System and method for measurement report compression

Abstract

Systems, apparatuses, and methods for measurement report compression are presented. One method can include receiving, by a first wireless device from a second wireless device, a plurality of signals, each signal being associated with a respective one of a plurality of time units. The method can include obtaining, by the first wireless device, a plurality of measurements of the plurality of signals. The method can include performing, by the first wireless device, compression on the plurality of measurements to obtain a compressed output in a channel impulse response (CIR) related format.

Description

Technical Field

[0001] This disclosure generally relates to wireless communications, including but not limited to systems and methods for compressing measurement reports. Background Technology

[0002] The standards organization 3GPP is currently specifying a new radio interface called 5G New Radio (5GNR) and a next-generation packet core network (NG-CN or NGC). 5G NR will have three main components: the 5G Access Network (5G-AN), the 5G Core Network (5GC), and the User Equipment (UE). To facilitate the implementation of different data services and needs, the elements of 5GC (also known as network functions) have been simplified, some of which are software-based and some hardware-based, so that they can be adapted as needed. Summary of the Invention

[0003] The exemplary embodiments disclosed herein are intended to address problems related to one or more difficulties existing in the prior art and provide additional features that will become apparent when taken in conjunction with the accompanying drawings and the following detailed description. Exemplary systems, methods, apparatuses, and computer program products are disclosed herein according to various embodiments. However, it should be understood that these embodiments are presented by way of example and not as limiting, and that various modifications can be made to the disclosed embodiments by those skilled in the art who read this disclosure, while remaining within the scope of this disclosure.

[0004] At least one aspect relates to a system, method, apparatus, or computer-readable medium. A method may include receiving multiple signals from a second wireless device by a first wireless device, each signal associated with a corresponding one of a plurality of time units. The method may include obtaining multiple measurements of the multiple signals by the first wireless device. The method may include performing compression on the multiple measurements by the first wireless device to obtain a compressed output in a channel impulse response (CIR) correlated format.

[0005] In some embodiments, the multiple measurements may include multiple CIR matrices, each CIR matrix having a delay dimension and a spatial dimension; the delay dimension corresponds to the number of time samples in a time unit; and / or the spatial dimension corresponds to the number of antennas of the first wireless device.

[0006] In some embodiments, the method may include a first wireless device sending a compressed output to a wireless communication node to determine at least one of a current time location or velocity, or a future time location and velocity. The compressed output may be input into an artificial intelligence (AI) model to determine the current or future time location or velocity. The method may also include the first wireless device receiving auxiliary information from the wireless communication node. The auxiliary information may include at least one of the following: an indication of the compression method; predicted position and velocity results for the sensed target; a list of identifiers (IDs) of sending nodes for group reporting; periodic or trigger indicators for reporting area or location-related information; and / or periodic or trigger indicators for reporting predicted measurements.

[0007] In some embodiments, the predicted position and velocity results of the sensed target may include at least one of the following: multiple predicted positions and velocities of the sensed target for future time, or multiple probabilities corresponding to a respective one of the predicted position and velocity.

[0008] In some implementations, performing compression may include at least one of the following: performing compression on multiple measurements in a spatial dimension by a first wireless device; generating the compressed output by the first wireless device using the following items to represent each of the CIR matrices: (i) a reference CIR of a reference antenna and (ii) difference information of one or more remaining antennas; and / or generating difference information by the first wireless device to include differences in at least one of phase, amplitude, or reference signal received power (RSRP) between the reference antenna and one or more remaining antennas. Performing compression may include performing compression on multiple measurements in a delay dimension by the first wireless device. Performing compression may include generating the compressed output by the first wireless device using the following items to represent each CIR matrix: (i) a reference CIR of a reference antenna, and / or (ii) at least one selected portion of some corresponding time delay paths measured by the respective remaining antennas. Performing compression may include generating each of at least one selected portion by the first wireless device to include at least one of the following: delay dimension information relative to the reference CIR, amplitude information, and / or phase difference information. Performing compression includes at least one of the following: performing compression on multiple measurements in the Doppler dimension by a first wireless device; generating the compressed output by the first wireless device by representing each of the CIR matrices using the following items: (i) information on at least one valid path in the reference CIR matrix and (ii) information on the effective Doppler shift; and / or generating information on at least one valid path by the first wireless device to include at least one of the following: delay dimension information, amplitude information, or Doppler shift or phase difference information relative to the reference CIR matrix.

[0009] In some embodiments, the method may include sending area or location-related information from a first wireless device to a wireless communication node. The area or location-related information may include at least one of the following: at least one of the longitude, latitude, or location coordinates of the sensing area or the first wireless device; and / or an indication or identifier (ID) of the sensing area. The area or location-related information may also include at least one of the following: an identifier (ID) of the first wireless device; an ID of a second wireless device; a cell ID; a resource set indication corresponding to all or a portion of multiple signals; and / or a resource ID corresponding to all or a portion of multiple signals.

[0010] In some embodiments, the method may include a configuration for a first wireless device to receive scheduling reports for area or location-related information from a wireless communication node, the configuration including periodicity or time intervals for scheduling reports. The method may include at least one of the following: the first wireless device receiving from the wireless communication node an indication corresponding to or in response to an event reporting area or location-related information. This event may correspond to performance degradation of an AI model or the completion of a defined number of measurement reports. The method may include the first wireless device sending predictive measurements about a sensed target to the wireless communication node, wherein the predictive measurements include at least one of the following: at least one CIR matrix; at least one measurement data or intermediate variable, including at least one of the following: relative time of arrival (RTOA) for a future time, reference received signal power (RSRP), Doppler shift; and / or a timestamp or indication for a future time.

[0011] In some implementations, the predictive measurement may include at least one of the following: multiple predictive measurements for a future time, or multiple probabilities corresponding to a respective one of the predictive measurements. The indication of a future time may include at least one of the following: the time difference between the future time and the current time, the difference in intra-system frame number (SFN) between the future time and the current time, the slot number of the future time relative to the current time within a frame, or the symbol index of the future time relative to the current time.

[0012] In some implementations, the method may include determining a group of second wireless devices by a first wireless device or wireless communication node based on proximity or location. The method may include performing one or more measurements associated with the group of second wireless devices by the first wireless device. The method may include generating a joint measurement report for the group by the first wireless device based on one or more measurements.

[0013] In some implementations, the method may include a joint measurement report for a combination of one or more measurements associated with the group of second wireless devices, the combined measurement being associated with at least one of: an identifier (ID) of the first wireless device; and / or the ID of each of the second wireless devices. A method may include transmitting multiple signals from the second wireless devices to the first wireless device, each signal being associated with a corresponding one of a plurality of time units. The method may include enabling the first wireless device to acquire multiple measurements of the multiple signals and performing compression on the multiple measurements to obtain a compressed output in a Channel Impulse Response (CIR) correlated format.

[0014] In some embodiments, a non-transitory computer-readable medium may store instructions that, when executed by at least one processor, cause the at least one processor to perform any one or more of the methods disclosed herein. An apparatus may include at least one processor configured to perform any one or more of the methods disclosed herein. Attached Figure Description

[0015] Figure 1 An example cellular communication network according to an embodiment of the present disclosure is shown, in which the techniques disclosed herein can be implemented;

[0016] Figure 2 Block diagrams of example base stations and user terminal devices according to some embodiments of the present disclosure are shown;

[0017] Figure 3 An example implementation of a CIR matrix in an AI / ML ISAC for a multi-antenna scenario is shown according to some embodiments of the present disclosure;

[0018] Figure 4 An example implementation of a total CIR matrix of multiple sensing RSs to be reported by a receiving node according to some embodiments of the present disclosure is shown;

[0019] Figure 5 An example implementation of a CIR matrix compressed in the antenna dimension according to some embodiments of the present disclosure is shown;

[0020] Figure 6 An example implementation of a CIR matrix compression method in the antenna dimension according to some embodiments of the present disclosure is shown;

[0021] Figure 7 An example implementation of a CIR matrix compressed in the delay dimension according to some embodiments of the present disclosure is shown;

[0022] Figure 8 Example implementations of the CIR selection portion according to some embodiments of this disclosure are shown;

[0023] Figure 9 An example implementation of a CIR matrix compression method in the delay dimension according to some embodiments of this disclosure is shown;

[0024] Figure 10 Example implementations of a CIR matrix compressed in the Doppler dimension according to some embodiments of the present disclosure are shown;

[0025] Figure 11 This illustrates variations in perception modes according to some embodiments of the present disclosure;

[0026] Figure 12 Variations in the serving cell according to some embodiments of this disclosure are shown;

[0027] Figure 13 Variations in resources (e.g., sensing reference signals) according to some embodiments of this disclosure are shown;

[0028] Figure 14 Example implementations of periodically reporting region-related information according to some embodiments of this disclosure are shown;

[0029] Figure 15 An example implementation of a predictive model deployed on a sensing RS receiving node according to some embodiments of the present disclosure is shown;

[0030] Figure 16 Example implementations of a prediction model deployed on the network side according to some embodiments of the present disclosure are shown;

[0031] Figure 17 Example implementations of SF sending prediction results as auxiliary data or information to UE or BS according to some embodiments of the present disclosure are shown;

[0032] Figure 18 An example implementation of joint sensing for multiple transmitting and receiving nodes according to some embodiments of this disclosure is shown;

[0033] Figure 19 An example implementation of group reporting of measurements from different transmitting nodes in adjacent areas according to some embodiments of this disclosure is shown;

[0034] Figure 20 A flowchart of an example method for compression to obtain a measurement report, according to some embodiments of this disclosure, is shown. Detailed Implementation

[0035] 1. Mobile Communication Technology and Environment

[0036] Figure 1An example wireless communication network and / or system 100 according to embodiments of the present disclosure is illustrated, in which the techniques disclosed herein can be implemented. In the following discussion, wireless communication network 100 can be any wireless network, such as a cellular network or a narrowband Internet of Things (NB-IoT) network, and is referred to herein as "network 100". This example network 100 includes a base station 102 (hereinafter referred to as "BS 102"; also referred to as a wireless communication node) and a user terminal device 104 (hereinafter referred to as "UE 104"; also referred to as a wireless communication device), which can communicate with each other via a communication link 110 (e.g., a wireless communication channel) and a cluster of cells 126, 130, 132, 134, 136, 138, and 140 covering a geographic area 101. Figure 1 In this context, BS 102 and UE 104 are contained within the respective geographical boundaries of cell 126. Each of the other cells 130, 132, 134, 136, 138, and 140 may include at least one base station operating on its allocated bandwidth to provide sufficient radio coverage to its intended users.

[0037] For example, BS 102 can operate within the allocated channel transmission bandwidth to provide sufficient coverage to UE 104. BS 102 and UE 104 can communicate via downlink radio frame 118 and uplink radio frame 124, respectively. Each radio frame 118 / 124 can be further divided into subframes 120 / 127, which may include data symbols 122 / 128. In this disclosure, BS 102 and UE 104 are described herein as non-limiting examples of "communication nodes" that can generally practice the methods disclosed herein. According to various embodiments of this solution, such communication nodes may be able to perform wireless and / or wired communication.

[0038] Figure 2 A block diagram of an example wireless communication system 200 for transmitting and receiving wireless communication signals (e.g., OFDM / OFDMA signals) according to some embodiments of this solution is shown. System 200 may include components and elements configured to support known or conventional operating characteristics that do not need to be described in detail herein. In one illustrative embodiment, system 200 may be used in applications such as... Figure 1 In the wireless communication environment 100, data symbols are communicated (e.g., transmitted and received) as described above.

[0039] System 200 typically includes a base station 202 (hereinafter referred to as "BS 202") and a user terminal equipment 204 (hereinafter referred to as "UE 204"). BS 202 includes a BS (base station) transceiver module 210, a BS antenna 212, a BS processor module 214, a BS memory module 216, and a network communication module 218, each module being coupled and interconnected with each other as needed via a data communication bus 220. UE 204 includes a UE (user equipment) transceiver module 230, a UE antenna 232, a UE memory module 234, and a UE processor module 236, each module being coupled and interconnected with each other as needed via a data communication bus 240. BS 202 communicates with UE 204 through a communication channel 250, which can be any wireless channel or other medium suitable for data transmission as described herein.

[0040] As will be understood by those skilled in the art, system 200 may also include, in addition to Figure 2 Any number of modules other than those shown. Those skilled in the art will understand that the various illustrative blocks, modules, circuits, and processing logic described in conjunction with the embodiments disclosed herein can be implemented in hardware, computer-readable software, firmware, or any practical combination thereof. To clearly illustrate this interchangeability and compatibility of hardware, firmware, and software, the various illustrative components, blocks, modules, circuits, and steps are generally described according to their functionality. Whether this functionality is implemented as hardware, firmware, or software depends on the specific application and design constraints imposed on the system as a whole. Those skilled in the art can implement this functionality appropriately for each specific application; however, such implementation decisions should not be construed as limiting the scope of this disclosure.

[0041] According to some embodiments, UE transceiver 230 may be referred to herein as "uplink" transceiver 230, which includes a radio frequency (RF) transmitter and an RF receiver, each of which includes circuitry coupled to antenna 232. A duplex switch (not shown) may alternatively couple the uplink transmitter or receiver to the uplink antenna in a time-duplex manner. Similarly, according to some embodiments, BS transceiver 210 may be referred to herein as "downlink" transceiver 210, which includes an RF transmitter and an RF receiver, each of which includes circuitry coupled to antenna 212. A downlink duplex switch may alternatively couple the downlink transmitter or receiver to downlink antenna 212 in a time-duplex manner. The operation of the two transceiver modules 210 and 230 may be coordinated in time such that uplink receiver circuitry is coupled to uplink antenna 232 for receiving transmissions via wireless transmission link 250 while the downlink transmitter is coupled to downlink antenna 212. Conversely, the operation of the two transceivers 210 and 230 can be coordinated in time such that the downlink receiver is coupled to the downlink antenna 212 for receiving transmissions via the wireless transmission link 250 while the uplink transmitter is coupled to the uplink antenna 232. In some embodiments, there is tight time synchronization with a minimum guard time between changes in duplex direction.

[0042] UE transceiver 230 and base transceiver 210 are configured to communicate via wireless data communication link 250 and cooperate with RF antenna arrangements 212 / 232 that are appropriately configured to support specific wireless communication protocols and modulation schemes. In some illustrative embodiments, UE transceiver 230 and base transceiver 210 are configured to support industry standards such as Long Term Evolution (LTE) and emerging 5G standards. However, it should be understood that this disclosure is not necessarily limited to application to specific standards and related protocols. Rather, UE transceiver 230 and base transceiver 210 may be configured to support alternative or additional wireless data communication protocols, including future standards or variations thereof.

[0043] According to various embodiments, for example, BS 202 may be an evolved Node B (eNB), a serving eNB, a target eNB, a femtocell, or a picocell. In some embodiments, UE 204 may be embodied in various types of user equipment, such as mobile phones, smartphones, personal digital assistants (PDAs), tablets, laptops, wearable computing devices, etc. Processor modules 214 and 236 may be implemented or realized using a general-purpose processor, content-addressable memory, digital signal processor, application-specific integrated circuit, field-programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. In this way, the processor may be implemented as a microprocessor, a controller, a microcontroller, a state machine, etc. The processor may also be implemented as a combination of computing devices, such as a combination of a digital signal processor and a microprocessor, multiple microprocessors, a combination of one or more microprocessors and a digital signal processor core, or any other such configuration.

[0044] Furthermore, the steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be directly embodied in hardware, firmware, software modules executed by processor modules 214 and 236 respectively, or any actual combination thereof. Memory modules 216 and 234 can be implemented as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. In this regard, memory modules 216 and 234 can be coupled to processor modules 210 and 230 respectively, such that processor modules 210 and 230 can read information from and write information to memory modules 216 and 234 respectively. Memory modules 216 and 234 can also be integrated into their respective processor modules 210 and 230. In some embodiments, memory modules 216 and 234 may each include a cache memory for storing temporary variables or other intermediate information during the execution of instructions to be executed by processor modules 210 and 230 respectively. Memory modules 216 and 234 may each include non-volatile memory for storing instructions to be executed by processor modules 210 and 230, respectively.

[0045] Network communication module 218 typically represents the hardware, software, firmware, processing logic, and / or other components of base station 202 that enable bidirectional communication between base station transceiver 210 and other network components and communication nodes configured to communicate with base station 202. For example, network communication module 218 may be configured to support Internet or WiMAX communication. In a non-limiting typical deployment, network communication module 218 provides an 802.3 Ethernet interface, allowing base station transceiver 210 to communicate with conventional Ethernet-based computer networks. In this way, network communication module 218 may include a physical interface for connecting to a computer network (e.g., a mobile switching center (MSC)). The terms “configured for,” “configured to,” and variations thereof, used herein with respect to a specified operation or function, refer to devices, components, circuits, structures, machines, signals, etc., that are physically constructed, programmed, formatted, and / or arranged to perform the specified operation or function.

[0046] The Open Systems Interconnection (OSI) model (referred to herein as the "OSI model") is a conceptual and logical layout that defines network communication used by systems (e.g., wireless communication devices, wireless communication nodes) that are open to interconnecting and communicating with other systems. The model is divided into seven sub-components or layers, each representing a conceptual set of services provided to its upper and lower layers. The OSI model also defines a logical network and efficiently describes the delivery of computer data packets using different layer protocols. The OSI model may also be referred to as the seven-layer OSI model or the seven-layer model. In some embodiments, the first layer may be the physical layer. In some embodiments, the second layer may be the Media Access Control (MAC) layer. In some embodiments, the third layer may be the Radio Link Control (RLC) layer. In some embodiments, the fourth layer may be the Packet Data Convergence Protocol (PDCP) layer. In some embodiments, the fifth layer may be the Radio Resource Control (RRC) layer. In some embodiments, the sixth layer may be the Non-Access Stratum (NAS) layer or the Internet Protocol (IP) layer, and the seventh layer is another layer.

[0047] Various exemplary embodiments of the present solution are described below with reference to the accompanying drawings to enable those skilled in the art to manufacture and use the present solution. It will be apparent to those skilled in the art that various changes or modifications can be made to the examples described herein after reading this disclosure without departing from the scope of the present solution. Therefore, the present solution is not limited to the exemplary embodiments and applications described and illustrated herein. Furthermore, the specific order or hierarchy of steps in the methods disclosed herein is merely an example method. Based on design preferences, the specific order or hierarchy of steps in the disclosed methods or processes can be rearranged while remaining within the scope of the present solution. Therefore, those skilled in the art will understand that the methods and techniques disclosed herein present various steps or actions in an exemplary order, and the present solution is not limited to the specific order or hierarchy presented, unless otherwise expressly stated.

[0048] 2. Systems and methods for measurement report compression

[0049] In Artificial Intelligence (AI) / Machine Learning (ML) Integrated Sensing and Communication (ISAC), a sensing reference signal receiving node, such as a User Equipment (UE) or Base Station (BS), can measure reflected signals and report the measurement data to a sensing result computing node, such as a Sensing Function (SF). Unlike traditional positioning or sensing measurement reporting, the input data used for AI / ML algorithms can be much larger in size and have higher dimensionality; therefore, directly transmitting the input data can be challenging. Furthermore, the performance of AI / ML sensing models can be affected and may severely degrade due to the mobility of the sensed target. This disclosure proposes a compression method for AI / ML ISAC measurement reporting and considers several methods for reporting area-related information.

[0050] In ISAC, there can be a total of six sensing modes: BS-BS bistatic, BS monostatic, BS-UE bistatic, UE-BS bistatic, UE-UE bistatic, and UE monostatic (e.g., BS transmits and UE receives, BS transmits and receives itself, BS A transmits and BS B receives, UE transmits and BS receives, UE transmits and receives itself, and UE A transmits and UE B receives). In each sensing mode, the UE or BS can receive the sensing RS reflected from the sensing target and can transmit some measurement results to the SF. These measurement results can include target delay, angle, and velocity information. In some sensing methods, the measurement results can occupy tens of bits, making them easy to transmit; however, for AI / ML ISAC, the data size of the measurement results can exceed several thousand bits, which can lead to significant reporting overhead for the UE or BS. Specifically, in the current AI / ML physical layer framework, three types of AI / ML model inputs can be defined: Channel Impulse Response (CIR), Power Delay Distribution (PDP), and Delay Distribution (DP). CIR can have the largest data size and can carry channel amplitude, delay, and phase information; PDP can have a smaller data size and can carry amplitude and delay information; and DP can have the smallest data size and can carry delay information. Due to the Doppler shift (e.g., sometimes referred to as phase information) required for velocity estimation, CIR can be an efficient form of measurement results processed / used by AI / MLISAC systems. In this context, this disclosure describes methods by which a UE or BS can reduce the data size of measurement reports.

[0051] To account for the mobility of the sensed target, some implementations may include a node handover procedure during the sense session. For example, when the sensed target moves from area A to area B, the SF may reconfigure the BS and UE in both areas to select appropriate transmitting and receiving nodes. In this context, this disclosure refers to methods for the UE or BS to report information related to its sensed area.

[0052] Example 1

[0053] For AI / MLISAC scenarios where the receiving node (e.g., BS or UE) used to measure the sensing RS may have multiple antennas, the CIR matrix format for this context can be defined. Figure 3 This is a diagram of the CIR matrix when the receiving node (used to receive sensing RS) has multiple antennas. For example, as shown... Figure 3 As shown, the CIR matrix can include two dimensions: a delay dimension and a spatial / antenna dimension. The delay dimension can be related to time samples in a time unit, such as time samples in an OFDM symbol, and the antenna / spatial dimension can be related to the number of antennas that the UE or BS has.

[0054] Furthermore, when it may be necessary to estimate the target's velocity, the UE or BS can report multiple CIR matrices, such as Figure 4 As shown. The total data size to be reported by the UE or BS can be N. Doppler *N Antenna *N t In some embodiments, N Doppler This represents the maximum Doppler dimension index, and can be related to the number of time units used to measure Doppler, N. Antenna Indicates the maximum number of antennas and / or N t This represents the maximum number of time samples in a time unit. Taking an OFDM signal as an example of a sensing RS, if the UE or BS has 32 antennas and can receive such a sensing RS with 128 symbols, then there can be 1024 time samples per symbol. In this case, the size of the CIR matrix can be [32, 1024], and the total data size of the measurement report can be 128 × 32 × 1024. Figure 4 An example implementation is shown, illustrating the total data size collected from the sensing RS to be reported by the receiving node.

[0055] Example 1-1

[0056] As mentioned above, the total data size that the UE or BS can report can be N. Doppler *N Antenna *N t Furthermore, sensing accuracy can be closely related to the time units, time samples, and / or the number of antennas used for sensing. In other words, the larger the data size that the UE or BS needs to report, the higher the sensing accuracy can be achieved. However, excessive data in the measurement report can lead to significant overhead for the UE or BS. One solution could be to reduce the data size of the measurement report from a spatial dimension. Specifically, when the UE or BS uses / receives reflected signals from different antennas, elements with the same delay exponent can include different phase and amplitude information. In this case, the CIR matrix can be compressed according to the antenna / spatial dimension.

[0057] Figure 5 An example implementation of a CIR matrix compressed in the antenna / spatial dimension is shown. For example, such as... Figure 5 As shown, each CIR matrix can be decomposed / processed into a reference CIR and an additional list. The reference CIR can include all / complete information from the reference antenna channel, and the additional list can include difference information determined / calculated between the reference antenna and the remaining (or other) antennas. The difference information can include:

[0058] ■ Phase difference information between the reference antenna and the other antennas;

[0059] ■ Amplitude difference information between the reference antenna and the other antennas.

[0060] Figure 6 This is a diagram illustrating the compression of the CIR matrix from the antenna / space dimension. For example, as shown... Figure 6 As shown, the sensing RS node (e.g., the receiving node) can decompose each CIR matrix into a reference antenna CIR and an additional list. The total data size reported by the UE or BS can be N. Doppler *(1×N t +(N Antenna -1)×N t ). Where 1×N t This indicates the data size of the reference antenna CIR, and (N) Antenna -1)×N t Indicates the size of the data in the supplementary list.

[0061] In this compression method, the information that the UE or BS transmits / indicates to the SF may include several reference CIRs, an additional list of reference antennas, and / or indicators. The measurement information elements (IEs) used to convey / indicate this information can be summarized, for example, as follows:

[0062] AI-MeasElement ::= SEQUENCE {

[0063] ...,

[0064] ReferenceAntennaIDINTEGER(0..128),OPTIONAL,

[0065] Reference CIRCIR, OPTIONAL

[0066] AdditionalListAdditionalListOPTIONAL,

[0067] ...,

[0068] }

[0069] ReferenceAntennaID represents the reference antenna indicator.

[0070] Example 1-2:

[0071] Besides compressing the CIR matrix from the antenna / space dimension, another approach is to compress the CIR matrix from the delay dimension. Specifically, the UE or BS can filter each CIR matrix in the delay dimension and can report some specific delay paths of the CIRs.

[0072] Figure 7This is a diagram of the CIR matrix in the UE or BS compressed delay dimension. For example, as shown below. Figure 7 As shown, the UE or BS can divide / segment the CIR matrix into a reference CIR and several selected parts. Similar to antenna / spatial dimension compression, the reference CIR includes all information about the reference antenna, and each selected part includes some specific paths corresponding to the CIR of each antenna. The number of selected parts can be equal to the number of the remaining (e.g., non-reference) antennas.

[0073] Figure 8 This is a diagram of the CIR selection section. From Figure 8 As can be seen, the selection section may include at least one time-delayed path representing the target reflection path. The information included in the selection section may be as follows:

[0074] ■ Start and end indices in the delay dimension, or the start index in the delay dimension and the length of the selected portion or relative arrival time (RTOA);

[0075] ■ Amplitude information or Received Signal Reference Power (RSRP);

[0076] ■ Phase difference information relative to the reference CIR.

[0077] Figure 9 This is a flowchart illustrating the compression of the CIR matrix from the delay dimension. For example, as shown... Figure 9 As shown, the sensing RS receiving node can divide / process / transform each CIR matrix into a reference antenna CIR and several selectable parts. The total data size that the UE or BS can report can be... .in This indicates the data size of the selected portion in each antenna.

[0078]

[0079] In this compression method, the information that the UE or BS can transmit / indicate to the SF includes several reference CIRs, a list of selected sections, and / or indicators of reference antennas, wherein each list of selected sections may include several selected sections. The measurement IE used to convey / indicate this information can be summarized / configured / represented, for example, as follows:

[0080] AI-MeasElement ::= SEQUENCE {

[0081] ...,

[0082] RS-ReferenceAntennaIDINTEGER (0..255),OPTIONAL,

[0083] ReferenceCIRReference CIROPTIONAL,

[0084] AI-CIR-SelectionPart CIR-SelectionPart OPTIONAL,

[0085] ...,

[0086] }

[0087] Example 1-3:

[0088] Similar to Examples 1-1 and 1-2, measurement reports can also be compressed from / within the Doppler dimension. Specifically, the UE or BS can report some effective paths (or effective paths in the Doppler dimension) in the reference CIR matrix and the effective Doppler frequency shift. In some embodiments, the reference CIR matrix includes complete delay and spatial (antenna) information within a time unit (e.g., OFDM symbol). Information on effective paths can include at least one of the following:

[0089] ■ Utilizing Doppler frequency shift or phase difference information from a reference CIR matrix;

[0090] ■ Amplitude information or Received Signal Reference Power (RSRP);

[0091] ■ Delay information or delay dimension index or relative time of arrival (RTOA).

[0092] Figure 10 This is a diagram illustrating the compression of the CIR matrix in the Doppler dimension by the UE or BS. For example, as shown... Figure 10 As shown, the original CIR matrix can be reduced / compressed into a reference CIR matrix corresponding to Doppler index 0, one valid path corresponding to Doppler index 1, and two valid paths corresponding to Doppler index 2. In this method, the total data size that the UE or BS can report can be... Where N eD Indicates the number of valid Doppler indices. This indicates the number of valid paths in the valid Doppler index i. The IE used to indicate this can be summarized / configured, for example, as follows:

[0093]

[0094] AI-MeasElement ::= SEQUENCE {

[0095] ...,

[0096] RS-ReferenceDopplerIndexINTEGER (0..127),OPTIONAL,

[0097] ReferenceCIRMatrixReference-CIR-MatrixOPTIONAL,

[0098] AI-CIR-EffectivePathList Effective-Path-List OPTIONAL,

[0099] ...,

[0100] }

[0101] In some embodiments, the AI-CIR-EffectivePathList includes effective paths in each effective Doppler shift.

[0102] Example 2:

[0103] Unlike positioning and communication, in ISAC, passively sensed targets may be constantly moving, which can lead to drastic changes in the reflected signal channel. In this case, the performance of the AI ​​sensing model may degrade or even fail. Therefore, the transmitting and receiving nodes can be reconfigured to better sense the target. For example, at time t0, the sensed target may be located in area A, and the transmitting node may be BS1, while the receiving node may be UE1. At time t1, the sensed target may be located in area B, and the transmitting node may be BS2, while the receiving node may be UE2.

[0104] Because of this practical problem, if the UE or BS can report some area or location-related information (e.g., Rx-Tx, Rx, Tx, etc.) and measurement results to the SF, the SF can adjust the AI ​​perception model to adapt to (or fit) the current perception area.

[0105] Example 2-1:

[0106] The mobility of the sensed target primarily alters its geographic location; therefore, one approach is for the UE or BS to report direct or relative coordinates to the SF. In this approach, the UE or BS may send geographic coordinates such as longitude and latitude, or the ID of the sensed area, to the SF. In some embodiments, the area ID indicates one of multiple candidate areas (each candidate area can be assigned an ordered number) within the entire SF service area, and each area ID corresponds to a unique geographic area.

[0107] AI-MeasElement ::= SEQUENCE {

[0108] ...,

[0109] LongitudeandLatidue{longitude,latitude}OPTIONAL,

[0110] or

[0111] AreaIDINTEGER (0..127),OPTIONAL,

[0112] ...,

[0113] }

[0114] In some embodiments, one or both of Longitude and Latitude and AreaID may be reported.

[0115] Example 2-2:

[0116] In addition to reporting direct or relative coordinates, the BS or UE can report area-related information to the SF in another way. Specifically, area-related information can be described based on sensing mode information (e.g., one of six sensing modes), cell information, and / or resource information. In some embodiments, sensing mode information can provide / indicate the relationship between the sensing RS transmitting and receiving nodes, for example, whether BS A is transmitting and UE A is receiving, or whether BS A is transmitting and BS B is receiving. Cell information indicates the serving cell of the UE or BS, and resource information indicates the direction of the transmitting beam in the sensing session. Figure 11 , Figure 12 and Figure 13 It could be a diagram showing different patterns, serving cells, and beams caused by the perceived target mobility. Figure 11 Example variations in perception patterns are shown. Figure 12 Example variations in the serving cell are shown. Figure 13 Example variations of resources (e.g., sensing reference signals) are shown.

[0117] Therefore, the sensing RS receiving node can report area-related information based on three aspects. Specifically, the UE or BS can report the transmitting node ID, receiving node ID, serving cell ID, and / or sensing RS resource and resource set ID to the SF. Specific IEs used for reporting can be represented / configured, for example, as follows:

[0118] AI-MeasElement ::= SEQUENCE {

[0119] ...,

[0120] TransmittingandReceivingNodeListtransmittingandreceiving-node-listOPTIONAL,

[0121] TransmittingandReceivingCellListtransmittingandreceiving-cell-listOPTIONAL,

[0122] SensingRS-ResourceIDsensingRS-ResourceIDOPTIONAL,

[0123] SensingRS-ResourceSetIDsensingRS-ResourceSetIDOPTIONAL,

[0124] ...,

[0125] }

[0126] In some embodiments, TransmittingandReceivingNodeList may include the IDs of the transmitting and receiving nodes in the sensing area. For example, the list of transmitting and receiving nodes may be represented as {T}. i R i}, T i Represents the ID of the sending node, and R i This represents the ID of the receiving node. The TransmittingandReceivingCellList includes the physical cell ID and / or global cell ID of the transmitting and receiving nodes. The Sensing RS Resource Set ID and / or Sensing RS Resource ID can indicate the resource location of the Sensing RS.

[0127] In some cases, such as when a BS is performing both transmission and reception, it may not need to report cell information to the SF. In such cases, the BS can only report sensing mode information and resource information.

[0128] Example 2-3:

[0129] Area-related information can be reported periodically or triggered by events. Specifically, when a receiving node (e.g., a UE or BS) can report area-related information along with measurement results, the receiving node can report at fixed intervals or be triggered by specific events.

[0130] ■ If area-related information can be configured to be reported periodically, the SF can transmit / provide the reporting period configuration included in the auxiliary data or information to the UE or BS. The reporting period can be configured as an interval time unit. For example, the SF can configure the receiving node to report area-related information at fixed intervals of OFDM symbols, time slots, milliseconds, or seconds. Furthermore, the SF can also configure the receiving node to report area-related information after reporting measurement results a specific number of times. For example, as... Figure 14 As shown, relevant information about the region can be reported after every three measurement results. Figure 14 An example implementation of periodically reporting region-related information is shown.

[0131] ■ If area-related information can be configured to be reported via event triggering, the SF can transmit / send indicators to the receiving node. For example, if the sensed target may be located at the edge of an existing sensed area, the performance of the sensed model may degrade. When the performance of the sensed model degrades beyond a certain threshold, such as a correct detection probability below 80% or a positioning error exceeding 5 meters, the SF can transmit indicators to the receiving node to request the receiving node to report some area-related information.

[0132] Example 3:

[0133] In Example 2, the UE or BS can report some area-related information to the SF to ensure / support satisfactory performance of the perception model. However, in some cases, reporting area-related information at the current time may not be sufficient for the UE or BS. For example, in a UAV detection scenario, it can detect the UAV at the current moment and predict its location at the next moment. In this case, a prediction model can be deployed on the perception RS receiving side or the perception result calculation side, and for the UE or BS, transmitting the prediction information of the perceived target may be essential.

[0134] Example 3-1:

[0135] Figure 15 This is a diagram of a sensing model deployed on a sensing RS receiving node (e.g., UE or BS). For example, as... Figure 15 As shown, the AI ​​ISAC system can have two models: a perception result calculation model deployed on the SF / network side, and a perception model deployed on the UE side. When the UE receives a reflected signal, it can report the current perception measurement and can use the prediction model to report the predicted measurement for the next / future time step. After receiving the current time measurement, the SF can input the current time measurement into the perception result calculation model and can feed the estimation result back to the UE to monitor the performance of the prediction model.

[0136] The predicted measurement can be a CIR matrix or a perceived intermediate variable. For example, the reported measurement could be the relative time of arrival (RTOA), the reference signal received power (RSRP), Doppler, and other intermediate variables for the next / future time step.

[0137] Furthermore, the sensing RS receiving node can report a timestamp corresponding to the prediction time when reporting prediction measurements. The prediction timestamp can be reported directly or relative. For example, the time difference between the prediction time and the current time can be reported as a timestamp, such as the difference between the system frame number (SFN), intra-frame slot number, symbol index, and the current time.

[0138] AI-MeasElement ::= SEQUENCE {

[0139] ...,

[0140] PredictedMeasurementspredicted-meas-listOPTIONAL,

[0141] PredictedTimeStamppredicted-timestampOPTIONAL,

[0142] ...,

[0143] }

[0144] PredictedMeasurements can be values ​​or lists representing measurement data at the next time step. The contents of predicted-meas-list can be {(Meas0, P0), (Meas1, P1), ..., (Meas...}}. N ,P N )}. Among them, Meas i This represents the predicted measurement in the next time step, such as a CIR matrix or intermediate variables, and P i This indicates the probability of predicting the measurement in the next time step. PredictedTimeStamp can be a normal timestamp or the time difference between the predicted time and the current time.

[0145] Example 3-2:

[0146] Figure 16 This is a diagram illustrating a prediction model deployed on the network side. For example, such as... Figure 16 As shown, the UE can report current time measurement results, such as the CIR matrix, to the SF, and the prediction model can directly predict the trajectory or state of the target, such as the position and velocity at the next time step.

[0147] In this case, the SF can send the prediction estimate as auxiliary data to the UE (e.g., Figure 17 (The solid line in the middle). Figure 17 An example implementation is shown whereby the SF sends the prediction result as auxiliary data to the UE or BS. After receiving the prediction result, the UE can adjust its method of receiving the reflection-aware RS, for example, by adjusting the access cell to better receive the reflection-aware RS. Furthermore, the SF can also send the prediction estimate result as auxiliary information to the BS ( Figure 17 (The dotted line in the image). After receiving the prediction results, the BS can adjust the transmitted beam to better perceive the target.

[0148] Similar to Example 2-3, the receiving node's reporting of prediction results can also be configured to be periodic or event-triggered. If the receiving node needs to report prediction results periodically, the SF can transmit the reporting period, such as several OFDM symbols, time slots, milliseconds, seconds, or any other time unit. If the receiving node wants to report prediction results via event triggering, the SF can transmit / send an indicator / trigger to the receiving node.

[0149] Example 4:

[0150] In some AI / ML ISAC scenarios, due to the small effective radar cross-section (RCS) area of ​​the target, the power of the reflected / reflected signals to be received by the UE or BS may be so weak that they are difficult to detect. Furthermore, the sensing accuracy may be insufficient for a single transmitting and receiving node. Therefore, one approach could be for the receiving node to combine multiple sensing RS transmitting nodes to generate a joint measurement report.

[0151] Figure 18 This is a diagram illustrating the combination of multiple sending and receiving nodes for target sensing. For example, as... Figure 18 As shown, a sensing RS receiving node (e.g., BS) can receive sensing RS signals transmitted by multiple transmitting nodes (e.g., UE A, UE B, and UE C). In this case, the sensing RS node can integrate the measurements (multiple) corresponding to each transmitting node and report these measurements together to the SF.

[0152] However, since the channel from each transmitting node to each receiving node may be different, the measurement quality may also vary. For example, for Figure 18 In Example 2, the measurement corresponding to UE A in the BSB (Blank Base B) may have a much lower SNR (Signal-to-Noise Ratio) than the measurement corresponding to UE C. Therefore, for the BSB, merging the measurements from UE A and UE C and reporting them together to the SF (Signal Provider ID) may not be suitable. In Example 2, the Sensing RS (Signal Provider ID) receiving node can report area-related information to the SF, and one reporting solution could be to report the sending node ID to the SF.

[0153] Figure 19 This is an illustration of how RS receiving nodes group measurements based on the areas where sending nodes can be located and report them together to the SF. One solution is for sensing RS receiving nodes to group measurements from neighboring areas and report them to the SF. For example, as Figure 19 As shown, the sensing measurements can be divided into two groups, where measurements from UE A and UE B should be grouped together for BS A, and measurements from UE B and UE C should be grouped together for BS B.

[0154] In this scenario, the information that the sensing RS receiving node can report can be a set / list of measurements. Each element in this set / list can be a measurement result corresponding to a transmitting node. Furthermore, in Example 1, the measurement results can be some complete CIR matrices or some compressed measurement results. When the RS receiving node wants to combine measurements from different transmitting nodes, it can report a list of transmitting node IDs to the SF, such as {(T1,T2,..,T...} i ), R j In some embodiments, T i Indicates the sending node ID, and R j Indicates the receiving node ID.

[0155] Example 5:

[0156] This example can be a combination of features from Examples 1 to 4. Specifically, at the start of the awareness session, the SF can transmit auxiliary data or information to the transmitting and receiving nodes, such as the UE or BS. The auxiliary data or messages may include, for example, the following information:

[0157] ■ Indicators for compression methods, such as spatial dimension compression, delay dimension compression, and Doppler dimension compression for receiving nodes;

[0158] ■ Predictive estimates, such as information related to the target's current position and velocity;

[0159] ■ Group report configuration, such as a list of sending node IDs to be combined;

[0160] ■ Periodic or trigger indicators used for reporting regional information;

[0161] ■ Periodic or trigger indicators used to report predictive measurement results.

[0162] After receiving auxiliary data or information, the sensing RS transmitting node can transmit sensing RS, and the receiving node can receive the reflected signal from the target. The receiving node can determine whether to compress the measurement results, i.e., the CIR matrix, based on its capabilities or indicators from the SF. If measurement compression may be necessary, the receiving node can compress measurements from the antenna, delay, and / or Doppler dimensions.

[0163] ■ If measurements can be compressed from the space / antenna dimension as described in Example 1-1, the reported information may include several reference CIRs and supplementary lists, for example, {(Reference CIR1, Supplementary List 1), (Reference CIR2, Supplementary List 2), ..., (Reference CIR...} i Additional list i In some embodiments, reference is made to the CIR. i The supplementary list 1 can be derived from the CIR matrix i. Each supplementary list can include the phase, amplitude, and phase difference relative to the reference CIR.

[0164] ■ If measurements can be compressed from the delay dimension as described in Examples 1-2, the report information may include several reference CIRs and selection sections, such as {(reference CIR1, selection section)} (1,1) ,..,Selection section (1,NAntenna) (Refer to CIR2, select part) (2,1) ,..,Selection section (2,NAntenna) In some embodiments, reference is made to the CIR. i and selection section (i,j) From CIR matrix i. Each selected portion may include a delay dimension index relative to the reference CIR, amplitude information, and / or phase difference information.

[0165] ■ If measurements can be compressed from the Doppler dimension as described in Examples 1-3, the reported information may include a reference CIR matrix and / or several valid paths, such as {reference CIR matrix, valid paths in valid Doppler shift 1, valid paths in valid Doppler shift 2, ..., valid paths in Doppler shift i}. Each valid path may include one or more delay dimension indices, amplitude information, and / or Doppler shift information relative to the reference CIR matrix.

[0166] In addition to compressing and reporting measurements (e.g., CIR matrix), the receiving node can also report some region-related information to the sensing results calculation node (e.g., SF). There are two ways to report region-related information: direct reporting and relative reporting.

[0167] When regional information can be reported directly, the receiving node can report to SF the coordinates of the sending and receiving nodes, such as longitude and latitude, or other geographical information, or the ID of the sensed area.

[0168] If area-related information can be reported relatively readily, the receiving node may report one or more of the following to the sensing RS receiving node: transmitting and receiving node IDs, physical and global cell IDs, and resource and resource set IDs. It may not be necessary to report all three aspects of the area-related information. Whether to report all three aspects may depend on the receiving node's capabilities or SF configuration. For example, the sensing RS receiving node may report {transmitting and receiving node IDs}, or {transmitting and receiving node IDs and cell IDs}, or {transmitting and receiving node IDs and resource and resource set IDs}.

[0169] If it may be necessary to group multiple sending and receiving nodes together for sensing, the receiving node can report the group ID of the sending node, for example, {(T1,T2,..,T...}. i ),R j In some embodiments, T i Indicates the sending node ID, and R j This indicates the receiving node ID. Group information can be determined by the receiving node itself or configured by the SF. If the group information can be configured by the SF, the group list information can be included in auxiliary data or information and transmitted to the receiving node.

[0170] The receiving node can also report some sensing-related prediction information to the SF. This sensing-related information can be a CIR matrix or intermediate variables, such as Time of Arrival (TOA), Time Difference of Arrival (TDOA), Rx-Tx timing difference, Relative Time of Arrival (RTOA), Reference Signal Received Power (RSRP), Angle of Arrival (AOA), Doppler shift, and other intermediate variables for the next / future time step. The SF can also transmit / send some sensing prediction results as auxiliary data or information to the transmitting or receiving node. The sensing prediction results can include the target's position, velocity, and / or trajectory for the next / future time step. It should be noted that the various features described in the various implementations / examples are considered to be combinable in any order or manner.

[0171] It should be understood that one or more features from the above / below implementation examples are not specific to any particular implementation example, but can be combined in any way (e.g., in any priority and / or order, simultaneously or otherwise).

[0172] Figure 20 A flowchart of an example method for measurement report compression is shown. Method 2000 can be used in conjunction with this document. Figure 1-19The method 2000 may be implemented by any one or more components and devices described in detail. Generally, in some embodiments, method 2000 may be performed by a sensing receiver device (e.g., UE, BS), a wireless communication device (e.g., a first wireless device, a second wireless device), a wireless communication node (e.g., BS, gNB), and / or a network node (e.g., a core network, LMF, (Sensing Function) (SF)). The first wireless device may be a sensing RS receiving node. The second wireless device may be a sensing RS transmitting node. The wireless communication node may be a sensing function. Depending on the embodiment, additional, fewer, or different operations may be performed in method 2000. At least one aspect of the operation relates to a system, method, apparatus, or computer-readable medium.

[0173] Regarding (2005), and in some embodiments, a method may include receiving multiple signals from a second wireless device by a first wireless device, each signal associated with a corresponding one of a plurality of time units. The method may include analyzing the received signals by the first wireless device to determine a signal quality indicator for each of the plurality of time units. These indicators may include signal strength, signal-to-noise ratio (SNR), and bit error rate (BER), enabling the device to assess the quality and reliability of the communication link over time. The method may include selecting one or more optimal time units for communication by the first wireless device based on the analysis of the signal quality indicators. This selection process can improve the efficiency of data transmission by prioritizing time units with superior signal characteristics, thereby mitigating the effects of interference or signal fading. The first wireless device may process the received signals to extract perception-related information from each of the plurality of time units. Perception-related information may include changes in signal characteristics indicating the presence, size, movement, or other attributes of a target within the environment. By analyzing changes in signal attributes such as amplitude, phase shift, or time delay, the device may infer insights into the physical environment and dynamic objects within it. The first wireless device may utilize advanced signal processing techniques such as Doppler analysis or micro-Doppler features to identify and track the speed and trajectory of moving targets. This feature can be used in applications that require real-time monitoring and response, such as autonomous driving, security monitoring, and dynamic environment mapping.

[0174] Regarding (2010), the method may include multiple measurements of multiple signals obtained by a first wireless device. The obtained measurements may be used to extract signal characteristics, such as amplitude, phase, and frequency. The method may include comparing the extracted signal characteristics with predefined thresholds or patterns. This comparison may be used to identify anomalies, signal degradation, or potential sources of interference, thereby allowing timely corrective actions to maintain or improve communication quality.

[0175] Regarding (2015), the method may include performing compression on multiple measurements by a first wireless device to obtain a compressed output in a Channel Impulse Response (CIR) correlated format. The compression process may include applying algorithms (e.g., machine learning algorithms), such as filtering, Discrete Wavelet Transform (DWT), or Principal Component Analysis (PCA), to reduce the amount of measurement data while retaining essential information. The compressed output can facilitate efficient storage and transmission of CIR correlated data, optimize bandwidth usage, and enhance system performance. By analyzing the CIR correlated format data, the device can quickly infer multipath characteristics, signal attenuation, and propagation delay, thereby enabling the determination of target characteristics and / or dynamic adjustment of transmission strategies to improve communication reliability.

[0176] The method may include a first wireless device sending a compressed output to a wireless communication node to determine at least one of the following: the position or velocity of a target object at the current time, or the position and velocity at a future time. The compressed output may be input into an artificial intelligence (AI) model to determine the position or velocity at the current time or a future time. The method may include the first wireless device receiving auxiliary information from the wireless communication node. The auxiliary information may include at least one of the following: an indication of the compression method; predicted position and velocity results of the sensed target; a list of identifiers (IDs) of transmitting nodes for group reporting; periodic or trigger indicators for reporting area or location-related information; and / or periodic or trigger indicators for reporting predicted measurements. The predicted position and velocity results of the sensed target may include at least one of the following: multiple predicted positions and velocities of the sensed target for a future time, or multiple probabilities corresponding to a corresponding one of the predicted position and velocity, respectively. The multiple measurements may include multiple CIR matrices, each CIR matrix having a delay dimension and a spatial dimension; the delay dimension corresponds to the number of time samples in a time unit; and / or the spatial dimension corresponds to the number of antennas of the first wireless device.

[0177] After sending the compressed output to the wireless communication node, the first wireless device can receive auxiliary information from the wireless communication node to enhance its operational efficiency. This auxiliary information can guide the first wireless device to select the most efficient compression method for future transmissions. The auxiliary information received by the first wireless device may include a list of identifiers for the sending node, which is part of a group reporting mechanism that facilitates coordinated data sharing and processing among multiple devices. This method may include periodic or triggered indicators for reporting, enabling the first wireless device to optimize its data transmission scheduling. Incorporating periodic or triggered indicators can reduce network congestion and improve the timeliness of sharing critical information.

[0178] In some implementations, performing compression may include at least one of the following: performing compression on multiple measurements in a spatial dimension by a first wireless device; generating the compressed output by the first wireless device using the following items to represent each of the CIR matrices: (i) a reference CIR of a reference antenna and (ii) difference information of one or more remaining antennas; and / or generating difference information by the first wireless device to include differences in at least one of phase, amplitude, or reference signal received power (RSRP) between the reference antenna and one or more remaining antennas. Performing compression may include performing compression on multiple measurements in a delay dimension by the first wireless device. Performing compression may include generating the compressed output by the first wireless device using the following items to represent each of the CIR matrices: (i) a reference CIR of a reference antenna, and / or (ii) at least one selected portion of some corresponding time delay paths measured by the respective remaining antennas. Performing compression may include generating each of at least one selected portion by the first wireless device to include at least one of the following: delay dimension information relative to the reference CIR, amplitude information, and / or phase difference information. Performing compression includes at least one of the following: performing compression on multiple measurements in the Doppler dimension by a first wireless device; generating the compressed output by the first wireless device using the following items to represent the CIR matrix: (i) information on at least one valid path in the reference CIR matrix and (ii) information on the effective Doppler shift; and / or generating information on at least one valid path by the first wireless device to include at least one of the following: delay dimension information, amplitude information, or Doppler shift or phase difference relative to the reference CIR matrix.

[0179] The first wireless device may send area or location-related information to the wireless communication node. The area or location-related information may include at least one of the following: at least one of the longitude, latitude, or location coordinates of the sensing area or the first wireless device; and / or an indication or identifier (ID) of the sensing area. The area or location-related information may also include at least one of the following: an identifier (ID) of the first wireless device; an ID of the second wireless device; a cell ID; a resource set indication corresponding to all or part of multiple signals; and / or a resource ID corresponding to all or part of multiple signals.

[0180] The first wireless device can receive configuration from the wireless communication node for scheduling reports of area or location-related information, the configuration including periodicity or time intervals for scheduling reports. The first wireless device can receive from the wireless communication node an indication corresponding to or in response to an event reporting area or location-related information. This event may correspond to performance degradation of an AI model or the completion of a defined number of measurement reports. The first wireless device can send predictive measurements about a sensed target to the wireless communication node, wherein the predictive measurements include at least one of the following: at least one CIR matrix; at least one measurement data or intermediate variable, including at least one of the following: relative time of arrival (RTOA) for a future time, reference signal received power (RSRP), Doppler shift; and / or a timestamp or indication of a future time. The first wireless device can receive configuration from the wireless communication node for scheduling reports of area or location-related information, the configuration including periodicity or time intervals for scheduling reports. The first wireless device can receive from the wireless communication node an indication corresponding to or in response to an event reporting area or location-related information. This event may correspond to performance degradation of an AI model or the completion of a defined number of measurement reports. The first wireless device can send predictive measurements about the sensed target to the wireless communication node, wherein the predictive measurements include at least one of the following: at least one CIR matrix; at least one measurement data or intermediate variable, including at least one of the following: relative time of arrival (RTOA) for a future time, reference signal received power (RSRP), Doppler shift; and / or a timestamp or indication of a future time. This method can facilitate an efficient and adaptive reporting mechanism, wherein the first wireless device receives a specific configuration of periodicity or time intervals for the area or location information it should report. The structured approach can allow for consistent monitoring and updating of location-based data, optimize network resources, and improve the accuracy of location services. Scheduling can adapt to network requirements / conditions and device capabilities, ensuring that reporting is consistent with network operational priorities and device power limitations.

[0181] In some implementations, the predictive measurement may include at least one of the following: multiple predictive measurements for a future time, or multiple probabilities corresponding to a respective one of the predictive measurements. The indication of the future time may include at least one of the following: the time difference between the future time and the current time, the difference in the system's intra-frame number (SFN) between the future time and the current time, the slot number of the future time relative to the current time within a frame, or the symbol index of the future time relative to the current time. A first wireless device or wireless communication node may be determined by a group of second wireless devices based on proximity or location. The first wireless device may perform one or more measurements associated with the group of second wireless devices. The first wireless device may generate a joint measurement report for the group based on one or more measurements. The method may include a joint measurement report for a combination of one or more measurements associated with the group of second wireless devices, the combined measurement being associated with at least one of the following: the identifier (ID) of the first wireless device; and / or the ID of each of the second wireless devices.

[0182] Regarding (2020), a second wireless device can send multiple signals to a first wireless device, each signal associated with a corresponding one of multiple time units. The method may include enabling the first wireless device to acquire multiple measurements of the multiple signals and performing compression on the multiple measurements to obtain a compressed output in a channel impulse response (CIR) correlated format. This method can facilitate efficient data management and analysis. The compression step can reduce the amount of data that needs to be stored or transmitted while retaining critical information about channel characteristics. This efficiency is highly useful for applications requiring fast processing and minimal latency, such as real-time location tracking or high-speed data communication.

[0183] While various embodiments of the present solution have been described above, it should be understood that they are presented by way of example only and not limitation. Similarly, various figures may depict exemplary architectures or configurations provided to enable those skilled in the art to understand exemplary features and functionality of the present solution. However, those skilled in the art will understand that the solution is not limited to the exemplary architectures or configurations shown, but can be implemented using various alternative architectures and configurations. Furthermore, as will be understood by those skilled in the art, one or more features of one embodiment may be combined with one or more features of another embodiment described herein. Therefore, the breadth and scope of this disclosure should not be limited by any of the illustrative embodiments described above.

[0184] It should also be understood that any reference to elements in this document using names such as “first”, “second”, etc., generally does not restrict the number or order of those elements. Rather, these names may be used herein as a convenient means of distinguishing two or more elements or instances of elements. Therefore, referring to the first element and the second element does not imply that only two elements can be used, or that the first element must precede the second element in some way.

[0185] Furthermore, those skilled in the art will understand that information and signals can be represented using any of a variety of different technologies and processes. For example, data, instructions, commands, information, signals, bits, and symbols that may be referenced in the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0186] Those skilled in the art will further understand that any of the various illustrative logic blocks, modules, processors, devices, circuits, methods, and functions described in connection with the aspects disclosed herein can be implemented by electronic hardware (e.g., digital implementation, analog implementation, or a combination of both), firmware, various forms of program or design code containing instructions (which, for convenience, may be referred to herein as "software" or "software module"), or any combination of these technologies. To clearly illustrate this interchangeability of hardware, firmware, and software, the various illustrative components, blocks, modules, circuits, and blocks have been generally described above in terms of their functionality. Whether these functions are implemented as hardware, firmware, or software, or as a combination of these technologies, depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the described functions in various ways for each specific application, but such implementation decisions do not depart from the scope of this disclosure.

[0187] Furthermore, those skilled in the art will understand that the various illustrative logic blocks, modules, devices, components, and circuits described herein may be implemented within or executed by integrated circuits (ICs), which may include general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, or any combination thereof. Logic blocks, modules, and circuits may also include antennas and / or transceivers for communicating with various components within a network or device. A general-purpose processor may be a microprocessor, but optionally, it may be any conventional processor, controller, or state machine. A processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other suitable configuration performing the functions described herein.

[0188] If implemented in software, functionality can be stored as one or more instructions or code on a computer-readable medium. Therefore, blocks of methods or algorithms disclosed herein can be implemented as software stored on a computer-readable medium. Computer-readable media include computer storage media and communication media, with communication media including any medium capable of transferring computer programs or code from one place to another. Storage media can be any available medium accessible to a computer. By way of example and not limitation, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0189] In this application, the term "module" as used herein refers to software, firmware, hardware, and any combination of such elements for performing the relevant functions described herein. Furthermore, for the purposes of discussion, various modules are described as discrete modules; however, it will be apparent to those skilled in the art that two or more modules can be combined to form a single module that performs the relevant functions according to embodiments of this solution.

[0190] Furthermore, in embodiments of this solution, memory or other memory, as well as communication components, may be employed. It should be understood that, for clarity, the above description has referenced various functional units and processors in describing embodiments of this solution. However, it will be apparent that any suitable functional distribution among different functional units, processing logic elements, or domains can be used without departing from this solution. For example, functions illustrated to be performed by individual processing logic elements or controllers may be performed by the same processing logic element or controller. Therefore, references to specific functional units are merely references to suitable means of providing said functions and do not indicate a strict logical or physical structure or organization.

[0191] Various modifications to the embodiments described herein will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the embodiments shown herein, but is accorded the broadest scope consistent with the novel features and principles disclosed herein, as set forth in the following claims.

Claims

1. A method comprising: The first wireless device receives multiple signals from the second wireless device, each signal being associated with a corresponding one of multiple time units; Multiple measurements of the plurality of signals are obtained from the first wireless device; as well as The first wireless device performs compression on the plurality of measurements to obtain a compressed output in a channel impulse response (CIR) related format.

2. The method according to claim 1, comprising: The first wireless device sends the compressed output to the wireless communication node to determine at least one of the current time's position or velocity, or the future time's position or velocity. The compressed output is input into an artificial intelligence (AI) model to determine the position or velocity at the current time or the future time.

3. The method according to claim 1, comprising: The first wireless device receives auxiliary information from the wireless communication node. The auxiliary information includes at least one of the following: Instructions for compression method; The predicted position or velocity of the perceived target; A list of identifiers (IDs) for the sending nodes used in group reports; Periodic or trigger indicators used to report information related to a region or location; or Periodic or trigger indicators used to report predictive measurements.

4. The method according to claim 3, wherein, The predicted position and velocity results of the sensed target include at least one of the following: Multiple predicted positions and velocities of the perceived target at the future time, or multiple probabilities corresponding to one of the predicted positions and velocities respectively.

5. The method according to claim 1, wherein, Meet at least one of the following: The multiple measurements include multiple CIR matrices, each having a delay dimension and a spatial dimension. The delay dimension corresponds to the number of time samples in the time unit; and The spatial dimension corresponds to the number of antennas in the first wireless device.

6. The method according to claim 5, wherein, Performing compression includes at least one of the following: The first wireless device performs compression on the plurality of measurements in the spatial dimension; The compressed output is generated by the first wireless device by representing each of the CIR matrix, wherein (i) the reference CIR of the reference antenna and (ii) the difference information of one or more of the remaining antennas are used; or The difference information is generated by the first wireless device, including differences in at least one of phase, amplitude, or reference signal received power (RSRP) between the reference antenna and the one or more other antennas.

7. The method according to claim 5, wherein, Performing compression includes at least one of the following: The first wireless device performs compression on the plurality of measurements in the latency dimension; The compressed output is generated by the first wireless device by representing each of the CIR matrices, wherein (i) the reference CIR of the reference antenna and (ii) respectively correspond to at least a selected portion of some corresponding time delay paths measured by the respective remaining antennas; or Each of the at least one selection portion generated by the first wireless device includes at least one of the following: delay dimension information, amplitude information, or phase difference relative to the reference CIR.

8. The method according to claim 5, wherein, Performing compression includes at least one of the following: The first wireless device performs compression on the plurality of measurements in the Doppler dimension; The compressed output is generated by the first wireless device by representing the CIR matrix, wherein information of at least one effective path is used in (i) the reference CIR matrix and (ii) the effective Doppler shift; or The information generated by the first wireless device for the at least one valid path includes at least one of the following: delay dimension information, amplitude information, or Doppler frequency shift or phase difference relative to the reference CIR matrix.

9. The method according to claim 1 or 2, comprising: The first wireless device sends region or location-related information to the wireless communication node.

10. The method according to claim 9, wherein, The region or location-related information includes at least one of the following: The sensing area or at least one of the longitude, latitude, or location coordinates of the first wireless device; or The indication or identifier (ID) of the sensing area.

11. The method according to claim 9, wherein, The region or location-related information includes at least one of the following: The identifier (ID) of the first wireless device; The ID of the second wireless device; Community ID; Resource set indication corresponding to all or part of the plurality of signals; or Resource IDs corresponding to all or part of the plurality of signals.

12. The method according to claim 2 or 9, comprising at least one of the following: The configuration for the first wireless device to receive scheduling reports for the region or location-related information from the wireless communication node includes periodicity or time intervals for the scheduling reports.

13. The method according to claim 2, 3 or 9, comprising at least one of the following: The first wireless device receives from the wireless communication node an indication corresponding to or in response to an event report related to the area or location.

14. The method according to claim 13, wherein, The event corresponds to the performance degradation of the AI ​​model, or the completion of a defined number of measurement reports.

15. The method according to claim 1, comprising: The first wireless device sends a predictive measurement about the sensed target to the wireless communication node. The predicted measurement includes at least one of the following: At least one CIR matrix; At least one measurement data or intermediate variable, including at least one of the following: relative time of arrival (RTOA) at a future time, received reference signal power (RSRP), or Doppler shift; or The timestamp or indication of the future time.

16. The method according to claim 15, wherein, The predictive measurement includes at least one of the following: multiple predictive measurements for the future time, or multiple probabilities corresponding to a specific one of the predictive measurements.

17. The method according to claim 15, wherein, The indication of the future time includes at least one of the following: the time difference between the future time and the current time, the difference between the future time and the system intraframe number (SFN) of the current time, the slot number of the future time relative to the current time within the frame, or the symbol index of the future time relative to the current time.

18. The method of claim 1, comprising at least one of the following: A group of second wireless devices is determined by the first wireless device or the wireless communication node based on proximity or location; One or more measurements associated with the group of second wireless devices are performed by the first wireless device; or The first wireless device generates a joint measurement report for the group based on the one or more measurements.

19. The method according to claim 18, wherein, In the joint measurement report, for the combined measurement of one or more measurements associated with the group of second wireless devices, the combined measurement is associated with at least one of the following: The identifier (ID) of the first wireless device; or The ID of each of the second wireless devices.

20. A method comprising: The second wireless device sends multiple signals to the first wireless device, each signal being associated with a corresponding one of multiple time units; as well as The first wireless device acquires multiple measurements of the plurality of signals and performs compression on the plurality of measurements to obtain a compressed output in a channel impulse response (CIR) correlated format.

21. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform the method of any one of claims 1-20.

22. An apparatus comprising: At least one processor is configured to implement the method of any one of claims 1-21.

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