Device sensing method and apparatus, and storage medium

Abstract

The present disclosure provides a device sensing method and apparatus, and a storage medium. The method comprises: sending a sensing signal to a fourth device; determining a first signal, wherein the first signal is a signal obtained when receiving the sensing signal reflected by the fourth device; performing channel estimation on the basis of the first signal, and determining a channel state information (CSI) matrix; inputting the CSI matrix into an artificial intelligence (AI) model, and acquiring auxiliary information output by the AI model; and on the basis of the CSI matrix and the auxiliary information, according to a sensing algorithm, determining a sensing result obtained by sensing a first device. The present disclosure improves the accuracy and reliability of device sensing, and improves the availability of the ISAC technology.

Description

Device perception method and device, and storage medium Technical Field

[0001] The present disclosure relates to the field of communications, and in particular to a device perception method and apparatus, and a storage medium. Background Art

[0002] Integrated Sensing and Communication (ISAC) is a crucial research area in wireless communications. ISAC technology enables sensing services based on existing mobile communications infrastructure, leveraging the advantages of mobile communication networks to meet the sensing needs of various service scenarios. This technology facilitates numerous application services, including detection, positioning and tracking, environmental reconstruction, target imaging, and gesture and posture recognition.

[0003] Summary of the Invention

[0004] To improve the reliability of device perception and the availability of ISAC technology, embodiments of the present disclosure provide a device perception method and apparatus, and a storage medium.

[0005] According to a first aspect of an embodiment of the present disclosure, a device perception method is provided. The method is performed by a first device and includes:

[0006] sending a perception signal to a fourth device;

[0007] determining a first signal, where the first signal is a signal obtained when receiving a sensing signal reflected by a fourth device;

[0008] Perform channel estimation based on the first signal to determine a channel state information CSI matrix;

[0009] Input the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0010] Based on the CSI matrix and the auxiliary information, and according to the perception algorithm, a perception result obtained by perceiving the first device is determined.

[0011] According to a second aspect of an embodiment of the present disclosure, a device perception method is provided. The method is performed by a first device and includes:

[0012] Determine a first signal, where the first signal is a signal obtained when receiving a sensing signal reflected by a third device, and the sensing signal is sent by the second device to the third device;

[0013] Perform channel estimation based on the first signal to determine a channel state information CSI matrix;

[0014] Input the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0015] Based on the CSI matrix and the auxiliary information, and according to the sensing algorithm, a sensing result obtained by sensing at least one third device is determined.

[0016] According to a third aspect of an embodiment of the present disclosure, a device perception method is provided, including:

[0017] The first device sends a perception signal to the fourth device;

[0018] The fourth device reflects the sensing signal to the first device;

[0019] The first device determines a first signal, where the first signal is a signal obtained when receiving the sensing signal reflected by the fourth device;

[0020] The first device performs channel estimation based on the first signal and determines a channel state information CSI matrix;

[0021] The first device inputs the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0022] The first device determines, based on the CSI matrix and the auxiliary information and according to the perception algorithm, a perception result obtained by perceiving the first device.

[0023] According to a fourth aspect of an embodiment of the present disclosure, a device perception method is provided, including:

[0024] The second device sends a perception signal to at least one third device;

[0025] at least one third device reflects the sensing signal to the first device;

[0026] The first device determines a first signal, where the first signal is a signal obtained when receiving a sensing signal reflected by at least one third device;

[0027] The first device performs channel estimation based on the first signal and determines a channel state information CSI matrix;

[0028] The first device inputs the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0029] The first device determines, based on the CSI matrix and the auxiliary information and according to the perception algorithm, a perception result obtained by perceiving at least one third device.

[0030] According to a fifth aspect of an embodiment of the present disclosure, there is provided a first device, including:

[0031] a transceiver module, configured to send a perception signal to a fourth device;

[0032] a processing module configured to determine a first signal, where the first signal is a signal obtained when receiving a sensing signal reflected by a fourth device;

[0033] The processing module is further configured to perform channel estimation based on the first signal and determine a channel state information CSI matrix;

[0034] The processing module is further configured to input the CSI matrix into the artificial intelligence (AI) model and obtain auxiliary information output by the AI ​​model;

[0035] The processing module is further configured to determine a perception result obtained by perceiving the fourth device based on the CSI matrix and the auxiliary information and according to the perception algorithm.

[0036] According to a sixth aspect of an embodiment of the present disclosure, there is provided a first device, including:

[0037] a processing module configured to determine a first signal, where the first signal is a signal obtained when receiving a sensing signal reflected by a third device, where the sensing signal is sent by the second device to the third device;

[0038] The processing module is further configured to perform channel estimation based on the first signal and determine a channel state information CSI matrix;

[0039] The processing module is further configured to input the CSI matrix into the artificial intelligence (AI) model and obtain auxiliary information output by the AI ​​model;

[0040] The processing module is further configured to determine, based on the CSI matrix and the auxiliary information and in accordance with the perception algorithm, a perception result obtained by perceiving at least one third device.

[0041] According to a seventh aspect of an embodiment of the present disclosure, a first device is provided, including:

[0042] one or more processors;

[0043] The processor is used to execute the device perception method of any one of the first aspects.

[0044] According to an eighth aspect of an embodiment of the present disclosure, a first device is provided, including:

[0045] one or more processors;

[0046] The processor is used to execute the device perception method of any one of the second aspects.

[0047] According to a ninth aspect of an embodiment of the present disclosure, there is provided a communication system, including:

[0048] A first device, the first device being configured to implement the device perception method according to any one of the first aspects;

[0049] The fourth device is configured to reflect the perception signal sent by the first device.

[0050] According to a tenth aspect of an embodiment of the present disclosure, there is provided a communication system, including:

[0051] a second device, the second device being configured to send a perception signal to a third device;

[0052] a third device, the third device being configured to reflect the sensing signal toward the first device;

[0053] A first device, wherein the first device is configured to implement the device perception method of any one of the second aspects.

[0054] According to the eleventh aspect of an embodiment of the present disclosure, a storage medium is provided, which stores instructions. When the instructions are executed on a communication device, the communication device executes a device perception method as described in any one of the first, second, third or fourth aspects.

[0055] According to the twelfth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, which, when executed by a processor, is used to implement the device perception method of any one of the first aspect, the second aspect, the third aspect or the fourth aspect.

[0056] In the disclosed embodiments, a first device can perform channel estimation based on a received first signal to determine a CSI matrix. Furthermore, an AI model can be used to infer auxiliary information, thereby determining a perception result obtained by perceiving the first device or at least one third device based on the CSI matrix and the auxiliary information, in accordance with a perception algorithm. Under different perception modes, the accuracy and reliability of device perception are improved, thereby enhancing the usability of ISAC technology.

[0057] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0059] FIG1A is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.

[0060] FIG1B is another exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.

[0061] FIG1C is a schematic diagram of an exemplary scenario of a perception mode provided according to an embodiment of the present disclosure.

[0062] FIG1D is a schematic diagram of an exemplary scenario of signal types in a synaesthesia channel model provided according to an embodiment of the present disclosure.

[0063] FIG1E is an exemplary schematic diagram of determining a perception result based on a perception algorithm according to an embodiment of the present disclosure.

[0064] FIG2A is an exemplary interaction diagram of a device perception method provided according to an embodiment of the present disclosure.

[0065] FIG2B is an exemplary interaction diagram of a device perception method provided according to an embodiment of the present disclosure.

[0066] FIG2C is a flowchart illustrating an exemplary model training method according to an embodiment of the present disclosure.

[0067] FIG3A is a schematic diagram of an exemplary flow chart of a device perception method provided according to an embodiment of the present disclosure.

[0068] FIG3B is a schematic diagram of an exemplary flow chart of a device perception method provided according to an embodiment of the present disclosure.

[0069] FIG4 is a schematic diagram of an exemplary flow chart of an AI-based target perception method provided according to an embodiment of the present disclosure.

[0070] FIG5A is a schematic diagram of an exemplary interaction of a first device provided according to an embodiment of the present disclosure.

[0071] FIG5B is a schematic diagram of an exemplary interaction of a first device according to an embodiment of the present disclosure.

[0072] FIG6A is a schematic diagram of an exemplary interaction of a communication device according to an embodiment of the present disclosure.

[0073] FIG6B is an exemplary interaction diagram of a chip provided according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0074] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0075] The embodiments of the present disclosure provide a device perception method, apparatus, and storage medium.

[0076] In a first aspect, an embodiment of the present disclosure provides a device perception method, which is performed by a first device and includes:

[0077] sending a perception signal to a fourth device;

[0078] determining a first signal, where the first signal is a signal obtained when receiving a sensing signal reflected by a fourth device;

[0079] Perform channel estimation based on the first signal to determine a channel state information CSI matrix;

[0080] Input the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0081] Based on the CSI matrix and the auxiliary information, and according to the perception algorithm, a perception result obtained by perceiving the first device is determined.

[0082] In the above embodiment, the first device can infer auxiliary information through the AI ​​model, and in a self-transmitting and self-receiving scenario, reduce the impact of clutter, noise, etc. on the perception results during device perception, thereby improving the accuracy and reliability of device perception, and reducing the computational complexity of determining the perception results, thereby improving the usability of ISAC technology.

[0083] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0084] Obtain a sample CSI matrix and real auxiliary information corresponding to each sample CSI matrix;

[0085] Input the sample CSI matrix into the initial AI model to obtain the estimated auxiliary information output by the initial AI model;

[0086] Determine the loss function based on the difference between the estimated auxiliary information and the actual auxiliary information;

[0087] Based on the loss function, the initial AI model is trained until the stopping condition is met to obtain the AI ​​model.

[0088] In the above embodiment, the above method can be used to train the AI ​​model, which is simple to implement and has high usability.

[0089] In conjunction with some embodiments of the first aspect, in some embodiments, the stop condition includes at least one of the following:

[0090] Reach the number of training cycles;

[0091] The loss function falls within the tolerance range;

[0092] The accuracy of the initial AI model reaches the first value.

[0093] In the above embodiment, training can be stopped when the above stopping conditions are met, thereby obtaining an AI model and improving the reliability of AI model training.

[0094] In conjunction with some embodiments of the first aspect, in some embodiments, determining a perception result obtained by sensing the first device based on the CSI matrix and the auxiliary information according to the perception algorithm includes:

[0095] The CSI matrix and the auxiliary information are used as input values ​​of the perception algorithm, and the perception result corresponding to the first device is calculated according to the perception algorithm.

[0096] In the above embodiment, the CSI matrix and auxiliary information can be used as input values ​​of the perception algorithm at the same time, which reduces the computational complexity of determining the perception result and has high usability.

[0097] In conjunction with some embodiments of the first aspect, in some embodiments, the perception algorithm includes at least one of the following:

[0098] Multidimensional multi-signal classification MUSIC algorithm;

[0099] Multidimensional ESPRIT algorithm for estimating signal parameters using rotation invariance technique.

[0100] In the above embodiment, the perception algorithm may include but is not limited to at least one of the above, which reduces the computational complexity of determining the perception result and has high usability.

[0101] In conjunction with some embodiments of the first aspect, in some embodiments, the perception result includes at least one of the following:

[0102] a distance value between the first device and the fourth device;

[0103] The speed value of the first device;

[0104] A horizontal angle value and / or a zenith angle value between the first device and the fourth device.

[0105] In the above embodiment, the perception result may include but is not limited to at least one of the above items, thereby improving the reliability and availability of device perception.

[0106] In conjunction with some embodiments of the first aspect, in some embodiments, the first device is any of the following devices:

[0107] vehicle;

[0108] User equipment;

[0109] Unmanned vehicles;

[0110] Internet of Things (IoT) devices;

[0111] Passive Ambient IoT devices.

[0112] In the above embodiment, the first device may be any of the above devices, which improves the reliability and availability of the perception of the first device itself.

[0113] In a second aspect, an embodiment of the present disclosure provides a device perception method, which is performed by a first device and includes:

[0114] Determine a first signal, where the first signal is a signal obtained when receiving a sensing signal reflected by a third device, and the sensing signal is sent by the second device to the third device;

[0115] Perform channel estimation based on the first signal to determine a channel state information CSI matrix;

[0116] Input the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0117] Based on the CSI matrix and the auxiliary information, and according to the perception algorithm, a perception result obtained by perceiving at least one third device is determined.

[0118] In the above embodiment, the first device can infer auxiliary information through the AI ​​model, and in the scenario of receiving and transmitting at different stations, reduce the impact of clutter, noise, etc. on the perception results during device perception, thereby improving the accuracy and reliability of device perception, and reducing the computational complexity of determining the perception results, thereby improving the availability of ISAC technology.

[0119] In conjunction with some embodiments of the second aspect, in some embodiments, the auxiliary information includes:

[0120] The number of third devices.

[0121] In the above embodiment, the auxiliary information may include but is not limited to the number of third devices, thereby improving the accuracy and reliability of device perception, reducing the computational complexity of determining the perception result, and improving the usability of the ISAC technology.

[0122] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0123] Obtain a sample CSI matrix and real auxiliary information corresponding to each sample CSI matrix;

[0124] Input the sample CSI matrix into the initial AI model to obtain the estimated auxiliary information output by the initial AI model;

[0125] Determine the loss function based on the difference between the estimated auxiliary information and the actual auxiliary information;

[0126] Based on the loss function, the initial AI model is trained until the stopping condition is met to obtain the AI ​​model.

[0127] In conjunction with some embodiments of the second aspect, in some embodiments, the stop condition includes at least one of the following:

[0128] Reach the number of training cycles;

[0129] The loss function is reduced to within the tolerance range;

[0130] The accuracy of the initial AI model reaches the first value.

[0131] In conjunction with some embodiments of the second aspect, in some embodiments, determining a perception result obtained by sensing at least one third device based on the CSI matrix and the auxiliary information according to the perception algorithm includes:

[0132] The CSI matrix and the auxiliary information are used as input values ​​of the perception algorithm, and the perception result corresponding to each third device is calculated according to the perception algorithm.

[0133] In conjunction with some embodiments of the second aspect, in some embodiments, the perception algorithm includes at least one of the following:

[0134] Multidimensional multi-signal classification MUSIC algorithm;

[0135] Multidimensional ESPRIT algorithm for estimating signal parameters using rotation invariance technique.

[0136] In conjunction with some embodiments of the second aspect, in some embodiments, each perception result includes at least one of the following:

[0137] a distance value between the third device and the first device;

[0138] The speed value of the third device;

[0139] The horizontal angle value and / or the zenith angle value between the third device and the first device.

[0140] In conjunction with some embodiments of the second aspect, in some embodiments, the third device is any one of the following devices:

[0141] vehicle;

[0142] User equipment;

[0143] Unmanned vehicles;

[0144] Internet of Things (IoT) devices;

[0145] Passive Ambient IoT devices.

[0146] In a third aspect, an embodiment of the present disclosure provides a device perception method, including:

[0147] The first device sends a perception signal to the fourth device;

[0148] The fourth device reflects the sensing signal to the first device;

[0149] The first device determines a first signal, where the first signal is a signal obtained when receiving the sensing signal reflected by the fourth device;

[0150] The first device performs channel estimation based on the first signal and determines a channel state information CSI matrix;

[0151] The first device inputs the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0152] The first device determines, based on the CSI matrix and the auxiliary information and according to the perception algorithm, a perception result obtained by perceiving the first device.

[0153] In the above embodiment, the first device can send a perception signal and receive a perception signal transmitted by the fourth device, thereby obtaining a first signal and perceiving itself based on the first signal. In the self-transmitting and self-receiving scenario, the accuracy and reliability of device perception are improved, and the availability of ISAC technology is improved.

[0154] In conjunction with some embodiments of the third aspect, in some embodiments, the method further includes:

[0155] The first device obtains a sample CSI matrix and real auxiliary information corresponding to each sample CSI matrix;

[0156] The first device inputs the sample CSI matrix into the initial AI model to obtain estimated auxiliary information output by the initial AI model;

[0157] The first device determines a loss function based on a difference between the estimated auxiliary information and the true auxiliary information;

[0158] The first device trains the initial AI model based on the loss function until the stopping condition is met, thereby obtaining the AI ​​model.

[0159] In conjunction with some embodiments of the third aspect, in some embodiments, the stop condition includes at least one of the following:

[0160] Reach the number of training cycles;

[0161] The loss function is reduced to within the tolerance range;

[0162] The accuracy of the initial AI model reaches the first value.

[0163] In conjunction with some embodiments of the third aspect, in some embodiments, the first device determines, based on the CSI matrix and the auxiliary information, according to the sensing algorithm, a perception result obtained by sensing the first device, including:

[0164] The first device uses the CSI matrix and the auxiliary information as input values ​​of the perception algorithm, and calculates the perception result corresponding to the first device according to the perception algorithm.

[0165] In conjunction with some embodiments of the third aspect, in some embodiments, the perception algorithm includes at least one of the following:

[0166] Multidimensional multi-signal classification MUSIC algorithm;

[0167] Multidimensional ESPRIT algorithm for estimating signal parameters using rotation invariance technique.

[0168] In conjunction with some embodiments of the third aspect, in some embodiments, the perception result includes at least one of the following:

[0169] a distance value between the first device and the fourth device;

[0170] The speed value of the first device;

[0171] A horizontal angle value and / or a zenith angle value between the first device and the fourth device.

[0172] In conjunction with some embodiments of the third aspect, in some embodiments, the first device is any of the following devices:

[0173] vehicle;

[0174] User equipment;

[0175] Unmanned vehicles;

[0176] Internet of Things (IoT) devices;

[0177] Passive Ambient IoT devices.

[0178] In a fourth aspect, an embodiment of the present disclosure provides a device perception method, including:

[0179] The second device sends a perception signal to at least one third device;

[0180] at least one third device reflects the sensing signal to the first device;

[0181] The first device determines a first signal, where the first signal is a signal obtained when receiving a sensing signal reflected by at least one third device;

[0182] The first device performs channel estimation based on the first signal and determines a channel state information CSI matrix;

[0183] The first device inputs the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0184] The first device determines, based on the CSI matrix and the auxiliary information and according to the perception algorithm, a perception result obtained by perceiving at least one third device.

[0185] In the above embodiment, the third device can reflect the perception signal sent by the second device, the first device obtains the first signal, and perceives the third device based on the first signal. In the scenario of receiving and transmitting at different stations, the accuracy and reliability of device perception are improved, and the availability of ISAC technology is improved.

[0186] In conjunction with some embodiments of the fourth aspect, in some embodiments, the auxiliary information includes:

[0187] The number of third devices.

[0188] In conjunction with some embodiments of the fourth aspect, in some embodiments, the method further includes:

[0189] The first device obtains a sample CSI matrix and real auxiliary information corresponding to each sample CSI matrix;

[0190] The first device inputs the sample CSI matrix into the initial AI model to obtain estimated auxiliary information output by the initial AI model;

[0191] The first device determines a loss function based on a difference between the estimated auxiliary information and the true auxiliary information;

[0192] The first device trains the initial AI model based on the loss function until the stopping condition is met, thereby obtaining the AI ​​model.

[0193] In conjunction with some embodiments of the fourth aspect, in some embodiments, the stop condition includes at least one of the following:

[0194] Reach the number of training cycles;

[0195] The loss function is reduced to within the tolerance range;

[0196] The accuracy of the initial AI model reaches the first value.

[0197] In conjunction with some embodiments of the fourth aspect, in some embodiments, the first device determines, based on the CSI matrix and the auxiliary information, according to the sensing algorithm, a perception result obtained by sensing at least one third device, including:

[0198] The first device uses the CSI matrix and the auxiliary information as input values ​​of the perception algorithm, and calculates the perception result corresponding to each third device according to the perception algorithm.

[0199] In conjunction with some embodiments of the fourth aspect, in some embodiments, the perception algorithm includes at least one of the following:

[0200] Multidimensional multi-signal classification MUSIC algorithm;

[0201] Multidimensional ESPRIT algorithm for estimating signal parameters using rotation invariance technique.

[0202] In conjunction with some embodiments of the fourth aspect, in some embodiments, each perception result includes at least one of the following:

[0203] a distance value between the third device and the first device;

[0204] The speed value of the third device;

[0205] The horizontal angle value and / or the zenith angle value between the third device and the first device.

[0206] In conjunction with some embodiments of the fourth aspect, in some embodiments, the third device is any one of the following devices:

[0207] vehicle;

[0208] User equipment;

[0209] Unmanned vehicles;

[0210] Internet of Things (IoT) devices;

[0211] Passive Ambient IoT devices.

[0212] In a fifth aspect, an embodiment of the present disclosure provides a first device, including:

[0213] a transceiver module, configured to send a perception signal to a fourth device;

[0214] a processing module configured to determine a first signal, where the first signal is a signal obtained when receiving a sensing signal reflected by a fourth device;

[0215] The processing module is further configured to perform channel estimation based on the first signal and determine a channel state information CSI matrix;

[0216] The processing module is further configured to input the CSI matrix into the artificial intelligence (AI) model and obtain auxiliary information output by the AI ​​model;

[0217] The processing module is further configured to determine a perception result obtained by perceiving the first device based on the CSI matrix and the auxiliary information and according to the perception algorithm.

[0218] In a sixth aspect, an embodiment of the present disclosure provides a first device, including:

[0219] a processing module configured to determine a first signal, where the first signal is a signal obtained when receiving a perception signal reflected by a third device; wherein the perception signal is sent by the second device to the third device;

[0220] The processing module is further configured to perform channel estimation based on the first signal and determine a channel state information CSI matrix;

[0221] The processing module is further configured to input the CSI matrix into the artificial intelligence (AI) model and obtain auxiliary information output by the AI ​​model;

[0222] The processing module is further configured to determine, based on the CSI matrix and the auxiliary information and in accordance with the perception algorithm, a perception result obtained by perceiving at least one third device.

[0223] In a seventh aspect, an embodiment of the present disclosure provides a first device, including:

[0224] one or more processors;

[0225] The processor is used to execute the device perception method of any one of the first aspects.

[0226] In an eighth aspect, an embodiment of the present disclosure provides a first device, including:

[0227] one or more processors;

[0228] The processor is used to execute the device perception method of any one of the second aspects.

[0229] In a ninth aspect, an embodiment of the present disclosure provides a communication system, including:

[0230] A first device, the first device being configured to implement the device perception method according to any one of the first aspects;

[0231] The fourth device is configured to reflect the perception signal sent by the first device.

[0232] In a tenth aspect, an embodiment of the present disclosure provides a communication system, including:

[0233] a second device, the second device being configured to send a perception signal to a third device;

[0234] a third device, the third device being configured to reflect the sensing signal toward the first device;

[0235] A first device, wherein the first device is configured to implement the device perception method of any one of the second aspects.

[0236] In the eleventh aspect, an embodiment of the present disclosure proposes a storage medium storing instructions, which, when the instructions are executed on a communication device, enables the communication device to execute a device perception method such as any one of the first aspect, the second aspect, the third aspect or the fourth aspect.

[0237] In the twelfth aspect, an embodiment of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the device perception method of any one of the first aspect, the second aspect, the third aspect or the fourth aspect.

[0238] It is understandable that the first device, communication system, storage medium, and computer program are all used to execute the method proposed in the embodiment of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method and will not be repeated here.

[0239] The present disclosure provides a device perception method, apparatus, and storage medium. In some embodiments, the terms device perception method, perception processing method, target perception method, and communication method are interchangeable; the terms device perception device, perception processing device, target perception device, and communication device are interchangeable; and the terms device perception system, perception processing system, target perception system, and communication system are interchangeable.

[0240] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0241] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0242] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0243] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when articles such as "a", "an", "the" in English are used in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0244] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0245] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0246] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0247] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0248] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0249] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0250] In some embodiments, devices and equipment can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "entity", "subject", etc.

[0251] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0252] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0253] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0254] FIG1A and FIG1B are schematic diagrams showing the architecture of a communication system according to embodiments of the present disclosure.

[0255] As shown in FIG. 1A , a communication system 100 may include a first device 101 and a fourth device 104 .

[0256] In some embodiments, the first device 101 may be a device for sending a perception signal, receiving a perception signal reflected by the fourth device 104, and determining its own perception result, which may include but is not limited to any of the following:

[0257] vehicle;

[0258] User Equipment (UE);

[0259] Unmanned vehicles;

[0260] Internet of Things (IoT) devices;

[0261] Passive Ambient Internet of Things (Ambient IoT) devices.

[0262] In some embodiments, the fourth device 104 can be used to reflect the perception signal sent by the first device 101 in a self-transmitting and self-receiving scenario, which may include but is not limited to any of the following: network equipment, such as access network equipment; UE; vehicle.

[0263] In some embodiments, in FIG1A , self-awareness is achieved through self-transmission and self-reception.

[0264] As shown in FIG. 1B , a communication system 100 ′ may include a first device 101 , a second device 102 , and a third device 103 .

[0265] In some embodiments, the second device 102 may be a device for sending a perception signal, which may include but is not limited to any of the following:

[0266] Network equipment, such as access network equipment;

[0267] UE;

[0268] vehicle.

[0269] In some examples, there may be one or more third devices 103. For example, the third device 103 may be a device that needs to be sensed, and the third device 103 may also be referred to as a "sensing target."

[0270] For example, the third device 103 may include but is not limited to any of the following devices:

[0271] vehicle;

[0272] User equipment UE;

[0273] Unmanned vehicles;

[0274] IoT devices;

[0275] Ambient IoT devices.

[0276] Among them, unmanned driving equipment may include but is not limited to drones, unmanned vehicles, etc.

[0277] Among them, IoT devices may include but are not limited to smart wearable devices, such as smart watches, smart bracelets, etc., smart home devices, such as smart light bulbs, smart air conditioners, smart rice cookers, etc., smart electricity meters, smart water meters, etc.

[0278] Ambient IoT devices include, but are not limited to, devices that send data and / or signals after being triggered by other devices, such as terminals or network devices. They can be equipped with Radio Frequency Identification (RFID) tags, allowing other devices, such as terminals or network devices, to act as readers for operations such as tag inventory and data reporting.

[0279] In some instances, the first device 101 may be a device that senses the third devices 103 and determines a sensing result corresponding to each third device 103 .

[0280] In some embodiments, the first device 101 may include, but is not limited to, any of the following:

[0281] Network equipment, such as access network equipment;

[0282] UE;

[0283] vehicle.

[0284] In some instances, in FIG. 1B , the third device 103 may be perceived through inter-station transmission and reception.

[0285] In some embodiments, the above-mentioned user equipment UE includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0286] In some embodiments, the above-mentioned access network device is, for example, a node or device that accesses the terminal to the wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0287] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0288] In some embodiments, the network device may also be a core network device. The core network device may be a device including one or more network elements, or may be multiple devices or a group of devices. The network element may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0289] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0290] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0291] The following embodiments of the present disclosure may be applied to the communication system shown in FIG1A or FIG1B , or part of the subject, but are not limited thereto. The subjects shown in FIG1A or FIG1B are examples. The communication system may include all or part of the subjects in FIG1A , or may include other subjects other than those in FIG1A or FIG1B . The number and form of each subject are arbitrary. Each subject may be physical or virtual. The connection relationship between the subjects is an example. The subjects may be connected or disconnected. The connection may be in any manner, directly or indirectly, and wired or wireless.

[0292] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, systems utilizing other communication methods, and next-generation systems based on these. Furthermore, a combination of multiple systems (for example, a combination of LTE or LTE-A with 5G) may also be used.

[0293] In the disclosed embodiment, the ISAC may include but is not limited to the following six sensing modes, as shown in Figure 1C. Mono-static means self-transmission and self-reception, i.e., the transmitting end and the receiving end are the same; bi-static means inter-station transmission and reception, i.e., the transmitting end and the receiving end are different.

[0294] Sensing Mode 1: Self-transmission and self-reception. Both the transmitter and receiver are user equipment (UE), such as a person. The transmitter sends a sensing signal to another UE (car) and receives the reflected echo from the other UE (car), thereby sensing the UE's own (person's) distance, speed, angle, and other information.

[0295] Sensing mode 2: autonomous transmission and reception. Both the transmitter and receiver are user equipment (UE), such as a person. The transmitter sends a sensing signal to an access network device, such as a gNB, and receives the reflected echo from the gNB to sense the user's distance, speed, angle, and other information.

[0296] Perception mode 3: inter-site transmission and reception. The transmitter is the gNB and the receiver is the UE (car). For example, the gNB sends a perception signal, and the UE (car) receives the signal forwarded by the user (person). The UE (car) calculates the channel matrix based on the received signal to perceive information such as the distance, speed, and angle of the user (person) in the environment.

[0297] Perception mode 4: inter-site transmission and reception. The transmitter is the UE (car) and the receiver is the gNB. For example, the UE (car) sends a perception signal, and the gNB receives the signal forwarded by the user (person). The gNB calculates the channel matrix based on the received signal to perceive information such as the distance, speed, and angle of the target in the environment, such as the user (person).

[0298] Sensing mode 5, with inter-site transmission and reception, is used by gNB#1 as the transmitter and gNB#2 as the receiver. For example, gNB#1 transmits a sensing signal, and gNB#2 receives the signal forwarded by the user (human). gNB#2 calculates the channel matrix based on the received signal to sense the distance, speed, angle, and other information of the target in the environment, such as the user (human).

[0299] Perception mode 6: inter-station transmission and reception. The transmitter is UE#1 (car #1) and the receiver is UE#2 (car #2). For example, UE#1 (car #1) sends a perception signal, and UE#2 (car #2) receives the signal forwarded by the user (person). UE#2 (car #2) calculates the channel matrix based on the received signal to perceive information such as the distance, speed, and angle of targets in the environment, such as the user (person).

[0300] It is understood that the aforementioned sensing target, namely the third device 103, may include drones, people in indoor and outdoor scenarios, cars on highways in outdoor scenarios, automated guided vehicles in factories in indoor scenarios, and dangerous targets on roadways or railways. Synaesthesia integration primarily involves the receiver calculating information such as the distance, speed, and angle of the sensing target in the environment based on received signals. The sensing target can be the receiver itself or another object.

[0301] In some embodiments, taking self-transmission and self-reception as an example, first device 101 and second device 102 are the same device, both assumed to be gNBs, and third device 103 is assumed to be a vehicle. For example, as shown in Figure 1C, in this scenario, there are a vehicle, a gNB, and an obstruction, which may be another UE or debris. Therefore, four types of signals can be considered in the synaesthesia channel model, namely:

[0302] Perceived Line Of Sight (LOS) path; Perceived Non-Line Of Sight (NLOS) path; Clutter LOS path; Clutter NLOS path.

[0303] In addition to the above four types of signals, the influence of channel noise also needs to be considered.

[0304] For example, as shown in FIG1D , the four types of signals are described as follows:

[0305] Perception LOS path: The path between the sensing signal reflector and the sensing target is the perception LOS path, and the path between the sensing target and the sensing signal receiver is also the perception LOS path (for example, the black solid line path in Figure 1D).

[0306] Perception NLOS path: The path between the sensing signal transmitter and the sensing target that needs to pass through debris is the perception NLOS path. The path between the sensing target and the sensing signal receiver that needs to pass through debris is also the perception NLOS path (for example, the blue solid line path in Figure 1D).

[0307] Clutter LOS path: The path between the sensing signal transmitter and the scattering clusters in the environment (such as debris) is the clutter LOS path. The path between the scattering clusters in the environment and the sensing signal receiver is also the clutter LOS path (such as the red dashed path in Figure 1D).

[0308] Clutter NLOS path: The path between the sensing signal transmitter and the scattering clusters (such as debris) in the environment that still needs to pass through the debris is the sensing NLOS path. The path between the scattering clusters (such as debris) in the environment and the sensing signal receiver that still needs to pass through the debris is also a sensing NLOS path (for example, the green dashed path in Figure 1D).

[0309] In the embodiment of the present disclosure, the main information for sensing the target is the channel state information (CSI) matrix H of the receiving end. The CSI matrix H is the superposition of the sensing target channel, the clutter channel and the noise, and can reflect the channel environment in which the sensing signal is located and the state information of the sensing target in the environment. The dimension of the CSI matrix H is related to the specific parameter settings in the actual synaesthesia scenario, and the information of the data in its different dimensions can reflect different types of state information of the sensing target. In the embodiment of the present disclosure, one possible situation is given:

[0310] The CSI matrix H usually includes three dimensions: subcarrier, Orthogonal Frequency Division Multiplexing (OFDM) symbol, and receiving antenna port.

[0311] Specifically, the channel matrix H represents the channel state information CSI, which can be a complex matrix of M×S×P, where M is the number of OFDM symbols, S is the number of subcarriers, and P is the number of receive antenna ports. The element H at the position of the mth OFDM symbol, the sth subcarrier, and the pth receive antenna port in the channel matrix H is m,s,p is a complex number Z m,s,p , as shown in Formula 1, which represents the impact on the amplitude and phase of the signal during its propagation from the transmitting antenna to the receiving antenna: m,s,p =Z m,s,p =a m,s,p +i×b m,s,p Formula 1

[0312] Among them, a m,s,p 、b m,s,p Used to represent the complex number Z m,s,p The value of the real part and the value of the imaginary part. m,s,p 、b m,s,p Can be a real number.

[0313] According to the received signal model, the phase shifts in the three dimensions of the channel matrix do not affect each other, and different types of perception results of the target can be obtained based on the phase changes in the three dimensions. That is, the distance of the target can be estimated based on the phase difference caused by the time delay between subcarriers, the speed of the target can be estimated based on the phase difference caused by the Doppler effect between OFDM symbols, and the angle of the target can be estimated based on the phase change between the received signals of multiple antenna ports.

[0314] In some embodiments, the perception results of the three parameters of the distance, speed, and angle of the perception target can be obtained based on the CSI matrix through the perception algorithm, as shown in Figure 1E.

[0315] When determining perception results through perception algorithms, the applicable scenarios will be limited. For example, it will be affected by factors such as clutter and noise in the scene. In addition, the calculation of perception results determined by perception algorithms has high negative clutter and its accuracy needs to be improved.

[0316] Therefore, the present disclosure provides the following device perception method and apparatus, and storage medium.

[0317] FIG2A is an interactive diagram of a device perception method according to an embodiment of the present disclosure. As shown in FIG2A , the present disclosure embodiment relates to a device perception method, wherein the method is used in the scenario shown in FIG1A , i.e., a self-transmitting and self-receiving scenario, and the method includes:

[0318] Step S2101: The first device 101 sends a perception signal to the fourth device 104.

[0319] In some embodiments, the first device 101 is a device that sends a sensing signal, receives the sensing signal reflected by the fourth device 104, and determines its own sensing result. The first device 101 may also be referred to as a "sensing target."

[0320] In some embodiments, the fourth device 104 is configured to reflect the sensing signal in a self-transmitting and self-receiving scenario.

[0321] In some embodiments, the sensing signal is a signal that enables the first device 101 to sense itself. For example, the sensing signal may include but is not limited to a Channel State Information-Reference Signal (CSI-RS).

[0322] In some embodiments, the perception signal sent by the first device 101 needs to be reflected by the fourth device 104 before being received by the first device 101 , so that the first device 101 can perceive itself.

[0323] Illustratively, in the above-mentioned sensing mode 1 and sensing mode 2, the first device 101 itself sends a sensing signal and receives a reflected echo of the sensing signal passing through the fourth device 104 .

[0324] Exemplarily, in the above-mentioned perception mode 1 and perception mode 2, the first device 101 is the same user (person).

[0325] For example, in sensing mode 1, the fourth device 104 may be a vehicle.

[0326] Exemplarily, in perception mode 2, the fourth device 104 may be an access network device, such as a gNB.

[0327] For example, the fourth device 104 may also be a UE or other devices, which is not detailed in the present disclosure.

[0328] In some embodiments, the specific descriptions of the first device 101 and the fourth device 104 have been introduced in the embodiment of Figure 1A and will not be repeated here.

[0329] In step S2102 , the fourth device 104 reflects a sensing signal to the first device 101 .

[0330] In some embodiments, in perception mode 1 and perception mode 2, the first device 101 sends a perception signal to the fourth device 104, and the fourth device 104 reflects the perception signal to the first device 101. At this time, the signal received by the first device 101 is the first signal.

[0331] In step S2103 , the first device 101 performs channel estimation based on the first signal and determines a CSI matrix H.

[0332] In some embodiments, the first device 101 performs channel estimation based on the received first signal to determine the CSI matrix H. Assume that M is the number of OFDM symbols, S is the number of subcarriers, and P is the number of receiving antenna ports. The element H at the position of the mth OFDM symbol, the sth subcarrier, and the pth receiving antenna port in the CSI matrix H is m,s,p , which can be a complex number Z m,s,p , for example, expressed by formula 1: H m,s,p =Z m,s,p =a m,s,p +i×b m,s,p Formula 1

[0333] Among them, a m,s,p 、b m,s,p Used to represent the complex number Z m,s,p The value of the real part and the value of the imaginary part. m,s,p 、b m,s,p Can be a real number.

[0334] In step S2104, the first device 101 inputs the CSI matrix H into the AI ​​model to obtain auxiliary information output by the AI ​​model.

[0335] In some embodiments, the auxiliary information may be used to assist the first device 101 in determining a perception result of the perception of the first device 101 .

[0336] In some embodiments, the auxiliary information may include, but is not limited to, the number of first devices 101. In a self-transmitting and self-receiving scenario, the number of first devices 101 may be 1. In some embodiments, the name of the artificial intelligence (AI) model is not limited and can be interchangeable with an AI multi-target quantity estimation model, a target quantity estimation model, and the like.

[0337] In some embodiments, the AI ​​model can be used to infer auxiliary information, such as the number of first devices 101, that is, the number of perceived targets.

[0338] In some embodiments, the input value of the AI ​​model is the CSI matrix H, and the output value is the number n of first devices 101. In the self-transmitting and self-receiving scenario, n can be 1.

[0339] In some embodiments, the training process of the AI ​​model will be introduced in subsequent embodiments and will not be introduced here.

[0340] In step S2105, the first device 101 determines a perception result obtained by perceiving the first device 101 based on the CSI matrix and the auxiliary information according to the perception algorithm. In some embodiments, the perception result corresponding to the first device 101 may include but is not limited to at least one of the following:

[0341] A distance value between the first device 101 and the fourth device 104;

[0342] a speed value of the first device 101;

[0343] The horizontal angle value and / or the zenith angle value between the first device 101 and the fourth device 104 .

[0344] The distance value may be expressed as d, which may be used to indicate a distance value between the first device 101 and the fourth device 104 .

[0345] The speed value of the first device 101 may be expressed as v, which may be used to indicate the running speed of the first device 101 relative to a stationary object.

[0346] The horizontal angle value and / or zenith angle value between the first device 101 and the fourth device 104 can be expressed as θ. The horizontal angle value refers to the angle value obtained by projecting the lines connecting the first device 101 and the fourth device 104 onto the horizontal plane, such as the center of the earth or other specified location. The zenith angle value refers to the angle value of the line connecting the first device 101 and the fourth device 104 relative to the ground normal.

[0347] In some embodiments, the perception algorithm may be a non-AI perception algorithm, i.e., when the first device 101 calculates the perception result according to the perception algorithm, no AI model or AI technology is involved. For example, the non-AI perception algorithm may include, but is not limited to, at least one of the following: a Multiple Signal Classification (MUSIC) algorithm; or an Estimation of Signal Parameters using Rotational Invariance Techniques (ESPRIT) algorithm.

[0348] In some embodiments, the (non-AI) perception algorithm employed in the present disclosure may include, but is not limited to, at least one of the following:

[0349] Multidimensional MUSIC algorithm;

[0350] Multidimensional ESPRIT algorithm.

[0351] In one example, the dimension of the perception algorithm may be equal to the number of parameter types included in each perception result.

[0352] For example, each perception result includes three parameters: distance value, speed value, and angle value, so the dimension of the perception algorithm can be three-dimensional.

[0353] In some embodiments, the CSI matrix and the auxiliary information may be used together as input values ​​of a perception algorithm so as to perform calculations according to the perception algorithm, thereby obtaining a perception result corresponding to the first device 101 .

[0354] In some embodiments, the MUSIC algorithm is used for spatial spectrum estimation, estimating the number of spatial signals incident on an array, as well as the strength and direction of their sources. Its core is to perform eigendecomposition on the covariance matrix of the received data, separating the signal subspace from the noise subspace. Leveraging the orthogonality of the signal direction vector and the noise subspace, a spatial scanning spectrum can be constructed, allowing for a global search for spectral peaks and signal parameter estimation.

[0355] In some embodiments, the ESPRIT algorithm can utilize the rotational invariance of the antenna array to estimate the angle of the signal, etc.

[0356] The above description is merely an exemplary description, and the present disclosure does not limit the solution in which the first device 101 calculates the perception result corresponding to the first device 101 using the perception algorithm based on the CSI matrix H and the auxiliary information.

[0357] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0358] In some embodiments, terms such as "send", "reflect", "report", "send", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0359] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0360] In some embodiments, terms such as "certain", "preseted", "preset", "setting", "indicated", "a certain", "any", "first", and "designated" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0361] In some embodiments, the device perception method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2105. For example, step S2101 can be implemented as an independent embodiment, step S2102 can be implemented as an independent embodiment, steps S2101+S2102 can be implemented as an independent embodiment, step S2103 can be implemented as an independent embodiment, step S2104 can be implemented as an independent embodiment, step S2105 can be implemented as an independent embodiment, steps S2104+S2105 can be implemented as an independent embodiment, steps S2103+S2104+S2105 can be implemented as an independent embodiment, and steps S2101 to S2105 can be implemented as independent embodiments, but are not limited thereto.

[0362] In some embodiments, step S2101 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, when other execution entities, such as the second device 102, send a perception signal, step S2101 may not be performed.

[0363] In some embodiments, step S2102 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, if the first device 101 receives a sensing signal reflected by another execution entity, such as the third device 103, and thus obtains the first signal, step S2102 may not be performed.

[0364] In some embodiments, step S2103 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, when the first device 101 determines the CSI matrix using other methods, step S2103 may not be performed.

[0365] In some embodiments, step S2104 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, when the first device 101 does not need to determine the auxiliary information, step S2104 may not be performed.

[0366] In some embodiments, step S2105 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, when the first device 101 determines the perception result corresponding to the first device 101 based on the AI ​​model, step S2105 may not be performed.

[0367] In some embodiments, steps S2101 to S2105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0368] In some embodiments, the execution order of steps S2101 to S2105 is not limited.

[0369] In the above embodiment, the first device can send a perception signal to the fourth device, which then reflects the signal. The first device then performs channel estimation based on the received first signal, thereby determining the CSI matrix. Furthermore, auxiliary information can be inferred through an AI model, and the perception result corresponding to the first device can be calculated based on the CSI matrix and the auxiliary information according to the perception algorithm. In the self-transmitting and self-receiving scenario, the accuracy and reliability of device perception are improved, and the usability of ISAC technology is enhanced.

[0370] Figure 2B is an interactive diagram of a device perception method according to an embodiment of the present disclosure. As shown in Figure 2B, the embodiment of the present disclosure relates to a device perception method, wherein the method is used in the scenario shown in Figure 1B, that is, in a different station transmission and reception scenario, and the above method includes:

[0371] Step S2201: The second device 102 sends a perception signal to the third device 103.

[0372] In some embodiments, the second device 102 is a device that sends a sensing signal.

[0373] In some embodiments, the first device 101 is a device that determines a perception result.

[0374] In some embodiments, the third device 103 refers to a device that needs to be sensed, and may also be referred to as a "sensing target."

[0375] In some embodiments, the perception signal is a signal that enables the first device 101 to perceive the third device 103. Exemplarily, the perception signal may include but is not limited to a CSI-RS.

[0376] In some embodiments, in an inter-station transmission and reception scenario, the second device 102 sends a perception signal to the third device 103 , and the third device 103 reflects the perception signal to the first device 101 , so that the first device 101 perceives the third device 103 .

[0377] In some embodiments, in the above-mentioned sensing modes 3 to 6, the second device 102 sends a sensing signal to the third device 103.

[0378] In one example, in perception mode 3, the second device 102 may be an access network device, such as a gNB, the third device 103 may be a user (person), and the first device 101 may be a vehicle.

[0379] In one example, in perception mode 4, the second device 102 may be a vehicle, the third device 103 may be a user (person), and the first device 101 may be an access network device, such as a gNB.

[0380] In one example, in perception mode 5, the second device 102 may be an access network device, such as gNB#1, the third device 103 may be a user (person), and the first device 101 may be an access network device, such as gNB#2.

[0381] In one example, in perception mode 6, the second device 102 may be vehicle #1, the third device 103 may be a user (person), and the first device 101 may be vehicle #2.

[0382] In some embodiments, the specific descriptions of the first device 101, the second device 102, and the third device 103 have been introduced in the embodiment of Figure 1B and will not be repeated here.

[0383] In step S2202 , the third device 103 reflects a sensing signal to the first device 101 .

[0384] In some embodiments, in perception mode 3 to perception mode 6, the second device 102 sends a perception signal to the third device 103, and the third device 103 reflects the perception signal to the first device 101. At this time, the signal received by the first device 101 is the first signal.

[0385] In step S2203 , the first device 101 inputs the CSI matrix H into the AI ​​model to obtain auxiliary information output by the AI ​​model.

[0386] In some embodiments, the implementation of step S2203 is similar to the aforementioned step S2103 and will not be repeated here.

[0387] In step S2204, the first device 101 inputs the CSI matrix H into the AI ​​model to obtain auxiliary information output by the AI ​​model.

[0388] In some embodiments, the implementation of step S2204 is similar to the aforementioned step S2104 and will not be repeated here.

[0389] In step S2205 , the first device 101 determines a perception result obtained by perceiving at least one third device 103 based on the CSI matrix and the auxiliary information and according to a perception algorithm.

[0390] In some embodiments, the perception result corresponding to each third device 103 may include but is not limited to at least one of the following:

[0391] The distance between the third device 103 and the first device 101;

[0392] a speed value of the third device 103;

[0393] The horizontal angle value and / or the zenith angle value between the third device 103 and the first device 101 .

[0394] The distance value may be expressed as d, which may be used to indicate a distance value of the third device 103 relative to the first device 101 .

[0395] The speed value of the third device 103 may be expressed as v, which may be used to indicate the running speed of the third device 103 relative to the stationary object.

[0396] The horizontal angle value and / or zenith angle value between the third device 103 and the reference device can be expressed as θ, where the horizontal angle value refers to the angle value obtained by projecting the lines connecting the reference point, such as the center of the earth or other specified location, to the third device 103 and the first device 101 onto the horizontal plane. The zenith angle value refers to the angle value of the line connecting the third device 103 and the first device 101 relative to the ground normal.

[0397] In some embodiments, the implementation of step S2205 is similar to the aforementioned step S2105 and will not be repeated here.

[0398] In some embodiments, the device perception method involved in the embodiments of the present disclosure may include at least one of steps S2201 to S2205. For example, step S2201 can be implemented as an independent embodiment, step S2202 can be implemented as an independent embodiment, steps S2201+S2202 can be implemented as an independent embodiment, step S2203 can be implemented as an independent embodiment, step S2204 can be implemented as an independent embodiment, step S2205 can be implemented as an independent embodiment, steps S2204+S2205 can be implemented as an independent embodiment, steps S2203+S2204+S2205 can be implemented as an independent embodiment, and steps S2201 to S2205 can be implemented as independent embodiments, but are not limited thereto.

[0399] In some embodiments, step S2201 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, when other execution entities, such as the first device 101, send a perception signal, step S2201 may not be performed.

[0400] In some embodiments, step S2202 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, if the first device 101 receives a sensing signal reflected by another execution entity, such as the fourth device 104, and thus obtains the first signal, step S2202 may not be performed.

[0401] In some embodiments, step S2203 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, when the first device 101 determines the CSI matrix using other methods, step S2203 may not be performed.

[0402] In some embodiments, step S2204 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, when the first device 101 does not need to determine the auxiliary information, step S2204 may not be performed.

[0403] In some embodiments, step S2205 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For example, when the first device 101 determines the perception result corresponding to the third device 103 based on the AI ​​model, step S2205 may not be performed.

[0404] In some embodiments, steps S2201 to S2205 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0405] In some embodiments, the execution order of steps S2201 to S2205 is not limited.

[0406] In the above embodiment, the second device can send a perception signal to the third device, the third device reflects it, and the first device performs channel estimation based on the received first signal, thereby determining the CSI matrix. Furthermore, auxiliary information can be inferred through the AI ​​model, and based on the CSI matrix and auxiliary information, the perception results corresponding to n third devices can be calculated according to the perception algorithm. In the scenario of receiving and transmitting at different stations, the accuracy and reliability of device perception are improved, and the availability of ISAC technology is improved.

[0407] FIG2C is a flow chart of a model training method according to an embodiment of the present disclosure. As shown in FIG2C , the embodiment of the present disclosure relates to a model training method for training an AI model. The method can be executed by the first device 101 and specifically includes:

[0408] Step S2301: Acquire a sample CSI matrix and real auxiliary information corresponding to each sample CSI matrix.

[0409] In some embodiments, the first device 101 can obtain a training set, which can be used to train the initial AI model, and may include one or more sample CSI matrices and real auxiliary information corresponding to each sample CSI matrix, such as the real number of "perceived targets".

[0410] In some embodiments, the first device 101 may obtain a validation set, which may be used to verify the accuracy of the initial AI model, and may include one or more sample CSI matrices and real auxiliary information corresponding to each sample CSI matrix.

[0411] In one example, the training set and the validation set may be the same set, or the training set may be a subset of the validation set, or the validation set may be a subset of the training set, or the training set and the validation set may not have the same sample CSI matrix, or may have partially the same sample CSI matrix. This disclosure is not limited to this.

[0412] Step S2302: Input the sample CSI matrix into the initial AI model to obtain the estimated auxiliary information output by the initial AI model.

[0413] In some embodiments, the initial AI model can use a residual network (RESNET) or a Visual Geometry Group (VGG) network as its backbone network, and may include, but is not limited to, at least one of the following network layers: input layer; convolution layer; pooling layer; activation function layer; connection layer; and output layer. This disclosure does not limit the architecture of the initial AI model.

[0414] In some embodiments, the first device 101 may input the sample CSI matrix into the initial AI model, thereby obtaining estimated auxiliary information output by the initial AI model, such as the estimated number of “perception targets”.

[0415] Step S2303: Determine a loss function based on the difference between the estimated auxiliary information and the actual auxiliary information.

[0416] In some embodiments, the loss function may be determined based on the difference between the estimated number and the actual number of “perception targets”.

[0417] Step S2304: Based on the loss function, the initial AI model is trained until the stopping condition is met to obtain the AI ​​model.

[0418] In some embodiments, the first device 101 reduces the loss function based on the loss function by using, for example, a stochastic gradient descent (SGD) algorithm.

[0419] In some embodiments, the stop condition may include but is not limited to at least one of the following:

[0420] Reach the number of training cycles;

[0421] On the training set and / or validation set, the loss function falls within the tolerance range;

[0422] The accuracy of the initial AI model reaches a first value on the training set and / or the validation set.

[0423] In some embodiments, the number of training cycles may be determined by a protocol or configured by a network device, which is not limited in this disclosure. To reduce the loss function, the first device 101 increases the number of training cycles by 1 each time it adjusts the network layer parameters of the initial AI model. The first device 101 may stop training when the number of training cycles for the initial AI model reaches the specified number of training cycles, at which point the AI ​​model can be obtained.

[0424] In some embodiments, the fault tolerance range may be determined by a protocol or a network device configuration, which is not limited in this disclosure. The first device 101 may determine that when the loss function on the training set or the validation set falls within the fault tolerance range, stop training and obtain an AI model.

[0425] In one example, the verification set may be a set pre-acquired by the first device 101 for verifying the performance of the AI ​​model, which may include one or more sample CSI matrices and real auxiliary information corresponding to each sample CSI matrix.

[0426] In some embodiments, the first value may be determined by a protocol or configured by a network device, which is not limited in this disclosure. The first device 101 may determine the accuracy of the initial AI model on a training set or a validation set, and when the first value is reached, stop training to obtain the AI ​​model. The description of the training set and validation set has been introduced in the previous embodiment and will not be repeated here.

[0427] In some embodiments, steps S2301 to S2304 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0428] In some embodiments, the execution order of steps S2301 to S2304 is not limited.

[0429] In the above embodiment, the first device can be trained in the above manner to obtain an AI model, and the AI ​​model can be used to infer auxiliary information, which is simple to implement and has high usability.

[0430] FIG3A is a flow chart of a device perception method according to an embodiment of the present disclosure. As shown in FIG3A , the present disclosure embodiment relates to a device perception method, which can be performed by a first device 101 and includes:

[0431] Step S3101: Send a perception signal.

[0432] In some embodiments, the first device 101 sends a perception signal to the fourth device 104 .

[0433] In some embodiments, the fourth device 104 reflects the sensing signal.

[0434] In some embodiments, the optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts of the embodiment involved in Figure 2A, which will not be repeated here.

[0435] Step S3102, obtain a first signal.

[0436] In some embodiments, the first device 101 may receive a perception signal reflected by the fourth device 104 to obtain the first signal, but is not limited thereto. The first device 101 may also receive a perception signal reflected by other execution entities to obtain the first signal.

[0437] In some embodiments, the first device 101 obtains a first signal determined according to a predefined rule.

[0438] In some embodiments, the first device 101 performs processing to obtain the first signal.

[0439] In some embodiments, step S3102 is omitted, the first device 101 autonomously implements the function indicated by the first signal, or the first device 101 obtains the first signal based on predefined rules or protocol agreements, or the above function is default or default.

[0440] In some embodiments, the optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts of the embodiment involved in Figure 2A, which will not be repeated here.

[0441] Step S3103: determine the CSI matrix.

[0442] In some embodiments, the optional implementation of step S3103 can refer to the optional implementation of step S2103 in Figure 2A and other related parts of the embodiment involved in Figure 2A, which will not be repeated here.

[0443] Step S3104: determine auxiliary information.

[0444] In some embodiments, the optional implementation of step S3104 can refer to the optional implementation of step S2104 in Figure 2A and other related parts of the embodiment involved in Figure 2A, which will not be repeated here.

[0445] Step S3105: Determine the perception result corresponding to the first device 101.

[0446] In some embodiments, the optional implementation of step S3105 can refer to the optional implementation of step S2105 in Figure 2A and other related parts of the embodiment involved in Figure 2A, which will not be repeated here.

[0447] In some embodiments, steps S3101 to S3105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0448] In some embodiments, the execution order of steps S3101 to S3105 is not limited.

[0449] In the above embodiment, the first device can perform channel estimation based on the received first signal to determine the CSI matrix. Furthermore, auxiliary information can be inferred using an AI model, and the perception result obtained by perceiving the first device itself can be determined according to the perception algorithm. In the self-transmitting and self-receiving scenario, the accuracy and reliability of device perception are improved, and the usability of ISAC technology is enhanced.

[0450] FIG3B is a flow chart of a device perception method according to an embodiment of the present disclosure. As shown in FIG3B , the present disclosure embodiment relates to a device perception method, which can be performed by the first device 101 and includes:

[0451] Step S3201, obtain a first signal.

[0452] In some embodiments, the first device 101 can receive a perception signal reflected by the third device 103 to obtain the first signal, wherein the perception signal is sent by the second device 102 to the third device 103, but is not limited to this. The first device 101 can also receive a perception signal reflected by other execution entities to obtain the first signal.

[0453] In some embodiments, the first device 101 obtains a first signal determined according to a predefined rule.

[0454] In some embodiments, the first device 101 performs processing to obtain the first signal.

[0455] In some embodiments, step S3201 is omitted, the first device 101 autonomously implements the function indicated by the first signal, or the first device 101 obtains the first signal based on predefined rules or protocol agreements, or the above function is default or default.

[0456] In some embodiments, the optional implementation of step S3201 can refer to the optional implementation of step S2202 in Figure 2B and other related parts of the embodiment involved in Figure 2B, which will not be repeated here.

[0457] Step S3202: Determine the CSI matrix.

[0458] In some embodiments, the optional implementation of step S3203 can refer to the optional implementation of step S2203 in Figure 2B and other related parts of the embodiment involved in Figure 2B, which will not be repeated here.

[0459] Step S3203: determine auxiliary information.

[0460] In some embodiments, the optional implementation of step S3203 can refer to the optional implementation of step S2204 in Figure 2B and other related parts of the embodiment involved in Figure 2B, which will not be repeated here.

[0461] Step S3204: Determine the perception result corresponding to the third device 103.

[0462] In some embodiments, the optional implementation of step S3204 can refer to the optional implementation of step S2105 in Figure 2B and other related parts of the embodiment involved in Figure 2B, which will not be repeated here.

[0463] In some embodiments, steps S3201 to S3204 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0464] In some embodiments, the execution order of steps S3201 to S3204 is not limited.

[0465] In the above embodiment, the first device can perform channel estimation based on the received first signal to determine the CSI matrix. Furthermore, auxiliary information can be inferred through an AI model, and the perception result obtained by perceiving at least one third device can be determined according to the perception algorithm. In scenarios where transmission and reception are performed at different stations, the accuracy and reliability of device perception are improved, thereby enhancing the usability of ISAC technology.

[0466] The above process is further illustrated below with examples.

[0467] The AI-based integrated synaesthesia method proposed in this paper primarily detects the distance, speed, and angle of a target. The type of information required for perception may vary depending on the application scenario and service. While distance, speed, and angle information can meet the needs of most scenarios, the method proposed in this paper can also be applied to situations requiring the perception of other types of information.

[0468] This paper proposes an AI-assisted multi-target perception method that estimates the specific number of perceived targets in integrated communication and perception scenarios based on an AI neural network model. Training the perceived target estimation model requires first collecting a training dataset. The dataset contains a certain number of data samples, each of which consists of two parts: the channel state information matrix H (the input data of the AI ​​perceived target estimation model) and the actual number of perceived targets n′ (the output data of the AI ​​perceived target estimation model).

[0469] The multi-target population estimation model estimates the number of perceived targets n based on the input channel state information matrix H. Therefore, during model training, the error between the model output (the estimated number of targets n) and the labels in the training set (the actual number of perceived targets n′) is used as a loss function. Model training is performed using methods such as stochastic gradient descent (SGD) to reduce the loss function, allowing the model to learn the mapping relationship between the input channel matrix and the output number of perceived targets. Model training is completed after a specific number of training cycles or when the model accuracy meets certain requirements.

[0470] The main application process of the AI-assisted multi-target perception method proposed in this invention is shown in Figure 4. The AI ​​multi-target estimation model estimates the number of perceived targets n with higher accuracy based on the channel matrix. The highly accurate number of perceived targets n estimated by the AI ​​model serves as auxiliary information for the non-AI perception algorithm. The non-AI perception algorithm calculates the distance, speed, angle, and other perception results of the n targets based on this auxiliary information and the channel state information matrix H at the receiving end.

[0471] Among them, the specific model structure and implementation of the multi-target quantity estimation model depend on the design and deployment in the actual application process, but the training data processing method, model input and output data type and dimension, and model training and application process provided by the present invention are also applicable.

[0472] The embodiments of the present disclosure also propose an apparatus for implementing any of the above methods. For example, an apparatus is proposed, which includes units or modules for implementing each step performed by each device (such as the first device, the second device, the third device, the fourth device, etc.) in any of the above methods.

[0473] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0474] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0475] FIG5A is a schematic diagram of the structure of a first device according to an embodiment of the present disclosure. As shown in FIG5A , the first device 5100 may include a transceiver module 5101 and a processing module 5102 .

[0476] In some embodiments, the transceiver module 5101 is configured to send a perception signal to a fourth device.

[0477] In some embodiments, the processing module 5102 is configured to determine a first signal, where the first signal is a signal obtained when receiving a perception signal reflected by the fourth device.

[0478] In some embodiments, the processing module 5102 is further configured to:

[0479] Perform channel estimation based on the first signal to determine a channel state information CSI matrix;

[0480] Input the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0481] Based on the CSI matrix and the auxiliary information, and according to the perception algorithm, a perception result obtained by perceiving the first device is determined.

[0482] Optionally, the above-mentioned transceiver module 5101 is used to execute at least one of the communication steps such as sending and / or receiving (such as step S2101, but not limited to this) performed by the first device 5100 in any of the above methods, which will not be repeated here.

[0483] Optionally, the processing module 5102 is used to execute at least one of the other steps (such as step S2103, step S2104, step S2105, but not limited thereto) performed by the first device 5100 in any of the above methods, which will not be repeated here.

[0484] In some embodiments, the processing module 5102 is further configured to:

[0485] Obtain a sample CSI matrix and real auxiliary information corresponding to each sample CSI matrix;

[0486] Input the sample CSI matrix into the initial AI model to obtain the estimated auxiliary information output by the initial AI model;

[0487] Determine the loss function based on the difference between the estimated auxiliary information and the actual auxiliary information;

[0488] Based on the loss function, the initial AI model is trained until the stopping condition is met to obtain the AI ​​model.

[0489] In some embodiments, the stop condition includes at least one of the following:

[0490] Reach the number of training cycles;

[0491] The loss function is reduced to within the tolerance range;

[0492] The accuracy of the initial AI model reaches the first value.

[0493] In some embodiments, the processing module 5102 is further configured to:

[0494] The CSI matrix and the auxiliary information are used as input values ​​of the perception algorithm, and the perception result corresponding to the first device is calculated according to the perception algorithm.

[0495] In some embodiments, the perception algorithm includes at least one of the following:

[0496] Multidimensional multi-signal classification MUSIC algorithm;

[0497] Multidimensional ESPRIT algorithm for estimating signal parameters using rotation invariance technique.

[0498] In some embodiments, each perception result includes at least one of the following:

[0499] a distance value between the first device and the fourth device;

[0500] The speed value of the first device;

[0501] A horizontal angle value and / or a zenith angle value between the first device and the fourth device.

[0502] In some embodiments, the first device is any of the following:

[0503] vehicle;

[0504] User equipment;

[0505] Unmanned vehicles;

[0506] Internet of Things (IoT) devices;

[0507] Passive Ambient IoT devices.

[0508] FIG5B is a schematic diagram of the structure of a first device according to an embodiment of the present disclosure. As shown in FIG5B , the first device 5200 may include: a processing module 5201 .

[0509] In some embodiments, the processing module 5201 is configured to determine a first signal, where the first signal is a signal obtained when receiving a perception signal reflected by a third device; wherein the perception signal is sent by the second device to the third device.

[0510] The processing module 5201 is further configured to:

[0511] Perform channel estimation based on the first signal to determine a channel state information CSI matrix;

[0512] Input the CSI matrix into the artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model;

[0513] Based on the CSI matrix and the auxiliary information, and according to the perception algorithm, a perception result obtained by perceiving at least one third device is determined.

[0514] Optionally, the processing module 5201 is used to execute at least one of the other steps (such as step S2203, step S2204, step S2205, but not limited thereto) performed by the first device 5100 in any of the above methods, which will not be repeated here.

[0515] In some embodiments, the auxiliary information includes:

[0516] The number of third devices.

[0517] In some embodiments, the processing module 5201 is further configured to:

[0518] Obtain a sample CSI matrix and real auxiliary information corresponding to each sample CSI matrix;

[0519] Input the sample CSI matrix into the initial AI model to obtain the estimated auxiliary information output by the initial AI model;

[0520] Determine the loss function based on the difference between the estimated auxiliary information and the actual auxiliary information;

[0521] Based on the loss function, the initial AI model is trained until the stopping condition is met to obtain the AI ​​model.

[0522] In some embodiments, the stop condition includes at least one of the following:

[0523] Reach the number of training cycles;

[0524] The loss function is reduced to within the tolerance range;

[0525] The accuracy of the initial AI model reaches the first value.

[0526] In some embodiments, the processing module 5201 is further configured to:

[0527] The CSI matrix and the auxiliary information are used as input values ​​of the perception algorithm, and the perception result corresponding to each third device is calculated according to the perception algorithm.

[0528] In some embodiments, the perception algorithm includes at least one of the following:

[0529] Multidimensional multi-signal classification MUSIC algorithm;

[0530] Multidimensional ESPRIT algorithm for estimating signal parameters using rotation invariance technique.

[0531] In some embodiments, each perception result includes at least one of the following:

[0532] a distance value between the third device and the first device;

[0533] The speed value of the third device;

[0534] The horizontal angle value and / or the zenith angle value between the third device and the first device.

[0535] In some embodiments, the third device is any of the following:

[0536] vehicle;

[0537] User equipment;

[0538] Unmanned vehicles;

[0539] Internet of Things (IoT) devices;

[0540] Passive Ambient IoT devices.

[0541] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0542] In some embodiments, the processing module may be a single module or may include multiple submodules. Optionally, the multiple submodules each execute all or part of the steps required by the processing module. Optionally, the processing module and the processor may be interchangeable.

[0543] Figure 6A is a schematic diagram of the structure of a communication device 6100 proposed in an embodiment of the present disclosure. Communication device 6100 can be any device (e.g., a first device, a second device, a third device, a fourth device, etc.), or a chip, a chip system, or a processor that supports each device in implementing any of the above methods. It can also be a chip, a chip system, or a processor that supports a terminal in implementing any of the above methods. Communication device 6100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0544] As shown in Figure 6A, the communication device 6100 includes one or more processors 6101. The processor 6101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control communication devices (such as base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, vehicles, unmanned driving devices, IoT devices, Amibent IoT devices, etc.), execute programs, and process program data. The communication device 6100 is used to perform any of the above methods.

[0545] In some embodiments, the communication device 6100 further includes one or more memories 6102 for storing instructions. Optionally, all or part of the memories 6102 may be located outside the communication device 6100.

[0546] In some embodiments, the communication device 6100 further includes one or more transceivers 6103. When the communication device 6100 includes one or more transceivers 6103, the transceiver 6103 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2101, step S2102, step S2201, step S2202, but not limited thereto), and the processor 6101 performs at least one of the other steps (for example, step S2103, step S2104, step S2105, step S2203, step S2204, step S2205, step S2301, step S2302, step S2303, step S2304, but not limited thereto).

[0547] In some embodiments, a transceiver may include a receiver and / or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0548] In some embodiments, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuit 6104 is connected to the memory 6102. The interface circuit 6104 may be configured to receive signals from the memory 6102 or other devices, and may be configured to send signals to the memory 6102 or other devices. For example, the interface circuit 6104 may read instructions stored in the memory 6102 and send the instructions to the processor 6101.

[0549] The communication device 6100 described in the above embodiments may be any device, but the scope of the communication device 6100 described in the present disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited to FIG. 6A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0550] 6B is a schematic diagram of the structure of a chip 6200 according to an embodiment of the present disclosure. If the communication device 6200 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 6200 shown in FIG6B , but the present disclosure is not limited thereto.

[0551] The chip 6200 includes one or more processors 6201 , and the chip 6200 is configured to execute any of the above methods.

[0552] In some embodiments, the chip 6200 further includes one or more interface circuits 6202. Optionally, the interface circuit 6202 is connected to the memory 6203. The interface circuit 6202 can be used to receive signals from the memory 6203 or other devices, and can be used to send signals to the memory 6203 or other devices. For example, the interface circuit 6202 can read instructions stored in the memory 6203 and send the instructions to the processor 6201.

[0553] In some embodiments, the interface circuit 6202 executes at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2101, step S2102, step S2201, step S2202, but not limited to this), and the processor 6201 executes at least one of the other steps (for example, step S2103, step S2104, step S2105, step S2203, step S2204, step S2205, step S2301, step S2302, step S2303, step S2304, but not limited to this).

[0554] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0555] In some embodiments, the chip 6200 further includes one or more memories 6203 for storing instructions. Alternatively, all or part of the memories 6203 may be located outside the chip 6200.

[0556] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 6100, the communication device 6100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a transient storage medium.

[0557] The present disclosure also provides a program product, which, when executed by the communication device 6100, enables the communication device 6100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0558] The present disclosure also provides a computer program that, when executed on a computer, causes the computer to perform any of the above methods. Other embodiments of the present disclosure will be readily apparent to those skilled in the art after considering the specification and practicing the invention disclosed herein.

[0559] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0560] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A device perception method, characterized in that: The method is performed by a first device and includes: sending a perception signal to a fourth device; determining a first signal, where the first signal is a signal obtained when the sensing signal reflected by the fourth device is received; Perform channel estimation based on the first signal to determine a channel state information CSI matrix; Inputting the CSI matrix into an artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model; Based on the CSI matrix and the auxiliary information, and according to a perception algorithm, a perception result obtained by perceiving the first device is determined.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining a sample CSI matrix and real auxiliary information corresponding to each of the sample CSI matrices; Inputting the sample CSI matrix into an initial AI model to obtain estimated auxiliary information output by the initial AI model; determining a loss function based on a difference between the estimated auxiliary information and the true auxiliary information; Based on the loss function, the initial AI model is trained until a stopping condition is met, thereby obtaining the AI ​​model.

3. The method according to claim 2, characterized in that The stop condition includes at least one of the following: Reach the number of training cycles; The loss function is reduced to a tolerance range; The accuracy of the initial AI model reaches a first value.

4. The method according to any one of claims 1 to 3, characterized in that The determining, based on the CSI matrix and the auxiliary information and according to a sensing algorithm, a sensing result obtained by sensing the first device includes: The CSI matrix and the auxiliary information are used as input values ​​of the perception algorithm, and the perception result corresponding to the first device is calculated according to the perception algorithm.

5. The method according to any one of claims 1 to 4, characterized in that The perception algorithm includes at least one of the following: Multidimensional multi-signal classification MUSIC algorithm; Multidimensional ESPRIT algorithm for estimating signal parameters using rotation invariance technique.

6. The method according to any one of claims 1 to 5, characterized in that The perception result includes at least one of the following: a distance value between the first device and the fourth device; a speed value of the first device; The horizontal angle value and / or the zenith angle value between the first device and the fourth device.

7. The method according to any one of claims 1 to 6, characterized in that The first device is any of the following devices: vehicle; User equipment; Unmanned vehicles; Internet of Things (IoT) devices; Passive Ambient IoT devices.

8. A device perception method, characterized in that: The method is performed by a first device and includes: determining a first signal, where the first signal is a signal obtained when receiving a perception signal reflected by a third device, where the perception signal is sent by the second device to the third device; Perform channel estimation based on the first signal to determine a channel state information CSI matrix; Inputting the CSI matrix into an artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model; Based on the CSI matrix and the auxiliary information, and according to a perception algorithm, a perception result obtained by perceiving at least one of the third devices is determined.

9. The method according to claim 8, characterized in that The auxiliary information includes: The number of the third devices.

10. The method according to claim 8 or 9, characterized in that The method further comprises: Obtaining a sample CSI matrix and real auxiliary information corresponding to each of the sample CSI matrices; Inputting the sample CSI matrix into an initial AI model to obtain estimated auxiliary information output by the initial AI model; determining a loss function based on a difference between the estimated auxiliary information and the true auxiliary information; Based on the loss function, the initial AI model is trained until a stopping condition is met, thereby obtaining the AI ​​model.

11. The method according to claim 10, characterized in that The stop condition includes at least one of the following: Reach the number of training cycles; The loss function is reduced to a tolerance range; The accuracy of the initial AI model reaches a first value.

12. The method according to any one of claims 8 to 11, characterized in that The determining, based on the CSI matrix and the auxiliary information and according to a sensing algorithm, a sensing result obtained by sensing at least one of the third devices includes: The CSI matrix and the auxiliary information are used as input values ​​of the perception algorithm, and the perception result corresponding to each of the third devices is calculated according to the perception algorithm.

13. The method according to any one of claims 8 to 12, characterized in that: The perception algorithm includes at least one of the following: Multidimensional multi-signal classification MUSIC algorithm; Multidimensional ESPRIT algorithm for estimating signal parameters using rotation invariance technique.

14. The method according to any one of claims 8 to 13, characterized in that: Each of the perception results includes at least one of the following: a distance value between the third device and the first device; a speed value of the third device; The horizontal angle value and / or the zenith angle value between the third device and the first device.

15. The method according to any one of claims 8 to 14, characterized in that: The first device is any of the following devices: vehicle; User equipment; Unmanned vehicles; Internet of Things (IoT) devices; Passive Ambient IoT devices.

16. A device perception method, characterized in that: include: The first device sends a perception signal to the fourth device; The fourth device reflects the sensing signal to the first device; The first device determines a first signal, where the first signal is a signal obtained when receiving the sensing signal reflected by the fourth device; The first device performs channel estimation based on the first signal to determine a channel state information CSI matrix; The first device inputs the CSI matrix into an artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model; The first device determines, based on the CSI matrix and the auxiliary information and according to a perception algorithm, a perception result obtained by perceiving the first device.

17. The method according to claim 16, characterized in that The method further comprises: The first device obtains a sample CSI matrix and real auxiliary information corresponding to each of the sample CSI matrices; The first device inputs the sample CSI matrix into an initial AI model to obtain estimated auxiliary information output by the initial AI model; The first device determines a loss function based on a difference between the estimated auxiliary information and the true auxiliary information; The first device trains the initial AI model based on the loss function until a stopping condition is met, thereby obtaining the AI ​​model.

18. The method according to claim 16 or 17, characterized in that The first device determines, based on the CSI matrix and the auxiliary information and according to a sensing algorithm, a sensing result obtained by sensing the first device, including: The first device uses the CSI matrix and the auxiliary information as input values ​​of the perception algorithm, and calculates the perception result corresponding to the first device according to the perception algorithm.

19. The method according to any one of claims 16 to 18, characterized in that: The perception result includes at least one of the following: a distance value between the first device and the fourth device; a speed value of the first device; The horizontal angle value and / or the zenith angle value between the first device and the fourth device.

20. A device perception method, characterized in that: include: The second device sends a perception signal to at least one third device; At least one of the third devices reflects the sensing signal toward the first device; The first device determines a first signal, where the first signal is a signal obtained when receiving the sensing signal reflected by at least one of the third devices; The first device performs channel estimation based on the first signal to determine a channel state information CSI matrix; The first device inputs the CSI matrix into an artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model; The first device determines, based on the CSI matrix and the auxiliary information and according to a perception algorithm, a perception result obtained by perceiving at least one of the third devices.

21. The method according to claim 20, characterized in that The auxiliary information includes: The number of the third devices.

22. The method according to claim 20 or 21, characterized in that The method further comprises: The first device obtains a sample CSI matrix and real auxiliary information corresponding to each of the sample CSI matrices; The first device inputs the sample CSI matrix into an initial AI model to obtain estimated auxiliary information output by the initial AI model; The first device determines a loss function based on a difference between the estimated auxiliary information and the true auxiliary information; The first device trains the initial AI model based on the loss function until a stopping condition is met, thereby obtaining the AI ​​model.

23. The method according to any one of claims 20 to 22, characterized in that The first device determines, based on the CSI matrix and the auxiliary information and according to a sensing algorithm, a sensing result obtained by sensing at least one of the third devices, including: The first device uses the CSI matrix and the auxiliary information as input values ​​of the perception algorithm, and calculates the perception result corresponding to each of the third devices according to the perception algorithm.

24. The method according to any one of claims 20 to 23, characterized in that Each of the perception results includes at least one of the following: a distance value between the third device and the first device; a speed value of the third device; The horizontal angle value and / or the zenith angle value between the third device and the first device.

25. A first device, characterized in that: include: a transceiver module, configured to send a perception signal to a fourth device; a processing module configured to determine a first signal, where the first signal is a signal obtained when receiving the sensing signal reflected by the fourth device; The processing module is further configured to perform channel estimation based on the first signal and determine a channel state information CSI matrix; The processing module is further configured to input the CSI matrix into an artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model; The processing module is further configured to determine, based on the CSI matrix and the auxiliary information and according to a perception algorithm, a perception result obtained by perceiving the first device.

26. A first device, characterized in that: include: a processing module configured to determine a first signal, where the first signal is a signal obtained when receiving a perception signal reflected by a third device, where the perception signal is sent by the second device to the third device; The processing module is further configured to perform channel estimation based on the first signal and determine a channel state information CSI matrix; The processing module is further configured to input the CSI matrix into an artificial intelligence (AI) model to obtain auxiliary information output by the AI ​​model; The processing module is further configured to determine, based on the CSI matrix and the auxiliary information and according to a perception algorithm, a perception result obtained by perceiving at least one of the third devices.

27. A first device, characterized in that: include: one or more processors; The processor is used to execute the device perception method described in any one of claims 1-7 or 8-15.

28. A communication system, characterized in that: include: A first device, wherein the first device is configured to implement the device perception method according to any one of claims 1 to 7; A fourth device is configured to reflect the perception signal sent by the first device.

29. A communication system, characterized in that: include: a second device, the second device being configured to send a perception signal to a third device; the third device being configured to reflect the sensing signal toward the first device; The first device is configured to implement the device perception method according to any one of claims 8 to 15.

30. A storage medium storing instructions, characterized in that: When the instruction is executed on the communication device, the communication device is caused to execute the device perception method according to any one of claims 1-7, 8-15, 16-19 or 20-24.

31. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, it is used to implement the device perception method described in any one of claims 1-7, 8-15, 16-19 or 20-24.

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