Positioning methods and communication devices

Abstract

Provided in the present application are positioning methods and communication devices. A method comprises: a first device acquires first information of a first reference point, the first information of the first reference point being used for training a first model, and the first information comprising position information and / or channel state information between the reference point and a network device; and the first device inputs position information of a second reference point into the first model, so as to obtain channel state information between the second reference point and the network device, the first information of the first reference point and first information of the second reference point being used for training a second model, and the second model being used for positioning of a terminal device.

Description

Positioning method and communication device Technical Field

[0001] The present application relates to the field of communication technology, and more particularly, to a positioning method and communication device. Background Art

[0002] With the continuous development of science and technology, methods for locating terminal devices using models (such as artificial intelligence models) have emerged. Before using artificial intelligence models to locate terminal devices, the artificial intelligence models need to be trained using information from reference points.

[0003] The greater the number of reference points, the more accurate the AI ​​model and the more accurate the positioning results obtained using the AI ​​model. However, the greater the number of reference points, the more manpower and material resources are required to collect reference point information, which increases the cost of AI model training.

[0004] Summary of the Invention

[0005] The present application provides a method and communication device for positioning. The following introduces various aspects involved in the present application.

[0006] In a first aspect, a method for positioning is provided, including: a first device obtains first information of a first reference point, the first information of the first reference point is used to train a first model, the first information including location information and / or channel state information between the reference point and a network device; the first device inputs the location information of a second reference point into the first model to obtain channel state information between the second reference point and the network device; wherein the first information of the first reference point and the first information of the second reference point are used to train a second model, and the second model is used to locate the terminal device.

[0007] According to a second aspect, a method for positioning is provided, comprising: a network device receives a request message sent by a first device; in response to the request message, the network device sends a first message to the first device, the first message including channel state information between the first reference point and the network device and / or channel state information between the second reference point and the network device; wherein, the first information of the first reference point is used to train a first model, the first model is used to generate channel state information between the second reference point and the network device based on the first information of the first reference point and the position information of the second reference point, the first information of the first reference point and the first information of the second reference point are used to train a second model, and the second model is used to locate the terminal device.

[0008] According to a third aspect, a communication device is provided, which is a first device and includes: an acquisition unit for acquiring first information of a first reference point, the first information of the first reference point being used to train a first model, the first information including location information and / or channel state information between the reference point and a network device; an input unit for inputting the location information of a second reference point into the first model to obtain channel state information between the second reference point and the network device; wherein the first information of the first reference point and the first information of the second reference point are used to train a second model, and the second model is used to locate the terminal device.

[0009] In a fourth aspect, a network device is provided, comprising: a receiving unit for receiving a request message sent by a first device; a sending unit for sending a first message to the first device in response to the request message, wherein the first message includes channel state information between the first reference point and the network device and / or channel state information between the second reference point and the network device; wherein the first information of the first reference point is used to train a first model, and the first model is used to generate channel state information between the second reference point and the network device based on the first information of the first reference point and the position information of the second reference point, and the first information of the first reference point and the first information of the second reference point are used to train a second model, and the second model is used to locate the terminal device.

[0010] In a fifth aspect, a communication device is provided, which is a first device and includes a processor and a memory, the memory is used to store one or more computer programs, and the processor is used to call the computer program in the memory so that the communication device executes part or all of the steps in the method of the first aspect.

[0011] In a sixth aspect, a network device is provided, comprising a processor, a memory, and a transceiver, wherein the memory is used to store one or more computer programs, and the processor is used to call the computer program in the memory so that the network device executes part or all of the steps in the method of the second aspect.

[0012] In a seventh aspect, an embodiment of the present application provides a communication system, which includes the above-mentioned terminal device and / or network device. In another possible design, the system may also include other devices that interact with the terminal device or network device in the solution provided in the embodiment of the present application.

[0013] In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program enables a communication device (for example, a first device or a network device) to perform some or all of the steps in the methods of the above aspects.

[0014] In a ninth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a communication device (e.g., a first device or a network device) to perform some or all of the steps of the methods described in each of the above aspects. In some implementations, the computer program product can be a software installation package.

[0015] In the tenth aspect, an embodiment of the present application provides a chip, which includes a memory and a processor. The processor can call and run a computer program from the memory to implement some or all of the steps described in the methods of the above aspects.

[0016] The present application generates the first information of the second reference point through the first model, and then uses the second reference point to train the second model. Since the first information of the second reference point is generated through the first model, the solution of the present application can reduce the manpower and material resources consumed by manual measurement, which is conducive to reducing the cost of training the second model. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG1 is a wireless communication system 100 used in an embodiment of the present application.

[0018] FIG2 shows a neural network model applicable to an embodiment of the present application.

[0019] FIG3 shows a neural network model applicable to an embodiment of the present application.

[0020] FIG4 shows a convolutional neural network applicable to an embodiment of the present application.

[0021] FIG5 shows a long short-term memory (LSTM) model applicable to an embodiment of the present application.

[0022] FIG6 is a schematic diagram of collecting first information of a reference point.

[0023] FIG7 is a schematic flowchart for positioning provided in an embodiment of the present application.

[0024] FIG8 is a schematic diagram of first information for determining a second reference point provided by an embodiment of the present application.

[0025] FIG9 is a schematic flowchart of another positioning method provided in an embodiment of the present application.

[0026] FIG10 is a schematic diagram of generating a CIR prediction value of a second reference point provided by an embodiment of the present application.

[0027] FIG11 is a schematic block diagram of a communication device provided in an embodiment of the present application.

[0028] FIG12 is a schematic block diagram of a network device provided in an embodiment of the present application.

[0029] FIG13 is a schematic structural diagram of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The technical solution in this application will be described below with reference to the accompanying drawings.

[0031] Figure 1 illustrates a wireless communication system 100 used in an embodiment of the present application. The wireless communication system 100 may include a network device 110 and a terminal device 120. The network device 110 may be a device that communicates with the terminal device 120. The network device 110 may provide communication coverage for a specific geographic area and may communicate with the terminal device 120 within the coverage area.

[0032] FIG1 exemplarily shows a network device and two terminals. Optionally, the wireless communication system 100 may include multiple network devices and each network device may include other numbers of terminal devices within its coverage area, which is not limited in the embodiments of the present application.

[0033] Optionally, the wireless communication system 100 may further include other network entities such as a network controller and a mobility management entity, which is not limited in the embodiment of the present application.

[0034] It should be understood that the technical solutions of the embodiments of the present application can be applied to various communication systems, such as: fifth generation (5G) system or new radio (NR), long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD), etc. The technical solutions provided in this application can also be applied to future communication systems, such as the sixth generation mobile communication system, satellite communication system, etc.

[0035] The terminal device in the embodiments of the present application may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station (MS), mobile terminal (MT), remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device. The terminal device in the embodiments of the present application may refer to a device that provides voice and / or data connectivity to a user and can be used to connect people, objects and machines, such as a handheld device with wireless connection function, a vehicle-mounted device, etc. The terminal device in the embodiments of the present application can be a mobile phone, a tablet computer, a laptop computer, a PDA, a mobile internet device (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc. Optionally, the UE can be used to act as a base station. For example, the UE can act as a scheduling entity that provides sidelink signals between UEs in V2X or D2D, etc. For example, a cellular phone and a car communicate with each other using sidelink signals. The cellular phone and smart home devices communicate without relaying the communication signal through the base station.

[0036] The network device in the embodiments of the present application may be a device for communicating with a terminal device, and may also be referred to as an access network device or a radio access network device. For example, the network device may be a base station. The network device in the embodiments of the present application may refer to a radio access network (RAN) node (or device) that connects a terminal device to a wireless network. A base station can broadly cover various names as follows, or be replaced with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmission point (TRP), transmission point (TP), master station MeNB, secondary station SeNB, multi-standard radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), positioning node, etc. A base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. A base station can also refer to a communication module, a modem or a chip used to be set in the aforementioned device or apparatus. The base station can also be a mobile switching center and a device that performs base station functions in device-to-device D2D, vehicle-to-everything (V2X), and machine-to-machine (M2M) communications, a network-side device in a 6G network, or a device that performs base station functions in future communication systems. The base station can support networks with the same or different access technologies. The embodiments of this application do not limit the specific technology and specific device form used by the network equipment.

[0037] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0038] In some deployments, the network device in the embodiments of the present application may refer to a CU or a DU, or the network device may include a CU and a DU. The gNB may also include an AAU.

[0039] The network equipment and terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; they can also be deployed in the air on aircraft, balloons, and satellites. The embodiments of this application do not limit the scenarios in which the network equipment and terminal devices are located.

[0040] It should be understood that all or part of the functions of the communication device in this application can also be implemented through software functions running on hardware, or through virtualization functions instantiated on a platform (such as a cloud platform).

[0041] Neural Networks

[0042] In recent years, artificial intelligence research, exemplified by neural networks, has achieved remarkable success in many fields, and will continue to play a vital role in people's lives and production for a long time to come. A neural network can be understood as a computational model consisting of multiple interconnected neuron nodes. The connections between these nodes represent the weighted values ​​from input signals to output signals, often referred to as parameters. Each node performs a weighted summation of different input signals and outputs the result through a specific activation function.

[0043] As shown in Figure 2, neurons can rely on activation functions to implement nonlinear mapping, where the input of the neuron can be recorded as A, and each dimension of the input is recorded as a j , the corresponding parameter is recorded as w j , together with the summation units (SU), the input is strengthened or weakened. In addition, the output of SU can be input into the activation function f to obtain the output t, where the value of j is 1, 2, ..., n.

[0044] Common neural networks include convolutional neural network (CNN), recurrent neural network (RNN), deep neural network (DNN), etc.

[0045] The following describes a neural network applicable to embodiments of the present application in conjunction with FIG3 . The neural network shown in FIG3 can be divided into three categories based on the location of different layers: input layer 310 , hidden layer 320 , and output layer 330 . Generally speaking, the first layer is the input layer 310 , the last layer is the output layer 330 , and the intermediate layers between the first and last layers are all hidden layers 320 .

[0046] The input layer 310 is used to input data, where the input data can be, for example, a received signal received by a receiver. The hidden layer 320 is used to process the input data, for example, decompress the received signal. The output layer 330 is used to output processed output data, for example, a decompressed signal.

[0047] As shown in Figure 3, a neural network consists of multiple layers, each of which contains multiple neurons. The neurons between layers can be fully connected or partially connected. For connected neurons, the output of the neurons in the previous layer can serve as the input of the neurons in the next layer.

[0048] With the continuous advancement of neural network research, deep learning algorithms have been proposed in recent years. These algorithms introduce a large number of hidden layers into neural networks, forming DNNs. More hidden layers allow DNNs to better capture complex real-world situations. Theoretically, a model with more parameters has higher complexity and a greater "capacity," meaning it can handle more complex learning tasks. These neural network models are widely used in pattern recognition, signal processing, optimization and combination, anomaly detection, and other fields.

[0049] CNN is a deep neural network with a convolutional structure, and its structure is shown in FIG4 , which may include an input layer 410 , a convolutional layer 420 , a pooling layer 430 , a fully connected layer 440 , and an output layer 450 .

[0050] Each convolution layer 420 may include a plurality of convolution operators, which are also called kernels. The convolution operator can be regarded as a filter for extracting specific information from the input signal. The convolution operator can essentially be a parameter matrix, which is usually predefined.

[0051] The parameter values ​​in these parameter matrices need to be obtained through a lot of training in practical applications. The parameter matrices formed by the parameter values ​​obtained through training can extract information from the input signal, thereby helping CNN to make correct predictions.

[0052] When CNN has multiple convolutional layers, the initial convolutional layer tends to extract more general features, which can also be called low-level features. As the depth of CNN increases, the features extracted by the subsequent convolutional layers become more and more complex.

[0053] Pooling layers 430 are often required periodically after convolutional layers to reduce the number of training parameters. For example, a single convolutional layer can be followed by a pooling layer, as shown in Figure 4, or multiple convolutional layers can be followed by one or more pooling layers. In signal processing, the sole purpose of a pooling layer is to reduce the spatial size of the extracted information.

[0054] The fully connected layer 440, after being processed by the convolution layer 420 and the pooling layer 430, is not sufficient for CNN to output the required output information. Because as mentioned above, the convolution layer 420 and the pooling layer 430 only extract features and reduce the parameters brought by the input data. However, in order to generate the final output information (for example, the bit stream of the original information transmitted by the transmitter), CNN also needs to use the fully connected layer 440. Generally, the fully connected layer 440 may include multiple hidden layers, and the parameters contained in the multiple hidden layers may be pre-trained based on relevant training data of a specific task type. For example, the task type may include decoding a data signal received by a receiver. For another example, the task type may also include channel estimation based on a pilot signal received by the receiver.

[0055] Following the multiple hidden layers in the fully connected layer 440, the final layer of the CNN is the output layer 450, which is used to output the results. Typically, this output layer 450 is configured with a loss function (e.g., a loss function similar to categorical cross entropy) to calculate the prediction error, or to evaluate the degree of difference between the output of the CNN model (also known as the predicted value) and the ideal result (also known as the true value).

[0056] In order to minimize the loss function, the CNN model needs to be trained. In some implementations, the backpropagation algorithm (BP) can be used to train the CNN model. The BP training process consists of a forward propagation process and a backward propagation process. During the forward propagation process (e.g., the propagation from 410 to 450 in Figure 4 is forward propagation), the input data is input into the above-mentioned layers of the CNN model, processed layer by layer, and transmitted to the output layer. If the output result of the output layer differs significantly from the ideal result, the minimization of the above-mentioned loss function is used as the optimization goal, and the backpropagation process is switched to (e.g., the propagation from 450 to 410 in Figure 4 is backward propagation). The partial derivatives of the optimization goal with respect to each neuron weight are calculated layer by layer, forming the gradient of the optimization goal with respect to the weight vector, which serves as the basis for modifying the model parameters. The CNN training process is completed during the parameter modification process. When the above-mentioned error reaches the desired value, the CNN training process ends.

[0057] It should be noted that the CNN shown in Figure 4 is only an example of a convolutional neural network. In specific applications, the convolutional neural network can also exist in the form of other network models, and the embodiments of the present application are not limited to this.

[0058] RNNs are designed to process sequential data. In traditional neural network models (for example, CNN models), the layers are fully connected, from the input layer to the hidden layer to the output layer, and the nodes within each layer are disconnected. However, these ordinary neural networks are inadequate for many problems. For example, if you want to predict the next word in a sentence, you generally need to use the previous word, because the previous and next words in a sentence are not independent. RNNs are called recurrent neural networks because the current output of a sequence is also related to the previous output. Specifically, the network remembers the previous information and applies it to the calculation of the current output. That is, the nodes between hidden layers are no longer disconnected but connected, and the input of the hidden layer includes not only the output of the input layer but also the output of the hidden layer at the previous moment. In theory, RNNs can process sequence data of any length.

[0059] Training an RNN is similar to training a traditional ANN (artificial neural network). The same backpropagation error algorithm is used, but there is a slight difference. If the RNN is expanded, the parameters W, U, and V are shared, while traditional neural networks are not. Furthermore, when using the gradient descent algorithm, the output of each step depends not only on the network state at the current step, but also on the state of the network at the previous steps. For example, at t = 4, three steps need to be propagated backward, and various gradients need to be added to the three subsequent steps. This learning algorithm is called backpropagation through time (BPTT).

[0060] Given the existence of artificial neural networks and convolutional neural networks, why do we still need recurrent neural networks? The reason is simple. Both convolutional and artificial neural networks assume that elements are independent of each other, and that input and output are also independent, like cats and dogs. However, in the real world, many elements are interconnected, such as the changes in stock prices over time. For example, someone said, "I love traveling, and my favorite place is Yunnan. I must visit __ someday." Everyone knows to fill in the blank with "Yunnan." This is because we infer this based on the context, but achieving this is quite difficult. Therefore, recurrent neural networks were developed. Their essence is that they possess memory, just like humans. Therefore, their output depends on the current input and memory. To put it simply, an RNN is a unit structure that is reused.

[0061] At present, in order to solve the gradient explosion or vanishing problem of RNN, a transformation is made on the basis of RNN to obtain the long short-term memory (LSTM) model. As shown in Figure 5, LSTM introduces a new memory unit ct (also called "cell state"), which is used for linear cyclic information transmission and outputs information to the external state h of the hidden layer. t At each moment t, c t It records historical information up to the current moment. Unlike RNNs, which only consider the most recent state, memory cells determine which states should be retained and which should be forgotten, addressing the shortcomings of traditional RNNs in long-term memory.

[0062] Continuing to refer to FIG5, in order to achieve the above state selection, the memory unit introduces a gate control mechanism to control the path of information transmission, similar to the gate in the data circuit, "0" means closed, and "1" means open. The memory unit includes a forget gate 510, an input gate 520, and an output gate 530. Among them, the forget gate is used to control the memory unit c at the previous moment. t-1 How much information needs to be forgotten? The input gate is used to control the candidate state at the current moment. How much information needs to be stored? The output gate is used to control the memory unit c at the current moment. t How much information needs to be output to the external state h t .

[0063] With the continuous improvement of science and technology and the continuous development of the current era, various fields are showing a trend of vigorous development, and people's demand for location services is also increasing. Therefore, high-precision positioning methods have become one of the current research hotspots. The positioning method in the embodiment of this application can be applied to indoor positioning and outdoor positioning.

[0064] With the rapid development of neural networks, solutions for using models to locate terminal devices have emerged. For example, two models can be used to jointly locate a terminal device. One model provides more reference points for the other model. The two models here may include a first model and a second model. The second model can be any of the neural network models described above. The second model can be an AI model or an ML model. For example, the second model can be a CNN or RNN. In some embodiments, the second model can be an LSTM model.

[0065] Before using the second model to locate the terminal device, the second model can be trained. For example, the first information of the reference point can be used to train the second model. The first information may include the location information of the reference point (or sampling point) and the channel state information between the reference point and the network device. That is, the second model can establish an association relationship between the channel state information and the location information. During the training process, the channel state information can be used as the input of the second model, and the location information can be used as the label data to train the second model. For example, the channel state information between the first reference point and the network device and the channel state information between the second reference point and the network device can be used as the input of the second model, and the location information of the first reference point and the location information of the second reference point can be used as the label data to train the second model. In the subsequent positioning process, the channel state information measured by the terminal device can be input into the second model to obtain the location information of the terminal device.

[0066] Taking Figure 6 as an example, Figure 6 shows four reference points. When collecting the first information of the reference points, the terminal device can be placed at different reference points, and then the position coordinates of the terminal device at the different reference points and the corresponding channel state information are recorded. For example, the terminal device can be placed at reference point 1, and the terminal device measures the reference signal sent by the network device to obtain channel state information 1; the terminal device can be placed at reference point 2, and the terminal device measures the reference signal sent by the network device to obtain channel state information 2; the terminal device can be placed at reference point 3, and the terminal device measures the reference signal sent by the network device to obtain channel state information 3; the terminal device can be placed at reference point 4, and the terminal device measures the reference signal sent by the network device to obtain channel state information 4. The position information of different reference points can be known. The position information of multiple reference points and the corresponding channel state information are input into the second model, and the second model is trained.

[0067] It is understandable that the greater the number of reference points, the higher the accuracy of the second model, and the higher the accuracy of the positioning results obtained using the second model. When the number of reference points is small, the accuracy of the positioning results obtained using the second model is lower. For example, the second model was trained using 256, 128, 64, 16, and 9 reference points, respectively. After testing, it was found that the second model trained with 256 reference points had the highest positioning accuracy. As can be seen from the above, the method of using models for positioning requires a high amount of data.

[0068] At present, most of the first information of reference points is still collected manually. The more reference points there are, the higher the labor cost required to collect the first information of reference points, which requires a lot of time and energy.

[0069] Based on this, an embodiment of the present application provides a method for positioning, in which first information of a second reference point (or pseudo-reference point) is generated through a first model, and then the second model is trained using the second reference point. Since the first information of the second reference point is generated through the first model, the manpower and material resources consumed by manual measurement can be reduced. Therefore, the solution provided by the embodiment of the present application can reduce the labor cost required to collect reference point information while ensuring the accuracy of the second model.

[0070] The solution of the embodiment of the present application is described in detail below with reference to FIG7 .

[0071] 7 , in step S710 , the first device obtains first information of a first reference point.

[0072] The first device may be any device with computing capabilities. In some implementations, the first device may be one or more of the following: a positioning server, a location management function (LMF), a serving cell, a positioning reference unit, or a terminal device.

[0073] The first reference point may be a terminal device, or the first reference point may be a terminal device at the first reference point. Taking Figure 8 as an example, the dots in Figure 8 represent reference points. The first device may obtain first information of the terminal device at the reference point. In some embodiments, the reference point may also be referred to as a sampling point.

[0074] The number of first reference points may include multiple ones, and the embodiment of the present application does not impose any specific limitation on this, as long as the number of first reference points can meet the training requirements of the first model.

[0075] The first information may include location information of the reference point and / or channel state information between the reference point and the network device. The first information of the first reference point may include location information of the first reference point and / or channel state information between the first reference point and the network device. In some implementations, the first information may include location information of the reference point and channel state information between the reference point and the network device.

[0076] The channel state information between the first reference point and the network device may be measured by the first reference point. The first reference point may measure a reference signal sent by the network device to obtain the channel state information. In some implementations, the first reference point may transmit the measured channel state information to the network device. In some implementations, the network device may be a base station.

[0077] The channel state information may include, for example, a channel impulse response (CIR) and / or a channel frequency response (CFR).

[0078] In some embodiments, the first information of the first reference point may be used to train a first model. The first model may be used to establish an association relationship between the location information and the channel state information.

[0079] The embodiments of the present application do not specifically limit the manner in which the first device obtains the first information of the first reference point. In some implementations, the network device may send the first information of the first reference point to the first device. The first information of the first reference point may be proactively sent by the network device to the first device, or may be sent to the first device upon receiving a request from the first device. This will be described in detail below.

[0080] The first information of the first reference point can be used to train the first model. For example, the position information of the first reference point can be used as input to the first model, and the channel state information between the first reference point and the network device can be used as label information to train the first model.

[0081] In step S720, the first device inputs the location information of the second reference point into the first model to obtain channel state information between the second reference point and the network device. In other words, the input of the first model may include the location information of the second reference point, and the output of the first model may include the channel state information between the second reference point and the network device.

[0082] Since the channel state information between the second reference point and the network device can be obtained through the first model, the second reference point does not need to measure the reference signal sent by the network device, thereby reducing labor costs.

[0083] The embodiment of the present application can use the data information of a small amount of reference points as real marking information, and predict the data information of the remaining reference points through the first model to obtain pseudo marking information to expand the data volume of the reference points. The generated pseudo marking information can be used to supplement the real collected data to enhance the accuracy of the model and improve the positioning accuracy, while reducing the time cost and manpower required to measure the information of the reference points. Since the solution of the embodiment of the present application does not require the installation of a large amount of additional hardware equipment, nor does it require the modification of existing hardware equipment, it is easy to promote and has obvious advantages and commercial prospects in high-precision indoor positioning scenarios.

[0084] The first model can be any of the neural network models described above. The first model can be an AI model or an ML model. The first model can be, for example, a CNN or RNN. In some embodiments, the first model can be an LSTM model.

[0085] The LSTM model is a recurrent neural network suitable for processing sequential data. The most significant feature of the LSTM is the addition of three neural networks: an input gate, a forget gate, and an output gate. By setting the number of hidden layers, the neural network can analyze and organize data information. Based on the aforementioned characteristics of the LSTM model, embodiments of the present application can use the LSTM model to predict the first information of the second reference point.

[0086] The position of the second reference point is determined based on the position of the first reference point. As shown in FIG8 , the solid dot is the second reference point, and the hollow dot is the first reference point.

[0087] The second reference point may be a reference point that the user wishes to measure, and the position information of the second reference point may be determined by the user independently, or may be determined by the first device. For example, the first device may determine the position information of the second reference point based on the first rule.

[0088] The location information of the reference point may be an absolute location or a relative location, which is not specifically limited in the embodiments of the present application. As an example, for an outdoor positioning scenario, the location information of the reference point may be an absolute location. For example, the location information of the reference point includes longitude information and / or latitude information. As another example, for an indoor positioning scenario, the location information of the reference point may be a relative location. The location information of the reference point may be location information in a first coordinate system. For example, the location coordinates of the reference point may be expressed as (x, y).

[0089] In order to improve the accuracy of the positioning results, the embodiment of the present application can train the first model based on the channel state information between the first reference point and multiple network devices (such as multiple base stations).

[0090] For example, the first reference point can be connected to base station 1, and the reference signal sent by base station 1 is measured to obtain CSI-1; the first reference point can be connected to base station 2, and the reference signal sent by base station 2 is measured to obtain CSI-2; and so on.

[0091] The embodiments of the present application do not specifically limit the channel state information measurement process. For example, multiple terminal devices can be placed at multiple reference points, such as a terminal device at each reference point, and then the channel state information between each terminal device and the network device can be measured. For another example, a terminal device can be placed at different reference points, and the channel state information between the terminal device at different reference points and the network device can be measured.

[0092] In some embodiments, the channel state information may include a CIR. The CIR may be represented by a matrix. The embodiments of the present application do not specifically limit the sampling length of the CIR matrix. For example, the sampling length of the CIR matrix may be 4096, 2048, or the like. The sampling length of the CIR matrix can be understood as the dimension of the CIR matrix. To reduce data processing complexity, the first device may perform dimensionality reduction on the collected CIR matrix and use the reduced CIR matrix to train the first model.

[0093] In some embodiments, the first device may obtain a first CIR matrix between the first reference point and the network device. The first device may process the first CIR matrix to obtain a second CIR matrix. The dimension of the second CIR matrix is ​​smaller than the dimension of the first CIR matrix. The processing of the first CIR matrix by the first device may also be referred to as dimensionality reduction.

[0094] The embodiments of the present application do not specifically limit the method of dimensionality reduction processing. As an example, the first device may merge multiple rows of elements in the first CIR matrix into one row of elements. The merging method may be taking an average, or it may be summing, etc. For example, the first device may average multiple rows of elements in the first CIR matrix to obtain a row of elements in the second CIR matrix. As another example, the first device may delete some elements in the first CIR matrix, and the remaining elements form the second CIR matrix. The elements retained by the first device may be elements of the first M rows, elements of the last M rows, or elements of the middle rows. For example, the first device may truncate the first M rows of the first CIR matrix, that is, only retain the data information of the first M rows, thereby obtaining the second CIR matrix.

[0095] In some embodiments, the dimension of the first CIR matrix is ​​4096, or in other words, the sampling length of the first CIR matrix is ​​4096; the dimension of the second CIR matrix is ​​256, or in other words, the sampling length of the second CIR matrix is ​​256.

[0096] In some embodiments, due to interference factors such as noise during the measurement process, the first CIR matrix may include some noise and other interference information. In this case, the first device may further filter the first CIR matrix to obtain a second CIR matrix. In other words, the second CIR matrix is ​​a filtered matrix. By filtering the first CIR matrix, the impact of noise and other interference on the training accuracy of the first model can be reduced, thereby improving the accuracy of the first model.

[0097] The embodiments of the present application do not specifically limit the order of filtering and dimensionality reduction. As an example, the first device may first perform dimensionality reduction on the first CIR matrix, and then filter the reduced matrix to obtain a second CIR matrix. As another example, the first device may first perform filtering on the first CIR matrix, and then perform dimensionality reduction on the filtered CIR matrix to obtain a second CIR matrix.

[0098] The following describes the process of processing the first CIR matrix by taking the example of first performing dimensionality reduction processing and then performing filtering processing.

[0099] The first CIR matrix can be expressed as follows:

[0100] in, represents the first matrix, a represents the ath base station in the positioning area, b represents the bth reference point in the positioning area, R represents the real part, I represents the imaginary part, and N represents the sampling length.

[0101] There will be a matrix.

[0102] The first device can Truncate the first M rows and get the matrix matrix It can be expressed as follows:

[0103] The first device can Perform filtering and obtain the matrix matrix It can be expressed as follows:

[0104] matrix It can be the second CIR matrix described above.

[0105] The first device may train the first model using the second CIR matrix and the location information of the first reference point. In some embodiments, the first device may combine the second CIR matrix and the location information of the first reference point to obtain a labeled dataset. The first device may input the label data for each reference point into the first model to train the first model and obtain a trained first model.

[0106] The channel state information between the first reference point and the network device may be actively sent by the network device to the first device, or may be sent by the network device to the first device after the first device requests it. This embodiment of the present application does not specifically limit this.

[0107] In some implementations, referring to FIG. 9 , in step S910 , the first device may send a request message to the network device. The request message is used to request channel state information between the first reference point and the network device. In step S920 , the network device sends a first message to the first device, where the first message includes the channel state information between the first reference point and the network device.

[0108] The network device may maintain the channel state information between the first reference point and the network device. After obtaining the channel state information between the first reference point and the network device through measurement, the first reference point may send the measured channel state information to the network device.

[0109] In some embodiments, the request message may also include the ID information of the terminal device. After receiving the request message, the network device may send the channel state information corresponding to the ID of the terminal device to the first device. The ID information of the terminal device allows the network device to clearly identify which channel state information needs to be transmitted.

[0110] In some embodiments, the request message can be used to request the channel state information corresponding to all terminal devices within the coverage of the network device. After receiving the request message, the network device can send the channel state information corresponding to all terminal devices within the coverage to the first device. In some implementations, the request message may include first indication information, and the first indication information is used to indicate that the first reference point includes all terminal devices within the coverage of the network device. In other implementations, if the request message does not include the ID information of the terminal device, the request message is used by default to request the channel state information corresponding to all terminal devices within the coverage of the network device. This method can reduce the amount of data transmission and save signaling overhead. After the network device receives the request message, if the request message carries the ID information of the terminal device, the network device can send the channel state information corresponding to the ID information of the terminal device to the first device; if the request message does not carry the ID information of the terminal device, the network device can send the channel state information corresponding to all terminal devices within the coverage to the first device.

[0111] The following describes an example of the solution of the embodiment of the present application in conjunction with Figure 10. It should be understood that the following description is only an example for ease of understanding, and the embodiment of the present application should not be limited to the implementation described below.

[0112] 10 , the solution of the embodiment of the present application can be divided into a training phase and a testing phase.

[0113] During the training phase, some first reference points can be selected, their CIR values ​​measured, and their location coordinates recorded. The CIR measurements and location coordinates of the first reference points are combined to form a positioning fingerprint library. The data in the fingerprint library is input into the first model, and the first model is trained to obtain a trained first model.

[0114] During the testing phase, the position coordinates of the second reference point are input into the first model to obtain the CIR prediction value of the second reference point. By generating the CIR prediction value of the second reference point based on the first model, the data information in the positioning fingerprint library can be effectively expanded.

[0115] Taking Figure 8 as an example, the first reference point is a hollow dot and the second reference point is a solid dot. The positioning server can collect CIR data information of the hollow dot and predict CIR data information of the solid dot using the first model.

[0116] The following takes the channel state information as CIR as an example to describe the technical solution provided by the embodiment of the present application in detail. It should be understood that the following description is only an example for ease of understanding and the embodiment of the present application should not be limited to the implementation described below.

[0117] There are A base stations in the area to be positioned. B reference points are randomly selected in the area. The B reference points are divided into B1 training points and B2 prediction points. The terminal device is placed at the B1 training points in sequence. The CIR matrix between the terminal device and each base station at each training point is measured. And record the location coordinates of the reference point where the terminal device is located (x b ,y b ).

[0118] It can be expressed by the following formula:

[0119] For each CIR matrix, the first M rows are truncated to obtain the matrix matrix It can be expressed as follows:

[0120] Pair Matrix Perform filtering to remove noise and other interference and obtain the matrix matrix It can be expressed as follows:

[0121] The matrix obtained at each reference point The corresponding coordinate labels (x b ,y b ) are merged to construct the labeled dataset F.

[0122] The data set F of each reference point is sequentially input into the first model, and the first model is trained to obtain a trained first model.

[0123] The coordinates of the B2 prediction points are input into the first model to obtain CIR information corresponding to the B2 prediction points.

[0124] The method embodiment of the present application is described in detail above in conjunction with Figures 1 to 10. The device embodiment of the present application is described in detail below in conjunction with Figures 11 to 13. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment. Therefore, for parts not described in detail, reference can be made to the above method embodiment.

[0125] FIG11 is a schematic block diagram of a communication device provided in an embodiment of the present application. The communication device 1100 shown in FIG11 can be any of the first devices described above. The communication device 1100 can include an acquisition unit 1110 and a generation unit 1120.

[0126] The acquisition unit 1110 is used to acquire first information of a first reference point, where the first information of the first reference point is used to train a first model, and the first information includes location information and / or channel state information between the reference point and the network device.

[0127] The input unit 1120 is configured to input the location information of the second reference point into the first model to obtain the channel state information between the second reference point and the network device.

[0128] The first information of the first reference point and the first information of the second reference point are used to train a second model, and the second model is used to locate the terminal device.

[0129] In some implementations, the channel state information includes a channel impulse response (CIR).

[0130] In some implementations, the acquisition unit is used to: obtain a first CIR matrix between the first reference point and the network device; the communication device also includes: a processing unit, used to process the first CIR matrix to obtain a second CIR matrix, the dimension of the second CIR matrix is ​​smaller than the dimension of the first CIR matrix; a training unit, used to train the first model using the second CIR matrix and the position information of the first reference point.

[0131] In some implementations, the communication device further includes: a sending unit for sending a request message to the network device, wherein the request message is used to request channel state information between the first reference point and the network device; and a receiving unit for receiving a first message sent by the network device, wherein the first message includes the channel state information between the first reference point and the network device and / or the channel state information between the second reference point and the network device.

[0132] In some implementations, the request message includes ID information of the first reference point.

[0133] In some implementations, the request message includes first indication information, where the first indication information is used to indicate that the first reference point includes all terminal devices within the coverage of the network device.

[0134] In some implementations, the first device is a positioning server.

[0135] In an optional embodiment, the receiving unit and the sending unit may be a transceiver 1330, and the acquiring unit 1110 and the generating unit 1120 may be a processor 1310. The communication device 1100 may further include a transceiver 1330 and a memory 1320, as specifically shown in FIG13 .

[0136] Figure 12 is a schematic block diagram of a network device provided in an embodiment of the present application. The network device 1200 shown in Figure 12 can be any of the network devices described above. The network device 1200 can include a receiving unit 1210 and a sending unit 1220.

[0137] The receiving unit 1210 is configured to receive a request message sent by the first device.

[0138] The sending unit 1220 is used to send a first message to the first device in response to the request message, where the first message includes channel state information between the first reference point and the network device and / or channel state information between the second reference point and the network device.

[0139] Among them, the first information of the first reference point is used to train the first model, the first information of the first reference point is used to train the first model, the first model is used to generate channel state information between the second reference point and the network device based on the first information of the first reference point and the position information of the second reference point, the first information of the first reference point and the first information of the second reference point are used to train the second model, and the second model is used to locate the terminal device.

[0140] In some implementations, the channel state information includes a channel impulse response (CIR).

[0141] In some implementations, the request message includes ID information of the first reference point.

[0142] In some implementations, the request message includes first indication information, where the first indication information is used to indicate that the first reference point includes all terminal devices within the coverage of the network device.

[0143] In some implementations, the first device is a positioning server.

[0144] In an optional embodiment, the receiving unit 1210 and the sending unit 1220 may be a transceiver 1330. The network device 1200 may further include a processor 1310 and a memory 1320, as specifically shown in FIG13 .

[0145] Figure 13 is a schematic block diagram of a communication device according to an embodiment of the present application. The dashed lines in Figure 13 indicate that the unit or module is optional. Apparatus 1300 may be used to implement the method described in the above method embodiment. Apparatus 1300 may be a chip, a first device, or a network device.

[0146] The device 1300 may include one or more processors 1310. The processor 1310 may support the device 1300 to implement the method described in the above method embodiment. The processor 1310 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0147] The apparatus 1300 may further include one or more memories 1320. The memories 1320 store programs that can be executed by the processor 1310, causing the processor 1310 to perform the methods described in the above method embodiments. The memories 1320 may be independent of the processor 1310 or integrated into the processor 1310.

[0148] The apparatus 1300 may further include a transceiver 1330. The processor 1310 may communicate with other devices or chips via the transceiver 1330. For example, the processor 1310 may transmit and receive data with other devices or chips via the transceiver 1330.

[0149] The present invention also provides a computer-readable storage medium for storing a program. The computer-readable storage medium can be applied to the first device or network device provided in the present invention, and the program causes a computer to execute the method performed by the first device or network device in each embodiment of the present invention.

[0150] The present application also provides a computer program product. The computer program product includes a program. The computer program product can be applied to the first device or network device provided in the present application, and the program causes a computer to execute the method performed by the first device or network device in each embodiment of the present application.

[0151] The present application also provides a computer program that can be applied to the first device or network device provided in the present application, and enables a computer to execute the method performed by the first device or network device in each embodiment of the present application.

[0152] It should be understood that the terms "system" and "network" in this application can be used interchangeably. In addition, the terms used in this application are only used to explain the specific embodiments of this application and are not intended to limit this application. The terms "first", "second", "third", and "fourth" in the specification and claims of this application and the accompanying drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0153] In the embodiments of this application, the term "indication" may refer to a direct indication, an indirect indication, or an indication of an association. For example, "A indicates B" may refer to a direct indication of B, e.g., B can obtain information through A; it may refer to an indirect indication of B, e.g., A indicates C, e.g., B can obtain information through C; or it may refer to an association between A and B.

[0154] In the embodiment of the present application, "B corresponding to A" means that B is associated with A and B can be determined based on A. However, it should be understood that determining B based on A does not mean determining B based solely on A, but B can also be determined based on A and / or other information.

[0155] In the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship between indication and indication, configuration and configuration, etc.

[0156] In the embodiments of the present application, "pre-definition" or "pre-configuration" may be implemented by pre-storing corresponding codes, tables, or other methods that can be used to indicate relevant information in a device (e.g., a terminal device and a network device). The present application does not limit the specific implementation method. For example, pre-definition may refer to information defined in a protocol.

[0157] In the embodiments of the present application, the “protocol” may refer to a standard protocol in the communications field, for example, it may include an LTE protocol, an NR protocol, and related protocols used in future communication systems, and the present application does not limit this.

[0158] In the embodiments of this application, the term "and / or" is simply a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0159] In various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0161] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0162] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0163] 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 instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be read by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0164] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for positioning, characterized in that include: The first device acquires first information of a first reference point, where the first information of the first reference point is used to train a first model, where the first information includes location information and / or channel state information between the reference point and the network device; The first device inputs the position information of the second reference point into the first model to obtain channel state information between the second reference point and the network device; The first information of the first reference point and the first information of the second reference point are used to train a second model, and the second model is used to locate the terminal device.

2. The method according to claim 1, characterized in that The channel state information includes a channel impulse response CIR, and the first device acquires first information of a first reference point, including: Acquiring, by the first device, a first CIR matrix between the first reference point and the network device; The method further comprises: The first device processes the first CIR matrix to obtain a second CIR matrix, where a dimension of the second CIR matrix is ​​smaller than a dimension of the first CIR matrix; The first device trains the first model using the second CIR matrix and the position information of the first reference point.

3. The method according to claim 1 or 2, characterized in that The method further comprises: The first device sends a request message to the network device, where the request message is used to request channel state information between the first reference point and the network device; The first device receives a first message sent by the network device, where the first message includes channel state information between the first reference point and the network device and / or channel state information between the second reference point and the network device.

4. The method according to claim 3, characterized in that The request message includes the ID information of the first reference point.

5. The method according to claim 3 or 4, characterized in that The request message includes first indication information, and the first indication information is used to indicate that the first reference point includes all terminal devices within the coverage range of the network device.

6. The method according to any one of claims 1 to 5, characterized in that The first device is a positioning server.

7. A method for positioning, characterized in that include: The network device receives the request message sent by the first device; In response to the request message, the network device sends a first message to the first device, where the first message includes channel state information between the first reference point and the network device and / or channel state information between the second reference point and the network device; Among them, the first information of the first reference point is used to train the first model, the first model is used to generate channel state information between the second reference point and the network device based on the first information of the first reference point and the position information of the second reference point, the first information of the first reference point and the first information of the second reference point are used to train the second model, and the second model is used to locate the terminal device.

8. The method according to claim 7, characterized in that The channel state information includes a channel impulse response (CIR).

9. The method according to claim 7 or 8, characterized in that The request message includes the ID information of the first reference point.

10. The method according to any one of claims 7 to 9, characterized in that The request message includes first indication information, and the first indication information is used to indicate that the first reference point includes all terminal devices within the coverage range of the network device.

11. The method according to any one of claims 7 to 10, characterized in that The first device is a positioning server.

12. A communication device, characterized in that: The communication device is a first device, comprising: an acquiring unit, configured to acquire first information of a first reference point, where the first information of the first reference point is used to train a first model, the first information including location information and / or channel state information between the reference point and a network device; an input unit, configured to input the position information of the second reference point into the first model to obtain channel state information between the second reference point and the network device; The first information of the first reference point and the first information of the second reference point are used to train a second model, and the second model is used to locate the terminal device.

13. The communication device according to claim 12, wherein: The channel state information includes a channel impulse response CIR, and the acquiring unit is configured to: Obtaining a first CIR matrix between the first reference point and the network device; The communication device further includes: a processing unit, configured to process the first CIR matrix to obtain a second CIR matrix, where a dimension of the second CIR matrix is ​​smaller than a dimension of the first CIR matrix; A training unit is used to train the first model using the second CIR matrix and the position information of the first reference point.

14. The communication device according to claim 12 or 13, characterized in that The communication device further includes: a sending unit, configured to send a request message to the network device, wherein the request message is used to request channel state information between the first reference point and the network device; A receiving unit is used to receive a first message sent by the network device, where the first message includes channel state information between the first reference point and the network device and / or channel state information between the second reference point and the network device.

15. The communication device according to claim 14, wherein: The request message includes the identification ID information of the first reference point.

16. The communication device according to claim 14 or 15, characterized in that The request message includes first indication information, and the first indication information is used to indicate that the first reference point includes all terminal devices within the coverage range of the network device.

17. The communication device according to any one of claims 12 to 16, characterized in that: The first device is a positioning server.

18. A network device, characterized in that: include: A receiving unit, configured to receive a request message sent by the first device; a sending unit, configured to send a first message to the first device in response to the request message, where the first message includes channel state information between the first reference point and the network device and / or channel state information between the second reference point and the network device; Among them, the first information of the first reference point is used to train the first model, the first model is used to generate channel state information between the second reference point and the network device based on the first information of the first reference point and the position information of the second reference point, the first information of the first reference point and the first information of the second reference point are used to train the second model, and the second model is used to locate the terminal device.

19. The network device according to claim 18, wherein: The channel state information includes a channel impulse response (CIR).

20. The network device according to claim 18 or 19, characterized in that: The request message includes the identification ID information of the first reference point.

21. The network device according to any one of claims 18 to 20, characterized in that: The request message includes first indication information, and the first indication information is used to indicate that the first reference point includes all terminal devices within the coverage range of the network device.

22. The network device according to any one of claims 18 to 21, characterized in that: The first device is a positioning server.

23. A communication device, characterized in that: The communication device is a first device, comprising a memory and a processor, wherein the memory is used to store a program, and the processor is used to call the program in the memory, so that the communication device executes the method according to any one of claims 1 to 6.

24. A network device, characterized in that: The network device comprises a transceiver, a memory and a processor, wherein the memory is used to store a program, and the processor is used to call the program in the memory and control the transceiver to receive or send a signal so that the network device executes the method according to any one of claims 7 to 11.

25. A device, characterized in that The device comprises a processor configured to call a program from a memory so as to enable the device to execute the method according to any one of claims 1 to 11.

26. A chip, characterized in that: The device comprises a processor configured to call a program from a memory so that a device equipped with the chip executes the method according to any one of claims 1 to 11.

27. A computer-readable storage medium, characterized in that A program is stored thereon, and the program causes a computer to execute the method according to any one of claims 1 to 11.

28. A computer program product, characterized in that The method comprises a program for causing a computer to execute the method according to any one of claims 1 to 11.

29. A computer program, characterized in that The computer program enables a computer to execute the method according to any one of claims 1 to 11.

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