CN115967451B - Wireless router signal processing method and device and wireless router using same - Google Patents
Wireless router signal processing method and device and wireless router using same Download PDFInfo
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- CN115967451B CN115967451B CN202310231809.4A CN202310231809A CN115967451B CN 115967451 B CN115967451 B CN 115967451B CN 202310231809 A CN202310231809 A CN 202310231809A CN 115967451 B CN115967451 B CN 115967451B
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Abstract
The invention discloses a wireless router signal processing method and device and a wireless router using the same. The method comprises the steps of carrying out signal frequency sampling on a transmitting signal of a wireless router, obtaining a time domain distribution diagram of the transmitting signal through Fourier transformation after determining a discrete sequence, obtaining the time domain distribution diagram of the transmitting signal with speckle noise after sampling for a plurality of times, and obtaining a training reference time domain distribution diagram of the transmitting signal without speckle noise by collecting standard transmitting signal parameters of the wireless router; further, the hybrid structure is constructed to generate the countermeasure network, and the acquired parameters are used for training the hybrid structure to generate the countermeasure network, so that the hybrid structure generates the countermeasure network to form a trained generated countermeasure network, and the trained generated countermeasure network automatically performs speckle noise elimination on the transmitted signal before the wireless router transmits the signal, thereby achieving the purpose of improving the stability of the transmitted signal of the wireless router.
Description
Technical Field
The invention relates to the technical field of wireless routers and manufacturing, in particular to a wireless router signal processing method and device and a wireless router using the same.
Background
The wireless router is a router with wireless coverage function for users to surf the internet.
Because the wireless router relies on air media for signal transmission, such coherence inevitably generates speckle noise, resulting in a reduction in the transmission rate of wireless signals, and such speckle noise can affect the transmission quality of wireless signals, which is greatly reduced once it encounters a wall or a large-volume home.
Therefore, a solution is needed to reject speckle noise in wireless signals when the wireless router transmits the wireless signals.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a wireless router signal processing method and device and a wireless router using the same, so as to achieve the purposes of effectively eliminating speckle noise in a wireless router transmitting signal and improving the stability of the wireless router transmitting signal.
The aim of the invention is realized by the following technical scheme: a wireless router signal processing method, comprising the steps of:
step S1: performing signal frequency sampling on a transmitting signal of a wireless router, determining a discrete sequence, performing Fourier transform on the discrete sequence, and determining a time domain distribution map of the transmitting signal;
step S2: repeating the step S1 for multiple signal frequency sampling to obtain N transmitting signal time domain distribution diagrams with speckle noise;
step S3: importing standard signal transmission parameters of a wireless router into a database, determining a discrete sequence of standard signal transmission frequency parameters, and carrying out Fourier transform on the sequence to obtain a training reference time domain distribution diagram without speckle noise;
step S4: selecting a transmitting signal time domain distribution diagram containing speckle noise and a training reference time domain distribution diagram without speckle noise to be matched in pairs to form a data set, setting part of the data set as a training data set, and setting the rest part as a test data set;
step S5: constructing a hybrid structure to generate an countermeasure network;
step S6: importing the training data set in the step S4 into the mixed structure constructed in the step S5 to generate an countermeasure network, so as to obtain a trained generated countermeasure network;
step S7: and removing speckle noise in the transmitting signal according to the trained generation countermeasure network, and completing adjustment of the transmitting signal frequency domain.
Further, the implementation step of the step S5 is as follows:
step S51: carrying out normalization processing on the training data set and the test data set by adopting a normalization formula;
step S52: constructing a generator;
step S53: constructing a discriminator;
step S54: the generator parameter optimization objective function is designed with the following formula:
in the formula (i),
representing the output of the generator,/>
Representing a reference image +.>
Output representing VGG-19 network feature extraction, +.>
The width, the height and the channel number of the image are respectively, i and n are constant definition generation values, and the specific numerical value is determined by the quadrant value of the function;
step S55: updating the network parameters of the generator and the arbiter to complete the generation of the countermeasure network by the mixed structure.
Further, the step S4 further includes the following steps: the data set is segmented into a training data set and a test data set.
Preferably, the data set segmentation principle is as follows:
carrying out weighted average processing on data in the data set to obtain a weighted average value, and comparing a single data point in the data set with the weighted average value to obtain a maximum data difference value and a minimum data difference value;
taking the average value of the maximum data difference value and the minimum data difference value as a division point, dividing the data into a test data set when the data difference value is more than or equal to the division point, and dividing the data into a training data set when the data difference value is less than the division point.
It should be noted that the generator includes an encoder and a decoder constructed by densely connected networks to form a U-Net structure, and the time domain profile is input to the encoder for downsampling and output through upsampling of the decoder.
Preferably, the specific steps of adjusting the frequency of the transmitted signal according to the trained generation countermeasure network are as follows:
and eliminating a time domain distribution diagram of the transmitting signal with higher speckle noise through a trained generation countermeasure network, performing time domain-frequency domain transformation on the output time domain signal diagram, and performing inverse Fourier transformation on the frequency domain to determine the transmitting signal of the wireless router.
A wireless router signal processing apparatus, comprising:
the first signal processing module is used for sampling the frequency of a transmitting signal of the wireless router, determining a discrete sequence, carrying out Fourier transform on the discrete sequence and determining a time domain distribution diagram of the transmitting signal;
the second signal processing module is used for determining a transmitting signal time domain distribution map with speckle noise and a training reference time domain distribution map without speckle noise according to the acquired data;
the third signal processing module is used for constructing a hybrid structure generation countermeasure network and a trained generation countermeasure network, filtering signals through the trained generation countermeasure network and determining a time domain of a signal transmitted by the wireless router;
and the fourth signal processing module is used for carrying out time domain-frequency domain transformation and carrying out inverse Fourier transformation on the frequency domain at the same time so as to determine a wireless router transmitting signal.
A computer readable storage medium storing a computer program for performing the method of any one of the above aspects of the invention.
A wireless router signal processing terminal, comprising: a processor, a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the functions related to any of the above aspects of the present invention, for example, generate, receive, send, or process data and/or information related to the above methods;
the processing terminal may be formed by a chip, or may include a chip and other discrete devices.
In one possible design, the processing terminal further includes a memory for holding necessary program instructions and data. The processor and the memory may be decoupled, provided on different devices, respectively, connected by wire or wirelessly, or the processor and the memory may be coupled on the same device.
The beneficial effects of the invention are as follows:
1) According to the invention, the hardware and the structure of the conventional wireless router are not required to be changed, the time domain distribution map of the transmitted signal for removing noise such as speckle noise can be obtained only by integrating the deep learning network with the mixed structure, and the stability of the transmitted signal of the wireless router is effectively improved by removing the speckle noise.
2) The deep learning method provided by the invention has the capability of efficiently removing noise such as speckle noise, and the like, and can analyze tiny and important transmitting frequency signals covered by the speckle noise while removing the noise such as the speckle noise, and the like.
3) The invention avoids repeated sampling operation and greatly reduces the data processing capacity.
4) The invention avoids the input power loss of the sample arm of the time domain distribution map of the transmitted signal, maintains the detection sensitivity, enables the subsequent data analysis to be stored in a more accurate range, and greatly improves the signal transmission accuracy of the wireless router.
Drawings
FIG. 1 is a flow chart of the steps of signal processing according to the present invention;
FIG. 2 is a diagram of a hybrid structure-generated countermeasure network architecture constructed in accordance with the present invention;
fig. 3 is a schematic diagram of frequency characteristics of a signal transmitted by a wireless router without using the present invention in the embodiment;
fig. 4 is a schematic diagram of frequency characteristics of a signal transmitted by a wireless router according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a signal processing apparatus according to the present invention;
fig. 6 is a schematic diagram of a signal processing terminal according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present invention, based on the embodiments of the present invention.
Embodiment one:
referring to fig. 1, the signal processing method of the wireless router disclosed by the invention comprises the following steps:
step S1: performing signal frequency sampling on a transmitting signal of a wireless router, determining a discrete sequence, performing Fourier transform on the discrete sequence, and determining a time domain distribution map of the transmitting signal;
step S2: repeating the step S1 for multiple signal frequency sampling to obtain N transmitting signal time domain distribution diagrams with speckle noise;
step S3: importing standard signal transmission parameters of a wireless router into a database, determining a discrete sequence of standard signal transmission frequency parameters, and carrying out Fourier transform on the sequence to obtain a training reference time domain distribution diagram without speckle noise;
step S4: selecting a transmitting signal time domain distribution diagram containing speckle noise and a training reference time domain distribution diagram without speckle noise to be matched in pairs to form a data set, setting part of the data set as a training data set, and setting the rest part as a test data set;
here, the dataset is segmented by:
carrying out weighted average processing on data in the data set to obtain a weighted average value, and comparing a single data point in the data set with the weighted average value to obtain a maximum data difference value and a minimum data difference value;
taking the average value of the maximum data difference value and the minimum data difference value as a division point, dividing the data into a test data set when the data difference value is more than or equal to the division point, and dividing the data into a training data set when the data difference value is less than the division point;
step S5: constructing a hybrid structure to generate an countermeasure network;
step S6: importing the training data set in the step S4 into the mixed structure constructed in the step S5 to generate an countermeasure network, so as to obtain a trained generated countermeasure network;
step S7: and removing speckle noise in the transmitting signal according to the trained generation countermeasure network, and completing adjustment of the transmitting signal frequency domain.
The specific steps of adjusting the frequency of the transmission signal according to the trained generation countermeasure network are as follows:
and eliminating a time domain distribution diagram of the transmitting signal with higher speckle noise through a trained generation countermeasure network, performing time domain-frequency domain transformation on the output time domain signal diagram, and performing inverse Fourier transformation on the frequency domain to determine the transmitting signal of the wireless router.
According to the method, under the condition that the hardware and the structure of the conventional wireless router are not changed, the time domain distribution map of the transmitted signal for removing noise such as speckle noise can be obtained only by integrating the deep learning network with the mixed structure, and the strength of the transmitted signal of the wireless router is effectively improved by removing the speckle noise.
Embodiment two:
referring to fig. 2, the present invention discloses a specific method for generating a countermeasure network by a hybrid architecture:
firstly, carrying out normalization processing on a training data set and a test data set by adopting a normalization formula;
gradually constructing a generator;
constructing a discriminator after the generator;
the generator parameters are designed for the parameters of the generator to optimize the objective function, and the formula is as follows:
in the formula (i),
representing the output of the generator,/>
Representing a reference image +.>
Output representing VGG-19 network feature extraction, +.>
The width, the height and the channel number of the image are respectively, i and n are constant definition generation values, and the specific numerical value is determined by the quadrant value of the function;
and finally, updating network parameters of the generator and the discriminator through a formula to finish the generation of the countermeasure network by the mixed structure.
Embodiment III:
referring to fig. 3 and 4, this embodiment temporarily constructs a wireless router transmit signal acquisition for acquiring a transmit signal image containing a large amount of speckle noise and its corresponding speckle noise-free transmit signal reference image. The center wavelength of the transmitting signal source is 850 nm. A broadband signal source with full width at half maximum 165 nm, the output signal being divided by a single 50:50 to the sample and reference arms. The interference signal enters a 2048-pixel spectrometer, and the signal detected by the spectrometer is transmitted to a computer. In this embodiment, the spectrometer and the two-dimensional scanning galvanometer are synchronized by a computer generated trigger signal. The other elements also have two identical collimators and two polarization controllers.
The invention uses the data set manufactured by the method as input to train and generate the countermeasure network, and sets the optimizer as the Adam optimizer before training and the learning rate as follows
The batch size and the number of training steps were set to 1 and 35 ten thousand, respectively, to obtain the optimal model parameters. Coefficients in the generator objective function ∈ ->
Sum coefficient->
1 and 0.1 respectively, the arbiter uses the cross entropy loss function as its objective function.
The best parameters of the model are obtained after the countermeasure network training is finished, and the trained model and the parameters thereof are integrated on the conventional processor system to finish explanation of the embodiment. In order to prove that the present invention has the function of automatically removing noise such as electronic noise and speckle noise, compared with the conventional router, the present embodiment performs waveform comparison of the transmission signals for the wireless router not using the present invention and the wireless router using the present invention, respectively.
Fig. 3 shows waveforms of transmission signals collected by a wireless router without using the present invention, and fig. 4 shows waveforms of transmission signals collected by the same wireless router with the present invention. It can be seen that images not formed using the present invention have a significant amount of speckle noise that severely affects the waveform of the transmitted signal, the peaks of which are affected by the speckle noise to exhibit irregular peaks and valleys, as shown in fig. 4. The waveform diagram of the transmitted signal obtained by the method well removes the noise such as speckle noise, electronic noise and the like, improves the stability of the waveform diagram, and leads the transmitted signal of the wireless router to be stable.
In order to verify that the method has high time resolution, the example further uses the method to continuously acquire a multi-frame transmission signal time domain distribution map, and a test system obtains the time consumed by 800 frames of transmission signal time domain distribution map with noise such as speckle noise removed. In the test, the time-domain distribution diagram acquisition of the transmitting signal for removing noise such as speckle noise and the like of 800 frames takes 115.8 seconds, and each frame of picture only needs 144.7 milliseconds on average.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
Embodiment four:
referring to fig. 5, the present invention also includes a wireless router signal processing apparatus,
the first signal processing module is used for sampling the frequency of a transmitting signal of the wireless router, determining a discrete sequence, carrying out Fourier transform on the discrete sequence and determining a time domain distribution diagram of the transmitting signal;
the second signal processing module is used for determining a transmitting signal time domain distribution map with speckle noise and a training reference time domain distribution map without speckle noise according to the acquired data;
the third signal processing module is used for constructing a hybrid structure generation countermeasure network and a trained generation countermeasure network, filtering signals through the trained generation countermeasure network and determining a time domain of a signal transmitted by the wireless router;
and the fourth signal processing module is used for carrying out time domain-frequency domain transformation and carrying out inverse Fourier transformation on the frequency domain at the same time so as to determine a wireless router transmitting signal.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Fifth embodiment:
referring to fig. 6, a computer-readable storage medium stores a computer program for executing the method according to any one of the above aspects of the present invention.
A wireless router signal processing terminal, comprising: a processor, a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the functions related to any of the above aspects of the present invention, for example, generate, receive, send, or process data and/or information related to the above methods;
the memory is a storage unit in the chip, such as a register, a cache, etc., and may also be a storage unit in the terminal located outside the chip, such as a ROM or other type of static storage device, a RAM, etc., that may store static information and instructions.
It is to be understood that the memory in this application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory.
The nonvolatile memory may be a ROM, a Programmable ROM (PROM), an Erasable Programmable ROM (EPROM), an electrically erasable programmable EPROM (EEPROM), or a flash memory.
The volatile memory may be RAM, which acts as external cache. There are many different types of RAM, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and direct memory bus RAM.
The processing terminal may be formed by a chip, or may include a chip and other discrete devices.
In one possible design, the processing terminal further includes a memory for holding necessary program instructions and data. The processor and the memory may be decoupled, provided on different devices, respectively, connected by wire or wirelessly, or the processor and the memory may be coupled on the same device.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the invention as defined in the appended claims.
Claims (7)
1. A wireless router signal processing method, characterized in that: the method comprises the following steps:
step S1: performing signal frequency sampling on a transmitting signal of a wireless router, determining a discrete sequence, performing Fourier transform on the discrete sequence, and determining a time domain distribution map of the transmitting signal;
step S2: repeating the step S1 for multiple signal frequency sampling to obtain N transmitting signal time domain distribution diagrams with speckle noise;
step S3: importing standard signal transmission parameters of a wireless router into a database, determining a discrete sequence of standard signal transmission frequency parameters, and carrying out Fourier transform on the discrete sequence to obtain a training reference time domain distribution diagram without speckle noise;
step S4: selecting a time domain distribution diagram of a transmitting signal containing speckle noise and a training reference time domain distribution diagram without speckle noise to be matched in pairs to form a data set, setting part of the data set as a training data set and the rest as a test data set, wherein the data set segmentation principle is as follows:
carrying out weighted average processing on data in the data set to obtain a weighted average value, and comparing a single data point in the data set with the weighted average value to obtain a maximum data difference value and a minimum data difference value;
taking the average value of the maximum data difference value and the minimum data difference value as a division point, dividing the data into a test data set when the data difference value is more than or equal to the division point, and dividing the data into a training data set when the data difference value is less than the division point;
step S5: constructing a hybrid structure to generate an countermeasure network;
step S6: importing the training data set in the step S4 into the mixed structure constructed in the step S5 to generate an countermeasure network, so as to obtain a trained generated countermeasure network;
step S7: and removing speckle noise in the transmitting signal according to the trained generation countermeasure network, and completing adjustment of the transmitting signal frequency domain.
2. A method of wireless router signal processing according to claim 1, wherein: the implementation steps of the step S5 are as follows:
step S51: carrying out normalization processing on the training data set and the test data set by adopting a normalization formula;
step S52: constructing a generator;
step S53: constructing a discriminator;
step S54: designing a generator parameter optimization objective function;
step S55: updating the network parameters of the generator and the arbiter to complete the generation of the countermeasure network by the mixed structure.
3. A method of wireless router signal processing according to claim 2, wherein:
the generator comprises an encoder and a decoder which are constructed by densely connected networks to form a U-Net structure, and the time domain distribution map is input into the encoder for downsampling and is output through upsampling of the decoder.
4. A method of wireless router signal processing according to claim 1, wherein: the specific steps of eliminating speckle noise in the transmitting signal and completing adjustment of the transmitting signal frequency domain are as follows:
the time domain distribution diagram of the transmitting signal with speckle noise is eliminated through the trained generation, the output time domain signal diagram is subjected to time domain-frequency domain transformation, and the frequency domain is subjected to inverse Fourier transformation, so that the transmitting signal of the wireless router is determined.
5. A wireless router signal processing apparatus, characterized in that: comprising the following steps:
the first signal processing module is used for carrying out signal frequency sampling on a transmitting signal of the wireless router, determining a discrete sequence, carrying out Fourier transform on the discrete sequence and determining a time domain distribution diagram of the transmitting signal; repeating the signal frequency sampling for a plurality of times to obtain N transmitting signal time domain distribution diagrams with speckle noise;
the second signal processing module is used for importing standard signal transmission parameters of the wireless router into a database, determining a discrete sequence of standard signal transmission frequency parameters, and carrying out Fourier transform on the discrete sequence to obtain a training reference time domain distribution diagram without speckle noise;
the third signal processing module is used for selecting a transmission signal time domain distribution diagram containing speckle noise and a training reference time domain distribution diagram without the speckle noise to be matched in pairs to form a data set, setting part of the data set as a training data set and the rest as a test data set, wherein the data set segmentation principle is as follows:
carrying out weighted average processing on data in the data set to obtain a weighted average value, and comparing a single data point in the data set with the weighted average value to obtain a maximum data difference value and a minimum data difference value;
taking the average value of the maximum data difference value and the minimum data difference value as a division point, dividing the data into a test data set when the data difference value is more than or equal to the division point, and dividing the data into a training data set when the data difference value is less than the division point;
constructing a hybrid structure to generate an countermeasure network;
importing the training data set into a mixed structure to generate an countermeasure network, so as to obtain a trained generated countermeasure network;
a fourth signal processing module: and removing speckle noise in the transmitting signal according to the trained generation countermeasure network, and completing adjustment of the transmitting signal frequency domain.
6. A computer-readable storage medium, characterized by: the storage medium stores a computer program for executing a wireless router signal processing method according to any of the preceding claims 1-4.
7. A wireless router signal processing terminal, characterized by: comprising the following steps:
a processor, a memory for storing instructions executable by the processor;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement a wireless router signal processing method according to any of the preceding claims 1-4.
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