CN107181494B - A method of based on ANN Control transmitter operating mode - Google Patents
A method of based on ANN Control transmitter operating mode Download PDFInfo
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- CN107181494B CN107181494B CN201710341493.9A CN201710341493A CN107181494B CN 107181494 B CN107181494 B CN 107181494B CN 201710341493 A CN201710341493 A CN 201710341493A CN 107181494 B CN107181494 B CN 107181494B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/005—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission adapting radio receivers, transmitters andtransceivers for operation on two or more bands, i.e. frequency ranges
- H04B1/0053—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission adapting radio receivers, transmitters andtransceivers for operation on two or more bands, i.e. frequency ranges with common antenna for more than one band
- H04B1/006—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission adapting radio receivers, transmitters andtransceivers for operation on two or more bands, i.e. frequency ranges with common antenna for more than one band using switches for selecting the desired band
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/02—Transmitters
- H04B1/04—Circuits
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0251—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity
- H04W52/0258—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity controlling an operation mode according to history or models of usage information, e.g. activity schedule or time of day
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/02—Transmitters
- H04B1/04—Circuits
- H04B2001/0408—Circuits with power amplifiers
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/02—Transmitters
- H04B1/04—Circuits
- H04B2001/0491—Circuits with frequency synthesizers, frequency converters or modulators
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
A method of based on ANN Control transmitter operating mode, being related to electrical communication technology and nerual network technique.The input signal of transmitter generates the operating mode that different control signals is used to control transmitter after passing through neural network classification, wherein control signal includes but is not limited to power control signal, frequency control signal, gain control signal, linearity control signal, data rate control signal, modulation system control signal.The present invention can classify to input signal by neural network in the application environment for multi-emitting target, for the operating mode of each transmitting target adjustment transmitter, to reach automatic channel switching and energy-efficient purpose.
Description
Technical field
The present invention relates to the communication technologys and nerual network technique, particularly relate to a kind of based on ANN Control hair
The method for penetrating machine operating mode.
Background technique
The communication technology is the important branch of electronic engineering, while being also one of basic subject.Wherein transmitter is logical
Indispensable part in letter technology.One transmitter will complete modulation, up conversion and power amplification, and the purpose of modulation is that handle is wanted
The analog signal or digital signal of transmission become suitable for the signal of transmission, and the analog signal to be transmitted is known as modulated signal.
In the transmitting terminal of communication system, the frequency spectrum shift of baseband signal is known as modulating to the process in specific given channel passband.It adjusts
It is formed with the modulator approaches such as amplitude modulation, frequency modulation, phase modulation.According to different input signals, the transmitting terminal of transmitter need different power,
Different modulator approach, different carrier signals etc..
In general, input signal has uncertainty in the application environment for multi-emitting target.Different inputs
Signal needs to take different emission modes, can be only achieved the maximization of efficiency of transmission.In order to solve this problem, it first has to solve
The circuit that seeks to certainly identifies different types of input signal, and then transmitter is adjusted to corresponding operating mode.And it passes
System circuit is difficult to identify the difference of input signal and the operating mode of adjust automatically transmitter, so that the power consumption of entire circuit
Increase the waste with transmission channel.Therefore, it is necessary to provide a kind of type of energy intelligent recognition input signal, and change accordingly
The circuit of transmitter operating mode.
Summary of the invention
The present invention in order to solve as the prior art limit caused by can not automatic identification input signal and adjust automatically hair
The defect for penetrating the operating mode of machine provides a kind of method based on ANN Control transmitter operating mode, can reduce
The power consumption of circuit and the utilization rate for promoting channel.
The technical solution of the present invention is as follows:
A method of based on ANN Control transmitter operating mode, include the following steps:
Step 1: the input signal X of transmitter is classified to obtain the transmitter input signal X by neural network
Classification information；
Step 2: generating different control signals according to the classification information that step 1 obtains, the control signal include but
It is not limited to power control signal, frequency control signal, gain control signal, the linearity control signal, data rate control signal, tune
Mode processed controls signal；
Step 3: the control signal generated with the input signal X and step 2 of identical transmitter described in step 1 is defeated
Enter to transmitter, transmitter changes its operating mode according to the control signal, and in this operating mode to the transmitter
Input signal X handled.
Specifically, the course of work of the input signal X in the step 1 by neural network classification transmitter is divided into instruction
Practice stage and working stage；
Training stage:
A, all samples in the input signal X of transmitter are inputted into neural network；
B, transmitter working condition set Y, the transmitter are established in neural network according to the input signal X of transmitter
The variable in each element in working condition set Y determines according to the input signal X of the transmitter, the variable include but
It is not limited to power, frequency, gain, the linearity, data transfer rate, modulation system, each element in the transmitter working condition set Y
Including one or more variable；
C, neural network is learnt to a part of sample and is adjusted network weight matrix W, obtains function f (X), i.e., neural
The output valve of network, the network weight matrix W are the weighted value of function f (X)；
D, defeated to neural network output valve, that is, function f (X) and target using the remaining sample not learnt as test set
Out it is that the transmitter working condition set Y established in step b is compared, judges whether the two error is less than default precision, if two
Return step a when person's error is not less than default precision is less than default precision with the error of target output until neural network is exported,
Network weight matrix W is saved, training terminates；
Working stage: input signal X of the network weight matrix W of the neural network obtained according to the training stage to transmitter
Classification, be input to each time the transmitter of neural network input signal X correspond in the transmitter working condition set Y one
A element.
Specifically, the transmitter working condition set Y includes element Y1, element Y2, element Y3, the element Y1 expression
Transmitter working condition include working frequency be 2.4GHz, output power be -20dB, transmitted data rate 100kbps；The member
Plain Y2 indicate transmitter working condition include working frequency be 400MHz, gain 10dB, transmitted data rate 10kbps；It is described
Element Y3 indicate transmitter working condition include working frequency be 5-6GHz, gain 15dB, output IP3 be 10dBm, modulation methods
Formula is FSK.
Element in transmitter working condition set Y includes the working condition of all transmitters, and three above element is only
With reference to.
Specifically, the neural network is convolutional neural networks.
Specifically, the neural network module is feedforward neural network.
Specifically, the input signal of the transmitter before being input to the neural network, has also passed through one two choosing
One multiple selector, first register and second register, the data input pin of the alternative multiple selector
The input signal of transmitter is connected, the data of the first data output end output of the alternative multiple selector are posted by first
The data input pin of neural network, the number of the second data output end output of the alternative multiple selector are input to after storage
According to the data input pin by being input to neural network after the second register, the alternative multiple selector is enabled by one
Signal controls the first register and the second register, and when the enable signal is 0, the input signal of the transmitter passes through two choosings
The first register is input to after one multiple selector, while the second register, by the signal afferent nerve network of storage, first posts
Storage, which is filled with the rear enable signal, becomes 1, and the input signal of transmitter after alternative multiple selector by being input at this time
Second register, while the first register replaces the signal afferent nerve network of storage, the first register and the second register
Work, the continual afferent nerve network of input signal of transmitter.
Specifically, the neural network includes Recognition with Recurrent Neural Network and long memory models neural network in short-term.
Specifically, the frequency control signal is generated by phaselocked loop, the phaselocked loop is connected to the neural network and hair
It penetrates between machine, the signal in the classification information that the phaselocked loop is exported according to the neural network about frequency control category changes
The frequency of phaselocked loop, to change the working frequency of transmitter.
Specifically, the power control signal and linearity control signal are generated by power amplifier, the power
Amplifier is connected between the neural network and transmitter, the classification that the power amplifier is exported according to the neural network
Change the power and the linearity of power amplifier in information about the signal of power control classification and linearity control category, thus
Change the power and the linearity of transmitter.
As the bridge between neural network and transmitter, corresponding neural network is generated for the above phaselocked loop and power amplifier
Classification information generate control transmitter operating mode control signal.
Neural network is one based on the structure and function of biological brain, thin with the nerve that network node imitates brain
Born of the same parents, to be connected to the network the technology for weighing the level of drive for imitating brain.The technology can effectively handle non-linear, the ambiguity of problem
And uncertainty relationship.The neural network module can be made of one or more neural networks, such as the long mind of memory models in short-term
Through network (LSTM), Recognition with Recurrent Neural Network (RNN), feedforward neural network (FNN) or convolutional neural networks (CNN).Neural network
Module realizes that sample when to by the input signal of system based on training is classified using number or analog circuit.Transmitting
The classification that machine control module can divide input signal based on neural network module generates different control signals, for each
The operating mode of a transmitting target adjustment transmitter.Transmitter module is believed according to the control of input signal and transmitter control module
Number, change one of output power, working frequency, gain, the linearity and data transfer rate of transmitter or multiple performance.The lock
Phase ring belongs in emitter control module the device for controlling frequency, and the power amplifier, which belongs in emitter control module, to be controlled
The device of power and the linearity.
The invention has the benefit that in the application environment for multi-emitting target, it can be by neural network to defeated
Enter signal to classify, for the operating mode of each transmitting target adjustment transmitter, to reach automatic channel switching and section
The purpose of energy；The present invention does not need the data structure that input signal is known in advance, and operation is simpler, and compared with the prior art can
The operating mode of judgement is more；Present invention is particularly suitable for high speeds, high integration application environment.
Detailed description of the invention
Fig. 1 is that the present invention is based on the work flow diagrams that a kind of neural network classification mode controls transmitter operating mode；
Fig. 2 is a kind of transmitter circuitry structural schematic diagram neural network based proposed in the present invention；
Fig. 3 is the transmitter circuitry structural schematic diagram based on neural networks such as CNN/FNN in the embodiment of the present invention 1；
Fig. 4 is the transmitter circuitry structural schematic diagram based on RNN/LSTM neural network in the embodiment of the present invention 2；
Fig. 5 is that the transmitter neural network based control PLL adjustment transmitter frequency structure in the embodiment of the present invention 3 is shown
It is intended to；
Fig. 6 is transmitter neural network based control PA adjustment transmitter power in the embodiment of the present invention 4 and linear
Spend schematic diagram.
Specific embodiment
The detailed description that connection with figures illustrates below is intended as the description of currently preferred embodiment of the invention, and is not
It is intended to indicate that implementable sole mode of the invention.It is understood that identical or of equal value function can be by being intended to be included in the present invention
Spirit and scope in different embodiments complete.
As shown in Fig. 2, a kind of transmitter circuitry neural network based provided in the embodiment of the present invention, including nerve net
Network module, transmitter control module and transmitter module.Input signal can directly input transmitter module, can also pass through nerve net
Emitter module is input to after network module, wherein not being changed by the data of the input signal of neural network module, nerve
Network module classifies input signal, and based on it is sub-category by transmitter control module adjust transmitter work shape
State and operating mode.
With reference to the accompanying drawings and embodiments, the technical schemes of the invention are described in detail.
Embodiment 1
It is illustrated in figure 3 the neural based on CNN (convolutional neural networks) or FNN (fuzzy neural network) of the present embodiment proposition
The transmitter circuitry structure chart of network, including an alternative multiple selector, the first register, the second register, a CNN
Or FNN neural network module, transmitter control module and transmitter module.When starting input signal, alternative multi-path choice
Device enables the first register, and input signal is stored in the first register, after the first register is filled with, alternative multiple selector
Input signal is stored in the second register, while the signal parallel afferent nerve of the first register storage by enabled second register
Network.After the second register is filled with, alternative multiple selector enables the first register, by the first deposit of input signal deposit
Device, while the signal parallel afferent nerve network that the second register is stored.First register and the second register work alternatively,
The continual afferent nerve network of input signal.Neural network obtains network weight after learning to incoming input signal
Value W.At network weight weight values W, identical input signal may be implemented the classification to signal, and pass through hair based on institute is sub-category
The working condition and operating mode for penetrating machine control module adjustment transmitter, change output power, the working frequency, increasing of transmitter
One of benefit, the linearity and data transfer rate or multiple performance.
Embodiment 2
It is the present embodiment proposition as shown in Figure 4 based on RNN (Recognition with Recurrent Neural Network), LSTM (the long mind of memory models in short-term
Through network) etc. neural networks transmitter circuitry structure chart, including Recognition with Recurrent Neural Network RNN and long memory models nerve net in short-term
Neural network module, emitter control module and the transmitter module of network LSTM etc..The direct afferent nerve network of input signal, mind
Network weight weight values W is obtained after network learns input signal.At network weight weight values W, identical input signal can
To realize classification to signal, and based on the sub-category working condition and work by transmitter control module adjustment transmitter of institute
Mode changes one of output power, working frequency, gain, the linearity and data transfer rate of transmitter or multiple performance.
Embodiment 3
Fig. 5 is the transmitter circuitry structure chart neural network based that the present embodiment proposes comprising a phaselocked loop
PLL, a neural network module and a transmitter module, wherein phase-locked loop pll belongs to transmitter control module, for controlling
The working frequency of transmitter.Input signal inputs phase-locked loop pll and neural network module, and neural network is to input signal
Network weight weight values W is obtained after habit.At network weight weight values W, the letter of neural network output is may be implemented in identical input signal
Number control phase-locked loop pll frequency, and then change transmitter working frequency.Input signal passes through transmitter control in the present embodiment
Molding block is input to transmitter module, wherein not being changed by the data of the input signal of transmitter control module.
Embodiment 4
Fig. 6 is the transmitter circuitry structure chart neural network based that the present embodiment proposes comprising a power amplification
Module PA, a neural network module and a transmitter module, wherein power amplifier module PA belongs to transmitter control module,
For controlling the power and the linearity of transmitter.Input signal input power amplification module PA and neural network module, nerve net
Network module obtains network weight weight values W after learning to input signal.At network weight weight values W, identical input signal can
To realize the power and the linearity of the signal control power amplifier module PA of neural network module output, to change transmitter
Power and the linearity.Input signal is input to transmitter module by transmitter control module in the present embodiment, wherein passing through hair
The data for penetrating the input signal of machine control module are not changed.
Claims (8)
1. a kind of method based on ANN Control transmitter operating mode, which comprises the steps of:
Step 1: the input signal X of transmitter is classified to obtain dividing for the transmitter input signal X by neural network
Category information, detailed process are divided into training stage and working stage；
Training stage:
A, all samples in the input signal X of transmitter are inputted into neural network；
B, transmitter working condition set Y, the transmitter work are established in neural network according to the input signal X of transmitter
The variable in each element in state set Y determines according to the input signal X of the transmitter, and the variable includes but unlimited
Each element includes in power, frequency, gain, the linearity, data transfer rate, modulation system, the transmitter working condition set Y
One or more variable；
C, neural network is learnt to a part of sample and is adjusted network weight matrix W, obtains the output valve of neural network i.e.
Function f (X), the network weight matrix W are the weighted value of function f (X)；
D, using the remaining sample not learnt as test set, it is to neural network output valve, that is, function f (X) and target output
The transmitter working condition set Y established in step b is compared, and judges whether the two error is less than default precision, if the two is missed
Return step a when difference is not less than default precision is less than default precision, preservation with the error of target output until neural network is exported
Network weight matrix W, training terminate；
Working stage: the network weight matrix W of the neural network obtained according to the training stage divides the input signal X of transmitter
Class, the input signal X for being input to the transmitter of neural network each time correspond to one in the transmitter working condition set Y
Element；
Step 2: generating different control signals according to the classification information that step 1 obtains, and the control signal includes but unlimited
Signal, data rate control signal, modulation methods are controlled in power control signal, frequency control signal, gain control signal, the linearity
Formula controls signal；
Step 3: the control signal generated with the input signal X and step 2 of identical transmitter described in step 1 is input to
Transmitter, transmitter change its operating mode according to the control signal, and in this operating mode to the defeated of the transmitter
Enter signal X to be handled.
2. the method according to claim 1 based on ANN Control transmitter operating mode, which is characterized in that described
Transmitter working condition set Y includes element Y1, element Y2, element Y3, and the element Y1 expression transmitter working condition includes
Working frequency is 2.4GHz, output power is -20dB, transmitted data rate 100kbps；The element Y2 indicates transmitter work
State include working frequency be 400MHz, gain 10dB, transmitted data rate 10kbps；The element Y3 indicates transmitter work
As state include working frequency be 5-6GHz, gain 15dB, output IP3 be 10dBm, modulation system FSK.
3. the method according to claim 1 based on ANN Control transmitter operating mode, which is characterized in that described
Neural network is convolutional neural networks.
4. the method according to claim 1 based on ANN Control transmitter operating mode, which is characterized in that described
Neural network is feedforward neural network.
5. the method according to claim 3 or 4 based on ANN Control transmitter operating mode, which is characterized in that
The input signal X of the transmitter has also passed through alternative multiple selector, one before being input to the neural network
A first register and second register, the input of the data input pin connection transmitter of the alternative multiple selector
Signal, the data of the first data output end output of the alternative multiple selector are by being input to nerve after the first register
The data of the data input pin of network, the second data output end output of the alternative multiple selector pass through the second register
It is input to the data input pin of neural network afterwards, the alternative multiple selector passes through first deposit of enable signal control
Device and the second register, when the enable signal is 0, the input signal X of the transmitter passes through after alternative multiple selector
It is input to the first register, while the second register, by the signal afferent nerve network of storage, the first register is described after being filled with
Enable signal becomes 1, and the input signal X of transmitter is by being input to the second register after alternative multiple selector at this time, together
When the first register by the signal afferent nerve network of storage, the second register, which is filled with the rear enable signal, becomes 0, and first posts
Storage and the second register work alternatively, the continual afferent nerve network of input signal X of transmitter.
6. the method according to claim 1 based on ANN Control transmitter operating mode, which is characterized in that described
Neural network includes Recognition with Recurrent Neural Network and long memory models neural network in short-term.
7. the method according to claim 1 based on ANN Control transmitter operating mode, which is characterized in that described
Frequency control signal is generated by phaselocked loop, and the phaselocked loop is connected between the neural network and transmitter, the phaselocked loop
According to the frequency for changing phaselocked loop in the classification information of neural network output about the signal of frequency control category, to change
Become the working frequency of transmitter.
8. the method according to claim 1 based on ANN Control transmitter operating mode, which is characterized in that described
Power control signal and linearity control signal are generated by power amplifier, and the power amplifier is connected to the nerve
Between network and transmitter, about power control class in the classification information that the power amplifier is exported according to the neural network
Other and linearity control category signal changes the power and the linearity of power amplifier, to change the power and line of transmitter
Property degree.
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| CN107769801B (en) * | 2017-10-16 | 2019-04-26 | 成都市深思创芯科技有限公司 | A kind of method neural network based promoting radio-frequency transmitter intermediate frequency signal-to-noise ratio |
| CN109245861A (en) * | 2018-10-29 | 2019-01-18 | 广州海格通信集团股份有限公司 | A kind of physical layer communication method using deep layer artificial neural network |
| WO2020087293A1 (en) * | 2018-10-30 | 2020-05-07 | 华为技术有限公司 | Communication receiver and method for processing signal |
| CN111539178B (en) * | 2020-04-26 | 2023-05-05 | 成都市深思创芯科技有限公司 | Chip layout design method and system based on neural network and manufacturing method |
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| CN1241881A (en) * | 1998-05-27 | 2000-01-19 | 诺基亚流动电话有限公司 | Transmitter |
| WO2006006909A1 (en) * | 2004-07-08 | 2006-01-19 | Andrew Corporation | A radio transmitter and a method of operating a radio transmitter |
| CN101557206A (en) * | 2000-03-04 | 2009-10-14 | 高通股份有限公司 | Transmitter architecture for communications system |
| CN103516371A (en) * | 2013-09-18 | 2014-01-15 | 清华大学 | Configurable wireless transmitter |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1241881A (en) * | 1998-05-27 | 2000-01-19 | 诺基亚流动电话有限公司 | Transmitter |
| CN101557206A (en) * | 2000-03-04 | 2009-10-14 | 高通股份有限公司 | Transmitter architecture for communications system |
| WO2006006909A1 (en) * | 2004-07-08 | 2006-01-19 | Andrew Corporation | A radio transmitter and a method of operating a radio transmitter |
| CN103516371A (en) * | 2013-09-18 | 2014-01-15 | 清华大学 | Configurable wireless transmitter |
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