CN107070569A - Multipoint cooperative frequency spectrum sensing method based on HMM model - Google Patents
Multipoint cooperative frequency spectrum sensing method based on HMM model Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/373—Predicting channel quality or other radio frequency [RF] parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- 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
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Abstract
The invention discloses a kind of multipoint cooperative frequency spectrum sensing method based on HMM model, comprise the following steps：Cognitive radio networks are introduced with multiple users, hidden Markov model modeling is carried out to the frequency spectrum of primary user, the observation of each each time slot of user obtained according to frequency spectrum perception, parameter to hidden Markov model is trained, and the frequency spectrum state of each next time slot of user of recursive calculation is the prediction probability of " busy " or " spare time "；The perceived spectral state for counting all next time slots of user is the number of times of " busy " or " spare time ", if the ratio of " busy " is more than predetermined threshold value, then judge the frequency spectrum state of next time slot as " busy ", otherwise it is " spare time ", output spectrum state outcome, and result is back to frequency spectrum perception data fusion center.The present invention introduces the predicting function of multi-node collaboration frequency spectrum perception and hidden Markov model to frequency spectrum in cognitive radio networks, the frequency spectrum status predication of next time slot is carried out, so as to improve the reliability of spectrum prediction.
Description
Technical field
The present invention relates to the spectrum prediction field in cognitive radio networks.It is more particularly related to which one kind is recognized
Know the multipoint cooperative frequency spectrum sensing method based on HMM forecast models in radio net.
Background technology
With the arrival of information age, wireless frequency spectrum has turned into the indispensable precious resources of modern society.It leads at present
To be licensed by unification of the motherland distribution, a frequency range typically can only be independently operated, this static state for a wireless communication system
Wireless frequency spectrum way to manage, simply and effectively avoid interfering between different wireless communication system.But at these
Allocated mandate frequency range in unauthorized frequency range with having the disequilibrium that frequency spectrum resource is utilized：On the one hand, frequency range is authorized to account for
With a big chunk of whole frequency spectrum resource, but wherein many frequency ranges are in idle condition；On the other hand, what opening was used is non-
Frequency range is authorized to account for a seldom part for whole frequency spectrum resource, but the user in the frequency range is a lot, and portfolio is also very big, radio
Frequency range tends to saturation substantially.Then the today developed rapidly in wireless and mobile communication, the problem of frequency spectrum resource is poor also shows
Obtain increasingly serious.Therefore, seek a kind of more effective spectrum management mode, make full use of each department, the idle frequency of each period
Section, alleviates the demand contradictory of ever-increasing frequency spectrum, as problem of concern.In order to solve the above problems, basic ideas
Just it is to try to the utilization rate improved.Then, the concept of cognitive radio is arisen at the historic moment.Its basic thought is：With cognitive function
Wireless Telecom Equipment can in the way of certain " waiting for an opportunity (Opportunistic Way) " in the frequency range of insertion authority, and
Dynamically utilize frequency spectrum.This unemployed frequency spectrum resource occurred in spatial domain, time domain and frequency domain is referred to as " frequency spectrum sky
Cave ".The core concept of cognitive radio is exactly to have Wireless Telecom Equipment to find " spectrum interposition " and rationally utilize these frequently
Compose the ability in hole.The rise of cognitive radio (CR, Cognitive Radio) technology and develop into solution radio spectrum resources
In short supply the problem of, is there is provided brand-new approach.It is by allowing cognitive user adaptively to perceive mandate frequency range in time and space
On spectrum interposition, opportunistic using hole carry out signal transmission, reach improve frequency spectrum utilization rate purpose.CR also causes
Wireless communication system can not be conducive to the cost of balanced communication authorizedly using transmission characteristic is more preferable, the broader frequency range of bandwidth
And performance；Meanwhile, system of broadband wireless communication generally has the service traffics characteristic of Larger Dynamic scope, is just being suitable for wider
Dynamic available frequency band in carry out opportunistic transmission.Therefore, introduce Cognition Mechanism and be not only raising future broadband wireless communication systems frequency
The effective way of Utilizing question is composed, is also technology and applies upper active demand.
Frequency spectrum perception is as a main ring for cognitive radio, and the purpose is to find the frequency spectrum on time domain, frequency domain, spatial domain
Hole, to utilize frequency spectrum for cognitive user in chance mode.Secondary user refers to that unauthorized use only has authorized user's ability
The user of the frequency spectrum used, primary user is then to obtain the user for licensing frequency spectrum.It is cognitive in order to not interfered to primary user
User using spectrum interposition during being communicated, it is desirable to be able to the appearance again of quick sensing primary user, is carried out in time
Frequency spectrum switches, and vacates channel and is used to primary user, or is continuing with original frequency range, but need by adjust transimission power or
Change modulation system to avoid the interference to primary user.This, which is accomplished by cognitive radio system, has spectrum detection function, can
Frequency spectrum is continuously intercepted in real time, to improve the reliability of detection.
Just at present, people make great progress in this respect, and various cognitive methods emerge in an endless stream.Single user sense
Know that design complexities are low, using technology maturation, it is easy to accomplish.But its performance can draw with multipath in wireless environment and shadow fading
The decrease of the received signal strength risen and reduce, and the frequency spectrum perception algorithm based on detection, being necessarily required to the regular hour enters
Line frequency composes the detection of state, that is, there is detection time delay.Therefore, the influence of detection time delay how is reduced or eliminated, frequency spectrum shape is realized
The reliable prediction of state, improving perceptual performance has important Research Significance.Further, since in traditional cognitive radio networks
In, packet transmission has randomness and scattered property, and frequency spectrum State Transferring frequently also has going out for a large amount of problems such as " hidden terminal "
It is existing.The patent of invention (Application No. 201610235479.6) that applicant applies before this solves asking for delay to a certain extent
Topic, but still suffer from the not high defect of interference, precision.Therefore, a kind of high reliability, precision height are needed badly at present, primary user is disturbed
Small frequency spectrum sensing method.
The content of the invention
It is an object of the invention to solve at least the above, and provide the advantage that at least will be described later.
It is a still further object of the present invention to provide a kind of multipoint cooperative frequency spectrum sensing method based on HMM model, it is being recognized
Know and the predicting function of multi-node collaboration frequency spectrum perception and hidden Markov model to frequency spectrum is introduced in radio net, carry out next
The frequency spectrum status predication of time slot, so as to improve the reliability of spectrum prediction.
In order to realize that there is provided a kind of multiple spot association based on HMM model according to object of the present invention and further advantage
Make frequency spectrum sensing method, comprise the following steps：
Step 1: including multiple primary users and multiple users in cognitive radio networks, the frequency spectrum of primary user is carried out
Hidden Markov model is modeled, the observation of each each time slot of user obtained according to frequency spectrum perception, to hidden Markov model
Parameter be trained, the frequency spectrum state of each next time slot of user of recursive calculation is the prediction probability of " busy " or " spare time ", probability
High frequency spectrum state is next time slot frequency spectrum status predication result；
Step 2: the perceived spectral state of all next time slots of user of statistics is the number of times of " busy " or " spare time ", if
The ratio of " busy " is more than predetermined threshold value, then judges the frequency spectrum state of next time slot as " busy ", be otherwise " spare time ", output spectrum state
As a result, and by result it is back to frequency spectrum perception data fusion center.
Preferably, predetermined threshold value is 2/ in the multipoint cooperative frequency spectrum sensing method based on HMM model, step 2
3 or 1/2.
Preferably, the multipoint cooperative frequency spectrum sensing method based on HMM model, step one is specifically included：
S1. hidden Markov model modeling is carried out to the frequency spectrum of primary user, obtains λ={ Π, A, B }, wherein, Π is frequency spectrum
State is the initial state probabilities of " busy " or " spare time ", and A is state transition probability matrix, and B is emission probability matrix；
S2. frequency spectrum perception is carried out using energy detection algorithm, obtains the sequence of observations O of each user's T time sloti=
{oit∈ V | t=1,2 ..., T }, the state space V={ 0,1 } of the sequence of observations, 0 represents that time user adjudicates the time slot frequency spectrum shape
State is " spare time ", and 1 represents that time user adjudicates the time slot frequency spectrum state for " busy "；
S3. using the sequence of observations as training sequence, operation Baum-Welch algorithms carry out parameter Π, A, B training, obtained
To the parameter of estimation
S4. according to history sensing results sequence O={ o1,o2,…,ot,…,oTAnd estimation parameterPass
Return the prediction probability for calculating that next time slot frequency spectrum state is " busy " or " spare time "
If S5.Set up, then this user judges the frequency spectrum shape of the next time slot in family
State is " busy ", is otherwise " spare time ".
Preferably, in the multipoint cooperative frequency spectrum sensing method based on HMM model, S1, state transition probability square
Battle array A is A={ aij}(k+1)×(k+1), aij=P { qt+1=Sj|qt=SiRepresent current time t time-frequency spectrum state for SiIn lower a period of time
S is transferred to when carving t+1jTransition probability, wherein k be coded block size；Hidden Markov model λ={ Π, A, B } hiding shape
State space is S={ 0,1 ..., k }, wherein 0 represents that primary user's channel is in " spare time " state, 1~k is " busy " state；Obtain shape
State transition probability matrix A and emission probability matrix B are respectively：
Wherein, α is the probability that state 0 is converted to state 1, and β is the probability that state k is converted to state 0, and k is positive integer, empty
Alarm probability pfWith false dismissal probability pmBe calculated as follows：
Wherein, n is the sample number in a time slot, and detection threshold value is τ,For gamma letter
Number,WithRespectively descend incomplete gamma functions and upper incomplete gamma letter
Number.
Preferably, in the multipoint cooperative frequency spectrum sensing method based on HMM model, S3, sequence of observations O is utilized
={ ot∈ V | t=1,2 ..., T } as training sequence, and the iterative estimate of parameter is carried out, obtain the parameter of the r times iteration such as
Under：
Wherein γt(i) represent in known hidden Markov model and sequence of observations O={ ot∈ V | t=1,2 ..., T }
In the case of, t channel spectrum state is SiProbability；ξt(i, j) represents channel status S during moment tiTurn in subsequent time t+1
To state SjExpectation transition probability；
Therefore γ is obtainedtAnd and ξ (i)tThe expression formula of (i, j) is as follows：
Wherein αt(i)=P (o1o2…ot,qt=Si| λ), 1≤t≤T-1 is forward variable, i.e., in given hidden Ma Erke
Under husband's model λ, moment t state is SiAnd partial sequence is o1o2…otProbability；Correspondingly, βt(i)=P (ot+1ot+2…oT,
qt=Si| λ), 1≤t≤T-1 is backward variable；αtAnd β (i)t(i) calculating is obtained by forwards algorithms and backward algorithm respectively；
The parameter obtained when iterationWhen meeting the condition of convergence, terminate iteration, obtain parameter
Preferably, in the multipoint cooperative frequency spectrum sensing method based on HMM model, S4, the method for recursive calculation
It is as follows：
Wherein,Initial value be
The present invention at least includes following beneficial effect：
Firstth, the present invention can overcome conventional cognitive radio network intermediate frequency spectrum State Transferring frequently, frequency spectrum cavity-pocket is difficult
Spy the problems such as perceiving and existing frequency spectrum perception reliability is relatively low and " hidden terminal " additional reduction is to the interference of primary user
Point, analysis is modeled using hidden Markov model to primary user's channel spectrum state；
Secondth, the present invention, relative to traditional spectrum prediction algorithm based on HMM, is introduced during spectrum prediction
The fusion that corresponding multipoint cooperative is perceived, and then improve the reliability and rapidity of spectrum prediction and greatly reduce to primary
The interference at family；
3rd, relative to the patent applied before this, this patent solves the additional reduction pair of " hidden terminal " problem well
The features such as interference of primary user and the frequency spectrum state for more quickly predicting next time slot.The other application that compares HMM model
Carry out the example of predicted state, this patent simultaneously non-fully determines the state of next time slot using HMM, and is combined with last melt
It is legal, to predict the frequency spectrum state for judging next time slot, erroneous judgement and the interference to primary user are reduced well.Be conducive to dimension
Normal communication environment is held, influence on a large scale is not caused on existing primary user.
Further advantage, target and the feature of the present invention embodies part by following explanation, and part will also be by this
The research and practice of invention and be understood by the person skilled in the art.
Brief description of the drawings
Fig. 1 is flow chart of the invention；
Fig. 2 is cognitive radio networks model of the invention；
Fig. 3 be step 3 of the present invention) schematic flow sheet.
Embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings, to make those skilled in the art with reference to specification text
Word can be implemented according to this.
It should be appreciated that such as " having ", "comprising" and " comprising " term used herein do not allot one or many
The presence or addition of individual other elements or its combination.
Fig. 2 shows cognitive radio (described briefly below is CR) network model, and a CR model includes N number of primary user's channel
With M users, it is different to the appellation of CR terminals in different documents, have a variety of sides such as CR user, secondary user, unauthorized user
Referred to as secondary user in formula, the present invention, equally, through buying or authorizing and possessing the user of certain section of frequency spectrum right to use, appellation
There is same referred to as primary user in authorized user, primary user, the present invention, wherein, each primary user's channel is by primary user's base
Stand and L primary user's receiving node constitutes primary user's sub-network.In each primary user's sub-network, packet is with data block
Form reach primary user base station, and arrival process obeys Poisson distribution Poisson distributions, and Mean Speed is v.When encoding block is big
It is small be k when, primary user base station accumulation k according to after bag carry out random linear network encoding be broadcast to again each primary user receive section
Point.Secondary user perceives primary user's channel by frequency spectrum perception algorithm and transmits data using idle frequency spectrum.Assuming that each channel time
Synchronous, the time of a main user data bag transmission is a time slot.Experiment is perceived to 200 time slots, and each time slot is adopted
20 samples of sample.
A kind of multipoint cooperative frequency spectrum sensing method based on HMM model, as shown in figure 1, comprising the following steps：
1) initialization HMM parameter models λ={ Π, A, B }：Hidden Markov model (HMM) is a kind of statistical model, is used for
One Markov process containing implicit unknown parameter of description.Its difficult point is the process is determined from the parameter of observable hidden
Containing parameter, is then used these parameters to for further analysis.Including hidden state (unknown, to be solved), observation sequence (
Know, Observable is following), probability (, it is known that computer initial value), the state transition probability of hidden state of hidden state
(unknown, following training are obtained), hidden state shows as the observation probability of observation sequence (unknown, following training are obtained).It is right
The frequency spectrum of primary user carries out hidden Markov model modeling, obtains λ={ Π, A, B }, wherein, Π be frequency spectrum state for " busy " or
The initial state probabilities in " spare time ", A is state transition probability matrix, is represented with state-transition matrix, and B is emission probability matrix；
State transition probability matrix A is A={ aij}(k+1)×(k+1), aij=P { qt+1=Sj|qt=SiRepresent current time t
Time-frequency spectrum state is SiS is transferred to when subsequent time t+1jTransition probability, wherein k be coded block size, k is quantity of state, most
The numeral calculated afterwards works as 1 processing, i.e. " busy " state when having k；HMM parameter models λ={ Π, A, B } hidden state space is S=
{ 0,1 ..., k }, wherein 0 represents that primary user's channel is in " spare time " state, 1~k is " busy " state；Obtain state transition probability
Matrix A and emission probability matrix B are respectively：
Wherein, α is the probability that state 0 is converted to state 1, and β is the probability that state k is converted to state 0, and k is positive integer, empty
Alarm probability pfWith false dismissal probability pmBe calculated as follows：
Wherein, n is the sample number in a time slot, and detection threshold value is τ,For gamma letter
Number,WithRespectively descend incomplete gamma functions and upper incomplete gamma letter
Number.
2) mode of frequency spectrum perception has a variety of, for example matched filter detection, energy measuring, cyclostationary characteristic detection
Deng carrying out frequency spectrum perception using energy detection algorithm in this programme, obtain the sequence of observations O of each time user T time sloti=
{oit∈ V | t=1,2 ..., T }, the state space V={ 0,1 } of the sequence of observations, 0 represents that time user adjudicates the time slot frequency spectrum shape
State is " spare time ", and 1 represents that time user adjudicates the time slot frequency spectrum state for " busy "；
3) using the sequence of observations as training sequence, operation Baum-Welch algorithms carry out parameter Π, A, B training, obtained
To the parameter of estimationCalculated using forwards algorithms in some specific next observable status switch of HMM
Probability, most probable model is then found accordingly.Forward-backward algorithm algorithm carries out one firstly for HMM parameter and initial estimated
Meter, but this is likely to a wrong conjecture, then passes through the validity for given these parameters of data assessment
And reduce the mistake caused by them to update HMM parameters so that and the error of given training data diminishes, for each
State, forward-backward algorithm algorithm both calculate reach this state " forward direction " probability, again calculate generate this model end-state " after
To " probability, these probability can efficiently be calculated by recurrence；
Utilize sequence of observations O={ ot∈ V | t=1,2 ..., T } as training sequence, and carry out the iteration of parameter and estimate
Meter, the parameter for obtaining the r times iteration is as follows：
Wherein γt(i) represent in known hidden Markov model and sequence of observations O={ ot∈ V | t=1,2 ..., T }
In the case of, t channel spectrum state is SiProbability；ξt(i, j) represents channel status S during moment tiTurn in subsequent time t+1
To state SjExpectation transition probability；
Therefore γ is obtainedtAnd and ξ (i)tThe expression formula of (i, j) is as follows：
Wherein αt(i)=P (o1o2…ot,qt=Si| λ), 1≤t≤T-1 is forward variable, i.e., in given hidden Ma Erke
Under husband's model λ, moment t state is SiAnd partial sequence is o1o2…otProbability；Correspondingly, βt(i)=P (ot+1ot+2…oT,
qt=Si| λ), 1≤t≤T-1 is backward variable；αtAnd β (i)t(i) calculating is obtained by forwards algorithms and backward algorithm respectively；
The parameter obtained when iterationWhen meeting the condition of convergence, terminate iteration, obtain parameter
4) according to history sensing results sequence O={ o1,o2,…,ot,…,oTAnd estimation parameterRecurrence
Calculate the prediction probability that next time slot frequency spectrum state is " busy " or " spare time "
The method of recursive calculation is as follows：
Wherein,Initial value be
If 5)Set up, then this user judges the frequency spectrum shape of the next time slot in family
State is " busy ", is otherwise " spare time ".
6) the perceived spectral state for counting all next time slots of user is the number of times of " busy " or " spare time ", if the ratio of " busy "
Example is more than predetermined threshold value (being preferably 2/3 or 1/2), then judges the frequency spectrum state of next time slot as " busy ", be otherwise " spare time ", export
Frequency spectrum state outcome, and result is back to frequency spectrum perception data fusion center, data now will be used as next time slot
Training data.
Assuming that there are 4 authorized users in system, Poisson distribution is obeyed in their utilizations to frequency spectrum, and it is to frequency spectrum
Occupancy is not less than 50%, and frequency spectrum period of state is 20ms, and channel circumstance is white Gaussian noise, and data modulation is adjusted for QPSK
System, is modulated using network code OFDM, and produce continuous take respectively according to the setting of Parameter for Poisson Distribution takes frequency with discontinuous
The probability distribution of spectrum, respective spectrum mode is produced according to this method.The frequency spectrum perception of other manner uses energy accumulation method,
Spectrum cycle is 100ms, and detecting period is 25ms, and other to send data time, the frequency spectrum sensing method based on HMM model is more
The fusion of node judges to emulate using 2/3 and 1/2, and 2 algorithms are carried out to comparing.1st, initialization HMM parameter models λ=
{Π,A,B}.2nd, unauthorized secondary user intercepts spectrum information, obtains observed data Oi={ oit∈ V | t=1,2 ..., T }, pin
To observed data using the preceding training that parameter Π, A, B are carried out to Baum-Welch algorithms, the parameter estimatedSo thatLikelihood ratio is maximum.3rd, according to history sensing results sequence O={ o1,
o2,…,ot,…,oTAnd estimation parameterThe next time slot frequency spectrum state of recursive calculation is pre- for " busy " or " spare time "
Survey probability4th, according to the not busy and busy probability calculated, carrying out fusion criterion, (now fusion criterion is set
One thresholding W) calculating, the state of the subsequent time after being predicted.5th, according to simulation result, HMM modes is obtained and perceive frequency
Spectrum judge using 2/3 the not busy and busy bit error rate than use 1/2 it is high, error performance is good, but the two is all than the energy of frequency spectrum
Amount accumulation method judges that the not busy and busy error performance of frequency spectrum is high, judges that this method is higher than the reliability of commonsense method.6th, root
According to simulation result, HMM modes perceived spectral is obtained and judges using 2/3 not busy and busy false-alarm probability than using 1/2 high
The two is lower than the false dismissal probability of commonsense method, further reduces so as to judge to change interference of the method to primary user and is
The throughput of system is also improved, and reduces detection time.
Number of devices and treatment scale described herein are the explanations for simplifying the present invention.To the present invention application,
Modifications and variations will be readily apparent to persons skilled in the art.
Although embodiment of the present invention is disclosed as above, it is not restricted in specification and embodiment listed
With it can be applied to various suitable the field of the invention completely, can be easily for those skilled in the art
Other modification is realized, therefore under the universal limited without departing substantially from claim and equivalency range, the present invention is not limited
In specific details and shown here as the legend with description.
Claims (6)
1. a kind of multipoint cooperative frequency spectrum sensing method based on HMM model, it is characterised in that comprise the following steps：
Step 1: including multiple primary users and multiple users in cognitive radio networks, hidden horse is carried out to the frequency spectrum of primary user
Er Kefu model modelings, the observation of each each time slot of user obtained according to frequency spectrum perception, to the ginseng of hidden Markov model
Number is trained, and the frequency spectrum state of each next time slot of user of recursive calculation is the prediction probability of " busy " or " spare time ", and probability is high
Frequency spectrum state is next time slot frequency spectrum status predication result；
Step 2: the perceived spectral state of all next time slots of user of statistics is the number of times of " busy " or " spare time ", if " busy "
Ratio is more than predetermined threshold value, then judges the frequency spectrum state of next time slot as " busy ", be otherwise " spare time ", output spectrum state outcome,
And result is back to frequency spectrum perception data fusion center.
2. the multipoint cooperative frequency spectrum sensing method as claimed in claim 1 based on HMM model, it is characterised in that in step 2
Predetermined threshold value is 2/3 or 1/2.
3. the multipoint cooperative frequency spectrum sensing method as claimed in claim 1 based on HMM model, it is characterised in that step one has
Body includes：
S1. hidden Markov model modeling is carried out to the frequency spectrum of primary user, obtains λ={ Π, A, B }, wherein, Π is frequency spectrum state
For " busy " or the initial state probabilities in " spare time ", A is state transition probability matrix, and B is emission probability matrix；
S2. frequency spectrum perception is carried out using energy detection algorithm, obtains the sequence of observations O of each user's T time sloti={ oit
∈ V | t=1,2 ..., T }, the state space V={ 0,1 } of the sequence of observations, 0 represents that time user adjudicates the time slot frequency spectrum state and is
" spare time ", 1 represents that time user adjudicates the time slot frequency spectrum state for " busy "；
S3. using the sequence of observations as training sequence, operation Baum-Welch algorithms carry out parameter Π, A, B training, estimated
The parameter of meter
S4. according to history sensing results sequence O={ o1,o2,…,ot,…,oTAnd estimation parameterRecurrence meter
Calculate the prediction probability that next time slot frequency spectrum state is " busy " or " spare time "
If S5.Set up, then this user judges that the frequency spectrum state of the next time slot in family is
" busy ", is otherwise " spare time ".
4. the multipoint cooperative frequency spectrum sensing method as claimed in claim 3 based on HMM model, it is characterised in that in S1, state
Transition probability matrix A is A={ aij}(k+1)×(k+1), aij=P { qt+1=Sj|qt=SiRepresent that current time t time-frequency spectrum state is
SiS is transferred to when subsequent time t+1jTransition probability, wherein k be coded block size；Hidden Markov model λ=Π, A,
B } hidden state space be S={ 0,1 ..., k }, wherein 0 represents that primary user channel is in " spare time " state, 1~k is " busy "
State；Obtain state transition probability matrix A and emission probability matrix B is respectively：
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Wherein, α is the probability that state 0 is converted to state 1, and β is the probability that state k is converted to state 0, and k is positive integer, and false-alarm is general
Rate pfWith false dismissal probability pmBe calculated as follows：
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</mrow>
Wherein, n is the sample number in a time slot, and detection threshold value is τ,For gamma function,WithRespectively descend incomplete gamma functions and upper incomplete gamma functions.
5. the multipoint cooperative frequency spectrum sensing method as claimed in claim 4 based on HMM model, it is characterised in that in S3, is utilized
Sequence of observations O={ ot∈ V | t=1,2 ..., T } as training sequence, and the iterative estimate of parameter is carried out, obtain the r times repeatedly
The parameter in generation is as follows：
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Wherein γt(i) represent in known hidden Markov model and sequence of observations O={ ot∈ V | t=1,2 ..., T } situation
Under, t channel spectrum state is SiProbability；ξt(i, j) represents channel status S during moment tiShape is gone in subsequent time t+1
State SjExpectation transition probability；
Therefore γ is obtainedtAnd and ξ (i)tThe expression formula of (i, j) is as follows：
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<mfenced open = "" close = "">
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</mrow>
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<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mi>b</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>o</mi>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<msub>
<mi>&beta;</mi>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Wherein αt(i)=P (o1o2…ot,qt=Si| λ), 1≤t≤T-1 is forward variable, i.e., in given hidden Markov mould
Under type λ, moment t state is SiAnd partial sequence is o1o2…otProbability；Correspondingly, βt(i)=P (ot+1ot+2…oT,qt=
Si| λ), 1≤t≤T-1 is backward variable；αtAnd β (i)t(i) calculating is obtained by forwards algorithms and backward algorithm respectively；
The parameter obtained when iterationWhen meeting the condition of convergence, terminate iteration, obtain parameter
6. the multipoint cooperative frequency spectrum sensing method as claimed in claim 5 based on HMM model, it is characterised in that in S4, recurrence
The method of calculating is as follows：
<mrow>
<msub>
<mi>P</mi>
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</mover>
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<msub>
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<mi>&lambda;</mi>
<mo>^</mo>
</mover>
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<mrow>
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</mover>
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<mi>s</mi>
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<msub>
<mi>y</mi>
<mi>t</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
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</mover>
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</mrow>
</msub>
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<mi>b</mi>
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</mrow>
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<mrow>
<msub>
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</munder>
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<mover>
<mi>&lambda;</mi>
<mo>^</mo>
</mover>
</msub>
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<mo>(</mo>
<msub>
<mi>s</mi>
<mi>t</mi>
</msub>
<mo>|</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mi>b</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>t</mi>
</msub>
<mo>|</mo>
<msub>
<mi>s</mi>
<mi>t</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
</mrow>
Wherein,Initial value be
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| CN112087275A (en) * | 2020-08-24 | 2020-12-15 | 宁波大学 | Cooperative spectrum sensing method based on birth and death process and viscous hidden Markov model |
| CN112187383A (en) * | 2020-08-24 | 2021-01-05 | 宁波大学 | Multi-band spectrum sensing method based on viscous hidden Markov model |
| CN112202513A (en) * | 2020-08-24 | 2021-01-08 | 宁波大学 | Multi-band spectrum sensing method based on birth and death process and viscous hidden Markov model |
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| CN110826019A (en) * | 2019-10-15 | 2020-02-21 | 电子科技大学 | Space spectrum state prediction method based on hidden Markov model |
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| CN112702133A (en) * | 2020-07-20 | 2021-04-23 | 长春理工大学 | Main user channel state prediction method based on dynamic Bayesian hidden Markov model |
| CN112202513B (en) * | 2020-08-24 | 2022-05-20 | 宁波大学 | Multi-band spectrum sensing method based on birth and death process and viscous hidden Markov model |
| CN112087275B (en) * | 2020-08-24 | 2022-05-20 | 宁波大学 | Cooperative spectrum sensing method based on birth and death process and viscous hidden Markov model |
| CN112187383B (en) * | 2020-08-24 | 2022-05-20 | 宁波大学 | Multi-band spectrum sensing method based on viscous hidden Markov model |
| CN112202513A (en) * | 2020-08-24 | 2021-01-08 | 宁波大学 | Multi-band spectrum sensing method based on birth and death process and viscous hidden Markov model |
| CN112187383A (en) * | 2020-08-24 | 2021-01-05 | 宁波大学 | Multi-band spectrum sensing method based on viscous hidden Markov model |
| CN112087275A (en) * | 2020-08-24 | 2020-12-15 | 宁波大学 | Cooperative spectrum sensing method based on birth and death process and viscous hidden Markov model |
| CN113852432A (en) * | 2021-01-07 | 2021-12-28 | 上海应用技术大学 | RCS-GRU model-based spectrum prediction sensing method |
| CN113852432B (en) * | 2021-01-07 | 2023-08-25 | 上海应用技术大学 | Spectrum Prediction Sensing Method Based on RCS-GRU Model |
| CN115021842A (en) * | 2021-11-19 | 2022-09-06 | 荣耀终端有限公司 | Task processing method and device |
| CN115021842B (en) * | 2021-11-19 | 2023-10-31 | 荣耀终端有限公司 | Task processing method, device and storage medium |
| CN114222305A (en) * | 2021-12-24 | 2022-03-22 | 宁波大学 | Collaborative spectrum sensing method based on viscous hidden Markov model |
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