CN115829016B - Adjustment method and device of wireless modem based on neural network - Google Patents
Adjustment method and device of wireless modem based on neural network Download PDFInfo
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Abstract
The invention provides a method and a device for adjusting a wireless modem based on a neural network, wherein the method comprises the following steps: performing neural network large-scale training on the existing large-scale parameter set of the wireless modem by adopting data-driven parallel computation through an embedded neural network processor; building an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network; inputting parameters to be adjusted of the wireless modem into an embedded neural network to process an adaptive adjustment model, and outputting parameters after the model adjustment; and (3) continuously updating the embedded neural network to process the self-adaptive adjustment model to perform self-adaptive continuous updating intelligent adjustment of the wireless modem after the parameter characteristics are subjected to statistic analysis model adjustment.
Description
Technical Field
The invention relates to the technical field of wireless communication modem and artificial intelligence, in particular to a method and a device for adjusting a wireless modem based on a neural network.
Background
At present, the efficiency of the wireless modulation and demodulation technology and the traditional chip is still lower when the neural network is operated; problems include: how to improve the neural network processor to perform large-scale training of the neural network, how to establish an adjustment model of the wireless modem, how to input and output model adjustment parameters, update and adjust the wireless modem model, perform self-adaptive continuous update and intelligent adjustment of the wireless modem and the like still remain to be further solved; therefore, there is a need for a method and apparatus for tuning a wireless modem based on a neural network, which at least partially solve the problems in the prior art.
Disclosure of Invention
A series of concepts in simplified form are introduced in the summary section, which will be described in further detail in the detailed description section; the summary of the invention is not intended to define the key features and essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
To at least partially solve the above problems, the present invention provides a method for adjusting a wireless modem based on a neural network, including:
s100, performing neural network large-scale training on the existing large-scale parameter set of the wireless modem by adopting data-driven parallel computation through an embedded neural network processor;
s200, building an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network;
s300, inputting parameters to be adjusted of the wireless modem into an embedded neural network to process the adaptive adjustment model, and outputting parameters after the model adjustment;
and S400, continuously updating the embedded neural network processing self-adaptive adjustment model by the parameter characteristics after the statistic analysis model is adjusted, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem.
Preferably, S100 includes:
s101, performing system integration on an embedded neural network processing core and a standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem;
s102, carrying out data driving parallel calculation according to an embedded neural network processing standard modulation and demodulation integrated subsystem;
s103, processing the standard modulation and demodulation integrated subsystem and the data driving parallel calculation through the embedded neural network, and performing large-scale training on the neural network.
Preferably, S200 includes:
s201, using the existing wireless modem system as an initial training model;
s202, taking standard wireless modulation-demodulation parameters as model training target parameters, and performing embedded neural network large-scale training on an initial training model;
s203, setting a Lang discrete time recurrence parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
Preferably, S300 includes:
s301, obtaining parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise;
s302, inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model;
S303, processing parameter adjustment processing of the self-adaptive adjustment model through an embedded neural network, and outputting parameters after model adjustment.
Preferably, S400 includes:
s401, carrying out statistical processing on the feature classification data, and carrying out statistical analysis on the parameter features after the model adjustment;
s402, comparing the parameter characteristics after model adjustment with the parameter characteristics of the standard wireless modem to obtain parameter characteristic comparison differences;
s403, continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem.
The invention relates to a wireless modem adjusting device based on a neural network, which comprises:
the parallel data driving embedded neural network system carries out neural network large-scale training on the existing large-scale parameter set of the wireless modem by adopting data driving parallel calculation through an embedded neural network processor;
the wireless modem self-adaptive adjustment system establishes an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network;
the parameter input model adjustment output system inputs parameters to be adjusted of the wireless modem into the embedded neural network to process the adaptive adjustment model, and outputs parameters after the model adjustment;
The feature classification statistical intelligent adjustment system is used for continuously updating the embedded neural network processing self-adaptive adjustment model after the parameter features are adjusted by the statistical analysis model, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem.
Preferably, the parallel driving embedded neural network system includes:
the embedded kernel modulation and demodulation integrated unit is used for carrying out system integration on the embedded neural network processing kernel and the standard wireless modulation and demodulation unit to obtain an embedded neural network processing standard modulation and demodulation integrated subsystem;
the data driving parallel computing unit is used for performing data driving parallel computing according to the embedded neural network processing standard modulation and demodulation integrated subsystem;
and the neural network large-scale training unit is used for carrying out the neural network large-scale training through the embedded neural network processing standard modulation-demodulation integrated subsystem and the data driving parallel calculation.
Preferably, the wireless modem adaptive adjustment system includes:
the method comprises the steps that an existing wireless modem system importing unit takes an existing wireless modem system as an initial training model;
the embedded neural network large-scale training unit takes standard wireless modulation-demodulation parameters as model training target parameters, and carries out embedded neural network large-scale training on the initial training model;
And the parameter identification self-adaptive adjustment unit is used for setting a Lane discrete time recursive parameter identification self-adaptive adjustment mechanism and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
Preferably, the parameter input model adjustment output system includes:
an interference to-be-adjusted parameter obtaining unit for obtaining to-be-adjusted parameters of the wireless modem with wireless electromagnetic interference noise;
the parameter to be adjusted is input into the embedded neural network to process the self-adaptive adjustment model;
and the model adjustment parameter adjustment output unit is used for processing the parameter adjustment processing of the self-adaptive adjustment model through the embedded neural network and outputting the adjusted parameters of the model.
Preferably, the feature classification statistics intelligent adjustment system comprises:
the feature classification data statistics unit is used for carrying out statistical processing on the feature classification data, and carrying out statistical analysis on the parameter features after the model adjustment;
the parameter characteristic difference analysis unit is used for comparing the parameter characteristics after the model is adjusted with the standard wireless modulation-demodulation parameter characteristics to obtain parameter characteristic comparison differences;
and the self-adaptive intelligent modulation and demodulation adjusting unit continuously updates the embedded neural network processing self-adaptive adjusting model according to the parameter characteristic comparison difference and performs self-adaptive continuous updating intelligent adjustment of the wireless modem.
Compared with the prior art, the invention at least comprises the following beneficial effects:
the beneficial effects of the technical scheme are as follows: the invention provides a method and a device for adjusting a wireless modem based on a neural network, wherein the wireless modem is subjected to large-scale training of the neural network by an embedded neural network processor through data-driven parallel computation; building an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network; inputting parameters to be adjusted of the wireless modem into an embedded neural network to process an adaptive adjustment model, and outputting parameters after the model adjustment; the parameter characteristics after the statistic analysis model is adjusted are continuously updated, the embedded neural network processes the self-adaptive adjustment model, and the self-adaptive continuous update intelligent adjustment of the wireless modem is carried out; performing system integration on the embedded neural network processing kernel and the standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem; performing data driving parallel calculation according to the embedded neural network processing standard modulation and demodulation integrated subsystem; processing a standard modulation and demodulation integrated subsystem and performing data-driven parallel computation through an embedded neural network to perform large-scale training of the neural network; taking the existing wireless modem system as an initial training model; taking the standard wireless modulation-demodulation parameters as model training target parameters, and carrying out embedded neural network large-scale training on the initial training model; setting Lang discrete time recursion parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem; acquiring parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise; inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model; the parameter adjustment processing of the self-adaptive adjustment model is processed through the embedded neural network, and the adjusted parameters of the model are output; the parameter characteristics after the adjustment of the statistical analysis model are subjected to statistical treatment through the characteristic classification data; comparing the parameter characteristics after the model adjustment with the parameter characteristics of the standard wireless modulation and demodulation to obtain parameter characteristic comparison differences; continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem; the processing of the wireless modem can be improved from the solidification setting to intelligent self-adaptive adjustment, and the processing efficiency of the wireless modem is further greatly improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a flowchart illustrating steps of a method for adjusting a wireless modem based on a neural network according to the present invention.
Fig. 2 is a diagram of an embodiment of a method for adjusting a wireless modem based on a neural network according to the present invention.
Fig. 3 is a system block diagram of an adjusting device of a wireless modem based on a neural network according to the present invention.
Detailed Description
The present invention is described in further detail below with reference to the drawings and examples to enable those skilled in the art to practice the same and to refer to the description; as shown in fig. 1-3, the present invention provides a method for adjusting a wireless modem based on a neural network, comprising:
s100, performing neural network large-scale training on the existing large-scale parameter set of the wireless modem by adopting data-driven parallel computation through an embedded neural network processor;
S200, building an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network;
s300, inputting parameters to be adjusted of the wireless modem into an embedded neural network to process the adaptive adjustment model, and outputting parameters after the model adjustment;
and S400, continuously updating the embedded neural network processing self-adaptive adjustment model by the parameter characteristics after the statistic analysis model is adjusted, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem.
The working principle of the technical scheme is as follows: the invention provides a method for adjusting a wireless modem based on a neural network, which adopts data-driven parallel computation through an embedded neural network processor to train the wireless modem on a large scale in the prior large scale parameter set; building an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network; inputting parameters to be adjusted of the wireless modem into an embedded neural network to process an adaptive adjustment model, and outputting parameters after the model adjustment; the parameter characteristics after the statistic analysis model is adjusted are continuously updated, the embedded neural network processes the self-adaptive adjustment model, and the self-adaptive continuous update intelligent adjustment of the wireless modem is carried out; performing system integration on the embedded neural network processing kernel and the standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem; performing data driving parallel calculation according to the embedded neural network processing standard modulation and demodulation integrated subsystem; processing a standard modulation and demodulation integrated subsystem and performing data-driven parallel computation through an embedded neural network to perform large-scale training of the neural network; taking the existing wireless modem system as an initial training model; taking the standard wireless modulation-demodulation parameters as model training target parameters, and carrying out embedded neural network large-scale training on the initial training model; setting Lang discrete time recursion parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem; acquiring parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise; inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model; the parameter adjustment processing of the self-adaptive adjustment model is processed through the embedded neural network, and the adjusted parameters of the model are output; the parameter characteristics after the adjustment of the statistical analysis model are subjected to statistical treatment through the characteristic classification data; comparing the parameter characteristics after the model adjustment with the parameter characteristics of the standard wireless modulation and demodulation to obtain parameter characteristic comparison differences; and continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem.
The beneficial effects of the technical scheme are as follows: the invention provides a method for adjusting a wireless modem based on a neural network, which comprises the following steps: performing neural network large-scale training on the existing large-scale parameter set of the wireless modem by adopting data-driven parallel computation through an embedded neural network processor; building an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network; inputting parameters to be adjusted of the wireless modem into an embedded neural network to process an adaptive adjustment model, and outputting parameters after the model adjustment; the parameter characteristics after the statistic analysis model is adjusted are continuously updated, the embedded neural network processes the self-adaptive adjustment model, and the self-adaptive continuous update intelligent adjustment of the wireless modem is carried out; performing system integration on the embedded neural network processing kernel and the standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem; performing data driving parallel calculation according to the embedded neural network processing standard modulation and demodulation integrated subsystem; processing a standard modulation and demodulation integrated subsystem and performing data-driven parallel computation through an embedded neural network to perform large-scale training of the neural network; taking the existing wireless modem system as an initial training model; taking the standard wireless modulation-demodulation parameters as model training target parameters, and carrying out embedded neural network large-scale training on the initial training model; setting Lang discrete time recursion parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem; acquiring parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise; inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model; the parameter adjustment processing of the self-adaptive adjustment model is processed through the embedded neural network, and the adjusted parameters of the model are output; the parameter characteristics after the adjustment of the statistical analysis model are subjected to statistical treatment through the characteristic classification data; comparing the parameter characteristics after the model adjustment with the parameter characteristics of the standard wireless modulation and demodulation to obtain parameter characteristic comparison differences; continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem; the processing of the wireless modem can be improved from the solidification setting to intelligent self-adaptive adjustment, and the processing efficiency of the wireless modem is further greatly improved.
In one embodiment, S100 comprises:
s101, performing system integration on an embedded neural network processing core and a standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem;
s102, carrying out data driving parallel calculation according to an embedded neural network processing standard modulation and demodulation integrated subsystem;
s103, processing the standard modulation and demodulation integrated subsystem and the data driving parallel calculation through the embedded neural network, and performing large-scale training on the neural network.
The working principle of the technical scheme is as follows: s100 includes: s101, performing system integration on an embedded neural network processing core and a standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem; s102, carrying out data driving parallel calculation according to an embedded neural network processing standard modulation and demodulation integrated subsystem; s103, processing the standard modulation and demodulation integrated subsystem and the data driving parallel calculation through the embedded neural network, and performing large-scale training on the neural network.
The beneficial effects of the technical scheme are as follows: performing system integration on the embedded neural network processing kernel and the standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem; performing data driving parallel calculation according to the embedded neural network processing standard modulation and demodulation integrated subsystem; and processing the standard modulation and demodulation integrated subsystem and the data driving parallel calculation through the embedded neural network to carry out the large-scale training of the neural network.
In one embodiment, S200 includes:
s201, using the existing wireless modem system as an initial training model;
s202, taking standard wireless modulation-demodulation parameters as model training target parameters, and performing embedded neural network large-scale training on an initial training model;
s203, setting a Lang discrete time recurrence parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
The working principle of the technical scheme is as follows: s200 includes: s201, using the existing wireless modem system as an initial training model; s202, taking standard wireless modulation-demodulation parameters as model training target parameters, and performing embedded neural network large-scale training on an initial training model; s203, setting a Lang discrete time recurrence parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
The beneficial effects of the technical scheme are as follows: taking the existing wireless modem system as an initial training model; taking the standard wireless modulation-demodulation parameters as model training target parameters, and carrying out embedded neural network large-scale training on the initial training model; and setting a Lang discrete time recursion parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
In one embodiment, S300 includes:
s301, obtaining parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise;
s302, inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model;
s303, processing parameter adjustment processing of the self-adaptive adjustment model through an embedded neural network, and outputting parameters after model adjustment.
The working principle of the technical scheme is as follows: s300 includes: s301, obtaining parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise; s302, inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model; s303, processing parameter adjustment processing of the self-adaptive adjustment model through an embedded neural network, and outputting parameters after model adjustment.
The beneficial effects of the technical scheme are as follows: acquiring parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise; inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model; and (3) processing parameter adjustment processing of the self-adaptive adjustment model through an embedded neural network, and outputting the adjusted parameters of the model.
In one embodiment, S400 includes:
s401, carrying out statistical processing on the feature classification data, and carrying out statistical analysis on the parameter features after the model adjustment;
S402, comparing the parameter characteristics after model adjustment with the parameter characteristics of the standard wireless modem to obtain parameter characteristic comparison differences;
s403, continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem.
The working principle of the technical scheme is as follows: s400 includes: s401, carrying out statistical processing on the feature classification data, and carrying out statistical analysis on the parameter features after the model adjustment; s402, comparing the parameter characteristics after model adjustment with the parameter characteristics of the standard wireless modem to obtain parameter characteristic comparison differences; s403, continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem; making wireless modem adaptive continuous update intelligent adjustments includes: setting a neural network embedded serial-parallel weight priority control module in the modulation-demodulation self-adaptive intelligent adjustment unit, configuring a route in the neural network embedded serial-parallel weight priority control module, comparing difference information based on parameter characteristics, and creating a plurality of neural network embedded serial-parallel weight adjustment sequences; performing self-adaptive continuous update intelligent adjustment according to the adjustment conditions; the adaptive continuous update intelligent adjustment according to the adjustment conditions comprises: when the routing state is not information jammed, the highest adjusting sequence, the next highest adjusting sequence, the lowest adjusting sequence and the next lowest adjusting sequence in the embedded serial-parallel weight adjusting sequence of the neural network are respectively verified by the embedded serial-parallel weight priority control module of the neural network, the verified serial-parallel weight adjusting sequence is executed if the verification is consistent, the next highest adjusting sequence is continuously verified and the serial-parallel weight adjusting sequence is sequentially updated if the verification is inconsistent; when the routing state is information-congested, carrying out parallel processing of the wireless modems; and performing intelligent adjustment of adaptive continuous updating of the wireless modem.
The beneficial effects of the technical scheme are as follows: the parameter characteristics after the adjustment of the statistical analysis model are subjected to statistical treatment through the characteristic classification data; comparing the parameter characteristics after the model adjustment with the parameter characteristics of the standard wireless modulation and demodulation to obtain parameter characteristic comparison differences; continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem; making wireless modem adaptive continuous update intelligent adjustments includes: setting a neural network embedded serial-parallel weight priority control module in the modulation-demodulation self-adaptive intelligent adjustment unit, configuring a route in the neural network embedded serial-parallel weight priority control module, comparing difference information based on parameter characteristics, and creating a plurality of neural network embedded serial-parallel weight adjustment sequences; performing self-adaptive continuous update intelligent adjustment according to the adjustment conditions; the adaptive continuous update intelligent adjustment according to the adjustment conditions comprises: when the routing state is not information jammed, the highest adjusting sequence, the next highest adjusting sequence, the lowest adjusting sequence and the next lowest adjusting sequence in the embedded serial-parallel weight adjusting sequence of the neural network are respectively verified by the embedded serial-parallel weight priority control module of the neural network, the verified serial-parallel weight adjusting sequence is executed if the verification is consistent, the next highest adjusting sequence is continuously verified and the serial-parallel weight adjusting sequence is sequentially updated if the verification is inconsistent; when the routing state is information-congested, carrying out parallel processing of the wireless modems; performing self-adaptive continuous updating intelligent adjustment of the wireless modem; the processing of the wireless modem can be improved from the solidification setting to intelligent self-adaptive adjustment, and the processing efficiency of the wireless modem is further greatly improved.
The invention provides a wireless modem adjusting device based on a neural network, which comprises:
the parallel data driving embedded neural network system carries out neural network large-scale training on the existing large-scale parameter set of the wireless modem by adopting data driving parallel calculation through an embedded neural network processor;
the wireless modem self-adaptive adjustment system establishes an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network;
the parameter input model adjustment output system inputs parameters to be adjusted of the wireless modem into the embedded neural network to process the adaptive adjustment model, and outputs parameters after the model adjustment;
the feature classification statistical intelligent adjustment system is used for continuously updating the embedded neural network processing self-adaptive adjustment model after the parameter features are adjusted by the statistical analysis model, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem.
The working principle of the technical scheme is as follows: the invention provides a wireless modem adjusting device based on a neural network, which comprises: the parallel data driving embedded neural network system carries out neural network large-scale training on the existing large-scale parameter set of the wireless modem by adopting data driving parallel calculation through an embedded neural network processor; the wireless modem self-adaptive adjustment system establishes an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network; the parameter input model adjustment output system inputs parameters to be adjusted of the wireless modem into the embedded neural network to process the adaptive adjustment model, and outputs parameters after the model adjustment; the feature classification statistical intelligent adjustment system is used for continuously updating the embedded neural network processing self-adaptive adjustment model after the parameter features are adjusted by the statistical analysis model and performing self-adaptive continuous updating intelligent adjustment of the wireless modem; performing system integration on the embedded neural network processing kernel and the standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem; performing data driving parallel calculation according to the embedded neural network processing standard modulation and demodulation integrated subsystem; processing a standard modulation and demodulation integrated subsystem and performing data-driven parallel computation through an embedded neural network to perform large-scale training of the neural network; taking the existing wireless modem system as an initial training model; taking the standard wireless modulation-demodulation parameters as model training target parameters, and carrying out embedded neural network large-scale training on the initial training model; setting Lang discrete time recursion parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem; acquiring parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise; inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model; the parameter adjustment processing of the self-adaptive adjustment model is processed through the embedded neural network, and the adjusted parameters of the model are output; the parameter characteristics after the adjustment of the statistical analysis model are subjected to statistical treatment through the characteristic classification data; comparing the parameter characteristics after the model adjustment with the parameter characteristics of the standard wireless modulation and demodulation to obtain parameter characteristic comparison differences; and continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem.
The beneficial effects of the technical scheme are as follows: the invention provides a wireless modem adjusting device based on a neural network, which comprises a parallel drive embedded neural network system, wherein the embedded neural network processor adopts data drive parallel calculation to train the existing large-scale parameter set of the wireless modem on a large scale; the wireless modem self-adaptive adjustment system establishes an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network; the parameter input model adjustment output system inputs parameters to be adjusted of the wireless modem into the embedded neural network to process the adaptive adjustment model, and outputs parameters after the model adjustment; the feature classification statistical intelligent adjustment system is used for continuously updating the embedded neural network processing self-adaptive adjustment model after the parameter features are adjusted by the statistical analysis model and performing self-adaptive continuous updating intelligent adjustment of the wireless modem; performing system integration on the embedded neural network processing kernel and the standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem; performing data driving parallel calculation according to the embedded neural network processing standard modulation and demodulation integrated subsystem; processing a standard modulation and demodulation integrated subsystem and performing data-driven parallel computation through an embedded neural network to perform large-scale training of the neural network; taking the existing wireless modem system as an initial training model; taking the standard wireless modulation-demodulation parameters as model training target parameters, and carrying out embedded neural network large-scale training on the initial training model; setting Lang discrete time recursion parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem; acquiring parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise; inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model; the parameter adjustment processing of the self-adaptive adjustment model is processed through the embedded neural network, and the adjusted parameters of the model are output; the parameter characteristics after the adjustment of the statistical analysis model are subjected to statistical treatment through the characteristic classification data; comparing the parameter characteristics after the model adjustment with the parameter characteristics of the standard wireless modulation and demodulation to obtain parameter characteristic comparison differences; continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem; the processing of the wireless modem can be improved from the solidification setting to intelligent self-adaptive adjustment, and the processing efficiency of the wireless modem is further greatly improved.
In one embodiment, a parallel-drive embedded neural network system includes:
the embedded kernel modulation and demodulation integrated unit is used for carrying out system integration on the embedded neural network processing kernel and the standard wireless modulation and demodulation unit to obtain an embedded neural network processing standard modulation and demodulation integrated subsystem;
the data driving parallel computing unit is used for performing data driving parallel computing according to the embedded neural network processing standard modulation and demodulation integrated subsystem;
and the neural network large-scale training unit is used for carrying out the neural network large-scale training through the embedded neural network processing standard modulation-demodulation integrated subsystem and the data driving parallel calculation.
The working principle of the technical scheme is as follows: the parallel driving embedded neural network system comprises: the embedded kernel modulation and demodulation integrated unit is used for carrying out system integration on the embedded neural network processing kernel and the standard wireless modulation and demodulation unit to obtain an embedded neural network processing standard modulation and demodulation integrated subsystem; the data driving parallel computing unit is used for performing data driving parallel computing according to the embedded neural network processing standard modulation and demodulation integrated subsystem; and the neural network large-scale training unit is used for carrying out the neural network large-scale training through the embedded neural network processing standard modulation-demodulation integrated subsystem and the data driving parallel calculation.
The beneficial effects of the technical scheme are as follows: the embedded kernel modulation and demodulation integrated unit is used for carrying out system integration on the embedded neural network processing kernel and the standard wireless modulation and demodulation unit to obtain an embedded neural network processing standard modulation and demodulation integrated subsystem; the data driving parallel computing unit is used for performing data driving parallel computing according to the embedded neural network processing standard modulation and demodulation integrated subsystem; and the neural network large-scale training unit is used for carrying out the neural network large-scale training through the embedded neural network processing standard modulation-demodulation integrated subsystem and the data driving parallel calculation.
In one embodiment, a wireless modem adaptation system includes:
the method comprises the steps that an existing wireless modem system importing unit takes an existing wireless modem system as an initial training model;
the embedded neural network large-scale training unit takes standard wireless modulation-demodulation parameters as model training target parameters, and carries out embedded neural network large-scale training on the initial training model;
and the parameter identification self-adaptive adjustment unit is used for setting a Lane discrete time recursive parameter identification self-adaptive adjustment mechanism and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
The working principle of the technical scheme is as follows: the wireless modem self-adaptive adjustment system comprises: the method comprises the steps that an existing wireless modem system importing unit takes an existing wireless modem system as an initial training model; the embedded neural network large-scale training unit takes standard wireless modulation-demodulation parameters as model training target parameters, and carries out embedded neural network large-scale training on the initial training model; and the parameter identification self-adaptive adjustment unit is used for setting a Lane discrete time recursive parameter identification self-adaptive adjustment mechanism and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
The beneficial effects of the technical scheme are as follows: the method comprises the steps that an existing wireless modem system importing unit takes an existing wireless modem system as an initial training model; the embedded neural network large-scale training unit takes standard wireless modulation-demodulation parameters as model training target parameters, and carries out embedded neural network large-scale training on the initial training model; and the parameter identification self-adaptive adjustment unit is used for setting a Lane discrete time recursive parameter identification self-adaptive adjustment mechanism and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
In one embodiment, a parametric input model tuning output system includes:
an interference to-be-adjusted parameter obtaining unit for obtaining to-be-adjusted parameters of the wireless modem with wireless electromagnetic interference noise;
the parameter to be adjusted is input into the embedded neural network to process the self-adaptive adjustment model;
and the model adjustment parameter adjustment output unit is used for processing the parameter adjustment processing of the self-adaptive adjustment model through the embedded neural network and outputting the adjusted parameters of the model.
The working principle of the technical scheme is as follows: the parameter input model adjustment output system comprises: an interference to-be-adjusted parameter obtaining unit for obtaining to-be-adjusted parameters of the wireless modem with wireless electromagnetic interference noise; the parameter to be adjusted is input into the embedded neural network to process the self-adaptive adjustment model; the parameter adjustment output unit is used for processing the parameter adjustment processing of the self-adaptive adjustment model through the embedded neural network and outputting the adjusted parameter of the model; calculating the error square sum value of the parameters after model adjustment:
wherein PJQmin is the error square sum value of parameters after model adjustment, R is the total number of layers of the neural network, S is the total number of self-adaptive circulation, i is the operation layer of the neural network where the parameters to be adjusted are located, G (xi) is the adjustment weight of the ith layer of the neural network where the parameters to be adjusted are located, wj (xi) is the j-th self-adaptive circulation target adjustment value of the ith layer of the neural network, hi is the initial weight coefficient of the ith layer of the neural network, and F (yi) is the output adjusted parameter value of the ith layer of the neural network; and calculating the error square sum value of the parameters after the model adjustment, so that the error square sum value of the parameters after the model adjustment is minimum.
The beneficial effects of the technical scheme are as follows: an interference to-be-adjusted parameter obtaining unit for obtaining to-be-adjusted parameters of the wireless modem with wireless electromagnetic interference noise; the parameter to be adjusted is input into the embedded neural network to process the self-adaptive adjustment model; the parameter adjustment output unit is used for processing the parameter adjustment processing of the self-adaptive adjustment model through the embedded neural network and outputting the adjusted parameter of the model; calculating an error square sum value of parameters after model adjustment, wherein PJQmin is the error square sum value of the parameters after model adjustment, R is the total number of layers of the neural network, S is the total number of self-adaptive circulation, i is a neural network operation layer where the parameters to be adjusted are located, G (xi) is an ith layer neural network adjustment weight of the parameters to be adjusted, wj (xi) is a jth self-adaptive circulation target adjustment value of the ith layer neural network, hi is an initial weight coefficient of the ith layer neural network, and F (yi) is an output adjusted parameter value of the ith layer neural network; calculating the error square sum value of the parameters after the model adjustment, so that the error square sum value of the parameters after the model adjustment is minimum; the parameter adjustment of the embedded neural network processing adaptive adjustment model greatly reduces the processing adjustment error of the wireless modem.
In one embodiment, the feature classification statistics intelligent adjustment system comprises:
the feature classification data statistics unit is used for carrying out statistical processing on the feature classification data, and carrying out statistical analysis on the parameter features after the model adjustment;
the parameter characteristic difference analysis unit is used for comparing the parameter characteristics after the model is adjusted with the standard wireless modulation-demodulation parameter characteristics to obtain parameter characteristic comparison differences;
and the self-adaptive intelligent modulation and demodulation adjusting unit continuously updates the embedded neural network processing self-adaptive adjusting model according to the parameter characteristic comparison difference and performs self-adaptive continuous updating intelligent adjustment of the wireless modem.
The working principle of the technical scheme is as follows: the feature classification statistics intelligent adjustment system comprises: the feature classification data statistics unit is used for carrying out statistical processing on the feature classification data, and carrying out statistical analysis on the parameter features after the model adjustment; the parameter characteristic difference analysis unit is used for comparing the parameter characteristics after the model is adjusted with the standard wireless modulation-demodulation parameter characteristics to obtain parameter characteristic comparison differences; the self-adaptive intelligent adjustment unit for the modem continuously updates the processing self-adaptive adjustment model of the embedded neural network according to the contrast difference of the parameter characteristics, and performs self-adaptive continuous update intelligent adjustment of the wireless modem; making wireless modem adaptive continuous update intelligent adjustments includes: setting a neural network embedded serial-parallel weight priority control module in the modulation-demodulation self-adaptive intelligent adjustment unit, configuring a route in the neural network embedded serial-parallel weight priority control module, comparing difference information based on parameter characteristics, and creating a plurality of neural network embedded serial-parallel weight adjustment sequences; performing self-adaptive continuous update intelligent adjustment according to the adjustment conditions; the adaptive continuous update intelligent adjustment according to the adjustment conditions comprises: when the routing state is not information jammed, the highest adjusting sequence, the next highest adjusting sequence, the lowest adjusting sequence and the next lowest adjusting sequence in the embedded serial-parallel weight adjusting sequence of the neural network are respectively verified by the embedded serial-parallel weight priority control module of the neural network, the verified serial-parallel weight adjusting sequence is executed if the verification is consistent, the next highest adjusting sequence is continuously verified and the serial-parallel weight adjusting sequence is sequentially updated if the verification is inconsistent; when the routing state is information-congested, carrying out parallel processing of the wireless modems; and performing intelligent adjustment of adaptive continuous updating of the wireless modem.
The beneficial effects of the technical scheme are as follows: the feature classification data statistics unit is used for carrying out statistical processing on the feature classification data, and carrying out statistical analysis on the parameter features after the model adjustment; the parameter characteristic difference analysis unit is used for comparing the parameter characteristics after the model is adjusted with the standard wireless modulation-demodulation parameter characteristics to obtain parameter characteristic comparison differences; the self-adaptive intelligent adjustment unit for the modem continuously updates the processing self-adaptive adjustment model of the embedded neural network according to the contrast difference of the parameter characteristics, and performs self-adaptive continuous update intelligent adjustment of the wireless modem; making wireless modem adaptive continuous update intelligent adjustments includes: setting a neural network embedded serial-parallel weight priority control module in the modulation-demodulation self-adaptive intelligent adjustment unit, configuring a route in the neural network embedded serial-parallel weight priority control module, comparing difference information based on parameter characteristics, and creating a plurality of neural network embedded serial-parallel weight adjustment sequences; performing self-adaptive continuous update intelligent adjustment according to the adjustment conditions; the adaptive continuous update intelligent adjustment according to the adjustment conditions comprises: when the routing state is not information jammed, the highest adjusting sequence, the next highest adjusting sequence, the lowest adjusting sequence and the next lowest adjusting sequence in the embedded serial-parallel weight adjusting sequence of the neural network are respectively verified by the embedded serial-parallel weight priority control module of the neural network, the verified serial-parallel weight adjusting sequence is executed if the verification is consistent, the next highest adjusting sequence is continuously verified and the serial-parallel weight adjusting sequence is sequentially updated if the verification is inconsistent; when the routing state is information-congested, carrying out parallel processing of the wireless modems; performing self-adaptive continuous updating intelligent adjustment of the wireless modem; the processing of the wireless modem can be improved from the solidification setting to intelligent self-adaptive adjustment, and the processing efficiency of the wireless modem is further greatly improved.
Although embodiments of the present invention have been disclosed above, it is not limited to the details and embodiments shown and described, it is well suited to various fields of use for which the invention would be readily apparent to those skilled in the art, and accordingly, the invention is not limited to the specific details and illustrations shown and described herein, without departing from the general concepts defined in the claims and their equivalents.
Claims (8)
1. A method for adjusting a wireless modem based on a neural network, comprising:
s100, performing neural network large-scale training on the existing large-scale parameter set of the wireless modem by adopting data-driven parallel computation through an embedded neural network processor;
s200, building an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network;
s300, inputting parameters to be adjusted of the wireless modem into an embedded neural network to process the adaptive adjustment model, and outputting parameters after the model adjustment;
s400, the parameter characteristics after the statistic analysis model is adjusted are continuously updated, the embedded neural network processes the self-adaptive adjustment model, and the self-adaptive continuous update intelligent adjustment of the wireless modem is carried out;
S400 includes:
s401, carrying out statistical processing on the feature classification data, and carrying out statistical analysis on the parameter features after the model adjustment;
s402, comparing the parameter characteristics after model adjustment with the parameter characteristics of the standard wireless modem to obtain parameter characteristic comparison differences;
s403, continuously updating the embedded neural network processing self-adaptive adjustment model according to the parameter characteristic contrast difference, and performing self-adaptive continuous updating intelligent adjustment of the wireless modem;
making wireless modem adaptive continuous update intelligent adjustments includes: setting a neural network embedded serial-parallel weight priority control module in the modulation-demodulation self-adaptive intelligent adjustment unit, configuring a route in the neural network embedded serial-parallel weight priority control module, comparing difference information based on parameter characteristics, and creating a plurality of neural network embedded serial-parallel weight adjustment sequences; performing self-adaptive continuous update intelligent adjustment according to the adjustment conditions; the adaptive continuous update intelligent adjustment according to the adjustment conditions comprises: when the routing state is not information jammed, the highest adjusting sequence, the next highest adjusting sequence, the lowest adjusting sequence and the next lowest adjusting sequence in the embedded serial-parallel weight adjusting sequence of the neural network are respectively verified by the embedded serial-parallel weight priority control module of the neural network, the verified serial-parallel weight adjusting sequence is executed if the verification is consistent, the next highest adjusting sequence is continuously verified and the serial-parallel weight adjusting sequence is sequentially updated if the verification is inconsistent; when the routing state is information-congested, carrying out parallel processing of the wireless modems; and performing intelligent adjustment of adaptive continuous updating of the wireless modem.
2. The method for tuning a wireless modem based on a neural network according to claim 1, wherein S100 comprises:
s101, performing system integration on an embedded neural network processing core and a standard wireless modem unit to obtain an embedded neural network processing standard modem integrated subsystem;
s102, carrying out data driving parallel calculation according to an embedded neural network processing standard modulation and demodulation integrated subsystem;
s103, processing the standard modulation and demodulation integrated subsystem and the data driving parallel calculation through the embedded neural network, and performing large-scale training on the neural network.
3. The method for tuning a wireless modem based on a neural network of claim 1, wherein S200 comprises:
s201, using the existing wireless modem system as an initial training model;
s202, taking standard wireless modulation-demodulation parameters as model training target parameters, and performing embedded neural network large-scale training on an initial training model;
s203, setting a Lang discrete time recurrence parameter identification self-adaptive adjustment mechanism, and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
4. The method for tuning a wireless modem based on a neural network of claim 1, wherein S300 comprises:
S301, obtaining parameters to be adjusted of a wireless modem with wireless electromagnetic interference noise;
s302, inputting parameters to be adjusted into an embedded neural network to process an adaptive adjustment model;
s303, processing parameter adjustment processing of the self-adaptive adjustment model through an embedded neural network, and outputting parameters after model adjustment.
5. An apparatus for adjusting a wireless modem based on a neural network, comprising:
the parallel data driving embedded neural network system carries out neural network large-scale training on the existing large-scale parameter set of the wireless modem by adopting data driving parallel calculation through an embedded neural network processor;
the wireless modem self-adaptive adjustment system establishes an embedded neural network processing self-adaptive adjustment model of the wireless modem through large-scale training of the embedded neural network;
the parameter input model adjustment output system inputs parameters to be adjusted of the wireless modem into the embedded neural network to process the adaptive adjustment model, and outputs parameters after the model adjustment;
the feature classification statistical intelligent adjustment system is used for continuously updating the embedded neural network processing self-adaptive adjustment model after the parameter features are adjusted by the statistical analysis model and performing self-adaptive continuous updating intelligent adjustment of the wireless modem;
The feature classification statistics intelligent adjustment system comprises:
the feature classification data statistics unit is used for carrying out statistical processing on the feature classification data, and carrying out statistical analysis on the parameter features after the model adjustment;
the parameter characteristic difference analysis unit is used for comparing the parameter characteristics after the model is adjusted with the standard wireless modulation-demodulation parameter characteristics to obtain parameter characteristic comparison differences;
the self-adaptive intelligent adjustment unit for the modem continuously updates the processing self-adaptive adjustment model of the embedded neural network according to the contrast difference of the parameter characteristics, and performs self-adaptive continuous update intelligent adjustment of the wireless modem;
making wireless modem adaptive continuous update intelligent adjustments includes: setting a neural network embedded serial-parallel weight priority control module in the modulation-demodulation self-adaptive intelligent adjustment unit, configuring a route in the neural network embedded serial-parallel weight priority control module, comparing difference information based on parameter characteristics, and creating a plurality of neural network embedded serial-parallel weight adjustment sequences; performing self-adaptive continuous update intelligent adjustment according to the adjustment conditions; the adaptive continuous update intelligent adjustment according to the adjustment conditions comprises: when the routing state is not information jammed, the highest adjusting sequence, the next highest adjusting sequence, the lowest adjusting sequence and the next lowest adjusting sequence in the embedded serial-parallel weight adjusting sequence of the neural network are respectively verified by the embedded serial-parallel weight priority control module of the neural network, the verified serial-parallel weight adjusting sequence is executed if the verification is consistent, the next highest adjusting sequence is continuously verified and the serial-parallel weight adjusting sequence is sequentially updated if the verification is inconsistent; when the routing state is information-congested, carrying out parallel processing of the wireless modems; and performing intelligent adjustment of adaptive continuous updating of the wireless modem.
6. The tuning device for a wireless modem based on a neural network of claim 5, wherein the parallel-driving embedded neural network system comprises:
the embedded kernel modulation and demodulation integrated unit is used for carrying out system integration on the embedded neural network processing kernel and the standard wireless modulation and demodulation unit to obtain an embedded neural network processing standard modulation and demodulation integrated subsystem;
the data driving parallel computing unit is used for performing data driving parallel computing according to the embedded neural network processing standard modulation and demodulation integrated subsystem;
and the neural network large-scale training unit is used for carrying out the neural network large-scale training through the embedded neural network processing standard modulation-demodulation integrated subsystem and the data driving parallel calculation.
7. The wireless modem adjustment apparatus based on a neural network according to claim 5, wherein the wireless modem adaptation adjustment system comprises:
the method comprises the steps that an existing wireless modem system importing unit takes an existing wireless modem system as an initial training model;
the embedded neural network large-scale training unit takes standard wireless modulation-demodulation parameters as model training target parameters, and carries out embedded neural network large-scale training on the initial training model;
And the parameter identification self-adaptive adjustment unit is used for setting a Lane discrete time recursive parameter identification self-adaptive adjustment mechanism and establishing an embedded neural network processing self-adaptive adjustment model of the wireless modem.
8. The tuning device for a wireless modem based on a neural network of claim 5, wherein the parameter input model tuning output system comprises:
an interference to-be-adjusted parameter obtaining unit for obtaining to-be-adjusted parameters of the wireless modem with wireless electromagnetic interference noise;
the parameter to be adjusted is input into the embedded neural network to process the self-adaptive adjustment model;
and the model adjustment parameter adjustment output unit is used for processing the parameter adjustment processing of the self-adaptive adjustment model through the embedded neural network and outputting the adjusted parameters of the model.
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