CN108270496A - A kind of fast frequency spectrum detection method based on bayesian criterion and channel relevancy - Google Patents
A kind of fast frequency spectrum detection method based on bayesian criterion and channel relevancy Download PDFInfo
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- CN108270496A CN108270496A CN201711318856.3A CN201711318856A CN108270496A CN 108270496 A CN108270496 A CN 108270496A CN 201711318856 A CN201711318856 A CN 201711318856A CN 108270496 A CN108270496 A CN 108270496A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
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Abstract
The present invention relates to the technical fields of smart home communication, more specifically, it is related to a kind of fast frequency spectrum detection method based on bayesian criterion and channel relevancy, by the channel estimation based on channel relevancy in smart home and the channel estimating based on bayesian criterion, pass through the correlation between channel, channel is grouped, and a detection channel is found in each channel group, it is directly detected, the state with other channels in group then is estimated to obtain jointly by historic state and the relationship between detection channel；Contacting between channel history state and channel present situation is modeled based on bayesian criterion, predicts the probability of next channel status；Channel estimation based on channel relevancy and contradiction caused by the channel estimating both methods based on bayesian criterion, are solved using minimum entropy algorithm；Shorten detection time on the basis of certain accuracy is ensured, improve the efficiency of frequency spectrum detection.
Description
Technical field
The present invention relates to the technical fields of smart home communication, and bayesian criterion and letter are based on more particularly, to one kind
The fast frequency spectrum detection method of road correlation.
Background technology
Compared with common household, smart home not only has traditional inhabitation function, while is capable of providing information exchange work(
It can so that people can check Household information in outside and control the relevant device of household, effectively arrange the time convenient for people, make
It is safer, comfortable to obtain home life.System includes internet, intelligent appliance, controller, household network and gateway.And intelligence
The network and gateway of household are the key links for being capable of information exchange between intelligent appliance equipment room, internet and user, are out
Hair and the important content and difficult point of design phase.Smart home final goal is to allow domestic environment is more comfortable, safer, more ring
It protects, is more convenient.
The appearance of Internet of Things so that present intelligent domestic system function is more abundant, more diversified and personalized,
System function is concentrated mainly on intelligent lighting controls, intelligent appliance control, Video chat and intelligent security guard etc..Network it is broadband
Demand of the communication system to frequency spectrum is continuously increased with diversification, also promotes frequency spectrum resource more and more valuable.However, current frequency spectrum
Cavity cannot efficiently use, it is impossible to shorten frequency spectrum detection event while realizing and ensure accuracy and improve detection efficiency.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide the nothings of a kind of bayesian criterion and channel relevancy
Line communication fast frequency spectrum detection method, by the correlation between channel, is grouped channel, and looked in each channel group
To a detection channel, it is directly detected, the state of other channels is then believed by historic state and with detection in same group
Relationship between road is estimated to obtain, shortens detection time on the basis of certain accuracy is ensured, improve frequency spectrum detection jointly
Efficiency.
In order to solve the above technical problems, the technical solution adopted by the present invention is：
A kind of fast frequency spectrum detection method based on bayesian criterion and channel relevancy is provided, is included the following steps：
S10. correlated channels are divided into one group according to channel relevancy, and choose a channel in every group of correlated channels and make
For detection channel and it is directly detected；The state of other channels in same group is believed by channel history state and detection
Relationship between the state in road is estimated to obtain；
S20. estimate to obtain based on channel relevancy in foundation detection channel status, channel history state and step S10
Channel status calculate channel entropy H1；
S30. by the historic state of frequency spectrum perception channel, the probability function of parameter is calculated in cognitive user；Based on shellfish
Contacting between this criterion of leaf modeling channel history state and channel present situation, predicts the probability of next channel status；
S40. it predicts to obtain based on bayesian criterion in foundation detection channel status, channel history state and step S30
Channel status calculate channel entropy H2；
S50. compare the size of entropy H1 and entropy H2：If H1<H2 then exports the channel shape estimated based on channel relevancy
State is testing result；If H1>H2, then output is testing result based on the channel status that bayesian criterion is predicted.
The fast frequency spectrum detection method based on bayesian criterion and channel relevancy of the present invention, passes through the phase between channel
Guan Xing is grouped channel, and a detection channel is found in each channel group, it is directly detected, in group
The state of other channels then is estimated to obtain jointly by historic state and the relationship between detection channel；Based on Bayes's standard
Contacting between channel history state and channel present situation is then modeled, predicts the probability of next channel status；Based on channel correlation
Property channel estimation and the channel estimating both methods based on bayesian criterion caused by contradiction, minimum entropy algorithm is needed
It solves；Shorten detection time on the basis of certain accuracy is ensured, improve the efficiency of frequency spectrum detection.
Preferably, the judgement of correlated channels carries out according to the following steps in step S10：
S11. the state binary message 0 or 1 of channel in determining time slot is represented, 0 represents that channel is in idle shape
State, 1 expression channel reorganize and outfit the state of primary user's occupancy；And pass through channel state vectorCarry out table
Show the state of all channels, wherein, whereinRepresent states of the channel n in time slot m, M is total number of timeslots amount；
S12. the correlation factor between channel i and channel j is calculated, is calculated by formula (1)；What correlation factor represented is two letters
Road determines the probability that state is identical in time slot at certain；
Wherein, CijIt is the correlation factor between channel i and channel j, Θ is same or operator；
S13. the correlation factors in same service frequency domain between each channel are represented by channel relevancy matrix A, correlation
Matrix A is represented by formula (2)：
Wherein, CijIt is the correlation factor between channel i and channel j, N is the channel number in the service-domain of place；
S14. setting threshold value Cth, as the correlation factor C between channel i and channel jijMore than threshold value CthWhen, then judge
Channel i and channel j is correlated channels；Otherwise, then judge that channel i and channel j is non-correlation channel.
In order to accurately estimate channel status, the estimation operation with other channel status in group is divided into two aspects, a side
In same service network, the big channel of correlation is divided into one group in face, and a channel (i.e. detection letter is selected in every group
Road) it is directly detected, it can be estimated according to the state of detection channel with other channels (estimating channel) in organizing
State.On the other hand, there is very big correlation in channel status in time, in other words, as the present situation of channel can basis
Its historic state is predicted.
Preferably, the determination method of the detection channel is：It is obtained from every a line of channel relevancy matrix A and first closes letter
Road group Si, Si={ cj|Cij≥Cth, wherein cjRepresent j-th of channel, cjWith same group in correlation between other channels all very
Greatly, it is determined as channel group SiInterior detection channel.
Preferably, the method for predicting next channel status probability based on bayesian criterion in step S30 includes following step
Suddenly：
S31. the historic state of n channel is known by frequency spectrum perception, is expressed as X=[x1,x2, ... xn]；Wherein,
x1、x2……xnThe state of channel 1, channel 2 ... channel n are represented respectively；The probability function of cognitive user calculating parameter；
S32. the probability function that cognitive user can calculate parameter θ is P (θ/X), can be summarised as P according to bayesian criterion
(θ/X)=P (X/ θ) × P (θ)/P (X)；
S33. assume that A represents the quantity summation that busy condition is in historical information, B represents idle state in historical information
Quantity summation, it is also busy quantity that C, which represents current state as busy condition preceding state, and it is empty that D, which represents current state,
Not busy state preceding state is also idle quantity；It is busy probability to define P (A), and idle probability is P (B), and current state is busy
Commonplace and preceding state is also that busy probability is P (C/A), and current state is the general of free time for idle and preceding state
Rate P (D/B)；At known P (A), P (B), P (C/A), under the premise of P (D/B), then in the case that preceding state is busy and before
In the case that one state is the free time, current state is still the probability that busy probability is then next channel status.
Compared with prior art, the beneficial effects of the invention are as follows：
The fast frequency spectrum detection method based on bayesian criterion and channel relevancy of the present invention, passes through the phase between channel
Guan Xing is grouped channel, and a detection channel is found in each channel group, it is directly detected, in group
The state of other channels then is estimated to obtain jointly by historic state and the relationship between detection channel；Based on Bayes's standard
Contacting between channel history state and channel present situation is then modeled, predicts the probability of next channel status；Based on channel correlation
Property channel estimation and the channel estimating both methods based on bayesian criterion caused by contradiction, using minimum entropy algorithm come
It solves；Shorten detection time on the basis of certain accuracy is ensured, improve the efficiency of frequency spectrum detection.
Description of the drawings
Fig. 1 is the flow chart of the fast frequency spectrum detection method based on bayesian criterion and channel relevancy.
Specific embodiment
The present invention is further illustrated With reference to embodiment.Wherein, attached drawing only for illustration,
What is represented is only schematic diagram rather than pictorial diagram, it is impossible to be interpreted as the limitation to this patent；In order to which the reality of the present invention is better described
Example is applied, the certain components of attached drawing have omission, zoom in or out, and do not represent the size of actual product；To those skilled in the art
For, the omitting of some known structures and their instructions in the attached drawings are understandable.
The same or similar label correspond to the same or similar components in the attached drawing of the embodiment of the present invention；In retouching for the present invention
In stating, it is to be understood that if it is based on attached drawing to have the orientation of the instructions such as term " on ", " under ", "left", "right" or position relationship
Shown orientation or position relationship, are for only for ease of the description present invention and simplify description rather than instruction or imply meaning
Device or element must have specific orientation, with specific azimuth configuration and operation, therefore position relationship described in attached drawing
Term is only for illustration, it is impossible to the limitation to this patent is interpreted as, it for the ordinary skill in the art, can
To understand the concrete meaning of above-mentioned term as the case may be.
Embodiment 1
First for the fast frequency spectrum detection method based on bayesian criterion and channel relevancy of the present invention as shown in Figure 1
Embodiment includes the following steps：
S10. correlated channels are divided into one group according to channel relevancy, and choose a channel in every group of correlated channels and make
For detection channel and it is directly detected；The state of other channels in same group is believed by channel history state and detection
Relationship between the state in road is estimated to obtain；
S20. estimate to obtain based on channel relevancy in foundation detection channel status, channel history state and step S10
Channel status calculate channel entropy H1；
S30. by the historic state of frequency spectrum perception channel, the probability function of parameter is calculated in cognitive user；Based on shellfish
Contacting between this criterion of leaf modeling channel history state and channel present situation, predicts the probability of next channel status；
S40. it predicts to obtain based on bayesian criterion in foundation detection channel status, channel history state and step S30
Channel status calculate channel entropy H2；
S50. compare the size of entropy H1 and entropy H2：If H1<H2 then exports the channel shape estimated based on channel relevancy
State is testing result；If H1>H2, then output is testing result based on the channel status that bayesian criterion is predicted.
Wherein, the judgement of correlated channels carries out according to the following steps in step S10：
S11. the state binary message 0 or 1 of channel in determining time slot is represented, 0 represents that channel is in idle shape
State, 1 expression channel reorganize and outfit the state of primary user's occupancy；And pass through channel state vectorCarry out table
Show the state of all channels, wherein, whereinRepresent states of the channel n in time slot m, M is total number of timeslots amount；
S12. the correlation factor between channel i and channel j is calculated, is calculated by formula (1)；What correlation factor represented is two letters
Road determines the probability that state is identical in time slot at certain；
Wherein, CijIt is the correlation factor between channel i and channel j, Θ is same or operator；
S13. the correlation factors in same service frequency domain between each channel are represented by channel relevancy matrix A, correlation
Matrix A is represented by formula (2)：
Wherein, CijIt is the correlation factor between channel i and channel j, N is the channel number in the service-domain of place；
S14. setting threshold value Cth, as the correlation factor C between channel i and channel jijMore than threshold value CthWhen, then judge
Channel i and channel j is correlated channels；Otherwise, then judge that channel i and channel j is non-correlation channel.
Detection channel determination method be：It is obtained from every a line of channel relevancy matrix A and first closes channel group Si, Si=
{cj|Cij≥Cth, wherein cjRepresent j-th of channel, cjWith same group in correlation between other channels it is all very big, be determined as letter
Road group SiInterior detection channel.
Specifically, the method for predicting next channel status probability based on bayesian criterion in step S30 includes following step
Suddenly：
S31. the historic state of n channel is known by frequency spectrum perception, is expressed as X=[x1,x2, ... xn]；Wherein,
x1、x2……xnThe state of channel 1, channel 2 ... channel n are represented respectively；The probability function of cognitive user calculating parameter；
S32. the probability function that cognitive user can calculate parameter θ is P (θ/X), can be summarised as P according to bayesian criterion
(θ/X)=P (X/ θ) × P (θ)/P (X)；
S33. assume that A represents the quantity summation that busy condition is in historical information, B represents idle state in historical information
Quantity summation, it is also busy quantity that C, which represents current state as busy condition preceding state, and it is empty that D, which represents current state,
Not busy state preceding state is also idle quantity；It is busy probability to define P (A), and idle probability is P (B), and current state is busy
Commonplace and preceding state is also that busy probability is P (C/A), and current state is the general of free time for idle and preceding state
Rate P (D/B)；At known P (A), P (B), P (C/A), under the premise of P (D/B), then in the case that preceding state is busy and before
In the case that one state is the free time, current state is still the probability that busy probability is then next channel status.
Caused by channel estimation based on channel relevancy and the channel estimating both methods based on bayesian criterion
Contradiction needs minimum entropy algorithm to solve.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
All any modification, equivalent and improvement made within the spirit and principle of invention etc., should be included in the claims in the present invention
Protection domain within.
Claims (4)
1. a kind of fast frequency spectrum detection method based on bayesian criterion and channel relevancy, which is characterized in that including following step
Suddenly：
S10. correlated channels are divided into one group according to channel relevancy, and a channel is chosen as inspection in every group of correlated channels
It surveys channel and it is directly detected；By channel history state and detection channel status between relationship obtain with organize in its
The state of his channel；
S20. according to the letter estimated in detection channel status, channel history state and step S10 based on channel relevancy
The entropy H1 of road state computation channel；
S30. by the historic state of frequency spectrum perception channel, the probability function of parameter is calculated in cognitive user；Based on Bayes
Criterion models contacting between channel history state and channel present situation, predicts the probability of next channel status；
S40. according to the letter predicted in detection channel status, channel history state and step S30 based on bayesian criterion
The entropy H2 of road state computation channel；
S50. compare the size of entropy H1 and entropy H2：If H1<H2, the then channel status that output is estimated based on channel relevancy are
Testing result；If H1>H2, then output is testing result based on the channel status that bayesian criterion is predicted.
2. the fast frequency spectrum detection method according to claim 1 based on bayesian criterion and channel relevancy, feature
Be, in step S10 the judgement of correlated channels carry out according to the following steps：
S11. the state binary message 0 or 1 of channel in determining time slot being represented, 0 expression channel is in idle condition, and 1
Represent that channel reorganizes and outfit the state of primary user's occupancy；And pass through channel state vectorTo represent
There is the state of channel, wherein, whereinRepresent states of the channel n in time slot m, M is total number of timeslots amount；
S12. the correlation factor between channel i and channel j is calculated, is calculated by formula (1)；What correlation factor represented is that two channels exist
Certain determines the probability that state is identical in time slot；
Wherein, CijIt is the correlation factor between channel i and channel j, Θ is same or operator；
S13. the correlation factors in same service frequency domain between each channel are represented by channel relevancy matrix A, correlation matrix A
It is represented by formula (2)：
Wherein, CijIt is the correlation factor between channel i and channel j, N is the channel number in the service-domain of place；
S14. setting threshold value Cth, as the correlation factor C between channel i and channel jijMore than threshold value CthWhen, then judge channel
I and channel j is correlated channels；Otherwise, then judge that channel i and channel j is non-correlation channel.
3. the fast frequency spectrum detection method according to claim 2 based on bayesian criterion and channel relevancy, feature
It is, the determination method of the detection channel is：It is obtained from every a line of channel relevancy matrix A and first closes channel group Si, Si=
{cj|Cij≥Cth, wherein cjRepresent j-th of channel, cjWith same group in correlation between other channels it is all very big, be determined as letter
Road group SiInterior detection channel.
4. the fast frequency spectrum detection method according to claim 1 based on bayesian criterion and channel relevancy, feature
It is, the method for predicting next channel status probability based on bayesian criterion in step S30 includes the following steps：
S31. the historic state of n channel is known by frequency spectrum perception, is expressed as X=[x1,x2, ... xn]；Wherein, x1、
x2……xnThe state of channel 1, channel 2 ... channel n are represented respectively；The probability function of cognitive user calculating parameter；
S32. cognitive user can calculate the probability function of parameter θ as P (θ/X), can be summarised as according to bayesian criterion P (θ/
X)=P (X/ θ) × P (θ)/P (X)；
S33. assume that A represents the quantity summation that busy condition is in historical information, B represents the number of idle state in historical information
Summation is measured, it is also busy quantity that C, which represents current state as busy condition preceding state, and D represents current state as idle shape
State preceding state is also idle quantity；Define P (A) be busy probability, idle probability be P (B), current state be it is busy simultaneously
And it is P (C/A) that preceding state, which is also busy probability, current state is idle probability P for idle and preceding state
(D/B)；At known P (A), P (B), P (C/A) are under the premise of P (D/B), then in the case that preceding state is busy and previous
In the case that a state is the free time, current state is still the probability that busy probability is then next channel status.
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| CN110572181A (en) * | 2019-08-12 | 2019-12-13 | 广东电网有限责任公司 | Channel detection method, device and equipment of power line |
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