the k-th spatial position indicated by the first sensing information. p min represents the minimum power indicated by the first sensing information, and p max represents the maximum power indicated by the first sensing information.

[0135] According to Formula 2, it can be seen that if p k is larger, then w k is larger. That is, in this implementation manner, a larger weighting coefficient can be configured for the spatial position with a larger corresponding first power, thereby being able to reduce the power error corresponding to this spatial position and improve the recovery accuracy of the power corresponding to this spatial position. And the larger the first power corresponding to a certain spatial position, the greater the power of the signal used to implement the sensing function reflected or scattered by this spatial position, and the greater the probability that there is a sensing target at this spatial position. In the embodiments of the present application, the power error corresponding to the spatial position with a larger corresponding first power is reduced by the weighting coefficient, and the recovery accuracy of the power corresponding to this spatial position is improved, which helps to improve the accuracy of determining the position of the sensing target according to the power corresponding to this spatial position.

[0136] Alternatively, as another optional implementation, the value of the k-th weighting coefficient is, for example, the value corresponding to the power range of the first power corresponding to the k-th spatial location. This power range is, for example, called the first power range, and can be determined based on the maximum power indicated by the first sensing information and / or the minimum power indicated by the first sensing information. For example, multiple power ranges can be divided based on the maximum power indicated by the first sensing information and / or the minimum power indicated by the first sensing information, and the first power range can be one of these multiple power ranges. Each of the multiple power ranges can include at least one power, and each of the multiple power ranges can correspond to a weighting coefficient. For a given spatial location, the power range corresponding to that spatial location can be determined based on the first power indicated by the first sensing information, and the weighting coefficient corresponding to that power range is the weighting coefficient corresponding to that spatial location. For example, the power range to which the k-th weighting coefficient belongs can satisfy the following relationship:

[0137] Among them, w k Let p represent the k-th weighting coefficient. min p represents the minimum power indicated by the first perceived information. max This represents the maximum power indicated by the first perceived information. For example, if p k ≥p max If the value is -25, then the k-th weighting coefficient is 1. The various values ​​in Formula 3 are merely examples and are not limited to these in practice. Formula 3 uses a division of three power ranges as an example, but is not limited to this in practice.

[0138] In this embodiment, the loss function between the first perceived information and the second perceived information may consist only of the distortion function. For example, the loss function satisfies the following relationship: Loss = L d (Formula 4)

[0139] Where Loss represents the loss function, L d This represents the distortion function.

[0140] Alternatively, the loss function may include other information besides the distortion function. Optionally, the loss function may also include a compression ratio function between the first compressed information and the first perceived information. For example, if the number of bits in the first perceived information is A and the number of bits in the first compressed information is B, then the compression ratio function... Among them, L r This represents the compression ratio function. Alternatively, the compression ratio function can also be understood as the compression ratio function that entropy-encodes the first compressed information. For example, the loss function satisfies the following relationship: Loss = L d +βLr (Formula 5)

[0141] Where Loss represents the loss function, L d L represents the distortion function. r Let represent the compression ratio function, and β represent the scaling factor. β can be a real number.

[0142] The first device can update the parameters of the compression model and / or decompression model based on the loss function. For example, each time the first device obtains a piece of second sensing information, it can calculate the value of the loss function and update the parameters of the compression model and / or decompression model based on the value of the loss function.

[0143] Training ends when the loss function converges; alternatively, training ends when the first set of training iterations is reached, which can be a pre-configured maximum number of training iterations. This first set of iterations may be configured by the first device or predefined by the protocol. Optionally, after training, the first device can send updated compressed model information to the second device, such as the parameters of the updated compressed model. The second device can then store the updated compressed model to perform compression based on it. The first device can also store an updated decompression model to perform decompression based on it. For example, during perception, the first and second devices can utilize this compressed and decompressed model to compress and decompress perceived information, thereby saving on the transmission overhead of perceived information.

[0144] This application embodiment can train compression and decompression models, thereby compressing the first sensing information using the compression model. The compressed first sensing information has a smaller data volume compared to the uncompressed first sensing information, thus reducing transmission overhead. The first sensing information may include a first power corresponding to at least one spatial location. The loss function used for training in this application embodiment is related to the power difference corresponding to the at least one spatial location and the maximum power indicated by the first sensing information. For example, among the first powers corresponding to the at least one spatial location, the larger the first power, the smaller the error caused by compression, making the receiver more accurate in recovering larger first powers. This makes the determined location of the sensing target more accurate, which is beneficial to improving sensing accuracy.

[0145] This application provides another perception method, please refer to Figure 7, which is a flowchart of the method. In the embodiment shown in Figure 7, the model training process can be completed by multiple nodes, for example, by two nodes: a first device and a second device. Alternatively, in the embodiment shown in Figure 7, the model training process can be completed by one node (e.g., the first device), while the second device can provide assistance. The training method provided in the embodiment shown in Figure 7 is, for example, an online training method. It can be seen that the compression model and / or decompression model in the various embodiments of this application can be trained using offline or online training methods, which is quite flexible.

[0146] S701, the first device inputs first compression information into the decompression model and obtains second sensing information. The second sensing information is, for example, the output information of the decompression model.

[0147] For more information on S701, such as the introduction of the decompression model and / or compression model, and the introduction of the first compression information, please refer to S501 in the embodiment shown in Figure 5.

[0148] Optionally, the method may further include S702, in which the first device obtains first compression information. The first compression information is related to the first sensing information. S702 occurs, for example, before S701.

[0149] An optional implementation of the first device obtaining the first compressed information includes a second device sending the first compressed information and the first device receiving the first compressed information, as exemplified by S702 in FIG7. In this implementation, the first compressed information is determined by the second device and then sent to the first device by the second device. Optionally, the second device may first obtain the first sensing information and then input the first sensing information into a compression model to obtain the first compressed information. The first compressed information may be, for example, the output information of the compression model. An optional implementation of the second device obtaining the first sensing information includes S703 and S704. S703 and S704 occur, for example, before S702.

[0150] S703, the first device sends a second signal. Correspondingly, the second device receives the second signal.

[0151] The second signal can be a signal used for sensing, such as a sensing signal or a fusion signal. This paper takes the example of the second signal being a sensing signal, and it can be referred to as the second sensing signal. The second sensing signal received by the second device can be a signal reflected, scattered, or diffracted by a sensing target in the environment after the second sensing signal sent by the first device has passed through it. The second device can obtain the first sensing information using algorithms such as back projection (BP) or discrete Fourier transform (DFT). Taking the first device as a network device and the second device as a UE as an example, the second sensing signal may include, for example, a channel state information reference signal (CSI-RS) and / or a synchronization signal and a physical broadcast channel (PBCH) block (SSB).

[0152] S704. The second device determines the first sensing information based on the second sensing signal.

[0153] Optionally, the second device can be configured with a first parameter, for example, the first parameter can be configured by the first device or other devices. The second device can determine at least one spatial location based on the first parameter, thereby determining the first sensing information. For example, the second device can determine the first sensing information based on the at least one spatial location and in conjunction with measurements of the second sensing signal.

[0154] The first parameter may include one or more of the following: number of subcarriers N0, number of horizontal ports N1, number of vertical ports N2, subcarrier oversampling factor O0, horizontal oversampling factor O1, or vertical oversampling factor O2. The number of horizontal ports may refer to the number of horizontal ports of the antenna of the first device, and the number of vertical ports may refer to the number of vertical ports of the antenna of the first device. Taking each spatial location in at least one spatial location as characterized by three parameters—distance, horizontal angle, and vertical angle—as an example, the second device can determine N0O0 values ​​in the distance dimension, N1O1 values ​​in the horizontal angle dimension, and N2O2 values ​​in the vertical angle dimension based on the first parameter. Each spatial location in the at least one spatial location can be uniquely determined by a distance, a horizontal angle, and a vertical angle; therefore, the number of the at least one spatial location is N0O0N1O1N2O2. Each spatial location in the at least one spatial location can correspond to a first power P(s,m,n), where s∈[0,N0O0], m∈[0,N1O1], and n∈[0,N2O2].

[0155] For example, the at least one spatial location is a spatial range with the first device as the origin of the coordinate system, in the subcarrier dimension (or distance dimension), horizontal dimension, and vertical dimension, which can be determined by the distance R(s) to the second device in the subcarrier dimension, the horizontal angle θ(m) in the horizontal dimension, and the vertical angle θ(n) in the vertical dimension.

[0156] R(s) can satisfy the following relationship:

[0157] Where c represents the speed of light. Δf represents the spacing between adjacent subcarriers used to transmit the second sensing signal. s represents the oversampled subcarrier index, and the value of s can be in the range [0, N0O0]. The "*" in the various embodiments of this application means multiplication, and will not be explained again below. θ(m)

[0158] θ(m) can satisfy the following relationship:

[0159] Where λ represents the wavelength of the subcarrier. d1 represents the spacing between the antenna ports in the horizontal direction. m represents the oversampled horizontal index, and the value of m can be in the range [0, N1O1]. [x] represents rounding up x. w1 represents the spacing between the antenna ports in the horizontal direction.

[0160] θ(n) can satisfy the following relationship:

[0161] Where λ represents the wavelength of the subcarrier. d2 represents the spacing between the antenna ports in the vertical direction. N represents the oversampled vertical index, and the value of n can be in the range [0, N2O2]. w2 represents the spacing between the antenna ports in the vertical direction.

[0162] Please refer to Figure 8 for an example of the at least one spatial location. Each spatial location in the at least one spatial location can be considered as a location grid, where each location grid can be characterized by three dimensions: distance, horizontal angle, and vertical angle.

[0163] The second device, having received the first sensing information, can input it into a compression model to obtain the first compressed information. After obtaining the first compressed information, the second device can directly send it to the first device; alternatively, the second device can quantize the first compressed information and then send the quantized first compressed information to the first device; or, after obtaining the first compressed information, the second device can quantize it, then perform arithmetic encoding and decoding on the quantized first compressed information, and then send the arithmetic-encoded and decoded first compressed information to the first device. By quantizing and / or performing arithmetic encoding and decoding on the first compressed information, the data volume of the first compressed information can be further reduced, thereby reducing reporting overhead.

[0164] In this scenario, if the second device directly sends the first compressed information to the first device, the first device can input the first compressed information into the decompression model. Alternatively, if the second device sends quantized first compressed information, the first device can input the quantized first compressed information into the decompression model, or the first device can first recover the quantized first compressed information and then input the recovered first compressed information into the decompression model. Alternatively, if the second device sends arithmetic-encoded and arithmetic-decoded first compressed information, the first device can input this arithmetic-encoded and arithmetic-decoded first compressed information into the decompression model. Alternatively, if the second device sends arithmetic-encoded and arithmetic-decoded first compressed information, and the second device quantizes the first compressed information and then performs arithmetic encoding and decoding on the quantized first compressed information, the first device, after obtaining the arithmetic-encoded and arithmetic-decoded first compressed information, can recover the quantized first compressed information and then input the recovered first compressed information (e.g., arithmetic-encoded and arithmetic-decoded first compressed information) into the decompression model.

[0165] S705. The first device updates the parameters of the compression model and / or the decompression model according to the loss function.

[0166] The first device can calculate a loss function based on the first and second sensing information, and then update the parameters of the compression model and / or the parameters of the decompression model based on the loss function.

[0167] Through steps S701 to S704, the first device obtains the second sensing information, but has not yet obtained the first sensing information. Therefore, the first device still needs to obtain the first sensing information. Optionally, the first device can obtain the first sensing information through steps S706 and S707.

[0168] S706, The second device sends the first signal. Correspondingly, the first device receives the first signal.

[0169] The first signal can be a signal used for sensing, such as a sensing signal or a fusion signal. This paper takes the example of the first signal being a sensing signal, and it can be referred to as the first sensing signal. The first sensing signal received by the first device can be a signal reflected, scattered, or diffracted by a sensing target in the environment after the first sensing signal sent by the second device has passed through it. Taking the first device as a network device and the second device as a UE as an example, the first sensing signal may include, for example, a channel sounding reference signal (SRS).

[0170] Optionally, taking the example of the first device being a network device and the second device being a UE, the first sensing signal can be configured by the first device for the second device, and the second device can send the first sensing signal according to the configuration.

[0171] S707, The first device determines the third sensing information based on the first sensing signal.

[0172] If the first device receives the first sensing signal, it can determine the third sensing information based on the first sensing signal. Optionally, the method by which the first device determines the third sensing information can be similar to the method by which the second device determines the first sensing information, as described above, and will not be elaborated further.

[0173] Considering the dissimilarity of the downlink (DL) channel and the uplink (UL) channel, the third sensing information can be considered the same as the first sensing information, or the third sensing information can be considered as the first sensing information. That is, through S706 and S707, the first device obtains the third sensing information, which is also equivalent to obtaining the first sensing information. Therefore, in S705, the first device can calculate the loss function based on the third sensing information (or understood as the first sensing information) and the second sensing information, and then update the parameters of the compression model and / or the parameters of the decompression model according to the loss function.

[0174] For more details on S705, please refer to S503 in the embodiment shown in Figure 5, which will not be elaborated further.

[0175] This application embodiment can train compression and decompression models, thereby compressing the first sensing information using the compression model. The compressed first sensing information has a smaller data volume compared to the uncompressed first sensing information, thus reducing transmission overhead. The first sensing information may include a first power corresponding to at least one spatial location. The loss function used for training in this application embodiment is related to the power difference corresponding to the at least one spatial location and the maximum power indicated by the first sensing information. For example, among the first powers corresponding to the at least one spatial location, the larger the first power, the smaller the error caused by compression, making the receiver more accurate in recovering larger first powers. This makes the determined location of the sensing target more accurate, which is beneficial to improving sensing accuracy. The training process of this application embodiment can be offline training or online training, which is quite flexible.

[0176] Figure 9 shows a schematic diagram of a device provided in an embodiment of this application. The device 900 can be the first device or its circuit system as shown in the embodiments of Figure 5 or Figure 7, used to implement the method corresponding to the first device in the above method embodiments. Alternatively, the device 900 can be the second device or its circuit system as shown in the embodiment of Figure 7, used to implement the method corresponding to the second device in the above method embodiments. For example, one type of circuit system is a chip system.

[0177] Since the device 900 in the embodiments of this application can implement the sensing method, the device 900 can also be called a sensing device. In implementation, the device 900 may have sensing function but no communication function, or it may have both sensing and communication functions. If the device 900 has communication function, it may also be called a communication device, etc., without limitation.

[0178] The device 900 includes at least one processor 901. The processor 901 can be used for internal processing within the device to implement certain control processing functions. Optionally, the processor 901 includes instructions. Optionally, the processor 901 can store data. Optionally, different processors can be independent devices, located in different physical locations, or located on different integrated circuits. Optionally, different processors can be integrated into one or more processors, for example, integrated on one or more integrated circuits.

[0179] Optionally, the device 900 includes one or more memories 903 for storing instructions. Optionally, the memories 903 may also store data. The processor and the memories may be separate or integrated together.

[0180] Optionally, the device 900 includes a communication line 902 and at least one communication interface 904. Since the memory 903, communication line 902, and communication interface 904 are all optional, they are all represented by dashed lines in FIG9.

[0181] Optionally, device 900 may further include a transceiver and / or an antenna. The transceiver can be used to send information to or receive information from other devices. The transceiver may be referred to as a transceiver unit, transceiver circuit, input / output interface, etc., and is used to realize the transmission and reception functions of device 900 via the antenna. Optionally, the transceiver includes a transmitter and a receiver. For example, the transmitter can be used to generate a radio frequency (RF) signal from a baseband signal, and the receiver can be used to convert the RF signal back into a baseband signal.

[0182] The processor 901 may include a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs according to the present application.

[0183] Communication line 902 may include a path for transmitting information between the aforementioned components.

[0184] Communication interface 904 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), wired access network, etc.

[0185] Memory 903 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 903 may exist independently and be connected to processor 901 via communication line 902. Alternatively, memory 903 may be integrated with processor 901.

[0186] The memory 903 stores computer execution instructions for implementing the present application's solution, and its execution is controlled by the processor 901. The processor 901 executes the computer execution instructions stored in the memory 903, thereby implementing the steps performed by the first or second device in the embodiments shown in FIG5 or FIG7.

[0187] Optionally, the computer execution instructions in the embodiments of this application may also be referred to as application code, and the embodiments of this application do not specifically limit this.

[0188] In a specific implementation, as one example, processor 901 may include one or more CPUs, such as CPU0 and CPU1 in FIG9.

[0189] In a specific implementation, as one embodiment, device 900 may include multiple processors, such as processor 901 and processor 905 in FIG. 9. Each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0190] When the device shown in Figure 9 is a chip, such as the chip of the first device or the chip of the second device, the chip includes a processor 901 (and may also include a processor 905), a communication line 902, and a communication interface 904. Optionally, it may include a memory 903. Specifically, the communication interface 904 may be an input interface, pins, or circuits, etc. The memory 903 may be a register, cache, etc. The processor 901 and processor 905 may be a general-purpose CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of a program for controlling the sensing method of any of the above embodiments.

[0191] This application embodiment can divide the device into functional modules according to the above method examples. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into a processing module. The integrated module can be implemented in hardware or software. The module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may exist in actual implementation. For example, when dividing the functional modules according to each function, Figure 10 is a schematic diagram of a device. This device 1000 can be the first or second device involved in the above method embodiments, or a chip in the first or second device. The device 1000 includes a processing unit 1002 and a transceiver unit 1001. Since the device 1000 in this application embodiment can implement a sensing method, the device 1000 can also be called a sensing device. In implementation, the device 1000 may have sensing functionality but no communication functionality, or it may have both sensing and communication functionality. If the device 1000 has communication functionality, it can also be called a communication device, etc., without limitation.

[0192] It should be understood that the device 1000 can be used to implement the steps performed by the first device or the second device in the sensing method of the embodiments of this application. The relevant features can be referred to the embodiments shown in Figure 5 or Figure 7 above, and will not be repeated here.

[0193] Optionally, the functions / implementation processes of the transceiver unit 1001 and processing unit 1002 in Figure 10 can be implemented by the processor 901 in Figure 9 calling computer execution instructions stored in memory 903. Alternatively, the functions / implementation processes of the processing unit 1002 in Figure 10 can be implemented by the processor 901 in Figure 9 calling computer execution instructions stored in memory 903, and the functions / implementation processes of the transceiver unit 1001 in Figure 10 can be implemented by the communication interface 904 in Figure 9.

[0194] Optionally, when the device 1000 is a chip or circuit, the function / implementation process of the transceiver unit 1001 can also be implemented through pins or circuits, etc. Optionally, the transceiver unit 1001 may include a transmitting unit and / or a receiving unit, whereby the transmitting unit implements the transmitting function and the receiving unit implements the receiving function; or, the transceiver unit 1001 may be an integral module capable of implementing both transmitting and / or receiving functions. Optionally, the transceiver unit 1001 can be implemented using a transceiver.

[0195] This application also provides a computer-readable storage medium storing a computer program or instructions that, when executed, implement the methods performed by the first or second device in the aforementioned method embodiments. Thus, the functions described in the above embodiments can be implemented as software functional units and sold or used as independent products. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to it, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0196] This application also provides a computer program product comprising: computer program code, which, when run on a computer, causes the computer to perform the method executed by the first device or the second device in any of the foregoing method embodiments.

[0197] This application also provides a processing apparatus, including a processor and an interface; the processor is used to execute the method executed by the first device or the second device involved in any of the above method embodiments.

[0198] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0199] The various illustrative logic units and circuits described in the embodiments of this application can be implemented or operate the described functions using a general-purpose processor, digital signal processor (DSP), ASIC, field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. The general-purpose processor can be a microprocessor; alternatively, it can be any conventional processor, controller, microcontroller, or state machine. The processor can also be implemented using a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0200] The steps of the methods or algorithms described in the embodiments of this application can be directly embedded in hardware, software units executed by a processor, or a combination of both. The software units can be stored in RAM, flash memory, ROM, erasable programmable read-only memory (EPROM), EEPROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and storage medium can be disposed in an ASIC, which can be disposed in the terminal device. Optionally, the processor and storage medium can also be disposed in different components of the terminal device.

[0201] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0202] The contents of the various embodiments of this application can be referenced to each other. Unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0203] It is understood that in the embodiments of this application, the first device and / or the second device may perform some or all of the steps in the embodiments of this application. These steps or operations are merely examples. In the embodiments of this application, other operations or variations of various operations may also be performed. Furthermore, the steps may be performed in different orders as presented in the embodiments of this application, and it is not necessary to perform all the operations in the embodiments of this application.

Claims

1. A sensing method, characterized in that, The method includes: Input first compression information into the decompression model to obtain second sensing information output by the decompression model, wherein the first compression information is related to the first sensing information, the first sensing information includes information indicating a first power corresponding to at least one spatial location, and the second sensing information includes information indicating a second power corresponding to the at least one spatial location. The parameters of the compression model and / or the decompression model are updated according to the loss function, wherein the loss function includes a distortion function between the first sensing information and the second sensing information, the distortion function being related to the power difference corresponding to the at least one spatial location and the maximum first power indicated by the first sensing information, wherein the power difference corresponding to one of the at least one spatial locations refers to the difference between the first power and the second power.

2. The method according to claim 1, characterized in that, The distortion function includes a weighting coefficient corresponding to the at least one spatial location, the weighting coefficient being related to the maximum first power indicated by the first sensing information.

3. The method according to claim 1 or 2, characterized in that, The distortion function satisfies the following relationship: where, L d represents the value of the distortion function, p′ k represents the second power corresponding to the k-th spatial position indicated by the second sensing information, p k represents the first power corresponding to the k-th spatial position indicated by the first sensing information, 0 < k < K, where K represents the number of the at least one spatial position, w k represents the weighting coefficient corresponding to the k-th spatial position.

4. The method according to claim 2 or 3, characterized in that, The kth spatial location in the at least one spatial location corresponds to the kth weighting coefficient, which is related to the first power indicated by the first sensing information corresponding to the kth spatial location, and the maximum and / or minimum first power indicated by the first sensing information.

5. The method according to claim 4, characterized in that, The k-th weighting coefficient satisfies the following relationship: Where, p k p represents the first power indicated by the first sensing information corresponding to the k-th spatial location. min p represents the minimum first power indicated by the first sensing information. max This indicates the maximum first power indicated by the first sensing information.

6. The method according to claim 4, characterized in that, The value of the kth weighting coefficient is the value corresponding to the power range of the first power at the kth spatial location, and the power range is determined based on the maximum and / or minimum first power indicated by the first sensing information.

7. The method according to any one of claims 1 to 6, characterized in that, The loss function also includes a compression ratio function between the first compressed information and the first perceived information.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Input the first sensing information into the compression model to obtain the first compression information output by the compression model; or, Receive the first compression information.

9. The method according to claim 8, characterized in that, The first compressed information is received, and the method further includes: Receive the first sensing signal; The first sensing information is determined based on the first sensing signal.

10. The method according to any one of claims 1 to 9, characterized in that, The method further includes: Send updated information about the compression model.

11. A sensing method, characterized in that, The method includes: First compression information is sent, which is used to determine a loss function. The loss function includes a distortion function between first sensing information and second sensing information. The first sensing information and the second sensing information are related to the first compression information. The first sensing information includes information indicating a first power corresponding to at least one spatial location. The second sensing information includes information indicating a second power corresponding to the at least one spatial location. The distortion function is related to the power difference corresponding to the at least one spatial location and the maximum first power included in the first sensing information. The power difference corresponding to one of the at least one spatial locations refers to the difference between the first power and the second power.

12. The method according to claim 11, characterized in that, The distortion function includes a weighting coefficient corresponding to the at least one spatial location, the weighting coefficient being related to the maximum first power indicated by the first sensing information.

13. The method according to claim 11 or 12, characterized in that, The distortion function satisfies the following relationship: where, L d represents the value of the distortion function, p′ k represents the second power corresponding to the k-th spatial position indicated by the second sensing information, p k represents the first power corresponding to the k-th spatial position indicated by the first sensing information, 0 < k < K, K represents the number of the at least one spatial position, w k represents the weighting coefficient corresponding to the k-th spatial position.

14. The method according to claim 12 or 13, characterized in that, The kth spatial location in the at least one spatial location corresponds to the kth weighting coefficient, which is related to the first power indicated by the first sensing information corresponding to the kth spatial location, and the maximum and / or minimum first power indicated by the first sensing information.

15. The method according to claim 14, characterized in that, The k-th weighting coefficient satisfies the following relationship: Where, p k p represents the first power indicated by the first sensing information corresponding to the k-th spatial location. min p represents the minimum first power indicated by the first sensing information. max This indicates the maximum first power indicated by the first sensing information.

16. The method according to claim 14, characterized in that, The value of the kth weighting coefficient is the value corresponding to the power range of the first power at the kth spatial location, and the power range is determined based on the maximum and / or minimum first power indicated by the first sensing information.

17. The method according to any one of claims 11 to 16, characterized in that, The loss function also includes a compression ratio function between the first compressed information and the first perceived information.

18. The method according to any one of claims 11 to 17, characterized in that, The method further includes: Receive the second sensing signal; The first sensing information is generated based on the second sensing signal; The first sensing information is input into the compression model to obtain the first compression information.

19. The method according to any one of claims 11 to 18, characterized in that, The method further includes: Send the first sensing signal.

20. The method according to any one of claims 11 to 19, characterized in that, The method further includes: Receive updated information about the compression model.

21. An apparatus, characterized in that, The apparatus includes a module for performing the method as described in any one of claims 1 to 10, or a module for performing the method as described in any one of claims 11 to 20.

22. An apparatus, characterized in that, The apparatus includes a processor for performing the method as claimed in any one of claims 1 to 10, or performing the method as claimed in any one of claims 11 to 20.

23. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program that, when run on a computer, causes the method as described in any one of claims 1 to 10 to be performed, or causes the method as described in any one of claims 11 to 20 to be performed.

24. A computer program product, characterized in that, The computer program product includes a computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 10, or causes the computer to perform the method as described in any one of claims 11 to 20.

25. A sensing system, characterized in that, The sensing system includes a first device and a second device, wherein... The first device is used to perform the method as described in any one of claims 1 to 10; The second device is used to perform the method as described in any one of claims 11 to 20.

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