An AI model-based terminal positioning method and device

Abstract

The present disclosure provides a terminal positioning method and apparatus based on an AI model, which can be applied to the field of communication technologies. The method includes: a first partial module of the AI model deployed on the terminal side processes the channel impulse response input into the first partial module to obtain quantized bit information, and sends the quantized bit information to a second partial module of the AI model on the network side. The first partial module of the AI model on the terminal side assists the AI model in terminal positioning, reducing the input amount of the channel impulse response on the network side, and thus reducing the occupancy of network-side transmission resources.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular, to a terminal positioning method and apparatus based on an AI model. Background Art

[0002] In related technologies, for example, in industrial scenarios, the requirement for positioning accuracy is very high. After an artificial intelligence (AI) model is trained, the trained AI model can be deployed on the network side or the terminal side for inference. When inferring, the input to the AI model is still the measurement result, and based on the measurement result, the AI model will output the corresponding terminal position.

[0003] In the current positioning solution, the input to the AI model is the channel impulse response. Generally, the input dimension is relatively large. The terminal needs to obtain the channel impulse response according to the measurement quantity, quantize the channel impulse response, and then feedback it to the network. In this scenario, the feedback quantity is relatively large, so the occupied transmission resources are relatively many. Summary of the Invention

[0004] In a first aspect, an embodiment of the present disclosure provides a terminal positioning method based on an AI model. The method is applied to the terminal side. The AI model includes a first part module and a second part module. The first part module is deployed on the terminal side. The method includes:

[0005] Input the channel impulse response into the first part module for processing to obtain quantized bit information;

[0006] Send the quantized bit information to the network side.

[0007] In one implementation, the first part module includes a quantization module. The step of inputting the channel impulse response into the first part module for processing to obtain quantized bit information includes:

[0008] Based on the quantization module, perform quantization processing on the input channel impulse response to obtain quantized bit information.

[0009] In one implementation, the first part module includes a compression module and a quantization module. The step of inputting the channel impulse response into the first part module for processing to obtain quantized bit information includes:

[0010] Based on the compression module, perform compression processing on the channel impulse response to obtain a compressed channel impulse response;

[0011] Based on the quantization module, perform quantization processing on the compressed channel impulse response to obtain quantized bit information.

[0012] In one implementation, the first part module and the second part module included in the AI model are obtained through joint training.

[0013] In a second aspect, an embodiment of the present disclosure provides a terminal positioning method based on an AI model. The method is applied to the network side. The AI model includes a first part module and a second part module, and the second part module is deployed on the network side. The method includes:

[0014] Receiving the quantized bit information sent by the terminal side;

[0015] Inputting the quantized bit information into the second part module for processing to obtain the positioning information of the terminal.

[0016] In one implementation, the second part module includes a dequantization module. The step of inputting the quantized bit information into the second part module for processing to obtain the positioning information of the terminal includes:

[0017] Based on the dequantization module, performing dequantization processing on the quantized bit information to obtain the positioning information of the terminal.

[0018] In one implementation, the second part module includes a dequantization module and a decompression module. The step of inputting the quantized bit information into the second part module for processing to obtain the positioning information of the terminal includes:

[0019] Based on the dequantization module, performing dequantization processing on the quantized bit information to obtain the compressed channel impulse response;

[0020] Based on the decompression module, processing the compressed channel impulse response to obtain the positioning information of the terminal.

[0021] In one implementation, the first part module and the second part module included in the AI model are obtained through joint training.

[0022] In one implementation, the positioning information is positioning coordinates or parameters required for positioning.

[0023] In one implementation, the parameters required for positioning are any one of the following:

[0024] Time of arrival of the signal, angle of arrival of the signal, non-line-of-sight information NLOS, or line-of-sight information LOS.

[0025] In a third aspect, an embodiment of the present disclosure provides a terminal positioning device based on an AI model. The device is applied to the terminal side. The AI model includes a first part module and a second part module, and the first part module is deployed on the terminal side. The device includes:

[0026] A processing unit, configured to input a channel impulse response into the first part module for processing to obtain quantized bit information;

[0027] A sending unit, configured to send the quantized bit information to the network side.

[0028] In one implementation, the first part module includes a quantization module,

[0029] The processing unit is further configured to perform quantization processing on the input channel impulse response based on the quantization module to obtain quantized bit information.

[0030] In one implementation, the first part module includes a compression module and a quantization module,

[0031] The processing unit is further configured to perform compression processing on the channel impulse response based on the compression module to obtain a compressed channel impulse response;

[0032] Perform quantization processing on the compressed channel impulse response based on the quantization module to obtain quantized bit information.

[0033] In one implementation, the first part module and the second part module included in the AI model are jointly trained.

[0034] In a fourth aspect, an embodiment of the present disclosure provides a terminal positioning device based on an AI model. The device is applied to the network side. The AI model includes a first part module and a second part module. The second part module is deployed on the network side. The device includes:

[0035] A receiving unit, configured to receive the quantized bit information sent by the terminal side;

[0036] A processing unit, configured to input the quantized bit information into the second part module for processing to obtain the positioning information of the terminal.

[0037] In one implementation, the second part module includes a dequantization module. The processing unit is further configured to perform dequantization processing on the quantized bit information based on the dequantization module to obtain the positioning information of the terminal.

[0038] In one implementation, the second part module includes a dequantization module and a decompression module. The processing unit is further configured to:

[0039] Perform dequantization processing on the quantized bit information based on the dequantization module to obtain a compressed channel impulse response;

[0040] Based on the decompression module, the compressed channel impulse response is processed to obtain the positioning information of the terminal.

[0041] In one implementation, the first part module and the second part module included in the AI model are jointly trained.

[0042] In one implementation, the positioning information is positioning coordinates or parameters required for positioning.

[0043] In one implementation, the parameters required for positioning are any one of the following:

[0044] Time of arrival of the signal, angle of arrival of the signal, non-line-of-sight information NLOS or line-of-sight information LOS.

[0045] In a fifth aspect, an embodiment of the present disclosure provides a computer-readable storage medium for storing instructions used for the above-mentioned terminal positioning device based on the AI model. When the instructions are executed, the terminal positioning device based on the AI model executes the method described in the first aspect or the second aspect above.

[0046] In a sixth aspect, an embodiment of the present disclosure further provides a computer program product including a computer program. When it runs on a computer, the computer is caused to execute the method described in the first aspect or the second aspect above.

[0047] In a seventh aspect, an embodiment of the present disclosure provides a chip system. The chip system includes at least one processor and an interface, and is used to support a communication device to implement the functions involved in the first aspect or the second aspect. For example, to determine or process at least one of the data and information involved in the above method. In a possible design, the chip system further includes a memory for storing necessary computer programs and data of the communication device. The chip system may be composed of chips or may include chips and other discrete devices.

[0048] In an eighth aspect, an embodiment of the present disclosure further provides a computer program. When it runs on a computer, the computer is caused to execute the method described in the first aspect or the second aspect above. Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the background art, the following will describe the drawings required to be used in the embodiments of the present disclosure or the background art.

[0050] Figure 1 It is a schematic diagram of the architecture of a communication system provided by an embodiment of the present disclosure;

[0051] Figure 2Schematic flowchart of a terminal positioning method based on an AI model provided by an embodiment of the present disclosure;

[0052] Figure 3 Schematic flowchart of another terminal positioning method based on an AI model provided by an embodiment of the present disclosure;

[0053] Figure 4 Schematic flowchart of another terminal positioning method based on an AI model provided by an embodiment of the present disclosure;

[0054] Figure 5 Schematic flowchart of another terminal positioning method based on an AI model provided by an embodiment of the present disclosure;

[0055] Figure 6 Schematic flowchart of another terminal positioning method based on an AI model provided by an embodiment of the present disclosure;

[0056] Figure 7 Schematic flowchart of another terminal positioning method based on an AI model provided by an embodiment of the present disclosure;

[0057] Figure 8 Schematic structural diagram of a terminal positioning device based on an AI model provided by an embodiment of the present disclosure;

[0058] Figure 9 Schematic structural diagram of another terminal positioning device based on an AI model provided by an embodiment of the present disclosure;

[0059] Figure 10 Schematic structural diagram of a communication device provided by an embodiment of the present disclosure;

[0060] Figure 11 Schematic structural diagram of a chip provided by an embodiment of the present disclosure. Detailed implementation manners

[0061] To better understand a terminal positioning method and device based on an AI model disclosed by an embodiment of the present disclosure, the communication system applicable to the embodiment of the present disclosure will be described below first.

[0062] Please refer to Figure 1 , Figure 1 , which is a schematic architecture diagram of a communication system provided by an embodiment of the present disclosure. The communication system may include, but is not limited to, a network device and a terminal device. Figure 1 The number and form of the devices shown are only for illustration and do not constitute a limitation to the embodiment of the present disclosure. In practical applications, there may include two or more network devices and two or more terminal devices. Figure 1 The communication system shown takes a network device 11 and a terminal device 12 as an example.

[0063] It should be noted that the technical solutions of the embodiments of the present disclosure can be applied to various communication systems. For example: Long Term Evolution (LTE) systems, 5th generation (5G) mobile communication systems, 5G New Radio (NR) systems, or other future new mobile communication systems, etc.

[0064] The network device 11 in the embodiments of the present disclosure is an entity on the network side for transmitting or receiving signals. For example, the network device 101 can be an evolved NodeB (eNB), a transmission reception point (TRP), a next generation NodeB (gNB) in an NR system, a base station in other future mobile communication systems, or an access node in a Wireless Fidelity (WiFi) system, etc. The embodiments of the present disclosure do not limit the specific technologies and specific device forms adopted by the network device. The network device provided by the embodiments of the present disclosure can be composed of a central unit (CU) and a distributed unit (DU). Among them, the CU can also be called a control unit. Adopting the CU-DU structure can split the protocol layer of the network device, such as a base station. The functions of some protocol layers are centrally controlled by the CU, and the functions of the remaining part or all protocol layers are distributed in the DU, and the DU is centrally controlled by the CU.

[0065] The terminal device 12 in the embodiments of the present disclosure is an entity on the user side for receiving or transmitting signals, such as a mobile phone. The terminal device can also be referred to as a terminal, a user equipment (UE), a mobile station (MS), a mobile terminal (MT), etc. The terminal device can be a car with communication functions, a smart car, a mobile phone, a wearable device, a tablet computer (Pad), a computer with wireless transceiver functions, 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, a wireless terminal device in a smart home, and so on. The embodiments of the present disclosure do not limit the specific technologies and specific device forms adopted by the terminal device.

[0066] It can be understood that the communication system described in the embodiments of the present disclosure is to more clearly illustrate the technical solutions of the embodiments of the present disclosure, and does not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art know that with the evolution of the system architecture and the emergence of new service scenarios, the technical solutions provided by the embodiments of the present disclosure are equally applicable to similar technical problems.

[0067] In related technologies, for example, in industrial scenarios, the requirement for positioning accuracy is very high. After the artificial intelligence (AI) model is trained, the trained AI model can be deployed on the network side or the terminal side for inference. When inferring, the input to the AI model is still the measurement result. Based on the measurement result, the AI model will output the corresponding terminal position. In the current positioning scheme, the input to the AI model is the channel impulse response, and generally the input dimension is relatively large. The terminal needs to extract the channel impulse response from the measurement quantity, quantize the channel impulse response and then feedback it to the network. In this scenario, the feedback quantity is relatively large, so the occupied transmission resources are relatively many.

[0068] Please refer to Figure 2 , Figure 2Schematic flowchart of a terminal positioning method provided by an embodiment of the present disclosure. This method is applied to the terminal side. The AI model includes a first part module and a second part module. The first part module is deployed on the terminal side, as Figure 2 shown. This method may include but is not limited to the following steps:

[0069] Step S201: Input the channel impulse response into the first part module for processing to obtain quantized bit information.

[0070] In an embodiment of the present application, the AI model includes a first part module and a second part module. The first part module of the AI model is deployed on the terminal side, and the second part module is deployed on the network side.

[0071] On the terminal side, the channel impulse response is obtained according to the measurement quantity, and then the channel impulse response is input into the first part module on the terminal side to obtain quantized bit information. This processing process is executed on the terminal side.

[0072] Step S202: Send the quantized bit information to the network side.

[0073] The quantized bit information obtained based on the first part module on the terminal side is sent to the second part module on the network side, so that the second part module on the network side can obtain the positioning information of the terminal according to the quantized bit information. It can be seen that in the method provided by the embodiment of the present disclosure, the input of the AI model is the channel impulse response on the terminal side, and the output of the AI model is the positioning information of the terminal executed on the network side.

[0074] In all embodiments of the present disclosure, the first part module is deployed on the terminal side and is used to compress the first channel impulse response, perform compression processing, and perform quantization processing on the compressed first channel impulse response to obtain the data after quantization of the first channel impulse response. The second part module is deployed on the network side and is used to determine the positioning information of the terminal according to the received data after quantization of the first channel impulse response.

[0075] In all embodiments of the present disclosure, bit information refers to one or more bits, or data composed of one or more bits; or rather, bit information is information in bit form.

[0076] In some embodiments, the positioning information of the terminal includes but is not limited to: positioning coordinates or parameters used to determine the terminal positioning.

[0077] The first part of the AI model deployed on the terminal side processes the input channel impulse response. After obtaining the quantized bit information, the quantized bit information is sent to the second part of the AI model on the network side. The first part of the AI model on the terminal side assists the AI model in terminal positioning, reducing the input amount of the channel impulse response on the network side, and thus reducing the occupancy of network-side transmission resources.

[0078] Embodiments of the present disclosure provide another terminal positioning method based on an AI model. Figure 3 As shown in the flowchart of another terminal positioning method based on an AI model provided by the embodiments of the present disclosure, this method is applied to the terminal side. Figure 3 As shown, the terminal positioning method based on the AI model may include the following steps:

[0079] Step S301: Quantize the input channel impulse response based on the quantization module to obtain quantized bit information.

[0080] In the embodiments of the present application, the AI model includes a first part module and a second part module. The first part module of the AI model is deployed on the terminal side, where the first part module includes a quantization module, and the second part module is deployed on the network side.

[0081] On the terminal side, the channel impulse response is obtained according to the measurement quantity, and then the channel impulse response is input into the quantization module of the first part module on the terminal side. Based on the quantization module, the input channel impulse response is quantized to obtain quantized bit information, and this processing process is executed on the terminal side.

[0082] Step S302: Send the quantized bit information to the network side.

[0083] The quantized bit information obtained by the terminal side based on the first part module is sent to the second part module on the network side, so that the second part module on the network side can obtain the positioning information of the terminal according to the quantized bit information. It can be seen that in the method provided by the embodiments of the present disclosure, the input of the AI model is the channel impulse response on the terminal side, and the output of the AI model is the positioning information of the terminal executed on the network side.

[0084] In some embodiments, the positioning information of the terminal includes but is not limited to: positioning coordinates or parameters required for terminal positioning.

[0085] The first part of the AI model deployed on the terminal side executes to input the channel impulse response into the quantization module of the first part of the module, and based on the quantization module, quantizes the input channel impulse response to obtain quantized bit information, and sends the quantized bit information to the second part of the AI model on the network side. The first part of the AI model on the terminal side assists the AI model in terminal positioning, reducing the input amount of the channel impulse response on the network side, and thus reducing the network side transmission resource occupancy.

[0086] The embodiments of the present disclosure provide another terminal positioning method based on an AI model. Figure 4 As shown in the flowchart of another terminal positioning method based on an AI model provided by the embodiments of the present disclosure, this method is applied to the terminal side. Figure 4 As shown, the terminal positioning method based on the AI model may include the following steps:

[0087] Step S4011: Compress the channel impulse response based on the compression module to obtain a compressed channel impulse response.

[0088] In the embodiments of the present application, the AI model includes a first part module and a second part module. The first part module of the AI model is deployed on the terminal side, where the first part module includes a compression module and a quantization module, and the second part module is deployed on the network side.

[0089] On the terminal side, the channel impulse response is obtained according to the measurement quantity, and then the channel impulse response is compressed based on the compression module to obtain a compressed channel impulse response.

[0090] Step S4012: Quantize the compressed channel impulse response based on the quantization module to obtain quantized bit information.

[0091] The compressed channel impulse response is input into the quantization module of the first part of the module on the terminal side, and based on the quantization module, the input compressed channel impulse response is quantized to obtain quantized bit information, and this processing process is executed on the terminal side.

[0092] Step S402: Send the quantized bit information to the network side.

[0093] The quantized bit information obtained on the terminal side based on the first part of the module is sent to the second part of the module on the network side, so that the second part of the module on the network side can obtain the positioning information of the terminal according to the quantized bit information. It can be seen that in the method provided by the embodiments of the present disclosure, the input of the AI model is the channel impulse response on the terminal side, and the output of the AI model is the positioning information of the terminal executed on the network side.

[0094] In some embodiments, the positioning information of the terminal includes but is not limited to: positioning coordinates or parameters required for terminal positioning.

[0095] The first part of the AI model deployed on the terminal side first compresses the channel impulse response based on the compression module of the first part of the module, inputs the compressed channel impulse response into the quantization module of the first part of the module, and quantizes the input compressed channel impulse response based on the quantization module to obtain quantized bit information. The quantized bit information is sent to the second part of the AI model on the network side. The compression module and quantization module of the first part of the AI model on the terminal side assist the AI model in terminal positioning, further reducing the input amount of the channel impulse response on the network side, and thus reducing the occupation of network-side transmission resources.

[0096] In some embodiments, although the AI model deploys the first part of the module on the terminal side and the second part of the module on the network side, its essence is still a complete AI model composed of the first part of the module and the second part of the module. The first part of the module on the terminal side and the second part of the module on the network side complement each other, and the combined use of the two realizes the positioning of the terminal. Therefore, during training, the first part of the module on the terminal side and the second part of the module on the network side need to be jointly trained, that is, the AI model is obtained in the same training process. The specific training process is not described in detail in this embodiment of the present disclosure.

[0097] Please refer to Figure 5 , Figure 5 which is a schematic flowchart of a terminal positioning method based on an AI model provided by an embodiment of the present disclosure. This method is applied to the network side. As Figure 5 shown, this method may include but is not limited to the following steps:

[0098] Step S501: Receive the quantized bit information sent from the terminal side.

[0099] The AI model includes a first part of the module and a second part of the module. The first part of the module is deployed on the terminal side, and the second part of the module is deployed on the network side. The channel impulse response is obtained according to the measurement quantity on the terminal side, and then the channel impulse response is input into the first part of the module on the terminal side to obtain quantized bit information. This processing process is executed on the terminal side. The second part of the module on the network side receives the quantized bit information sent by the first part of the module on the terminal side.

[0100] Step S502: Input the quantized bit information into the second part of the module for processing to obtain the positioning information of the terminal.

[0101] Input the received quantized bit information into the second part module of the AI model, and the second part module performs the calculation of the terminal's positioning information.

[0102] In some embodiments, the positioning information of the terminal includes but is not limited to: positioning coordinates or parameters required for terminal positioning. The parameters required for positioning are any one of the following: time of arrival of the signal, angle of arrival of the signal, non-line-of-sight information (Non-line-of-sight propagation, NLOS), or line-of-sight information (line-of-sight propagation, LOS).

[0103] After the first part module of the AI model deployed on the terminal side sends the quantized bit information to the second part module of the AI model on the network side, the second part module on the network side receives the quantized bit information, and the dequantization module in the second part module of the AI model performs the confirmation of the terminal positioning information. In this terminal positioning process, the first part module of the AI model on the terminal side assists the second part module of the AI model on the network side to complete the confirmation of the terminal positioning information, reducing the input amount of the channel impulse response on the network side, and thus reducing the occupancy of the network side transmission resources.

[0104] The embodiments of the present disclosure provide another terminal positioning method based on an AI model. Figure 6 For the flowchart of another terminal positioning method provided by the embodiments of the present disclosure, as Figure 6 shown, the terminal positioning method based on the AI model may include the following steps:

[0105] Step S601: Receive the quantized bit information sent by the terminal side.

[0106] Step S602: Based on the dequantization module, perform dequantization processing on the quantized bit information to obtain the positioning information of the terminal.

[0107] Input the received quantized bit information into the second part module of the AI model. The second part module includes a dequantization module. After the dequantization module performs dequantization processing on the quantized bit information, it then performs the calculation of the terminal's positioning information.

[0108] In some embodiments, the positioning information of the terminal includes but is not limited to: positioning coordinates or parameters required for terminal positioning. The parameters required for positioning are any one of the following: time of arrival of the signal, angle of arrival of the signal, NLOS, or LOS.

[0109] After the first part of the AI model deployed on the terminal side sends the quantized bit information to the second part of the AI model on the network side, the second part on the network side receives the quantized bit information. After the dequantization module in the second part performs dequantization, the confirmation of the terminal location information is performed based on the dequantized bit information. In this terminal location process, the first part of the AI model on the terminal side assists the second part of the AI model on the network side to complete the confirmation of the terminal location information, reducing the input amount of the channel impulse response on the network side, and thus reducing the occupation of network side transmission resources.

[0110] The embodiments of the present disclosure provide another terminal location method based on an AI model. Figure 7 It is a schematic flowchart of another terminal location method based on an AI model provided by the embodiments of the present disclosure. As Figure 7 shown, the terminal location method based on the AI model may include the following steps:

[0111] Step S701: Receive the quantized bit information sent by the terminal side.

[0112] Step S702: Based on the dequantization module, perform dequantization processing on the quantized bit information to obtain the compressed channel impulse response.

[0113] On the terminal side, the channel impulse response is obtained according to the measurement quantity, and then based on the compression module, the channel impulse response is compressed to obtain the compressed channel impulse response. The compressed channel impulse response is input into the quantization module of the first part of the module on the terminal side, and based on this quantization module, the input compressed channel impulse response is quantized to obtain the quantized bit information. This processing process is executed on the terminal side.

[0114] On the network side, the received quantized bit information sent by the terminal is input into the second part of the AI model. The second part of the module includes a dequantization module and a decompression module. The dequantization module performs dequantization processing on the quantized bit information to obtain the compressed channel impulse response.

[0115] Step S703: Based on the decompression module, process the compressed channel impulse response to obtain the location information of the terminal.

[0116] The compressed channel impulse response obtained after the processing of the dequantization module is input into the decompression module in the second part of the module to process the compressed channel impulse response to obtain the location information of the terminal.

[0117] In some embodiments, the positioning information of the terminal includes, but is not limited to: positioning coordinates or parameters required for terminal positioning. The parameters required for positioning are any one of the following: time of arrival of the signal, angle of arrival of the signal, NLOS or LOS.

[0118] After the first part module of the AI model deployed on the terminal side sends the quantized bit information to the second part module of the AI model on the network side, the second part module on the network side receives the quantized bit information, and the dequantization module in the second part performs dequantization to obtain the compressed channel impulse response. After the decompression module decompresses the compressed channel impulse response, the confirmation of the terminal positioning information is performed based on the decompressed bit information. In this terminal positioning process, the first part module of the AI model on the terminal side assists the second part module of the network side AI model to complete the confirmation of the terminal positioning information, reducing the input amount of the channel impulse response on the network side, and thus reducing the occupation of network side transmission resources.

[0119] In some embodiments, although the AI model deploys the first part module on the terminal side and the second part module on the network side respectively, its essence is still a complete AI model composed of the first part module and the second part module. The first part module on the terminal side and the second part module on the network side complement each other, and the combined use of the two realizes the positioning of the terminal. Because, during training, the first part module on the terminal side and the second part module on the network side need to be jointly trained, that is, the AI model is obtained in the same training process. The specific training process is not described in detail in the embodiments of the present disclosure.

[0120] Corresponding to Figures 2 to 4 the terminal positioning method based on the AI model provided in the above Figures 2 to 4 embodiment, the present disclosure also provides a terminal positioning device based on the AI model. Since the terminal positioning device based on the AI model provided in the embodiments of the present disclosure corresponds to

[0121] Figure 8 the terminal positioning method based on the AI model provided in the above

[0122] processing unit 81, configured to input the channel impulse response into the first part module for processing to obtain quantized bit information;

[0123] A sending unit 82, configured to send the quantized bit information to the network side.

[0124] As a possible implementation manner of the embodiments of the present disclosure, the first part of the module includes a quantization module.

[0125] The processing unit 81 is further configured to perform quantization processing on the input channel impulse response based on the quantization module to obtain quantized bit information.

[0126] As a possible implementation manner of the embodiments of the present disclosure, the first part of the module includes a compression module and a quantization module.

[0127] The processing unit is further configured to perform compression processing on the channel impulse response based on the compression module to obtain a compressed channel impulse response.

[0128] Based on the quantization module, perform quantization processing on the compressed channel impulse response to obtain quantized bit information.

[0129] As a possible implementation manner of the embodiments of the present disclosure, the first part of the module and the second part of the module included in the AI model are obtained through joint training.

[0130] Corresponding to the terminal positioning method based on the AI model provided in the above Figures 5 to 7 embodiment, the present disclosure further provides a terminal positioning device based on the AI model. Since the terminal positioning device based on the AI model provided in the embodiments of the present disclosure corresponds to the Figures 5 to 7 terminal positioning method based on the AI model provided in the above embodiment, the implementation manners of the terminal positioning method based on the AI model are also applicable to the terminal positioning device based on the AI model provided in the embodiments of the present disclosure, and will not be described in detail in the embodiments of the present disclosure.

[0131] Figure 9 It is a schematic structural diagram of a terminal positioning device based on the AI model provided in the embodiments of the present disclosure. The device is applied to the network side. The AI model includes a first part of the module and a second part of the module. The second part of the module is deployed on the network side. The device includes:

[0132] A receiving unit 91, configured to receive the quantized bit information sent by the terminal side.

[0133] A processing unit 92, configured to input the quantized bit information into the second part of the module for processing to obtain the positioning information of the terminal.

[0134] As a possible implementation of an embodiment of the present disclosure, the second part of the module includes a dequantization module, and the processing unit 92 is further configured to perform dequantization processing on the quantized bit information based on the dequantization module to obtain the positioning information of the terminal.

[0135] As a possible implementation of an embodiment of the present disclosure, the second part of the module includes a dequantization module and a decompression module, and the processing unit 92 is further configured to:

[0136] Perform dequantization processing on the quantized bit information based on the dequantization module to obtain the compressed channel impulse response;

[0137] Process the compressed channel impulse response based on the decompression module to obtain the positioning information of the terminal.

[0138] As a possible implementation of an embodiment of the present disclosure, the first part of the module and the second part of the module included in the AI model are jointly trained.

[0139] As a possible implementation of an embodiment of the present disclosure, the positioning information is a positioning coordinate or a parameter required for positioning.

[0140] As a possible implementation of an embodiment of the present disclosure, the parameter required for positioning is any one of the following:

[0141] Time of arrival of the signal, angle of arrival of the signal, non-line-of-sight information NLOS or line-of-sight information LOS.

[0142] Please refer to Figure 10 , Figure 10 which is a schematic structural diagram of another communication device provided by an embodiment of the present disclosure. Figure 10 In this, the communication device 1000 may be a network device, a terminal device, a chip, a chip system, or a processor that supports the network device to implement the above method, or may also be a chip, a chip system, or a processor that supports the terminal device to implement the above method. This device can be used to implement the method described in the above method embodiment, and for details, please refer to the description in the above method embodiment.

[0143] The communication device 1000 may include one or more processors 1001. The processor 1001 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute computer programs, and process the data of the computer programs.

[0144] Optionally, the communication device 1000 may further include one or more memories 1002, on which a computer program 1004 may be stored, and the processor 1001 executes the computer program 1004 so that the communication device 1000 performs the method described in the above method embodiment. Optionally, data may also be stored in the memory 1002. The communication device 1000 and the memory 1002 may be provided separately or integrated together.

[0145] Optionally, the communication device 1000 may further include a transceiver 1005 and an antenna 1006. The transceiver 1005 may be referred to as a transceiver unit, a transceiver, or a transceiver circuit, etc., and is used to implement a transceiver function. The transceiver 1005 may include a receiver and a transmitter, the receiver may be referred to as a receiver or a receiving circuit, etc., and is used to implement a receiving function; the transmitter may be referred to as a transmitter or a transmitting circuit, etc., and is used to implement a transmitting function.

[0146] Optionally, the communication device 1000 may further include one or more interface circuits 1007. The interface circuit 1007 is used to receive code instructions and transmit them to the processor 1001. The processor 1001 executes the code instructions to enable the communication device 1000 to execute the method described in the above method embodiment.

[0147] The communication device 1000 is a first node: the transceiver 1005 is used to execute Figure 2 Step 201 and other steps in.

[0148] The communication device 1000 is a network device: the transceiver 1005 is used to perform Figure 4 Step 402 and other steps in.

[0149] In one implementation, the processor 1001 may include a transceiver for implementing receiving and sending functions. For example, the transceiver may be a transceiver circuit, an interface, or an interface circuit. The transceiver circuit, interface, or interface circuit for implementing the receiving and sending functions may be separate or integrated. The above-mentioned transceiver circuit, interface, or interface circuit may be used for reading and writing code / data, or the above-mentioned transceiver circuit, interface, or interface circuit may be used for transmitting or delivering signals.

[0150] In one implementation, the processor 1001 may store a computer program 1003, which runs on the processor 1001 and enables the communication device 1000 to perform the method described in the above method embodiment. The computer program 1003 may be fixed in the processor 1001, in which case the processor 1001 may be implemented by hardware.

[0151] In one implementation, the communication device 1000 may include circuitry that can implement the functions of transmitting, receiving, or communicating in the foregoing method embodiments. The processor and transceiver described in this disclosure may be implemented on an integrated circuit (IC), analog IC, radio frequency integrated circuit (RFIC), mixed-signal IC, application specific integrated circuit (ASIC), printed circuit board (PCB), electronic device, etc. The processor and transceiver may also be fabricated using various IC process technologies, such as complementary metal oxide semiconductor (CMOS), N-type metal oxide semiconductor (NMOS), P-type metal oxide semiconductor (PMOS), bipolar junction transistor (BJT), BiCMOS, silicon germanium (SiGe), gallium arsenide (GaAs), etc.

[0152] The communication device described in the above embodiments may be a network device or a terminal device. However, the scope of the communication device described in this disclosure is not limited thereto, and the structure of the communication device may not be limited by Figure 10 . The communication device may be an independent device or may be a part of a larger device. For example, the communication device may be:

[0153] (1) An independent integrated circuit (IC), or chip, or chip system or subsystem;

[0154] (2) A collection of one or more ICs. Optionally, the IC collection may also include a storage component for storing data and computer programs;

[0155] (3) An ASIC, such as a modem;

[0156] (4) A module that can be embedded in other devices;

[0157] (5) A receiver, terminal device, smart terminal device, cellular phone, wireless device, handset, mobile unit, vehicle-mounted device, network device, cloud device, artificial intelligence device, etc.;

[0158] (6) Others, etc.

[0159] For the case where the communication device may be a chip or a chip system, reference may be made to Figure 11 the structural schematic diagram of the chip shown. Figure 11 The chip 1100 shown includes a processor 1101 and an interface 1103. Among them, the number of processors 1101 may be one or more, and the number of interfaces 1103 may be multiple.

[0160] For the case where the chip is used to implement the functions of the terminal in the embodiments of the present disclosure:

[0161] The interface 1103 is used to execute Figure 2 step 202 in Figure 3 step 302 in Figure 4 step 402 in etc.

[0162] For the case where the chip is used to implement the functions of the network in the embodiments of the present disclosure:

[0163] The interface 1103 is used to execute Figure 5 step 501 in Figure 6 step 601 in etc.

[0164] Optionally, the chip 1100 further includes a memory 1102, and the memory 1102 is used to store necessary computer programs and data.

[0165] Those skilled in the art can also understand that the various illustrative logical blocks and steps listed in the embodiments of the present disclosure can be implemented by electronic hardware, computer software, or a combination of both. Whether such a function is implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art can use various methods to implement the described function for each specific application, but such implementation should not be construed as exceeding the scope protected by the embodiments of the present disclosure.

[0166] The present disclosure also provides a readable storage medium, on which instructions are stored, and when the instructions are executed by a computer, the functions of any of the above method embodiments are implemented.

[0167] The present disclosure also provides a computer program product, and when the computer program product is executed by a computer, the functions of any of the above method embodiments are implemented.

[0168] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, the processes or functions described in the embodiments of the present disclosure are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer program may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer program may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a high-definition digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0169] Those of ordinary skill in the art can understand that the various digital numbers such as the first and the second involved in the present disclosure are only for the convenience of description and are not used to limit the scope of the embodiments of the present disclosure, nor do they represent the order of precedence.

[0170] At least one in the present disclosure may also be described as one or more. The plurality may be two, three, four, or more, and the present disclosure does not make any restrictions. In the embodiments of the present disclosure, for a technical feature, the technical features in this technical feature are distinguished by "the first", "the second", "the third", "A", "B", "C", and "D", etc. There is no order of precedence or size order among the technical features described by "the first", "the second", "the third", "A", "B", "C", and "D".

[0171] It can be further understood that in the present disclosure, "a plurality of" means two or more, and other quantifiers are similar thereto. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. The singular forms of "a", "the", and "said" are also intended to include the plural forms unless the context clearly indicates otherwise.

[0172] It can be further understood that the meanings of words such as "in response to", "if", and "in case" involved in the present disclosure depend on the context and the actual usage scenario. For example, the word "in case" as used herein can be interpreted as "when" or "while".

[0173] The corresponding relationships shown in each table in the present disclosure can be configured or predefined. The values taken by the information in each table are only examples and can be configured as other values, which are not limited in the present disclosure. When configuring the corresponding relationships between the configuration information and each parameter, it is not necessarily required to configure all the corresponding relationships shown in each table. For example, in the tables in the present disclosure, the corresponding relationships shown in some rows can also not be configured. Another example is that appropriate deformation adjustments can be made based on the above tables, such as splitting, merging, etc. The names of the parameters shown in the titles of the above tables can also be other names understandable by the communication device, and the values taken or the representation methods of the parameters can also be other values or representation methods understandable by the communication device. When implementing the above tables, other data structures can also be used, such as arrays, queues, containers, stacks, linear lists, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables or hash maps, etc.

Claims

1. A terminal positioning method based on an AI model, characterized in that, The method is applied to the terminal side. The AI model includes a first part module and a second part module. The first part module is deployed on the terminal side, and the second part module is deployed on the network side. The method includes: Input the channel impulse response into the first part module for processing to obtain quantized bit information; Send the quantized bit information to the network side. The quantized bit information is used to obtain the positioning information of the terminal after being processed by the second part module.

2. The method according to claim 1, wherein The first part module includes a quantization module. The step of inputting the channel impulse response into the first part module for processing to obtain quantized bit information includes: Based on the quantization module, perform quantization processing on the input channel impulse response to obtain quantized bit information.

3. The method according to claim 1, characterized in that The first part module includes a compression module and a quantization module. The step of inputting the channel impulse response into the first part module for processing to obtain quantized bit information includes: Based on the compression module, perform compression processing on the channel impulse response to obtain a compressed channel impulse response; Based on the quantization module, perform quantization processing on the compressed channel impulse response to obtain quantized bit information.

4. The method according to any one of claims 1 to 3, characterized in that The first part module and the second part module included in the AI model are obtained by joint training.

5. A terminal positioning method based on an AI model, characterized in that, The method is applied to the network side. The AI model includes a first part module and a second part module. The first part module is deployed on the terminal side, and the second part module is deployed on the network side. The method includes: Receive the quantized bit information sent from the terminal side. The quantized bit information is obtained by processing of the first part module; Input the quantized bit information into the second part module for processing to obtain the positioning information of the terminal.

6. The method according to claim 5, characterized in that, The second part module includes a dequantization module. The step of inputting the quantized bit information into the second part module for processing to obtain the positioning information of the terminal includes: Based on the dequantization module, perform dequantization processing on the quantized bit information to obtain the positioning information of the terminal.

7. The method according to claim 5, wherein The second part module includes a dequantization module and a decompression module. The step of inputting the quantized bit information into the second part module for processing to obtain the positioning information of the terminal includes: Based on the dequantization module, perform dequantization processing on the quantized bit information to obtain a compressed channel impulse response; Based on the decompression module, perform processing on the compressed channel impulse response to obtain the positioning information of the terminal.

8. The method according to any one of claims 5 to 7, characterized in that The first part module and the second part module included in the AI model are obtained by joint training.

9. The method according to claim 8, characterized in that, The positioning information is positioning coordinates or parameters required for positioning.

10. The method according to claim 9, characterized in that, The parameters required for positioning are any one of the following: Time of signal arrival, angle of signal arrival, non-line-of-sight information NLOS or line-of-sight information LOS.

11. A terminal positioning device based on an AI model, characterized in that, The device is applied to the terminal side. The AI model includes a first part module and a second part module. The first part module is deployed on the terminal side, and the second part module is deployed on the network side. The device includes: A processing unit, configured to input a channel impulse response into a first part module for processing to obtain quantized bit information; A sending unit, configured to send the quantized bit information to the network side, where the quantized bit information is used to obtain the positioning information of the terminal after being processed by the second part module.

12. The device according to claim 11, characterized in that, The first part module includes a quantization module, The processing unit is further configured to perform quantization processing on the input channel impulse response based on the quantization module to obtain quantized bit information.

13. The device according to claim 11, characterized in that, The first part module includes a compression module and a quantization module, The processing unit is further configured to perform compression processing on the channel impulse response based on the compression module to obtain a compressed channel impulse response; Perform quantization processing on the compressed channel impulse response based on the quantization module to obtain quantized bit information.

14. The device according to any one of claims 11-13, characterized in that, The first part module and the second part module included in the AI model are obtained through joint training.

15. A terminal positioning device based on an AI model, characterized in that, The apparatus is applied to the network side. The AI model includes a first part module and a second part module. The first part module is deployed on the terminal side, and the second part module is deployed on the network side. The apparatus includes: A receiving unit, configured to receive the quantized bit information sent from the terminal side, where the quantized bit information is obtained by processing of the first part module; A processing unit, configured to input the quantized bit information into the second part module for processing to obtain the positioning information of the terminal.

16. The device according to claim 15, characterized in that, The second part module includes a dequantization module. The processing unit is further configured to perform dequantization processing on the quantized bit information based on the dequantization module to obtain the positioning information of the terminal.

17. The device according to claim 15, characterized in that, The second part module includes a dequantization module and a decompression module. The processing unit is further configured to: Perform dequantization processing on the quantized bit information based on the dequantization module to obtain a compressed channel impulse response; Perform processing on the compressed channel impulse response based on the decompression module to obtain the positioning information of the terminal.

18. The device according to any one of claims 15 - 17, characterized in that The first part module and the second part module included in the AI model are obtained through joint training.

19. The device according to claim 18, characterized in that, The positioning information is a positioning coordinate or a parameter required for positioning.

20. The device according to claim 19, wherein The parameter required for positioning is any one of the following: Time of arrival of the signal, angle of arrival of the signal, non-line-of-sight information NLOS or line-of-sight information LOS.

21. A communication device, characterized in that, The apparatus includes a processor and a memory. A computer program is stored in the memory. The processor executes the computer program stored in the memory so that the apparatus executes the method according to any one of claims 1 to 4, or executes the method according to any one of claims 5 to 10.

22. A computer-readable storage medium, configured to store instructions, which when executed, cause the method according to any one of claims 1 to 4, or execute the method according to any one of claims 5 to 10.

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