CN114297747B - Modeling method and electronic terminal of hybrid channel in subway tunnel - Google Patents
Modeling method and electronic terminal of hybrid channel in subway tunnel Download PDFInfo
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Abstract
The invention provides a subway tunnel mixed channel modeling method and an electronic terminal, the method comprises the steps of establishing a three-dimensional model of a subway tunnel, configuring parameters of the three-dimensional model of the subway tunnel, radiating signals to a receiver by a transmitter based on a ray tracing method, constructing a radial basis function neural network in the process that the transmitter radiates signals to the receiver, configuring a radial basis function neural network structure, selecting test data to train the radial basis function neural network, taking the difference obtained by subtracting a simulation path loss value from the test path loss as the input of the RBF neural network, and outputting the difference as a path loss predicted value at a receiving point to form a required radial basis function neural network model of the subway tunnel mixed channel. Compared with the traditional ray tracing method, the method effectively reduces the adjustment times of simulation parameters, and has higher efficiency when used for predicting the wireless channel characteristics under various complex scenes.
Description
Technical Field
The invention belongs to the technical field of software simulation modeling, and particularly relates to a subway tunnel mixed channel modeling method and an electronic terminal.
Background
The existing wireless channel simulation modeling has poor prediction accuracy on the wave propagation characteristics in the tunnel. The traditional ray tracing simulation modeling lacks a scientific method for adjusting simulation parameters such as roughness, reflection times, ray intervals and the like, a prediction result which is relatively close to reality can be obtained through multiple simulation experiments, and in addition, the original simulation parameters need to be recalibrated once a propagation scene is changed.
Disclosure of Invention
In view of the above drawbacks of the prior art, the present invention aims to provide a subway tunnel hybrid channel modeling method and an electronic terminal, which are used for solving the technical problem of poor accuracy of channel simulation modeling prediction in the prior art.
To achieve the above and other related objects, an embodiment of the present invention provides a method for modeling a subway tunnel hybrid channel, including establishing a three-dimensional model of a subway tunnel, configuring parameters of the three-dimensional model of the subway tunnel, radiating signals from a transmitter to a receiver based on a ray tracing method, constructing a radial basis function neural network in the process of radiating signals from the transmitter to the receiver, configuring the radial basis function neural network structure, selecting test data to train the radial basis function neural network, taking a difference obtained by subtracting a simulated path loss value from a test path loss as an input of the RBF neural network, and outputting the difference as a path loss predicted value at a receiving point to form a radial basis function neural network model of a desired subway tunnel hybrid channel.
In one embodiment of the present application, the parameters of the three-dimensional model of the configured subway tunnel include the roughness of the tunnel wall, the ray interval, the ray reflection number, and the ray diffraction number.
In one embodiment of the application, the radiation of the signal from the transmitter to the receiver based on the ray tracing method comprises the steps that the transmitter radiates the signal in all directions, the radiated signal reaches the receiver after being transmitted by a space channel, and the receiver is a plurality of concentric spheres with different radiuses, wherein the radiuses of the concentric spheres are correspondingly matched with the path lengths of rays and included angles between adjacent rays.
In an embodiment of the present application, the configuring the radial basis function neural network structure includes configuring an input layer as a vector x, a dimension as m, a number of samples as n, and a hidden layer fully connected with the input layer without connection in the layers.
In an embodiment of the present application, a maximum value of the number of neurons in the hidden layer is equal to the number of samples, and the number of neurons in the hidden layer is set to be 30-40.
In one embodiment of the present application, the transmission/reception distance, the roughness of the tunnel wall, the ray interval, the ray reflection number, and the ray diffraction number are used as the detailed information of the input layer vector x.
In an embodiment of the present application, the transfer function of the radial basis function neural network is a radial basis function, and the distribution density of the radial basis function is configured to be 1.5.
In one embodiment of the present application, the basis functions of the radial basis function neural network are configured as gaussian functions.
In an embodiment of the present application, verifying the radial basis function neural network model of the subway tunnel hybrid channel using the remaining test data is further included.
The embodiment of the invention also provides the electronic terminal which is characterized by comprising a processor and a memory, wherein the memory stores program instructions, and the processor runs the program instructions to realize the subway tunnel mixed channel modeling method.
As described above, the subway tunnel mixed channel modeling method and the electronic terminal have the following beneficial effects:
1. according to the invention, the radial basis function (Radial Basis Function, RBF) neural network algorithm is used for constructing the radial basis function neural network model of the subway tunnel mixed channel, so that the accuracy of prediction of single ray tracking simulation is improved.
2. Compared with the traditional ray tracing method, the method effectively reduces the adjustment times of simulation parameters, and has higher efficiency when being used for predicting the wireless channel characteristics under various complex scenes.
Drawings
Fig. 1 shows a schematic flow chart of a subway tunnel mixed channel modeling method of the invention.
Fig. 2 is a schematic diagram of a radial basis function neural network structure in the subway tunnel mixed channel modeling method of the invention.
Fig. 3 is a schematic diagram showing comparison of path loss prediction results in the subway tunnel mixed channel modeling method according to the invention.
Fig. 4 shows a functional block diagram of an electronic terminal of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
The embodiment aims to provide a subway tunnel mixed channel modeling method and an electronic terminal, which are used for solving the technical problem that monitoring of multi-stage clock timing precision is not realized in the prior art.
According to the subway tunnel mixed channel modeling method and the electronic terminal, a three-dimensional tunnel model and a train model for ray tracing simulation are designed, and the influence of simulation parameters on the difference between actual measurement data and simulation data is obtained through training by a radial basis function (Radial Basis Function, RBF) neural network algorithm, so that a mixed channel model based on ray tracing and a neural network is built. Compared with the traditional ray tracing method, the mixed model effectively reduces the adjustment times of simulation parameters, improves the accuracy of prediction, and can be used for predicting the characteristics of wireless channels in various scenes.
The principle and implementation of the subway tunnel mixed channel modeling method and the electronic terminal of the present embodiment will be described in detail below, so that those skilled in the art can understand the subway tunnel mixed channel modeling method and the electronic terminal of the present invention without creative labor.
Example 1
As shown in fig. 1, the present embodiment provides a subway tunnel mixed channel modeling method, including:
Step 100, establishing a three-dimensional model of a subway tunnel;
Step 200, configuring parameters of a three-dimensional model of a subway tunnel;
Step 300, based on ray tracing method, the transmitter radiates signal to the receiver;
step 400, constructing a radial basis function neural network in the process of radiating signals from a transmitter to a receiver, and configuring the radial basis function neural network structure;
And S500, selecting test data to train the radial basis function neural network, taking the difference obtained by subtracting the simulation path loss value from the test path loss as the input of the RBF neural network, and outputting the difference as the path loss predicted value at the receiving point to form the required radial basis function neural network model of the subway tunnel mixed channel.
The following describes in detail steps S100 to S500 of the subway tunnel mixed channel modeling method of the present embodiment with reference to fig. 2 and 3.
And step 100, establishing a three-dimensional model of the subway tunnel.
And step 200, configuring parameters of a three-dimensional model of the subway tunnel.
In this embodiment, parameters of the three-dimensional model of the configured subway tunnel include, but are not limited to, roughness of the tunnel wall, ray interval, ray reflection number, and ray diffraction number.
Step S300, the transmitter radiates a signal to the receiver based on the ray tracing method.
When using the ray tracing method, the signal emission point is regarded as a point source and the electromagnetic wave emitted outwards is regarded as rays in geometry according to the incident and rebound ray algorithm. Each emission line is tracked, and before the emission line is completely lost, the emission line is hit on a plane, and the emission line is subjected to transmission, reflection and diffraction treatment according to the geometrical optics principle. The signal receiving point is also regarded as a geometric point, all signal rays passing through the point are regarded as received, electromagnetic field intensities are vector-added, and propagation characteristics are predicted.
Specifically, in the embodiment, the radiation of the signal from the transmitter to the receiver based on the ray tracing method includes that the transmitter radiates the signal in all directions, the radiated signal reaches the receiver after being transmitted by the space channel, and the receiver is a plurality of concentric spheres with different radiuses, and the radiuses of the concentric spheres are correspondingly matched with the path lengths of rays and the included angles between adjacent rays.
And step 400, constructing a radial basis function neural network in the process of radiating signals from a transmitter to a receiver, and configuring the radial basis function neural network structure.
And S500, selecting test data to train the radial basis function neural network, taking the difference obtained by subtracting the simulation path loss value from the test path loss as the input of the RBF neural network, and outputting the difference as the path loss predicted value at the receiving point to form the required radial basis function neural network model of the subway tunnel mixed channel.
In this embodiment, the configuration of the radial basis function neural network structure includes configuring an input layer as a vector x, a dimension as m, and the number of samples as n, where the hidden layer is fully connected with the input layer and there is no connection in the layers.
In this embodiment, the maximum number of neurons in the hidden layer is equal to the number of samples, and the number of neurons in the hidden layer is set to 30-40, for example, the number of neurons in the hidden layer is set to 40.
In this embodiment, the transmission/reception distance, the roughness of the tunnel wall, the ray interval, the number of ray reflections, and the number of ray diffraction are used as the detailed information of the input layer vector x.
In the embodiment, the transfer function of the radial basis function neural network is a radial basis function, the distribution density of the radial basis function is configured to be 1.5, and the basis function of the radial basis function neural network is configured to be a Gaussian function.
In this embodiment, when a Radial Basis Function (RBF) neural network is used for channel modeling, parameters of a network model including an input layer vector, dimensions and the number of samples are set first, a hidden layer is fully connected with the input layer, no connection exists in the layer, the maximum number of neurons of the hidden layer is equal to the number of samples, a transfer function is an RBF function, and the basis function is set as a gaussian function.
In this embodiment, the method further includes verifying the radial basis function neural network model of the subway tunnel hybrid channel by using the remaining test data.
The method for combining the ray tracing method and the neural network comprises the following steps of implementing data by using field test data, taking the difference obtained by subtracting the simulation path loss value from the measured path loss as the input of the RBF neural network on the basis of ray tracing, outputting the path loss predicted value at a receiving point, using part of samples as a training network, enabling the RBF neural network to learn the influence of fine environmental information and simulation parameters on the simulation result into the network through training, and verifying the model by the rest samples.
In this embodiment, taking a mixed channel modeling of 28GHz based on a ray tracing and neural network algorithm for a typical subway tunnel as an example, the subway tunnel mixed channel modeling method of this embodiment is described.
1) And establishing a three-dimensional model of the subway tunnel.
2) Tunnel roughness was set to 0.01m, 0.005m, 0.001m, 0.0005m, 0.0003m, and 0.0001m, respectively. The ray intervals were set to 0.05 °, 0.1 °, 0.2 °, and 0.5 °, the reflection numbers were set to 6, 12, 18, and 24, and the diffraction numbers were set to 0,1, 3, and 5, respectively.
3) In the operation of the transmission bouncing ray method, a transmitter radiates signals in all directions, and the signals reach a receiver after being transmitted by a space channel. The receiver is a plurality of concentric spheres of different radii, the radius of which depends on the path length of the rays and the angle between adjacent rays.
4) On the basis of ray tracing, an RBF neural network is constructed, an input layer of the RBF neural network is a vector x, the dimension is m, the number of samples is n, a hidden layer is fully connected with the input layer, no connection exists in the layer, the maximum value of the number of neurons of the hidden layer is equal to the number of the samples, and the number of neurons of the hidden layer is generally set to 40, as shown in fig. 2.
5) Parameters of network modeling are set, and the transceiving distance, the roughness of tunnel walls, the ray interval, the ray reflection times and the ray diffraction times are used as detailed information of an input layer vector x.
6) The neural network transfer function is RBF. The basis function is set to a gaussian function. To avoid the occurrence of the overfitting phenomenon, the RBF function distribution density was set to 1.5.
7) The network is trained by using test data in the 28GHz frequency band, and the difference obtained by subtracting the simulation path loss value from the test path loss is taken as the input of the RBF neural network, and the output is taken as the path loss predicted value at the receiving point. Through training, the RBF neural network learns the influence of the fine environmental information and simulation parameters on the simulation result into the network.
8) And finally, verifying the modeling by the residual test data.
9) Path loss prediction experiments in the 28GHz frequency band are carried out by using the constructed mixed channel modeling, and compared with a ray tracing method and measured data, as shown in figure 3. It can be found that the path loss value predicted by using the mixed channel modeling based on the ray tracing and RBF neural network algorithm is closer to the measured value.
Example 2
As shown in fig. 4, the present embodiment further provides an electronic terminal 10, where the electronic terminal 10 includes a processor 101 and a memory 102.
The memory 102 is connected to the processor 101 through a system bus and performs communication with each other, the memory 102 is used for storing a computer program, the processor 101 is coupled to the memory 1002, and the processor 101 is used for running the computer program, so that the electronic terminal 10 performs the subway tunnel mixed channel modeling method described in embodiment 1. Embodiment 1 has already described the subway tunnel hybrid channel modeling method in detail, and will not be described in detail here.
The subway tunnel mixed channel modeling method described can be applied to various types of electronic terminals 10.
In an actual implementation manner, the electronic terminal 10100 is, for example, an electronic terminal 10100 that installs an Android operating system or an iOS operating system, or operating systems such as Palm OS, symbian (plug) or Black Berry OS, windows Phone, etc.
In an exemplary embodiment, the electronic terminal 10 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, cameras or other electronic elements for performing the above-described subway tunnel mixing channel modeling method.
It should be noted that the system bus mentioned above may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, abbreviated as PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated as EISA) bus. The system bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write libraries, and read-only libraries). The memory may include random access memory (Random Access Memory, RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor 101 may be a general-purpose processor, including a central Processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a digital signal processor (DIGITAL SIGNAL Processing, DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, or discrete hardware components.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of implementing the method embodiments described above may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. The program, when executed, performs the steps comprising the method embodiments described above, and the storage medium described above includes various media capable of storing program code, such as ROM, RAM, magnetic or optical disk.
Example 3
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the subway tunnel hybrid channel modeling method. The foregoing detailed description of the subway tunnel hybrid channel modeling method is omitted herein.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of implementing the method embodiments described above may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. The program, when executed, performs the steps comprising the method embodiments described above, and the storage medium described above includes various media capable of storing program code, such as ROM, RAM, magnetic or optical disk.
In summary, the invention realizes the monitoring of the time service precision of the multi-stage clock, namely the precision between the output time of the monitoring system host and the output time of the secondary time server (or ETS server), the precision between the output time of the monitoring system station host and the output time of the system host, and the precision between the output time of the station slave and the output time of the station host. Therefore, the invention effectively overcomes the defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
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